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You can then easily specify a high-quality manual meta-analysis and execute it in the cloud,\nrapidly generating results that you can inspect and share."),(0,n.kt)("admonition",{type:"tip"},(0,n.kt)("p",{parentName:"admonition"},"We will perform a slimmed down replication of the following meta-analysis:\n",(0,n.kt)("a",{parentName:"p",href:"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4494985/"},"Neurobiological impact of nicotinic acetylcholine receptor agonists: An ALE meta-analysis of pharmacological neuroimaging studies"),"."),(0,n.kt)("p",{parentName:"admonition"},"For more guidance on how to choose a topic for meta-analysis, see the ",(0,n.kt)("a",{parentName:"p",href:"https://www.bmj.com/content/372/bmj.n71"},"PRISMA statement")," and the ",(0,n.kt)("a",{parentName:"p",href:"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5918306/"},"10 simple rules for neuroimaging meta-analyses"),".")),(0,n.kt)("h2",{id:"creating-a-new-project"},"Creating a new Project"),(0,n.kt)("p",null,(0,n.kt)("em",{parentName:"p"},"Projects")," contain all the Steps necessary to create a new meta-analysis in Neurosynth Compose.\nWe'll step through this process in detail."),(0,n.kt)("p",null,"To get started, ",(0,n.kt)("em",{parentName:"p"},"sign in"),", and select ",(0,n.kt)("strong",{parentName:"p"},"New Project"),". "),(0,n.kt)("p",null,"You'll now see the Project page, showing the three stages of a meta-analysis:\n",(0,n.kt)("strong",{parentName:"p"},"Search & Curate, Extract & Annotate")," and ",(0,n.kt)("strong",{parentName:"p"},"Specify Meta-Analyses"),"."),(0,n.kt)("p",null,(0,n.kt)("img",{alt:"New Project",src:a(4819).Z,width:"1557",height:"501"}),". "),(0,n.kt)("h2",{id:"search--curate"},"Search & Curate"),(0,n.kt)("p",null,"The first step in a meta-analysis is to ",(0,n.kt)("em",{parentName:"p"},"Search")," for studies, and ",(0,n.kt)("em",{parentName:"p"},"Curate")," these studies into a\nfinal ",(0,n.kt)("em",{parentName:"p"},"StudySet")," that contains the studies we want to include in a Meta-Analysis."),(0,n.kt)("p",null,"To get started, we must choose a Curation workflow. There are three options: Simple, PRISMA and Custom. The main difference between these options is the number of review steps involved in creating a final list of studies. ",(0,n.kt)("strong",{parentName:"p"},'For a rigorous Manual Meta-Analysis, select "PRISMA".')),(0,n.kt)("admonition",{type:"tip"},(0,n.kt)("p",{parentName:"admonition"},"Reviewers typically require Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) reporting\nfor gold-standard meta-analyses. The PRISMA guidelines ensure a systematic selection and reporting process.\nHowever, a Simple workflow may be useful for exploratory and automated meta-analyses. ")),(0,n.kt)("p",null,"Next, you will be presented with the Curation board, consisting of four columns representing\nthe steps of a PRISMA workflow:"),(0,n.kt)("ul",null,(0,n.kt)("li",{parentName:"ul"},(0,n.kt)("p",{parentName:"li"},(0,n.kt)("strong",{parentName:"p"},"Identification"),": Remove duplicate studies")),(0,n.kt)("li",{parentName:"ul"},(0,n.kt)("p",{parentName:"li"},(0,n.kt)("strong",{parentName:"p"},"Screening"),": Remove irrelevant studies")),(0,n.kt)("li",{parentName:"ul"},(0,n.kt)("p",{parentName:"li"},(0,n.kt)("strong",{parentName:"p"},"Elibility"),": Remove studies that do not meet inclusion criteria or do not have retrievable information")),(0,n.kt)("li",{parentName:"ul"},(0,n.kt)("p",{parentName:"li"},(0,n.kt)("strong",{parentName:"p"},"Included"),": Double check the studies and view which studies will be included in the meta-analysis"))),(0,n.kt)("p",null,"The overall goal is to go from a large number of studies from a broad search results, to only the\nstudies that are relevant to your research question. These steps should be completed in order!"),(0,n.kt)("h3",{id:"importing-studies"},"Importing studies"),(0,n.kt)("p",null,"But first, we must add studies to our Project. Click the ",(0,n.kt)("strong",{parentName:"p"},"Import Studies")," button. "),(0,n.kt)("p",null,"You can search for studies within the ",(0,n.kt)("em",{parentName:"p"},"NeuroStore")," database\u2014which we maintain and indexes over 20,000 pre-processed neuroimaging studies\u2014or from external sources, such as PubMed or a Citation manager file. "),(0,n.kt)("p",null,(0,n.kt)("img",{alt:"Import Studies",src:a(3569).Z,width:"622",height:"238"}),". "),(0,n.kt)("p",null,(0,n.kt)("em",{parentName:"p"},"Import via NeuroStore")," is simpler and faster, as these studies already exist on in our system\nand have pre-extracted imaging data (e.g. coordinates).\nAs a result, there will be fewer irrelevant studies to exclude, and less data extraction to complete."),(0,n.kt)("p",null,"However, ",(0,n.kt)("em",{parentName:"p"},"searching other sources")," is often recommended for a comprehensive literature search, as a single source can never index all possible studies."),(0,n.kt)("p",null,"To replicate the Nicotine meta-analysis, we will replicate the author's PubMed search."),(0,n.kt)("admonition",{type:"info"},(0,n.kt)("p",{parentName:"admonition"},"Searching NeuroStore is a valid option for a manual meta-analysis to balance rigor and efficiency. To learn more, see the ",(0,n.kt)("a",{parentName:"p",href:"/compose-docs/tutorial/automated"},"Automated Meta-Analysis tutorial"),". ")),(0,n.kt)("h4",{id:"searching-pubmed"},"Searching PubMed"),(0,n.kt)("p",null,"We can export any PubMed search result to file, and import that file into Neurosynth Compose.\nIn this example, we perform the following search which in the ",(0,n.kt)("a",{parentName:"p",href:"https://pubmed.ncbi.nlm.nih.gov/"},"PubMed")," search field:"),(0,n.kt)("pre",null,(0,n.kt)("code",{parentName:"pre"},'("fMRI" OR "PET" OR "neuroimaging" OR "Functional magnetic resonance imaging" OR "functional MRI") \nAND ("nicotine" OR "cigarette" OR "smok*" OR "DMXB-A")\n')),(0,n.kt)("p",null,"This results in over 3,000 studies that are potentially relevant for this meta-analysis."),(0,n.kt)("p",null,"Next, we save the results to ",(0,n.kt)("inlineCode",{parentName:"p"},"PMID")," format, with a PubMed ID on each line."),(0,n.kt)("p",null,(0,n.kt)("img",{alt:"Download PubMed",src:a(6652).Z,width:"1053",height:"475"})),(0,n.kt)("admonition",{type:"info"},(0,n.kt)("p",{parentName:"admonition"},"Reviewing 3,000 studies can take a long time!\nOnly a small percentage of these studies will meet all inclusion criteria. For the sake of the tutorial, we provide a shortened list of ",(0,n.kt)(o.aBF,{size:20,mdxType:"FaDownload"})," ",(0,n.kt)("a",{target:"_blank",href:a(7555).Z},"PUBMED IDS"),".")),(0,n.kt)("p",null,"We can import this file into our Project by clicking ",(0,n.kt)("strong",{parentName:"p"},"Upload File"),". Give this import a name for future reference.\nAll studies imported from this search will be ",(0,n.kt)("strong",{parentName:"p"},"Tagged")," with the search name. "),(0,n.kt)("p",null,(0,n.kt)("img",{alt:"Import name",src:a(212).Z,width:"1282",height:"354"})),(0,n.kt)("h3",{id:"identification"},"Identification"),(0,n.kt)("p",null,"All the imported studies are now visible in the first column of our PRISMA curation board. "),(0,n.kt)("p",null,(0,n.kt)("img",{alt:"Identification",src:a(8665).Z,width:"3595",height:"939"})),(0,n.kt)("p",null,"The purpose ",(0,n.kt)("strong",{parentName:"p"},"Identification")," is to find ",(0,n.kt)("em",{parentName:"p"},"duplicate studies"),", which is common when importing from multiple sources.\n",(0,n.kt)("em",{parentName:"p"},"Neurosynth Compose")," will automatically find potential duplicates (based on the Title, DOI, and PMID).\nHowever, you can also manually review studies to identify any duplicates we might have missed."),(0,n.kt)("p",null,"To review studies, click on the ",(0,n.kt)("strong",{parentName:"p"},"Identification"),' column header, or an individual study.\nFor each study, you can choose to "Promote" it to the next phase, "Exclude" as a duplicate, or flag for later review.'),(0,n.kt)("p",null,(0,n.kt)("img",{alt:"Identification review",src:a(3615).Z,width:"2251",height:"1211"})),(0,n.kt)("admonition",{type:"tip"},(0,n.kt)("p",{parentName:"admonition"},"To remain PRISMA compliant, you should only exclude studies for being a duplicate in the Identification step.")),(0,n.kt)("p",null,"In this example, there are no duplicates. To quickly advance, click ",(0,n.kt)("strong",{parentName:"p"},"Promote All Uncategorized Studies")," under ",(0,n.kt)("em",{parentName:"p"},"Identification"),"."),(0,n.kt)("h3",{id:"screening"},"Screening"),(0,n.kt)("p",null,"The goal of ",(0,n.kt)("em",{parentName:"p"},"Screening")," is to determine if imported studies are relevant to your research question, based on the the Tile\nand Abstract of each."),(0,n.kt)("p",null,"To begin, click on the Screening column header from the main Curation board. The interface is\nidentical to the previous phase, except the default Exclusion reason is now ",(0,n.kt)("strong",{parentName:"p"},"irrelevant"),". "),(0,n.kt)("p",null,"Go ahead and review all 13 studies to determine if they are relevant to the topic of ",(0,n.kt)("em",{parentName:"p"},'"Nicotine administration'),'".\nAny studies that are not relevant will remain in this column and not advance.'),(0,n.kt)("h3",{id:"eligibility"},"Eligibility"),(0,n.kt)("p",null,"The goal ",(0,n.kt)("em",{parentName:"p"},"Eligibility")," is to determine studies meet the eligibility criteria of your meta-analysis.\nYou will need to read the ",(0,n.kt)("strong",{parentName:"p"},"full text")," to make this determination. "),(0,n.kt)("admonition",{type:"note"},(0,n.kt)("p",{parentName:"admonition"},"The eligibility criteria depends on your research question!\nThis is where your expertise is most necessary, in order to create an interesting research question,\nand filter studies accordingly. The results of this step depends on the researcher, and there may be reasonable scientific disagreements.")),(0,n.kt)("p",null,'To begin, click on the "Eligibility" column header, or a study, as before.'),(0,n.kt)("p",null,(0,n.kt)("img",{alt:"Custom Eligibility",src:a(940).Z,width:"2341",height:"1449"})),(0,n.kt)("p",null,"For this example, we will include studies that meet these criteria:"),(0,n.kt)("pre",null,(0,n.kt)("code",{parentName:"pre"},"* fMRI or PET\n* Reported brain activity in stereotaxic coordinates (Talairach or MNI space)\n* Reported a set of coordinates (i.e., foci) from a within-subjects or between-subjects contrast assessing the effects of nAChR agonist administration (i.e., pharmacological administration or cigarette smoking) relative to a baseline condition (i.e., placebo administration or smoking-abstinence condition)\n* Examined brain activity using a cognitive or affective task paradigm or at rest. \n* Studies examining functional connectivity, brain morphology, or neurochemistry are excluded.\n")),(0,n.kt)("p",null,"For this tutorial, go ahead and review all studies in this column, and Promote relevant studies to the next phase."),(0,n.kt)("admonition",{type:"tip"},(0,n.kt)("p",{parentName:"admonition"},"Given that this phase is more open-ended, there is no default Exclusion reason. We provide four default options for you, and\nyou may define custom Exclusion reasons if you see fit.")),(0,n.kt)("admonition",{type:"tip"},(0,n.kt)("p",{parentName:"admonition"},'For open source studies, we will link to the article full text (PDF). If not available, click\n"view article link" to view article\'s PubMed page, and access the full text using your credentials.')),(0,n.kt)("h3",{id:"included"},"Included"),(0,n.kt)("p",null,"Congratulations! Once you have reviewed all studies, you should have a set of studies that you want to include in your meta-analysis. "),(0,n.kt)("p",null,"At this point, you can review this final list, and view a PRISMA diagram visually outlining your review process:"),(0,n.kt)("p",null,(0,n.kt)("img",{alt:"PRISMA",src:a(8718).Z,width:"1714",height:"1448"})),(0,n.kt)("p",null,"To finish ",(0,n.kt)("em",{parentName:"p"},"Curation"),", and create a final StudySet, click ",(0,n.kt)("em",{parentName:"p"},"Move on to Extraction")," at the top right. "),(0,n.kt)("h2",{id:"extract-and-annotation"},"Extract and Annotation"),(0,n.kt)("p",null,"The goal of this phase is to ",(0,n.kt)("strong",{parentName:"p"},"extract data")," from the text of studies (such as coordinates)\nthat will be used in the meta-analysis. You will also want to ",(0,n.kt)("strong",{parentName:"p"},"annotate")," relevant Study meta-data.\nMost commonly, for each Study you will designate which Analyses (i.e. Contrasts) to included in your meta-analysis."),(0,n.kt)("admonition",{type:"tip"},(0,n.kt)("p",{parentName:"admonition"},"Studies that are already indexed by NeuroStore will have automatically extracted data (such as coordinates)\nsaving you a lot of time and effort. However, you may want to verify and improve this information.")),(0,n.kt)("h3",{id:"ingestion"},"Ingestion"),(0,n.kt)("p",null,"But first, we must create a StudySet containing your studies! "),(0,n.kt)("p",null,"New studies not in the NeuroStore database will be created, and studies that match existing studies will be added to a newly created StudySet."),(0,n.kt)("admonition",{type:"info"},(0,n.kt)("p",{parentName:"admonition"},"Studies in NeuroStore have multiple Versions, including those created by other users, as well as the original copy."),(0,n.kt)("p",{parentName:"admonition"},"If you ingest a study that is already indexed in the database, we will match to the newest possible Version, by default.\nIdeally, this will be a Version that another user has already improved, saving you even more time!"),(0,n.kt)("p",{parentName:"admonition"},"You can switch Study Versions at anytime (including to the original automate copy) by clicking ",(0,n.kt)("em",{parentName:"p"},"Switch Study Version")," at the bottom\nof Study's page. Any edits you make will automatically be saved as a new Version.")),(0,n.kt)("h3",{id:"editing-studies"},"Editing studies"),(0,n.kt)("p",null,"Once your StudySet is created, it's time to edit studies. You will see a list of your studies:"),(0,n.kt)("p",null,(0,n.kt)("img",{alt:"Extraction",src:a(9774).Z,width:"1655",height:"799"})),(0,n.kt)("p",null,"Click on any Study to edit:"),(0,n.kt)("p",null,(0,n.kt)("img",{alt:"Extraction study view",src:a(5362).Z,width:"2418",height:"856"})),(0,n.kt)("h4",{id:"analysis-data"},"Analysis Data"),(0,n.kt)("p",null,"Let's start by editing a Study's Analyses."),(0,n.kt)("p",null,(0,n.kt)("img",{alt:"Extraction study view",src:a(369).Z,width:"2128",height:"923"})),(0,n.kt)("admonition",{type:"info"},(0,n.kt)("p",{parentName:"admonition"},(0,n.kt)("em",{parentName:"p"},"Analyses"),' are groups of images or coordinates reported in a given study fir a specific analysis. This is often referred to as "Contrasts", but we use the more general term "Analyses" to accommodate a wider range of fMRI models. ')),(0,n.kt)("p",null,"For new studies, we will not have any data, and it is necessary to manually enter these data from the text of a study.\nWe can create a new Analysis for each group of Coordinates, and input the data from the text:"),(0,n.kt)("p",null,"For studies already indexed by NeuroStore, we will already have pre-extracted Coordinates. You can use this same interface to verify and correct these data. "),(0,n.kt)("admonition",{type:"caution"},(0,n.kt)("p",{parentName:"admonition"},"Automatically extracted coordinates can contain a number of errors. Mostly commonly, several distinct Analyses (i.e. contrasts), will get grouped into a single Analyses. You will want to create new Analyses to split up the Coordinates into distinct units. "),(0,n.kt)("p",{parentName:"admonition"},"Another common error is that Analyses are duplicated, meaning you may want to delete extra Analyses. ")),(0,n.kt)("admonition",{type:"tip"},(0,n.kt)("p",{parentName:"admonition"},"You can copy and paste coordinates from Microsoft Excel or Google Sheets.")),(0,n.kt)("h4",{id:"annotations"},"Annotations"),(0,n.kt)("p",null,"A key goal of the Extraction phase, is to add Annotations that can help us distinguish Analyses, and include/exclude specific Analyses from a meta-analysis. Annotations are simply columns of data with a value for all Analyses within a StudySet."),(0,n.kt)("p",null,"Annotations can be explained as a way to categorize analyses within each study. For example, they can be categorized by task (e.g., Stroop, N-back, etc.), by modality (e.g., fMRI, PET, etc.), or by any other category that you want to use to filter the analyses. "),(0,n.kt)("admonition",{type:"tip"},(0,n.kt)("p",{parentName:"admonition"},'By default, a single Annotation called "included" is created, which includes all Analyses.\nYou can modify this Annotation to select the relevant Analyses of interest to your research question')),(0,n.kt)("p",null,"For this replication, we are interested in the effects of nAChR agonists on the brain, which can either be excitatory or inhibitory, so we add both an \u201cactivation\u201d and \u201cdeactivation\u201d column. "),(0,n.kt)("p",null,'First create an Annotation. From the main Extraction page, click "View Annotations" on the top right. Here you can view all annotations and the value for each Analyses, as well as create new Annotations. You can choose the data type of each column as either a Number, String, or Boolean. Selected a Boolean data type for the \u201cactivation\u201d and \u201cdeactivation\u201d columns, which will allow you to filter the analyses by whether they are excitatory or inhibitory. By default you will have an \u201cinclude\u201d column to help get you started.'),(0,n.kt)("p",null,'Below, I am creating a new Annotation for "deactivations"'),(0,n.kt)("p",null,(0,n.kt)("img",{alt:"annotation for a study",src:a(8669).Z,width:"1795",height:"758"})),(0,n.kt)("p",null,'Now, when I look at a study, I can edit the value for Analyses, assigning each group of coordinates as "activations" or "deactivations":'),(0,n.kt)("p",null,(0,n.kt)("img",{alt:"annotation for a study",src:a(4235).Z,width:"850",height:"128"})),(0,n.kt)("h2",{id:"meta-analysis-specification"},"Meta-analysis specification"),(0,n.kt)("p",null,"You are finally ready to specify a meta-analysis! "),(0,n.kt)("p",null,(0,n.kt)("img",{alt:"start meta-analysis specification",src:a(234).Z,width:"834",height:"308"})),(0,n.kt)("p",null,"After clicking ",(0,n.kt)("strong",{parentName:"p"},'"+ Meta-Analysis Specification"'),", you'll see the following dialog:"),(0,n.kt)("p",null,(0,n.kt)("img",{alt:"Meta-analysis wizard",src:a(588).Z,width:"1762",height:"1014"})),(0,n.kt)("p",null,'For each Meta-Analysis, you will select an Annotation to filter the Analyses to include.\nRemember, by default, the "included" column will be created and include ',(0,n.kt)("em",{parentName:"p"},"all")," Analyses. "),(0,n.kt)("p",null,"Next, you will ",(0,n.kt)("strong",{parentName:"p"},"select a meta-analysis Algorithm and Corrector"),":"),(0,n.kt)("p",null,(0,n.kt)("img",{alt:"Meta-analysis algorithm",src:a(3556).Z,width:"1762",height:"1014"})),(0,n.kt)("p",null,'A variety of common meta-analysis algorithms such as "ALE" and "MKDA" are available, as well as two\nstrategies for controlling for multiple comparisons: FDR (false detection rate) and FWE (family wise error).'),(0,n.kt)("p",null,'For this example, we\'ll choose "MKDADensity" and and "FDRCorrection". You can modify the parameters for each, if you want,\nbut we provide sane defaults for all. '),(0,n.kt)("admonition",{type:"tip"},(0,n.kt)("p",{parentName:"admonition"},"Learn more about meta-analysis algorithms in the ",(0,n.kt)("a",{parentName:"p",href:"https://nimare.readthedocs.io/en/stable/index.html"},"NiMARE Documentation"))),(0,n.kt)("p",null,"Next, you'll give your meta-analysis a name, and review the details of your specification. "),(0,n.kt)("p",null,(0,n.kt)("img",{alt:"Meta-analysis review",src:a(7832).Z,width:"1771",height:"1575"})),(0,n.kt)("admonition",{type:"tip"},(0,n.kt)("p",{parentName:"admonition"},"You can define multiple Meta-Analysis specifications in a Project, paired to the same StudySet")),(0,n.kt)("h2",{id:"run-your-meta-analysis"},"Run your meta-analysis!"),(0,n.kt)("p",null,"Congratulations! 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\ No newline at end of file diff --git a/assets/js/2367c334.cba4408d.js b/assets/js/2367c334.cba4408d.js new file mode 100644 index 0000000..b76bcc0 --- /dev/null +++ b/assets/js/2367c334.cba4408d.js @@ -0,0 +1 @@ +"use strict";(self.webpackChunkns_compose_docs=self.webpackChunkns_compose_docs||[]).push([[7068],{4894:(e,t,a)=>{a.r(t),a.d(t,{assets:()=>d,contentTitle:()=>l,default:()=>m,frontMatter:()=>s,metadata:()=>r,toc:()=>c});var i=a(7462),n=(a(7294),a(3905)),o=a(5154);const s={sidebar_label:"Manual Meta-Analysis",sidebar_position:1},l="Manual Meta-Analysis",r={unversionedId:"tutorial/manual",id:"tutorial/manual",title:"Manual Meta-Analysis",description:"How to create a custom, manual meta-analysis.",source:"@site/docs/tutorial/manual.md",sourceDirName:"tutorial",slug:"/tutorial/manual",permalink:"/compose-docs/tutorial/manual",draft:!1,editUrl:"https://github.com/neurostuff/compose-docs/edit/master/docs/tutorial/manual.md",tags:[],version:"current",lastUpdatedBy:"Adam Thomas",lastUpdatedAt:1722613388,formattedLastUpdatedAt:"Aug 2, 2024",sidebarPosition:1,frontMatter:{sidebar_label:"Manual Meta-Analysis",sidebar_position:1},sidebar:"tutorialSidebar",previous:{title:"Tutorials",permalink:"/compose-docs/tutorial/"},next:{title:"Automated Meta-Analysis",permalink:"/compose-docs/tutorial/automated"}},d={},c=[{value:"Creating a new Project",id:"creating-a-new-project",level:2},{value:"Search & Curate",id:"search--curate",level:2},{value:"Importing studies",id:"importing-studies",level:3},{value:"Searching PubMed",id:"searching-pubmed",level:4},{value:"Identification",id:"identification",level:3},{value:"Screening",id:"screening",level:3},{value:"Eligibility",id:"eligibility",level:3},{value:"Included",id:"included",level:3},{value:"Extract and Annotation",id:"extract-and-annotation",level:2},{value:"Ingestion",id:"ingestion",level:3},{value:"Editing studies",id:"editing-studies",level:3},{value:"Analysis Data",id:"analysis-data",level:4},{value:"Annotations",id:"annotations",level:4},{value:"Meta-analysis specification",id:"meta-analysis-specification",level:2},{value:"Run your meta-analysis!",id:"run-your-meta-analysis",level:2}],u={toc:c},p="wrapper";function m(e){let{components:t,...s}=e;return(0,n.kt)(p,(0,i.Z)({},u,s,{components:t,mdxType:"MDXLayout"}),(0,n.kt)("h1",{id:"manual-meta-analysis"},"Manual Meta-Analysis"),(0,n.kt)("p",null,(0,n.kt)("em",{parentName:"p"},"How to create a custom, manual meta-analysis.")),(0,n.kt)("admonition",{type:"info"},(0,n.kt)("p",{parentName:"admonition"},"This is a condensed adaptation of a course taught at OHBM 2023.\nSee the ",(0,n.kt)("a",{parentName:"p",href:"https://neurostuff.github.io/meta-analysis-book/"},"course materials")," for a complete overview.")),(0,n.kt)("p",null,(0,n.kt)("em",{parentName:"p"},"Neurosynth Compose")," provides a streamlined workflow to facilitate study selection and\ndata extraction. You can then easily specify a high-quality manual meta-analysis and execute it in the cloud,\nrapidly generating results that you can inspect and share."),(0,n.kt)("admonition",{type:"tip"},(0,n.kt)("p",{parentName:"admonition"},"We will perform a slimmed down replication of the following meta-analysis:\n",(0,n.kt)("a",{parentName:"p",href:"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4494985/"},"Neurobiological impact of nicotinic acetylcholine receptor agonists: An ALE meta-analysis of pharmacological neuroimaging studies"),"."),(0,n.kt)("p",{parentName:"admonition"},"For more guidance on how to choose a topic for meta-analysis, see the ",(0,n.kt)("a",{parentName:"p",href:"https://www.bmj.com/content/372/bmj.n71"},"PRISMA statement")," and the ",(0,n.kt)("a",{parentName:"p",href:"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5918306/"},"10 simple rules for neuroimaging meta-analyses"),".")),(0,n.kt)("h2",{id:"creating-a-new-project"},"Creating a new Project"),(0,n.kt)("p",null,(0,n.kt)("em",{parentName:"p"},"Projects")," contain all the Steps necessary to create a new meta-analysis in Neurosynth Compose.\nWe'll step through this process in detail."),(0,n.kt)("p",null,"To get started, ",(0,n.kt)("em",{parentName:"p"},"sign in"),", and select ",(0,n.kt)("strong",{parentName:"p"},"New Project"),". "),(0,n.kt)("p",null,"You'll now see the Project page, showing the three stages of a meta-analysis:\n",(0,n.kt)("strong",{parentName:"p"},"Search & Curate, Extract & Annotate")," and ",(0,n.kt)("strong",{parentName:"p"},"Specify Meta-Analyses"),"."),(0,n.kt)("p",null,(0,n.kt)("img",{alt:"New Project",src:a(4819).Z,width:"1557",height:"501"}),". "),(0,n.kt)("h2",{id:"search--curate"},"Search & Curate"),(0,n.kt)("p",null,"The first step in a meta-analysis is to ",(0,n.kt)("em",{parentName:"p"},"Search")," for studies, and ",(0,n.kt)("em",{parentName:"p"},"Curate")," these studies into a\nfinal ",(0,n.kt)("em",{parentName:"p"},"StudySet")," that contains the studies we want to include in a Meta-Analysis."),(0,n.kt)("p",null,"To get started, we must choose a Curation workflow. There are three options: Simple, PRISMA and Custom. The main difference between these options is the number of review steps involved in creating a final list of studies. ",(0,n.kt)("strong",{parentName:"p"},'For a rigorous Manual Meta-Analysis, select "PRISMA".')),(0,n.kt)("admonition",{type:"tip"},(0,n.kt)("p",{parentName:"admonition"},"Reviewers typically require Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) reporting\nfor gold-standard meta-analyses. The PRISMA guidelines ensure a systematic selection and reporting process.\nHowever, a Simple workflow may be useful for exploratory and automated meta-analyses. ")),(0,n.kt)("p",null,"Next, you will be presented with the Curation board, consisting of four columns representing\nthe steps of a PRISMA workflow:"),(0,n.kt)("ul",null,(0,n.kt)("li",{parentName:"ul"},(0,n.kt)("p",{parentName:"li"},(0,n.kt)("strong",{parentName:"p"},"Identification"),": Remove duplicate studies")),(0,n.kt)("li",{parentName:"ul"},(0,n.kt)("p",{parentName:"li"},(0,n.kt)("strong",{parentName:"p"},"Screening"),": Remove irrelevant studies")),(0,n.kt)("li",{parentName:"ul"},(0,n.kt)("p",{parentName:"li"},(0,n.kt)("strong",{parentName:"p"},"Elibility"),": Remove studies that do not meet inclusion criteria or do not have retrievable information")),(0,n.kt)("li",{parentName:"ul"},(0,n.kt)("p",{parentName:"li"},(0,n.kt)("strong",{parentName:"p"},"Included"),": Double check the studies and view which studies will be included in the meta-analysis"))),(0,n.kt)("p",null,"The overall goal is to go from a large number of studies from a broad search results, to only the\nstudies that are relevant to your research question. These steps should be completed in order!"),(0,n.kt)("h3",{id:"importing-studies"},"Importing studies"),(0,n.kt)("p",null,"But first, we must add studies to our Project. Click the ",(0,n.kt)("strong",{parentName:"p"},"Import Studies")," button. "),(0,n.kt)("p",null,"You can search for studies within the ",(0,n.kt)("em",{parentName:"p"},"NeuroStore")," database\u2014which we maintain and indexes over 20,000 pre-processed neuroimaging studies\u2014or from external sources, such as PubMed or a Citation manager file. "),(0,n.kt)("p",null,(0,n.kt)("img",{alt:"Import Studies",src:a(3569).Z,width:"622",height:"238"}),". "),(0,n.kt)("p",null,(0,n.kt)("em",{parentName:"p"},"Import via NeuroStore")," is simpler and faster, as these studies already exist on in our system\nand have pre-extracted imaging data (e.g. coordinates).\nAs a result, there will be fewer irrelevant studies to exclude, and less data extraction to complete."),(0,n.kt)("p",null,"However, ",(0,n.kt)("em",{parentName:"p"},"searching other sources")," is often recommended for a comprehensive literature search, as a single source can never index all possible studies."),(0,n.kt)("p",null,"To replicate the Nicotine meta-analysis, we will replicate the author's PubMed search."),(0,n.kt)("admonition",{type:"info"},(0,n.kt)("p",{parentName:"admonition"},"Searching NeuroStore is a valid option for a manual meta-analysis to balance rigor and efficiency. To learn more, see the ",(0,n.kt)("a",{parentName:"p",href:"/compose-docs/tutorial/automated"},"Automated Meta-Analysis tutorial"),". ")),(0,n.kt)("h4",{id:"searching-pubmed"},"Searching PubMed"),(0,n.kt)("p",null,"We can export any PubMed search result to file, and import that file into Neurosynth Compose.\nIn this example, we perform the following search which in the ",(0,n.kt)("a",{parentName:"p",href:"https://pubmed.ncbi.nlm.nih.gov/"},"PubMed")," search field:"),(0,n.kt)("pre",null,(0,n.kt)("code",{parentName:"pre"},'("fMRI" OR "PET" OR "neuroimaging" OR "Functional magnetic resonance imaging" OR "functional MRI") \nAND ("nicotine" OR "cigarette" OR "smok*" OR "DMXB-A")\n')),(0,n.kt)("p",null,"This results in over 3,000 studies that are potentially relevant for this meta-analysis."),(0,n.kt)("p",null,"Next, we save the results to ",(0,n.kt)("inlineCode",{parentName:"p"},"PMID")," format, with a PubMed ID on each line."),(0,n.kt)("p",null,(0,n.kt)("img",{alt:"Download PubMed",src:a(6652).Z,width:"1053",height:"475"})),(0,n.kt)("admonition",{type:"info"},(0,n.kt)("p",{parentName:"admonition"},"Reviewing 3,000 studies can take a long time!\nOnly a small percentage of these studies will meet all inclusion criteria. For the sake of the tutorial, we provide a shortened list of ",(0,n.kt)(o.aBF,{size:20,mdxType:"FaDownload"})," ",(0,n.kt)("a",{target:"_blank",href:a(7555).Z},"PUBMED IDS"),".")),(0,n.kt)("p",null,"We can import this file into our Project by clicking ",(0,n.kt)("strong",{parentName:"p"},"Upload File"),". Give this import a name for future reference.\nAll studies imported from this search will be ",(0,n.kt)("strong",{parentName:"p"},"Tagged")," with the search name. "),(0,n.kt)("p",null,(0,n.kt)("img",{alt:"Import name",src:a(212).Z,width:"1282",height:"354"})),(0,n.kt)("h3",{id:"identification"},"Identification"),(0,n.kt)("p",null,"All the imported studies are now visible in the first column of our PRISMA curation board. "),(0,n.kt)("p",null,(0,n.kt)("img",{alt:"Identification",src:a(8665).Z,width:"3595",height:"939"})),(0,n.kt)("p",null,"The purpose ",(0,n.kt)("strong",{parentName:"p"},"Identification")," is to find ",(0,n.kt)("em",{parentName:"p"},"duplicate studies"),", which is common when importing from multiple sources.\n",(0,n.kt)("em",{parentName:"p"},"Neurosynth Compose")," will automatically find potential duplicates (based on the Title, DOI, and PMID).\nHowever, you can also manually review studies to identify any duplicates we might have missed."),(0,n.kt)("p",null,"To review studies, click on the ",(0,n.kt)("strong",{parentName:"p"},"Identification"),' column header, or an individual study.\nFor each study, you can choose to "Promote" it to the next phase, "Exclude" as a duplicate, or flag for later review.'),(0,n.kt)("p",null,(0,n.kt)("img",{alt:"Identification review",src:a(3615).Z,width:"2251",height:"1211"})),(0,n.kt)("admonition",{type:"tip"},(0,n.kt)("p",{parentName:"admonition"},"To remain PRISMA compliant, you should only exclude studies for being a duplicate in the Identification step.")),(0,n.kt)("p",null,"In this example, there are no duplicates. To quickly advance, click ",(0,n.kt)("strong",{parentName:"p"},"Promote All Uncategorized Studies")," under ",(0,n.kt)("em",{parentName:"p"},"Identification"),"."),(0,n.kt)("h3",{id:"screening"},"Screening"),(0,n.kt)("p",null,"The goal of ",(0,n.kt)("em",{parentName:"p"},"Screening")," is to determine if imported studies are relevant to your research question, based on the the Tile\nand Abstract of each."),(0,n.kt)("p",null,"To begin, click on the Screening column header from the main Curation board. The interface is\nidentical to the previous phase, except the default Exclusion reason is now ",(0,n.kt)("strong",{parentName:"p"},"irrelevant"),". "),(0,n.kt)("p",null,"Go ahead and review all 13 studies to determine if they are relevant to the topic of ",(0,n.kt)("em",{parentName:"p"},'"Nicotine administration'),'".\nAny studies that are not relevant will remain in this column and not advance.'),(0,n.kt)("h3",{id:"eligibility"},"Eligibility"),(0,n.kt)("p",null,"The goal ",(0,n.kt)("em",{parentName:"p"},"Eligibility")," is to determine studies meet the eligibility criteria of your meta-analysis.\nYou will need to read the ",(0,n.kt)("strong",{parentName:"p"},"full text")," to make this determination. "),(0,n.kt)("admonition",{type:"note"},(0,n.kt)("p",{parentName:"admonition"},"The eligibility criteria depends on your research question!\nThis is where your expertise is most necessary, in order to create an interesting research question,\nand filter studies accordingly. The results of this step depends on the researcher, and there may be reasonable scientific disagreements.")),(0,n.kt)("p",null,'To begin, click on the "Eligibility" column header, or a study, as before.'),(0,n.kt)("p",null,(0,n.kt)("img",{alt:"Custom Eligibility",src:a(940).Z,width:"2341",height:"1449"})),(0,n.kt)("p",null,"For this example, we will include studies that meet these criteria:"),(0,n.kt)("pre",null,(0,n.kt)("code",{parentName:"pre"},"* fMRI or PET\n* Reported brain activity in stereotaxic coordinates (Talairach or MNI space)\n* Reported a set of coordinates (i.e., foci) from a within-subjects or between-subjects contrast assessing the effects of nAChR agonist administration (i.e., pharmacological administration or cigarette smoking) relative to a baseline condition (i.e., placebo administration or smoking-abstinence condition)\n* Examined brain activity using a cognitive or affective task paradigm or at rest. \n* Studies examining functional connectivity, brain morphology, or neurochemistry are excluded.\n")),(0,n.kt)("p",null,"For this tutorial, go ahead and review all studies in this column, and Promote relevant studies to the next phase."),(0,n.kt)("admonition",{type:"tip"},(0,n.kt)("p",{parentName:"admonition"},"Given that this phase is more open-ended, there is no default Exclusion reason. We provide four default options for you, and\nyou may define custom Exclusion reasons if you see fit.")),(0,n.kt)("admonition",{type:"tip"},(0,n.kt)("p",{parentName:"admonition"},'For open source studies, we will link to the article full text (PDF). If not available, click\n"view article link" to view article\'s PubMed page, and access the full text using your credentials.')),(0,n.kt)("h3",{id:"included"},"Included"),(0,n.kt)("p",null,"Congratulations! Once you have reviewed all studies, you should have a set of studies that you want to include in your meta-analysis. "),(0,n.kt)("p",null,"At this point, you can review this final list, and view a PRISMA diagram visually outlining your review process:"),(0,n.kt)("p",null,(0,n.kt)("img",{alt:"PRISMA",src:a(8718).Z,width:"1714",height:"1448"})),(0,n.kt)("p",null,"To finish ",(0,n.kt)("em",{parentName:"p"},"Curation"),", and create a final StudySet, click ",(0,n.kt)("em",{parentName:"p"},"Move on to Extraction")," at the top right. "),(0,n.kt)("h2",{id:"extract-and-annotation"},"Extract and Annotation"),(0,n.kt)("p",null,"The goal of this phase is to ",(0,n.kt)("strong",{parentName:"p"},"extract data")," from the text of studies (such as coordinates)\nthat will be used in the meta-analysis. You will also want to ",(0,n.kt)("strong",{parentName:"p"},"annotate")," relevant Study meta-data.\nMost commonly, for each Study you will designate which Analyses (i.e. Contrasts) to included in your meta-analysis."),(0,n.kt)("admonition",{type:"tip"},(0,n.kt)("p",{parentName:"admonition"},"Studies that are already indexed by NeuroStore will have automatically extracted data (such as coordinates)\nsaving you a lot of time and effort. However, you may want to verify and improve this information.")),(0,n.kt)("h3",{id:"ingestion"},"Ingestion"),(0,n.kt)("p",null,"But first, we must create a StudySet containing your studies! "),(0,n.kt)("p",null,"New studies not in the NeuroStore database will be created, and studies that match existing studies will be added to a newly created StudySet."),(0,n.kt)("admonition",{type:"info"},(0,n.kt)("p",{parentName:"admonition"},"Studies in NeuroStore have multiple Versions, including those created by other users, as well as the original copy."),(0,n.kt)("p",{parentName:"admonition"},"If you ingest a study that is already indexed in the database, we will match to the newest possible Version, by default.\nIdeally, this will be a Version that another user has already improved, saving you even more time!"),(0,n.kt)("p",{parentName:"admonition"},"You can switch Study Versions at anytime (including to the original automate copy) by clicking ",(0,n.kt)("em",{parentName:"p"},"Switch Study Version")," at the bottom\nof Study's page. Any edits you make will automatically be saved as a new Version.")),(0,n.kt)("h3",{id:"editing-studies"},"Editing studies"),(0,n.kt)("p",null,"Once your StudySet is created, it's time to edit studies. You will see a list of your studies:"),(0,n.kt)("p",null,(0,n.kt)("img",{alt:"Extraction",src:a(9774).Z,width:"1655",height:"799"})),(0,n.kt)("p",null,"Click on any Study to edit:"),(0,n.kt)("p",null,(0,n.kt)("img",{alt:"Extraction study view",src:a(5362).Z,width:"2418",height:"856"})),(0,n.kt)("h4",{id:"analysis-data"},"Analysis Data"),(0,n.kt)("p",null,"Let's start by editing a Study's Analyses."),(0,n.kt)("p",null,(0,n.kt)("img",{alt:"Extraction study view",src:a(369).Z,width:"2128",height:"923"})),(0,n.kt)("admonition",{type:"info"},(0,n.kt)("p",{parentName:"admonition"},(0,n.kt)("em",{parentName:"p"},"Analyses"),' are groups of images or coordinates reported in a given study fir a specific analysis. This is often referred to as "Contrasts", but we use the more general term "Analyses" to accommodate a wider range of fMRI models. ')),(0,n.kt)("p",null,"For new studies, we will not have any data, and it is necessary to manually enter these data from the text of a study.\nWe can create a new Analysis for each group of Coordinates, and input the data from the text:"),(0,n.kt)("p",null,"For studies already indexed by NeuroStore, we will already have pre-extracted Coordinates. You can use this same interface to verify and correct these data. "),(0,n.kt)("admonition",{type:"caution"},(0,n.kt)("p",{parentName:"admonition"},"Automatically extracted coordinates can contain a number of errors. Mostly commonly, several distinct Analyses (i.e. contrasts), will get grouped into a single Analyses. You will want to create new Analyses to split up the Coordinates into distinct units. "),(0,n.kt)("p",{parentName:"admonition"},"Another common error is that Analyses are duplicated, meaning you may want to delete extra Analyses. ")),(0,n.kt)("admonition",{type:"tip"},(0,n.kt)("p",{parentName:"admonition"},"You can copy and paste coordinates from Microsoft Excel or Google Sheets.")),(0,n.kt)("h4",{id:"annotations"},"Annotations"),(0,n.kt)("p",null,"A key goal of the Extraction phase, is to add Annotations that can help us distinguish Analyses, and include/exclude specific Analyses from a meta-analysis. Annotations are simply columns of data with a value for all Analyses within a StudySet."),(0,n.kt)("p",null,"Annotations can be explained as a way to categorize analyses within each study. For example, they can be categorized by task (e.g., Stroop, N-back, etc.), by modality (e.g., fMRI, PET, etc.), or by any other category that you want to use to filter the analyses. "),(0,n.kt)("admonition",{type:"tip"},(0,n.kt)("p",{parentName:"admonition"},'By default, a single Annotation called "included" is created, which includes all Analyses.\nYou can modify this Annotation to select the relevant Analyses of interest to your research question')),(0,n.kt)("p",null,"For this replication, we are interested in the effects of nAChR agonists on the brain, which can either be excitatory or inhibitory, so we add both an \u201cactivation\u201d and \u201cdeactivation\u201d column. "),(0,n.kt)("p",null,'First create an Annotation. From the main Extraction page, click "View Annotations" on the top right. Here you can view all annotations and the value for each Analyses, as well as create new Annotations. You can choose the data type of each column as either a Number, String, or Boolean. Selected a Boolean data type for the \u201cactivation\u201d and \u201cdeactivation\u201d columns, which will allow you to filter the analyses by whether they are excitatory or inhibitory. By default you will have an \u201cinclude\u201d column to help get you started.'),(0,n.kt)("p",null,'Below, I am creating a new Annotation for "deactivations"'),(0,n.kt)("p",null,(0,n.kt)("img",{alt:"annotation for a study",src:a(8669).Z,width:"1795",height:"758"})),(0,n.kt)("p",null,'Now, when I look at a study, I can edit the value for Analyses, assigning each group of coordinates as "activations" or "deactivations":'),(0,n.kt)("p",null,(0,n.kt)("img",{alt:"annotation for a study",src:a(4235).Z,width:"850",height:"128"})),(0,n.kt)("h2",{id:"meta-analysis-specification"},"Meta-analysis specification"),(0,n.kt)("p",null,"You are finally ready to specify a meta-analysis! "),(0,n.kt)("p",null,(0,n.kt)("img",{alt:"start meta-analysis specification",src:a(234).Z,width:"834",height:"308"})),(0,n.kt)("p",null,"After clicking ",(0,n.kt)("strong",{parentName:"p"},'"+ Meta-Analysis Specification"'),", you'll see the following dialog:"),(0,n.kt)("p",null,(0,n.kt)("img",{alt:"Meta-analysis wizard",src:a(588).Z,width:"1762",height:"1014"})),(0,n.kt)("p",null,'For each Meta-Analysis, you will select an Annotation to filter the Analyses to include.\nRemember, by default, the "included" column will be created and include ',(0,n.kt)("em",{parentName:"p"},"all")," Analyses. "),(0,n.kt)("p",null,"Next, you will ",(0,n.kt)("strong",{parentName:"p"},"select a meta-analysis Algorithm and Corrector"),":"),(0,n.kt)("p",null,(0,n.kt)("img",{alt:"Meta-analysis algorithm",src:a(3556).Z,width:"1762",height:"1014"})),(0,n.kt)("p",null,'A variety of common meta-analysis algorithms such as "ALE" and "MKDA" are available, as well as two\nstrategies for controlling for multiple comparisons: FDR (false detection rate) and FWE (family wise error).'),(0,n.kt)("p",null,'For this example, we\'ll choose "MKDADensity" and and "FDRCorrection". You can modify the parameters for each, if you want,\nbut we provide sane defaults for all. '),(0,n.kt)("admonition",{type:"tip"},(0,n.kt)("p",{parentName:"admonition"},"Learn more about meta-analysis algorithms in the ",(0,n.kt)("a",{parentName:"p",href:"https://nimare.readthedocs.io/en/stable/index.html"},"NiMARE Documentation"))),(0,n.kt)("p",null,"Next, you'll give your meta-analysis a name, and review the details of your specification. "),(0,n.kt)("p",null,(0,n.kt)("img",{alt:"Meta-analysis review",src:a(7832).Z,width:"1771",height:"1575"})),(0,n.kt)("admonition",{type:"tip"},(0,n.kt)("p",{parentName:"admonition"},"You can define multiple Meta-Analysis specifications in a Project, paired to the same StudySet")),(0,n.kt)("h2",{id:"run-your-meta-analysis"},"Run your meta-analysis!"),(0,n.kt)("p",null,"Congratulations! You now have a Meta-Analysis specification that is ready to run."),(0,n.kt)("p",null,"You can execute your Meta-Analysis for free in the cloud on Google Colab by copying the unique ",(0,n.kt)("em",{parentName:"p"},"meta-analysis id"),"\nand pasting it into our Google Colab notebook."),(0,n.kt)("p",null,(0,n.kt)("img",{alt:"Meta-analysis run",src:a(3349).Z,width:"2994",height:"1030"})))}m.isMDXComponent=!0},7555:(e,t,a)=>{a.d(t,{Z:()=>i});const i=a.p+"assets/files/tutorial_pmids-434d072c0087956ffdef61013ba6d0da.txt"},369:(e,t,a)=>{a.d(t,{Z:()=>i});const i=a.p+"assets/images/add_coordinates-8bea486d4904648bcafa7ea0aa3ccf05.png"},5362:(e,t,a)=>{a.d(t,{Z:()=>i});const i=a.p+"assets/images/annotate_and_extract_study-ca3e4d3930ebf9dc60ad23185a36eefc.png"},8669:(e,t,a)=>{a.d(t,{Z:()=>i});const i=a.p+"assets/images/annotation_create-1b42a849c3fa25379a4492e899c883bd.png"},4235:(e,t,a)=>{a.d(t,{Z:()=>i});const i=a.p+"assets/images/annotation_view-bb152fb9777b96fa48ecf95ada750d97.png"},940:(e,t,a)=>{a.d(t,{Z:()=>i});const i=a.p+"assets/images/custom_exclusion-16ad73f8fbab6041651f04bf3deb4f80.png"},6652:(e,t,a)=>{a.d(t,{Z:()=>i});const i=a.p+"assets/images/download_search_results-d5ec087db9b8527d9891715d987540a0.png"},9774:(e,t,a)=>{a.d(t,{Z:()=>i});const i=a.p+"assets/images/extraction_and_annotation-3f2588aa81794ea975ca13e54b3ae968.png"},8665:(e,t,a)=>{a.d(t,{Z:()=>i});const i=a.p+"assets/images/identification-7f834ceb21aa8027ee29b527ead7700d.png"},3615:(e,t,a)=>{a.d(t,{Z:()=>i});const i=a.p+"assets/images/identification_review-95da15912e8e3b0ecd45612d88ce8ae7.png"},212:(e,t,a)=>{a.d(t,{Z:()=>i});const i=a.p+"assets/images/import_name-47b2b52fb0476898def227448666b9c9.png"},3569:(e,t,a)=>{a.d(t,{Z:()=>i});const i=a.p+"assets/images/import_studies_options-d58951af444810f76b35e77050c7deb4.png"},3556:(e,t,a)=>{a.d(t,{Z:()=>i});const i=a.p+"assets/images/ma_algorithm-76b551077f36af384173d025dd8d39cb.png"},7832:(e,t,a)=>{a.d(t,{Z:()=>i});const i=a.p+"assets/images/ma_review-dca9072320631a4c2b7bd8a3939763bf.png"},3349:(e,t,a)=>{a.d(t,{Z:()=>i});const i=a.p+"assets/images/ma_run-21ff60898e902524975d8fc965d4a08c.png"},588:(e,t,a)=>{a.d(t,{Z:()=>i});const i=a.p+"assets/images/ma_wizard-94f17423e2d4a4df7e23f88cb26c4595.png"},4819:(e,t,a)=>{a.d(t,{Z:()=>i});const i=a.p+"assets/images/new_project_view-865234e6c83825dde8320121d1d9c773.png"},8718:(e,t,a)=>{a.d(t,{Z:()=>i});const i=a.p+"assets/images/prisma_diagram-399aba8441e1b00d3ffb3cdc7b5c6d9c.png"},234:(e,t,a)=>{a.d(t,{Z:()=>i});const i=a.p+"assets/images/proceed_meta_analysis-ccd673905b3f5425823cc947b282bd48.png"}}]); \ No newline at end of file diff --git a/assets/js/4c0219fe.7dd70b81.js b/assets/js/4c0219fe.7dd70b81.js new file mode 100644 index 0000000..059eb9e --- /dev/null +++ b/assets/js/4c0219fe.7dd70b81.js @@ -0,0 +1 @@ +"use strict";(self.webpackChunkns_compose_docs=self.webpackChunkns_compose_docs||[]).push([[3730],{5267:(e,t,a)=>{a.r(t),a.d(t,{assets:()=>c,contentTitle:()=>r,default:()=>h,frontMatter:()=>o,metadata:()=>l,toc:()=>d});var n=a(7462),i=(a(7294),a(3905)),s=a(6464);const o={sidebar_label:"MKDA Chi-Squared Association",sidebar_position:3},r="MKDA Chi-Squared and large-scale association tests",l={unversionedId:"tutorial/advanced/mkda_association",id:"tutorial/advanced/mkda_association",title:"MKDA Chi-Squared and large-scale association tests",description:"How to perform large-scale association tests using MKDA Chi-Squared Meta-Analysis, with a Social Processing example",source:"@site/docs/tutorial/advanced/mkda_association.md",sourceDirName:"tutorial/advanced",slug:"/tutorial/advanced/mkda_association",permalink:"/compose-docs/tutorial/advanced/mkda_association",draft:!1,editUrl:"https://github.com/neurostuff/compose-docs/edit/master/docs/tutorial/advanced/mkda_association.md",tags:[],version:"current",lastUpdatedBy:"Adam Thomas",lastUpdatedAt:1722613388,formattedLastUpdatedAt:"Aug 2, 2024",sidebarPosition:3,frontMatter:{sidebar_label:"MKDA Chi-Squared Association",sidebar_position:3},sidebar:"tutorialSidebar",previous:{title:"Advanced tutorials",permalink:"/compose-docs/tutorial/advanced/"}},c={},d=[{value:"The Reverse Inference Problem",id:"the-reverse-inference-problem",level:2},{value:"MKDA Chi-Squared",id:"mkda-chi-squared",level:2},{value:"How to run MKDA Chi-Squared on Neurosynth Compose",id:"how-to-run-mkda-chi-squared-on-neurosynth-compose",level:2},{value:"Specification",id:"specification",level:3},{value:"Executing your analysis",id:"executing-your-analysis",level:3},{value:"Interpreting results",id:"interpreting-results",level:2},{value:"Example: Pintos Lobo (2022) - All Social Processing Tasks",id:"example-pintos-lobo-2022---all-social-processing-tasks",level:2},{value:"Results",id:"results",level:3},{value:"Footnotes & Limitations",id:"footnotes--limitations",level:2},{value:"References & Further Reading",id:"references--further-reading",level:2}],p={toc:d},m="wrapper";function h(e){let{components:t,...o}=e;return(0,i.kt)(m,(0,n.Z)({},p,o,{components:t,mdxType:"MDXLayout"}),(0,i.kt)("h1",{id:"mkda-chi-squared-and-large-scale-association-tests"},"MKDA Chi-Squared and large-scale association tests"),(0,i.kt)("p",null,(0,i.kt)("em",{parentName:"p"},"How to perform large-scale association tests using MKDA Chi-Squared Meta-Analysis, with a Social Processing example")),(0,i.kt)("h2",{id:"the-reverse-inference-problem"},"The Reverse Inference Problem"),(0,i.kt)("p",null,"A common goal of neuroimaging meta-analysis, is to pool a set of studies that invoke common psychological constructs to identify where brain activity is consistently activated."),(0,i.kt)("p",null,"Although this is a useful approach, there is a significant inferential challenge-- namely, determining how ",(0,i.kt)("em",{parentName:"p"},"specific")," the relationship between activity in a given region and the cognitive state invoked by the target task. Ideally, we would like to infer the probability of a mental state given activity in a given region. However, this is exceedingly difficult due to the well-established problem of ",(0,i.kt)("em",{parentName:"p"},"reverse inference")," (Poldrack, 2011). "),(0,i.kt)("p",null,"Even if we establish that a given task (e.g. working memory) activates a region 100% of the time (e.g. lateral prefrontal cortex), this observation only establishes that working memory engagement is a sufficient condition for LPFC activity, but ",(0,i.kt)("em",{parentName:"p"},"not")," that LPFC activity indicates working memory engagement (Poldrack & Yarkoni, 2015). In practice, we know that brain regions are activated by a variety of cognitive processes, and that certain regions of the brain- such as the insula, lateral PFC and medial frontal cingulate cotex (MFCC)- have a high base rate of activation, making it difficult to establish specificity. Using the Neurosynth database (15,000+ studies), we can map the probability of activation of all voxels. Across this large and diverse dataset, certain voxels in MFCC and insula are activate in as many as 20% of studies. "),(0,i.kt)("p",null,(0,i.kt)("img",{alt:"Prob-A",src:a(9569).Z,width:"1000",height:"180"}),"\n",(0,i.kt)("em",{parentName:"p"},"Probability of Activity for all Voxels across the Neurosynth Dataset")),(0,i.kt)("p",null,"The reverse inference problem is a challenge even for rigorous, high-quality meta-analyses. For example, a recent meta-analysis of RDoC social constructs across 864 fMRI contrasts, ",(0,i.kt)("a",{parentName:"p",href:"https://pubmed.ncbi.nlm.nih.gov/36436737/"},"Pintos Lobo et al., (2022)"),' found converging activation across a variety of regions for "All Social Processing Tasks", including mPFC, ACC, PCC, TPJ, bilateral insula, amygdala, fusiform gyrus, precuneus, and thalamus. However, some of the regions have a high base rate of activation, making it difficult to know how strongly associated their activity is with social processing.'),(0,i.kt)("p",null,(0,i.kt)("img",{alt:"Lobos Pinto",src:a(9377).Z,width:"517",height:"135"})),(0,i.kt)("p",null,(0,i.kt)("em",{parentName:"p"},"Fig 5a (condensed) from Pintos Lobo et al., (2022). Convergent Activation Patterns Across all social processing tasks (864 contrasts, 1,109 total annotations). ")),(0,i.kt)("p",null,"Although reverse inference poses a serious challenge, there are certain questions we can ask using large-scale meta-analytic databases that can help. Specifically: ",(0,i.kt)("strong",{parentName:"p"},"does activity occur ",(0,i.kt)("em",{parentName:"strong"},"more consistently")," for studies that elicit by the mental construct of interest (in this case, social processing) than studies that ",(0,i.kt)("em",{parentName:"strong"},"do not")," elicit that construct")," Large-scale meta-analytic datasets can serve as a useful reference, as they consists of tens of thousands of diverse neuroimaging studies automatically sampled from the literature."),(0,i.kt)("h2",{id:"mkda-chi-squared"},"MKDA Chi-Squared"),(0,i.kt)("p",null,"We can answer this question using a ",(0,i.kt)("inlineCode",{parentName:"p"},"Multilevel kernel density (MKDA) analysis - Chi-square")," analysis, originally introduced in ",(0,i.kt)("a",{parentName:"p",href:"https://doi.org/10.1093/scan/nsm015"},"Wager et al.,"),". For every voxel, we test if a greater proportion of studies in our meta-analysis activate a given voxel than in a large set of studies that ",(0,i.kt)("em",{parentName:"p"},"we did not select")," for our inclusion criteria. "),(0,i.kt)("p",null,"Conceptually, this tests if there's evidence of a ",(0,i.kt)("em",{parentName:"p"},"population level")," association between the task or psychological construct in our meta-analysis and brain activation (for every voxel). It is equivalent to conducting a chi-squared test of independence for a 2-by-2 table of counts for each voxel, where the binary variables are foci occurrence in the meta-analysis of interest and foci occurrence in the reference set of unselected studies."),(0,i.kt)("h2",{id:"how-to-run-mkda-chi-squared-on-neurosynth-compose"},"How to run MKDA Chi-Squared on Neurosynth Compose"),(0,i.kt)("h3",{id:"specification"},"Specification"),(0,i.kt)("p",null,"Specifying an MKDA Chi-Square meta-analysis in Neurosynth is easy. Simply, select a target set of Analyses to include from your StudySet as you would for any other meta-analysis. "),(0,i.kt)("p",null,'In Step 3 ("Create Meta-Analysis Specification") of your Project, select ',(0,i.kt)("em",{parentName:"p"},"MKDAChi2")," as the ",(0,i.kt)("em",{parentName:"p"},"algorithm"),". "),(0,i.kt)("p",null,(0,i.kt)("img",{alt:"MKDA Chi Squared",src:a(541).Z,width:"1169",height:"408"})),(0,i.kt)("admonition",{type:"note"},(0,i.kt)("p",{parentName:"admonition"},"By default, the ",(0,i.kt)("inlineCode",{parentName:"p"},"FDRCorrector")," is selected, which will perform cluster correction using False Detection Rate with an ",(0,i.kt)("em",{parentName:"p"},"alpha")," of 0.05.\nThis is a fast algorithm, however, it is recommended to use ",(0,i.kt)("inlineCode",{parentName:"p"},"FWECorrector")," (family-wise-error) with the ",(0,i.kt)("inlineCode",{parentName:"p"},"montecarlo")," method for more accurate, publication-quality results.")),(0,i.kt)("p",null,'Next, select the annotation inclusion column you want to use, as before (by default, the "included" column will be used).'),(0,i.kt)("p",null,"Now, select a reference dataset from the dropdown list below. The Neurosynth dataset represents the latest release of the legacy ",(0,i.kt)("em",{parentName:"p"},"Neurosynth")," dataset (version 7), released July, 2018. The ",(0,i.kt)("em",{parentName:"p"},"Neurostore"),' dataset represents the latest update of our continuously updating "live" dataset, spanning over 20,000 neuroimaging studies. '),(0,i.kt)("p",null,(0,i.kt)("img",{alt:"MKDA Chi Squared Reference",src:a(6344).Z,width:"1136",height:"243"})),(0,i.kt)("p",null,"Now simply complete the rest of the meta-analysis specification wizard to finish. "),(0,i.kt)("h3",{id:"executing-your-analysis"},"Executing your analysis"),(0,i.kt)("p",null,"As usual, you can execute your meta-analysis using Google Colab or on a local computational resource using Docker. "),(0,i.kt)("admonition",{type:"tip"},(0,i.kt)("p",{parentName:"admonition"},"The ",(0,i.kt)("inlineCode",{parentName:"p"},"MKDAChi2")," algorithm takes between ~30s-2minutes to run. However, the ",(0,i.kt)("inlineCode",{parentName:"p"},"FWECorrector")," with 5,000+ montecarlo iterations can take several hours to complete.\nWe recommend using a workstation or HPC and specifying ",(0,i.kt)("inlineCode",{parentName:"p"},"--n-cores")," at run-time.")),(0,i.kt)("h2",{id:"interpreting-results"},"Interpreting results"),(0,i.kt)("p",null,"The ",(0,i.kt)("em",{parentName:"p"},"MKDA Chi-Squared")," Workflow outputs two key maps: ",(0,i.kt)("strong",{parentName:"p"},"uniformity")," and ",(0,i.kt)("strong",{parentName:"p"},"association")," test maps."),(0,i.kt)("ul",null,(0,i.kt)("li",{parentName:"ul"},(0,i.kt)("strong",{parentName:"li"},"Uniformity test map:")," z-scores from a one-way ANOVA testing whether the proportion of studies that report activation at a given voxel differs from the rate that would be expected if activations were uniformly distributed throughout gray matter.")),(0,i.kt)("p",null,'The uniformity test map can be interpreted in roughly the same way as most standard whole-brain fMRI analysis: it displays the degree to which each voxel is consistently activated in studies that use a given term. For instance, for a meta-analysis of "emotion" high z-scores in the amygdala implies that studies that use the word emotion a lot tend to consistently report activation in the amygdala--at least, more consistently than one would expect if activation were uniformly distributed throughout gray matter. '),(0,i.kt)("ul",null,(0,i.kt)("li",{parentName:"ul"},(0,i.kt)("strong",{parentName:"li"},"Association test map"),": z-scores from a two-way ANOVA testing for the presence of a non-zero association between term use and voxel activation.")),(0,i.kt)("p",null,"The association test maps tell you whether activation in a region ",(0,i.kt)("strong",{parentName:"p"},"XXX")," occurs more consistently for studies in your meta-analytic sample ",(0,i.kt)("strong",{parentName:"p"},"m")," than for other studies in the reference dataset. In other words, a large positive z-score implies that studies in a meta-analysis are more likely to report ",(0,i.kt)("strong",{parentName:"p"},"XXX")," activation than studies whose abstracts don't include the word 'emotion'. "),(0,i.kt)("p",null,"Note that association maps ",(0,i.kt)("em",{parentName:"p"},"do not")," tell you what the probability of a given psychological concept or task is. High Z-scores do not imply that a certain region or voxel is ",(0,i.kt)("em",{parentName:"p"},"selective")," for a given concept or task. Instead, it just means there is evidence that there is at least a non-zero difference between reference studies, and studies in the meta-analysis."),(0,i.kt)("admonition",{type:"note"},(0,i.kt)("p",{parentName:"admonition"},(0,i.kt)("em",{parentName:"p"},"NiMARE")," outputs a variety of maps, including cluster-corrected and uncorrected versions of all maps. "),(0,i.kt)("p",{parentName:"admonition"},"See the documentation sections on ",(0,i.kt)("a",{parentName:"p",href:"https://nimare.readthedocs.io/en/stable/outputs.html"},"Outputs of NIMARE")," and ",(0,i.kt)("a",{parentName:"p",href:"https://nimare.readthedocs.io/en/stable/cbma.html#the-monte-carlo-multiple-comparisons-correction-method"},"Monte Carlo multiple comparisons")," for more details.")),(0,i.kt)("h2",{id:"example-pintos-lobo-2022---all-social-processing-tasks"},"Example: Pintos Lobo (2022) - All Social Processing Tasks"),(0,i.kt)("p",null,"To demonstrate, we used Neurosynth-Compose to replicate the ",(0,i.kt)("a",{parentName:"p",href:"https://pubmed.ncbi.nlm.nih.gov/36436737/"},"Pintos Lobo et al., (2022)")," meta-analysis for All Social Processing Tasks. For this example, we have already created a ",(0,i.kt)("inlineCode",{parentName:"p"},"Project")," and ",(0,i.kt)("inlineCode",{parentName:"p"},"StudySet")," with the coordinates used in this meta-analysis."),(0,i.kt)("p",null,"We then specified a ",(0,i.kt)("inlineCode",{parentName:"p"},"MKDAChi2")," Meta-Analysis with ",(0,i.kt)("inlineCode",{parentName:"p"},"FWECorrector")," with the ",(0,i.kt)("inlineCode",{parentName:"p"},"montecarlo")," method with 5,000 iterations. "),(0,i.kt)(s.Z,{variant:"contained",color:"primary",href:"https://compose.neurosynth.org/projects/4x4NsrWg8heS/meta-analyses/7K9BVG9hJQRu",mdxType:"Button"},"Meta-Analysis Specification and Results on Neurosynth Compose"),(0,i.kt)("h3",{id:"results"},"Results"),(0,i.kt)("p",null,"First, let's look at the FWE cluster corrected ",(0,i.kt)("strong",{parentName:"p"},"uniformity test")," map."),(0,i.kt)("p",null,(0,i.kt)("inlineCode",{parentName:"p"},"z_desc-uniformityMass_level-cluster_corr-FWE_method-montecarlo"),"\n",(0,i.kt)("img",{alt:"Uniformity",src:a(4568).Z,width:"950",height:"180"})),(0,i.kt)("p",null,"In this analysis, we replicate the findings of Pinto Lobos (2022), showing consistent activation for social processing across a variety of regions."),(0,i.kt)("p",null,"Next, let's look at the FWE cluster corrected ",(0,i.kt)("strong",{parentName:"p"},"association map"),":"),(0,i.kt)("p",null,(0,i.kt)("inlineCode",{parentName:"p"},"z_desc-associationMass_level-cluster_corr-FWE_method-montecarlo"),"\n",(0,i.kt)("img",{alt:"Association",src:a(2167).Z,width:"950",height:"180"})),(0,i.kt)("p",null,"As before, regions which have been previously implicated with social processing, such as the tempo-parietal junction (TPJ), and dorso-medial and ventro-medial PFC are present, meaning that activity in these social processing studies report activity in these regions with greater frequency than other studies in the Neurosynth database."),(0,i.kt)("p",null,"However, certain regions which we know to have low specificity, such as the insula, medial frontal cingulate cortex (MFCC) and parts of dorso-lateral PFC, are absent, meaning that there is ",(0,i.kt)("em",{parentName:"p"},"no evidence")," that social processing tasks report activity in these regions ",(0,i.kt)("em",{parentName:"p"},"more frequently")," than other studies in the database."),(0,i.kt)("p",null,"This example demonstrates how ",(0,i.kt)("inlineCode",{parentName:"p"},"MKDA Chi-Squared")," association analysis can help determine the specificity activity and tasks in a meta-analysis, even for high-quality manual meta-analyses."),(0,i.kt)("h2",{id:"footnotes--limitations"},"Footnotes & Limitations"),(0,i.kt)("p",null,(0,i.kt)("strong",{parentName:"p"},'What happened to the "forward inference" and "reverse inference" maps?')),(0,i.kt)("p",null,'We renamed the pre-generated forward and reverse inference maps; they\'re now referred to as the "uniformity test" and "association test" maps that we discuss here.'),(0,i.kt)("p",null,"Although the method we used hasn't changed (",(0,i.kt)("inlineCode",{parentName:"p"},"MKDA Chi-Squared"),"), the latter names more accurately capture what these maps actually mean. It was a mistake on our part to have used the forward and reverse inference labels; those labels should properly be reserved for posterior probability maps generated via a Bayesian estimation analysis, rather than for z-scores resulting from a frequentist inferential test of association. Probability maps are more difficult to interpret and use correctly, as they depend on the ",(0,i.kt)("em",{parentName:"p"},"prior")," assumed by the researcher. Since setting an appropriate prior is highly non-trivial, these maps are disabled by default."),(0,i.kt)("p",null,(0,i.kt)("strong",{parentName:"p"}," Using MKDA Chi Squared on manual meta-analyses ")),(0,i.kt)("p",null,"In this tutorial, we applied ",(0,i.kt)("inlineCode",{parentName:"p"},"MKDA Chi-Squared"),' to a manual meta-analysis. However, this is not a perfect comparison, as there are differences between the reference sample (Neurosynth), the high-quality manual annotations given as input. Studies in large-scale meta-analytic databases are automatically populated, meaning there are potential sampling biases. Most notably, studies in Neurosynth include all reported coordinates, not only "target" analyses/contrasts. Thus, it is possible that low-level task > no task contrasts are over-represented in this reference sample. '),(0,i.kt)("h2",{id:"references--further-reading"},"References & Further Reading"),(0,i.kt)("p",null,"If you want to understand the nuances of what inferences you can and cannot make using these maps, we recommend reading Tal Yarkoni's blog posts on how these maps do not provide evidence that the dACC is select for pain: ",(0,i.kt)("a",{parentName:"p",href:"https://www.talyarkoni.org/blog/2015/12/05/no-the-dorsal-anterior-cingulate-is-not-selective-for-pain-comment-on-lieberman-and-eisenberger-2015/"},"Post 1"),", ",(0,i.kt)("a",{parentName:"p",href:"https://www.talyarkoni.org/blog/2015/12/14/still-not-selective-comment-on-comment-on-comment-on-lieberman-eisenberger-2015/"},"Post 2"),", as well as a commentary by ",(0,i.kt)("a",{parentName:"p",href:"https://www.pnas.org/doi/10.1073/pnas.1600282113"},"Tor Wager et al., 2016")),(0,i.kt)("p",null,"Poldrack RA. Inferring mental states from neuroimaging data: from reverse inference to large-scale decoding. Neuron. 2011 Dec 8;72(5):692-7. doi: 10.1016/j.neuron.2011.11.001. PMID: 22153367; PMCID: PMC3240863."),(0,i.kt)("p",null,"Poldrack RA, Yarkoni T. From Brain Maps to Cognitive Ontologies: Informatics and the Search for Mental Structure. Annu Rev Psychol. 2016;67:587-612. doi: 10.1146/annurev-psych-122414-033729. Epub 2015 Sep 21. PMID: 26393866; PMCID: PMC4701616."))}h.isMDXComponent=!0},541:(e,t,a)=>{a.d(t,{Z:()=>n});const n=a.p+"assets/images/mkda_chi_squared_algo-ccaec6b8f53a020b314e09162dccf9b7.png"},6344:(e,t,a)=>{a.d(t,{Z:()=>n});const n=a.p+"assets/images/mkda_chi_squared_reference-418d3c236f463dcdd4f007e4a649c1ba.png"},9377:(e,t,a)=>{a.d(t,{Z:()=>n});const n=a.p+"assets/images/pinto_lobos_figa-115dccb8db33fc1a969fa252124b9e96.png"},2167:(e,t,a)=>{a.d(t,{Z:()=>n});const n=a.p+"assets/images/pinto_lobos_z_desc-associationMass_level-cluster_corr-FWE_method-montecarlo.nii.gz-7f5395616d7b1943c618393fca6e4e88.png"},4568:(e,t,a)=>{a.d(t,{Z:()=>n});const n=a.p+"assets/images/pinto_lobos_z_desc-uniformityMass_level-cluster_corr-FWE_method-montecarlo.nii.gz-db297c21a1e3a0c952b683a8d0f07981.png"},9569:(e,t,a)=>{a.d(t,{Z:()=>n});const n=a.p+"assets/images/prob-A_neurosynth-8d7c0db5edbb4d6862b9e503cc30e694.png"}}]); \ No newline at end of file diff --git a/assets/js/4c0219fe.d571c3ba.js b/assets/js/4c0219fe.d571c3ba.js deleted file mode 100644 index 058bdcd..0000000 --- a/assets/js/4c0219fe.d571c3ba.js +++ /dev/null @@ -1 +0,0 @@ -"use strict";(self.webpackChunkns_compose_docs=self.webpackChunkns_compose_docs||[]).push([[3730],{5267:(e,t,a)=>{a.r(t),a.d(t,{assets:()=>c,contentTitle:()=>r,default:()=>h,frontMatter:()=>o,metadata:()=>l,toc:()=>d});var n=a(7462),i=(a(7294),a(3905)),s=a(6464);const o={sidebar_label:"MKDA Chi-Squared Association",sidebar_position:3},r="MKDA Chi-Squared and large-scale association tests",l={unversionedId:"tutorial/advanced/mkda_association",id:"tutorial/advanced/mkda_association",title:"MKDA Chi-Squared and large-scale association tests",description:"How to perform large-scale association tests using MKDA Chi-Squared Meta-Analysis, with a Social Processing example",source:"@site/docs/tutorial/advanced/mkda_association.md",sourceDirName:"tutorial/advanced",slug:"/tutorial/advanced/mkda_association",permalink:"/compose-docs/tutorial/advanced/mkda_association",draft:!1,editUrl:"https://github.com/neurostuff/compose-docs/edit/master/docs/tutorial/advanced/mkda_association.md",tags:[],version:"current",lastUpdatedBy:"Alejandro de la Vega",lastUpdatedAt:1706982719,formattedLastUpdatedAt:"Feb 3, 2024",sidebarPosition:3,frontMatter:{sidebar_label:"MKDA Chi-Squared Association",sidebar_position:3},sidebar:"tutorialSidebar",previous:{title:"Advanced tutorials",permalink:"/compose-docs/tutorial/advanced/"}},c={},d=[{value:"The Reverse Inference Problem",id:"the-reverse-inference-problem",level:2},{value:"MKDA Chi-Squared",id:"mkda-chi-squared",level:2},{value:"How to run MKDA Chi-Squared on Neurosynth Compose",id:"how-to-run-mkda-chi-squared-on-neurosynth-compose",level:2},{value:"Specification",id:"specification",level:3},{value:"Executing your analysis",id:"executing-your-analysis",level:3},{value:"Interpreting results",id:"interpreting-results",level:2},{value:"Example: Pintos Lobo (2022) - All Social Processing Tasks",id:"example-pintos-lobo-2022---all-social-processing-tasks",level:2},{value:"Results",id:"results",level:3},{value:"Footnotes & Limitations",id:"footnotes--limitations",level:2},{value:"References & Further Reading",id:"references--further-reading",level:2}],p={toc:d},m="wrapper";function h(e){let{components:t,...o}=e;return(0,i.kt)(m,(0,n.Z)({},p,o,{components:t,mdxType:"MDXLayout"}),(0,i.kt)("h1",{id:"mkda-chi-squared-and-large-scale-association-tests"},"MKDA Chi-Squared and large-scale association tests"),(0,i.kt)("p",null,(0,i.kt)("em",{parentName:"p"},"How to perform large-scale association tests using MKDA Chi-Squared Meta-Analysis, with a Social Processing example")),(0,i.kt)("h2",{id:"the-reverse-inference-problem"},"The Reverse Inference Problem"),(0,i.kt)("p",null,"A common goal of neuroimaging meta-analysis, is to pool a set of studies that invoke common psychological constructs to identify where brain activity is consistently activated."),(0,i.kt)("p",null,"Although this is a useful approach, there is a significant inferential challenge-- namely, determining how ",(0,i.kt)("em",{parentName:"p"},"specific")," the relationship between activity in a given region and the cognitive state invoked by the target task. Ideally, we would like to infer the probability of a mental state given activity in a given region. However, this is exceedingly difficult due to the well-established problem of ",(0,i.kt)("em",{parentName:"p"},"reverse inference")," (Poldrack, 2011). "),(0,i.kt)("p",null,"Even if we establish that a given task (e.g. working memory) activates a region 100% of the time (e.g. lateral prefrontal cortex), this observation only establishes that working memory engagement is a sufficient condition for LPFC activity, but ",(0,i.kt)("em",{parentName:"p"},"not")," that LPFC activity indicates working memory engagement (Poldrack & Yarkoni, 2015). In practice, we know that brain regions are activated by a variety of cognitive processes, and that certain regions of the brain- such as the insula, lateral PFC and medial frontal cingulate cotex (MFCC)- have a high base rate of activation, making it difficult to establish specificity. Using the Neurosynth database (15,000+ studies), we can map the probability of activation of all voxels. Across this large and diverse dataset, certain voxels in MFCC and insula are activate in as many as 20% of studies. "),(0,i.kt)("p",null,(0,i.kt)("img",{alt:"Prob-A",src:a(9569).Z,width:"1000",height:"180"}),"\n",(0,i.kt)("em",{parentName:"p"},"Probability of Activity for all Voxels across the Neurosynth Dataset")),(0,i.kt)("p",null,"The reverse inference problem is a challenge even for rigorous, high-quality meta-analyses. For example, a recent meta-analysis of RDoC social constructs across 864 fMRI contrasts, ",(0,i.kt)("a",{parentName:"p",href:"https://pubmed.ncbi.nlm.nih.gov/36436737/"},"Pintos Lobo et al., (2022)"),' found converging activation across a variety of regions for "All Social Processing Tasks", including mPFC, ACC, PCC, TPJ, bilateral insula, amygdala, fusiform gyrus, precuneus, and thalamus. However, some of the regions have a high base rate of activation, making it difficult to know how strongly associated their activity is with social processing.'),(0,i.kt)("p",null,(0,i.kt)("img",{alt:"Lobos Pinto",src:a(9377).Z,width:"517",height:"135"})),(0,i.kt)("p",null,(0,i.kt)("em",{parentName:"p"},"Fig 5a (condensed) from Pintos Lobo et al., (2022). Convergent Activation Patterns Across all social processing tasks (864 contrasts, 1,109 total annotations). ")),(0,i.kt)("p",null,"Although reverse inference poses a serious challenge, there are certain questions we can ask using large-scale meta-analytic databases that can help. Specifically: ",(0,i.kt)("strong",{parentName:"p"},"does activity occur ",(0,i.kt)("em",{parentName:"strong"},"more consistently")," for studies that elicit by the mental construct of interest (in this case, social processing) than studies that ",(0,i.kt)("em",{parentName:"strong"},"do not")," elicit that construct")," Large-scale meta-analytic datasets can serve as a useful reference, as they consists of tens of thousands of diverse neuroimaging studies automatically sampled from the literature."),(0,i.kt)("h2",{id:"mkda-chi-squared"},"MKDA Chi-Squared"),(0,i.kt)("p",null,"We can answer this question using a ",(0,i.kt)("inlineCode",{parentName:"p"},"Multilevel kernel density (MKDA) analysis - Chi-square")," analysis, originally introduced in ",(0,i.kt)("a",{parentName:"p",href:"https://doi.org/10.1093/scan/nsm015"},"Wager et al.,"),". For every voxel, we test if a greater proportion of studies in our meta-analysis activate a given voxel than in a large set of studies that ",(0,i.kt)("em",{parentName:"p"},"we did not select")," for our inclusion criteria. "),(0,i.kt)("p",null,"Conceptually, this tests if there's evidence of a ",(0,i.kt)("em",{parentName:"p"},"population level")," association between the task or psychological construct in our meta-analysis and brain activation (for every voxel). It is equivalent to conducting a chi-squared test of independence for a 2-by-2 table of counts for each voxel, where the binary variables are foci occurrence in the meta-analysis of interest and foci occurrence in the reference set of unselected studies."),(0,i.kt)("h2",{id:"how-to-run-mkda-chi-squared-on-neurosynth-compose"},"How to run MKDA Chi-Squared on Neurosynth Compose"),(0,i.kt)("h3",{id:"specification"},"Specification"),(0,i.kt)("p",null,"Specifying an MKDA Chi-Square meta-analysis in Neurosynth is easy. Simply, select a target set of Analyses to include from your StudySet as you would for any other meta-analysis. "),(0,i.kt)("p",null,'In Step 3 ("Create Meta-Analysis Specification") of your Project, select ',(0,i.kt)("em",{parentName:"p"},"MKDAChi2")," as the ",(0,i.kt)("em",{parentName:"p"},"algorithm"),". "),(0,i.kt)("p",null,(0,i.kt)("img",{alt:"MKDA Chi Squared",src:a(541).Z,width:"1169",height:"408"})),(0,i.kt)("admonition",{type:"note"},(0,i.kt)("p",{parentName:"admonition"},"By default, the ",(0,i.kt)("inlineCode",{parentName:"p"},"FDRCorrector")," is selected, which will perform cluster correction using False Detection Rate with an ",(0,i.kt)("em",{parentName:"p"},"alpha")," of 0.05.\nThis is a fast algorithm, however, it is recommended to use ",(0,i.kt)("inlineCode",{parentName:"p"},"FWECorrector")," (family-wise-error) with the ",(0,i.kt)("inlineCode",{parentName:"p"},"montecarlo")," method for more accurate, publication-quality results.")),(0,i.kt)("p",null,'Next, select the annotation inclusion column you want to use, as before (by default, the "included" column will be used).'),(0,i.kt)("p",null,"Now, select a reference dataset from the dropdown list below. The Neurosynth dataset represents the latest release of the legacy ",(0,i.kt)("em",{parentName:"p"},"Neurosynth")," dataset (version 7), released July, 2018. The ",(0,i.kt)("em",{parentName:"p"},"Neurostore"),' dataset represents the latest update of our continuously updating "live" dataset, spanning over 20,000 neuroimaging studies. '),(0,i.kt)("p",null,(0,i.kt)("img",{alt:"MKDA Chi Squared Reference",src:a(6344).Z,width:"1136",height:"243"})),(0,i.kt)("p",null,"Now simply complete the rest of the meta-analysis specification wizard to finish. "),(0,i.kt)("h3",{id:"executing-your-analysis"},"Executing your analysis"),(0,i.kt)("p",null,"As usual, you can execute your meta-analysis using Google Colab or on a local computational resource using Docker. "),(0,i.kt)("admonition",{type:"tip"},(0,i.kt)("p",{parentName:"admonition"},"The ",(0,i.kt)("inlineCode",{parentName:"p"},"MKDAChi2")," algorithm takes between ~30s-2minutes to run. However, the ",(0,i.kt)("inlineCode",{parentName:"p"},"FWECorrector")," with 5,000+ montecarlo iterations can take several hours to complete.\nWe recommend using a workstation or HPC and specifying ",(0,i.kt)("inlineCode",{parentName:"p"},"--n-cores")," at run-time.")),(0,i.kt)("h2",{id:"interpreting-results"},"Interpreting results"),(0,i.kt)("p",null,"The ",(0,i.kt)("em",{parentName:"p"},"MKDA Chi-Squared")," Workflow outputs two key maps: ",(0,i.kt)("strong",{parentName:"p"},"uniformity")," and ",(0,i.kt)("strong",{parentName:"p"},"association")," test maps."),(0,i.kt)("ul",null,(0,i.kt)("li",{parentName:"ul"},(0,i.kt)("strong",{parentName:"li"},"Uniformity test map:")," z-scores from a one-way ANOVA testing whether the proportion of studies that report activation at a given voxel differs from the rate that would be expected if activations were uniformly distributed throughout gray matter.")),(0,i.kt)("p",null,'The uniformity test map can be interpreted in roughly the same way as most standard whole-brain fMRI analysis: it displays the degree to which each voxel is consistently activated in studies that use a given term. For instance, for a meta-analysis of "emotion" high z-scores in the amygdala implies that studies that use the word emotion a lot tend to consistently report activation in the amygdala--at least, more consistently than one would expect if activation were uniformly distributed throughout gray matter. '),(0,i.kt)("ul",null,(0,i.kt)("li",{parentName:"ul"},(0,i.kt)("strong",{parentName:"li"},"Association test map"),": z-scores from a two-way ANOVA testing for the presence of a non-zero association between term use and voxel activation.")),(0,i.kt)("p",null,"The association test maps tell you whether activation in a region ",(0,i.kt)("strong",{parentName:"p"},"XXX")," occurs more consistently for studies in your meta-analytic sample ",(0,i.kt)("strong",{parentName:"p"},"m")," than for other studies in the reference dataset. In other words, a large positive z-score implies that studies in a meta-analysis are more likely to report ",(0,i.kt)("strong",{parentName:"p"},"XXX")," activation than studies whose abstracts don't include the word 'emotion'. "),(0,i.kt)("p",null,"Note that association maps ",(0,i.kt)("em",{parentName:"p"},"do not")," tell you what the probability of a given psychological concept or task is. High Z-scores do not imply that a certain region or voxel is ",(0,i.kt)("em",{parentName:"p"},"selective")," for a given concept or task. Instead, it just means there is evidence that there is at least a non-zero difference between reference studies, and studies in the meta-analysis."),(0,i.kt)("admonition",{type:"note"},(0,i.kt)("p",{parentName:"admonition"},(0,i.kt)("em",{parentName:"p"},"NiMARE")," outputs a variety of maps, including cluster-corrected and uncorrected versions of all maps. "),(0,i.kt)("p",{parentName:"admonition"},"See the documentation sections on ",(0,i.kt)("a",{parentName:"p",href:"https://nimare.readthedocs.io/en/stable/outputs.html"},"Outputs of NIMARE")," and ",(0,i.kt)("a",{parentName:"p",href:"https://nimare.readthedocs.io/en/stable/cbma.html#the-monte-carlo-multiple-comparisons-correction-method"},"Monte Carlo multiple comparisons")," for more details.")),(0,i.kt)("h2",{id:"example-pintos-lobo-2022---all-social-processing-tasks"},"Example: Pintos Lobo (2022) - All Social Processing Tasks"),(0,i.kt)("p",null,"To demonstrate, we used Neurosynth-Compose to replicate the ",(0,i.kt)("a",{parentName:"p",href:"https://pubmed.ncbi.nlm.nih.gov/36436737/"},"Pintos Lobo et al., (2022)")," meta-analysis for All Social Processing Tasks. For this example, we have already created a ",(0,i.kt)("inlineCode",{parentName:"p"},"Project")," and ",(0,i.kt)("inlineCode",{parentName:"p"},"StudySet")," with the coordinates used in this meta-analysis."),(0,i.kt)("p",null,"We then specified a ",(0,i.kt)("inlineCode",{parentName:"p"},"MKDAChi2")," Meta-Analysis with ",(0,i.kt)("inlineCode",{parentName:"p"},"FWECorrector")," with the ",(0,i.kt)("inlineCode",{parentName:"p"},"montecarlo")," method with 5,000 iterations. "),(0,i.kt)(s.Z,{variant:"contained",color:"primary",href:"https://compose.neurosynth.org/projects/4x4NsrWg8heS/meta-analyses/7K9BVG9hJQRu",mdxType:"Button"},"Meta-Analysis Specification and Results on Neurosynth Compose"),(0,i.kt)("h3",{id:"results"},"Results"),(0,i.kt)("p",null,"First, let's look at the FWE cluster corrected ",(0,i.kt)("strong",{parentName:"p"},"uniformity test")," map."),(0,i.kt)("p",null,(0,i.kt)("inlineCode",{parentName:"p"},"z_desc-uniformityMass_level-cluster_corr-FWE_method-montecarlo"),"\n",(0,i.kt)("img",{alt:"Uniformity",src:a(4568).Z,width:"950",height:"180"})),(0,i.kt)("p",null,"In this analysis, we replicate the findings of Pinto Lobos (2022), showing consistent activation for social processing across a variety of regions."),(0,i.kt)("p",null,"Next, let's look at the FWE cluster corrected ",(0,i.kt)("strong",{parentName:"p"},"association map"),":"),(0,i.kt)("p",null,(0,i.kt)("inlineCode",{parentName:"p"},"z_desc-associationMass_level-cluster_corr-FWE_method-montecarlo"),"\n",(0,i.kt)("img",{alt:"Association",src:a(2167).Z,width:"950",height:"180"})),(0,i.kt)("p",null,"As before, regions which have been previously implicated with social processing, such as the tempo-parietal junction (TPJ), and dorso-medial and ventro-medial PFC are present, meaning that activity in these social processing studies report activity in these regions with greater frequency than other studies in the Neurosynth database."),(0,i.kt)("p",null,"However, certain regions which we know to have low specificity, such as the insula, medial frontal cingulate cortex (MFCC) and parts of dorso-lateral PFC, are absent, meaning that there is ",(0,i.kt)("em",{parentName:"p"},"no evidence")," that social processing tasks report activity in these regions ",(0,i.kt)("em",{parentName:"p"},"more frequently")," than other studies in the database."),(0,i.kt)("p",null,"This example demonstrates how ",(0,i.kt)("inlineCode",{parentName:"p"},"MKDA Chi-Squared")," association analysis can help determine the specificity activity and tasks in a meta-analysis, even for high-quality manual meta-analyses."),(0,i.kt)("h2",{id:"footnotes--limitations"},"Footnotes & Limitations"),(0,i.kt)("p",null,(0,i.kt)("strong",{parentName:"p"},'What happened to the "forward inference" and "reverse inference" maps?')),(0,i.kt)("p",null,'We renamed the pre-generated forward and reverse inference maps; they\'re now referred to as the "uniformity test" and "association test" maps that we discuss here.'),(0,i.kt)("p",null,"Although the method we used hasn't changed (",(0,i.kt)("inlineCode",{parentName:"p"},"MKDA Chi-Squared"),"), the latter names more accurately capture what these maps actually mean. It was a mistake on our part to have used the forward and reverse inference labels; those labels should properly be reserved for posterior probability maps generated via a Bayesian estimation analysis, rather than for z-scores resulting from a frequentist inferential test of association. Probability maps are more difficult to interpret and use correctly, as they depend on the ",(0,i.kt)("em",{parentName:"p"},"prior")," assumed by the researcher. Since setting an appropriate prior is highly non-trivial, these maps are disabled by default."),(0,i.kt)("p",null,(0,i.kt)("strong",{parentName:"p"}," Using MKDA Chi Squared on manual meta-analyses ")),(0,i.kt)("p",null,"In this tutorial, we applied ",(0,i.kt)("inlineCode",{parentName:"p"},"MKDA Chi-Squared"),' to a manual meta-analysis. However, this is not a perfect comparison, as there are differences between the reference sample (Neurosynth), the high-quality manual annotations given as input. Studies in large-scale meta-analytic databases are automatically populated, meaning there are potential sampling biases. Most notably, studies in Neurosynth include all reported coordinates, not only "target" analyses/contrasts. Thus, it is possible that low-level task > no task contrasts are over-represented in this reference sample. '),(0,i.kt)("h2",{id:"references--further-reading"},"References & Further Reading"),(0,i.kt)("p",null,"If you want to understand the nuances of what inferences you can and cannot make using these maps, we recommend reading Tal Yarkoni's blog posts on how these maps do not provide evidence that the dACC is select for pain: ",(0,i.kt)("a",{parentName:"p",href:"https://www.talyarkoni.org/blog/2015/12/05/no-the-dorsal-anterior-cingulate-is-not-selective-for-pain-comment-on-lieberman-and-eisenberger-2015/"},"Post 1"),", ",(0,i.kt)("a",{parentName:"p",href:"https://www.talyarkoni.org/blog/2015/12/14/still-not-selective-comment-on-comment-on-comment-on-lieberman-eisenberger-2015/"},"Post 2"),", as well as a commentary by ",(0,i.kt)("a",{parentName:"p",href:"https://www.pnas.org/doi/10.1073/pnas.1600282113"},"Tor Wager et al., 2016")),(0,i.kt)("p",null,"Poldrack RA. Inferring mental states from neuroimaging data: from reverse inference to large-scale decoding. Neuron. 2011 Dec 8;72(5):692-7. doi: 10.1016/j.neuron.2011.11.001. PMID: 22153367; PMCID: PMC3240863."),(0,i.kt)("p",null,"Poldrack RA, Yarkoni T. From Brain Maps to Cognitive Ontologies: Informatics and the Search for Mental Structure. Annu Rev Psychol. 2016;67:587-612. doi: 10.1146/annurev-psych-122414-033729. Epub 2015 Sep 21. 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To filter analyses for inclusion, we can create\nan "include" column may have a corresponding values of "True" or "False"\nfor each analysis indicating whether to include the analysis in the meta-analysis.'),(0,o.kt)("h3",{id:"functions-1"},"Functions"),(0,o.kt)("p",null,"Annotations can also be cloned if you disagree with an existing annotation\nyou do not own and want to edit it."),(0,o.kt)("h2",{id:"study"},"Study"),(0,o.kt)("h3",{id:"overview-2"},"Overview"),(0,o.kt)("p",null,"A study is a publishable unit of research containing neuroimaging\ndata.\nThe data can be represented as peak coordinates or actual images.\nThe study is connected to the original journal it was published in."),(0,o.kt)("h3",{id:"functions-2"},"Functions"),(0,o.kt)("p",null,"Studies can be created, cloned, and edited to accomodate your needs for your meta-analysis"),(0,o.kt)("h2",{id:"analysis"},"Analysis"),(0,o.kt)("h3",{id:"overview-3"},"Overview"),(0,o.kt)("p",null,"An analysis represents a single statistical contrast between any number of groups/conditions.\nThe contents of an analysis include the terms applied to the groups/conditions and their respective\nweights in the contrast.\nAn analysis also contains the results of the statistical contrast either with an image and/or a table\nindicating significant results "),(0,o.kt)("h2",{id:"condition"},"Condition"),(0,o.kt)("h3",{id:"overview-4"},"Overview"),(0,o.kt)("p",null,"A condition is term/word that represents a psychological (e.g., 2-back), physical (e.g., biking)"),(0,o.kt)("h2",{id:"weights"},"Weights"),(0,o.kt)("h2",{id:"point"},"Point"),(0,o.kt)("h3",{id:"overview-5"},"Overview"),(0,o.kt)("h2",{id:"image"},"Image"))}v.isMDXComponent=!0}}]); \ No newline at end of file diff --git a/assets/js/a3562974.4bbe0efc.js b/assets/js/a3562974.4bbe0efc.js deleted file mode 100644 index 8515866..0000000 --- a/assets/js/a3562974.4bbe0efc.js +++ /dev/null @@ -1 +0,0 @@ -"use strict";(self.webpackChunkns_compose_docs=self.webpackChunkns_compose_docs||[]).push([[9735],{3905:(e,t,o)=>{o.d(t,{Zo:()=>d,kt:()=>h});var n=o(7294);function i(e,t,o){return t in e?Object.defineProperty(e,t,{value:o,enumerable:!0,configurable:!0,writable:!0}):e[t]=o,e}function a(e,t){var o=Object.keys(e);if(Object.getOwnPropertySymbols){var n=Object.getOwnPropertySymbols(e);t&&(n=n.filter((function(t){return Object.getOwnPropertyDescriptor(e,t).enumerable}))),o.push.apply(o,n)}return o}function r(e){for(var t=1;t=0||(i[o]=e[o]);return i}(e,t);if(Object.getOwnPropertySymbols){var a=Object.getOwnPropertySymbols(e);for(n=0;n=0||Object.prototype.propertyIsEnumerable.call(e,o)&&(i[o]=e[o])}return i}var l=n.createContext({}),u=function(e){var t=n.useContext(l),o=t;return e&&(o="function"==typeof e?e(t):r(r({},t),e)),o},d=function(e){var t=u(e.components);return n.createElement(l.Provider,{value:t},e.children)},c="mdxType",p={inlineCode:"code",wrapper:function(e){var t=e.children;return n.createElement(n.Fragment,{},t)}},m=n.forwardRef((function(e,t){var o=e.components,i=e.mdxType,a=e.originalType,l=e.parentName,d=s(e,["components","mdxType","originalType","parentName"]),c=u(o),m=i,h=c["".concat(l,".").concat(m)]||c[m]||p[m]||a;return o?n.createElement(h,r(r({ref:t},d),{},{components:o})):n.createElement(h,r({ref:t},d))}));function h(e,t){var o=arguments,i=t&&t.mdxType;if("string"==typeof e||i){var a=o.length,r=new Array(a);r[0]=m;var s={};for(var l in t)hasOwnProperty.call(t,l)&&(s[l]=t[l]);s.originalType=e,s[c]="string"==typeof e?e:i,r[1]=s;for(var u=2;u{o.r(t),o.d(t,{assets:()=>l,contentTitle:()=>r,default:()=>p,frontMatter:()=>a,metadata:()=>s,toc:()=>u});var n=o(7462),i=(o(7294),o(3905));const a={title:"Curation",sidebar_position:0},r="Curation",s={unversionedId:"guide/Project/Curation",id:"guide/Project/Curation",title:"Curation",description:"Curation is the first step in creating a meta-analysis, and begins by searching for and importing studies into the project. 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This involves excluding irrelevant studies, and including relevant ones.",source:"@site/docs/guide/Project/Curation.md",sourceDirName:"guide/Project",slug:"/guide/Project/Curation",permalink:"/compose-docs/guide/Project/Curation",draft:!1,editUrl:"https://github.com/neurostuff/compose-docs/edit/master/docs/guide/Project/Curation.md",tags:[],version:"current",lastUpdatedBy:"Alejandro de la Vega",lastUpdatedAt:1706982719,formattedLastUpdatedAt:"Feb 3, 2024",sidebarPosition:0,frontMatter:{title:"Curation",sidebar_position:0},sidebar:"tutorialSidebar",previous:{title:"Project",permalink:"/compose-docs/guide/Project/"},next:{title:"Extraction",permalink:"/compose-docs/guide/Project/Extraction"}},l={},u=[{value:"Getting Started",id:"getting-started",level:2},{value:"PRISMA Workflow",id:"prisma-workflow",level:3},{value:"Simple Workflow",id:"simple-workflow",level:3},{value:"Importing",id:"importing",level:2},{value:"Import from Neurostore",id:"import-from-neurostore",level:3},{value:"Import from PubMed",id:"import-from-pubmed",level:3},{value:"Import from BibTex/RIS/endnote",id:"import-from-bibtexrisendnote",level:3},{value:"Custom Studies",id:"custom-studies",level:3},{value:"Duplicates",id:"duplicates",level:3},{value:"Duplicates are detected between the studies being imported and the studies already in the project",id:"duplicates-are-detected-between-the-studies-being-imported-and-the-studies-already-in-the-project",level:4},{value:"Duplicates are detected within the file you are importing",id:"duplicates-are-detected-within-the-file-you-are-importing",level:4},{value:"Excluding and Promoting Studies",id:"excluding-and-promoting-studies",level:2},{value:"Exclude",id:"exclude",level:3},{value:"Promote",id:"promote",level:3},{value:"On to Extraction",id:"on-to-extraction",level:2}],d={toc:u},c="wrapper";function p(e){let{components:t,...o}=e;return(0,i.kt)(c,(0,n.Z)({},d,o,{components:t,mdxType:"MDXLayout"}),(0,i.kt)("h1",{id:"curation"},"Curation"),(0,i.kt)("p",null,(0,i.kt)("em",{parentName:"p"},"Curation")," is the first step in creating a meta-analysis, and begins by ",(0,i.kt)("em",{parentName:"p"},"searching")," for and ",(0,i.kt)("em",{parentName:"p"},"importing")," studies into the project. Next, you will ",(0,i.kt)("em",{parentName:"p"},"review")," studies for inclusion based on their relevancy to your research question. This involves ",(0,i.kt)("strong",{parentName:"p"},"excluding")," irrelevant studies, and ",(0,i.kt)("strong",{parentName:"p"},"including")," relevant ones. "),(0,i.kt)("p",null,"At the end of the process, you will be ready to create a ",(0,i.kt)("a",{parentName:"p",href:"/compose-docs/guide/glossary#studyset"},(0,i.kt)("strong",{parentName:"a"},"Studyset"))," of related studies that are amenable for neuroimaging meta-analysis"),(0,i.kt)("h2",{id:"getting-started"},"Getting Started"),(0,i.kt)("p",null,"The curation interface uses columns as a way to label studies based on their current status.\nThe left most column is the starting point and all studies imported into the project will begin there.\nThe right most column is the ",(0,i.kt)("strong",{parentName:"p"},"inclusion column")," and is the place where studies will go to be included in the meta-analysis. "),(0,i.kt)("p",null,'The goal is to get studies from the left most starting column, and narrow them down to a final subset of included studies in the right most column.\nIf a study is not eligible for inclusion at any point, it should be marked as "excluded" before reaching the inclusion column.'),(0,i.kt)("p",null,"The curation step is complete when all studies have been categorized, either by being included or excluded."),(0,i.kt)("p",null,"When you first begin ",(0,i.kt)("em",{parentName:"p"},"Curation"),", you will choose between different workflows, which vary in how rigorous or systematic the selection of studies will be."),(0,i.kt)("admonition",{title:"How specific to be?",type:"tip"},(0,i.kt)("p",{parentName:"admonition"},"Performing a systematic meta-analysis involves a substantial amount of effort. It is up to you, the researcher, how rigorous to be in this process. We reccomend thinking about your ",(0,i.kt)("em",{parentName:"p"},"goals")," prior to starting this process so that you can have clear inclusion guidelines. If you are looking for an exploratory analysis, we reccomend following the tutorial for ",(0,i.kt)("em",{parentName:"p"},"automated meta-analysis"),", which replaces manual curation with an automated selection of studies. Note that automated meta-analyses are not a replacement for a careful systematic meta-analysis.")),(0,i.kt)("h3",{id:"prisma-workflow"},"PRISMA Workflow"),(0,i.kt)("p",null,"PRISMA stands for the ",(0,i.kt)("strong",{parentName:"p"},"Preferred Reporting Items for Systematic Review and Meta-Analyses"),". The\n",(0,i.kt)("a",{parentName:"p",href:"http://www.prisma-statement.org/?AspxAutoDetectCookieSupport=1"},"PRISMA guidelines")," are a set of rules for reporting a\nsystematic review, and are the gold standard for producing a proper, rigorous, and transparent meta-analysis."),(0,i.kt)("p",null,"If you are trying to create a rigorous ",(0,i.kt)("a",{parentName:"p",href:"/compose-docs/tutorial/manual"},"PRISMA compliant manual meta-analysis")," you will want to select this option."),(0,i.kt)("p",null,"When you select this option, neurosynth-compose will automatically initialize the curation step with 4 distinct columns as\ndictated by PRISMA guidelines: ",(0,i.kt)("strong",{parentName:"p"},"Identification, Screening, Eligibility, Included"),"."),(0,i.kt)("p",null,"After importing studies into neurosynth, they will be placed into the identification column and studies are triaged from there."),(0,i.kt)("admonition",{title:"PRISMA Summary",type:"info"},(0,i.kt)("p",{parentName:"admonition"},"The ",(0,i.kt)("strong",{parentName:"p"},"Identification")," column is where all imported studies are deposited into initially. As alluded to by the name, the identification\ncolumn is where you identify all records yielded from your search. In this column, you wil exclude studies purely based on whether they are\nduplicates of existing studies. All other studies are promoted."),(0,i.kt)("p",{parentName:"admonition"},"The ",(0,i.kt)("strong",{parentName:"p"},"Screening")," column consists of all records that have been screened for duplicates. In this column, you want to review the titles/abstracts of studies\nand exclude purely based on whether they are irrelevant to your research question or domain. All other studies are promoted. "),(0,i.kt)("p",{parentName:"admonition"},"The ",(0,i.kt)("strong",{parentName:"p"},"Eligibility")," column consists of all records that have been screened for duplicates and irrelevant content. In this column, you want to\nreview the full text of studies and exclude based on whether the study described aligns with the standards of the meta-analysis itself. Reasons\nfor exclusion may include wrong setting, wrong patient population, wrong intervention, wrong paragdigm, etc."),(0,i.kt)("p",{parentName:"admonition"},"The ",(0,i.kt)("strong",{parentName:"p"},"Included")," column consists of all records that have passed previous levels of exclusion and can be considered for the next step of the project."),(0,i.kt)("p",{parentName:"admonition"},"For more information, consult the ",(0,i.kt)("a",{parentName:"p",href:"http://www.prisma-statement.org/?AspxAutoDetectCookieSupport=1"},"PRISMA guidelines"),".")),(0,i.kt)("h3",{id:"simple-workflow"},"Simple Workflow"),(0,i.kt)("p",null,"If you want to create a semi-automated meta-analysis (i.e. perform manual review on a large-scale study search), you'll want to select this option."),(0,i.kt)("p",null,"This workflow is initalized with only two columns. As before, the left most column is where studies will be placed when they are imported. However, unlike a full PRISMA workflow, all exclusion occurs in this column. All studies not excluded are then promoted to the right most column for inclusion into the meta-analysis."),(0,i.kt)("h2",{id:"importing"},"Importing"),(0,i.kt)("p",null,"To begin importing studies into your project clicking the ",(0,i.kt)("strong",{parentName:"p"},"IMPORT STUDIES")," button."),(0,i.kt)("p",null,"You will be asked to choose a source, name your import, and address\nany potential duplicate studies."),(0,i.kt)("admonition",{type:"tip"},(0,i.kt)("p",{parentName:"admonition"},"We recommend giving each import a meaningful name. This will be useful when you come back\nand want to know where a certain study was imported from.")),(0,i.kt)("h3",{id:"import-from-neurostore"},"Import from Neurostore"),(0,i.kt)("p",null,"Neurostore indexes a large number of neuroimaging studies which are ready for meta-analysis. Studies in Neurostore have been pre-processed, including extracting peak activation coordinates from Tables in the text, and computing semantic features from the abstract/full text. Neurostore also indexes studies which other users have annotated and made available to others for re-use."),(0,i.kt)("p",null,"Importing from Neurostore utilizes the Study search UI similar to the ",(0,i.kt)("a",{parentName:"p",href:"https://compose.neurosynth.org/studies"},"Study Page"),".\nAfter entering your search, click the bottom right button to import the searched studies into your project."),(0,i.kt)("admonition",{type:"tip"},(0,i.kt)("p",{parentName:"admonition"},"Importing from Neurostore can save you a lot of time, as these studies are much more likely to be amenable to meta-analysis, and have pre-extracted coordinates. However, note that some manual annotation may still be required to verify the coordinate extraction, and choose the relevant Analysis (i.e. contrast) for final inclusion.")),(0,i.kt)("h3",{id:"import-from-pubmed"},"Import from PubMed"),(0,i.kt)("p",null,"Use this option to import studies directly from PubMed. To start, you need to go to the ",(0,i.kt)("a",{parentName:"p",href:"https://pubmed.ncbi.nlm.nih.gov/"},"PubMed Site"),"\nand either enter in a search or navigate to a previously created collection. "),(0,i.kt)("p",null,"To import the search or collection from PubMed into neurosynth-compose, you will need a text file containing a list of PMIDs.\nYou can obtain this by going to your collection/search and clicking the ",(0,i.kt)("strong",{parentName:"p"},"Save")," button. Set ",(0,i.kt)("strong",{parentName:"p"},"Selection")," to ",(0,i.kt)("strong",{parentName:"p"},"All results"),"\nand set ",(0,i.kt)("strong",{parentName:"p"},"Format")," to ",(0,i.kt)("strong",{parentName:"p"},"PMID"),". Click ",(0,i.kt)("strong",{parentName:"p"},"Create file")," and your text file containing the PMIDs will be generated and downloaded."),(0,i.kt)("admonition",{type:"caution"},(0,i.kt)("p",{parentName:"admonition"},"Neurosynth-Compose uses the PubMed API to import studies. As a result, the maximum number of PMIDs that can be imported at once is 1,500.\nIf your collection has more than 1,500 PMIDs, split the import into multiple files.")),(0,i.kt)("h3",{id:"import-from-bibtexrisendnote"},"Import from BibTex/RIS/endnote"),(0,i.kt)("p",null,"Use this option to import studies via a .bib, .RIS, or .enw file. This may be useful if you want to import from a citation manager like Zotero."),(0,i.kt)("h3",{id:"custom-studies"},"Custom Studies"),(0,i.kt)("p",null,"If there is any record that cannot be easily imported using one of the methods listed above, you can also manually create a study. This may be necessary\nto include resources like unpublished studies."),(0,i.kt)("h3",{id:"duplicates"},"Duplicates"),(0,i.kt)("p",null,'If duplicates are detected in your import, you will be asked how to re-concile them by choosing which of the studies to keep by choosing "KEEP THIS STUDY". Matching duplicates will be automatically marked for exclusion. Note that as a user, you can over-ride any of these selection at any time, and choose which studies to keep or exclude. '),(0,i.kt)("admonition",{type:"info"},(0,i.kt)("p",{parentName:"admonition"},(0,i.kt)("em",{parentName:"p"},"Neurosynth Compose")," does not delete studies. If a study is marked as a duplicate, it is marked as ",(0,i.kt)("strong",{parentName:"p"},"excluded")," but it is not discarded to ensure complete provenance. ")),(0,i.kt)("p",null,"There are two potential ways that duplicates can occur:"),(0,i.kt)("h4",{id:"duplicates-are-detected-between-the-studies-being-imported-and-the-studies-already-in-the-project"},"Duplicates are detected between the studies being imported and the studies already in the project"),(0,i.kt)("p",null,"If any study being important has the same title, PMID, or DOI is the same as\none already in the project, you will be asked reconcile these duplicates."),(0,i.kt)("p",null,'Note that if you mark a study that is already promoted as a duplicate, it will be "demoted" back to the first column and marked as "duplicate". It is reccomended to mark as duplicate the incoming study to avoid this. '),(0,i.kt)("h4",{id:"duplicates-are-detected-within-the-file-you-are-importing"},"Duplicates are detected within the file you are importing"),(0,i.kt)("p",null,"Although rare, it is possible to have duplicates within a given import. For example, if within a RIS file there are duplicate entries. In this case, you will be asked to select which study to keep. Studies marked as duplicates will still be imported but marked as excluded. "),(0,i.kt)("h2",{id:"excluding-and-promoting-studies"},"Excluding and Promoting Studies"),(0,i.kt)("p",null,"Once studies have been imported into the first column of the curation phase, they need to be reviewed for inclusion into your meta-analysis.\nAll studies must be either excluded or moved to the inclusion column in order to progress to the extraction phase."),(0,i.kt)("p",null,"To begin, either click on the button at the top of the column or click on any study in the column. This will open up a page which will show the study\nalong with the following options: ",(0,i.kt)("strong",{parentName:"p"},"PROMOTE, NEEDS REVIEW"),", and ",(0,i.kt)("strong",{parentName:"p"},"EXCLUDE"),". There is also a button to ",(0,i.kt)("strong",{parentName:"p"},"ADD TAGS")," which will assign an informational tag to the study."),(0,i.kt)("h3",{id:"exclude"},"Exclude"),(0,i.kt)("p",null,"To exclude a study, click the ",(0,i.kt)("strong",{parentName:"p"},"EXCLUDE")," button and select the exclusion reason. You can either choose from the preset exclusion reasons or you can begin\ntyping to create a new one."),(0,i.kt)("admonition",{type:"tip"},(0,i.kt)("p",{parentName:"admonition"},"For the PRISMA workflow, the default exclusion reason wil depend on the phase you are in (identification vs screening vs eligibility), to match the PRISMA guidelines. While we do not recommend it, you can click the arrow button and start typing in the input to create a new exclusion reason.\nRevisit the ",(0,i.kt)("a",{parentName:"p",href:"./Curation#prisma-workflow"},"PRISMA workflow")," to review reccomended exclusion criteria.")),(0,i.kt)("h3",{id:"promote"},"Promote"),(0,i.kt)("p",null,"If a study meets inclusion critera (for the current phase), click ",(0,i.kt)("strong",{parentName:"p"},"PROMOTE")," to move the study forward to the next curation column. If it is moved into the right most inclusion column, then it will be included in the meta-analysis."),(0,i.kt)("admonition",{type:"tip"},(0,i.kt)("p",{parentName:"admonition"},"For the first column (especially in a PRISMA workflow) it can be tedious to promote non-duplicates to the next column. If all duplicates have been resolved, you can exit the dialog and click ",(0,i.kt)("strong",{parentName:"p"},"PROMOTE ALL UNCATEGORIZED STUDIES")," to advance all non-duplicate studies to the next column.")),(0,i.kt)("h2",{id:"on-to-extraction"},"On to Extraction"),(0,i.kt)("p",null,"When you have categorized all imported studies by either excluding them or moving them to the inclusion column, then you have\nsuccessfully completed the curation portion of the meta-analysis."),(0,i.kt)("p",null,(0,i.kt)("em",{parentName:"p"},"Neurosynth Compose")," will detect this and reveal a new button: ",(0,i.kt)("strong",{parentName:"p"},"MOVE TO EXTRACTION PHASE"),". 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"),(0,i.kt)("p",null,"At the end of the process, you will be ready to create a ",(0,i.kt)("a",{parentName:"p",href:"/compose-docs/guide/glossary#studyset"},(0,i.kt)("strong",{parentName:"a"},"Studyset"))," of related studies that are amenable for neuroimaging meta-analysis"),(0,i.kt)("h2",{id:"getting-started"},"Getting Started"),(0,i.kt)("p",null,"The curation interface uses columns as a way to label studies based on their current status.\nThe left most column is the starting point and all studies imported into the project will begin there.\nThe right most column is the ",(0,i.kt)("strong",{parentName:"p"},"inclusion column")," and is the place where studies will go to be included in the meta-analysis. "),(0,i.kt)("p",null,'The goal is to get studies from the left most starting column, and narrow them down to a final subset of included studies in the right most column.\nIf a study is not eligible for inclusion at any point, it should be marked as "excluded" before reaching the inclusion column.'),(0,i.kt)("p",null,"The curation step is complete when all studies have been categorized, either by being included or excluded."),(0,i.kt)("p",null,"When you first begin ",(0,i.kt)("em",{parentName:"p"},"Curation"),", you will choose between different workflows, which vary in how rigorous or systematic the selection of studies will be."),(0,i.kt)("admonition",{title:"How specific to be?",type:"tip"},(0,i.kt)("p",{parentName:"admonition"},"Performing a systematic meta-analysis involves a substantial amount of effort. It is up to you, the researcher, how rigorous to be in this process. 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The\n",(0,i.kt)("a",{parentName:"p",href:"http://www.prisma-statement.org/?AspxAutoDetectCookieSupport=1"},"PRISMA guidelines")," are a set of rules for reporting a\nsystematic review, and are the gold standard for producing a proper, rigorous, and transparent meta-analysis."),(0,i.kt)("p",null,"If you are trying to create a rigorous ",(0,i.kt)("a",{parentName:"p",href:"/compose-docs/tutorial/manual"},"PRISMA compliant manual meta-analysis")," you will want to select this option."),(0,i.kt)("p",null,"When you select this option, neurosynth-compose will automatically initialize the curation step with 4 distinct columns as\ndictated by PRISMA guidelines: ",(0,i.kt)("strong",{parentName:"p"},"Identification, Screening, Eligibility, Included"),"."),(0,i.kt)("p",null,"After importing studies into neurosynth, they will be placed into the identification column and studies are triaged from there."),(0,i.kt)("admonition",{title:"PRISMA Summary",type:"info"},(0,i.kt)("p",{parentName:"admonition"},"The ",(0,i.kt)("strong",{parentName:"p"},"Identification")," column is where all imported studies are deposited into initially. As alluded to by the name, the identification\ncolumn is where you identify all records yielded from your search. In this column, you wil exclude studies purely based on whether they are\nduplicates of existing studies. All other studies are promoted."),(0,i.kt)("p",{parentName:"admonition"},"The ",(0,i.kt)("strong",{parentName:"p"},"Screening")," column consists of all records that have been screened for duplicates. In this column, you want to review the titles/abstracts of studies\nand exclude purely based on whether they are irrelevant to your research question or domain. All other studies are promoted. "),(0,i.kt)("p",{parentName:"admonition"},"The ",(0,i.kt)("strong",{parentName:"p"},"Eligibility")," column consists of all records that have been screened for duplicates and irrelevant content. In this column, you want to\nreview the full text of studies and exclude based on whether the study described aligns with the standards of the meta-analysis itself. 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Studies in Neurostore have been pre-processed, including extracting peak activation coordinates from Tables in the text, and computing semantic features from the abstract/full text. Neurostore also indexes studies which other users have annotated and made available to others for re-use."),(0,i.kt)("p",null,"Importing from Neurostore utilizes the Study search UI similar to the ",(0,i.kt)("a",{parentName:"p",href:"https://compose.neurosynth.org/studies"},"Study Page"),".\nAfter entering your search, click the bottom right button to import the searched studies into your project."),(0,i.kt)("admonition",{type:"tip"},(0,i.kt)("p",{parentName:"admonition"},"Importing from Neurostore can save you a lot of time, as these studies are much more likely to be amenable to meta-analysis, and have pre-extracted coordinates. However, note that some manual annotation may still be required to verify the coordinate extraction, and choose the relevant Analysis (i.e. contrast) for final inclusion.")),(0,i.kt)("h3",{id:"import-from-pubmed"},"Import from PubMed"),(0,i.kt)("p",null,"Use this option to import studies directly from PubMed. To start, you need to go to the ",(0,i.kt)("a",{parentName:"p",href:"https://pubmed.ncbi.nlm.nih.gov/"},"PubMed Site"),"\nand either enter in a search or navigate to a previously created collection. "),(0,i.kt)("p",null,"To import the search or collection from PubMed into neurosynth-compose, you will need a text file containing a list of PMIDs.\nYou can obtain this by going to your collection/search and clicking the ",(0,i.kt)("strong",{parentName:"p"},"Save")," button. Set ",(0,i.kt)("strong",{parentName:"p"},"Selection")," to ",(0,i.kt)("strong",{parentName:"p"},"All results"),"\nand set ",(0,i.kt)("strong",{parentName:"p"},"Format")," to ",(0,i.kt)("strong",{parentName:"p"},"PMID"),". Click ",(0,i.kt)("strong",{parentName:"p"},"Create file")," and your text file containing the PMIDs will be generated and downloaded."),(0,i.kt)("admonition",{type:"caution"},(0,i.kt)("p",{parentName:"admonition"},"Neurosynth-Compose uses the PubMed API to import studies. As a result, the maximum number of PMIDs that can be imported at once is 1,500.\nIf your collection has more than 1,500 PMIDs, split the import into multiple files.")),(0,i.kt)("h3",{id:"import-from-bibtexrisendnote"},"Import from BibTex/RIS/endnote"),(0,i.kt)("p",null,"Use this option to import studies via a .bib, .RIS, or .enw file. This may be useful if you want to import from a citation manager like Zotero."),(0,i.kt)("h3",{id:"custom-studies"},"Custom Studies"),(0,i.kt)("p",null,"If there is any record that cannot be easily imported using one of the methods listed above, you can also manually create a study. This may be necessary\nto include resources like unpublished studies."),(0,i.kt)("h3",{id:"duplicates"},"Duplicates"),(0,i.kt)("p",null,'If duplicates are detected in your import, you will be asked how to re-concile them by choosing which of the studies to keep by choosing "KEEP THIS STUDY". Matching duplicates will be automatically marked for exclusion. Note that as a user, you can over-ride any of these selection at any time, and choose which studies to keep or exclude. '),(0,i.kt)("admonition",{type:"info"},(0,i.kt)("p",{parentName:"admonition"},(0,i.kt)("em",{parentName:"p"},"Neurosynth Compose")," does not delete studies. If a study is marked as a duplicate, it is marked as ",(0,i.kt)("strong",{parentName:"p"},"excluded")," but it is not discarded to ensure complete provenance. ")),(0,i.kt)("p",null,"There are two potential ways that duplicates can occur:"),(0,i.kt)("h4",{id:"duplicates-are-detected-between-the-studies-being-imported-and-the-studies-already-in-the-project"},"Duplicates are detected between the studies being imported and the studies already in the project"),(0,i.kt)("p",null,"If any study being important has the same title, PMID, or DOI is the same as\none already in the project, you will be asked reconcile these duplicates."),(0,i.kt)("p",null,'Note that if you mark a study that is already promoted as a duplicate, it will be "demoted" back to the first column and marked as "duplicate". It is reccomended to mark as duplicate the incoming study to avoid this. '),(0,i.kt)("h4",{id:"duplicates-are-detected-within-the-file-you-are-importing"},"Duplicates are detected within the file you are importing"),(0,i.kt)("p",null,"Although rare, it is possible to have duplicates within a given import. For example, if within a RIS file there are duplicate entries. In this case, you will be asked to select which study to keep. Studies marked as duplicates will still be imported but marked as excluded. "),(0,i.kt)("h2",{id:"excluding-and-promoting-studies"},"Excluding and Promoting Studies"),(0,i.kt)("p",null,"Once studies have been imported into the first column of the curation phase, they need to be reviewed for inclusion into your meta-analysis.\nAll studies must be either excluded or moved to the inclusion column in order to progress to the extraction phase."),(0,i.kt)("p",null,"To begin, either click on the button at the top of the column or click on any study in the column. This will open up a page which will show the study\nalong with the following options: ",(0,i.kt)("strong",{parentName:"p"},"PROMOTE, NEEDS REVIEW"),", and ",(0,i.kt)("strong",{parentName:"p"},"EXCLUDE"),". There is also a button to ",(0,i.kt)("strong",{parentName:"p"},"ADD TAGS")," which will assign an informational tag to the study."),(0,i.kt)("h3",{id:"exclude"},"Exclude"),(0,i.kt)("p",null,"To exclude a study, click the ",(0,i.kt)("strong",{parentName:"p"},"EXCLUDE")," button and select the exclusion reason. You can either choose from the preset exclusion reasons or you can begin\ntyping to create a new one."),(0,i.kt)("admonition",{type:"tip"},(0,i.kt)("p",{parentName:"admonition"},"For the PRISMA workflow, the default exclusion reason wil depend on the phase you are in (identification vs screening vs eligibility), to match the PRISMA guidelines. While we do not recommend it, you can click the arrow button and start typing in the input to create a new exclusion reason.\nRevisit the ",(0,i.kt)("a",{parentName:"p",href:"./Curation#prisma-workflow"},"PRISMA workflow")," to review reccomended exclusion criteria.")),(0,i.kt)("h3",{id:"promote"},"Promote"),(0,i.kt)("p",null,"If a study meets inclusion critera (for the current phase), click ",(0,i.kt)("strong",{parentName:"p"},"PROMOTE")," to move the study forward to the next curation column. If it is moved into the right most inclusion column, then it will be included in the meta-analysis."),(0,i.kt)("admonition",{type:"tip"},(0,i.kt)("p",{parentName:"admonition"},"For the first column (especially in a PRISMA workflow) it can be tedious to promote non-duplicates to the next column. If all duplicates have been resolved, you can exit the dialog and click ",(0,i.kt)("strong",{parentName:"p"},"PROMOTE ALL UNCATEGORIZED STUDIES")," to advance all non-duplicate studies to the next column.")),(0,i.kt)("h2",{id:"on-to-extraction"},"On to Extraction"),(0,i.kt)("p",null,"When you have categorized all imported studies by either excluding them or moving them to the inclusion column, then you have\nsuccessfully completed the curation portion of the meta-analysis."),(0,i.kt)("p",null,(0,i.kt)("em",{parentName:"p"},"Neurosynth Compose")," will detect this and reveal a new button: ",(0,i.kt)("strong",{parentName:"p"},"MOVE TO EXTRACTION PHASE"),". 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"),(0,n.kt)("p",null,"In a ",(0,n.kt)("strong",{parentName:"p"},"manual meta-analysis"),", researchers cast a wide net to find a wide range of potentially relevant articles, and use their expertise (read: painstakingly review hundreds of articles) to decide which of articles are relevant to their research question. Our platform seeks streamline this process through a user-friendly interface and pre-extracted data for over 20,000 neuroimaging studies. Yet, ",(0,n.kt)("strong",{parentName:"p"},"a gold standard meta-analysis still requires a significant time investment"),", limiting their application in routine scientific practice."),(0,n.kt)("p",null,"In an ",(0,n.kt)("strong",{parentName:"p"},"automated meta-analysis"),", we instead use data-driven text mining metrics to ",(0,n.kt)("em",{parentName:"p"},"select")," articles. The original ",(0,n.kt)("em",{parentName:"p"},"Neurosynth")," pioneered this approach by developing text mining techniques to automatically extract brain coordinates and semantic text features from thousands of articles. "),(0,n.kt)("p",null,"Surprisingly, this works! For example, by meta-analyzing all studies that mention the term ",(0,n.kt)("a",{parentName:"p",href:"https://neurosynth.org/analyses/terms/emotional/"},'"emotional"')," above a certain frequency, we observe a strong association with activity in the amygdala. By and large, the sheer number of studies overcomes the inherent noisiness of automated data extraction and study selection. "),(0,n.kt)("h3",{id:"flexible-automated-meta-analysis-in-neurosynth-compose"},"Flexible automated meta-analysis in Neurosynth Compose"),(0,n.kt)("p",null,"Although automated meta-analyses have proved to be a useful tool, there are several limitations. The overall goal of ",(0,n.kt)("em",{parentName:"p"},"Neurosynth Compose")," is to give users a flexible data curation platform, to overcome these limitations using their expert knowledge. For example:"),(0,n.kt)("ul",null,(0,n.kt)("li",{parentName:"ul"},(0,n.kt)("em",{parentName:"li"},"Flexible selection criteria.")," The original Neurosynth has a fixed number of terms and meta-analyses. With",(0,n.kt)("em",{parentName:"li"},"Neurosynth Compose")," you can flexibly search the NeuroStore database using a powerful and flexible search to precisely define your search criteria. "),(0,n.kt)("li",{parentName:"ul"},(0,n.kt)("em",{parentName:"li"},"Combine expert knowledge with automated selection.")," Automated study selection is inherently an noisy and imperfect measure. With ",(0,n.kt)("em",{parentName:"li"},"Neurosynth Compose"),", you can use automated study selection as a first pass, and later apply your own expert criteria to refine study inclusion criteria. "),(0,n.kt)("li",{parentName:"ul"},(0,n.kt)("em",{parentName:"li"},"Correct data extraction errors.")," Automated extraction can miss entire tables of coordinates (e.g. supplementary materials), duplicate coordinates, and groups distinct sets of Analyses (e.g. Contrasts) into a single group. Now, you can correct these data to make your meta-analysis more precise. ")),(0,n.kt)("h2",{id:"tutorial"},"Tutorial"),(0,n.kt)("p",null,"An automated meta-analysis in ",(0,n.kt)("em",{parentName:"p"},"Neurosynth Compose")," looks a lot like a manual one, except data curation is optional. 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")),(0,n.kt)("h4",{id:"import-from-neurostore"},"Import from Neurostore"),(0,n.kt)("p",null,"Let's add studies to our curation board by clicking ",(0,n.kt)("strong",{parentName:"p"},"Import Studies"),".\nIn an automated meta-analysis, you'll want to select ",(0,n.kt)("em",{parentName:"p"},"Import via NeuroStore"),", as all indexed studies are guaranteed to contain imaging data (saving you from manual data extraction)."),(0,n.kt)("admonition",{type:"caution"},(0,n.kt)("p",{parentName:"admonition"},"Although the NeuroStore database is continuously growing, it is necessarily an incomplete snapshot of the neuroimaging literature")),(0,n.kt)("p",null,"Input any search term to narrow down studies. This will search the Title and Abstract fields. You may also add additional search filters using the ",(0,n.kt)("strong",{parentName:"p"},"+ Add Filter")," button, and ",(0,n.kt)("em",{parentName:"p"},"select the desired modality of the imaging data"),". "),(0,n.kt)("p",null,(0,n.kt)("img",{alt:"Neurostore Search",src:a(8506).Z,width:"1849",height:"1523"}),". "),(0,n.kt)("p",null,'To import your search, click "Import Studies From Neurostore" at the bottom right. Give your import a name to Tag all imported studies, and continue back to your Curation board. '),(0,n.kt)("admonition",{type:"note"},(0,n.kt)("p",{parentName:"admonition"},"You must import an entire set of search results into your curation board. If you want to exclude any specific studies, you will do so on your board. This allows the Search & Curate process to be fully reproducible.")),(0,n.kt)("h4",{id:"promote-studies"},"Promote studies"),(0,n.kt)("p",null,"Back to your Curation board, you will now see all studies from your NeuroStore search on the left most column."),(0,n.kt)("p",null,(0,n.kt)("img",{alt:"Curation board",src:a(761).Z,width:"2583",height:"1604"}),". "),(0,n.kt)("p",null,"At this point, you have two options: manually review the search results and select which studies to include, or perform a fully automated meta-analysis by including all search results. "),(0,n.kt)("p",null,"For this tutorial, we'll skip manual curation and ",(0,n.kt)("strong",{parentName:"p"},"Promote All Uncategorized Studies"),' to the right-most "Included" column. '),(0,n.kt)("p",null,"We can now click ",(0,n.kt)("em",{parentName:"p"},"Move to Extraction Phase"),"."),(0,n.kt)("h3",{id:"extraction-and-annotation"},"Extraction and Annotation"),(0,n.kt)("p",null,"At this point, you will create a StudySet containing all of your Studies. Advance through the dialog to begin ",(0,n.kt)("em",{parentName:"p"},"Extraction"),"."),(0,n.kt)("p",null,"The goal of this phase is to add or correct imaging data (e.g. Coordinates) in imported studies, and create Annotations to determine which Analyses (e.g. Contrasts), should be included in your meta-analysis."),(0,n.kt)("p",null,"Since we are performing a manual meta-analysis, we're going to skip these steps!"),(0,n.kt)("p",null,"From the main ",(0,n.kt)("em",{parentName:"p"},"Project"),' page, we can click "Mark All as Complete".'),(0,n.kt)("p",null,(0,n.kt)("img",{alt:"Skip Extraction",src:a(1599).Z,width:"1263",height:"855"}),". "),(0,n.kt)("admonition",{type:"tip"},(0,n.kt)("p",{parentName:"admonition"},"It's up to you if you want to skip this step. The validity of your meta-analysis is highly dependant on input data, so we only recommend skipping all curation for exploratory analyses.")),(0,n.kt)("h3",{id:"specify-meta-analyses"},"Specify Meta-Analyses"),(0,n.kt)("p",null,"You can now specify your meta-analysis. This step will be identical between automated and manual meta-analyses."),(0,n.kt)("p",null,'First, we select the "included" column, which by default includes ',(0,n.kt)("em",{parentName:"p"},"all")," Study Analyses as inputs."),(0,n.kt)("p",null,(0,n.kt)("img",{alt:"Inclusion column",src:a(8376).Z,width:"1368",height:"1715"}),". "),(0,n.kt)("p",null,"Next, we select a meta-analysis algorithm. This time, we'll select MKDA Chi-Squared (",(0,n.kt)("inlineCode",{parentName:"p"},"MKDAChi2"),") with ",(0,n.kt)("inlineCode",{parentName:"p"},"FDRCorrector"),". "),(0,n.kt)("p",null,"MKDA Chi-Square compares your StudySet to a reference set of studies (all the studies in NeuroStore that you did ",(0,n.kt)("em",{parentName:"p"},"not")," select), allowing you to identify areas of stronger association with your selected studies. This is the algorithm used in the original Neurosynth Platform."),(0,n.kt)("h3",{id:"execute"},"Execute"),(0,n.kt)("p",null,"Once you specify your meta-analysis, you can execute it in the cloud using Google Colab using your unique meta-analysis id. "),(0,n.kt)("p",null,(0,n.kt)("img",{alt:"Automated execute",src:a(2770).Z,width:"1551",height:"1098"}),". 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"),(0,n.kt)("p",null,"In a ",(0,n.kt)("strong",{parentName:"p"},"manual meta-analysis"),", researchers cast a wide net to find a wide range of potentially relevant articles, and use their expertise (read: painstakingly review hundreds of articles) to decide which of articles are relevant to their research question. Our platform seeks streamline this process through a user-friendly interface and pre-extracted data for over 20,000 neuroimaging studies. Yet, ",(0,n.kt)("strong",{parentName:"p"},"a gold standard meta-analysis still requires a significant time investment"),", limiting their application in routine scientific practice."),(0,n.kt)("p",null,"In an ",(0,n.kt)("strong",{parentName:"p"},"automated meta-analysis"),", we instead use data-driven text mining metrics to ",(0,n.kt)("em",{parentName:"p"},"select")," articles. 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f=o.p+o.u(a),b=new Error;o.l(f,(t=>{if(o.o(e,a)&&(0!==(r=e[a])&&(e[a]=void 0),r)){var c=t&&("load"===t.type?"missing":t.type),f=t&&t.target&&t.target.src;b.message="Loading chunk "+a+" failed.\n("+c+": "+f+")",b.name="ChunkLoadError",b.type=c,b.request=f,r[1](b)}}),"chunk-"+a,a)}},o.O.j=a=>0===e[a];var a=(a,t)=>{var r,c,f=t[0],b=t[1],d=t[2],n=0;if(f.some((a=>0!==e[a]))){for(r in b)o.o(b,r)&&(o.m[r]=b[r]);if(d)var i=d(o)}for(a&&a(t);n Blog | Neurosynth Compose Docs - + @@ -14,7 +14,7 @@ and streamline user interfaces. Automated meta-analyses are ideal for generating exploratory results rapidly, enabling meta-analysis as part of routine scientific practice.

We've also made many small but important updates to our platform, including significant performance updates and improvements to the user interface. Neurosynth Compose is now more intuitive and easier to use. Give it a try by following our manual meta-analysis tutorial.

We also have some exciting new features in the pipeline that we'll release in early 2024 including:

  • Image-based Meta-Analysis (IBMA). Soon, you will be able to use NeuroVault data as inputs for IBMA-- a more powerful and sensitive alternative to Coordinate Based Meta-Analysis.
  • Advanced data extraction using Large Language Models (GPT). Early protypes to extract detailed information (such as participant demographics) from neuroimaging articles using LLMs have shown promise. We are working on incorporating these workflows into Neurosynth Compose, making it even easier to identify relevant studies for meta-analysis.

We look forward to your feedback!

-Alejandro

· 3 min read
Alejandro de la Vega

Dear Neurosynth Community,

My name is Alejandro, and I am the current project leader of the Neurosynth project.

I am very excited to announce to you that the Neurosynth project lives on, and we are officially announcing the (beta) release of the latest member of the ecosystem: Neurosynth Compose.

Neurosynth Compose enables users to easily perform custom neuroimaging meta-analyses using a web-based platform, with no programming experience required. This project addresses one of the most commonly request features, which is the ability to customize large-scale meta-analyses using you own expert knowledge.

Neurosynth Compose is free to use and helps users:

  • 🔎 Search across over 20,000 studies in the Neurosynth database, or import from external databses such as PubMed.
  • 🗃️ Curate your StudySet using systematic review tools conforming to the PRISMA guidelines.
  • 📝 Extract coordinates and metadata for each study, leveraging thousands of pre-extracted studies to minimize effort.
  • 📊 Analyze by specifying a reproducible NiMARE workflow, and execute it locally or in the cloud.
  • 🔗 Share with the community with complete provenance and reproducibility.

The goal of Neurosynth Compose is to enable researchers to go beyond the finite set of automatically generated meta-analyses from the original platform and overcome limitations from automated coordinate and semantic extraction. The end result is a gold standard meta-analysis, in much less time than a manual workflow, and with much greater reproducible.

Currently, Neurosynth Compose is in beta, and under active development. We welcome feedback to ensure our platform meets the needs of the community. Please leave us feedback using the button on the bottom right corner of the screen!

We are working on several upcoming features that will make the platform even better. Many of these features are already available in our Python meta-analysis library, NiMARE, and we are actively working on the user facing online interfaces.

  • Image-based Meta-Analysis (IBMA). We have developed algorithms in NiMARE for using whole-brain statistical maps as inputs to meta-analysis. This is more powerful and sensitive technique compared to Coordinate-base Meta-Analysis. Soon, you will be able to use NeuroVault data as inputs for your meta-analyses.
  • MKDA Chi-squared / Association test. A hallmark feature of Neurosynth is the ability to relate meta-analytic findings to the rest of the literature, to determine the strength and specificity of an association (this was previously called "reverse inference"). This will soon be possible on your custom meta-analyses.
  • A wide range of improvements to the user experience. We are in the process of re-working parts of the online interface to decrease friction when creating a StudySet, making study utilization, and editing more intuitive.

I would like to thank everyone involved in this highly-collaborative project, but especially James Kent, a postdoctoral fellow, and Nick Lee, a software engineer, who did the lion's share of the work.

We are excited for you to try it and let us know what you think.

-Alejandro

- + \ No newline at end of file diff --git a/blog/2024/1/31/new-year.html b/blog/2024/1/31/new-year.html index 6200ffd..4316d1c 100644 --- a/blog/2024/1/31/new-year.html +++ b/blog/2024/1/31/new-year.html @@ -5,13 +5,13 @@ New Year, New Features | Neurosynth Compose Docs - +

New Year, New Features

· 2 min read
Alejandro de la Vega

Hello Neurosynth Users,

2023 was a very exciting year for Neurosynth, having launched our Compose platform to the public and announced it on social media. In the December we’ve saw over 500 new user visits, with 200 users signing up for an account! 🚀

Help us keep this growth going by sharing our announcement with your colleagues. 🧑‍🔬

🌟 What’s New 🌟

We’ve also continued to introduce new features and improve the user experience. Here’s some highlights:

Large-scale association tests​

A key feature that set Neurosynth aside were large-scale association maps (previously known as “reverse inference”).

Whereas a typical meta-analysis tells you if activity is consistently reported in a target set of studies, an association test tells you if activation occurs more consistently in this set of studies versus a large and diverse reference sample.

That's important, because this allows you to control for base rate differences between regions. Certain regions, such as the insula or lateral PFC for instance, play a very broad role in cognition, and hence are consistently activated for many different tasks and cognitive states. Using MKDA Chi-Squared, you can test if brain activity in a region (such as the insula) is specifically associated with the studies in your meta-analysis.

Previously association tests were available for the automatically generated maps on neurosynth.org. Now you can perform large-scale association tests for your custom meta-analyses in Neurosynth Compose.

We have created a full primer and tutorial on MKDA Chi-Squared, including an example from a recent meta-analysis on social processing. Check it out!

MKDA Chi-Squared Tutorial 🧑‍🎓

UX Enhancements ✨​

Based on your valuable feedback, we've made numerous bug fixes and improvements:

  • Simplified Curation: The review import page has been removed, and summary information is now added directly to the tag step.

  • Searching UI: We've replaced the dropdown with a selection gallery, making it easier to choose your preferred search method, and we now auto-generate search import names. In addition, resolving duplicates is skipped if none are present.

  • Improved Editing Workflow: The editing interface has been improved, streamlining the extraction process.

  • Various UX Improvements and Fixes: We fixed many papercuts, especially in the Extraction phase.

We hope you enjoy these changes.

Email us any feedback, or ask a question on NeuroStars if you have issues.

Cheers,

The Neurosynth Team 🧠

- + \ No newline at end of file diff --git a/blog/announcing-ns-compose.html b/blog/announcing-ns-compose.html index ce24cac..6838414 100644 --- a/blog/announcing-ns-compose.html +++ b/blog/announcing-ns-compose.html @@ -5,13 +5,13 @@ Announcing Neurosynth Compose! | Neurosynth Compose Docs - +

Announcing Neurosynth Compose!

· 3 min read
Alejandro de la Vega

Dear Neurosynth Community,

My name is Alejandro, and I am the current project leader of the Neurosynth project.

I am very excited to announce to you that the Neurosynth project lives on, and we are officially announcing the (beta) release of the latest member of the ecosystem: Neurosynth Compose.

Neurosynth Compose enables users to easily perform custom neuroimaging meta-analyses using a web-based platform, with no programming experience required. This project addresses one of the most commonly request features, which is the ability to customize large-scale meta-analyses using you own expert knowledge.

Neurosynth Compose is free to use and helps users:

  • 🔎 Search across over 20,000 studies in the Neurosynth database, or import from external databses such as PubMed.
  • 🗃️ Curate your StudySet using systematic review tools conforming to the PRISMA guidelines.
  • 📝 Extract coordinates and metadata for each study, leveraging thousands of pre-extracted studies to minimize effort.
  • 📊 Analyze by specifying a reproducible NiMARE workflow, and execute it locally or in the cloud.
  • 🔗 Share with the community with complete provenance and reproducibility.

The goal of Neurosynth Compose is to enable researchers to go beyond the finite set of automatically generated meta-analyses from the original platform and overcome limitations from automated coordinate and semantic extraction. The end result is a gold standard meta-analysis, in much less time than a manual workflow, and with much greater reproducible.

Currently, Neurosynth Compose is in beta, and under active development. We welcome feedback to ensure our platform meets the needs of the community. Please leave us feedback using the button on the bottom right corner of the screen!

We are working on several upcoming features that will make the platform even better. Many of these features are already available in our Python meta-analysis library, NiMARE, and we are actively working on the user facing online interfaces.

  • Image-based Meta-Analysis (IBMA). We have developed algorithms in NiMARE for using whole-brain statistical maps as inputs to meta-analysis. This is more powerful and sensitive technique compared to Coordinate-base Meta-Analysis. Soon, you will be able to use NeuroVault data as inputs for your meta-analyses.
  • MKDA Chi-squared / Association test. A hallmark feature of Neurosynth is the ability to relate meta-analytic findings to the rest of the literature, to determine the strength and specificity of an association (this was previously called "reverse inference"). This will soon be possible on your custom meta-analyses.
  • A wide range of improvements to the user experience. We are in the process of re-working parts of the online interface to decrease friction when creating a StudySet, making study utilization, and editing more intuitive.

I would like to thank everyone involved in this highly-collaborative project, but especially James Kent, a postdoctoral fellow, and Nick Lee, a software engineer, who did the lion's share of the work.

We are excited for you to try it and let us know what you think.

-Alejandro

- + \ No newline at end of file diff --git a/blog/archive.html b/blog/archive.html index 2be8ae8..2bb3f5f 100644 --- a/blog/archive.html +++ b/blog/archive.html @@ -5,13 +5,13 @@ Archive | Neurosynth Compose Docs - +
- + \ No newline at end of file diff --git a/blog/tags.html b/blog/tags.html index 6df1d04..42914dc 100644 --- a/blog/tags.html +++ b/blog/tags.html @@ -5,13 +5,13 @@ Tags | Neurosynth Compose Docs - +
- + \ No newline at end of file diff --git a/blog/tags/hello.html b/blog/tags/hello.html index 05e0c3e..96f4f51 100644 --- a/blog/tags/hello.html +++ b/blog/tags/hello.html @@ -5,7 +5,7 @@ 2 posts tagged with "hello" | Neurosynth Compose Docs - + @@ -14,7 +14,7 @@ and streamline user interfaces. Automated meta-analyses are ideal for generating exploratory results rapidly, enabling meta-analysis as part of routine scientific practice.

We've also made many small but important updates to our platform, including significant performance updates and improvements to the user interface. Neurosynth Compose is now more intuitive and easier to use. Give it a try by following our manual meta-analysis tutorial.

We also have some exciting new features in the pipeline that we'll release in early 2024 including:

  • Image-based Meta-Analysis (IBMA). Soon, you will be able to use NeuroVault data as inputs for IBMA-- a more powerful and sensitive alternative to Coordinate Based Meta-Analysis.
  • Advanced data extraction using Large Language Models (GPT). Early protypes to extract detailed information (such as participant demographics) from neuroimaging articles using LLMs have shown promise. We are working on incorporating these workflows into Neurosynth Compose, making it even easier to identify relevant studies for meta-analysis.

We look forward to your feedback!

-Alejandro

· 3 min read
Alejandro de la Vega

Dear Neurosynth Community,

My name is Alejandro, and I am the current project leader of the Neurosynth project.

I am very excited to announce to you that the Neurosynth project lives on, and we are officially announcing the (beta) release of the latest member of the ecosystem: Neurosynth Compose.

Neurosynth Compose enables users to easily perform custom neuroimaging meta-analyses using a web-based platform, with no programming experience required. This project addresses one of the most commonly request features, which is the ability to customize large-scale meta-analyses using you own expert knowledge.

Neurosynth Compose is free to use and helps users:

  • 🔎 Search across over 20,000 studies in the Neurosynth database, or import from external databses such as PubMed.
  • 🗃️ Curate your StudySet using systematic review tools conforming to the PRISMA guidelines.
  • 📝 Extract coordinates and metadata for each study, leveraging thousands of pre-extracted studies to minimize effort.
  • 📊 Analyze by specifying a reproducible NiMARE workflow, and execute it locally or in the cloud.
  • 🔗 Share with the community with complete provenance and reproducibility.

The goal of Neurosynth Compose is to enable researchers to go beyond the finite set of automatically generated meta-analyses from the original platform and overcome limitations from automated coordinate and semantic extraction. The end result is a gold standard meta-analysis, in much less time than a manual workflow, and with much greater reproducible.

Currently, Neurosynth Compose is in beta, and under active development. We welcome feedback to ensure our platform meets the needs of the community. Please leave us feedback using the button on the bottom right corner of the screen!

We are working on several upcoming features that will make the platform even better. Many of these features are already available in our Python meta-analysis library, NiMARE, and we are actively working on the user facing online interfaces.

  • Image-based Meta-Analysis (IBMA). We have developed algorithms in NiMARE for using whole-brain statistical maps as inputs to meta-analysis. This is more powerful and sensitive technique compared to Coordinate-base Meta-Analysis. Soon, you will be able to use NeuroVault data as inputs for your meta-analyses.
  • MKDA Chi-squared / Association test. A hallmark feature of Neurosynth is the ability to relate meta-analytic findings to the rest of the literature, to determine the strength and specificity of an association (this was previously called "reverse inference"). This will soon be possible on your custom meta-analyses.
  • A wide range of improvements to the user experience. We are in the process of re-working parts of the online interface to decrease friction when creating a StudySet, making study utilization, and editing more intuitive.

I would like to thank everyone involved in this highly-collaborative project, but especially James Kent, a postdoctoral fellow, and Nick Lee, a software engineer, who did the lion's share of the work.

We are excited for you to try it and let us know what you think.

-Alejandro

- + \ No newline at end of file diff --git a/blog/tags/neurosynth.html b/blog/tags/neurosynth.html index 1ad310c..e68408e 100644 --- a/blog/tags/neurosynth.html +++ b/blog/tags/neurosynth.html @@ -5,7 +5,7 @@ 3 posts tagged with "neurosynth" | Neurosynth Compose Docs - + @@ -14,7 +14,7 @@ and streamline user interfaces. Automated meta-analyses are ideal for generating exploratory results rapidly, enabling meta-analysis as part of routine scientific practice.

We've also made many small but important updates to our platform, including significant performance updates and improvements to the user interface. Neurosynth Compose is now more intuitive and easier to use. Give it a try by following our manual meta-analysis tutorial.

We also have some exciting new features in the pipeline that we'll release in early 2024 including:

  • Image-based Meta-Analysis (IBMA). Soon, you will be able to use NeuroVault data as inputs for IBMA-- a more powerful and sensitive alternative to Coordinate Based Meta-Analysis.
  • Advanced data extraction using Large Language Models (GPT). Early protypes to extract detailed information (such as participant demographics) from neuroimaging articles using LLMs have shown promise. We are working on incorporating these workflows into Neurosynth Compose, making it even easier to identify relevant studies for meta-analysis.

We look forward to your feedback!

-Alejandro

· 3 min read
Alejandro de la Vega

Dear Neurosynth Community,

My name is Alejandro, and I am the current project leader of the Neurosynth project.

I am very excited to announce to you that the Neurosynth project lives on, and we are officially announcing the (beta) release of the latest member of the ecosystem: Neurosynth Compose.

Neurosynth Compose enables users to easily perform custom neuroimaging meta-analyses using a web-based platform, with no programming experience required. This project addresses one of the most commonly request features, which is the ability to customize large-scale meta-analyses using you own expert knowledge.

Neurosynth Compose is free to use and helps users:

  • 🔎 Search across over 20,000 studies in the Neurosynth database, or import from external databses such as PubMed.
  • 🗃️ Curate your StudySet using systematic review tools conforming to the PRISMA guidelines.
  • 📝 Extract coordinates and metadata for each study, leveraging thousands of pre-extracted studies to minimize effort.
  • 📊 Analyze by specifying a reproducible NiMARE workflow, and execute it locally or in the cloud.
  • 🔗 Share with the community with complete provenance and reproducibility.

The goal of Neurosynth Compose is to enable researchers to go beyond the finite set of automatically generated meta-analyses from the original platform and overcome limitations from automated coordinate and semantic extraction. The end result is a gold standard meta-analysis, in much less time than a manual workflow, and with much greater reproducible.

Currently, Neurosynth Compose is in beta, and under active development. We welcome feedback to ensure our platform meets the needs of the community. Please leave us feedback using the button on the bottom right corner of the screen!

We are working on several upcoming features that will make the platform even better. Many of these features are already available in our Python meta-analysis library, NiMARE, and we are actively working on the user facing online interfaces.

  • Image-based Meta-Analysis (IBMA). We have developed algorithms in NiMARE for using whole-brain statistical maps as inputs to meta-analysis. This is more powerful and sensitive technique compared to Coordinate-base Meta-Analysis. Soon, you will be able to use NeuroVault data as inputs for your meta-analyses.
  • MKDA Chi-squared / Association test. A hallmark feature of Neurosynth is the ability to relate meta-analytic findings to the rest of the literature, to determine the strength and specificity of an association (this was previously called "reverse inference"). This will soon be possible on your custom meta-analyses.
  • A wide range of improvements to the user experience. We are in the process of re-working parts of the online interface to decrease friction when creating a StudySet, making study utilization, and editing more intuitive.

I would like to thank everyone involved in this highly-collaborative project, but especially James Kent, a postdoctoral fellow, and Nick Lee, a software engineer, who did the lion's share of the work.

We are excited for you to try it and let us know what you think.

-Alejandro

- + \ No newline at end of file diff --git a/blog/tutorials-updates.html b/blog/tutorials-updates.html index c70d958..aea2ef6 100644 --- a/blog/tutorials-updates.html +++ b/blog/tutorials-updates.html @@ -5,7 +5,7 @@ New tutorials and updates | Neurosynth Compose Docs - + @@ -14,7 +14,7 @@ and streamline user interfaces. Automated meta-analyses are ideal for generating exploratory results rapidly, enabling meta-analysis as part of routine scientific practice.

We've also made many small but important updates to our platform, including significant performance updates and improvements to the user interface. Neurosynth Compose is now more intuitive and easier to use. Give it a try by following our manual meta-analysis tutorial.

We also have some exciting new features in the pipeline that we'll release in early 2024 including:

  • Image-based Meta-Analysis (IBMA). Soon, you will be able to use NeuroVault data as inputs for IBMA-- a more powerful and sensitive alternative to Coordinate Based Meta-Analysis.
  • Advanced data extraction using Large Language Models (GPT). Early protypes to extract detailed information (such as participant demographics) from neuroimaging articles using LLMs have shown promise. We are working on incorporating these workflows into Neurosynth Compose, making it even easier to identify relevant studies for meta-analysis.

We look forward to your feedback!

-Alejandro

- + \ No newline at end of file diff --git a/guide.html b/guide.html index a1bbf14..92785ba 100644 --- a/guide.html +++ b/guide.html @@ -5,13 +5,13 @@ User Guide | Neurosynth Compose Docs - +
-
- + + \ No newline at end of file diff --git a/guide/Explore.html b/guide/Explore.html index a643bc2..dabad58 100644 --- a/guide/Explore.html +++ b/guide/Explore.html @@ -5,15 +5,15 @@ Explore | Neurosynth Compose Docs - +

Explore

Here, you can browse and search existing public Studies, StudySets and Meta-Analyses created on the platform.

Studies​

The Studies page lets you browse and search all studies that exist on the NeuroStore server. This interface is similar to what you'll see when importing studies into your Project. However, here it's simply provided for your browsing pleasure.

For more information on how advanced search functionally, see Searching Studies

StudySets and Meta-Analyses​

For StudySets and Meta-Analyses, you can browse and search any user-contributed items, including those from other users.

Note that although you see all publically available items, you cannot edit somebody else's content.

note

We are currently working on a way to allow users to fork other users' StudySets and Meta-Analyses to create their own versions. Stay tuned! -::::

- +::::

+ \ No newline at end of file diff --git a/guide/Explore/Searching.html b/guide/Explore/Searching.html index 1811b97..8d2da9f 100644 --- a/guide/Explore/Searching.html +++ b/guide/Explore/Searching.html @@ -5,7 +5,7 @@ Searching | Neurosynth Compose Docs - + @@ -15,8 +15,8 @@ certain AND or NOT operators amongst multiple clauses.

For example, consider the search: nicotine OR smoking -marijuana. In this example, the search results returned to you will be all the results yielded from nicotine unioned with all the results of smoking -marijuana. Having -marijuana here does not relate to the entire search term - just smoking.

To ensure that you do not have any mention of marijuana in your returned papers, you must search: nicotine -marijuana or smoking -marijuana.

The same case applies for AND operations. In general, OR unions between various search groups consisting of and, not, and quoted search terms.

Study Data Type​

Studies can either report their findings as coordinate data, image data, or in some cases, both. Using the Study Data Type button, you can filter the results so that only coordinate or image data appears. For example, if you are doing a coordinate based meta-analysis, you -will want to filter the results to show only studies that report coordinates.

Sorting Results​

Use the Sort By feature to sort the results based on a given property. You can also set this to be ascending or descending.

Filtering​

To filter the results of the search, click on the orange Add Filter button. You have the option of filtering by title, description, author, or publication. Enter the string you want to filter by and click add to apply the filter.

Only one filter can be applied for each field. In order to remove a filter, simply click on the delete button on the given filter.

- +will want to filter the results to show only studies that report coordinates.

Sorting Results​

Use the Sort By feature to sort the results based on a given property. You can also set this to be ascending or descending.

Filtering​

To filter the results of the search, click on the orange Add Filter button. You have the option of filtering by title, description, author, or publication. Enter the string you want to filter by and click add to apply the filter.

Only one filter can be applied for each field. In order to remove a filter, simply click on the delete button on the given filter.

+ \ No newline at end of file diff --git a/guide/Project.html b/guide/Project.html index 224e852..36d1d09 100644 --- a/guide/Project.html +++ b/guide/Project.html @@ -5,7 +5,7 @@ Project | Neurosynth Compose Docs - + @@ -13,8 +13,8 @@

Project

A project organizes the the various steps needed to create a meta-analysis from start to finish.

Within a project you will be able to:

  1. Curate studies of interest and select the ones to be included in the meta-analysis
  2. Extract the relevant data such as activation coordinates and other meta-data
  3. Specify the algorithm and corrector you would like to use

In each project, you can define a define a single StudySet (i.e. a collection of related studies), and one or more MetaAnalysis specifications.

You can open a specific project by logging in, navigating to the My Projects page, and selecting a project you've created.

When you view a project for the first time, you'll notice that you'll default to the "Edit Project" tab. The "View Meta-Analyses" tab will be disabled and will become enabled when you have created the first meta-analysis specification -for the project.

- +for the project.

+ \ No newline at end of file diff --git a/guide/Project/Curation.html b/guide/Project/Curation.html index 01dd3d0..1470b0c 100644 --- a/guide/Project/Curation.html +++ b/guide/Project/Curation.html @@ -5,7 +5,7 @@ Curation | Neurosynth Compose Docs - + @@ -35,8 +35,8 @@ along with the following options: PROMOTE, NEEDS REVIEW, and EXCLUDE. There is also a button to ADD TAGS which will assign an informational tag to the study.

Exclude​

To exclude a study, click the EXCLUDE button and select the exclusion reason. You can either choose from the preset exclusion reasons or you can begin typing to create a new one.

tip

For the PRISMA workflow, the default exclusion reason wil depend on the phase you are in (identification vs screening vs eligibility), to match the PRISMA guidelines. While we do not recommend it, you can click the arrow button and start typing in the input to create a new exclusion reason. Revisit the PRISMA workflow to review reccomended exclusion criteria.

Promote​

If a study meets inclusion critera (for the current phase), click PROMOTE to move the study forward to the next curation column. If it is moved into the right most inclusion column, then it will be included in the meta-analysis.

tip

For the first column (especially in a PRISMA workflow) it can be tedious to promote non-duplicates to the next column. If all duplicates have been resolved, you can exit the dialog and click PROMOTE ALL UNCATEGORIZED STUDIES to advance all non-duplicate studies to the next column.

On to Extraction​

When you have categorized all imported studies by either excluding them or moving them to the inclusion column, then you have -successfully completed the curation portion of the meta-analysis.

Neurosynth Compose will detect this and reveal a new button: MOVE TO EXTRACTION PHASE. This will move you back to the project page and get you started on the next major component to building your meta-analysis: extraction.

- +successfully completed the curation portion of the meta-analysis.

Neurosynth Compose will detect this and reveal a new button: MOVE TO EXTRACTION PHASE. This will move you back to the project page and get you started on the next major component to building your meta-analysis: extraction.

+ \ No newline at end of file diff --git a/guide/Project/Extraction.html b/guide/Project/Extraction.html index 70f18c4..b4ebf3b 100644 --- a/guide/Project/Extraction.html +++ b/guide/Project/Extraction.html @@ -5,7 +5,7 @@ Extraction | Neurosynth Compose Docs - + @@ -24,8 +24,8 @@ are imported into the curation phase are excluded during the curation process. Inserting these studies into the database would clutter it and create a lot of empty entries which don't have coordinates and might not even be used. By waiting until we have our finalized included subset of studies, we reduce the number of empty, useless studies in the database.

Annotations​

Study Editing​

Studies in the extraction phase are filtered and categorized to help better organize and facilitate the process. Initially, all studies -start as Uncategorized. The user can then decide to mark them as Save For Later if they want to revisit the study, or Completed.

Read Only Studies​

Study Annotations​

Study Edit Interface​

Syncing Between Curation and Extraction​

- +start as Uncategorized. The user can then decide to mark them as Save For Later if they want to revisit the study, or Completed.

Read Only Studies​

Study Annotations​

Study Edit Interface​

Syncing Between Curation and Extraction​

+ \ No newline at end of file diff --git a/guide/Project/Specification.html b/guide/Project/Specification.html index c931c78..594826d 100644 --- a/guide/Project/Specification.html +++ b/guide/Project/Specification.html @@ -5,13 +5,13 @@ Specification | Neurosynth Compose Docs - +
-
- + + \ No newline at end of file diff --git a/guide/Running.html b/guide/Running.html index d5f2445..ed42d63 100644 --- a/guide/Running.html +++ b/guide/Running.html @@ -5,7 +5,7 @@ Running Analyses | Neurosynth Compose Docs - + @@ -17,8 +17,8 @@ of the results, or browse the results in the Neurosynth Compose web interface, in the Meta-Analysis section of your Project.

tip

The Colab notebook has limited and varying freely available resources, and may not be able to run large analyses. If your analysis fails, try running it again, or using one of the other methods below.

Docker​

The easiest way to run analyses locally is to use the nsc-runner Docker image provided by Neurosynth Compose.

Docker is a containerization technology that allows you to run software in a consistent environment, regardless of the underlying operating system.

To run the Docker image, you will need to install Docker on your local machine. Instructions for installing Docker can be found here.

Once Docker is installed, you can run your analysis using the using the following command:

docker run -it -v -v /local/dir:/results ghcr.io/neurostuff/nsc-runner:latest <meta-analysis-id>

where /local/dir is the path to a local directory where you would like to save the results of your analysis, and <meta-analysis-id> is the ID of the meta-analysis you would like to run.

The Docker image will download all required software, run the analysis, and upload the results to Neurovault & Neurosynth Compose. -An HTML report will be saved in the results directory, and the results will be available in the Meta-Analysis section of your Project on Neurosynth Compose.

Updating the Docker image​

For every release of nsc-runner, we publish a corresponding Docker image.

You can manually download a specific neuroscout-cli release as follows:

docker pull ghcr.io/neurostuff/nsc-runner:<version>

where <version> is the version of nsc-runner that you want to download. If you omit version, the latest stable version will be downloaded.

You can see the tags available for download on GitHub

Manually prepared environment using pip​

danger

Manually installing nsc-runner may be difficult due to complex dependencies in the SciPy stack, or fMRI-specific tooling. Proceed only if you know what you’re doing.

Use pip to install nsc-runner from PyPI:

pip install nsc-runner

and then run the analysis using the following command:

nsc-runner <meta-analysis-id>
- +An HTML report will be saved in the results directory, and the results will be available in the Meta-Analysis section of your Project on Neurosynth Compose.

Updating the Docker image​

For every release of nsc-runner, we publish a corresponding Docker image.

You can manually download a specific neuroscout-cli release as follows:

docker pull ghcr.io/neurostuff/nsc-runner:<version>

where <version> is the version of nsc-runner that you want to download. If you omit version, the latest stable version will be downloaded.

You can see the tags available for download on GitHub

Manually prepared environment using pip​

danger

Manually installing nsc-runner may be difficult due to complex dependencies in the SciPy stack, or fMRI-specific tooling. Proceed only if you know what you’re doing.

Use pip to install nsc-runner from PyPI:

pip install nsc-runner

and then run the analysis using the following command:

nsc-runner <meta-analysis-id>
+ \ No newline at end of file diff --git a/guide/glossary.html b/guide/glossary.html index 1f9270e..c10cd1d 100644 --- a/guide/glossary.html +++ b/guide/glossary.html @@ -5,7 +5,7 @@ Glossary | Neurosynth Compose Docs - + @@ -28,8 +28,8 @@ The contents of an analysis include the terms applied to the groups/conditions and their respective weights in the contrast. An analysis also contains the results of the statistical contrast either with an image and/or a table -indicating significant results

Condition​

Overview​

A condition is term/word that represents a psychological (e.g., 2-back), physical (e.g., biking)

Weights​

Point​

Overview​

Image​

- +indicating significant results

Condition​

Overview​

A condition is term/word that represents a psychological (e.g., 2-back), physical (e.g., biking)

Weights​

Point​

Overview​

Image​

+ \ No newline at end of file diff --git a/index.html b/index.html index a15ccd7..7af923c 100644 --- a/index.html +++ b/index.html @@ -5,13 +5,13 @@ What is Neurosynth Compose? | Neurosynth Compose Docs - +
-

What is Neurosynth Compose?

Neurosynth Compose is the next-generation of Neurosynth, enabling users to easily perform custom neuroimaging meta-analyses using a fully web-based platform, with no programming experience required.

Neurosynth Compose is free to use and helps users:

  • 🔎 Search across over 20,000 studies in the Neurosynth database, or import from external databases such as PubMed.
  • 🗃️ Curate your StudySet using systematic review tools conforming to the PRISMA guidelines.
  • 📝 Extract coordinates and metadata for each study, leveraging thousands of pre-extracted studies to minimize effort.
  • 📊 Analyze by specifying a reproducible NiMARE workflow, and execute it locally or in the cloud.
  • 🔗 Share with the community with complete provenance and reproducibility.

Get started! 🚀​

Tutorials

Tutorials show you how to use Neurosynth Compose with complete end-to-end examples.

Go to Tutorials

User Guide

The User Guide provides a detailed overview of the platform, and explains key concepts and features.

Go to User Guide

Getting Help​

  • Ask a question on Neurostars using the neurosynth-compose tag.
  • Report a bug or request a feature on GitHub
  • Help us improve the platform by using the feedback button in the bottom right corner of the page.
- +

What is Neurosynth Compose?

Neurosynth Compose is the next-generation of Neurosynth, enabling users to easily perform custom neuroimaging meta-analyses using a fully web-based platform, with no programming experience required.

Neurosynth Compose is free to use and helps users:

  • 🔎 Search across over 20,000 studies in the Neurosynth database, or import from external databases such as PubMed.
  • 🗃️ Curate your StudySet using systematic review tools conforming to the PRISMA guidelines.
  • 📝 Extract coordinates and metadata for each study, leveraging thousands of pre-extracted studies to minimize effort.
  • 📊 Analyze by specifying a reproducible NiMARE workflow, and execute it locally or in the cloud.
  • 🔗 Share with the community with complete provenance and reproducibility.

Get started! 🚀​

Tutorials

Tutorials show you how to use Neurosynth Compose with complete end-to-end examples.

Go to Tutorials

User Guide

The User Guide provides a detailed overview of the platform, and explains key concepts and features.

Go to User Guide

Getting Help​

  • Ask a question on Neurostars using the neurosynth-compose tag.
  • Report a bug or request a feature on GitHub
  • Help us improve the platform by using the feedback button in the bottom right corner of the page.
+ \ No newline at end of file diff --git a/introduction/ecosystem.html b/introduction/ecosystem.html index 289fc6e..9167047 100644 --- a/introduction/ecosystem.html +++ b/introduction/ecosystem.html @@ -5,7 +5,7 @@ Ecosystem for fMRI Meta-Analysis | Neurosynth Compose Docs - + @@ -14,22 +14,22 @@ synthesizing findings across 14,000+ fMRI studies. The philosophy was to leverage large scale meta-analysis to provide new insights into the literature, overcoming limitations of database with sheer scale. With regular updates, Neurosynth was able to keep up with the growth of the literature. -The database was released with a permissive license, and accompanied by a Python package to manipulate and analyze it.

Although this approach was surprsingly successful, there were several major limitations to Neurosynth 1.0:

  • Meta-analyses were limited by concepts that can be inferred from large scale text mining (i.e. frequency of terms in the text). -Although these features proved to be surprsingly useful for well-powered and broad cognitive constructs (e.g. 'emotion'), Neurosynth was not able +The database was released with a permissive license, and accompanied by a Python package to manipulate and analyze it.

    Although this approach was surprisingly successful, there were several major limitations to Neurosynth 1.0:

    • Meta-analyses were limited by concepts that can be inferred from large scale text mining (i.e. frequency of terms in the text). +Although these features proved to be surprisingly useful for well-powered and broad cognitive constructs (e.g. 'emotion'), Neurosynth was not able to capture the fine-grained details of the neuroimaging literature, or allow users to define their own grouping of studies.

    • The database is not curated, and therefore contains many inaccuracies and incomplete data at both the study and coordinate level. -Aside from obvious extraction erors, automated coordinate extraction lacks the ability to determine critical information, such as whether the activation is positive or negative. -In addition, it's not possible to segregate the coordinates into distinct contrast, conditions, or studies without manual curation.

    • Coordinate-based analyses are inherently inferior to image-based meta-analysis, which is becoming increasingly possible with sharing of unthresholded statisical maps in repositories like NeuroVault.

    Neurosynth Compose aims to address these limitations:

    • Provides a web-based platform for meta-analytic neuroimaging research, allowing users to curate studies, and specify meta-analytic models.

    • Flexible and easy to use, allowing users to perform both large-scale exploratory meta-analyses, as well as targeted, hypothesis-driven meta-analyses that conform to stringent standards such as the [PRISMA][https://prisma-statement.org] statement.

    • Allows users to perfom image-based meta-analysis using unthresholded images from NeuroVault (in progress).

    • Quick, reproducible and transparent sharing of results.

    • Incentivizes collaborative curation of neuroimaging studies in a accesible centralized repository (NeuroStore, see below).

    NeuroStore​

    NeuroStore is the centralized repository for neuroimaging data amenable to meta-analysis, including coordinates, images, and metadata. +Aside from obvious extraction errors, automated coordinate extraction lacks the ability to determine critical information, such as whether the activation is positive or negative. +In addition, it's not possible to segregate the coordinates into distinct contrast, conditions, or studies without manual curation.

  • Coordinate-based analyses are inherently inferior to image-based meta-analysis, which is becoming increasingly possible with sharing of unthresholded statistical maps in repositories like NeuroVault.

Neurosynth Compose aims to address these limitations:

  • Provides a web-based platform for meta-analytic neuroimaging research, allowing users to curate studies, and specify meta-analytic models.

  • Flexible and easy to use, allowing users to perform both large-scale exploratory meta-analyses, as well as targeted, hypothesis-driven meta-analyses that conform to stringent standards such as the PRISMA statement.

  • Allows users to perfom image-based meta-analysis using unthresholded images from NeuroVault (in progress).

  • Quick, reproducible and transparent sharing of results.

  • Incentivizes collaborative curation of neuroimaging studies in a accessible centralized repository (NeuroStore, see below).

NeuroStore​

NeuroStore is the centralized repository for neuroimaging data amenable to meta-analysis, including coordinates, images, and metadata. NeuroStore serves as the data backend for Neurosynth Compose, but is also designed to be a standalone interoperable resource for the neuroimaging community.

NeuroStore ingests data from a number of sources, including NeuroVault, NeuroQuery, and Neurosynth (ACE), and provides a unified API for querying the database.

Importantly, data on NeuroStore can be curated the community, allowing users to correct errors in coordinate extraction, and add new studies and coordinates. By providing a centralized repository for curated neuroimaging data, NeuroStore aims to facilitate the development of new meta-analytic tools, and to provide a common data format for the neuroimaging community.

NiMARE​

NiMARE (Neuroimaging Meta-Analysis Research Environment) is a Python package for performing neuroimaging meta-analysis.

NiMARE is an open source, collaboratively-developed package that implements a range of meta-analytic algorithms, including coordinate- and image-based meta-analyses, automated annotation, functional decoding, and meta-analytic coactivation modeling.

By consolidating meta-analytic methods under a common library and syntax, NiMARE makes it straightforward for users to employ the appropriate approach for a given analysis.

NeuroSynth Compose uses NiMARE to execute meta-analyses, by exporting datasets in the common NIMADS format (see below), and specifying reproducible NiMARE workflows for common meta-analytic models.

PyMARE​

PyMARE is a Python library for effect-size meta-analysis. NiMARE uses PyMARE for its image-based meta-analysis algorithms (with some light wrapping to convert image objects to arrays compatible with PyMARE functions).

NIMADS​

NIMADS is a new standard for organizing and representing meta-analytic neuroimaging data. NIMADS is used as a common, interoperable format by NeuroStore, pyNIMADS, and NiMARE.

NeuroQuery​

NeuroQuery is a web service, Python library, and coordinate database built for large-scale, predictive meta-analysis. -Predictive meta-analysis generates non-statistical brain maps from text, using a database of coordinates and associated texts.

The NeuroQuery dataset is accurate and maintainble than the existing Neurosynth 1.0 database, +Predictive meta-analysis generates non-statistical brain maps from text, using a database of coordinates and associated texts.

The NeuroQuery dataset is accurate and maintainable than the existing Neurosynth 1.0 database, this new database will effectively replace the old one within the meta-analytic ecosystem. NiMARE can ingest the NeuroQuery database and convert it automatically to a NiMARE Dataset object for analysis. Additionally, the NeuroQuery database will feed directly into NeuroStore as a source of coordinates.

NeuroVault​

NeuroVault is a repository for unthresholded statistical maps from neuroimaging studies.

As of early 2023, NeuroVault contains over 8,000 collections of statistical images, with over 100,000 images in total. It is the largest repository of unthresholded statistical maps in the world, and is the primary source of data for image-based meta-analyses using Neurosynth Compose.

Currently, NeuroVault supports some basic meta-analytic functionality. However, as other tools in this ecosystem are developed, -it is planned that NeuroVault will focus exclusively on image storage and sharing, and will rely on other tools for meta-analysis.

- +it is planned that NeuroVault will focus exclusively on image storage and sharing, and will rely on other tools for meta-analysis.

+ \ No newline at end of file diff --git a/introduction/faq.html b/introduction/faq.html index 6a362d1..703de19 100644 --- a/introduction/faq.html +++ b/introduction/faq.html @@ -5,13 +5,13 @@ Frequently Asked Questions | Neurosynth Compose Docs - +
-

Frequently Asked Questions

Is this service free to use?
Yes! Note, however, that NS-Compose is a web-based engine for neuroimaging meta-analysis specification; at the moment, we don’t (yet?) provide free computing resources for the execution of the resulting meta-analysis specifications. However, you can easily run your meta-analysis in the cloud using Google Colab, or locally using Python. Instructions are provided after you complete a meta-analysis.
Are there any restrictions on meta-analyses created?
Yes. Once a meta-analysis specification is executed and results are uploaded to our platform, you will no longer be able to delete or edit the analysis specification. A complete copy of the StudySet and Analysis is kept on our system to ensure complete provenance. You can, however, keep the analysis as private to ensure it is unlisted in the public search.
If you wish to make any edits, you can edit the StudySet and create a new Analysis specification, which will receive a new unique ID.

In the event that you publish any results generated using the Neurosynth Compose, you MUST provide a link to the corresponding meta-analysis specification ID on the platform.
I've noticed that a study on your platform contains errors or is incomplete, can I fix them?
Yes! We welcome user contributions. You can correct or add details on a study, including meta-data and peak activation coordinates. A key piece of information that you may want to correct is how Activation coordinates are grouped into distinct Analyses (i.e., Contrasts). Please ensure that any edits you make are as objective as possible and reflect what is represented in the original Study, and *not* the goals of your meta-analysis.

To avoid debates about the ground truth of a Study, when you make edits a new Version of the study is created, which is associated with your User.
If I contribute new studies to the platform, or edit existing studies, will other users be able to see them?
Yes! Although a new Version of the study is created when you make any edits, we default to displaying user edited Versions over the automatically extracted versions. This is because we assume that any edits made by users will be improvements on the extraction algorithm. Please ensure any changes you make reflect this. You may also make a Version private if you don't want to share your edits with others.
How does this project relate to the original Neurosynth?
Neurosynth 1.0 was an online platform for browsing automatically generated large-scale neuroimaging meta-analyses. However, because all analyses were pre-generated, users were unable to define custom meta-analyses using the Neurosynth database. Instead, Neurosynth 1.0 used text mining techniques to automatically group studies based on the frequency of the terms mentioned in the text. Neurosynth Compose is focused on allowing users to overcome the limitations of automated large-scale meta-analysis, by enabling users to annotate studies, and curate sets of studies amenable for meta-analysis. This way, users can systematically define meta-analyses using their own expertise, while still leveraging the Neurosynth database, and an easy-to-use web-based analysis builder to accelerate the meta-analysis process.
- +

Frequently Asked Questions

Is this service free to use?
Yes! Note, however, that NS-Compose is a web-based engine for neuroimaging meta-analysis specification; at the moment, we don’t (yet?) provide free computing resources for the execution of the resulting meta-analysis specifications. However, you can easily run your meta-analysis in the cloud using Google Colab, or locally using Python. Instructions are provided after you complete a meta-analysis.
Are there any restrictions on meta-analyses created?
Yes. Once a meta-analysis specification is executed and results are uploaded to our platform, you will no longer be able to delete or edit the analysis specification. A complete copy of the StudySet and Analysis is kept on our system to ensure complete provenance. You can, however, keep the analysis as private to ensure it is unlisted in the public search.
If you wish to make any edits, you can edit the StudySet and create a new Analysis specification, which will receive a new unique ID.

In the event that you publish any results generated using the Neurosynth Compose, you MUST provide a link to the corresponding meta-analysis specification ID on the platform.
I've noticed that a study on your platform contains errors or is incomplete, can I fix them?
Yes! We welcome user contributions. You can correct or add details on a study, including meta-data and peak activation coordinates. A key piece of information that you may want to correct is how Activation coordinates are grouped into distinct Analyses (i.e., Contrasts). Please ensure that any edits you make are as objective as possible and reflect what is represented in the original Study, and *not* the goals of your meta-analysis.

To avoid debates about the ground truth of a Study, when you make edits a new Version of the study is created, which is associated with your User.
If I contribute new studies to the platform, or edit existing studies, will other users be able to see them?
Yes! Although a new Version of the study is created when you make any edits, we default to displaying user edited Versions over the automatically extracted versions. This is because we assume that any edits made by users will be improvements on the extraction algorithm. Please ensure any changes you make reflect this. You may also make a Version private if you don't want to share your edits with others.
How does this project relate to the original Neurosynth?
Neurosynth 1.0 was an online platform for browsing automatically generated large-scale neuroimaging meta-analyses. However, because all analyses were pre-generated, users were unable to define custom meta-analyses using the Neurosynth database. Instead, Neurosynth 1.0 used text mining techniques to automatically group studies based on the frequency of the terms mentioned in the text. Neurosynth Compose is focused on allowing users to overcome the limitations of automated large-scale meta-analysis, by enabling users to annotate studies, and curate sets of studies amenable for meta-analysis. This way, users can systematically define meta-analyses using their own expertise, while still leveraging the Neurosynth database, and an easy-to-use web-based analysis builder to accelerate the meta-analysis process.
+ \ No newline at end of file diff --git a/introduction/team.html b/introduction/team.html index fac3888..78ed0a1 100644 --- a/introduction/team.html +++ b/introduction/team.html @@ -5,14 +5,14 @@ Our Team | Neurosynth Compose Docs - +

Our Team

Neurosynth-Compose is collaborative effort across several laboratories, and is supported by the National Institute of Mental Health award 5R01MH096906. -Together, we developed Neurosynth-Compose, and related tools, such as NiMARE and Neurostore.

Alejandro de la Vega

Alejandro de la Vega

Principal Investigator
University of Texas at Austin

James Kent

James Kent

Postdoctoral Fellow & Principal Engineer
University of Texas at Austin

Nicholas Lee

Nicholas Lee

Frontend Engineer
McGill University

Taylor Salo

Taylor Salo

Postdoctoral fellow
University of Pennsylvania

Katie Bottenhorn

Katie Bottenhorn

Postdoctoral Fellow
University of Southern California

Jean-Baptiste Poline

Jean-Baptiste Poline

Associate Professor
McGill University

Angela Laird

Angela Laird

Professor
Florida International University

Julio Peraza

Julio Peraza

Graduate Student
Florida International University

Russ Poldrack

Russ Poldrack

Professor
Stanford University

Tom Nichols

Tom Nichols

Professor
Oxford University

Yifan Yu

Yifan Yu

Graduate Student
Oxford University

Kendra Oudyk

Kendra Oudyk

Graduate Student
McGill University

- +Together, we developed Neurosynth-Compose, and related tools, such as NiMARE and Neurostore.

Alejandro de la Vega

Alejandro de la Vega

Principal Investigator
University of Texas at Austin

James Kent

James Kent

Postdoctoral Fellow & Principal Engineer
University of Texas at Austin

Nicholas Lee

Nicholas Lee

Frontend Engineer
McGill University

Taylor Salo

Taylor Salo

Postdoctoral fellow
University of Pennsylvania

Katie Bottenhorn

Katie Bottenhorn

Postdoctoral Fellow
University of Southern California

Jean-Baptiste Poline

Jean-Baptiste Poline

Associate Professor
McGill University

Angela Laird

Angela Laird

Professor
Florida International University

Julio Peraza

Julio Peraza

Graduate Student
Florida International University

Russ Poldrack

Russ Poldrack

Professor
Stanford University

Tom Nichols

Tom Nichols

Professor
Oxford University

Yifan Yu

Yifan Yu

Graduate Student
Oxford University

Kendra Oudyk

Kendra Oudyk

Graduate Student
McGill University

+ \ No newline at end of file diff --git a/markdown-page.html b/markdown-page.html index 1c789c1..a0c0d17 100644 --- a/markdown-page.html +++ b/markdown-page.html @@ -5,13 +5,13 @@ Markdown page example | Neurosynth Compose Docs - +

Markdown page example

You don't need React to write simple standalone pages.

- + \ No newline at end of file diff --git a/tutorial.html b/tutorial.html index 1ba47df..ecd3eb7 100644 --- a/tutorial.html +++ b/tutorial.html @@ -5,15 +5,15 @@ Tutorials | Neurosynth Compose Docs - +

Tutorials

Quickstart​

Neurosynth Compose supports a range of workflows, from exploratory large-scale automated analyses to highly rigorous manual analyses.

The choice of workflow depends on your research question and resources available for manual curation. -We reccomend starting with the manual meta-analysis tutorial if you are new.

Manual

Most stringent and rigorous.

  • Search across multiple sources, such as: PubMed, Web of Science, NeuroStore
  • Careful curation for inclusion/exclusion criteria
  • Extract coordinates from studies not already indexed
  • Meets PRISMA guidelines

Automated

Fast exploratory analysis.

  • Query studies at scale
  • Search using terms, topics or activation coordinates
  • Replicate results from original Neurosynth
  • Immediate results, ideal for a exploratory analysis

Advanced tutorials​

After you've completed the core tutorials above, you can continue your learning journey with advanced tutorials.

- +We reccomend starting with the manual meta-analysis tutorial if you are new.

Manual

Most stringent and rigorous.

  • Search across multiple sources, such as: PubMed, Web of Science, NeuroStore
  • Careful curation for inclusion/exclusion criteria
  • Extract coordinates from studies not already indexed
  • Meets PRISMA guidelines

Automated

Fast exploratory analysis.

  • Query studies at scale
  • Search using terms, topics or activation coordinates
  • Replicate results from original Neurosynth
  • Immediate results, ideal for a exploratory analysis

Advanced tutorials​

After you've completed the core tutorials above, you can continue your learning journey with advanced tutorials.

+ \ No newline at end of file diff --git a/tutorial/advanced.html b/tutorial/advanced.html index caf5a80..d3afffa 100644 --- a/tutorial/advanced.html +++ b/tutorial/advanced.html @@ -5,13 +5,13 @@ Advanced tutorials | Neurosynth Compose Docs - +
-
- + + \ No newline at end of file diff --git a/tutorial/advanced/mkda_association.html b/tutorial/advanced/mkda_association.html index 2ce8fb5..1282ad5 100644 --- a/tutorial/advanced/mkda_association.html +++ b/tutorial/advanced/mkda_association.html @@ -5,7 +5,7 @@ MKDA Chi-Squared and large-scale association tests | Neurosynth Compose Docs - + @@ -15,8 +15,8 @@ This is a fast algorithm, however, it is recommended to use FWECorrector (family-wise-error) with the montecarlo method for more accurate, publication-quality results.

Next, select the annotation inclusion column you want to use, as before (by default, the "included" column will be used).

Now, select a reference dataset from the dropdown list below. The Neurosynth dataset represents the latest release of the legacy Neurosynth dataset (version 7), released July, 2018. The Neurostore dataset represents the latest update of our continuously updating "live" dataset, spanning over 20,000 neuroimaging studies.

MKDA Chi Squared Reference

Now simply complete the rest of the meta-analysis specification wizard to finish.

Executing your analysis​

As usual, you can execute your meta-analysis using Google Colab or on a local computational resource using Docker.

tip

The MKDAChi2 algorithm takes between ~30s-2minutes to run. However, the FWECorrector with 5,000+ montecarlo iterations can take several hours to complete. We recommend using a workstation or HPC and specifying --n-cores at run-time.

Interpreting results​

The MKDA Chi-Squared Workflow outputs two key maps: uniformity and association test maps.

  • Uniformity test map: z-scores from a one-way ANOVA testing whether the proportion of studies that report activation at a given voxel differs from the rate that would be expected if activations were uniformly distributed throughout gray matter.

The uniformity test map can be interpreted in roughly the same way as most standard whole-brain fMRI analysis: it displays the degree to which each voxel is consistently activated in studies that use a given term. For instance, for a meta-analysis of "emotion" high z-scores in the amygdala implies that studies that use the word emotion a lot tend to consistently report activation in the amygdala--at least, more consistently than one would expect if activation were uniformly distributed throughout gray matter.

  • Association test map: z-scores from a two-way ANOVA testing for the presence of a non-zero association between term use and voxel activation.

The association test maps tell you whether activation in a region XXX occurs more consistently for studies in your meta-analytic sample m than for other studies in the reference dataset. In other words, a large positive z-score implies that studies in a meta-analysis are more likely to report XXX activation than studies whose abstracts don't include the word 'emotion'.

Note that association maps do not tell you what the probability of a given psychological concept or task is. High Z-scores do not imply that a certain region or voxel is selective for a given concept or task. Instead, it just means there is evidence that there is at least a non-zero difference between reference studies, and studies in the meta-analysis.

note

NiMARE outputs a variety of maps, including cluster-corrected and uncorrected versions of all maps.

See the documentation sections on Outputs of NIMARE and Monte Carlo multiple comparisons for more details.

Example: Pintos Lobo (2022) - All Social Processing Tasks​

To demonstrate, we used Neurosynth-Compose to replicate the Pintos Lobo et al., (2022) meta-analysis for All Social Processing Tasks. For this example, we have already created a Project and StudySet with the coordinates used in this meta-analysis.

We then specified a MKDAChi2 Meta-Analysis with FWECorrector with the montecarlo method with 5,000 iterations.

Meta-Analysis Specification and Results on Neurosynth Compose

Results​

First, let's look at the FWE cluster corrected uniformity test map.

z_desc-uniformityMass_level-cluster_corr-FWE_method-montecarlo Uniformity

In this analysis, we replicate the findings of Pinto Lobos (2022), showing consistent activation for social processing across a variety of regions.

Next, let's look at the FWE cluster corrected association map:

z_desc-associationMass_level-cluster_corr-FWE_method-montecarlo -Association

As before, regions which have been previously implicated with social processing, such as the tempo-parietal junction (TPJ), and dorso-medial and ventro-medial PFC are present, meaning that activity in these social processing studies report activity in these regions with greater frequency than other studies in the Neurosynth database.

However, certain regions which we know to have low specificity, such as the insula, medial frontal cingulate cortex (MFCC) and parts of dorso-lateral PFC, are absent, meaning that there is no evidence that social processing tasks report activity in these regions more frequently than other studies in the database.

This example demonstrates how MKDA Chi-Squared association analysis can help determine the specificity activity and tasks in a meta-analysis, even for high-quality manual meta-analyses.

Footnotes & Limitations​

What happened to the "forward inference" and "reverse inference" maps?

We renamed the pre-generated forward and reverse inference maps; they're now referred to as the "uniformity test" and "association test" maps that we discuss here.

Although the method we used hasn't changed (MKDA Chi-Squared), the latter names more accurately capture what these maps actually mean. It was a mistake on our part to have used the forward and reverse inference labels; those labels should properly be reserved for posterior probability maps generated via a Bayesian estimation analysis, rather than for z-scores resulting from a frequentist inferential test of association. Probability maps are more difficult to interpret and use correctly, as they depend on the prior assumed by the researcher. Since setting an appropriate prior is highly non-trivial, these maps are disabled by default.

Using MKDA Chi Squared on manual meta-analyses

In this tutorial, we applied MKDA Chi-Squared to a manual meta-analysis. However, this is not a perfect comparison, as there are differences between the reference sample (Neurosynth), the high-quality manual annotations given as input. Studies in large-scale meta-analytic databases are automatically populated, meaning there are potential sampling biases. Most notably, studies in Neurosynth include all reported coordinates, not only "target" analyses/contrasts. Thus, it is possible that low-level task > no task contrasts are over-represented in this reference sample.

References & Further Reading​

If you want to understand the nuances of what inferences you can and cannot make using these maps, we recommend reading Tal Yarkoni's blog posts on how these maps do not provide evidence that the dACC is select for pain: Post 1, Post 2, as well as a commentary by Tor Wager et al., 2016

Poldrack RA. Inferring mental states from neuroimaging data: from reverse inference to large-scale decoding. Neuron. 2011 Dec 8;72(5):692-7. doi: 10.1016/j.neuron.2011.11.001. PMID: 22153367; PMCID: PMC3240863.

Poldrack RA, Yarkoni T. From Brain Maps to Cognitive Ontologies: Informatics and the Search for Mental Structure. Annu Rev Psychol. 2016;67:587-612. doi: 10.1146/annurev-psych-122414-033729. Epub 2015 Sep 21. PMID: 26393866; PMCID: PMC4701616.

- +Association

As before, regions which have been previously implicated with social processing, such as the tempo-parietal junction (TPJ), and dorso-medial and ventro-medial PFC are present, meaning that activity in these social processing studies report activity in these regions with greater frequency than other studies in the Neurosynth database.

However, certain regions which we know to have low specificity, such as the insula, medial frontal cingulate cortex (MFCC) and parts of dorso-lateral PFC, are absent, meaning that there is no evidence that social processing tasks report activity in these regions more frequently than other studies in the database.

This example demonstrates how MKDA Chi-Squared association analysis can help determine the specificity activity and tasks in a meta-analysis, even for high-quality manual meta-analyses.

Footnotes & Limitations​

What happened to the "forward inference" and "reverse inference" maps?

We renamed the pre-generated forward and reverse inference maps; they're now referred to as the "uniformity test" and "association test" maps that we discuss here.

Although the method we used hasn't changed (MKDA Chi-Squared), the latter names more accurately capture what these maps actually mean. It was a mistake on our part to have used the forward and reverse inference labels; those labels should properly be reserved for posterior probability maps generated via a Bayesian estimation analysis, rather than for z-scores resulting from a frequentist inferential test of association. Probability maps are more difficult to interpret and use correctly, as they depend on the prior assumed by the researcher. Since setting an appropriate prior is highly non-trivial, these maps are disabled by default.

Using MKDA Chi Squared on manual meta-analyses

In this tutorial, we applied MKDA Chi-Squared to a manual meta-analysis. However, this is not a perfect comparison, as there are differences between the reference sample (Neurosynth), the high-quality manual annotations given as input. Studies in large-scale meta-analytic databases are automatically populated, meaning there are potential sampling biases. Most notably, studies in Neurosynth include all reported coordinates, not only "target" analyses/contrasts. Thus, it is possible that low-level task > no task contrasts are over-represented in this reference sample.

References & Further Reading​

If you want to understand the nuances of what inferences you can and cannot make using these maps, we recommend reading Tal Yarkoni's blog posts on how these maps do not provide evidence that the dACC is select for pain: Post 1, Post 2, as well as a commentary by Tor Wager et al., 2016

Poldrack RA. Inferring mental states from neuroimaging data: from reverse inference to large-scale decoding. Neuron. 2011 Dec 8;72(5):692-7. doi: 10.1016/j.neuron.2011.11.001. PMID: 22153367; PMCID: PMC3240863.

Poldrack RA, Yarkoni T. From Brain Maps to Cognitive Ontologies: Informatics and the Search for Mental Structure. Annu Rev Psychol. 2016;67:587-612. doi: 10.1146/annurev-psych-122414-033729. Epub 2015 Sep 21. PMID: 26393866; PMCID: PMC4701616.

+ \ No newline at end of file diff --git a/tutorial/automated.html b/tutorial/automated.html index 6e7b49c..49c205f 100644 --- a/tutorial/automated.html +++ b/tutorial/automated.html @@ -5,7 +5,7 @@ Automated Meta-Analysis | Neurosynth Compose Docs - + @@ -13,8 +13,8 @@

Automated Meta-Analysis

How to create a fully automated meta-analysis

caution

This tutorial, and the functionality for automated meta-analysis, is under construction. To view existing automated meta-analyses, you can use Neurosynth Original

Why automated meta-analysis?​

The principal difference between an automated and a manual meta-analysis is the process used to select the final set of Studies and Analyses to include into your meta-analysis.

In a manual meta-analysis, researchers cast a wide net to find a wide range of potentially relevant articles, and use their expertise (read: painstakingly review hundreds of articles) to decide which of articles are relevant to their research question. Our platform seeks streamline this process through a user-friendly interface and pre-extracted data for over 20,000 neuroimaging studies. Yet, a gold standard meta-analysis still requires a significant time investment, limiting their application in routine scientific practice.

In an automated meta-analysis, we instead use data-driven text mining metrics to select articles. The original Neurosynth pioneered this approach by developing text mining techniques to automatically extract brain coordinates and semantic text features from thousands of articles.

Surprisingly, this works! For example, by meta-analyzing all studies that mention the term "emotional" above a certain frequency, we observe a strong association with activity in the amygdala. By and large, the sheer number of studies overcomes the inherent noisiness of automated data extraction and study selection.

Flexible automated meta-analysis in Neurosynth Compose​

Although automated meta-analyses have proved to be a useful tool, there are several limitations. The overall goal of Neurosynth Compose is to give users a flexible data curation platform, to overcome these limitations using their expert knowledge. For example:

  • Flexible selection criteria. The original Neurosynth has a fixed number of terms and meta-analyses. WithNeurosynth Compose you can flexibly search the NeuroStore database using a powerful and flexible search to precisely define your search criteria.
  • Combine expert knowledge with automated selection. Automated study selection is inherently an noisy and imperfect measure. With Neurosynth Compose, you can use automated study selection as a first pass, and later apply your own expert criteria to refine study inclusion criteria.
  • Correct data extraction errors. Automated extraction can miss entire tables of coordinates (e.g. supplementary materials), duplicate coordinates, and groups distinct sets of Analyses (e.g. Contrasts) into a single group. Now, you can correct these data to make your meta-analysis more precise.

Tutorial​

An automated meta-analysis in Neurosynth Compose looks a lot like a manual one, except data curation is optional. We reccomeend following the manual meta-analysis tutorial to learn in depth about our platform.

Search & Curate​

One of the main differences between a manual and automated meta-analysis, is the steps required to select studies. As such, we reccomeend selecting the "Simple" curation workflow, which only consists of a single data curation step (which is optional).

Simple Workflow.

tip

Decide ahead of time if you want to perform a Coordinate or an Image based meta-analysis. Image-based Meta-Analysis (IBMA) is more precise and powerful, but there are much fewer studies available.

Import from Neurostore​

Let's add studies to our curation board by clicking Import Studies. In an automated meta-analysis, you'll want to select Import via NeuroStore, as all indexed studies are guaranteed to contain imaging data (saving you from manual data extraction).

caution

Although the NeuroStore database is continuously growing, it is necessarily an incomplete snapshot of the neuroimaging literature

Input any search term to narrow down studies. This will search the Title and Abstract fields. You may also add additional search filters using the + Add Filter button, and select the desired modality of the imaging data.

Neurostore Search.

To import your search, click "Import Studies From Neurostore" at the bottom right. Give your import a name to Tag all imported studies, and continue back to your Curation board.

note

You must import an entire set of search results into your curation board. If you want to exclude any specific studies, you will do so on your board. This allows the Search & Curate process to be fully reproducible.

Promote studies​

Back to your Curation board, you will now see all studies from your NeuroStore search on the left most column.

Curation board.

At this point, you have two options: manually review the search results and select which studies to include, or perform a fully automated meta-analysis by including all search results.

For this tutorial, we'll skip manual curation and Promote All Uncategorized Studies to the right-most "Included" column.

We can now click Move to Extraction Phase.

Extraction and Annotation​

At this point, you will create a StudySet containing all of your Studies. Advance through the dialog to begin Extraction.

The goal of this phase is to add or correct imaging data (e.g. Coordinates) in imported studies, and create Annotations to determine which Analyses (e.g. Contrasts), should be included in your meta-analysis.

Since we are performing a manual meta-analysis, we're going to skip these steps!

From the main Project page, we can click "Mark All as Complete".

Skip Extraction.

tip

It's up to you if you want to skip this step. The validity of your meta-analysis is highly dependant on input data, so we only recommend skipping all curation for exploratory analyses.

Specify Meta-Analyses​

You can now specify your meta-analysis. This step will be identical between automated and manual meta-analyses.

First, we select the "included" column, which by default includes all Study Analyses as inputs.

Inclusion column.

Next, we select a meta-analysis algorithm. This time, we'll select MKDA Chi-Squared (MKDAChi2) with FDRCorrector.

MKDA Chi-Square compares your StudySet to a reference set of studies (all the studies in NeuroStore that you did not select), allowing you to identify areas of stronger association with your selected studies. This is the algorithm used in the original Neurosynth Platform.

Execute​

Once you specify your meta-analysis, you can execute it in the cloud using Google Colab using your unique meta-analysis id.

Automated execute.

Congratulations, you have now run an automated meta-analysis!

Remember, just because it is easy to run a meta-analysis and get results, does not mean the results are valid. -It is important to think about the validity of your study selection process, for both manual and automated meta-analyses.

- +It is important to think about the validity of your study selection process, for both manual and automated meta-analyses.

+ \ No newline at end of file diff --git a/tutorial/manual.html b/tutorial/manual.html index aca0484..b3d85d2 100644 --- a/tutorial/manual.html +++ b/tutorial/manual.html @@ -5,7 +5,7 @@ Manual Meta-Analysis | Neurosynth Compose Docs - + @@ -48,8 +48,8 @@ Remember, by default, the "included" column will be created and include all Analyses.

Next, you will select a meta-analysis Algorithm and Corrector:

Meta-analysis algorithm

A variety of common meta-analysis algorithms such as "ALE" and "MKDA" are available, as well as two strategies for controlling for multiple comparisons: FDR (false detection rate) and FWE (family wise error).

For this example, we'll choose "MKDADensity" and and "FDRCorrection". You can modify the parameters for each, if you want, but we provide sane defaults for all.

tip

Learn more about meta-analysis algorithms in the NiMARE Documentation

Next, you'll give your meta-analysis a name, and review the details of your specification.

Meta-analysis review

tip

You can define multiple Meta-Analysis specifications in a Project, paired to the same StudySet

Run your meta-analysis!​

Congratulations! You now have a Meta-Analysis specification that is ready to run.

You can execute your Meta-Analysis for free in the cloud on Google Colab by copying the unique meta-analysis id -and pasting it into our Google Colab notebook.

Meta-analysis run

- +and pasting it into our Google Colab notebook.

Meta-analysis run

+ \ No newline at end of file