Lab research

Lab research themes

Combining brain imaging and genetics. The lab's work combined genetics and brain imaging, which allows for the examination of gene effects on the brain, thus identifying susceptible neural structures and functions influenced by putative risk genes. Genes code for brain structure and function, rather than psychiatric symptoms, and thus, gene effects are likely more penetrant at the level of the brain rather than behaviour. This approach may also allow for early identification of vulnerable brain structures conferred by risk genes, before the onset of psychiatric symptoms, where early identification of disease is a major goal in clinical neuroscience research in psychiatry. This approach is applied to several severe mental illnesses across the human lifespan, from childhood and youth into late-life.

Neuroimaging in clinical trials. The lab has also recently been using neuroimaging in the context of clinical trials to understand the effects of existing and novel therapeutics on the brain, and to better understand brain-based mechanisms of treatment response.

Data-driven applications toward dissecting heterogeneity in psychiatric disorders. The lab is also a lead of multi-centre neuroimaging studies, and uses data-driven methods to disentangle the heterogeneity within and across psychiatric disorders using multiple levels of data.

Current projects

A list with a bit of information about each project in the archive is here. Similar and sometimes more detailed information can be reviewed on the Dashboard and in XNAT.

See our Foundation reports for a summary description of each lab member's projects:

• 2017
• 2016
• 2015
• 2014
• 2013
• 2012

Most significant (recent) publications

Note: this list is taken from Dr. Voineskos' general CV, updated 04-30-2018.

1. Roostaei T, Nazeri A, Felsky D, De Jager PL, Schneider JA, Pollock BG, Bennett DA, Voineskos AN, Alzheimer’s Disease Neuroimaging Initiative (ADNI). Genome-wide interaction study of brain beta-amyloid burden and cognitive impairment in Alzheimer’s disease. Molecular Psychiatry. 2017, Feb;22(2):287-295.

   This paper helped clarify the roles of a key risk factor, beta-amyloid, in the context of genetic, neuroimaging, and neurocognitive profiles in dementia. Beta-amyloid build-up has demonstrated poor correlation with cognitive performance, an issue that has called the amyloid hypothesis into question. Our findings here identified a genome-wide significant genetic variant in the amylin gene, such that there was a genome-wide by amyloid interaction predicting cognitive performance. Our findings explain some of the heterogeneity in the amyloid-cognition relationship in human populations.

2. Wheeler AL, Wessa M, Szeszko PR, Foussias G, Chakravarty MM, Lerch JP, DeRosse P, Remington G, Mulsant BH,Linke J, Malhotra AK, Voineskos AN. Further neuroimaging evidence for the deficit subtype of schizophrenia: a cortical connectomics analysis JAMA Psychiatry. 2015 May 1;72(5):446-55.

   This paper used multi-modal neuroimaging to dissect the heterogeneity of schizophrenia. By using three different data sets, along with graph theory based network analyses, which were crucial to heterogeneity dissection, validation of schizophrenia subtypes was established. The schizophrenia subtype was different than other schizophrenia patients, bipolar patients, and healthy controls.

3. Nazeri, A, Mulsant BH, Rajji TK, Levesque ML, Pipitone J, Stefanik L, Shahab S, Roostaei T, Wheeler AL, Chavez S,Voineskos AN. Gray matter neuritic microstructure deficits in schizophrenia and bipolar disorder. Biological Psychiatry. 2017 Nov 15;82(10):726-736.

   This paper used a novel diffusion MRI approach, NODDI-based imaging in schizophrenia and bipolar patients for the first time. We established the neural correlates from NODDI-based imaging of cognitive impairment, which was dimensional in nature cutting across patient groups and healthy control groups. We then applied a machine learning-based classification method that used NODDI-based measures to classify patient and healthy control groups. This method identified stable schizophrenia patients from euthymic bipolar patients and healthy controls with greater than 90 percent accuracy. This paper was featured on the cover of Biological Psychiatry.

4. Stefanik L, Erdman L, Ameis SH, Foussias G, Mulsant BH, Behdinan T, Goldenberg A, O'Donnell LJ, Voineskos AN. Brain-Behavior Participant Similarity Networks Among Youth and Emerging Adults with Schizophrenia Spectrum, Autism Spectrum, or Bipolar Disorder and Matched Controls. Neuropsychopharmacology. 2018 Apr;43(5):1180-1188.

   In this paper, we used a novel data-driven approach known as similarity network fusion to identify new groups of individuals cutting across DSM-disorders, each with distinct neural circuit-cognitive profiles. Cutting across diagnostic boundaries, our approach identified new groups of people based on a profile of neuroimaging and behavioral data. Our findings bring us closer to disease subtyping that can be leveraged toward the targeting of specific neural circuitry among participant subgroups to ameliorate social cognitive and neurocognitive deficits.

5. Viviano J, Buchanan R, Calarco N, Gold J, Foussias G, Bhagwat N, Stefanik L, Hawco C, DeRosse P, Argyelan M,Turner J, Chavez S, Kochunov P, Kingsley P, Zhou X, Malhotra A, Voineskos AN. Resting-state connectivity biomarkers of cognitive performance and social function in Schizophrenia Spectrum Disorders and healthy controls. Biological Psychiatry. In Press.

   In this multi-centre research domain criteria study, we found different patterns of neural circuit activation during functional brain imaging using machine learning tools that were independent of diagnostic group. These patterns were related to neurocognitive and social cognitive performance. We then replicated these findings in an independent sample. Our findings provide new neuroimaging endpoints for cognitive function, that can be tested in target-engagement-based intervention studies.