Update README.md

README.md

@@ -8,7 +8,7 @@ Habenula ROIs were manually delineated on a per-subject basis, on subjects' T1-w
 `group-level.py` performs nonlinear transformations on each subject's SBFC _Z_-map to put it in MNI space, then concatenates subject-level SBFC maps, and runs a group-level test using FSL's Randomise, with 5000 permutations and threshold-free cluster enhancement.
 ## 2. Replication in Human Connectome Project 7T data
 `sbfc-hb-hcp.py` is analogous to `sbfc-hb.py`, but performs per-run SBFC per subject and then collapses across runs.\
-`group-level.py` is used similarly here, but without nonlinear transformation to MNI space as preprocessed HCP data is alread in MNI space.\
+`group-level.py` is used similarly here, but without nonlinear transformation to MNI space as preprocessed HCP data is alread in MNI space.
 ## 3. Extension to understand individual differences in habenula connectivity with respect to a myriad of depression-, reward-, and mood-related behavioral variables.
 `hcp-indiv-diff-hcp.py` assesses individual differences using a permuted OLS anaysis, controlling for age, gender, and handedness. Each variable in the `model` variables are treated as independent (which, admittedly, may not be the case). Significance threshold for related functional conectivity within each model is corrected by an adapted Sidak procedure introduced by Li & Ji (2005).\
 `decoding_maps.py` leverages prior literature indexed in the Neurosynth database to perform meta-analytic functional decoding for a relatively unbiased look at the terms frequently used across manuscripts that report similar activation patterns to our significant results from the HCP dataset.