Fig. 5: Generation and evaluation of individual habenula ROIs.

a The MAGeTbrain algorithm produced high-resolution individualised habenula masks (red outline) using the 7-Tesla anatomical images (0.75 mm isotropic). Three example habenula masks are presented here and displayed on their corresponding T1-weighted whole-brain image in the native space. b The individualised habenula masks were normalised to the MNI space using the DARTEL flow fields produced during anatomical image pre-processing. These high-resolution normalised masks were used as reference label images with which we evaluated the functional resolution habenula masks. c To create the habenula ROIs for functional analysis, each individual’s anatomical-resolution habenula mask were resampled to the functional resolution (2 mm isotropic) and transformed to the standard space. An iterative volume optimisation procedure was developed to minimise the impact of down-sampling on the habenula mask. In brief, adjustment was made to the re-binarising threshold in each iteration to ensure that the resultant habenula ROI had a volume that is within 10% of the reference label image (i.e., normalised habenula mask in anatomical resolution) after the removal of CSF voxels. d The subject-specific habenula ROIs in functional resolution were further validated via an rsFC analysis. Results of this analysis replicated the habenula functional connectivity pattern reported in past studies. ACC anterior cingulate cortex, CSF cerebrospinal fluid, DARTEL Diffeomorphic Anatomical Registration Through Exponentiated Lie Algebra, FSL FMRIB Software Library, L left, MAGeT Multiple Automatically Generated Templates brain segmentation, PCC posterior cingulate cortex, pSMA pre-supplementary motor area, R right, rsFC resting-state functional connectivity, SPM Statistical Parametric Mapping, VOI volume-of-interest, VTA ventral tegmental area.