Improved correspondence of fMRI visual field localizer data after cortex-based macroanatomical alignment

Abstract

Studying the visual system with fMRI often requires using localizer paradigms to define regions of interest (ROIs). However, the considerable interindividual variability of the cerebral cortex represents a crucial confound for group-level analyses. Cortex-based alignment (CBA) techniques reliably reduce interindividual macroanatomical variability. Yet, their utility has not been assessed for visual field localizer paradigms, which map specific parts of the visual field within retinotopically organized visual areas. We evaluated CBA for an attention-enhanced visual field localizer, mapping homologous parts of each visual quadrant in 50 participants. We compared CBA with volume-based alignment and a surface-based analysis, which did not include macroanatomical alignment. CBA led to the strongest increase in the probability of activation overlap (up to 86%). At the group level, CBA led to the most consistent increase in ROI size while preserving vertical ROI symmetry. Overall, our results indicate that in addition to the increased signal-to-noise ratio of a surface-based analysis, macroanatomical alignment considerably improves statistical power. These findings confirm and extend the utility of CBA for the study of the visual system in the context of group analyses. CBA should be particularly relevant when studying neuropsychiatric disorders with abnormally increased interindividual macroanatomical variability.

A cortical surface template for human neuroscience

“…Functional magnetic resonance imaging (fMRI) data, which were acquired as 3D volumes, can be projected onto this surface for analysis and visualization in a 2D space 5 . Compared with the 3D volumetric analysis of fMRI data, surface-based analysis affords better inter-subject alignment, higher statistical power, more accurate localization of functional areas, and better brain-based prediction of cognitive and personality traits [6][7][8][9][10][11][12][13] . Due to these advantages, surface-based analysis has been widely adopted by the neuroimaging community, including software [14][15][16][17] , large-scale datasets [18][19][20] , and cortical atlases and parcellations [21][22][23][24][25] .…”