PT  - JOURNAL ARTICLE
AU  - J. Swaroop Guntupalli
AU  - James V. Haxby
TI  - A computational model of shared fine-scale structure in the human connectome
AID  - 10.1101/108738
DP  - 2017 Jan 01
TA  - bioRxiv
PG  - 108738
4099  - http://biorxiv.org/content/early/2017/02/15/108738.short
4100  - http://biorxiv.org/content/early/2017/02/15/108738.full
AB  - Local variation in cortical connectivity profiles is typically modeled as having a coarse spatial scale that can be parcellated into cortical systems of large, interconnected brain areas. We created a common model of the human connectome using a new algorithm – “connectivity hyperalignment” – that discovers shared basis functions for connectivity topographies. Transforming individual connectivity data into the common model connectome dramatically increases intersubject correlation of connectivity profiles. More importantly, the common model connectome captures shared fine-scale spatial variation in connectivity profiles that is obscured by other methods of aggregating multi-subject data into a common space. We found similar results using fMRI data obtained in the resting state and while subjects watched a movie. Fine-scale variations in connectivity profiles are closely related to fine-scale distinctions in representations of information. These results reveal the existence of a shared fine-scale structure in the human connectome that was not incorporated in previous models.