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/2017/04/24/108738.short
4100  - http://biorxiv.org/content/early/2017/04/24/108738.full
AB  - Variation in cortical connectivity profiles is typically modeled as having a coarse spatial scale parcellated into interconnected brain areas. We created a high-dimensional common model of the human connectome to search for fine-scale structure that is shared across brains. Projecting individual connectivity data into this new common model connectome accounts for over three times more variance in the human connectome than do previous models. This newly discovered shared structure resides in fine-scale local variation that is closely related to fine-scale distinctions in representations of information. These results reveal a shared fine-scale structure that is a markedly larger component of the human connectome than coarse-scale, areal structure. This shared fine-scale structure was not captured in previous models and was, therefore, inaccessible to analysis and study.Author Summary Resting state fMRI has become a ubiquitous tool for measuring connectivity in normal and diseased brains. Current dominant models of connectivity are based on coarse regional connectivity ignoring fine-scale structure within those regions. We developed a high-dimensional model common model of the human connectome that captures both coarse and fine-scale structure of connectivity shared across brains. We showed that this shared fine-scale structure is related to fine-scale distinctions in representation of information, and our model accounts for over three times more shared variance of connectivity compared to previous models. Our model opens new territory – shared fine-scale structure, a dominant but mostly unexplored component of the human connectome – for analysis and study.