Abstract
Since the advent of neuroimaging, there has been a considerable interest in mapping the visual system. This has led to exquisite maps of visual selectivity, but mapping is a purely descriptive approach to understanding the visual brain. In order to uncover mechanistic explanations, rather than descriptive maps of the visual system, neuroimagers will need to incorporate neural connectivity into their analyses. After all, connectivity is the principal constraint on the domain of information that a brain region can process, and thus should be highly predictive of neural selectivity. We previously showed that connectivity fingerprints can define a region so well that they can be used to predict the location of visual regions even in the absence of functional localizers, using only an individual’s connectivity patterns (Osher et al. 2015; Osher et al. 2018). Here we present a suite of analytic tools that will allow researchers to derive the connectivity fingerprints that best define any specific visual region, offering answers to questions such as “what connectivity pattern does a region need to be highly selective to faces?”Our software suite is applicable to DWI as well as functional connectivity data, any set of brain regions as seeds and targets, any fMRI task, and any number of individuals. We also demonstrate how connectivity fingerprints can be used to predict behavioral data, in addition to neural activation in each subject. We present a few specific results from the application of connectivity fingerprints to predict neural activation in individual subjects and behavioral variation in various mental tasks
Acknowledgement: Sloan Foundation (ZMS)