Constructing Compact Brain Connectomes for Individual Fingerprinting
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Recent neuroimaging studies have shown that functional connectomes are unique to individuals, i.e., two distinct fMRIs taken over different sessions of the same subject are more similar in terms of their connectomes than those from two different subjects. In this study, we present significant new results that identify specific parts of the connectome that code the unique signatures. We show that a very small part of the connectome (under 100 features from among over 64K total features) is responsible for the signatures. A network of these features is shown to achieve excellent training and test accuracy in matching imaging datasets. We show that these features are statistically significant, robust to perturbations, invariant across populations, and are localized to a small number of structural regions of the brain (12 regions from among 180). We develop an innovative matrix sampling technique to derive computationally efficient and accurate methods for identifying the discriminating sub-connectome and support all of our claims using state of the art statistical tests and computational techniques.
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