Human brain imaging such as Functional Magnetic Resonance Imaging (fMRI) has made transformational impacts on neuroscience. However, due to technology limitations in the past, researchers have constrained their analyses by making assumptions that are biased or false, leading to missed opportunities for science discovery, and in the worst case, incorrect inferences.Realizing that the processes in the brain may depend on widely distributed interactions among regions, we propose a novel method of analyzing the brain by its full correlation structure and show the effectiveness of the correlation analysis. The full correlation matrices lead to dramatic increase of data amount by at most extracting 7 orders of magnitude more information. With careful data computing, allocating and appropriate feature selection, we design a parallel tool to get the full correlation matrices in distribution and analyze them efficiently via classification to show the effectiveness of connectivity analysis. The critical voxels picked by our tool well match the existing theory of cognitive neuroscience in terms of location. And based on the selected voxels, experimental results demonstrate that the correlation based classification gets a comparable accuracy as traditional activation based classification within a subject, and is able to attain substantially higher accuracy than activation when analyzing across subjects, which, for the first time, shows the stability of brain correlation structures among different subjects.

This work is advised by Prof. Moses Charikar and Prof. Kai Li at Princeton Computer Science Department, and Prof. Nicholas Turk-Browne and Prof. Jonathan Cohen at Princeton Neuroscience Institute.

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