Dynamic Functional Connectivity Captures Individuals’ Unique Brain Signatures
Gandhi, Rohan; Garimella, Arun; Toiviainen, Petri; Alluri, Vinoo (2020). Dynamic Functional Connectivity Captures Individuals’ Unique Brain Signatures. In Mahmud, Mufti; Vassanelli, Stefano; Kaiser, M. Shamim; Zhong, Ning (Eds.) BI 2020 : 13th International Conference on Brain Informatics, Proceedings (pp. 97-106). Lecture Notes in Computer Science, 12241. Cham: Springer. DOI: 10.1007/978-3-030-59277-6_9
Published inLecture Notes in Computer Science
© Springer Nature Switzerland AG 2020
Recent neuroimaging evidence suggest that there exists a unique individual-specific functional connectivity (FC) pattern consistent across tasks. The objective of our study is to utilize FC patterns to identify an individual using a supervised machine learning approach. To this end, we use two previously published data sets that comprises resting-state and task-based fMRI responses. We use static FC measures as input to a linear classifier to evaluate its performance. We additionally extend this analysis to capture dynamic FC using two approaches: the common sliding window approach and the more recent phase synchrony-based measure. We found that the classification models using dynamic FC patterns as input outperform their static analysis counterpart by a significant margin for both data sets. Furthermore, sliding window-based analysis proved to capture more individual-specific brain connectivity patterns than phase synchrony measures for resting-state data while the reverse pattern was observed for the task-based data set. Upon investigating the effects of feature reduction, we found that feature elimination significantly improved results up to a point with near-perfect classification accuracy for the task-based data set while a gradual decrease in the accuracy was observed for resting-state data set. The implications of these findings are discussed. The results we have are promising and present a novel direction to investigate further. ...