Functional Connectome Fingerprinting Through Tucker Tensor Decomposition

The human functional connectome (FC) is a representation of the functional couplings between brain regions derived from blood oxygen level-dependent (BOLD) signals. Over the past decade, studies related to FC fingerprinting have sought to uncover functional patterns that enable uniquely identifying individuals across repeated scanning sessions, hence demonstrating the stability and distinctiveness of functional brain organization. In this study, it is hypothesized that tensor decomposition techniques, given their ability to project high-dimensional data into lower-dimensional spaces, enable detecting the brain fingerprint with high accuracy. A mathematical framework based on Tucker decomposition is presented to uncover the FC fingerprint of 426 unrelated participants from the Young-Adult Human Connectome Project (HCP) Dataset. An analysis of how brain parcellation granularity, decomposition rank, and scan length relate to within- and between-condition (resting state-task) fingerprinting was conducted. Relative to FC matrices as well as to Principal Components Analysis (PCA), tensor decomposition significantly increases the functional connectome’s fingerprint. For parcellation granularity of 214 in the within-condition setting, an improvement of 11–36% was seen across all fMRI conditions. Similarly, a substantial improvement, ranging from 43 to 72%, was observed in the between-condition setting relative to FC matrices. Compared to matching rates obtained directly on FCs and when applying other data-driven decomposition methods, Tucker decomposition led to higher or the same level of matching rates for all analyses. Furthermore, in the context of between-condition fingerprinting, results from the proposed framework suggest that partially sampling time points from resting-state time series is sufficient to uncover FC fingerprints with high accuracy.