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Title: Uncovering individual differences in fine-scale dynamics of functional connectivity
Abstract Functional connectivity (FC) profiles contain subject-specific features that are conserved across time and have potential to capture brain–behavior relationships. Most prior work has focused on spatial features (nodes and systems) of these FC fingerprints, computed over entire imaging sessions. We propose a method for temporally filtering FC, which allows selecting specific moments in time while also maintaining the spatial pattern of node-based activity. To this end, we leverage a recently proposed decomposition of FC into edge time series (eTS). We systematically analyze functional magnetic resonance imaging frames to define features that enhance identifiability across multiple fingerprinting metrics, similarity metrics, and data sets. Results show that these metrics characteristically vary with eTS cofluctuation amplitude, similarity of frames within a run, transition velocity, and expression of functional systems. We further show that data-driven optimization of features that maximize fingerprinting metrics isolates multiple spatial patterns of system expression at specific moments in time. Selecting just 10% of the data can yield stronger fingerprints than are obtained from the full data set. Our findings support the idea that FC fingerprints are differentially expressed across time and suggest that multiple distinct fingerprints can be identified when spatial and temporal characteristics are considered simultaneously.  more » « less
Award ID(s):
2023985
PAR ID:
10399640
Author(s) / Creator(s):
; ; ;
Publisher / Repository:
Oxford University Press
Date Published:
Journal Name:
Cerebral Cortex
Volume:
33
Issue:
5
ISSN:
1047-3211
Format(s):
Medium: X Size: p. 2375-2394
Size(s):
p. 2375-2394
Sponsoring Org:
National Science Foundation
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