Poster F80, Tuesday, March 27, 8:00-10:00 am, Exhibit Hall C
Temporal variability of functional brain connectivity predicts individual differences in attention
Angus Ho Ching Fong1, Kwangsun Yoo1, Monica D Rosenberg1, Marvin M Chun1; 1Yale University
Dynamic functional connectivity (DFC) aims to increase resolvable information from brain scans by considering temporal changes in network structure. Recent work has demonstrated that static (time-invariant) resting-state and task-based FC predicts individual differences in behavior, including attention (Rosenberg et al., 2016, Nature Neurosci; Rosenberg et al., 2017, Trends Cog Sci). Here, we show that DFC predicts attention performance across individuals. Sliding-window FC matrices were generated from fMRI data collected during rest and attention task performance (n=25) by calculating Pearson’s r between every pair of nodes of a whole-brain atlas within overlapping 20-60s time segments. Next, variance in r values across windows was taken to quantify the extent of temporal variability of each connection, resulting in a node-by-node “FC variability (FCV) matrix” for each individual. In a leave-one-subject-out-cross-validation approach, partial-least-square-regression (PLSR) models were then constructed to predict attention scores from FCV matrices. Predicted and observed attention scores were significantly correlated (task model: r=0.78, p=7.85*10^-6; rest model: r=0.40, p=4.76*10^-2), indicating successful out-of-sample predictions across rest and task conditions. We furthermore show that combining DFC and static FC features improves predictions over either model alone. Combined PLSR models successfully predicted attention in task-(r=0.86, p=2.19*10-6) and rest-(r=0.55, p=5.31*10-3) based scans; in addition, they generalized to two independent data sets (participants performing the Attention Network Task and the stop-signal task). FCV with significant PLSR coefficients clustered in visuo-motor and executive-control brain networks; moreover, most of these coefficients were negative. Thus, better attention may rely on stable (less variable) information flow between regions processing ongoing tasks.
Topic Area: METHODS: Neuroimaging