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Dynamic functional connectivity during task performance and rest predicts individual differences in attention across studies.
Neuroimage 2019; 188:14-25N

Abstract

Dynamic functional connectivity (DFC) aims to maximize resolvable information from functional brain scans by considering temporal changes in network structure. Recent work has demonstrated that static, i.e. time-invariant resting-state and task-based FC predicts individual differences in behavior, including attention. 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 by calculating Pearson's r between every pair of nodes of a whole-brain atlas within overlapping 10-60s time segments. Next, variance in r values across windows was taken to quantify temporal variability in the strength of each connection, resulting in a DFC connectome for each individual. In a leave-one-subject-out-cross-validation approach, partial-least-square-regression (PLSR) models were then trained to predict attention task performance from DFC matrices. Predicted and observed attention scores were significantly correlated, indicating successful out-of-sample predictions across rest and task conditions. Combining DFC and static FC features numerically improves predictions over either model alone, but the improvement was not statistically significant. Moreover, dynamic and combined models generalized to two independent data sets (participants performing the Attention Network Task and the stop-signal task). Edges with significant PLSR coefficients concentrated in visual, motor, and executive-control brain networks; moreover, most of these coefficients were negative. Thus, better attention may rely on more stable, i.e. less variable, information flow between brain regions.

Authors+Show Affiliations

Department of Psychology, Yale University, USA. Electronic address: hoching.fong@yale.edu.Department of Psychology, Yale University, USA.Department of Psychology, Yale University, USA.Department of Psychiatry, Yale School of Medicine, USA.Department of Psychiatry, Yale School of Medicine, USA; Department of Neuroscience, Yale School of Medicine, USA; Interdepartmental Neuroscience Program, Yale University, USA.Department of Radiology and Biomedical Imaging, Yale School of Medicine, USA.Interdepartmental Neuroscience Program, Yale University, USA; Department of Radiology and Biomedical Imaging, Yale School of Medicine, USA; Department of Neurosurgery, Yale School of Medicine, New Haven, CT 06520, USA.Department of Psychology, Yale University, USA; Department of Neuroscience, Yale School of Medicine, USA; Interdepartmental Neuroscience Program, Yale University, USA.

Pub Type(s)

Journal Article
Research Support, N.I.H., Extramural
Research Support, U.S. Gov't, Non-P.H.S.

Language

eng

PubMed ID

30521950

Citation

Fong, Angus Ho Ching, et al. "Dynamic Functional Connectivity During Task Performance and Rest Predicts Individual Differences in Attention Across Studies." NeuroImage, vol. 188, 2019, pp. 14-25.
Fong AHC, Yoo K, Rosenberg MD, et al. Dynamic functional connectivity during task performance and rest predicts individual differences in attention across studies. Neuroimage. 2019;188:14-25.
Fong, A. H. C., Yoo, K., Rosenberg, M. D., Zhang, S., Li, C. R., Scheinost, D., ... Chun, M. M. (2019). Dynamic functional connectivity during task performance and rest predicts individual differences in attention across studies. NeuroImage, 188, pp. 14-25. doi:10.1016/j.neuroimage.2018.11.057.
Fong AHC, et al. Dynamic Functional Connectivity During Task Performance and Rest Predicts Individual Differences in Attention Across Studies. Neuroimage. 2019;188:14-25. PubMed PMID: 30521950.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Dynamic functional connectivity during task performance and rest predicts individual differences in attention across studies. AU - Fong,Angus Ho Ching, AU - Yoo,Kwangsun, AU - Rosenberg,Monica D, AU - Zhang,Sheng, AU - Li,Chiang-Shan R, AU - Scheinost,Dustin, AU - Constable,R Todd, AU - Chun,Marvin M, Y1 - 2018/12/03/ PY - 2018/11/02/received PY - 2018/11/26/revised PY - 2018/11/30/accepted PY - 2020/03/01/pmc-release PY - 2018/12/7/pubmed PY - 2018/12/7/medline PY - 2018/12/7/entrez KW - Dynamic functional connectivity KW - Individual differences KW - Partial least squares regression KW - Predictive modeling KW - Sustained attention SP - 14 EP - 25 JF - NeuroImage JO - Neuroimage VL - 188 N2 - Dynamic functional connectivity (DFC) aims to maximize resolvable information from functional brain scans by considering temporal changes in network structure. Recent work has demonstrated that static, i.e. time-invariant resting-state and task-based FC predicts individual differences in behavior, including attention. 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 by calculating Pearson's r between every pair of nodes of a whole-brain atlas within overlapping 10-60s time segments. Next, variance in r values across windows was taken to quantify temporal variability in the strength of each connection, resulting in a DFC connectome for each individual. In a leave-one-subject-out-cross-validation approach, partial-least-square-regression (PLSR) models were then trained to predict attention task performance from DFC matrices. Predicted and observed attention scores were significantly correlated, indicating successful out-of-sample predictions across rest and task conditions. Combining DFC and static FC features numerically improves predictions over either model alone, but the improvement was not statistically significant. Moreover, dynamic and combined models generalized to two independent data sets (participants performing the Attention Network Task and the stop-signal task). Edges with significant PLSR coefficients concentrated in visual, motor, and executive-control brain networks; moreover, most of these coefficients were negative. Thus, better attention may rely on more stable, i.e. less variable, information flow between brain regions. SN - 1095-9572 UR - https://www.unboundmedicine.com/medline/citation/30521950/Dynamic_functional_connectivity_during_task_performance_and_rest_predicts_individual_differences_in_attention_across_studies_ L2 - https://linkinghub.elsevier.com/retrieve/pii/S1053-8119(18)32138-4 DB - PRIME DP - Unbound Medicine ER -