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Predicting an individual's dorsal attention network activity from functional connectivity fingerprints
David E Osher1, James A Brissenden2, David C Somers2
1Department of Psychology, The Ohio State University , Columbus, Ohio.
Journal of Neurophysiology
|May 9, 2019
Summary
Individual brain connectivity patterns predict the dorsal attention network's (DAN) activation. Computational models identified "connectivity fingerprints" that accurately forecast how specific brain regions respond to attention tasks, outperforming group averages.
Area of Science:
- Neuroscience
- Cognitive Neuroscience
- Computational Neuroscience
Background:
- The dorsal attention network (DAN), comprising frontal and parietal regions, is crucial for attentionally demanding tasks.
- While attentional deployment consistently activates the DAN across individuals, significant variations exist in individual activation patterns.
Purpose of the Study:
- To investigate if an individual's unique functional connectivity patterns can predict their specific cortical dorsal attention network (DAN) activity.
- To identify and utilize "connectivity fingerprints" of the DAN for predicting individual activation patterns.
Main Methods:
- Developed computational models to predict task-related DAN activation based on whole-brain resting-state functional connectivity patterns.
- Modeled task activation as a function of connectivity to define frontal and parietal DAN connectivity fingerprints.
- Validated predictions against group-average benchmarks and cross-subject predictions.
Main Results:
- Individual connectivity patterns accurately predicted subjects' characteristic DAN activation, outperforming standard group-average predictions.
- Connectivity fingerprints successfully predicted individual DAN activation beyond predictions derived from other subjects' connectivity.
- The predictive connections (fingerprints) primarily involved coactive regions, including visual cortices in the occipital and temporal lobes.
Conclusions:
- An individual's distinctive functional connectivity pattern significantly explains variance in DAN functional responses.
- Connectivity fingerprints serve as defining computational characteristics of the DAN, indicating regions capable of exerting top-down attentional bias.
- Resting-state connectivity alone is sufficient to accurately predict individual dorsal attention network activation patterns.