Short-Time Windows of Correlation Between Large-Scale Functional Brain Networks Predict Vigilance Intraindividually and Interindividually

• Type of Material: Article
• Authors: Garth John Thompson, Georgia Institute of Technology; Matthew Evan Magnuson, Georgia Institute of Technology; Michael Donelyn Merritt, Georgia Institute of Technology; Hillary Schwarb, Georgia Institute of Technology; Wenju Pan, Emory University; Andrew McKinley, Air Force Research Laboratory, Ohio; Lloyd D. Tripp, Air Force Research Laboratory, Ohio; Eric H. Schumacher, Georgia Institute of Technology; Shella Keilholz, Emory University
• Language: English
• Date: 2013-12-01
• Publisher: John Wiley & Sons
• Publication Version: Final Published Version
• Copyright Statement: © 2012 Wiley Periodicals, Inc.
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1002/hbm.22140
• Title of Journal or Parent Work: Human Brain Mapping
• Volume: 34
• Issue: 12
• Start Page: 3280
• End Page: 3298
• Grant/Funding Information: This research was performed under an appointment to the U.S. Department of Homeland Security (DHS) Scholarship and Fellowship Program, administered by the Oak Ridge Institute for Science and Education (ORISE) through an interagency agreement between the U.S. Department of Energy (DOE) and DHS.
• Keywords: Neural basis; functional connectivity; spontaneous fluctuations; Response time; Intrinsic connectivity; Neurosciences; default mode; Task; Radiology, Nuclear Medicine & Medical Imaging; Global signal; pvt; Life Sciences & Biomedicine; psychomotor vigilance task; Sleep deprivation; large scale cerebral networks; performance prediction; Default mode network; Science & Technology; task positive; Psychomotor vigilance; resting state; Neurosciences & Neurology; Resting state networks; windowed correlation; Neuroimaging; Alzheimer's disease
• Research Categories: Biology, Neuroscience; Engineering, Biomedical; Psychology, Psychobiology

Abstract

A better understanding of how behavioral performance emerges from interacting brain systems may come from analysis of functional networks using functional magnetic resonance imaging. Recent studies comparing such networks with human behavior have begun to identify these relationships, but few have used a time scale small enough to relate their findings to variation within a single individual's behavior. In the present experiment we examined the relationship between a psychomotor vigilance task and the interacting default mode and task positive networks. Two time-localized comparative metrics were calculated: difference between the two networks' signals at various time points around each instance of the stimulus (peristimulus times) and correlation within a 12.3-s window centered at each peristimulus time. Correlation between networks was also calculated within entire resting-state functional imaging runs from the same individuals. These metrics were compared with response speed on both an intraindividual and an interindividual basis. In most cases, a greater difference or more anticorrelation between networks was significantly related to faster performance. While interindividual analysis showed this result generally, using intraindividual analysis it was isolated to peristimulus times 4 to 8 s before the detected target. Within that peristimulus time span, the effect was stronger for individuals who tended to have faster response times. These results suggest that the relationship between functional networks and behavior can be better understood by using shorter time windows and also by considering both intraindividual and interindividual variability.