Literature DB >> 22736565

Short-time windows of correlation between large-scale functional brain networks predict vigilance intraindividually and interindividually.

Garth John Thompson1, Matthew Evan Magnuson, Michael Donelyn Merritt, Hillary Schwarb, Wen-Ju Pan, Andrew McKinley, Lloyd D Tripp, Eric H Schumacher, Shella Dawn Keilholz.   

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.
Copyright © 2012 Wiley Periodicals, Inc.

Entities:  

Keywords:  default mode; functional connectivity; large scale cerebral networks; performance prediction; psychomotor vigilance task; pvt; resting state; spontaneous fluctuations; task positive; windowed correlation

Mesh:

Year:  2012        PMID: 22736565      PMCID: PMC6870033          DOI: 10.1002/hbm.22140

Source DB:  PubMed          Journal:  Hum Brain Mapp        ISSN: 1065-9471            Impact factor:   5.038


  49 in total

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4.  Competition between functional brain networks mediates behavioral variability.

Authors:  A M Clare Kelly; Lucina Q Uddin; Bharat B Biswal; F Xavier Castellanos; Michael P Milham
Journal:  Neuroimage       Date:  2007-08-23       Impact factor: 6.556

5.  Lapsing during sleep deprivation is associated with distributed changes in brain activation.

Authors:  Michael W L Chee; Jiat Chow Tan; Hui Zheng; Sarayu Parimal; Daniel H Weissman; Vitali Zagorodnov; David F Dinges
Journal:  J Neurosci       Date:  2008-05-21       Impact factor: 6.167

6.  Intrinsic connectivity between the hippocampus and posteromedial cortex predicts memory performance in cognitively intact older individuals.

Authors:  Liang Wang; Peter Laviolette; Kelly O'Keefe; Deepti Putcha; Akram Bakkour; Koene R A Van Dijk; Maija Pihlajamäki; Bradford C Dickerson; Reisa A Sperling
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7.  Altered resting state networks in mild cognitive impairment and mild Alzheimer's disease: an fMRI study.

Authors:  Serge A R B Rombouts; Frederik Barkhof; Rutger Goekoop; Cornelis J Stam; Philip Scheltens
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9.  Prediction of human errors by maladaptive changes in event-related brain networks.

Authors:  Tom Eichele; Stefan Debener; Vince D Calhoun; Karsten Specht; Andreas K Engel; Kenneth Hugdahl; D Yves von Cramon; Markus Ullsperger
Journal:  Proc Natl Acad Sci U S A       Date:  2008-04-21       Impact factor: 11.205

10.  A method for evaluating dynamic functional network connectivity and task-modulation: application to schizophrenia.

Authors:  Unal Sakoğlu; Godfrey D Pearlson; Kent A Kiehl; Y Michelle Wang; Andrew M Michael; Vince D Calhoun
Journal:  MAGMA       Date:  2010-02-17       Impact factor: 2.310

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  98 in total

Review 1.  Recent theoretical, neural, and clinical advances in sustained attention research.

Authors:  Francesca C Fortenbaugh; Joseph DeGutis; Michael Esterman
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2.  Effects of severing the corpus callosum on electrical and BOLD functional connectivity and spontaneous dynamic activity in the rat brain.

Authors:  Matthew E Magnuson; Garth J Thompson; Wen-Ju Pan; Shella D Keilholz
Journal:  Brain Connect       Date:  2014-01-23

3.  Integration of temporal and spatial properties of dynamic connectivity networks for automatic diagnosis of brain disease.

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Journal:  Med Image Anal       Date:  2018-04-04       Impact factor: 8.545

4.  Different dynamic resting state fMRI patterns are linked to different frequencies of neural activity.

Authors:  Garth John Thompson; Wen-Ju Pan; Shella Dawn Keilholz
Journal:  J Neurophysiol       Date:  2015-06-03       Impact factor: 2.714

5.  When the brain takes a break: a model-based analysis of mind wandering.

Authors:  Matthias Mittner; Wouter Boekel; Adrienne M Tucker; Brandon M Turner; Andrew Heathcote; Birte U Forstmann
Journal:  J Neurosci       Date:  2014-12-03       Impact factor: 6.167

6.  Ongoing dynamics in large-scale functional connectivity predict perception.

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Journal:  Proc Natl Acad Sci U S A       Date:  2015-06-23       Impact factor: 11.205

Review 7.  Noise and non-neuronal contributions to the BOLD signal: applications to and insights from animal studies.

Authors:  Shella D Keilholz; Wen-Ju Pan; Jacob Billings; Maysam Nezafati; Sadia Shakil
Journal:  Neuroimage       Date:  2016-12-22       Impact factor: 6.556

8.  Making group inferences using sparse representation of resting-state functional mRI data with application to sleep deprivation.

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9.  Dynamic brain connectivity is a better predictor of PTSD than static connectivity.

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Journal:  Hum Brain Mapp       Date:  2017-06-12       Impact factor: 5.038

10.  Evaluation of sliding window correlation performance for characterizing dynamic functional connectivity and brain states.

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Journal:  Neuroimage       Date:  2016-03-04       Impact factor: 6.556

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