TY - JOUR
T1 - Distributed effects of methylphenidate on the network structure of the resting brain
T2 - A connectomic pattern classification analysis
AU - Sripada, Chandra Sekhar
AU - Kessler, Daniel
AU - Welsh, Robert
AU - Angstadt, Michael
AU - Liberzon, Israel
AU - Phan, K. Luan
AU - Scott, Clayton
N1 - Funding Information:
C.S. Sripada's research was supported by the National Center for Advancing Translational Sciences of the National Institutes of Health under Award Number UL1TR000433 , NIH grant K23-AA-020297 , a Center for Computational Medicine and Bioinformatics Pilot Grant , and the John Templeton Foundation . R.C. Welsh's research was supported by R01NS052514 . C. Scott's research was supported by NIH grant P01CA087634 .
Copyright:
Copyright 2013 Elsevier B.V., All rights reserved.
PY - 2013/11/1
Y1 - 2013/11/1
N2 - Methylphenidate is a psychostimulant medication that produces improvements in functions associated with multiple neurocognitive systems. To investigate the potentially distributed effects of methylphenidate on the brain's intrinsic network architecture, we coupled resting state imaging with multivariate pattern classification. In a within-subject, double-blind, placebo-controlled, randomized, counterbalanced, cross-over design, 32 healthy human volunteers received either methylphenidate or placebo prior to two fMRI resting state scans separated by approximately one week. Resting state connectomes were generated by placing regions of interest at regular intervals throughout the brain, and these connectomes were submitted for support vector machine analysis. We found that methylphenidate produces a distributed, reliably detected, multivariate neural signature. Methylphenidate effects were evident across multiple resting state networks, especially visual, somatomotor, and default networks. Methylphenidate reduced coupling within visual and somatomotor networks. In addition, default network exhibited decoupling with several task positive networks, consistent with methylphenidate modulation of the competitive relationship between these networks. These results suggest that connectivity changes within and between large-scale networks are potentially involved in the mechanisms by which methylphenidate improves attention functioning.
AB - Methylphenidate is a psychostimulant medication that produces improvements in functions associated with multiple neurocognitive systems. To investigate the potentially distributed effects of methylphenidate on the brain's intrinsic network architecture, we coupled resting state imaging with multivariate pattern classification. In a within-subject, double-blind, placebo-controlled, randomized, counterbalanced, cross-over design, 32 healthy human volunteers received either methylphenidate or placebo prior to two fMRI resting state scans separated by approximately one week. Resting state connectomes were generated by placing regions of interest at regular intervals throughout the brain, and these connectomes were submitted for support vector machine analysis. We found that methylphenidate produces a distributed, reliably detected, multivariate neural signature. Methylphenidate effects were evident across multiple resting state networks, especially visual, somatomotor, and default networks. Methylphenidate reduced coupling within visual and somatomotor networks. In addition, default network exhibited decoupling with several task positive networks, consistent with methylphenidate modulation of the competitive relationship between these networks. These results suggest that connectivity changes within and between large-scale networks are potentially involved in the mechanisms by which methylphenidate improves attention functioning.
KW - Connectome
KW - FMRI
KW - Intrinsic connectivity networks
KW - Methylphenidate
KW - Multivariate pattern classification
KW - Resting state
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U2 - 10.1016/j.neuroimage.2013.05.016
DO - 10.1016/j.neuroimage.2013.05.016
M3 - Article
C2 - 23684862
AN - SCOPUS:84878834789
VL - 81
SP - 213
EP - 221
JO - NeuroImage
JF - NeuroImage
SN - 1053-8119
ER -