Literature DB >> 21854968

Spectral characteristics of resting state networks.

Rami K Niazy1, Jingyi Xie, Karla Miller, Christian F Beckmann, Stephen M Smith.   

Abstract

Resting state networks (RSNs), as imaged by functional MRI, are distributed maps of areas believed to be involved in the function of the "resting" brain, which appear in both resting and task data. The current dominant view is that such networks are associated with slow (∼0.015Hz), spontaneous fluctuations in the BOLD signal. To date, limited work has investigated the frequency characteristics of RSNs; here we investigate a range of issues relating to their spectral and phase characteristics. Our results indicate that RSNs, although dominated by low frequencies in the raw BOLD signal, are in fact broadband processes that show temporal coherences across a wide frequency spectrum. In addition, we show that RSNs exhibit different levels of phase synchrony at different frequencies. These findings challenge the notion that FMRI resting signals are simple "low frequency" spontaneous signal fluctuations.
Copyright © 2011 Elsevier B.V. All rights reserved.

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Year:  2011        PMID: 21854968     DOI: 10.1016/B978-0-444-53839-0.00017-X

Source DB:  PubMed          Journal:  Prog Brain Res        ISSN: 0079-6123            Impact factor:   2.453


  93 in total

1.  Functional magnetic resonance imaging phase synchronization as a measure of dynamic functional connectivity.

Authors:  Enrico Glerean; Juha Salmi; Juha M Lahnakoski; Iiro P Jääskeläinen; Mikko Sams
Journal:  Brain Connect       Date:  2012-06-11

2.  Age-related differences in the dynamic architecture of intrinsic networks.

Authors:  Tara M Madhyastha; Thomas J Grabowski
Journal:  Brain Connect       Date:  2014-01-30

3.  Functional connectivity arises from a slow rhythmic mechanism.

Authors:  Jingfeng M Li; William J Bentley; Abraham Z Snyder; Marcus E Raichle; Lawrence H Snyder
Journal:  Proc Natl Acad Sci U S A       Date:  2015-04-27       Impact factor: 11.205

Review 4.  Characterizing Resting-State Brain Function Using Arterial Spin Labeling.

Authors:  J Jean Chen; Kay Jann; Danny J J Wang
Journal:  Brain Connect       Date:  2015-10-06

5.  Group comparison of spatiotemporal dynamics of intrinsic networks in Parkinson's disease.

Authors:  Tara M Madhyastha; Mary K Askren; Jing Zhang; James B Leverenz; Thomas J Montine; Thomas J Grabowski
Journal:  Brain       Date:  2015-07-14       Impact factor: 13.501

6.  The encoding/retrieval flip: interactions between memory performance and memory stage and relationship to intrinsic cortical networks.

Authors:  Willem Huijbers; Aaron P Schultz; Patrizia Vannini; Donald G McLaren; Sarah E Wigman; Andrew M Ward; Trey Hedden; Reisa A Sperling
Journal:  J Cogn Neurosci       Date:  2013-02-05       Impact factor: 3.225

7.  BOLD fractional contribution to resting-state functional connectivity above 0.1 Hz.

Authors:  Jingyuan E Chen; Gary H Glover
Journal:  Neuroimage       Date:  2014-12-12       Impact factor: 6.556

8.  Improving the use of principal component analysis to reduce physiological noise and motion artifacts to increase the sensitivity of task-based fMRI.

Authors:  David A Soltysik; David Thomasson; Sunder Rajan; Nadia Biassou
Journal:  J Neurosci Methods       Date:  2014-12-04       Impact factor: 2.390

Review 9.  The Human Connectome Project's neuroimaging approach.

Authors:  Matthew F Glasser; Stephen M Smith; Daniel S Marcus; Jesper L R Andersson; Edward J Auerbach; Timothy E J Behrens; Timothy S Coalson; Michael P Harms; Mark Jenkinson; Steen Moeller; Emma C Robinson; Stamatios N Sotiropoulos; Junqian Xu; Essa Yacoub; Kamil Ugurbil; David C Van Essen
Journal:  Nat Neurosci       Date:  2016-08-26       Impact factor: 24.884

10.  Evaluating the effects of systemic low frequency oscillations measured in the periphery on the independent component analysis results of resting state networks.

Authors:  Yunjie Tong; Lia M Hocke; Lisa D Nickerson; Stephanie C Licata; Kimberly P Lindsey; Blaise deB Frederick
Journal:  Neuroimage       Date:  2013-03-21       Impact factor: 6.556

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