Literature DB >> 28899744

Neural and metabolic basis of dynamic resting state fMRI.

Garth J Thompson1.   

Abstract

Resting state fMRI (rsfMRI) as a technique showed much initial promise for use in psychiatric and neurological diseases where diagnosis and treatment were difficult. To realize this promise, many groups have moved towards examining "dynamic rsfMRI," which relies on the assumption that rsfMRI measurements on short time scales remain relevant to the underlying neural and metabolic activity. Many dynamic rsfMRI studies have demonstrated differences between clinical or behavioral groups beyond what static rsfMRI measured, suggesting a neurometabolic basis. Correlative studies combining dynamic rsfMRI and other physiological measurements have supported this. However, they also indicate multiple mechanisms and, if using correlation alone, it is difficult to separate cause and effect. Hypothesis-driven studies are needed, a few of which have begun to illuminate the underlying neurometabolic mechanisms that shape observed differences in dynamic rsfMRI. While the number of potential noise sources, potential actual neurometabolic sources, and methodological considerations can seem overwhelming, dynamic rsfMRI provides a rich opportunity in systems neuroscience. Even an incrementally better understanding of the neurometabolic basis of dynamic rsfMRI would expand rsfMRI's research and clinical utility, and the studies described herein take the first steps on that path forward.
Copyright © 2017 Elsevier Inc. All rights reserved.

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Year:  2017        PMID: 28899744      PMCID: PMC5844792          DOI: 10.1016/j.neuroimage.2017.09.010

Source DB:  PubMed          Journal:  Neuroimage        ISSN: 1053-8119            Impact factor:   6.556


  209 in total

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Journal:  J Cereb Blood Flow Metab       Date:  2015-12-21       Impact factor: 6.200

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5.  Functional connectivity in blood oxygenation level-dependent and cerebral blood volume-weighted resting state functional magnetic resonance imaging in the rat brain.

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Journal:  Neuroimage       Date:  2015-12-19       Impact factor: 6.556

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8.  Spontaneous physiological variability modulates dynamic functional connectivity in resting-state functional magnetic resonance imaging.

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  13 in total

1.  EEG Microstates Predict Concurrent fMRI Dynamic Functional Connectivity States.

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Authors:  Behnaz Yousefi; Shella Keilholz
Journal:  Neuroimage       Date:  2021-02-05       Impact factor: 7.400

3.  A systematic investigation of the association between network dynamics in the human brain and the state of consciousness.

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Journal:  Neurosci Conscious       Date:  2020-06-14

4.  The Profiles of Non-stationarity and Non-linearity in the Time Series of Resting-State Brain Networks.

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Journal:  Front Neurosci       Date:  2020-06-11       Impact factor: 4.677

5.  Brain parcellation driven by dynamic functional connectivity better capture intrinsic network dynamics.

Authors:  Liangwei Fan; Qi Zhong; Jian Qin; Na Li; Jianpo Su; Ling-Li Zeng; Dewen Hu; Hui Shen
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6.  Ranking of communities in multiplex spatiotemporal models of brain dynamics.

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7.  Changes in Dynamics Within and Between Resting-State Subnetworks in Juvenile Myoclonic Epilepsy Occur at Multiple Frequency Bands.

Authors:  Zhe Zhang; Guangyao Liu; Zhijun Yao; Weihao Zheng; Yuanwei Xie; Tao Hu; Yu Zhao; Yue Yu; Ying Zou; Jie Shi; Jing Yang; Tiancheng Wang; Jing Zhang; Bin Hu
Journal:  Front Neurol       Date:  2018-06-14       Impact factor: 4.003

Review 8.  Intrinsic connectome organization across temporal scales: New insights from cross-modal approaches.

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9.  Altered dynamic effective connectivity of the default mode network in newly diagnosed drug-naïve juvenile myoclonic epilepsy.

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Review 10.  Questions and controversies in the study of time-varying functional connectivity in resting fMRI.

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Journal:  Netw Neurosci       Date:  2020-02-01
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