Literature DB >> 22432423

A sliding time-window ICA reveals spatial variability of the default mode network in time.

Vesa Kiviniemi1, Tapani Vire, Jukka Remes, Ahmed Abou Elseoud, Tuomo Starck, Osmo Tervonen, Juha Nikkinen.   

Abstract

Recent evidence on resting-state networks in functional (connectivity) magnetic resonance imaging (fcMRI) suggests that there may be significant spatial variability of activity foci over time. This study used a sliding time window approach with the spatial domain-independent component analysis (SliTICA) to detect spatial maps of resting-state networks over time. The study hypothesis was that the spatial distribution of a functionally connected network would present marked variability over time. The spatial stability of successive sliding-window maps of the default mode network (DMN) from fcMRI data of 12 participants imaged in the resting state was analyzed. Control measures support previous findings on the stability of independent component analysis in measuring sliding-window sources accurately. The spatial similarity of successive DMN maps varied over time at low frequencies and presented a 1/f power spectral pattern. SliTICA maps show marked temporal variation within the DMN; a single voxel was detected inside a group DMN map in maximally 82% of time windows. Mapping of incidental connectivity reveals centrifugally increasing connectivity to the brain cortex outside the DMN core areas. In conclusion, SliTICA shows marked spatial variance of DMN activity in time, which may offer a more comprehensive measurement of the overall functional activity of a network.

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Year:  2011        PMID: 22432423     DOI: 10.1089/brain.2011.0036

Source DB:  PubMed          Journal:  Brain Connect        ISSN: 2158-0014


  107 in total

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

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

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5.  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

6.  Dynamic functional connectivity revealed by resting-state functional near-infrared spectroscopy.

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7.  Enhanced subject-specific resting-state network detection and extraction with fast fMRI.

Authors:  Burak Akin; Hsu-Lei Lee; Jürgen Hennig; Pierre LeVan
Journal:  Hum Brain Mapp       Date:  2016-10-03       Impact factor: 5.038

8.  Dynamic Functional Magnetic Resonance Imaging Connectivity Tensor Decomposition: A New Approach to Analyze and Interpret Dynamic Brain Connectivity.

Authors:  Fatemeh Mokhtari; Paul J Laurienti; W Jack Rejeski; Grey Ballard
Journal:  Brain Connect       Date:  2018-12-26

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

Authors:  Sadia Shakil; Chin-Hui Lee; Shella Dawn Keilholz
Journal:  Neuroimage       Date:  2016-03-04       Impact factor: 6.556

10.  Association between heart rate variability and fluctuations in resting-state functional connectivity.

Authors:  Catie Chang; Coraline D Metzger; Gary H Glover; Jeff H Duyn; Hans-Jochen Heinze; Martin Walter
Journal:  Neuroimage       Date:  2012-12-12       Impact factor: 6.556

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