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Non-Stationarity in the “Resting Brain’s” Modular Architecture

Task-free functional magnetic resonance imaging (TF-fMRI) has great potential for advancing the understanding and treatment of neurologic illness. However, as with all measures of neural activity, variability is a hallmark of intrinsic connectivity networks (ICNs) identified by TF-fMRI. This variabi...

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Autores principales: Jones, David T., Vemuri, Prashanthi, Murphy, Matthew C., Gunter, Jeffrey L., Senjem, Matthew L., Machulda, Mary M., Przybelski, Scott A., Gregg, Brian E., Kantarci, Kejal, Knopman, David S., Boeve, Bradley F., Petersen, Ronald C., Jack, Clifford R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3386248/
https://www.ncbi.nlm.nih.gov/pubmed/22761880
http://dx.doi.org/10.1371/journal.pone.0039731
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author Jones, David T.
Vemuri, Prashanthi
Murphy, Matthew C.
Gunter, Jeffrey L.
Senjem, Matthew L.
Machulda, Mary M.
Przybelski, Scott A.
Gregg, Brian E.
Kantarci, Kejal
Knopman, David S.
Boeve, Bradley F.
Petersen, Ronald C.
Jack, Clifford R.
author_facet Jones, David T.
Vemuri, Prashanthi
Murphy, Matthew C.
Gunter, Jeffrey L.
Senjem, Matthew L.
Machulda, Mary M.
Przybelski, Scott A.
Gregg, Brian E.
Kantarci, Kejal
Knopman, David S.
Boeve, Bradley F.
Petersen, Ronald C.
Jack, Clifford R.
author_sort Jones, David T.
collection PubMed
description Task-free functional magnetic resonance imaging (TF-fMRI) has great potential for advancing the understanding and treatment of neurologic illness. However, as with all measures of neural activity, variability is a hallmark of intrinsic connectivity networks (ICNs) identified by TF-fMRI. This variability has hampered efforts to define a robust metric of connectivity suitable as a biomarker for neurologic illness. We hypothesized that some of this variability rather than representing noise in the measurement process, is related to a fundamental feature of connectivity within ICNs, which is their non-stationary nature. To test this hypothesis, we used a large (n = 892) population-based sample of older subjects to construct a well characterized atlas of 68 functional regions, which were categorized based on independent component analysis network of origin, anatomical locations, and a functional meta-analysis. These regions were then used to construct dynamic graphical representations of brain connectivity within a sliding time window for each subject. This allowed us to demonstrate the non-stationary nature of the brain’s modular organization and assign each region to a “meta-modular” group. Using this grouping, we then compared dwell time in strong sub-network configurations of the default mode network (DMN) between 28 subjects with Alzheimer’s dementia and 56 cognitively normal elderly subjects matched 1∶2 on age, gender, and education. We found that differences in connectivity we and others have previously observed in Alzheimer’s disease can be explained by differences in dwell time in DMN sub-network configurations, rather than steady state connectivity magnitude. DMN dwell time in specific modular configurations may also underlie the TF-fMRI findings that have been described in mild cognitive impairment and cognitively normal subjects who are at risk for Alzheimer’s dementia.
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spelling pubmed-33862482012-07-03 Non-Stationarity in the “Resting Brain’s” Modular Architecture Jones, David T. Vemuri, Prashanthi Murphy, Matthew C. Gunter, Jeffrey L. Senjem, Matthew L. Machulda, Mary M. Przybelski, Scott A. Gregg, Brian E. Kantarci, Kejal Knopman, David S. Boeve, Bradley F. Petersen, Ronald C. Jack, Clifford R. PLoS One Research Article Task-free functional magnetic resonance imaging (TF-fMRI) has great potential for advancing the understanding and treatment of neurologic illness. However, as with all measures of neural activity, variability is a hallmark of intrinsic connectivity networks (ICNs) identified by TF-fMRI. This variability has hampered efforts to define a robust metric of connectivity suitable as a biomarker for neurologic illness. We hypothesized that some of this variability rather than representing noise in the measurement process, is related to a fundamental feature of connectivity within ICNs, which is their non-stationary nature. To test this hypothesis, we used a large (n = 892) population-based sample of older subjects to construct a well characterized atlas of 68 functional regions, which were categorized based on independent component analysis network of origin, anatomical locations, and a functional meta-analysis. These regions were then used to construct dynamic graphical representations of brain connectivity within a sliding time window for each subject. This allowed us to demonstrate the non-stationary nature of the brain’s modular organization and assign each region to a “meta-modular” group. Using this grouping, we then compared dwell time in strong sub-network configurations of the default mode network (DMN) between 28 subjects with Alzheimer’s dementia and 56 cognitively normal elderly subjects matched 1∶2 on age, gender, and education. We found that differences in connectivity we and others have previously observed in Alzheimer’s disease can be explained by differences in dwell time in DMN sub-network configurations, rather than steady state connectivity magnitude. DMN dwell time in specific modular configurations may also underlie the TF-fMRI findings that have been described in mild cognitive impairment and cognitively normal subjects who are at risk for Alzheimer’s dementia. Public Library of Science 2012-06-28 /pmc/articles/PMC3386248/ /pubmed/22761880 http://dx.doi.org/10.1371/journal.pone.0039731 Text en Jones et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Jones, David T.
Vemuri, Prashanthi
Murphy, Matthew C.
Gunter, Jeffrey L.
Senjem, Matthew L.
Machulda, Mary M.
Przybelski, Scott A.
Gregg, Brian E.
Kantarci, Kejal
Knopman, David S.
Boeve, Bradley F.
Petersen, Ronald C.
Jack, Clifford R.
Non-Stationarity in the “Resting Brain’s” Modular Architecture
title Non-Stationarity in the “Resting Brain’s” Modular Architecture
title_full Non-Stationarity in the “Resting Brain’s” Modular Architecture
title_fullStr Non-Stationarity in the “Resting Brain’s” Modular Architecture
title_full_unstemmed Non-Stationarity in the “Resting Brain’s” Modular Architecture
title_short Non-Stationarity in the “Resting Brain’s” Modular Architecture
title_sort non-stationarity in the “resting brain’s” modular architecture
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3386248/
https://www.ncbi.nlm.nih.gov/pubmed/22761880
http://dx.doi.org/10.1371/journal.pone.0039731
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