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Brain state transition analysis using ultra-fast fMRI differentiates MCI from cognitively normal controls

PURPOSE: Conventional resting-state fMRI studies indicate that many cortical and subcortical regions have altered function in Alzheimer’s disease (AD) but the nature of this alteration has remained unclear. Ultrafast fMRIs with sub-second acquisition times have the potential to improve signal contra...

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Autores principales: Palmer, William C., Park, Sung Min, Levendovszky, Swati Rane
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9555083/
https://www.ncbi.nlm.nih.gov/pubmed/36248645
http://dx.doi.org/10.3389/fnins.2022.975305
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author Palmer, William C.
Park, Sung Min
Levendovszky, Swati Rane
author_facet Palmer, William C.
Park, Sung Min
Levendovszky, Swati Rane
author_sort Palmer, William C.
collection PubMed
description PURPOSE: Conventional resting-state fMRI studies indicate that many cortical and subcortical regions have altered function in Alzheimer’s disease (AD) but the nature of this alteration has remained unclear. Ultrafast fMRIs with sub-second acquisition times have the potential to improve signal contrast and enable advanced analyses to understand temporal interactions between brain regions as opposed to spatial interactions. In this work, we leverage such fast fMRI acquisitions from Alzheimer’s disease Neuroimaging Initiative to understand temporal differences in the interactions between resting-state networks in 55 older adults with mild cognitive impairment (MCI) and 50 cognitively normal healthy controls. METHODS: We used a sliding window approach followed by k-means clustering. At each window, we computed connectivity i.e., correlations within and across the regions of the default mode, salience, dorsal attention, and frontoparietal network. Visual and somatosensory networks were excluded due to their lack of association with AD. Using the Davies–Bouldin index, we identified clusters of windows with distinct connectivity patterns, also referred to as brain states. The fMRI time courses were converted into time courses depicting brain state transition. From these state time course, we calculated the dwell time for each state i.e., how long a participant spent in each state. We determined how likely a participant transitioned between brain states. Both metrics were compared between MCI participants and controls using a false discovery rate correction of multiple comparisons at a threshold of. 0.05. RESULTS: We identified 8 distinct brain states representing connectivity within and between the resting state networks. We identified three transitions that were different between controls and MCI, all involving transitions in connectivity between frontoparietal, dorsal attention, and default mode networks (p<0.04). CONCLUSION: We show that ultra-fast fMRI paired with dynamic functional connectivity analysis allows us to capture temporal transitions between brain states. Most changes were associated with transitions between the frontoparietal and dorsal attention networks connectivity and their interaction with the default mode network. Although future work needs to validate these findings, the brain networks identified in our work are known to interact with each other and play an important role in cognitive function and memory impairment in AD.
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spelling pubmed-95550832022-10-13 Brain state transition analysis using ultra-fast fMRI differentiates MCI from cognitively normal controls Palmer, William C. Park, Sung Min Levendovszky, Swati Rane Front Neurosci Neuroscience PURPOSE: Conventional resting-state fMRI studies indicate that many cortical and subcortical regions have altered function in Alzheimer’s disease (AD) but the nature of this alteration has remained unclear. Ultrafast fMRIs with sub-second acquisition times have the potential to improve signal contrast and enable advanced analyses to understand temporal interactions between brain regions as opposed to spatial interactions. In this work, we leverage such fast fMRI acquisitions from Alzheimer’s disease Neuroimaging Initiative to understand temporal differences in the interactions between resting-state networks in 55 older adults with mild cognitive impairment (MCI) and 50 cognitively normal healthy controls. METHODS: We used a sliding window approach followed by k-means clustering. At each window, we computed connectivity i.e., correlations within and across the regions of the default mode, salience, dorsal attention, and frontoparietal network. Visual and somatosensory networks were excluded due to their lack of association with AD. Using the Davies–Bouldin index, we identified clusters of windows with distinct connectivity patterns, also referred to as brain states. The fMRI time courses were converted into time courses depicting brain state transition. From these state time course, we calculated the dwell time for each state i.e., how long a participant spent in each state. We determined how likely a participant transitioned between brain states. Both metrics were compared between MCI participants and controls using a false discovery rate correction of multiple comparisons at a threshold of. 0.05. RESULTS: We identified 8 distinct brain states representing connectivity within and between the resting state networks. We identified three transitions that were different between controls and MCI, all involving transitions in connectivity between frontoparietal, dorsal attention, and default mode networks (p<0.04). CONCLUSION: We show that ultra-fast fMRI paired with dynamic functional connectivity analysis allows us to capture temporal transitions between brain states. Most changes were associated with transitions between the frontoparietal and dorsal attention networks connectivity and their interaction with the default mode network. Although future work needs to validate these findings, the brain networks identified in our work are known to interact with each other and play an important role in cognitive function and memory impairment in AD. Frontiers Media S.A. 2022-09-28 /pmc/articles/PMC9555083/ /pubmed/36248645 http://dx.doi.org/10.3389/fnins.2022.975305 Text en Copyright © 2022 Palmer, Park and Levendovszky. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Palmer, William C.
Park, Sung Min
Levendovszky, Swati Rane
Brain state transition analysis using ultra-fast fMRI differentiates MCI from cognitively normal controls
title Brain state transition analysis using ultra-fast fMRI differentiates MCI from cognitively normal controls
title_full Brain state transition analysis using ultra-fast fMRI differentiates MCI from cognitively normal controls
title_fullStr Brain state transition analysis using ultra-fast fMRI differentiates MCI from cognitively normal controls
title_full_unstemmed Brain state transition analysis using ultra-fast fMRI differentiates MCI from cognitively normal controls
title_short Brain state transition analysis using ultra-fast fMRI differentiates MCI from cognitively normal controls
title_sort brain state transition analysis using ultra-fast fmri differentiates mci from cognitively normal controls
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9555083/
https://www.ncbi.nlm.nih.gov/pubmed/36248645
http://dx.doi.org/10.3389/fnins.2022.975305
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