B - 34 Brain Network Organization over Time after TBI

Abstract Objective Resting-state functional magnetic resonance (rsfMRI) has identified that brain networks become less specialized through declining network segregation as people age. Yet, it is unclear how segregation progresses over time after traumatic brain injury (TBI). This study evaluated segregation in older adults (M = 64.17 years) over a 2-year time period to better understand this phenomenon. Method 45 participants with TBI (mild-complicated, moderate, and severe) and 41 healthy controls (HCs) received a 20-minute rsfMRI scan at timepoint one, and 25 TBI and 15 HCs received a scan at timepoint two. Data were preprocessed with fMRIPrep. XCP_D was used to divide the brain into 274 regions (19 networks) and 1000 regions (17 networks) using the Brainnetome and Schaefer 1000 atlases. Fisher r-to-z transformed connectivity matrices were used to calculate segregation (strength of connections within a network relative to strength of connections between networks). Results Segregation didn’t differ significantly (p > 0.05) between the TBI and HC groups at either timepoint, nor did segregation differ between the TBI group’s first and second timepoint. Segregation did significantly decline between the HC group’s first and second timepoint, but this was only observed using the Schaefer 1000 atlas, t(21.46) = −2.09, p = 0.049 95% CI [−0.049, −0.0001]. Conclusions The results indicate that brain network segregation remains relatively stable after TBI but declines in healthy, older adults over time. This suggests brain network organization is less flexible post-injury and could potentially serve as a brain biomarker. However, it’s worth noting that choice of brain atlas affects the observation of this finding.

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Publication Details

Journal
Archives of Clinical Neuropsychology
Published
2024-09-12
DOI
https://doi.org/10.1093/arclin/acae067.195
Primary Topic
EEG and Brain-Computer Interfaces
Type
article
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article

B - 34 Brain Network Organization over Time after TBI

Hollie A. Mullin, Frank Gerard Hillary
Archives of Clinical Neuropsychology
EEG and Brain-Computer Interfaces
article

B - 34 Brain Network Organization over Time after TBI

Hollie A. Mullin, Frank Gerard Hillary
article en

Abstract

Abstract Objective Resting-state functional magnetic resonance (rsfMRI) has identified that brain networks become less specialized through declining network segregation as people age. Yet, it is unclear how segregation progresses over time after traumatic brain injury (TBI). This study evaluated segregation in older adults (M = 64.17 years) over a 2-year time period to better understand this phenomenon. Method 45 participants with TBI (mild-complicated, moderate, and severe) and 41 healthy controls (HCs) received a 20-minute rsfMRI scan at timepoint one, and 25 TBI and 15 HCs received a scan at timepoint two. Data were preprocessed with fMRIPrep. XCP_D was used to divide the brain into 274 regions (19 networks) and 1000 regions (17 networks) using the Brainnetome and Schaefer 1000 atlases. Fisher r-to-z transformed connectivity matrices were used to calculate segregation (strength of connections within a network relative to strength of connections between networks). Results Segregation didn’t differ significantly (p > 0.05) between the TBI and HC groups at either timepoint, nor did segregation differ between the TBI group’s first and second timepoint. Segregation did significantly decline between the HC group’s first and second timepoint, but this was only observed using the Schaefer 1000 atlas, t(21.46) = −2.09, p = 0.049 95% CI [−0.049, −0.0001]. Conclusions The results indicate that brain network segregation remains relatively stable after TBI but declines in healthy, older adults over time. This suggests brain network organization is less flexible post-injury and could potentially serve as a brain biomarker. However, it’s worth noting that choice of brain atlas affects the observation of this finding.

Archives of Clinical Neuropsychology
Openalex Percentile: Top 84%
EEG and Brain-Computer Interfaces
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