Longitudinal Analysis of Brain Recovery after Mild Traumatic Brain Injury Based on Groupwise Consistent Brain Network Clusters

Traumatic brain injury (TBI) affects over 1.5 million Americans each year, and more than 75% of TBI cases are classified as mild (mTBI). Several functional network alternations have been reported after mTBI; however, the network alterations on a large scale, particularly on connectome scale, are sti...

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Published inMedical Image Computing and Computer-Assisted Intervention -- MICCAI 2015 pp. 194 - 201
Main Authors Chen, Hanbo, Iraji, Armin, Jiang, Xi, Lv, Jinglei, Kou, Zhifeng, Liu, Tianming
Format Book Chapter
LanguageEnglish
Published Cham Springer International Publishing 2015
SeriesLecture Notes in Computer Science
Subjects
Online AccessGet full text
ISBN3319245708
9783319245706
ISSN0302-9743
1611-3349
DOI10.1007/978-3-319-24571-3_24

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Abstract Traumatic brain injury (TBI) affects over 1.5 million Americans each year, and more than 75% of TBI cases are classified as mild (mTBI). Several functional network alternations have been reported after mTBI; however, the network alterations on a large scale, particularly on connectome scale, are still unknown. To analyze brain network, in a previous work, 358 landmarks named dense individualized common connectivity based cortical landmarks (DICCCOL) were identified on cortical surface. These landmarks preserve structural connection consistency and maintain functional correspondence across subjects. Hence DICCCOLs have been shown powerful in identifying connectivity signatures in affected brains. However, on such fine scales, the longitudinal changes in brain network of mTBI patients were complicated by the noise embedded in the systems as well as the normal variability of individuals at different times. Faced with such problems, we proposed a novel framework to analyze longitudinal changes from the perspective of network clusters. Specifically, multiview spectral clustering algorithm was applied to cluster brain networks based on DICCCOLs. And both structural and functional networks were analyzed. Our results showed that significant longitudinal changes were identified from mTBI patients that can be related to the neurocognitive recovery and the brain’s effort to compensate the effect of injury.
AbstractList Traumatic brain injury (TBI) affects over 1.5 million Americans each year, and more than 75% of TBI cases are classified as mild (mTBI). Several functional network alternations have been reported after mTBI; however, the network alterations on a large scale, particularly on connectome scale, are still unknown. To analyze brain network, in a previous work, 358 landmarks named dense individualized common connectivity based cortical landmarks (DICCCOL) were identified on cortical surface. These landmarks preserve structural connection consistency and maintain functional correspondence across subjects. Hence DICCCOLs have been shown powerful in identifying connectivity signatures in affected brains. However, on such fine scales, the longitudinal changes in brain network of mTBI patients were complicated by the noise embedded in the systems as well as the normal variability of individuals at different times. Faced with such problems, we proposed a novel framework to analyze longitudinal changes from the perspective of network clusters. Specifically, multiview spectral clustering algorithm was applied to cluster brain networks based on DICCCOLs. And both structural and functional networks were analyzed. Our results showed that significant longitudinal changes were identified from mTBI patients that can be related to the neurocognitive recovery and the brain’s effort to compensate the effect of injury.
Author Kou, Zhifeng
Lv, Jinglei
Chen, Hanbo
Jiang, Xi
Iraji, Armin
Liu, Tianming
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Snippet Traumatic brain injury (TBI) affects over 1.5 million Americans each year, and more than 75% of TBI cases are classified as mild (mTBI). Several functional...
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StartPage 194
SubjectTerms brain network clustering
DTI
fMRI
longitudinal analysis
t-test
traumatic brain injury
Title Longitudinal Analysis of Brain Recovery after Mild Traumatic Brain Injury Based on Groupwise Consistent Brain Network Clusters
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