Applications of Resting State Functional MR Imaging to Traumatic Brain Injury

Thomas O'Neill; Elizabeth M. Davenport; Gowtham Murugesan; Albert Montillo; Joseph A Maldjian

doi:10.1016/j.nic.2017.06.006

Applications of Resting State Functional MR Imaging to Traumatic Brain Injury

Thomas O'Neill, Elizabeth M. Davenport, Gowtham Murugesan, Albert Montillo, Joseph A Maldjian

Research output: Contribution to journal › Review article › peer-review

19 Scopus citations

Abstract

Traumatic brain injury (TBI) is an important public health issue. TBI includes a broad spectrum of injury severities and abnormalities. Functional MR imaging (fMR imaging), both resting state (rs) and task, has been used often in research to study the effects of TBI. Although rs-fMR imaging is not currently applicable in clinical diagnosis of TBI, computer-aided tools are making this a possibility for the future. Specifically, graph theory is being used to study the change in networks after TBI. Machine learning methods allow researchers to build models capable of predicting injury severity and recovery trajectories.

Original language	English (US)
Pages (from-to)	685-696
Number of pages	12
Journal	Neuroimaging Clinics of North America
Volume	27
Issue number	4
DOIs	https://doi.org/10.1016/j.nic.2017.06.006
State	Published - Nov 2017

Keywords

BOLD
Graph theory
Machine learning
Magnetoencephalography
Resting state
TBI
fMR imaging

ASJC Scopus subject areas

Radiology Nuclear Medicine and imaging
Clinical Neurology

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10.1016/j.nic.2017.06.006

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title = "Applications of Resting State Functional MR Imaging to Traumatic Brain Injury",

abstract = "Traumatic brain injury (TBI) is an important public health issue. TBI includes a broad spectrum of injury severities and abnormalities. Functional MR imaging (fMR imaging), both resting state (rs) and task, has been used often in research to study the effects of TBI. Although rs-fMR imaging is not currently applicable in clinical diagnosis of TBI, computer-aided tools are making this a possibility for the future. Specifically, graph theory is being used to study the change in networks after TBI. Machine learning methods allow researchers to build models capable of predicting injury severity and recovery trajectories.",

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AU - Davenport, Elizabeth M.

AU - Murugesan, Gowtham

AU - Montillo, Albert

AU - Maldjian, Joseph A

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AB - Traumatic brain injury (TBI) is an important public health issue. TBI includes a broad spectrum of injury severities and abnormalities. Functional MR imaging (fMR imaging), both resting state (rs) and task, has been used often in research to study the effects of TBI. Although rs-fMR imaging is not currently applicable in clinical diagnosis of TBI, computer-aided tools are making this a possibility for the future. Specifically, graph theory is being used to study the change in networks after TBI. Machine learning methods allow researchers to build models capable of predicting injury severity and recovery trajectories.

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KW - Machine learning

KW - Magnetoencephalography

KW - Resting state

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KW - fMR imaging

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Applications of Resting State Functional MR Imaging to Traumatic Brain Injury

Abstract

Keywords

ASJC Scopus subject areas

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