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Neuroinformatics Challenges to the Structural, Connectomic, Functional, and Electrophysiological Multimodal Imaging of Human Traumatic Brain Injury

Institution:
Department of Neurology, Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
Publication Date:
Feb-2014
Journal:
Front Neuroinform.
Volume Number:
8
Issue Number:
19
Pages:
1-12
Citation:
Front Neuroinform. 2014 Feb 26;8(19):1-12.
PubMed ID:
24616696
PMCID:
PMC3935464
Keywords:
neuroinformatics, traumatic brain injury, neuroanatomy, Connectomics, ehabilitation, MRI, DTI
Appears in Collections:
NA-MIC, SLICER
Sponsors:
P41 EB015922/EB/NIBIB NIH HHS/United States
R41 NS081792/NS/NINDS NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
Generated Citation:
Goh S.Y.M., Irimia A., Torgerson C.M., Van Horn J.D. Neuroinformatics Challenges to the Structural, Connectomic, Functional, and Electrophysiological Multimodal Imaging of Human Traumatic Brain Injury. Front Neuroinform. 2014 Feb 26;8(19):1-12. PMID: 24616696. PMCID: PMC3935464.
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Throughout the past few decades, the ability to treat and rehabilitate traumatic brain injury (TBI) patients has become critically reliant upon the use of neuroimaging to acquire adequate knowledge of injury-related effects upon brain function and recovery. As a result, the need for TBI neuroimaging analysis methods has increased in recent years due to the recognition that spatiotemporal computational analyses of TBI evolution are useful for capturing the effects of TBI dynamics. At the same time, however, the advent of such methods has brought about the need to analyze, manage, and integrate TBI neuroimaging data using informatically inspired approaches which can take full advantage of their large dimensionality and informational complexity. Given this perspective, we here discuss the neuroinformatics challenges for TBI neuroimaging analysis in the context of structural, connectivity, and functional paradigms. Within each of these, the availability of a wide range of neuroimaging modalities can be leveraged to fully understand the heterogeneity of TBI pathology; consequently, large-scale computer hardware resources and next-generation processing software are often required for efficient data storage, management, and analysis of TBI neuroimaging data. However, each of these paradigms poses challenges in the context of informatics such that the ability to address them is critical for augmenting current capabilities to perform neuroimaging analysis of TBI and to improve therapeutic efficacy.

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