Statistical machine learning to identify traumatic brain injury (TBI) from structural disconnections of white matter networks

Mitra, Jhimli, Shen, Kai-kai, Ghose, Soumya, Bourgeat, Pierrick, Fripp, Jurgen, Salvado, Olivier, Pannek, Kerstin, Taylor, D. Jamie, Mathias, Jane L. and Rose, Stephen (2016) Statistical machine learning to identify traumatic brain injury (TBI) from structural disconnections of white matter networks. NeuroImage, 129 247-259. doi:10.1016/j.neuroimage.2016.01.056

Author Mitra, Jhimli
Shen, Kai-kai
Ghose, Soumya
Bourgeat, Pierrick
Fripp, Jurgen
Salvado, Olivier
Pannek, Kerstin
Taylor, D. Jamie
Mathias, Jane L.
Rose, Stephen
Title Statistical machine learning to identify traumatic brain injury (TBI) from structural disconnections of white matter networks
Journal name NeuroImage   Check publisher's open access policy
ISSN 1095-9572
Publication date 2016-04-01
Year available 2016
Sub-type Article (original research)
DOI 10.1016/j.neuroimage.2016.01.056
Open Access Status Not Open Access
Volume 129
Start page 247
End page 259
Total pages 13
Place of publication Amsterdam, Netherlands
Publisher Academic Press Inc.
Collection year 2017
Language eng
Formatted abstract
Identifying diffuse axonal injury (DAI) in patients with traumatic brain injury (TBI) presenting with normal appearing radiological MRI presents a significant challenge. Neuroimaging methods such as diffusion MRI and probabilistic tractography, which probe the connectivity of neural networks, show significant promise. We present a machine learning approach to classify TBI participants primarily with mild traumatic brain injury (mTBI) based on altered structural connectivity patterns derived through the network based statistical analysis of structural connectomes generated from TBI and age-matched control groups. In this approach, higher order diffusion models were used to map white matter connections between 116 cortical and subcortical regions. Tracts between these regions were generated using probabilistic tracking and mean fractional anisotropy (FA) measures along these connections were encoded in the connectivity matrices. Network-based statistical analysis of the connectivity matrices was performed to identify the network differences between a representative subset of the two groups. The affected network connections provided the feature vectors for principal component analysis and subsequent classification by random forest. The validity of the approach was tested using data acquired from a total of 179 TBI patients and 146 controls participants. The analysis revealed altered connectivity within a number of intra- and inter-hemispheric white matter pathways associated with DAI, in consensus with existing literature. A mean classification accuracy of 68.16% ± 1.81% and mean sensitivity of 80.0% ± 2.36% were achieved in correctly classifying the TBI patients evaluated on the subset of the participants that was not used for the statistical analysis, in a 10-fold cross-validation framework. These results highlight the potential for statistical machine learning approaches applied to structural connectomes to identify patients with diffusive axonal injury.
Keyword Diffusion tractography
Machine learning
Network-based statistics
Structural network connections
Traumatic brain injury
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: UQ Centre for Clinical Research Publications
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