Format

Send to

Choose Destination
Neuroimage. 2016 Apr 1;129:247-259. doi: 10.1016/j.neuroimage.2016.01.056. Epub 2016 Jan 28.

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

Author information

1
CSIRO Health and Biosecurity, The Australian e-Health & Research Centre, Herston, QLD, Australia; Center for Computational Imaging and Personalized Diagnostics, Case Western Reserve University, Cleveland, OH, USA. Electronic address: jhimli.mitra@case.edu.
2
CSIRO Health and Biosecurity, The Australian e-Health & Research Centre, Herston, QLD, Australia. Electronic address: kaikai.shen@csiro.au.
3
CSIRO Health and Biosecurity, The Australian e-Health & Research Centre, Herston, QLD, Australia; Center for Computational Imaging and Personalized Diagnostics, Case Western Reserve University, Cleveland, OH, USA. Electronic address: soumya.ghose@case.edu.
4
CSIRO Health and Biosecurity, The Australian e-Health & Research Centre, Herston, QLD, Australia. Electronic address: pierrick.bourgeat@csiro.au.
5
CSIRO Health and Biosecurity, The Australian e-Health & Research Centre, Herston, QLD, Australia. Electronic address: jurgen.fripp@csiro.au.
6
CSIRO Health and Biosecurity, The Australian e-Health & Research Centre, Herston, QLD, Australia. Electronic address: olivier.salvado@csiro.au.
7
Department of Computing, Imperial College London, London, United Kingdom. Electronic address: kerstin.pannek@gmail.com.
8
Department of Radiology, The Royal Adelaide Hospital, Adelaide, SA, Australia. Electronic address: jamie.taylor@health.sa.gov.au.
9
School of Psychology, University of Adelaide, Adelaide, SA, Australia. Electronic address: jane.mathias@adelaide.edu.au.
10
CSIRO Health and Biosecurity, The Australian e-Health & Research Centre, Herston, QLD, Australia. Electronic address: stephen.rose@csiro.au.

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.

KEYWORDS:

Diffusion tractography; Machine learning; Network-based statistics; Structural network connections; Traumatic brain injury

[Indexed for MEDLINE]

Supplemental Content

Full text links

Icon for Elsevier Science
Loading ...
Support Center