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

• Published in: NeuroImage (Orlando, Fla.) Vol. 129; pp. 247 - 259
• Main Authors: Mitra, Jhimli, Shen, Kai-kai, Ghose, Soumya, Bourgeat, Pierrick, Fripp, Jurgen, Salvado, Olivier, Pannek, Kerstin, Taylor, D. Jamie, Mathias, Jane L., Rose, Stephen
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.04.2016; Elsevier Limited
• Subjects: Adult; Brain Injuries, Traumatic - diagnostic imaging; Connectome - methods; Diffuse Axonal Injury - diagnostic imaging; Diffusion Tensor Imaging - methods; Diffusion tractography; Female; Humans; Image Interpretation, Computer-Assisted; Machine Learning; Male; Middle Aged; Network-based statistics; Neural networks; Neural Pathways - pathology; Structural network connections; Studies; Traumatic brain injury; White Matter - pathology; Traumatic brain injury; Network-based statistics; Diffusion tractography; Machine learning; Structural network connections
• DOI: 10.1016/j.neuroimage.2016.01.056
• ISSN: 1053-8119

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. [Display omitted] •Method to identify diffuse axonal injury in mild traumatic brain injury (mTBI) patients from structural connectivity patterns.•Network-based statistics (NBS) is used to find significant network differences in mTBI and controls.•Fractional anisotropy (FA) features of the different structural connections obtained from NBS used as features.•Random forest classifier discriminates between mTBI and controls based on FA features.•Discriminative and significant network differences obtained from feature importance of random forest.