Get access

Individual prediction of white matter injury following traumatic brain injury

Authors

  • Peter J. Hellyer MRes,

    1. Computational, Cognitive, and Clinical Neuroimaging Laboratory, Division of Experimental Medicine, London, United Kingdom
    Search for more papers by this author
  • Robert Leech PhD,

    Corresponding author
    • Computational, Cognitive, and Clinical Neuroimaging Laboratory, Division of Experimental Medicine, London, United Kingdom
    Search for more papers by this author
  • Timothy E. Ham MD,

    1. Computational, Cognitive, and Clinical Neuroimaging Laboratory, Division of Experimental Medicine, London, United Kingdom
    Search for more papers by this author
  • Valerie Bonnelle PhD,

    1. Computational, Cognitive, and Clinical Neuroimaging Laboratory, Division of Experimental Medicine, London, United Kingdom
    2. MRC Clinical Sciences Centre, Experimental and Clinical Neuroscience Section, Cognitive Neuroimaging Research Group, Faculty of Medicine, Imperial College London, Hammersmith Hospital Campus, London, United Kingdom
    Search for more papers by this author
  • David J. Sharp MD, PhD

    1. Computational, Cognitive, and Clinical Neuroimaging Laboratory, Division of Experimental Medicine, London, United Kingdom
    Search for more papers by this author

Address correspondence to Dr Leech, Computational, Cognitive, and Clinical Neuroimaging Laboratory, 3rd Floor, Burlington Danes Building, Hammersmith Hospital, Du Cane Road, London W12 0NN, United Kingdom. E-mail: r.leech@imperial.ac.uk

Abstract

Objective

Traumatic brain injury (TBI) often results in traumatic axonal injury (TAI). This can be difficult to identify using conventional imaging. Diffusion tensor imaging (DTI) offers a method of assessing axonal damage in vivo, but has previously mainly been used to investigate groups of patients. Machine learning techniques are increasingly used to improve diagnosis based on complex imaging measures. We investigated whether machine learning applied to DTI data can be used to diagnose white matter damage after TBI and to predict neuropsychological outcome in individual patients.

Methods

We trained pattern classifiers to predict the presence of white matter damage in 25 TBI patients with microbleed evidence of TAI compared to neurologically healthy age-matched controls. We then applied these classifiers to 35 additional patients with no conventional imaging evidence of TAI. Finally, we used regression analyses to predict indices of neuropsychological outcome for information processing speed, executive function, and associative memory in a group of 70 heterogeneous patients.

Results

The classifiers discriminated between patients with microbleeds and age-matched controls with a high degree of accuracy, and outperformed other methods. When the trained classifiers were applied to patients without microbleeds, patients having likely TAI showed evidence of greater cognitive impairment in information processing speed and executive function. The classifiers were also able to predict the extent of impairments in information processing speed and executive function.

Interpretation

The work provides a proof of principle that multivariate techniques can be used with DTI to provide diagnostic information about clinically significant TAI. ANN NEUROL 2013;73:489–499

Ancillary