Combining Multiple Indices of Diffusion Tensor Imaging Can Better Differentiate Patients with Traumatic Brain Injury from Healthy Subjects

Abstract

Aim

Diffuse axonal injury (DAI) is one of the most common pathological features of traumatic brain injury (TBI). Diffusion tensor imaging (DTI) indices can be used to identify and quantify white matter microstructural changes following DAI. Recently, many studies have used DTI with various machine learning approaches to predict white matter microstructural changes following TBI. The current study sought to examine whether our classification approach using multiple DTI indices in conjunction with machine learning is a useful tool for diagnosing/classifying TBI patients and healthy controls.

Methods

Participants were adult patients with chronic TBI (n = 26) with DAI pathology, and age- and sex-matched healthy controls (n = 26). DTI images were obtained from all participants. Tract-based spatial statistics analyses were applied to DTI images. Classification models were built using principal component analysis and support vector machines. Receiver operator characteristic curve analysis and area under the curve were used to assess the classification performance of the different classifiers.

Results

Tract-based spatial statistics revealed significantly decreased fractional anisotropy, as well as increased mean diffusivity, axial diffusivity, and radial diffusivity in patients with TBI compared with healthy controls (all p-values < 0.01). The principal component analysis and support vector machine-based machine learning classification using combined DTI indices classified patients with TBI and healthy controls with an accuracy of 90.5% with an area under the curve of 93 ± 0.09.

Conclusion

These results highlight the potential of our approach combining multiple DTI measures to identify patients with TBI.

Keywords:

• diffuse axonal injury
• diffusion tensor imaging
• machine learning
• screening
• traumatic brain injury

View correction statement:

Combining Multiple Indices of Diffusion Tensor Imaging Can Better Differentiate Patients with Traumatic Brain Injury from Healthy Subjects [Corrigendum]

Abbreviations

TBI, traumatic brain injury; mTBI, mild traumatic brain injury; DAI, diffusion axonal injury; DTI, diffusion tensor imaging; FA, fractional anisotropy; MD, mean diffusivity; AD, axial diffusivity; RD, radial diffusivity; ALL, voxel-wise combination of FA, MD, AD, and RD in one dataset; TBSS, tract-based spatial statistics; TFCE, threshold-free cluster enhancement; FEW, family-wise error rate; ML, machine learning; PCA, principal component analysis; SVM, support vector machine; PCs, principal components; GCS, Glasgow Coma Scale; JCS, Japan Coma Scale; ROC, receiving operating curve; AUC, area under the curve; bmTBI, blast-related mild traumatic brain injury.

Data Sharing Statement

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Ethics Approval and Consent to Participate

This study was approved by the Committee on Medical Ethics of Kyoto University and all procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Written informed consent was obtained from all individual participants included in the study.

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Disclosure

The authors declare that they have no competing interests.

Additional information

Funding

This work was supported by an Industrial Disease Clinical Research Grant (150502-02); a Health Labor Sciences Research Grant; a Grant-in-Aid for Young Scientists (19K17110), B (21H02805), and C (17K10327, 18K07712) from the Japan Society for the Promotion of Science; Innovative Areas (16H06402) from the Ministry of Education, Culture, Sports, Science, and Technology of Japan (MEXT); a research grant from the National Mutual Insurance Federation of Agricultural Cooperatives; and ISHIZUE 2020 from the Kyoto University Research Development Program. The funding sources had no role in in study design; in the collection, analysis, and interpretation of data; in the writing of the report; and in the decision to submit the article for publication.