Sensor-level MEG combined with machine learning yields robust classification of mild traumatic brain injury patients

Juho Aaltonen; Verna Heikkinen; Hanna Kaltiainen; Riitta Salmelin; Hanna Renvall

doi:10.1016/j.clinph.2023.06.010

Sensor-level MEG combined with machine learning yields robust classification of mild traumatic brain injury patients

Clin Neurophysiol. 2023 Sep:153:79-87. doi: 10.1016/j.clinph.2023.06.010. Epub 2023 Jun 30.

Authors

Juho Aaltonen¹, Verna Heikkinen², Hanna Kaltiainen³, Riitta Salmelin⁴, Hanna Renvall⁵

Affiliations

¹ BioMag Laboratory, HUS Medical Imaging Center, Helsinki University Hospital, Helsinki University and Aalto University School of Science, P.O. Box 340, 00029 HUS Helsinki, Finland; Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Aalto University, P.O. Box 12200, 00760 AALTO, Finland. Electronic address: juho.aaltonen@aalto.fi.
² BioMag Laboratory, HUS Medical Imaging Center, Helsinki University Hospital, Helsinki University and Aalto University School of Science, P.O. Box 340, 00029 HUS Helsinki, Finland; Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Aalto University, P.O. Box 12200, 00760 AALTO, Finland.
³ Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Aalto University, P.O. Box 12200, 00760 AALTO, Finland; Department of Neurology, Helsinki University Hospital and Clinical Neurosciences, Neurology, University of Helsinki, P.O. Box 340, 00029 HUS, Helsinki, Finland.
⁴ Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Aalto University, P.O. Box 12200, 00760 AALTO, Finland.
⁵ BioMag Laboratory, HUS Medical Imaging Center, Helsinki University Hospital, Helsinki University and Aalto University School of Science, P.O. Box 340, 00029 HUS Helsinki, Finland; Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Aalto University, P.O. Box 12200, 00760 AALTO, Finland; Department of Neurology, Helsinki University Hospital and Clinical Neurosciences, Neurology, University of Helsinki, P.O. Box 340, 00029 HUS, Helsinki, Finland.

PMID: 37459668
DOI: 10.1016/j.clinph.2023.06.010

Abstract

Objective: Diagnosis of mild traumatic brain injury (mTBI) is challenging despite its high incidence, due to the unspecificity and variety of symptoms and the frequent lack of structural imaging findings. There is a need for reliable and simple-to-use diagnostic tools that would be feasible across sites and patient populations.

Methods: We evaluated linear machine learning (ML) methods' ability to separate mTBI patients from healthy controls, based on their sensor-level magnetoencephalographic (MEG) power spectra in the subacute phase (<2 months) after a head trauma. We recorded resting-state MEG data from 25 patients and 25 age-sex matched controls and utilized a previously collected data set of 20 patients and 20 controls from a different site. The data sets were analyzed separately with three ML methods.

Results: The median classification accuracies varied between 80 and 95%, without significant differences between the applied ML methods or data sets. The classification accuracies were significantly higher with ML than with traditional sensor-level MEG analysis based on detecting pathological low-frequency activity.

Conclusions: Easily applicable linear ML methods provide reliable and replicable classification of mTBI patients using sensor-level MEG data.

Significance: Power spectral estimates combined with ML can classify mTBI patients with high accuracy and have high promise for clinical use.

Keywords: Machine learning; Magnetoencephalography; Mild traumatic brain injury; Resting-state.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Brain / physiology
Brain Concussion* / diagnosis
Humans
Learning
Magnetoencephalography / methods