Routine magnetoencephalography in memory clinic patients: A machine learning approach

Abstract

Abstract Introduction We report the routine application of magnetoencephalography (MEG) in a memory clinic, and its value in the discrimination of patients with Alzheimer's disease (AD) dementia from controls. Methods Three hundred sixty‐six patients visiting our memory clinic underwent MEG recording. Source‐reconstructed MEG data were visually assessed and evaluated in the context of clinical findings and other diagnostic markers. We analyzed the diagnostic accuracy of MEG spectral measures in the discrimination of individual AD dementia patients (n = 40) from subjective cognitive decline (SCD) patients (n = 40) using random forest models. Results Best discrimination was obtained using a combination of relative theta and delta power (accuracy 0.846, sensitivity 0.855, specificity 0.837). The results were validated in an independent cohort. Hippocampal and thalamic regions, besides temporal‐occipital lobes, contributed considerably to the model. Discussion MEG has been implemented successfully in the workup of memory clinic patients and has value in diagnostic decision‐making.

Keywords

• Alzheimer's disease
• diagnostic biomarker
• machine learning
• magnetoencephalography
• random forest classifier