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Elsevier, Alzheimer's && Dementia :: Diagnosis, Assessment && Disease Monitoring, 1(13), 2021

DOI: 10.1002/dad2.12227

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Routine magnetoencephalography in memory clinic patients: A machine learning approach

This paper was not found in any repository, but could be made available legally by the author.
This paper was not found in any repository, but could be made available legally by the author.

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Data provided by SHERPA/RoMEO

Abstract

AbstractIntroductionWe 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.MethodsThree 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.ResultsBest 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.DiscussionMEG has been implemented successfully in the workup of memory clinic patients and has value in diagnostic decision‐making.