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Research Paper|Volume 12, Issue 23|pp 24101—24116

Predicting the outcome of non-pharmacological treatment for patients with dementia-related mild cognitive impairment

Yoshihito Shigihara1,2, Hideyuki Hoshi1, Jesús Poza3,4,5, Víctor Rodríguez-González3,4, Carlos Gómez3,4, Takao Kanzawa6,7
  • 1Precision Medicine Centre, Hokuto Hospital, Obihiro 080-0833, Hokkaido, Japan
  • 2MEG Centre, Kumagaya General Hospital, Kumagaya 360-8567, Saitama, Japan
  • 3Biomedical Engineering Group, Higher Technical School of Telecommunications Engineering, University of Valladolid, Valladolid 47011, Castilla y León, Spain
  • 4Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, (CIBER-BBN), Valladolid 47011, Castilla y León, Spain
  • 5Instituto de Investigación en Matemáticas (IMUVA), University of Valladolid, Valladolid 47011, Castilla y León, Spain
  • 6The Dementia Center, Institute of Brain and Vessels Mihara Memorial Hospital, Isehara 372-0006, Gunma, Japan
  • 7Isesaki Clinic, Gunma, Isehara 372-0056, Gunma, Japan
Received: September 5, 2020Accepted: November 8, 2020Published: December 7, 2020
https://doi.org/10.18632/aging.202270

Copyright: © 2020 Shigihara et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Dementia is a progressive cognitive syndrome, with few effective pharmacological treatments that can slow its progress. Hence, non-pharmacological treatments (NPTs) play an important role in improving patient symptoms and quality of life. Designing the optimal personalised NPT strategy relies on objectively and quantitatively predicting the treatment outcome. Magnetoencephalography (MEG) findings can reflect the cognitive status of patients with dementia, and thus potentially predict NPT outcome. In the present study, 16 participants with cognitive impairment underwent NPT for several months. Their cognitive performance was evaluated based on the Mini-Mental State Examination and the Alzheimer's Disease Assessment Scale - Cognitive at the beginning and end of the NPT period, while resting-state brain activity was evaluated using MEG during the NPT period. Our results showed that the spectral properties of MEG signals predicted the changes in cognitive performance scores. High frequency oscillatory intensity at the right superior frontal gyrus medial segment, opercular part of the inferior frontal gyrus, triangular part of the inferior frontal gyrus, post central gyrus, and angular gyrus predicted the changes in cognitive performance scores. Thus, resting-state brain activity may be a powerful tool in designing personalised NPT.