Predicting Alzheimer’s disease severity by means of TMS–EEG coregistration

Bagattini, C., Mutanen, T. P., Fracassi, C., Manenti, R., Cotelli, M., Ilmoniemi, R. J., Miniussi, C. and Bortoletto, M. (2019) Predicting Alzheimer’s disease severity by means of TMS–EEG coregistration. Neurobiology of Aging, 80, pp. 38-45. (doi: 10.1016/j.neurobiolaging.2019.04.008) (PMID:31077959)

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Abstract

Clinical manifestations of Alzheimer's disease (AD) are associated with a breakdown in large-scale communication, such that AD may be considered as a “disconnection syndrome.” An established method to test effective connectivity is the combination of transcranial magnetic stimulation with electroencephalography (TMS–EEG) because the TMS-induced cortical response propagates to distant anatomically connected regions. To investigate whether prefrontal connectivity alterations may predict disease severity, we explored the relationship of dorsolateral prefrontal cortex connectivity (derived from TMS–EEG) with cognitive decline (measured with Mini Mental State Examination and a face–name association memory task) in 26 patients with AD. The amplitude of TMS–EEG evoked component P30, which was found to be generated in the right superior parietal cortex, predicted Mini Mental State Examination and face–name memory scores: higher P30 amplitudes predicted poorer cognitive and memory performances. The present results indicate that advancing disease severity might be associated with effective connectivity increase involving long-distance frontoparietal connections, which might represent a maladaptive pathogenic mechanism reflecting a damaged excitatory–inhibitory balance between anterior and posterior regions.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Mutanen, Mr Tuomas
Authors: Bagattini, C., Mutanen, T. P., Fracassi, C., Manenti, R., Cotelli, M., Ilmoniemi, R. J., Miniussi, C., and Bortoletto, M.
College/School:College of Medical Veterinary and Life Sciences > Institute of Neuroscience and Psychology
Journal Name:Neurobiology of Aging
Publisher:Elsevier
ISSN:0197-4580
ISSN (Online):1558-1497
Published Online:13 April 2019
Copyright Holders:Copyright © 2019 Elsevier Inc.
First Published:First published in Neurobiology of Aging 80:38-45
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher

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