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Alzheimers Dement (Amst). 2019 Jun 14;11:450-462. doi: 10.1016/j.dadm.2019.04.009. eCollection 2019 Dec.

Biomagnetic biomarkers for dementia: A pilot multicentre study with a recommended methodological framework for magnetoencephalography.

Author information

1
Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.
2
MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK.
3
Department of Psychiatry, University of Cambridge, Cambridge, UK.
4
Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Centre for Biomedical Technology, Madrid, Spain.
5
Department of Industrial Engineering, Instituto Universitario de Neurociencia, Universidad de La Laguna, Tenerife, Spain.
6
Biomedical Research Networking Center in Bioengineering Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain.
7
Department of Experimental Psychology, Faculty of Psychology, Universidad Complutense de Madrid, Madrid, Spain.
8
Oxford Centre for Human Brain Activity, University of Oxford, Oxford, UK.
9
Wellcome Centre for Integrative Neuroscience, University of Oxford, Oxford, UK.
10
Department of Experimental Psychology, University of Oxford, Oxford, UK.

Abstract

Introduction:

An increasing number of studies are using magnetoencephalography (MEG) to study dementia. Here we define a common methodological framework for MEG resting-state acquisition and analysis to facilitate the pooling of data from different sites.

Methods:

Two groups of patients with mild cognitive impairment (MCI, n = 84) and healthy controls (n = 84) were combined from three sites, and site and group differences inspected in terms of power spectra and functional connectivity. Classification accuracy for MCI versus controls was compared across three different types of MEG analyses, and compared with classification based on structural MRI.

Results:

The spectral analyses confirmed frequency-specific differences in patients with MCI, both in power and connectivity patterns, with highest classification accuracy from connectivity. Critically, site acquisition differences did not dominate the results.

Discussion:

This work provides detailed protocols and analyses that are sensitive to cognitive impairment, and that will enable standardized data sharing to facilitate large-scale collaborative projects.

KEYWORDS:

Functional connectivity; Harmonization; Magnetoencephalography; Mild cognitive impairment; Multi-site; Spectral analysis

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