Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

Aberrant MEG multi-frequency phase temporal synchronization predicts conversion from mild cognitive impairment-to-Alzheimer's disease

Pusil, Sandra, Dimitriadis, Stavros, lopez, Maria Eugenia, Pereda, Ernesto and Maestu, Fernando 2019. Aberrant MEG multi-frequency phase temporal synchronization predicts conversion from mild cognitive impairment-to-Alzheimer's disease. NeuroImage: Clinical 24 , 101972. 10.1016/j.nicl.2019.101972

[img]
Preview
PDF - Published Version
Available under License Creative Commons Attribution.

Download (2MB) | Preview

Abstract

Many neuroimaging studies focus on a frequency-specific or a multi-frequency network analysis showing that functional brain networks are disrupted in patients with Alzheimer's disease (AD). Although those studies enriched our knowledge of the impact of AD in brain's functionality, our goal is to test the effectiveness of combining neuroimaging with network neuroscience to predict with high accuracy subjects with mild cognitive impairment (MCI) that will convert to AD. In this study, eyes-closed resting-state magnetoencephalography (MEG) recordings from 27 stable MCI (sMCI) and 27 progressive MCI (pMCI) from two scan sessions (baseline and follow-up after approximately 3 years) were projected via beamforming onto an atlas-based set of regions of interest (ROIs). Dynamic functional connectivity networks were constructed independently for the five classical frequency bands while a multivariate phase-based coupling metric was adopted. Thus, computing the distance between the fluctuation of functional strength of every pair of ROIs between the two conditions with dynamic time wrapping (DTW), a large set of features was extracted. A machine learning algorithm revealed 49 DTW-based features in the five frequency bands that can distinguish the sMCI from pMCI with absolute accuracy (100%). Further analysis of the selected links revealed that most of the connected ROIs were part of the default mode network (DMN), the cingulo-opercular (CO), the fronto-parietal and the sensorimotor network. Overall, our dynamic network multi-frequency analysis approach provides an effective framework of constructing a sensitive MEG-based connectome biomarker for the prediction of conversion from MCI to Alzheimer's disease.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Psychology
Cardiff University Brain Research Imaging Centre (CUBRIC)
Publisher: Elsevier
ISSN: 2213-1582
Funders: MRC
Date of First Compliant Deposit: 6 August 2019
Date of Acceptance: 3 August 2019
Last Modified: 18 Sep 2019 11:33
URI: http://orca-mwe.cf.ac.uk/id/eprint/124749

Actions (repository staff only)

Edit Item Edit Item

Downloads

Downloads per month over past year

View more statistics