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

the BioFIND Working Group

doi:10.1016/j.dadm.2019.04.009

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

the BioFIND Working Group

Psychology

Research output: Contribution to journal › Article › peer-review

16 Citations (Scopus)

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.

Original language	English
Pages (from-to)	450-462
Number of pages	13
Journal	Alzheimer's and Dementia: Diagnosis, Assessment and Disease Monitoring
Volume	11
DOIs	https://doi.org/10.1016/j.dadm.2019.04.009
Publication status	Published - Dec 2019

Bibliographical note

Funding Information:
The authors would like to thank all the BioFIND members for their input, including Arjan Hillebrand, Alida Gouw, and Jyrki Makela. They thank Christophe Phillips for his help implementing MKL within the PRONTO toolbox. The study was funded by JPND “Harmonisation and Alignment in Brain Imaging Methods for Neurodegeneration” (MR/P502017/1). R.N.H. was additionally supported by MRC grant SUAG/010-RG91365; L.E.H. and J.B.R. were additionally supported by MRC grant SUAG/004-RG91365, the Cambridge NIHR Biomedical Research Centre and Wellcome Trust (103838) and the Dementias Platform UK. Cam-CAN data collection was supported by the Biotechnology and Biological Sciences Research Council (BB/H008217/1). A.J.Q. M.W.W. and A.C.N. are additionally supported by a NIHR Oxford Health Biomedical Research Centre and Wellcome Investigator Awards to MWW (106183/Z/14/Z) and ACN (104571/Z/14/Z), The Wellcome Centre for Integrative Neuroimaging is supported by core funding from the Wellcome Trust (203139/Z/16/Z).

Funding Information:
The authors would like to thank all the BioFIND members for their input, including Arjan Hillebrand, Alida Gouw, and Jyrki Makela. They thank Christophe Phillips for his help implementing MKL within the PRONTO toolbox. The study was funded by JPND “Harmonisation and Alignment in Brain Imaging Methods for Neurodegeneration” ( MR/P502017/1 ). R.N.H. was additionally supported by MRC grant SUAG/010-RG91365 ; L.E.H. and J.B.R. were additionally supported by MRC grant SUAG/004-RG91365 , the Cambridge NIHR Biomedical Research Centre and Wellcome Trust ( 103838 ) and the Dementias Platform UK. Cam-CAN data collection was supported by the Biotechnology and Biological Sciences Research Council ( BB/H008217/1 ). A.J.Q., M.W.W., and A.C.N. are additionally supported by a NIHR Oxford Health Biomedical Research Centre and Wellcome Investigator Awards to MWW (106183/Z/14/Z) and ACN (104571/Z/14/Z), The Wellcome Centre for Integrative Neuroimaging is supported by core funding from the Wellcome Trust ( 203139/Z/16/Z ).

Publisher Copyright:
© 2019 The Authors

Keywords

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

ASJC Scopus subject areas

Clinical Neurology
Psychiatry and Mental health

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1016/j.dadm.2019.04.009

Cite this

@article{7f83fcf6a97a4fce986e30f2f9e028f2,

title = "Biomagnetic biomarkers for dementia: A pilot multicentre study with a recommended methodological framework for magnetoencephalography",

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",

author = "{the BioFIND Working Group} and Hughes, {Laura E.} and Henson, {Richard N.} and Ernesto Pereda and Ricardo Bru{\~n}a and David L{\'o}pez-Sanz and Quinn, {Andrew J.} and Woolrich, {Mark W.} and Nobre, {Anna C.} and Rowe, {James B.} and Fernando Maest{\'u}",

note = "Funding Information: The authors would like to thank all the BioFIND members for their input, including Arjan Hillebrand, Alida Gouw, and Jyrki Makela. They thank Christophe Phillips for his help implementing MKL within the PRONTO toolbox. The study was funded by JPND “Harmonisation and Alignment in Brain Imaging Methods for Neurodegeneration” (MR/P502017/1). R.N.H. was additionally supported by MRC grant SUAG/010-RG91365; L.E.H. and J.B.R. were additionally supported by MRC grant SUAG/004-RG91365, the Cambridge NIHR Biomedical Research Centre and Wellcome Trust (103838) and the Dementias Platform UK. Cam-CAN data collection was supported by the Biotechnology and Biological Sciences Research Council (BB/H008217/1). A.J.Q. M.W.W. and A.C.N. are additionally supported by a NIHR Oxford Health Biomedical Research Centre and Wellcome Investigator Awards to MWW (106183/Z/14/Z) and ACN (104571/Z/14/Z), The Wellcome Centre for Integrative Neuroimaging is supported by core funding from the Wellcome Trust (203139/Z/16/Z). Funding Information: The authors would like to thank all the BioFIND members for their input, including Arjan Hillebrand, Alida Gouw, and Jyrki Makela. They thank Christophe Phillips for his help implementing MKL within the PRONTO toolbox. The study was funded by JPND “Harmonisation and Alignment in Brain Imaging Methods for Neurodegeneration” ( MR/P502017/1 ). R.N.H. was additionally supported by MRC grant SUAG/010-RG91365 ; L.E.H. and J.B.R. were additionally supported by MRC grant SUAG/004-RG91365 , the Cambridge NIHR Biomedical Research Centre and Wellcome Trust ( 103838 ) and the Dementias Platform UK. Cam-CAN data collection was supported by the Biotechnology and Biological Sciences Research Council ( BB/H008217/1 ). A.J.Q., M.W.W., and A.C.N. are additionally supported by a NIHR Oxford Health Biomedical Research Centre and Wellcome Investigator Awards to MWW (106183/Z/14/Z) and ACN (104571/Z/14/Z), The Wellcome Centre for Integrative Neuroimaging is supported by core funding from the Wellcome Trust ( 203139/Z/16/Z ). Publisher Copyright: {\textcopyright} 2019 The Authors",

year = "2019",

month = dec,

doi = "10.1016/j.dadm.2019.04.009",

language = "English",

volume = "11",

pages = "450--462",

journal = "Alzheimer's and Dementia: Diagnosis, Assessment and Disease Monitoring",

issn = "2352-8729",

publisher = "Wiley Open Access",

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T1 - Biomagnetic biomarkers for dementia

T2 - A pilot multicentre study with a recommended methodological framework for magnetoencephalography

AU - the BioFIND Working Group

AU - Hughes, Laura E.

AU - Henson, Richard N.

AU - Pereda, Ernesto

AU - Bruña, Ricardo

AU - López-Sanz, David

AU - Quinn, Andrew J.

AU - Woolrich, Mark W.

AU - Nobre, Anna C.

AU - Rowe, James B.

AU - Maestú, Fernando

N1 - Funding Information: The authors would like to thank all the BioFIND members for their input, including Arjan Hillebrand, Alida Gouw, and Jyrki Makela. They thank Christophe Phillips for his help implementing MKL within the PRONTO toolbox. The study was funded by JPND “Harmonisation and Alignment in Brain Imaging Methods for Neurodegeneration” (MR/P502017/1). R.N.H. was additionally supported by MRC grant SUAG/010-RG91365; L.E.H. and J.B.R. were additionally supported by MRC grant SUAG/004-RG91365, the Cambridge NIHR Biomedical Research Centre and Wellcome Trust (103838) and the Dementias Platform UK. Cam-CAN data collection was supported by the Biotechnology and Biological Sciences Research Council (BB/H008217/1). A.J.Q. M.W.W. and A.C.N. are additionally supported by a NIHR Oxford Health Biomedical Research Centre and Wellcome Investigator Awards to MWW (106183/Z/14/Z) and ACN (104571/Z/14/Z), The Wellcome Centre for Integrative Neuroimaging is supported by core funding from the Wellcome Trust (203139/Z/16/Z). Funding Information: The authors would like to thank all the BioFIND members for their input, including Arjan Hillebrand, Alida Gouw, and Jyrki Makela. They thank Christophe Phillips for his help implementing MKL within the PRONTO toolbox. The study was funded by JPND “Harmonisation and Alignment in Brain Imaging Methods for Neurodegeneration” ( MR/P502017/1 ). R.N.H. was additionally supported by MRC grant SUAG/010-RG91365 ; L.E.H. and J.B.R. were additionally supported by MRC grant SUAG/004-RG91365 , the Cambridge NIHR Biomedical Research Centre and Wellcome Trust ( 103838 ) and the Dementias Platform UK. Cam-CAN data collection was supported by the Biotechnology and Biological Sciences Research Council ( BB/H008217/1 ). A.J.Q., M.W.W., and A.C.N. are additionally supported by a NIHR Oxford Health Biomedical Research Centre and Wellcome Investigator Awards to MWW (106183/Z/14/Z) and ACN (104571/Z/14/Z), The Wellcome Centre for Integrative Neuroimaging is supported by core funding from the Wellcome Trust ( 203139/Z/16/Z ). Publisher Copyright: © 2019 The Authors

PY - 2019/12

Y1 - 2019/12

N2 - 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.

AB - 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.

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KW - Harmonization

KW - Magnetoencephalography

KW - Mild cognitive impairment

KW - Multi-site

KW - Spectral analysis

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DO - 10.1016/j.dadm.2019.04.009

M3 - Article

AN - SCOPUS:85067188976

SN - 2352-8729

VL - 11

SP - 450

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JO - Alzheimer's and Dementia: Diagnosis, Assessment and Disease Monitoring

JF - Alzheimer's and Dementia: Diagnosis, Assessment and Disease Monitoring

ER -

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

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

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Cite this