Disrupted core-periphery structure of multimodal brain networks in Alzheimer’s disease
In Alzheimer’s disease (AD), the progressive atrophy leads to aberrant network reconfigurations both at structural and functional levels. In such network reorganization, the core and peripheral nodes appear to be crucial for the prediction of clinical outcome because of their ability to influence la...
Main Authors: | , , , , , , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
The MIT Press
2019-04-01
|
Series: | Network Neuroscience |
Subjects: | |
Online Access: | https://www.mitpressjournals.org/doi/pdf/10.1162/netn_a_00087 |
id |
doaj-520967c0517646798ec86e89db4e07e1 |
---|---|
record_format |
Article |
spelling |
doaj-520967c0517646798ec86e89db4e07e12020-11-24T22:15:26ZengThe MIT PressNetwork Neuroscience2472-17512019-04-013263565210.1162/netn_a_00087netn_a_00087Disrupted core-periphery structure of multimodal brain networks in Alzheimer’s diseaseJeremy Guillon0Mario Chavez1Federico Battiston2Yohan Attal3Valentina La Corte4Michel Thiebaut de Schotten5Bruno Dubois6Denis Schwartz7Olivier Colliot8Fabrizio De Vico Fallani9Institut du Cerveau et de la Moelle Epiniere, ICM, Inserm, U 1127, CNRS, UMR 7225, Sorbonne Universite, Paris, FranceCNRS, UMR 7225, Paris, FranceInria Paris, Aramis Project Team, Paris, FranceMyBrain Technologies, Paris, FranceDepartment of Neurology, Institute of Memory and Alzheimer’s Disease, Assistance Publique - Hopitaux de Paris, Pitié-Salpêtrière Hospital, Paris, FranceInstitut du Cerveau et de la Moelle Epiniere, ICM, Inserm, U 1127, CNRS, UMR 7225, Sorbonne Universite, Paris, FranceInstitut de la Memoire et de la Maladie d’Alzheimer - IM2A, AP-HP, Sorbonne Universite, Paris, FranceInstitut du Cerveau et de la Moelle Epiniere, ICM, Inserm, U 1127, CNRS, UMR 7225, Sorbonne Universite, Ecole Normale Superieure, ENS, Centre MEG-EEG, Paris, FranceInstitut du Cerveau et de la Moelle Epiniere, ICM, Inserm, U 1127, CNRS, UMR 7225, Sorbonne Universite, Paris, FranceInstitut du Cerveau et de la Moelle Epiniere, ICM, Inserm, U 1127, CNRS, UMR 7225, Sorbonne Universite, Paris, FranceIn Alzheimer’s disease (AD), the progressive atrophy leads to aberrant network reconfigurations both at structural and functional levels. In such network reorganization, the core and peripheral nodes appear to be crucial for the prediction of clinical outcome because of their ability to influence large-scale functional integration. However, the role of the different types of brain connectivity in such prediction still remains unclear. Using a multiplex network approach we integrated information from DWI, fMRI, and MEG brain connectivity to extract an enriched description of the core-periphery structure in a group of AD patients and age-matched controls. Globally, the regional coreness—that is, the probability of a region to be in the multiplex core—significantly decreased in AD patients as result of a random disconnection process initiated by the neurodegeneration. Locally, the most impacted areas were in the core of the network—including temporal, parietal, and occipital areas—while we reported compensatory increments for the peripheral regions in the sensorimotor system. Furthermore, these network changes significantly predicted the cognitive and memory impairment of patients. Taken together these results indicate that a more accurate description of neurodegenerative diseases can be obtained from the multimodal integration of neuroimaging-derived network data. Alzheimer’s disease includes a progressive destruction of axonal pathways leading to global network changes. While these changes affect both the anatomy and the function of the brain, a joint characterization of the impact on the nodes of the network is still lacking. By integrating information from multiple neuroimaging data, within a modern complex systems framework, we show that the nodes constituting the core of the brain network are the most impacted by the disconnection process. Furthermore, these network alterations significantly predict the cognitive and memory impairment of patients and represent potential biomarkers of disease progression. We posit that a more accurate description of neurodegenerative diseases can be obtained by analyzing and modeling brain networks derived from multimodal neuroimaging data.https://www.mitpressjournals.org/doi/pdf/10.1162/netn_a_00087Neurodegenerative diseasesBrain connectivityMultilayer network theoryMEGDWIfMRI |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jeremy Guillon Mario Chavez Federico Battiston Yohan Attal Valentina La Corte Michel Thiebaut de Schotten Bruno Dubois Denis Schwartz Olivier Colliot Fabrizio De Vico Fallani |
spellingShingle |
Jeremy Guillon Mario Chavez Federico Battiston Yohan Attal Valentina La Corte Michel Thiebaut de Schotten Bruno Dubois Denis Schwartz Olivier Colliot Fabrizio De Vico Fallani Disrupted core-periphery structure of multimodal brain networks in Alzheimer’s disease Network Neuroscience Neurodegenerative diseases Brain connectivity Multilayer network theory MEG DWI fMRI |
author_facet |
Jeremy Guillon Mario Chavez Federico Battiston Yohan Attal Valentina La Corte Michel Thiebaut de Schotten Bruno Dubois Denis Schwartz Olivier Colliot Fabrizio De Vico Fallani |
author_sort |
Jeremy Guillon |
title |
Disrupted core-periphery structure of multimodal brain networks in Alzheimer’s disease |
title_short |
Disrupted core-periphery structure of multimodal brain networks in Alzheimer’s disease |
title_full |
Disrupted core-periphery structure of multimodal brain networks in Alzheimer’s disease |
title_fullStr |
Disrupted core-periphery structure of multimodal brain networks in Alzheimer’s disease |
title_full_unstemmed |
Disrupted core-periphery structure of multimodal brain networks in Alzheimer’s disease |
title_sort |
disrupted core-periphery structure of multimodal brain networks in alzheimer’s disease |
publisher |
The MIT Press |
series |
Network Neuroscience |
issn |
2472-1751 |
publishDate |
2019-04-01 |
description |
In Alzheimer’s disease (AD), the progressive atrophy leads to aberrant network reconfigurations both at structural and functional levels. In such network reorganization, the core and peripheral nodes appear to be crucial for the prediction of clinical outcome because of their ability to influence large-scale functional integration. However, the role of the different types of brain connectivity in such prediction still remains unclear. Using a multiplex network approach we integrated information from DWI, fMRI, and MEG brain connectivity to extract an enriched description of the core-periphery structure in a group of AD patients and age-matched controls. Globally, the regional coreness—that is, the probability of a region to be in the multiplex core—significantly decreased in AD patients as result of a random disconnection process initiated by the neurodegeneration. Locally, the most impacted areas were in the core of the network—including temporal, parietal, and occipital areas—while we reported compensatory increments for the peripheral regions in the sensorimotor system. Furthermore, these network changes significantly predicted the cognitive and memory impairment of patients. Taken together these results indicate that a more accurate description of neurodegenerative diseases can be obtained from the multimodal integration of neuroimaging-derived network data. Alzheimer’s disease includes a progressive destruction of axonal pathways leading to global network changes. While these changes affect both the anatomy and the function of the brain, a joint characterization of the impact on the nodes of the network is still lacking. By integrating information from multiple neuroimaging data, within a modern complex systems framework, we show that the nodes constituting the core of the brain network are the most impacted by the disconnection process. Furthermore, these network alterations significantly predict the cognitive and memory impairment of patients and represent potential biomarkers of disease progression. We posit that a more accurate description of neurodegenerative diseases can be obtained by analyzing and modeling brain networks derived from multimodal neuroimaging data. |
topic |
Neurodegenerative diseases Brain connectivity Multilayer network theory MEG DWI fMRI |
url |
https://www.mitpressjournals.org/doi/pdf/10.1162/netn_a_00087 |
work_keys_str_mv |
AT jeremyguillon disruptedcoreperipherystructureofmultimodalbrainnetworksinalzheimersdisease AT mariochavez disruptedcoreperipherystructureofmultimodalbrainnetworksinalzheimersdisease AT federicobattiston disruptedcoreperipherystructureofmultimodalbrainnetworksinalzheimersdisease AT yohanattal disruptedcoreperipherystructureofmultimodalbrainnetworksinalzheimersdisease AT valentinalacorte disruptedcoreperipherystructureofmultimodalbrainnetworksinalzheimersdisease AT michelthiebautdeschotten disruptedcoreperipherystructureofmultimodalbrainnetworksinalzheimersdisease AT brunodubois disruptedcoreperipherystructureofmultimodalbrainnetworksinalzheimersdisease AT denisschwartz disruptedcoreperipherystructureofmultimodalbrainnetworksinalzheimersdisease AT oliviercolliot disruptedcoreperipherystructureofmultimodalbrainnetworksinalzheimersdisease AT fabriziodevicofallani disruptedcoreperipherystructureofmultimodalbrainnetworksinalzheimersdisease |
_version_ |
1725794304959447040 |