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Netw Neurosci. 2019 May 1;3(2):635-652. doi: 10.1162/netn_a_00087. eCollection 2019.

Disrupted core-periphery structure of multimodal brain networks in Alzheimer's disease.

Author information

1
Institut du Cerveau et de la Moelle Epiniere, ICM, Inserm, U 1127, CNRS, UMR 7225, Sorbonne Universite, Paris, France.
2
CNRS, UMR 7225, Paris, France.
3
Inria Paris, Aramis Project Team, Paris, France.
4
MyBrain Technologies, Paris, France.
5
Department of Neurology, Institute of Memory and Alzheimer's Disease, Assistance Publique - Hopitaux de Paris, Pitié-Salpêtrière Hospital, Paris, France.
6
Institut de la Memoire et de la Maladie d'Alzheimer - IM2A, AP-HP, Sorbonne Universite, Paris, France.
7
Institut du Cerveau et de la Moelle Epiniere, ICM, Inserm, U 1127, CNRS, UMR 7225, Sorbonne Universite, Ecole Normale Superieure, ENS, Centre MEG-EEG, Paris, France.

Abstract

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.

KEYWORDS:

Brain connectivity; DWI; MEG; Multilayer network theory; Neurodegenerative diseases; fMRI

Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

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