Sanchez-Bornot, Jose M., Lopez, Maria E., Bruña, Ricardo, Maestu, Fernando, Youssofzadeh, Vahab, Yang, Su ORCID: https://orcid.org/0000-0002-6618-7483, McLean, Paula L., Prasad, Girijesh and Wong-Lin, KongFatt (2019) Cluster-permutation statistical analysis for high-dimensional brain-wide functional connectivity mapping. biorxiv.org. (Submitted)

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Abstract

Brain functional connectivity (FC) analyses based on magnetoencephalographic (MEG) signals have yet to exploit the intrinsic high-dimensional information. Typically, these analyses are constrained to regions of interest to avoid the curse of dimensionality, which leads to conservative hypothesis testing. We removed such constraint by extending cluster-permutation statistics for high-dimensional MEG-FC analyses. We demonstrated the feasibility of this approach by identifying MEG-FC resting-state changes in mild cognitive impairment (MCI), a prodromal stage of Alzheimer’s disease. We found dense clusters of increased connectivity strength in MCI compared to healthy controls (hypersynchronization), in delta (1-4 Hz) and higher-theta (6-8 Hz) bands oscillations. These clusters mainly consisted of interactions between occipitofrontal and occipitotemporal regions in the left hemisphere and could potentially be used as neuromarkers of early progression in Alzheimer’s disease. Our novel approach can be used to generate high-resolution statistical FC maps for neuroimaging studies in general.

Item Type:	Article
Identifier:	10.1101/849554
Subjects:	Computing Medicine and health
Depositing User:	Su Yang
Date Deposited:	07 Jun 2021 13:21
Last Modified:	04 Nov 2024 11:31
URI:	https://repository.uwl.ac.uk/id/eprint/7941

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