Visualizing modules of coordinated structural brain atrophy during the course of conversion to Alzheimer's disease by applying methodology from gene co-expression analysis.

Kenichiro Sato,Ryoko Ihara,Michio Senda,Takeshi Ikeuchi,Kazushi Suzuki,Hiroyuki Arai,Kenji Ishii,Atsushi Iwata,Tatsuo Mano,Takeshi Iwatsubo,Kengo Ito,Tatsushi Toda,Ryozo Kuwano,Hiroshi Matsuda

doi:10.1016/j.nicl.2019.101957

Abstract

ObjectiveWe aimed to identify modularized structural atrophy of brain regions with a high degree of connectivity and its longitudinal changes associated with the progression of Alzheimer's disease (AD) using weighted gene co-expression network analysis (WGCNA), which is an unsupervised hierarchical clustering method originally used in genetic analysis.MethodsWe included participants with late mild cognitive impairment (MCI) at baseline from the Japanese Alzheimer's Disease Neuroimaging Initiative (J-ADNI) study. We imputed normalized and Z-transformed structural volume or cortical thickness data of 164 parcellated brain regions/structures based on the calculations of the FreeSurfer software. We applied the WGCNA to extract modules with highly interconnected structural atrophic patterns and examined the correlation between the identified modules and clinical AD progression.ResultsWe included 204 participants from the baseline dataset, and performed a follow-up with 100 in the 36-month dataset of MCI cohort participants from the J-ADNI. In the univariate correlation or variable importance analysis, baseline atrophy in temporal lobe regions/structures significantly predicted clinical AD progression. In the WGCNA consensus analysis, co-atrophy modules associated with MCI conversion were first distributed in the temporal lobe and subsequently extended to adjacent parietal cortical regions in the following 36 months.ConclusionsWe identified coordinated modules of brain atrophy and demonstrated their longitudinal extension along with the clinical course of AD progression using WGCNA, which showed a good correspondence with previous pathological studies of the tau propagation theory. Our results suggest the potential applicability of this methodology, originating from genetic analyses, for the surrogate visualization of the underlying pathological progression in neurodegenerative diseases not limited to AD.

Highlights

Regional brain atrophy indicates a decline in its corresponding function; the structural features of brain atrophy associated with the disease course are important hallmarks to accurately predict the conversion of mild cognitive impairment (MCI) to Alzheimer'sNeuroImage: Clinical 24 (2019) 101957 disease (AD) and the subsequent disease progression
We aimed to identify intra-modular brain regions/structures with significantly similar structural changes across the samples, agnostic of anatomical/functional knowledge, by applying this method to the structural brain Magnetic resonance imaging (MRI) data of MCI participants from the Japanese Alzheimer's Disease Neuroimaging Initiative (J-ADNI) (Iwatsubo et al, 2018; Iwata et al, 2018), which is a multi-center prospective observational study for the progression of MCI and mild Alzheimer's disease (AD) in the Japanese population
We evaluated correlations between the modules and clinical prognostic metrics associated with MCI conversion and ADAS-cog13 progression, over a longitudinal timecourse

Summary

Introduction

Regional brain atrophy indicates a decline in its corresponding function; the structural features of brain atrophy associated with the disease course are important hallmarks to accurately predict the conversion of mild cognitive impairment (MCI) to Alzheimer'sNeuroImage: Clinical 24 (2019) 101957 disease (AD) and the subsequent disease progression. With regard to AD/MCI, structural and functional network analysis has revealed the reduced connectivity metrics between temporal, parietal, and frontal lobes in AD (Yao et al, 2010; Griffa et al, 2013; Zhu et al, 2014; Prescott et al, 2016; Filippi et al, 2018). These studies report connectivity metrics between individual nodes, they have not fully considered modularization of multiple inter-correlated nodes. By modularizing highly interconnected regions/structures and measuring their association with the longitudinal clinical prognosis, it may be possible to visualize the underlying pathological propagation of AD indirectly

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