Quantifying Deviations and Asymmetry of Brain Structure and Function in Alzheimer's Disease

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Abstract Submission 

Cui Zhao¹, Yong Liu¹

¹School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China

Cui Zhao  
School of Artificial Intelligence, Beijing University of Posts and Telecommunications
Beijing, China

Yong Liu  
School of Artificial Intelligence, Beijing University of Posts and Telecommunications
Beijing, China

Altered brain structure and function have been implicated in the pathophysiology of Alzheimer's dementia (AD), mild cognitive impairment (MCI), and other neurodegenerative diseases. Identifying neurobiological differences between patients with impaired cognitions and healthy individuals has been a majority of clinical neuroscience for decades. However, recent meta-analyses have raised concerns regarding the replicability and clinical relevance of brain alternations, especially for the stage of MCI. The present study aimed to quantify the upper bounds of univariate effect sizes across neuroimaging modalities and to evaluate the predictive value of the maximum effect variables in MCI progression.

Participants from two independent datasets were included in the present study, including AD, MCI patients, and healthy controls with normal cognition (NC). Univariate statistical effect size, distribution overlapping coefficient, and classification accuracy were calculated for metrics derived from T1w structural imaging, diffusion MRI, resting-state functional MRI, and brain connectome. After determining the maximum effect variable, we further performed the Kaplan Meier analyses and Cox proportional hazards regression analyses to estimate the predictive value of this regional index and its hemispheric asymmetry in the progression of MCI.

A total of 2689 participants from two independent datasets (MCADI and ADNI) were included in the present study, including 682 AD patients, 1067 MCI patients, and 940 NC adults. Using the general linear model, we found the regional gray matter volume (GMV) of the caudal hippocampus (cHipp) exhibited the highest effect size in differentiating AD, MCI, and NC participants (MCADI dataset: partial η^2 = 0.35 [95%CI: 0.32~0.39], false discovery rate [FDR]-corrected P = 1.79×10^-95; ADNI dataset: partial η^2 = 0.24 [95%CI: 0.21~0.27], FDR-corrected P = 2.69×10^-79). Based on the longitudinal follow-up of ADNI, MCI participants were divided into three gro×ups, including stable (sMCI, n = 399), reversion to NC (rMCI, n = 23), and progression to AD (pMCI, n = 164). Among three groups of MCI, significant group differences were observed in the cognitive performance at baseline (FDR-corrected Ps < 0.05). Furthermore, the probability of progression to dementia was much greater in MCI patients with hemispheric asymmetry of cortical atrophy in the cHipp (P = 0.0005). Cox proportional hazards regression analysis revealed that the lateral coefficients of cHipp had the highest Hazard Ratio (HR = 7.79, 95% CI: 1.81~33.55, P = 0.006) than the regional GMV of the bilateral cHipp.

Results of this case-control study suggest that cortical atrophy in the hippocampus was the most significant univariable in differentiating AD, MCI patients, and cognitively healthy individuals in two dependent datasets. Hemispheric asymmetry of hippocampal atrophy exhibited a remarkably significant ability to reveal the longitudinal progression from MCI to dementia, which may serve as a valuable reference for future research on the prediction of MCI progression. Behavioral and socioemotional measures are needed to understand hemispheric asymmetry in AD.

Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) ¹

Aging ²

Multivariate Approaches

Neurophysiology of Imaging Signals

Aging

Cognition

MRI

Multivariate

Univariate