Voxel-based morphometry and a deep learning model for the diagnosis of early Alzheimer’s disease based on cerebral gray matter changes

Abstract

This study aimed to analyse cerebral grey matter changes in mild cognitive impairment (MCI) using voxel-based morphometry and to diagnose early Alzheimer's disease using deep learning methods based on convolutional neural networks (CNNs) evaluating these changes. Participants (111 MCI, 73 normal cognition) underwent 3-T structural magnetic resonance imaging. The obtained images were assessed using voxel-based morphometry, including extraction of cerebral grey matter, analyses of statistical differences, and correlation analyses between cerebral grey matter and clinical cognitive scores in MCI. The CNN-based deep learning method was used to extract features of cerebral grey matter images. Compared to subjects with normal cognition, participants with MCI had grey matter atrophy mainly in the entorhinal cortex, frontal cortex, and bilateral frontotemporal lobes (p < 0.0001). This atrophy was significantly correlated with the decline in cognitive scores (p < 0.01). The accuracy, sensitivity, and specificity of the CNN model for identifying participants with MCI were 80.9%, 88.9%, and 75%, respectively. The area under the curve of the model was 0.891. These findings demonstrate that research based on brain morphology can provide an effective way for the clinical, non-invasive, objective evaluation and identification of early Alzheimer's disease.

Adolescent neurodevelopment and psychopathology: The interplay between adversity exposure and genetic risk for accelerated brain ageing

“…Stronger genetic effects are though reportedly observed on structural (relative to functional) brain indices, while structure-function coupling in connectomic features is also under substantial genetic modulation ( Arnatkeviciute et al, 2021a , Arnatkeviciute et al, 2021b , Baum et al, 2020 , Gu et al, 2021 ). Consequently, investigations of synchronised structure-function development would augment the findings herein reported, particularly given the robust predictive power of both white and grey matter indices for AD and MDD ( Huang et al, 2022 , Lu et al, 2022 ), as well as psychopathology risk ( Aggarwal et al, 2022 , Fenchel et al, 2022 , Lalousis et al, 2022 , Park et al, 2022 , Patel et al, 2022 , Zhao et al, 2021a , Zhao et al, 2021b ).…”

Psychological Resilience in Adolescence as a function of Genetic Risk for Major Depressive Disorder and Alzheimer’s Disease

“…Third, stronger genetic effects are reportedly observed on structural (relative to functional) brain indices, while structure-function coupling in connectomic features is also under substantial genetic modulation (85). Consequently, investigations of synchronised structure-function development would augment the findings herein reported, particularly given the robust predictive power of both white and gray matter indices for AD and MDD, as well as psychological resilience (86)(87)(88).…”

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