Uncovering neural substrates across Alzheimer's disease stages using contrastive variational autoencoder

Alzheimer's disease is the most common major neurocognitive disorder. Although currently, no cure exists, understanding the neurobiological substrate underlying Alzheimer's disease progression will facilitate early diagnosis and treatment, slow disease progression, and improve prognosis. I...

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Published in:Cerebral cortex (New York, N.Y. 1991) Vol. 34; no. 10
Main Authors: Tang, Yan, Yang, Chao, Wang, Yuqi, Zhang, Yunhao, Xin, Jiang, Zhang, Hao, Xie, Hua
Format: Journal Article
Language:English
Published: United States 03.10.2024
Subjects:
Aged
Aged, 80 and over
Alzheimer Disease - diagnostic imaging
Alzheimer Disease - pathology
Biomarkers
Brain - diagnostic imaging
Brain - pathology
Cognitive Dysfunction - diagnostic imaging
Cognitive Dysfunction - pathology
Cognitive Dysfunction - physiopathology
Disease Progression
Female
Humans
Magnetic Resonance Imaging - methods
Male
Middle Aged
Neurofilament Proteins - metabolism
brain morphology
structural magnetic resonance imaging
neurofilament light chain
Alzheimer’s disease
contrastive variational autoencoder
ISSN:1460-2199, 1460-2199
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Summary:Alzheimer's disease is the most common major neurocognitive disorder. Although currently, no cure exists, understanding the neurobiological substrate underlying Alzheimer's disease progression will facilitate early diagnosis and treatment, slow disease progression, and improve prognosis. In this study, we aimed to understand the morphological changes underlying Alzheimer's disease progression using structural magnetic resonance imaging data from cognitively normal individuals, individuals with mild cognitive impairment, and Alzheimer's disease via a contrastive variational autoencoder model. We used contrastive variational autoencoder to generate synthetic data to boost the downstream classification performance. Due to the ability to parse out the nonclinical factors such as age and gender, contrastive variational autoencoder facilitated a purer comparison between different Alzheimer's disease stages to identify the pathological changes specific to Alzheimer's disease progression. We showed that brain morphological changes across Alzheimer's disease stages were significantly associated with individuals' neurofilament light chain concentration, a potential biomarker for Alzheimer's disease, highlighting the biological plausibility of our results.
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ISSN:1460-2199
1460-2199
DOI:10.1093/cercor/bhae393