Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026
Elucidating the neuropathological and molecular heterogeneity of amyloid beta and tau in Alzheimer's disease through machine learning and transcriptomic integration.
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Zhao Kanhao, Xie Hua, Jacobs Tovia, Gaggi Naomi L, Fortea Juan, Carlisle Nancy B, Fonzo Gregory A, Pohl Kilian M, Osorio Ricardo S, Zhang Yu, ADNI Study Group and the PREVENT‐AD Research Group
Аннотация
INTRODUCTION: Functional brain network alterations associated with Alzheimer's disease (AD) pathology, including amyloid beta (Aβ) and phosphorylated tau (p-tau), are difficult to interpret due to overlapping aging-associated and non-amyloid biological processes.
METHODS: We analyzed resting-state functional magnetic resonance imaging (fMRI) from 289 older adults classified as Aβ-positive (A+, n = 129) or Aβ-negative (A-, n = 160) based on cerebrospinal fluid biomarkers. A contrastive deep learning framework was used to identify A+-specific network dimensions and predict individual Aβ and p-tau levels.
RESULTS: A+-specific signatures were localized to the right superior temporal and anterior cingulate cortices and linked to attention and memory functions, with transcriptomic enrichment implicating synaptic dysfunction and glial activity. In contrast, signature dimensions shared between A+ and A- individuals involved language-related regions and aging-associated molecular pathways.
CONCLUSION: These findings suggest that contrastive graph learning may help separate amyloid-associated functional network variation from broader background biological variability, providing insight into the heterogeneity of AD-related biomarkers and cognitive dysfunction.
METHODS: We analyzed resting-state functional magnetic resonance imaging (fMRI) from 289 older adults classified as Aβ-positive (A+, n = 129) or Aβ-negative (A-, n = 160) based on cerebrospinal fluid biomarkers. A contrastive deep learning framework was used to identify A+-specific network dimensions and predict individual Aβ and p-tau levels.
RESULTS: A+-specific signatures were localized to the right superior temporal and anterior cingulate cortices and linked to attention and memory functions, with transcriptomic enrichment implicating synaptic dysfunction and glial activity. In contrast, signature dimensions shared between A+ and A- individuals involved language-related regions and aging-associated molecular pathways.
CONCLUSION: These findings suggest that contrastive graph learning may help separate amyloid-associated functional network variation from broader background biological variability, providing insight into the heterogeneity of AD-related biomarkers and cognitive dysfunction.