ArticleNeuroImage. Clinical2018
Profiling heterogeneity of Alzheimer's disease using white-matter impairment factors.
Article in NeuroImage. Clinical, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Major depressive disorder on a neuromorphic continuum.Nature communications · 2025Article
- Sparse Deep Neural Network for Encoding and Decoding the Structural Connectome.IEEE journal of translational engineering in health and medicine · 2024Article
- An eXplainability Artificial Intelligence approach to brain connectivity in Alzheimer's disease.Frontiers in aging neuroscience · 2023Article
- Fecal Bile Acids and Neutral Sterols Are Associated with Latent Microbial Subgroups in the Human Gut.Metabolites · 2022Article
- Associations between habitual diet, metabolic disease, and the gut microbiota using latent Dirichlet allocation.Microbiome · 2021Article
- Disentangling Heterogeneity in Alzheimer's Disease and Related Dementias Using Data-Driven Methods.Biological psychiatry · 2020Review
- White Matter Microstructural Damage as an Early Sign of Subjective Cognitive Decline.Frontiers in aging neuroscience · 2019Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
The clinical presentation of Alzheimer's disease (AD) is not unitary as heterogeneity exists in the disease's clinical and anatomical characteristics. MRI studies have revealed that heterogeneous gray matter atrophy patterns are associated with specific traits of cognitive decline. Although white matter (WM) impairment also contributes to AD pathology, its heterogeneity remains unclear. The Latent Dirichlet Allocation (LDA) method is a suitable framework to study heterogeneity and allows to identify latent impairment factors of AD instead of simply mapping an overall disease effect. By exploring whole brain WM skeleton images by using LDA, three latent factors were revealed in AD: a temporal-frontal impairment factor (temporal and frontal lobes, especially hippocampus and para-hippocampus), a parietal factor (parietal lobe, especially precuneus), and a long fibre bundle factor (corpus callosum and superior longitudinal fasciculus). As revealed by longitudinal analysis, the latent factors have distinct impact on cognitive decline: for executive function (EF), the temporal-frontal factor was more strongly associated with baseline EF compared with the parietal factor, while the long-fibre bundle factor was most associated with decline rate of EF; for memory, the three factors showed almost equal effect on the baseline memory and decline rate. For each participant, LDA estimates his/her composition profile of latent impairment factors, which indicates disease subtype. We also found that the APOE genotype affects the AD subtype. Specifically, APOE ε4 was more associated with the long fibre bundle factor and APOE ε2 was more associated with temporal-frontal factor. By investigating heterogeneity and subtypes of AD through white matter impairment factors, our study could facilitate precision medicine.
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