Machine learning-derived Alzheimer's disease dimensions: neuroanatomical, cognitive, clinical, functional, and genetic risk heterogeneity signatures
- 1Washington University in St. Louis
Abstract
Alzheimers disease (AD) is characterized by substantial clinical and biological heterogeneity, with diverse neuroanatomical, cognitive, clinical, functional, and genetic risk profiles that are poorly captured by current diagnostic frameworks. Identifying reproducible disease dimensions is critical for understanding divergent pathophysiological mechanisms and designing future stratified therapeutic interventions. Herein, we applied a semi-supervised machine learning clustering framework and robust reproducibility validation strategies to structural MRI data from 1,757 participants, comprising 1,108 cognitively normal controls (CN) and 649 patients with mild cognitive impairment (MCI) or dementia due to AD. We identified two MRI-based neuroanatomical dimensions of AD: Dimension 1 (n = 393; prevalence [~] 61%) and Dimension 2 (n = 256; prevalence [~] 39%). Dimension 1 exhibited widespread cortical and subcortical atrophy, including involvement of the hippocampus, amygdala, parahippocampal, temporal, frontal, and occipital regions, while Dimension 2 showed relatively preserved brain patterns. The two dimensions were not statistically different in demographic characteristics, including age, sex, race, and education (all p > 0.05). Compared to Dimension 2, Dimension 1 had more severe cognitive and clinical impairment, greater functional impairment, elevated APOE {varepsilon}4 carrier burden, and higher polygenic risk for AD (p < 0.05). Analysis of individual-level summary neuroanatomical signature expression scores, quantifying each subjects continuous position along two disease dimensions, revealed that Dimension 1 signature expression correlated strongly with cognitive impairment, clinical severity, functional impairment, and genetic risk, while Dimensional 2 signature expression showed relatively weaker associations. These results reveal that AD encompasses at least two biologically distinct dimensions identifiable from structural MRI profiles and their distinct associations with cognitive, clinical, functional, and genetic profiles. These findings may aid in AD patient stratification for designing targeted therapeutic approaches in the future.
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BibTeX
@misc{le2026machine,
title = {{Machine learning-derived Alzheimer's disease dimensions: neuroanatomical, cognitive, clinical, functional, and genetic risk heterogeneity signatures}},
author = {H. N. Le and S. Ishrat and B. Yaghooti and T. Murad and R. Sapkota and D. S. Thakuri and D. F. Wong and C. Cruchaga and A. D. N. Initiative and G. B. Chand},
howpublished = {medRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.02.26361668},
url = {https://www.medrxiv.org/content/10.64898/2026.09.02.26361668v1},
note = {CC BY-NC},
}
RIS
TY - UNPB
TI - Machine learning-derived Alzheimer's disease dimensions: neuroanatomical, cognitive, clinical, functional, and genetic risk heterogeneity signatures
AU - H. N. Le
AU - S. Ishrat
AU - B. Yaghooti
AU - T. Murad
AU - R. Sapkota
AU - D. S. Thakuri
AU - D. F. Wong
AU - C. Cruchaga
AU - A. D. N. Initiative
AU - G. B. Chand
PY - 2026
DA - 2026/09/02
PB - medRxiv
DO - 10.64898/2026.09.02.26361668
UR - https://www.medrxiv.org/content/10.64898/2026.09.02.26361668v1
AB - Alzheimers disease (AD) is characterized by substantial clinical and biological heterogeneity, with diverse neuroanatomical, cognitive, clinical, functional, and genetic risk profiles that are poorly captured by current diagnostic frameworks. Identifying reproducible disease dimensions is critical for understanding divergent pathophysiological mechanisms and designing future stratified therapeutic interventions. Herein, we applied a semi-supervised machine learning clustering framework and robust reproducibility validation strategies to structural MRI data from 1,757 participants, comprising 1,108 cognitively normal controls (CN) and 649 patients with mild cognitive impairment (MCI) or dementia due to AD. We identified two MRI-based neuroanatomical dimensions of AD: Dimension 1 (n = 393; prevalence [~] 61%) and Dimension 2 (n = 256; prevalence [~] 39%). Dimension 1 exhibited widespread cortical and subcortical atrophy, including involvement of the hippocampus, amygdala, parahippocampal, temporal, frontal, and occipital regions, while Dimension 2 showed relatively preserved brain patterns. The two dimensions were not statistically different in demographic characteristics, including age, sex, race, and education (all p > 0.05). Compared to Dimension 2, Dimension 1 had more severe cognitive and clinical impairment, greater functional impairment, elevated APOE {varepsilon}4 carrier burden, and higher polygenic risk for AD (p < 0.05). Analysis of individual-level summary neuroanatomical signature expression scores, quantifying each subjects continuous position along two disease dimensions, revealed that Dimension 1 signature expression correlated strongly with cognitive impairment, clinical severity, functional impairment, and genetic risk, while Dimensional 2 signature expression showed relatively weaker associations. These results reveal that AD encompasses at least two biologically distinct dimensions identifiable from structural MRI profiles and their distinct associations with cognitive, clinical, functional, and genetic profiles. These findings may aid in AD patient stratification for designing targeted therapeutic approaches in the future.
ER -