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DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data
Published on: December 15, 2023
Atypical Alzheimer disease: a multi-axis framework toward defining heterogeneity
Lea T Grinberg1,2, Melissa E Murray3,4
1Department of Neuroscience, Mayo Clinic Florida, Jacksonville, FL, USA. grinberg.lea@mayo.edu.
Abstract:
Alzheimer disease (AD) is defined biologically by the presence of amyloid-β (Aβ) plaques and tau neurofibrillary tangles in the brain; however, these pathologies do not affect all brain regions equally. Typical AD usually presents with memory impairment, whereas atypical forms of AD, including posterior cortical atrophy, logopenic variant primary progressive aphasia, behavioural and dysexecutive AD, and corticobasal syndrome, manifest with prominent non-memory symptoms. Aβ biomarkers usually confirm that AD pathology is present, whereas regional tau, neurodegeneration and dysfunction more closely track the affected network and the presenting symptoms. The atypical variants, which tend to present at a younger age than typical AD, provide human models to study selective vulnerability of neurons and circuits. They also help us to test whether immune-glial, vascular, protein-handling, synaptic or genetic factors shape regional vulnerability and rate of progression beyond total Aβ and tau burden. In this Review, we integrate clinical, neuropathological, imaging, genetic and molecular evidence to describe atypical AD within the broader AD spectrum. We propose a practical multi-axis framework comprising clinical phenotype, AD biological context (AD pathology plus co-pathologies and molecular modifiers), network topography (regional patterns) and tempo (rate of clinical and biomarker progression). This framework could reduce diagnostic mismatches, make cohorts more comparable and support trials that include outcomes tailored to the affected brain networks.
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