Biology subjects

Poulakis, K.

Publications and source records attributed to Poulakis, K..

2 recordsLinked to original sources

bioRxiv2025

Translating the Post-Mortem Brain Multi-Omics Molecular Taxonomy of Alzheimer's Dementia to Living Humans

Alzheimers disease (AD) dementia is characterized by significant molecular and phenotypic heterogeneity, which confounds its mechanistic understanding, diagnosis, and effective treatment. In this study, we harness the most comprehensive dataset of paired ante-mortem blood omics, clinical, psychological, and post-mortem brain multi-omics data and neuroimaging to extensively characterize and translate the molecular taxonomy of AD dementia to living individuals. First, utilizing a comprehensive integration of eight complementary molecular layers from brain multi-omics data (N = 1,189), we identified three distinct molecular AD dementia subtypes exhibiting strong associations with cognitive decline, sex, psychological traits, brain morphology, and characterized by specific cellular and molecular drivers involving immune, vascular, and oligodendrocyte precursor cells. Next, in a significant translational effort, we developed predictive models to convert these advanced brain-derived molecular profiles (AD dementia pseudotimes and subtypes) into blood-, MRI- and psychological traits-based markers. The translation results underscore both the promise of these models and the opportunities for further enhancement. Our findings enhance the understanding of AD heterogeneity, underscore the value of multi-scale molecular approaches for elucidating causal mechanisms, and lay the groundwork for the development of novel therapies in living persons that target multi-level brain molecular subtypes of AD dementia.

neuroscience↗

bioRxiv2019

Fully Bayesian longitudinal unsupervised learning for the assessment and visualization of AD heterogeneity and progression

Tau pathology and regional brain atrophy are the closest correlate of cognitive decline in Alzheimers disease (AD). Understanding heterogeneity and longitudinal progression of brain atrophy during the disease course will play a key role in understanding AD pathogenesis. We propose a framework for longitudinal clustering that: 1) incorporates whole brain data, 2) leverages unequal visits per individual, 3) compares clusters with a control group, 4) allows to study confounding effects, 5) provides clusters visualization, 6) measures clustering uncertainty, all these simultaneously. We used amyloid-{beta} positive AD and negative healthy subjects, three longitudinal sMRI scans (cortical thickness and subcortical volume) over two years. We found 3 distinct longitudinal AD brain atrophy patterns: a typical diffuse pattern (n=34, 47.2%), and 2 atypical patterns: Minimal atrophy (n=23 31.9%) and Hippocampal sparing (n=9, 12.5%). We also identified outliers (n=3, 4.2%) and observations with uncertain classification (n=3, 4.2%). The clusters differed not only in regional distributions of atrophy at baseline, but also longitudinal atrophy progression, age at AD onset, and cognitive decline. A framework for the longitudinal assessment of variability in cohorts with several neuroimaging measures was successfully developed. We believe this framework may aid in disentangling distinct subtypes of AD from disease staging.

neuroscience↗