AI Insight
Researchers developed a novel computational framework to characterize Alzheimer's disease (AD) progression and subtypes using geometric brain signatures derived from three neuroimaging modalities: amyloid-PET (AV45), metabolic PET (FDG), and structural MRI. These signatures were generated by decomposing brain maps into eigenmodes, which are resonant geometric patterns of the cortical surface operating at different spatial scales. The resulting features successfully identified distinct disease progression trajectories (quantified as pseudotime) and patient subtypes that correlated strongly with biological and cognitive measures, outperforming conventional region-based brain analysis approaches.
Why it matters
This framework could help reduce diagnostic delays in Alzheimer's disease by providing more sensitive and robust biomarkers than current localization-based methods, and may ultimately support better patient stratification for clinical trials and personalized treatment strategies.
⚠️ Preprint – Noch nicht peer-reviewed
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Alzheimer’s disease (AD) patients suffer from consequential diagnostic delay due to the lack of accessible biomarkers. They also show different responses to treatments due to disease heterogeneity and progression. Here, we developed a novel framework to identify disease progression and subtypes by using geometric brain signatures derived from multiple neuroimaging modalities, including [18F]-Florbetapir (AV45) Positron Emission Tomography (PET), [18F]-Fludeoxyglucose (FDG) PET, and structural Magnetic Resonance Imaging (MRI). These signatures were derived by decomposing corresponding maps of amyloid-beta levels, metabolic activity, and cortical thickness in terms of the fundamental, resonant modes-eigenmodes-of cortical geometry, each tied to a specific spatial resolution scale. Our results showed that geometric eigenmode-based features identified trajectories of disease progression, quantified as pseudotime, in distinct subtypes. The disease progression trajectories and subtypes are identified with high stability and are highly related to biological and cognitive measures. These performances are superior to those obtained using conventional localised features and remain robust across datasets, indicating that geometric signatures of brain structure and function can be used to uncover new markers of AD diagnosis and prognosis that are missed by conventional localisation approaches.
Source: Geometric brain signatures of Alzheimer's disease progression and subtypes