Biomarkers.

Area of Science:

• Neuroimaging
• Network Neuroscience
• Alzheimer's Disease Research

Background:

• Positron Emission Tomography (PET) neuroimaging quantifies in vivo amyloid and tau accumulation.
• Network approaches are valuable for studying brain structure and function in Alzheimer's Disease (AD).
• Estimating participant-level networks from static PET data has been challenging, hindering multimodal integration.

Purpose of the Study:

• To develop a novel framework for deriving participant-level tau similarity networks from PI-2620 tau PET data.
• To analyze tau accumulation patterns in a large and diverse community cohort (HABS-HD study).
• To explore the potential for multimodal network integration in AD research.

Main Methods:

• Extracted Standardized Uptake Value Ratios (SUVr) from 100 cortical regions (Schaefer parcellation).
• Generated two participant-level network types: similarity networks and reciprocal of absolute difference (RAD) networks.
• Compared these networks with existing intersubject correlation (covariance) networks and stratified participants into Tau+ and Tau- subgroups.

Main Results:

• All three network types exhibited a block structure within canonical resting-state networks.
• Moderate correlations were observed between covariance networks and the two participant-level network types (r=0.49, r=0.32).
• Tau positivity stratified networks showed high correlation for similarity networks (r=0.80) and moderate correlation for RAD networks (r=0.67).

Conclusions:

• Participant-level tau PET networks offer a promising approach for investigating AD neurobiology and topological network properties.
• RAD networks preserve SUVr differences, facilitating patient-centric multimodal network approaches.
• These PET networks hold potential for integration with MRI data for precision diagnostics and AD subtyping.