Abstract Details

Title Whole-brain functional connectivity predicts tau PET in preclincal Alzheimer’s disease

Topic Aging, Dementia, and Behavioral Neurology

Presentation(s) N4 - Neuroscience in the Clinic: Time for Tau: Developments in Biomarkers and Therapeutic Trials (1:35 PM-1:45 PM)

Poster/Presentation
Number 002

Objective

Our study aims to develop robust connectomic models to predict amyloid and tau deposition and assess functional connectivity changes in preclinical Alzheimer's Disease (AD).

Background Preclinical AD is characterized by initial amyloid accumulation without cognitive symptoms. While functional connectivity changes have been observed, the relationship between AD pathology and the functional connectome during this stage remains unclear.

Design/Methods We applied Connectome-based Predictive Modeling (CPM), an approach that predicts outcome variables from the functional connectome, to baseline tau PET (18F-flortaucipir), amyloid PET (18F-florbetapir) and resting-state fMRI from the Anti-Amyloid in Asymptomatic Alzheimer's disease study (n=394, aged 65-85). Model performances were assessed using Spearman's correlations (?) of predicted vs. observed values. Model significance was assessed against permuted models (n=1000 iterations), corrected for the false-discovery rate. We characterized models and assessed generalizability using an external symptomatic AD cohort (ADNI, n=469, aged 55-90).

Results Whole-brain functional connectivity robustly predicted regional tau PET, outperforming amyloid PET models. The best-performing models were for regions associated with Braak stages IV/V (posterior cingulate ?=0.30, precuneus ? = 0.22; p<0.05), while models for regions first impacted by tau accumulation performed poorly (parahippocampal ?=0.05, entorhinal ?=0.06, fusiform ?=0.09; p>0.05). The association between connectivity and regional tau is predominantly linear and prediction accuracies were robust to temporal and default mode network node lesions. Tau model accuracies correlated with global connectivity of brain regions (?=0.54, p<0.05) rather than underlying tau burden (?=0.17, p>0.05). Models generalized to ADNI, particularly in individuals with elevated tau (SUVR >1.19).

Conclusions Whole-brain functional connectivity predicts tau PET in preclinical AD and generalizes to a clinical dataset with abnormal tau PET, highlighting the functional connectome's potential as a biomarker for early AD detection and monitoring.

Authors/Disclosures
Hamid Abuwarda PRESENTER	Mr. Abuwarda has nothing to disclose.
Anne Trainer (Yale University)	Anne Trainer has nothing to disclose.
Corey Horien, MD, PhD	Dr. Horien has nothing to disclose.
Xilin Shen, PhD	The institution of Dr. Shen has received research support from NIH.
Suyeon Ju	Suyeon Ju has nothing to disclose.
Todd Constable	Todd Constable has nothing to disclose.
Carolyn Fredericks, MD (Yale School of Medicine, Department of Neurology)	The institution of Dr. Fredericks has received research support from Alzheimer's Association. The institution of Dr. Fredericks has received research support from National Institute on Aging.

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