# A causal deep learning approach to identifying metabolic signatures of cognitive and functional decline in alzheimer’s disease

## TL;DR

This paper uses deep learning and causal modeling to identify brain regions linked to cognitive and functional decline in Alzheimer’s disease using FDG-PET scans.

## Contribution

A novel deep learning model (FDG CogNet) with causal inference and attention mechanisms improves prediction of cognitive and functional decline in Alzheimer’s.

## Key findings

- Temporal, parietal, and hippocampal regions are most influential for cognitive performance in early Alzheimer’s.
- FDG CogNet achieved high predictive accuracy (R² = 0.90 for MMSE and R² = 0.94 for FAQ).
- Cerebellar regions show compensatory activity in MCI but decline in AD, indicating reduced neural resilience.

## Abstract

Cognitive and functional decline in Alzheimer’s disease (AD) arises from disruptions in specific brain networks. Identifying the most affected regions is essential for understanding disease progression and developing targeted interventions. Fluorodeoxyglucose positron emission tomography (FDG-PET) offers a sensitive method for detecting early metabolic dysfunction, often before structural changes become apparent. We examined regional brain glucose metabolism in relation to cognitive performance and functional independence across cognitively normal individuals, those with mild cognitive impairment (MCI), and AD patients. Cognitive function was measured using the Mini-Mental State Examination (MMSE), and daily functioning was assessed via the Functional Activities Questionnaire (FAQ). Imaging and clinical data were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Structural causal modeling was used to identify brain regions with a strong causal influence on MMSE and FAQ scores. These causally validated regions were then used as input to the proposed FDG-PET-based Cognition Prediction Network (FDG CogNet), a deep learning model, which includes a feature-wise attention mechanism to dynamically weight each region’s contribution to prediction. Temporal, parietal, and hippocampal regions were most influential for cognitive performance, particularly in early stages of disease. Functional abilities were more strongly associated with executive and integrative regions, including the angular gyrus, temporal poles, posterior cingulate, and frontal cortices. Cerebellar regions showed compensatory activity in MCI but diminished in AD, suggesting reduced neural resilience. FDG CogNet achieved high predictive accuracy, with R² = 0.90 for MMSE and R² = 0.94 for FAQ, demonstrating that limiting inputs to causally relevant regions improved both performance and interpretability. These findings clarify stage-specific neural mechanisms in AD and show that combining causal inference with an attention-based deep learning model provides a powerful framework for accurate and interpretable prediction. This approach highlights the clinical utility of FDG PET for early diagnosis and suggests that timely, region-specific interventions offer the best opportunity to preserve cognitive and functional abilities in AD.