Brain metabolic patterns improve alzheimer diagnosis using gut information
BRIDGE: Learning Brain-Gut Metabolic Evidence for Alzheimer's Disease Diagnosis
Computer Vision and Pattern Recognition
Summary
Diagnosing Alzheimer's disease usually looks at brain scans alone, missing helpful gut-related signals. The authors developed BRIDGE, a method that learns connections between brain and gut metabolism from special scans that include both areas. It then uses this knowledge to improve Alzheimer's diagnosis just from brain scans when gut scans aren't available. Their tests show this approach better detects disease stages like mild cognitive impairment and Alzheimer’s than brain-only methods.
What this means in practice
- •For clinical imaging teams: Use BRIDGE to enhance Alzheimer's diagnosis accuracy from brain PET scans by incorporating inferred gut metabolic signals without extra imaging.
- •For medical ai developers: Integrate BRIDGE’s cross-organ metabolic knowledge transfer into diagnostic tools to improve detection of Alzheimer's and mild cognitive impairment.
Authors
Pengli Zhu, Ruxi Chang, Tianliang Yao, Yu Jiang, Shengyuan Liu, Yiyang Lin, Xiaoyi Duan, Shuang Wu, Yixuan Yuan, the Alzheimer's Disease Neuroimaging Initiative
Abstract
Alzheimer's disease (AD) diagnosis may benefit from gut metabolic information associated with brain-gut interactions, yet existing models largely analyze cerebral glucose metabolism alone. Although whole-body fluorodeoxyglucose positron emission tomography (FDG-PET) can provide paired brain-gut observations, routine brain examinations rarely include gut imaging, limiting direct use of this information for diagnosis. In this paper, we present BRIDGE (Brain and Gut Representation Integration via Distribution-Guided Enhancement), a two-stage framework for transferring brain-gut metabolic knowledge to brain-only diagnosis. Specifically, BRIDGE integrates a conditional brain-to-gut latent transport module and a disease-evidence-guided cerebral pathway to capture subject-specific cross-organ correspondence and disease-relevant cerebral representations. For the cerebral pathway, a large language model (LLM) helps organize literature-derived descriptors into human-approved semantic anchors that guide feature learning. Additionally, we introduce a counterfactual-guided residual fusion mechanism, which uses matched and non-matched virtual-gut evidence to supervise a confidence gate and dynamically regulate the contribution of transported cross-organ information. At inference, BRIDGE exploits complementary gut-related context using brain PET alone, without measured gut PET. Extensive experiments across public and private cohorts demonstrate that BRIDGE outperforms brain-only and cross-organ baselines, achieving AUC gains of 6.0% and 5.2% over its brain-only counterpart for CN/MCI and CN/AD diagnosis, respectively. Notably, its ability to transfer complementary brain-gut metabolic context while preserving brain PET as the sole inference input highlights its potential for clinically practical diagnosis. Our code is available at https://github.com/CUHK-AIM-Group/BRIDGE.