Bridging Brain Connectomes and Clinical Reports for Early Alzheimer's Disease Diagnosis
This work addresses the problem of early Alzheimer's diagnosis for clinicians by providing a novel multimodal approach that links objective imaging with subjective reports, though it is incremental in combining existing methods for a known bottleneck.
The paper tackled the challenge of integrating brain imaging data with clinical reports for early Alzheimer's disease diagnosis by proposing a framework that aligns brain connectomes and clinical reports in a shared latent space, achieving state-of-the-art predictive performance on the ADNI dataset.
Integrating brain imaging data with clinical reports offers a valuable opportunity to leverage complementary multimodal information for more effective and timely diagnosis in practical clinical settings. This approach has gained significant attention in brain disorder research, yet a key challenge remains: how to effectively link objective imaging data with subjective text-based reports, such as doctors' notes. In this work, we propose a novel framework that aligns brain connectomes with clinical reports in a shared cross-modal latent space at both the subject and connectome levels, thereby enhancing representation learning. The key innovation of our approach is that we treat brain subnetworks as tokens of imaging data, rather than raw image patches, to align with word tokens in clinical reports. This enables a more efficient identification of system-level associations between neuroimaging findings and clinical observations, which is critical since brain disorders often manifest as network-level abnormalities rather than isolated regional alterations. We applied our method to mild cognitive impairment (MCI) using the ADNI dataset. Our approach not only achieves state-of-the-art predictive performance but also identifies clinically meaningful connectome-text pairs, offering new insights into the early mechanisms of Alzheimer's disease and supporting the development of clinically useful multimodal biomarkers.