Interpretable Fusion Analytics Framework for fMRI Connectivity: Self-Attention Mechanism and Latent Space Item-Response Model
This work addresses the interpretability issue in deep learning for fMRI-based disease classification, which is crucial for clinicians and researchers in neuroscience, though it appears incremental as it builds on existing methods for connectivity analysis.
The authors tackled the problem of interpreting deep learning classification of cognitive impairment diseases from fMRI data by proposing a novel framework that combines self-attention mechanisms and a latent space item-response model to identify significant brain functions, showing validity in determining important ROI functions across four disease types.
There have been several attempts to use deep learning based on brain fMRI signals to classify cognitive impairment diseases. However, deep learning is a hidden black box model that makes it difficult to interpret the process of classification. To address this issue, we propose a novel analytical framework that interprets the classification result from deep learning processes. We first derive the region of interest (ROI) functional connectivity network (FCN) by embedding functions based on their similar signal patterns. Then, using the self-attention equipped deep learning model, we classify diseases based on their FCN. Finally, in order to interpret the classification results, we employ a latent space item-response interaction network model to identify the significant functions that exhibit distinct connectivity patterns when compared to other diseases. The application of this proposed framework to the four types of cognitive impairment shows that our approach is valid for determining the significant ROI functions.