Semi-Nonlinear Deep Efficient Reconstruction for Unveiling Linear and Nonlinear Spatial Features of the Human Brain

Deep learning has become indispensable for identifying hierarchical spatial features (SFs), which are crucial for linking neurological disorders to brain functionality, from functional Magnetic Resonance Imaging (fMRI). Unfortunately, existing methods are constrained by architectures that are either linear or nonlinear, limiting a comprehensive categorization of spatial features. To overcome this limitation, we introduce the Semi-Nonlinear Deep Efficient Reconstruction (SENDER) framework, a novel hybrid approach designed to simultaneously capture both linear and nonlinear spatial features, providing a holistic understanding of brain functionality. In our approach, linear SFs are formed by directly integrating multiple spatial features at shallow layers, whereas nonlinear SFs emerge from combining partial regions of these features, yielding complex patterns at deeper layers. We validated SENDER through extensive qualitative and quantitative evaluations with four state-of-the-art methods. Results demonstrate its superior performance, identifying five reproducible linear SFs and eight reproducible nonlinear SFs.