What type of study is this?

This is a Quantitative Study study.

October 16, 2025

Efficient 4D fMRI analysis via spatio-temporal screening and region-aware feature extraction for template-free brain disorder classification

Key Points

Proposed model achieves 97% classification accuracy on ADNI dataset, with a notable reduction in computational demands.
The model outperforms existing techniques by 5% accuracy on a private disorder dataset, showcasing significant improvements.
Method includes a hierarchical encoder for extracting spatial and temporal features from 4D fMRI data effectively.
Results indicate potential for improved biomarker identification, supporting template-free approaches in clinical settings.

Abstract

Abstract Objective:Functional magnetic resonance imaging (fMRI) is crucial for identifying neurological disorder biomarkers, but current deep learning methods face some limitations. Template-dependent methods lack inter-subject specificity and generalizability due to fixed anatomical priors. Emerging template-free models often separate spatial and temporal processing, discarding temporal continuity. To address these limitations, we propose a novel axial slice-centric model that jointly models spatiotemporal representations through end-to-end processing of native 4D fMRI data. This eliminates template dependency while preserving intrinsic brain activity patterns.Approach:Our framework redefines 4D fMRI analysis by decomposing it into 3D spatiotemporal manifolds along the axial axis, enabling joint learning of spatial and temporal features and preserving individualized structure organization. A hierarchical encoder extracts local spatiotemporal interactions within each slice, progressively aggregating information to capture multi-granularity neural patterns. To maintain temporal continuity and computational efficiency, a differentiable TopK operation adaptively selects informative slices and time points, balancing computational demands with long-range temporal dependencies.Main results:Experimental results on the ADNI dataset (324 subjects) and a private disorder of consciousness dataset (164 subjects) demonstrate the effectiveness of our 4D fMRI framework in classifying neurological disorders. Specifically, on the ADNI dataset, our proposed model achieves 97% classification accuracy with over 25% reduction in FLOPs compared to baseline methods. On the private dataset, our model outperforms state-of-the-art approaches by 5% accuracy. Visualization of slice-level attention maps identify biomarkers consistent with previous research, demonstrating that our template-free framework can discover biomarkers comparable to those identified by template-dependent methods.Significance:Our joint spatiotemporal modeling framework, enabled by axial slice-centric decomposition of 4D fMRI data while preserving temporal continuity, achieves excellent complexity-accuracy trade-offs for brain disorder analysis. Biomarker visualization confirms its template-free capability to identify clinically-relevant neural patterns, offering an efficient and interpretable solution for 4D fMRI-based diagnosis.

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