Comparative Evaluation of Resting-State fMRI Representations for Schizophrenia Classification Using Deep Learning

Background: Schizophrenia is a complex and heterogeneous psychiatric disorder characterized by widespread impairments in cognitive, emotional, and perceptual processes. The increasing demand for objective, biologically grounded biomarkers in clinical diagnostics has catalyzed significant interest in functional Magnetic Resonance Imaging (fMRI) integrated with deep learning-based methodologies. However, the prevailing literature relies on pre-derived features such as functional connectivity matrices, independent component analysis outputs, and time-series representations. These approaches often introduce additional computational costs, methodological complexity, and sensitivity to parameter selection, thereby limiting scalability. Methods: To address these limitations, this study proposes a simplified end-to-end deep learning framework that does not require derived features and instead learns directly from preprocessed fMRI (pre-fMRI) data. In addition, an Amplitude of Low-Frequency Fluctuation (ALFF)-based dimensionality reduction strategy was employed, enabling direct classification from resting-state fMRI data without requiring functional connectivity or correlation matrix computation. Resting-state fMRI data from publicly available datasets, including participants aged 18–62 years (individuals with schizophrenia: 78; healthy controls: 158), were modelled using multiple representations, including three-dimensional (3D) blood oxygen level-dependent (BOLD) volumes, ALFF maps, mean- and standard deviation-based statistical summaries, and four-dimensional (4D) spatiotemporal structures. These representations were evaluated using Convolutional Neural Network (CNN)-based approaches with five-fold cross-validation. Results: The findings clearly demonstrate that data representation, rather than model architecture, serves as the primary determinant of classification performance. The highest performance was achieved using ALFF-based 3D representations, with an accuracy of 80.10%. In contrast, 4D approaches that jointly model spatial and temporal information did not yield a significant performance improvement despite increased architectural complexity and computational cost. This suggests that biologically meaningful and information-rich signal representations play a more critical role than model depth or temporal modelling strategies. Conclusions: The proposed approach demonstrates that effective classification can be achieved without relying on complex derived features such as functional connectivity, offering a simpler, more computationally efficient, and generalizable framework. The findings challenge the prevailing trend toward increasing architectural complexity in the literature and emphasize the critical importance of data representation. This study provides valuable insights for developing more scalable, interpretable, and clinically applicable artificial intelligence models in fMRI-based studies.