High-Frequency Spatial Feature Fusion with 3D CNN for Early Stage Schizophrenia Classification

Abstract

Early detection of schizophrenia (SZ) remains challenging due to the subtlety of early-stage brain alterations and reliance on subjective clinical assessment. We propose a frequency-aware 3D convolutional neural network (CNN) pipeline that integrates NeuroMark-HiFi high-pass spatial filtering with a modified VGGNet3D architecture featuring 3D Laplacian kernel initialization and dilated convolutions. Using the FBIRN dataset (N=311; 150 healthy controls, 161 SZ) with all 53 intrinsic connectivity networks (ICNs) per subject, we evaluate four experimental conditions across two hyperparameter configurations to isolate the contributions of enhanced input representations and frequency-aware model design. Under the optimized configuration, Condition 3 (HiFi + Laplacian initialization) achieved the best mean test accuracy of 75.54% with a peak single-fold accuracy of 87.10%, representing a 5.44% absolute gain over the optimized baseline. These results demonstrate that high-frequency spatial features are more discriminative for SZ classification than raw intensities, and that aligning Laplacian-initialized kernels with HiFi-filtered input creates a beneficial inductive bias--even with a compact model of approximately 1.4M parameters.