EAAI Journal 2026 Journal Article
A consistency-driven pseudo-labeling framework for robust functional connectivity modeling in neuropsychiatric disorder diagnosis
- Xin Wen
- Shijie Guo
- Li Dong
- Xiaobo Liu
- Wenbo Ning
- Jie Shi
- Songhua Liu
- Cheng Luo
The incidence of neuropsychiatric disorders such as Autism Spectrum Disorder (ASD), Attention Deficit Hyperactivity Disorder (ADHD), and Major Depressive Disorder (MDD) continues to rise. Deep learning-based computer-aided diagnosis (CAD) has emerged as a promising approach to alleviate the increasing burden on neuroimaging-based clinical resources. However, neuroimaging modalities such as functional magnetic resonance imaging (fMRI) involve complex spatiotemporal characteristics, making their representations susceptible to various types of noise and interference, which in turn hampers the effectiveness of CAD. To address this challenge, we propose a pseudo-label consistency-driven framework for functional connectivity (FC) reconstruction and discriminative modeling (PL-FCDM), aiming to enhance both the representational quality and discriminative power of FC features. Specifically, two complementary pseudo-labeling models are developed to independently capture discriminative features from the temporal domain (time series) and spatial domain (dynamic functional connectivity), enabling pseudo label prediction from distinct modalities. Then a consistency-based filtering strategy is applied to construct high-confidence reconstructed functional connectivity. These graphs are subsequently fed into a classification model comprising a Feature Optimization Autoencoder and a Depthwise Separable Convolutional Neural Network for efficient identification of neuropsychiatric disorders. Extensive experiments conducted on four publicly available multi-site datasets—ABIDE I, ABIDE II, ADHD-200, and REST-meta-MDD demonstrate that the proposed method achieves classification accuracies of 76. 14%, 74. 37%, 72. 89%, and 71. 15%, respectively. These results consistently outperform several state-of-the-art approaches, validating the effectiveness and robustness of the proposed framework in feature refinement and multi-disorder recognition.