Psychiatry stands at a pivotal turning point shaped by rapid technological advances and pressing clinical demands (Yan et al., 2019). Mental health disorders, defined by multifaceted etiologies and heterogeneous presentations, have traditionally relied on subjective assessments and qualitative interviews. However, the advent of high-dimensional neuroimaging and electrophysiological data-such as EEG and MRI-offers an opportunity to move toward objective, biomarker-based diagnostics. These modalities capture the brain's dynamic, non-linear, and spatially distributed activity, yet analyzing such data remains challenging due to noise, high dimensionality, and inter-individual variability.Deep learning has demonstrated exceptional potential across biomedical domains, including neuroimaging and genomics, by automatically learning hierarchical representations from raw or minimally processed data.Its capacity to model complex, non-linear patterns makes it particularly suitable for deciphering psychiatric signals. This computational capability enables researchers to transcend traditional linear methods and uncover subtle biomarkers underlying mental disorders.To conceptualize this integrative approach, Figure 1 illustrates a four-stage workflow that encapsulates the synergy between deep learning and high-dimensional signal processing in computational psychiatry.The pipeline begins with multimodal data inputs (EEG (Hasanzadeh et al., 2024), MRI (Ke et al., 2024a), Computed Tomography (Strambo et al., 2018)), progresses through advanced analytical methods (deep learning architectures, signal processing techniques, multimodal fusion), and culminates in clinically relevant applications such as precision diagnosis, treatment prediction (Voigt et al., 2021), and longitudinal monitoring. This structured framework highlights how computational intelligence (Richards et al., 2019) can transform raw data into actionable clinical insights, thereby bridging the gap between algorithmic innovation and psychiatric practice.As the second volume in our series, this Research Topic consolidates cutting-edge work at the intersection of deep learning and high-dimensional signal processing in psychiatry. By highlighting methods that enhance interpretability, accuracy, and robustness in computational diagnostics, we aim to accelerate innovation and foster collaboration between computational scientists and clinicians, ultimately translating algorithmic progress into practical tools for mental healthcare. This Research Topic seeks to advance computational psychiatry through innovations that integrate deep learning with advanced signal processing. The overarching objective is to move beyond conventional analytical methods and develop robust, interpretable, and clinically actionable tools capable of deciphering the complexity of psychiatric data for improved diagnosis and personalized treatment prediction.The featured studies exemplify this effort through diverse approaches: predicting Cognitive Behavioral Therapy efficacy in post-stroke depression using interpretable machine learning (doi: Yang and Ye (doi: 10.3389/fpsyt.2025.1667107) present a Sobel Network that intrinsically incorporates gradient-based operations into convolutional layers for EEG-based depression detection. Unlike standard preprocessing pipelines, this end-to-end architecture emphasizes edge-like spatial gradients in EEG topographies-features closely associated with depression-related disruptions in neural connectivity. The network achieves 98.67% accuracy under the experimental conditions reported, showcasing a principled integration of image-processing priors with deep learning for neurodiagnostic applications.Pan (doi: 10.3389/fpsyt.2025.1659536) addresses the challenge of integrating heterogeneous data by proposing a multimodal deep learning framework. It features a Fusion-Aware Relational Encoder (FARE) to model high-order interactions between modalities like EEG and MRI, and a Modality-Aligned Optimization Strategy (MAOS) to ensure balanced learning. This approach advances comprehensive psychiatric assessment by effectively combining multiple data sources, directly tackling the multimodal fusion challenge critical for future research.The contributions in this volume, while promising, must be scrutinized within the broader landscape of computational psychiatry. Recent methodological innovations, such as deep wavelet self-attention non-negative tensor factorization (Wang et al., 2025a) and deep wavelet temporal-frequency attention (Wang et al., 2025b), represent significant strides in modeling non-linear fMRI dynamics, yet they also reveal a persistent tension between model complexity and interpretability. While these approaches offer enhanced discriminability for conditions like autism spectrum disorder and depression (Wang et al., 2025c;Ke et al., 2024b), their clinical translation remains hampered by a lack of standardized validation across diverse cohorts and imaging protocols. Furthermore, the reliance on high-quality, labeled data poses a critical bottleneck; generative adversarial networks coupled with tensor decomposition (Wang et al., 2025c) attempt to mitigate data scarcity, but such synthetic data may inadvertently amplify biases or obscure biologically plausible variations.Beyond fMRI, EEG-based frameworks have made commendable progress in scalability and real-time applicability (Ke et al., 2020). However, the pursuit of ever-higher accuracy (often exceeding 98%) risks creating an illusion of perfection, masking underlying issues of generalizability, demographic bias, and sensor variability. The field must therefore pivot from isolated performance metrics toward robust, ethically aligned deployment-a shift that demands closer collaboration between computational researchers, clinicians, and ethicists.Ultimately, the true measure of success for computational psychiatry (Epalle et al., 2021) lies not in algorithmic sophistication alone, but in its capacity to generate clinically actionable, interpretable, and equitable insights. Future work must prioritize transparent model architectures, rigorous external validation, and integrative frameworks that bridge the gap between high-dimensional data and holistic patient narratives.Collectively, the studies in this volume underscore the transformative potential of deep learning and advanced signal processing in modern psychiatry. From predicting therapeutic outcomes to decoding complex neurophysiological signals and integrating multimodal data, these contributions chart a clear course toward more precise, personalized, and data-informed mental healthcare. However, as highlighted in the Discussion, several critical challenges must be addressed to realize this vision.Future research should focus on:
Ke et al. (Tue,) studied this question.