As large language models (LLMs) continue to revolutionize AI research, there is a growing interest in building large-scale brain foundation models to advance neuroscience. While most existing brain foundation models are pre-trained on time-series signals or connectome features, we propose a novel graph-based pre-training paradigm for constructing a brain graph foundation model. In this paper, we introduce the Brain Graph Foundation Model, termed BrainGFM, a unified framework that leverages graph contrastive learning and graph masked autoencoders for large-scale fMRI-based pre-training. BrainGFM is pre-trained on a diverse mixture of brain atlases with varying parcellations, significantly expanding the pre-training corpus and enhancing the model's ability to generalize across heterogeneous fMRI-derived brain representations. To support efficient and versatile downstream transfer, we integrate both graph prompts and language prompts into the model design, enabling BrainGFM to flexibly adapt to a wide range of atlases, neurological and psychiatric disorders, and task settings. Furthermore, we employ meta-learning to optimize the graph prompts, facilitating strong generalization to previously unseen disorders under both few-shot and zero-shot learning conditions via language-guided prompting. BrainGFM is pre-trained on 27 neuroimaging datasets spanning 25 common neurological and psychiatric disorders, encompassing 2 types of brain atlases (functional and anatomical) across 8 widely-used parcellations, and covering over 25,000 subjects, 60,000 fMRI scans, and a total of 400,000 graph samples aggregated across all atlases and parcellations. The code is available at: https://github.com/weixinxu666/BrainGFM

Analyzing functional brain networks using functional magnetic resonance imaging (fMRI) is crucial for understanding psychiatric disorders and addictive behaviors. While existing fMRI-based graph convolutional networks (GCNs) show considerable promise for feature extraction, they often fall short in characterizing complex relationships between brain regions and demographic factors and accounting for interpretable variables linked to psychiatric conditions. We propose NeuroTree to overcome these limitations, integrating a k-hop AGE-GCN with neural ordinary differential equations (ODEs). This framework leverages an attention mechanism to optimize functional connectivity (FC), thereby enhancing dynamic FC feature learning for brain disease classification. Furthermore, NeuroTree effectively decodes fMRI network features into tree structures, which improves the capture of high-order brain regional pathway features and enables the identification of hierarchical neural behavioral patterns essential for understanding disease-related brain subnetworks. Our empirical evaluations demonstrate that NeuroTree achieves state-of-the-art performance across two distinct mental disorder datasets and provides valuable insights into age-related deterioration patterns. These findings underscore the model's efficacy in predicting psychiatric disorders and elucidating their underlying neural mechanisms.

In light of the NIMH's Research Domain Criteria (RDoC), the advent of functional neuroimaging, novel technologies and methods provide new opportunities to develop precise and personalized prognosis and diagnosis of mental disorders. Machine learning (ML) and artificial intelligence (AI) technologies are playing an increasingly critical role in the new era of precision psychiatry. Combining ML/AI with neuromodulation technologies can potentially provide explainable solutions in clinical practice and effective therapeutic treatment. Advanced wearable and mobile technologies also call for the new role of ML/AI for digital phenotyping in mobile mental health. In this review, we provide a comprehensive review of the ML methodologies and applications by combining neuroimaging, neuromodulation, and advanced mobile technologies in psychiatry practice. Additionally, we review the role of ML in molecular phenotyping and cross-species biomarker identification in precision psychiatry. We further discuss explainable AI (XAI) and causality testing in a closed-human-in-the-loop manner, and highlight the ML potential in multimedia information extraction and multimodal data fusion. Finally, we discuss conceptual and practical challenges in precision psychiatry and highlight ML opportunities in future research.

Artificial intelligence (AI) machine learning is rapidly being deployed to help accelerate neuroscience, psychology, and psychiatry research. A new study published in Molecular Psychiatry by researchers affiliated with Yale University shows how AI machine learning can identify patterns of neural connections in the brain associated with aggressive behavior in children. According to the Yale researchers, this study is a first of its kind. "Disruptions in frontoparietal networks supporting emotion regulation have been long implicated in maladaptive childhood aggression," wrote the researchers. "However, the association of connectivity between large-scale functional networks with aggressive behavior has not been tested."

Machine learning models may have greater accuracy than gold-standard risk assessment tools for predicting criminal offense among people with psychiatric disorders, according to study results published in Journal of Psychiatric Research. "Knowing the type of crime an individual is likely to commit, before the offense occurs, is urgently needed in order to guide more targeted and precise risk assessment strategies and frontline therapeutic interventions," Devon Watts, of the department of psychiatry and behavioral neurosciences at McMaster University in Canada, and colleagues wrote. "Furthermore, the vast majority of work thus far has focused on predicting recidivism in non-psychiatric prison populations. Importantly, it is largely unclear whether such models can be appropriately extrapolated to offenses committed by those with severe mental illness." Current, actuarial risk estimates are unable to individually predict criminal offense type a patient will go on to commit, and they frequently simply evaluate the general risk for crime occurring among a group sample, according to the researchers. In the current study, Watts and colleagues sought to create a machine learning model able to predict criminal offense type committed among a large transdiagnostic sample of psychiatry patients, on the individual level.

Artificial Intelligence (AI) has invaded the healthcare sector long back. It is making accountable impacts on treatment and overviewing of patients. However, psychiatry department stands out when it comes to utilising AI applications. It has taken a long way before reaching the current initial stage where AI is being used for analyzing patients but only by a handful of psychiatrists. Medicine is already reaping a fruitful benefit from artificial intelligence and big data.

Graph theory is the study of graphs, mathematical structures that model the relationships between objects. In this example, we see a social network. A line represents a friendship between the people that it connects. In more technical terms, every person would be called a "node" or "vertex," while every line that connects would be called a "link" or "edge." So, this graph has 5 vertices and 7 edges.

The identification of reproducible biomarkers is an important step toward personalized medicine of psychiatric disorders. A large repertoire of machine learning tools is available that can aid in identifying such biomarker patterns from high-dimensional biological data. However, in psychiatry, the identification of clinically useful patterns has been challenging, due to the biological complexity and heterogeneity of the disorders, and the low effect sizes of individual biological markers. The incorporation of additional biological knowledge, such as information on biological network structure, or data from diverse modalities, is a promising route to make high-dimensional data more accessible for machine learning, and to identify more meaningful biological illness signatures. Here, we describe opportunities of such integrative analytics approaches and discuss unresolved challenges.

Ranking score levels and functional categories are as in the key in (B). Highlighted ranks and terms correspond to examples shown in (D).

Our understanding of the pathophysiology of psychiatric disorders, including autism spectrum disorder (ASD), schizophrenia (SCZ), and bipolar disorder (BD), lags behind other fields of medicine. The diagnosis and study of these disorders currently depend on behavioral, symptomatic characterization. Defining genetic contributions to disease risk allows for biological, mechanistic understanding but is challenged by genetic complexity, polygenicity, and the lack of a cohesive neurobiological model to interpret findings. The transcriptome represents a quantitative phenotype that provides biological context for understanding the molecular pathways disrupted in major psychiatric disorders. RNA sequencing (RNA-seq) in a large cohort of cases and controls can advance our knowledge of the biology disrupted in each disorder and provide a foundational resource for integration with genomic and genetic data.