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Applications of machine learning in the biomedical sciences are growing rapidly. This growth has been spurred by diverse cross-institutional and interdisciplinary collaborations, public availability of large datasets, an increase in the accessibility of analytic routines, and the availability of powerful computing resources. With this increased access and exposure to machine learning comes a responsibility for education and a deeper understanding of its bases and bounds, borne equally by data scientists seeking to ply their analytic wares in medical research and by biomedical scientists seeking to harness such methods to glean knowledge from data. This article provides an accessible and critical review of machine learning for a biomedically informed audience, as well as its applications in psychiatry. The review covers definitions and expositions of commonly used machine learning methods, and historical trends of their use in psychiatry. We also provide a set of standards, namely Guidelines for REporting Machine Learning Investigations in Neuropsychiatry (GREMLIN), for designing and reporting studies that use machine learning as a primary data-analysis approach. Lastly, we propose the establishment of the Machine Learning in Psychiatry (MLPsych) Consortium, enumerate its objectives, and identify areas of opportunity for future applications of machine learning in biological psychiatry. This review serves as a cautiously optimistic primer on machine learning for those on the precipice as they prepare to dive into the field, either as methodological practitioners or well-informed consumers., (© 2023. The Author(s), under exclusive licence to Springer Nature Limited.)

Anxiety disorders are common, complex psychiatric disorders with twin heritabilities of 30-60%. We conducted a genome-wide association study of Lifetime Anxiety Disorder (n case  = 25 453, n control  = 58 113) and an additional analysis of Current Anxiety Symptoms (n case  = 19 012, n control  = 58 113). The liability scale common variant heritability estimate for Lifetime Anxiety Disorder was 26%, and for Current Anxiety Symptoms was 31%. Five novel genome-wide significant loci were identified including an intergenic region on chromosome 9 that has previously been associated with neuroticism, and a locus overlapping the BDNF receptor gene, NTRK2. Anxiety showed significant positive genetic correlations with depression and insomnia as well as coronary artery disease, mirroring findings from epidemiological studies. We conclude that common genetic variation accounts for a substantive proportion of the genetic architecture underlying anxiety.

Abnormalities in social interaction are a common feature of several psychiatric disorders, aligning with the recent move towards using Research Domain Criteria (RDoC) to describe disorders in terms of observable behaviours rather than using specific diagnoses. Neuroeconomic games are an effective measure of social decision-making that can be adapted for use in neuroimaging, allowing investigation of the biological basis for behaviour. This review summarises findings of neuroeconomic gameplay studies in Axis 1 psychiatric disorders and advocates the use of these games as measures of the RDoC Affiliation and Attachment, Reward Responsiveness, Reward Learning and Reward Valuation constructs. Although research on neuroeconomic gameplay is in its infancy, consistencies have been observed across disorders, particularly in terms of impaired integration of social and cognitive information, avoidance of negative social interactions and reduced reward sensitivity, as well as a reduction in activity in brain regions associated with processing and responding to social information.

In mammals, the neuropeptide vasopressin is a key molecule for complex emotional and social behaviours. Two microsatellite polymorphisms, RS1 and RS3, near the promoter of AVPR1A, encoding the receptor subtype most heavily implicated in behaviour regulation, have been linked to autism and behavioural traits. However, the impact of these variants on human brain function is unknown. Here we show that human amygdala function is strongly associated with genetic variation in AVPR1A. Using an imaging genetics approach in a sample of 121 volunteers studied with an emotional face-matching paradigm, we found that differential activation of amygdala is observed in carriers of risk alleles for RS3 and RS1. Alleles in RS1 previously reported to be significantly over- and undertransmitted to autistic probands showed opposing effects on amygdala activation. Furthermore, we show functional difference in human brain between short and long repeat lengths that mirror findings recently obtained in a corresponding variant in voles. Our results indicate a neural mechanism mediating genetic risk for autism through an impact on amygdala signalling and provide a rationale for exploring therapeutic strategies aimed at abnormal amygdala function in this disorder.

The etiology of schizophrenia is thought to include both epistasis and gene-environment interactions. We sought to test whether a set of schizophrenia candidate genes regulated by hypoxia or involved in vascular function in the brain (AKT1, BDNF, CAPON, CHRNA7, COMT, DTNBP1, GAD1, GRM3, NOTCH4, NRG1, PRODH, RGS4, TNF-alpha) interacted with serious obstetric complications to influence risk for schizophrenia. A family-based study of transmission disequilibrium was conducted in 116 trios. Twenty-nine probands had at least one serious obstetric complication (OC) using the McNeil-Sjostrom Scale, and many of the OCs reported were associated with the potential for fetal hypoxia. Analyses were conducted using conditional logistic regression and a likelihood ratio test (LRT) between nested models was performed to assess significance. Of the 13 genes examined, four (AKT1 (three SNPs), BDNF (two SNPs), DTNBP1 (one SNP) and GRM3 (one SNP)) showed significant evidence for gene-by-environment interaction (LRT P-values ranged from 0.011 to 0.037). Although our sample size was modest and the power to detect interactions was limited, we report significant evidence for genes involved in neurovascular function or regulated by hypoxia interacting with the presence of serious obstetric complications to increase risk for schizophrenia.