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Informatics paradigms for brain and mental health research have seen significant advances in recent years. These developments can largely be attributed to the emergence of new technologies such as machine learning, deep learning, and artificial intelligence. Data-driven methods have the potential to support mental health care by providing more precise and personalised approaches to detection, diagnosis, and treatment of depression. In particular, precision psychiatry is an emerging field that utilises advanced computational techniques to achieve a more individualised approach to mental health care. This survey provides an overview of the ways in which artificial intelligence is currently being used to support precision psychiatry. Advanced algorithms are being used to support all phases of the treatment cycle. These systems have the potential to identify individuals suffering from mental health conditions, allowing them to receive the care they need and tailor treatments to individual patients who are mostly to benefit. Additionally, unsupervised learning techniques are breaking down existing discrete diagnostic categories and highlighting the vast disease heterogeneity observed within depression diagnoses. Artificial intelligence also provides the opportunity to shift towards evidence-based treatment prescription, moving away from existing methods based on group averages. However, our analysis suggests there are several limitations currently inhibiting the progress of data-driven paradigms in care. Significantly, none of the surveyed articles demonstrate empirically improved patient outcomes over existing methods. Furthermore, greater consideration needs to be given to uncertainty quantification, model validation, constructing interdisciplinary teams of researchers, improved access to diverse data and standardised definitions within the field. Empirical validation of computer algorithms via randomised control trials which demonstrate measurable improvement to patient outcomes are the next step in progressing models to clinical implementation. [ABSTRACT FROM AUTHOR]

The reduction of hippocampal volume remains controversial in depression because of the variability among individuals in clinical studies. Here, a reliable experimental rat model of depression, established by chronic unpredictable mild stress (CUMS), was used. Thirty rats were randomly divided into two groups (CUMS group and control group). Hippocampal volume was dynamically measured every 2 weeks in a 56-day chronic stress procedure using structural magnetic resonance imaging, and the correlation between the hippocampal volume and the learning and memory changes was investigated. Our results demonstrated that CUMS rats showed significantly smaller volumes of the bilateral hippocampus compared to that of the controls, changing dramatically with the development of CUMS procedure. The left hippocampal volume was reduced earlier and more markedly than the right one from the 2nd week to the 8th week of the CUMS procedure (on the 8th week: left: approximately 15.3 %; right: approximately 8.4 % reduction). Additionally, the hippocampal volume of CUMS rats was significantly negatively correlated with the learning and memory changes. Of note, it showed that the more obviously the hippocampal volume reduced, the more severely the learning and memory damaged. In conclusion, the hippocampal volume decreased gradually and dynamically and was correlated with the impairment of the learning and memory in depression. [ABSTRACT FROM AUTHOR]