Correlates and predictors of the severity of suicidal ideation in adolescence: an examination of brain connectomics and psychosocial characteristics

Classification of suicidality by training supervised machine learning models with brain MRI findings: A systematic review

BACKGROUND

Suicide is a global public health issue causing around 700,000 deaths worldwide each year. Therefore, identifying suicidal thoughts and behaviors in patients can help lower the suicide-related mortality rate. This review aimed to investigate the feasibility of suicidality identification by applying supervised Machine Learning (ML) methods to Magnetic Resonance Imaging (MRI) data.

METHODS

We conducted a systematic search on PubMed, Scopus, and Web of Science to identify studies examining suicidality by applying ML methods to MRI features. Also, the Prediction Model Risk of Bias Assessment Tool (PROBAST) was employed for the quality assessment.

RESULTS

23 studies met the inclusion criteria. Of these, 20 developed prediction models without external validation and 3 developed prediction models with external validation. The performance of ML models varied among the reviewed studies, with the highest reported values of accuracies and Area Under the Curve (AUC) ranging from 51.7 % to 100 % and 0.52 to 1, respectively. Over half of the studies that reported accuracy (12/21) or AUC (13/16) achieved values of ≥0.8. Our comparative analysis indicated that deep learning exhibited the highest predictive performance compared to other ML models. The most commonly identified discriminative imaging features were resting-state functional connectivity and grey matter volume within prefrontal-limbic structures.

LIMITATIONS

Small sample sizes, lack of external validation, heterogeneous study designs, and ML model development.

CONCLUSIONS

Most of the studies developed ML models capable of ML-based suicide identification, although ML models' predictive performance varied across the reviewed studies. Thus, further well-designed is necessary to uncover the true potential of different ML models in this field.

Editorial: Prevention is the best cure

In this issue of Journal of Child Psychology and Psychiatry, many interesting research findings converge to suggest that prevention and early intervention strategies can be the key to building healthier societies with happier people. Across different societies, we observe that scientific attention and practice is becoming more evenly divided between the traditionally dominant focus on clinical disorders and residential treatments on the one hand and a burgeoning focus on prevention and resilience in ambulant settings and younger populations on the other hand.

Exploration of the Mechanism Underlying the Association of Incident Microinfarct and Motor Deficit: A Preliminary Functional MRI Study

BACKGROUND

Cerebral microinfarcts (CMIs) might cause measurable disruption to brain connections and are associated with cognitive decline, but the association between CMIs and motor impairment is still unclear.

OBJECTIVE

To assess the CMIs effect on motor function in vivo and explore the potential neuropathological mechanism based on graph-based network method.

METHODS

We identified 133 non-demented middle-aged and elderly participants who underwent MRI scanning, cognitive, and motor assessment. The short physical performance battery (SPPB) assessed motor function, including balance, walking speed, and chair stand. We grouped participants into 34 incident CMIs carriers and 99 non-CMIs carriers as controls, depending on diffusion-weighted imaging. Then we assessed the independent CMIs effects on motor function and explored neural mechanisms of CMIs on motor impairment via mapping of degree centrality (DC) and eigenvector centrality (EC).

RESULTS

CMIs carriers had worse motor function than non-carriers. Linear regression analyses showed that CMIs independently contributed to motor function. CMIs carriers had decreased EC in the precuneus, while increased DC and EC in the middle temporal gyrus and increased DC in the inferior frontal gyrus compared to controls (p < 0.05, corrected). Correlation analyses showed that EC of precuneus was related to SPPB (r = 0.25) and balance (r = 0.27); however, DC (r = -0.25) and EC (r = -0.25) of middle temporal gyrus was related with SPPB in all participants (p < 0.05, corrected).

CONCLUSION

CMIs represent an independent risk factor for motor dysfunction. The relationship between CMIs and motor function may be attributed to suppression of functional hub region and compensatory activation of motor-related regions.