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Objective A growing body of evidence has put forward clinical risk factors associated with patients with mood disorders that attempt suicide. However, what is not known is how to integrate clinical variables into a clinically useful tool in order to estimate the probability of an individual patient attempting suicide. Method A total of 144 patients with mood disorders were included. Clinical variables associated with suicide attempts among patients with mood disorders and demographic variables were used to ‘train’ a machine learning algorithm. The resulting algorithm was utilized in identifying novel or ‘unseen’ individual subjects as either suicide attempters or non-attempters. Three machine learning algorithms were implemented and evaluated. Results All algorithms distinguished individual suicide attempters from non-attempters with prediction accuracy ranging between 65%-72% (p<0.05). In particular, the relevance vector machine (RVM) algorithm correctly predicted 103 out of 144 subjects translating into 72% accuracy (72.1% sensitivity and 71.3% specificity) and an area under the curve of 0.77 (p<0.0001). The most relevant predictor variables in distinguishing attempters from non-attempters included previous hospitalizations for depression, a history of psychosis, cocaine dependence and post-traumatic stress disorder (PTSD) comorbidity. Conclusion Risk for suicide attempt among patients with mood disorders can be estimated at an individual subject level by incorporating both demographic and clinical variables. Future studies should examine the performance of this model in other populations and its subsequent utility in facilitating selection of interventions to prevent suicide.

The lifetime risk of suicide and suicide attempt in patients with schizophrenia are 5% and 25%-50%, respectively. The current meta-analysis aims to determine risk factors associated with suicidality in subjects with schizophrenia. We searched Pubmed, Web of Science, EMBASE, and the reference lists of included studies. Inclusion criteria were met if an article reported a dichotomous sample of patients with schizophrenia with suicidal ideation, attempted suicide, or suicide compared to patients without. We also performed a cohort study meta-analysis as a supplemental analysis. A total of 96 studies with 80488 participants were included in our analysis. Depressive symptoms (P < .0001), Positive and Negative Symptom Scale (PANSS) general score (P < .0001) and number of psychiatric hospitalizations (P < .0001) were higher in patients with suicide ideation. History of alcohol use (P = .0001), family history of psychiatric illness (P < .0001), physical comorbidity (P < .0001), history of depression (P < .0001), family history of suicide (P < .0001), history of drug use (P = .0024), history of tobacco use (P = .0034), being white (P = .0022), and depressive symptoms (P < .0001) were the most consistent variables associated with suicide attempts. The first two were also significant in the cohort meta-analysis. Being male (P = .0005), history of attempted suicide (P < .0001), younger age (P = .0266), higher intelligence quotient (P < .0001), poor adherence to treatment (P < .0001), and hopelessness (P < .0001) were the most consistently associated with suicide. The first three were also significant in the cohort meta-analysis. Our findings may help with future development of preventive strategies to combat suicide. Future studies may combine the above-mentioned variables by using multivariate predictive analysis techniques to objectively stratify suicidality in schizophrenia.

Illness trajectories are largely variable, and illness progression is not a general rule in BD. The number of manic episodes seems to be the clinical marker more robustly associated with neuroprogression in BD. However, the majority of the evidence came from cross-sectional studies that are prone to bias. Longitudinal studies may help to identify signatures of neuroprogression and integrate findings from the field of neuroimaging, neurocognition, and biomarkers.

Cortical gyrification of the brain represents the folding characteristic of the cerebral cortex. How the brain cortical gyrification changes from childhood to old age in healthy human subjects is still unclear. Additionally, studies have shown regional gyrification alterations in patients with major psychiatric disorders, such as major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia (SCZ). However, whether the lifespan trajectory of gyrification over the brain is altered in patients diagnosed with major psychiatric disorders is still unknown. In this study, we investigated the trajectories of gyrification in three independent cohorts based on structural brain images of 881 subjects from age 4 to 83. We discovered that the trajectory of gyrification during normal development and aging was not linear and could be modeled with a logarithmic function. We also found that the gyrification trajectories of patients with MDD, BD and SCZ were deviated from the healthy one during adulthood, indicating altered aging in the brain of these patients.

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Background Neuroanatomical abnormalities in Bipolar disorder (BD) have previously been reported. However, the utility of these abnormalities in distinguishing individual BD patients from Healthy controls and stratify patients based on overall illness burden has not been investigated in a large cohort. Methods In this study, we examined whether structural neuroimaging scans coupled with a machine learning algorithm are able to distinguish individual BD patients from Healthy controls in a large cohort of 256 subjects. Additionally, we investigated the relationship between machine learning predicted probability scores and subjects’ clinical characteristics such as illness duration and clinical stages. Neuroimaging scans were acquired from 128 BD patients and 128 Healthy controls. Gray and white matter density maps were obtained and used to ‘train’ a relevance vector machine (RVM) learning algorithm which was used to distinguish individual patients from Healthy controls. Results The RVM algorithm distinguished patients from Healthy controls with 70.3 % accuracy (74.2 % specificity, 66.4 % sensitivity, chi-square p<0.005) using white matter density data and 64.9 % accuracy (71.1 % specificity, 58.6 % sensitivity, chi-square p<0.005) with gray matter density. Multiple brain regions – largely covering the fronto – limbic system were identified as ‘most relevant’ in distinguishing both groups. Patients identified by the algorithm with high certainty (a high probability score) – belonged to a subgroup with more than ten total lifetime manic episodes including hospitalizations (late stage). Conclusions These results indicate the presence of widespread structural brain abnormalities in BD which are associated with higher illness burden – which points to neuroprogression.