Neural mechanisms underlying suicide behavior in youth with bipolar disorder

Resting-state functional connectivity indicators of risk and resilience for self-harm in adolescent bipolar disorder

BACKGROUND

Suicide is the second leading cause of death in all youth and among adults with bipolar disorder (BD). The risk of suicide in BD is among the highest of all psychiatric conditions. Self-harm, including suicide attempts and non-suicidal self-injury, is a leading risk factor for suicide. Neuroimaging studies suggest reward circuits are implicated in both BD and self-harm; however, studies have yet to examine self-harm related resting-state functional connectivity (rsFC) phenotypes within adolescent BD.

METHODS

Resting-state fMRI data were analyzed for 141 adolescents, ages 13-20 years, including 38 with BD and lifetime self-harm (BD_SH+), 33 with BD and no self-harm (BD_SH-), and 70 healthy controls (HC). The dorsolateral prefrontal cortex (dlPFC), orbitofrontal cortex (OFC) and amygdala were examined as regions of interest in seed-to-voxel analyses. A general linear model was used to explore the bivariate correlations for each seed.

RESULTS

BD_SH- had increased positive rsFC between the left amygdala and left lateral occipital cortex, and between the right dlPFC and right frontal pole, and increased negative rsFC between the left amygdala and left superior frontal gyrus compared to BD_SH+ and HC. BD_SH+ had increased positive rsFC of the right OFC with the precuneus and left paracingulate gyrus compared to BD_SH- and HC.

CONCLUSIONS

This study provides preliminary evidence of altered reward-related rsFC in relation to self-harm in adolescents with BD. Between-group differences conveyed a combination of putative risk and resilience connectivity patterns. Future studies are warranted to evaluate changes in rsFC in response to treatment and related changes in self-harm.

Prediction of suicidality in bipolar disorder using variability of intrinsic brain activity and machine learning

Bipolar disorder (BD) is associated with marked suicidal susceptibility, particularly during a major depressive episode. However, the evaluation of suicidal risk remains challenging since it relies mainly on self-reported information from patients. Hence, it is necessary to complement neuroimaging features with advanced machine learning techniques in order to predict suicidal behavior in BD patients. In this study, a total of 288 participants, including 75 BD suicide attempters, 101 BD nonattempters and 112 healthy controls, underwent a resting-state functional magnetic resonance imaging (rs-fMRI). Intrinsic brain activity was measured by amplitude of low-frequency fluctuation (ALFF). We trained and tested a two-level k-nearest neighbors (k-NN) model based on resting-state variability of ALFF with fivefold cross-validation. BD suicide attempters had increased dynamic ALFF values in the right anterior cingulate cortex, left thalamus and right precuneus. Compared to other machine learning methods, our proposed framework had a promising performance with 83.52% accuracy, 78.75% sensitivity and 87.50% specificity. The trained models could also replicate and validate the results in an independent cohort with 72.72% accuracy. These findings based on a relatively large data set, provide a promising way of combining fMRI data with machine learning technique to reliably predict suicide attempt at an individual level in bipolar depression. Overall, this work might enhance our understanding of the neurobiology of suicidal behavior by detecting clinically defined disruptions in the dynamics of instinct brain activity.

Temporal dynamics alterations of spontaneous neuronal activity in anterior cingulate cortex predict suicidal risk in bipolar II patients

Bipolar disorder type II (BD-II) is linked to an increased suicidal risk. Since a prior suicide attempt (SA) is the single most important risk factor for sequent suicide, the elucidation of involved neural substrates is critical for its prevention. Therefore, we examined the spontaneous brain activity and its temporal variabilities in suicide attempters with bipolar II during a major depressive episode. In this cross-sectional study, 101 patients with BD-II, including 44 suicidal attempters and 57 non-attempters, and 60 non-psychiatric controls underwent a resting-state functional magnetic resonance imaging (fMRI). Participants were assessed with Hamilton Rating Scale for Depression (HAMD) and Nurses^, Global Assessment of Suicide Risk (NGASR). The dynamics of low-frequency fluctuation (dALFF) was measured using sliding-window analysis and its correlation with suicidal risk was conducted using Pearson correlation. Compared to non-attempters, suicidal attempters showed an increase in brain activity and temporal dynamics in the anterior cingulate cortex (ACC). In addition, the temporal variabilities of ACC activity positively correlated with suicidal risk (R = 0.45, p = 0.004), while static ACC activity failed to (R = 0.08, p > 0.05). Our findings showed that an aberrant static ALFF and temporal variability could affect suicidal behavior in BD-II patients. However, temporal variability of neuronal activity was more sensitive than static amplitude in reflecting diathesis for suicide in BD-II. Dynamics of brain activity could be considered in developing neuromarkers for suicide prevention.