Aberrant dynamic functional connectivity in corticostriatal circuitry in depressed bipolar II disorder with recent suicide attempt

Effects of bright light therapy on cingulate cortex dynamic functional connectivity and neurotransmitter activity in young adults with subthreshold depression

BACKGROUND

The neurobiological mechanisms behind the antidepressant effect of bright light therapy (BLT) are unclear. We aimed to explore the dynamic functional connectivity (dFC) changes of the cingulate cortex (CC) in subthreshold depression (StD).

METHODS

The StD participants (38 BLT and 39 placebo) underwent resting-state functional magnetic resonance imaging (rs-fMRI) and mood assessment before and after eight-week BLT. Seed-based whole-brain dFC analysis was conducted and multivariate regression model was adopted to predict Hamilton Depression Rating Scale (HDRS) and Centre for Epidemiologic Studies Depression Scale (CESD) scores changes after BLT. JuSpace toolbox was used to calculate the associations between dFC and neurotransmitter activity in the BLT group.

RESULTS

BLT group showed decreased CESD and HDRS scores. Also, BLT group showed increased dFC of the right supracallosal anterior cingulate cortex (supACC)-right temporal pole (TP), left middle cingulate cortex (MCC)-right insula, and left supACC-pons, and decreased dFC of the right supACC- right middle frontal gyrus (MFG). Changes in dFC of the right supACC-right TP showed positive correlation with changes in CESD and HDRS. Moreover, combining the baseline dFC variability of the CC could predict HDRS changes in BLT. Finally, compared to baseline, the supACC and MCC dFC changes showed significant correlations with the neurotransmitter activities.

CONCLUSIONS

BLT alleviates depressive symptoms and changes the CC dFC variability in StD, and pre-treatment dFC variability of the CC could be used as a biomarker for improved BLT treatment in StD. Furthermore, dFC changes with specific neurotransmitter systems after BLT may underline the antidepressant mechanisms of BLT.

Challenges and Frontiers in Computational Metabolic Psychiatry

One of the primary challenges in metabolic psychiatry is that the disrupted brain functions that underlie psychiatric conditions arise from a complex set of downstream and feedback processes that span multiple spatiotemporal scales. Importantly, the same circuit can have multiple points of failure, each of which results in a different type of dysregulation, and thus elicits distinct cascades downstream that produce divergent signs and symptoms. Here, we illustrate this challenge by examining how subtle differences in circuit perturbations can lead to divergent clinical outcomes. We also discuss how computational models can perform the spatially heterogeneous integration and bridge in vitro and in vivo paradigms. By leveraging recent methodological advances and tools, computational models can integrate relevant processes across scales (e.g., tricarboxylic acid cycle, ion channel, neural microassembly, whole-brain macrocircuit) and across physiological systems (e.g., neural, endocrine, immune, vascular), providing a framework that can unite these mechanistic processes in a manner that goes beyond the conceptual and descriptive to the quantitative and generative. These hold the potential to sharpen our intuitions toward circuit-based models for personalized diagnostics and treatment.

Abnormal ReHo and ALFF values in drug-naïve depressed patients with suicidal ideation or attempts: Evidence from the REST-meta-MDD consortium

BACKGROUND

The assessment of suicide risk in patients with major depressive disorder (MDD) is somewhat subjective in clinical diagnosis and may lead to diagnostic bias and serious consequences. Therefore, the aim of this study was to determine whether MDD patients with suicidal ideation or suicide attempts exhibited local brain functional synchrony and spontaneous activity intensity, thus providing certain imaging basis for suicide assessment.

METHODS

This study was conducted using ReHo and ALFF analyses on 213 first episode drug-naïve MDD patients from the REST-meta-MDD consortium. All patients were categorized into MDD with SI or SA group and MDD without SI and SA. A voxel-based two-sample t-test was then used to identify brain regions with significant differences in ReHo or ALFF values. Finally, Reho or ALFF values of those brain regions in MDD with SI or SA group were extracted for correlation analysis with suicide severity.

RESULTS

Compared with MDD patients without SI or SA, MDD patients with SI or SA had increased ReHo in the triangular part of left inferior frontal gyrus, orbital part of right inferior frontal gyrus and right precuneus gyrus, and increased ALFF in the middle occipital gyrus. All of these brain region characteristics were positively correlated with suicide severity on the HAMD 3th item score and HAMD 9th item score.

CONCLUSION

Our findings suggest that abnormalities of regional spontaneous brain activity were found in IFG, precuneus gyrus, and MOG among MDD patients with suicidal thoughts or attempts, which provides a reliable imaging basis for identifying and preventing suicide.

Machine learning applied to the prediction of relapse, hospitalization, and suicide in bipolar disorder using neuroimaging and clinical data: A systematic review

BACKGROUND

Bipolar disorder (BD) is associated with increased morbidity/mortality. Adverse outcome prediction might help with the management of patients with BD.

METHODS

We systematically reviewed the performance of machine learning (ML) studies in predicting adverse outcomes (relapse or recurrence, hospital admission, and suicide-related events) in patients with BD. Demographic, clinical, and neuroimaging-related poor outcome predictors were also reviewed. Three databases (PubMed, Scopus, and Web of Science) were explored from inception to July 2023.

RESULTS

Eighteen studies, accounting for >30,000 patients, were included. Support vector machine, decision trees, random forest, and logistic regression were the most frequently used ML algorithms. ML models' area under the receiver operating characteristic (ROC) curve (AUC), sensitivity, and specificity ranged from 0.71 to 0.98, 72.7-92.8 %, and 59.0-95.2 % for relapse/recurrence prediction (4 studies (3 on relapses and 1 on recurrences). The corresponding values were 0.78-0.88, 21.4-100 %, and 77.0-99.7 % for hospital admissions (3 studies, 21,266 patients), and 0.71-0.99, 44.4-97.9 %, and 38.9-95.0 % for suicide-related events (10 studies, 5558 patients). Also, one study addressed a combination of the interest outcomes. Adverse outcome predictors included early onset BD, BD type I, comorbid psychiatric or substance use disorder, circadian rhythm disruption, hospitalization characteristics, and neuroimaging parameters, including increased dynamic amplitude of low-frequency fluctuation, decreased frontolimbic functional connectivity and aberrant dynamic functional connectivity in corticostriatal circuitry.

CONCLUSIONS

ML models can predict adverse outcomes of BD with relatively acceptable performance measures. Future studies with larger samples and nested cross-validation validation should be conducted to reach more reliable results.

Machine learning and the prediction of suicide in psychiatric populations: a systematic review

Machine learning (ML) has emerged as a promising tool to enhance suicidal prediction. However, as many large-sample studies mixed psychiatric and non-psychiatric populations, a formal psychiatric diagnosis emerged as a strong predictor of suicidal risk, overshadowing more subtle risk factors specific to distinct populations. To overcome this limitation, we conducted a systematic review of ML studies evaluating suicidal behaviors exclusively in psychiatric clinical populations. A systematic literature search was performed from inception through November 17, 2022 on PubMed, EMBASE, and Scopus following the PRISMA guidelines. Original research using ML techniques to assess the risk of suicide or predict suicide attempts in the psychiatric population were included. An assessment for bias risk was performed using the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) guidelines. About 1032 studies were retrieved, and 81 satisfied the inclusion criteria and were included for qualitative synthesis. Clinical and demographic features were the most frequently employed and random forest, support vector machine, and convolutional neural network performed better in terms of accuracy than other algorithms when directly compared. Despite heterogeneity in procedures, most studies reported an accuracy of 70% or greater based on features such as previous attempts, severity of the disorder, and pharmacological treatments. Although the evidence reported is promising, ML algorithms for suicidal prediction still present limitations, including the lack of neurobiological and imaging data and the lack of external validation samples. Overcoming these issues may lead to the development of models to adopt in clinical practice. Further research is warranted to boost a field that holds the potential to critically impact suicide mortality.

The altered temporal properties of dynamic functional connectivity associated with suicide attempt in bipolar disorders

OBJECTIVE

The suicide risk in bipolar disorder (BD) is the highest among psychiatric disorders, and the neurobiological mechanism of suicide in BD remains unclear. The study aimed to investigate the underlying relevance between the implicated abnormalities of dynamic functional connectivity (FC) and suicide attempt (SA) in BD.

METHODS

We used the sliding window method to analyze the dynamic FC patterns from resting-state functional MRI data in 81 healthy controls (HC) and 114 BD patients (50 with SA and 64 with none SA). Then, the temporal properties of dynamic FC and the relationship between altered measures and clinical variables were explored.

RESULTS

We found that one of the five captured brain functional states was more associated with SA. The SA patients showed significantly increased fractional window and dwell time in the suicide-related state, along with increased number of state transitions compared with none SA (NSA). In addition, the connections within subcortical network-subcortical network (SubC-SubC), default mode network-subcortical network (DMN-SubC), and attention network-subcortical network (AN-SubC) were significantly changed in SA patients relative to NSA and HC in the suicide-related state. Crucially, the above-altered measures were significantly correlated with suicide risk.

CONCLUSIONS

Our findings suggested that the impaired dynamic FC within SubC-SubC, DMN-SubC, and AN-SubC were the important underlying mechanism in understanding SA for BD patients. It highlights the temporal properties of whole-brain dynamic FC could serve as the valuable biomarker for suicide risk assessment in BD.

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.