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

Tian, Shui; Zhu, Rong; Chen, Zhilu; Wang, Huan; Chattun, Mohammad Ridwan; Zhang, Siqi; Shao, Junneng; Wang, Xinyi; Yao, Zhijian; Lü, Qing

doi:10.1002/hbm.26243

Cited by 8 publications

(1 citation statement)

References 58 publications

(77 reference statements)

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“…Only a limited number of studies have employed ML specifically to address suicide risk on the BPD population, and just a few of them use Magnetic Resonance Imaging (MRI) data. One is the study by Tian et al [11], where a k-NN model was developed on resting-state functional MRI variables of individuals with bipolar disorder (overall population composed by 288 subjects), resulting in a final suicide prediction accuracy of 0.72 on an independent dataset. From a ML methodology point of view, many of the previously mentioned works present some limitations.…”

Section: Introductionmentioning

confidence: 99%

Suicide Risk in Borderline Personality Disorder: a Machine Learning Tool based on Clinical and MRI Data

Crema,

Boccali,

Martinelli

et al. 2024

Preprint

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Borderline Personality Disorder (BPD) is a complex mental condition. Individuals with BPD have an average of three lifetime suicide attempts, and 10% of them die by suicide. Understanding risk factors linked to suicidal behaviors is crucial for effective intervention strategies. In recent years, machine learning (ML) approaches for predicting suicide risk in persons with mental disorders have been developed, but a reliable, BPD-specific tool is lacking. In this work, we developed DRAMA-BPD (Detecting Risk factors for suicide Attempts with Machine learning Approaches in Borderline Personality Disorder), a second-opinion tool to assess suicide risk in individuals with BPD. DRAMA-BPD, built upon a Support Vector Machine (SVM) classifier, is trained on the CLIMAMITHE (CLM) dataset, which encompasses sociodemographic, clinical, emotional assessments, and MRI data. Feature selection revealed that 6 out of the 7 most important features are MRI-derived, and a comprehensive review was conducted to ensure consistency with existing scientific literature. The classifier achieved an overall Area Under the Curve (AUC) of 0.73, Precision (P) of 0.75, Recall (R) of 0.70, and F1-score of 0.72. Tests were conducted on the independent SUDMEX_CONN dataset, yielding an AUC of 0.59, P of 0.46, R of 0.92, and F1 of 0.62. While there is a significant imbalance between Precision and Recall, these results demonstrate the potential utility of the proposed model.

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Section: Introductionmentioning

confidence: 99%

Suicide Risk in Borderline Personality Disorder: a Machine Learning Tool based on Clinical and MRI Data

Crema,

Boccali,

Martinelli

et al. 2024

Preprint

View full text Add to dashboard Cite

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

Tian

Zhu

Chen

et al. 2023

Human Brain Mapping

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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.

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Local brain abnormalities in emotional disorders: Evidence from resting state fMRI studies

Jia,

Li,

Wang

et al. 2024

WIRES Cognitive Science

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Emotional disorders inflict an enormous burden on society. Research on brain abnormalities implicated in emotional disorders has witnessed great progress over the past decades. Using cross‐sectional and longitudinal designs, resting state functional magnetic resonance imaging (rs‐fMRI) and its analytic approaches have been applied to characterize the local properties of patients with emotional disorders. Additionally, brain activity alterations of emotional disorders have shown frequency‐specific. Despite the gains in understanding the roles of brain abnormalities in emotional disorders, the limitation of the small sample size needs to be highlighted. Lastly, we proposed that evidence from the positive psychology research stream presents it as a viable discipline, whose suggestions could be developed in future emotional disorders research. Such interdisciplinary research may produce novel treatments and intervention options.This article is categorized under: Psychology > Brain Function and Dysfunction

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

Cited by 8 publications

References 58 publications

Suicide Risk in Borderline Personality Disorder: a Machine Learning Tool based on Clinical and MRI Data

Suicide Risk in Borderline Personality Disorder: a Machine Learning Tool based on Clinical and MRI Data

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

Local brain abnormalities in emotional disorders: Evidence from resting state fMRI studies

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