OBJECTIVE Childhood maltreatment (CM) is a significant risk factor for the development and severity of bipolar disorder (BD) with increased risk of suicide attempts (SA). This study evaluated whether a machine learning algorithm could be trained to predict if a patient with BD has a history of CM or previous SA based on brain metabolism measured by positron emission tomography. METHODS Thirty-six euthymic patients diagnosed with BD type I, with and without a history of CM were assessed using the Childhood Trauma Questionnaire. Suicide attempts were assessed through the Mini International Neuropsychiatric Interview (MINI-Plus) and a semi-structured interview. Resting-state positron emission tomography with 18F-fluorodeoxyglucose was conducted, electing only grey matter voxels through the Statistical Parametric Mapping toolbox. Imaging analysis was performed using a supervised machine learning approach following Gaussian Process Classification. RESULTS Patients were divided into 18 participants with a history of CM and 18 participants without it, along with 18 individuals with previous SA and 18 individuals without such history. The predictions for CM and SA were not significant (accuracy = 41.67%; p = 0.879). CONCLUSION Further investigation is needed to improve the accuracy of machine learning, as its predictive qualities could potentially be highly useful in determining histories and possible outcomes of high-risk psychiatric patients.

中文翻译：

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检查与躁郁症患者的童年虐待和自杀企图相关的脑代谢差异：一项 PET 和机器学习研究。

目的 童年虐待 (CM) 是双相情感障碍 (BD) 发展和严重程度的重要危险因素，会增加自杀未遂 (SA) 的风险。这项研究评估了是否可以训练机器学习算法，以根据正电子发射断层扫描测量的脑代谢来预测 BD 患者是否有 CM 病史或既往 SA 病史。方法 使用儿童创伤问卷对 36 名被诊断患有 I 型 BD，有或没有 CM 病史的心境正常的患者进行评估。自杀企图通过迷你国际神经精神病学访谈 (MINI-Plus) 和半结构化访谈进行评估。使用 18F-氟脱氧葡萄糖进行静息态正电子发射断层扫描，通过统计参数映射工具箱仅选择灰质体素。使用高斯过程分类后的监督机器学习方法进行成像分析。结果 患者被分为 18 名有 CM 病史的参与者和 18 名没有 CM 病史的参与者，以及 18 名有 SA 病史的人和 18 名没有这种病史的人。CM 和 SA 的预测不显着（准确度 = 41.67%；p = 0.879）。结论 需要进一步研究以提高机器学习的准确性，因为其预测质量可能对确定高危精神病患者的病史和可能的结果非常有用。以及 18 名以前有 SA 的人和 18 名没有这种历史的人。CM 和 SA 的预测不显着（准确度 = 41.67%；p = 0.879）。结论 需要进一步研究以提高机器学习的准确性，因为其预测质量可能对确定高危精神病患者的病史和可能的结果非常有用。以及 18 名以前有 SA 的人和 18 名没有这种历史的人。CM 和 SA 的预测不显着（准确度 = 41.67%；p = 0.879）。结论 需要进一步研究以提高机器学习的准确性，因为其预测质量可能对确定高危精神病患者的病史和可能的结果非常有用。