Blair Robert James Richard, Bashford-Largo Johannah, Dominguez Ahria, Dobbertin Matthew, Blair Karina S, Bajaj Sahil
Child and Adolescent Mental Health Center, Copenhagen University Hospital - Mental Health Services CPH, Copenhagen, Denmark.
Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
Psychol Med. 2024 Nov 18;54(15):1-10. doi: 10.1017/S003329172400240X.
Machine learning (ML) has developed classifiers differentiating patient groups despite concerns regarding diagnostic reliability. An alternative strategy, used here, is to develop a functional classifier (hyperplane) (e.g. distinguishing the neural responses to received reward received punishment in typically developing (TD) adolescents) and then determine the functional integrity of the response (reward response distance from the hyperplane) in adolescents with externalizing and internalizing conditions and its associations with symptom clusters.
Two hundred and ninety nine adolescents (mean age = 15.07 ± 2.30 years, 117 females) were divided into three groups: a training sample of TD adolescents where the Support Vector Machine (SVM) algorithm was applied ( = 65; 32 females), and two test groups- an independent sample of TD adolescents ( = 39; 14 females) and adolescents with a psychiatric diagnosis (major depressive disorder (MDD), generalized anxiety disorder (GAD), attention deficit hyperactivity disorder (ADHD) & conduct disorder (CD); = 195, 71 females).
SVM ML analysis identified a hyperplane with accuracy = 80.77%, sensitivity = 78.38% and specificity = 88.99% that implicated feature neural regions associated with reward punishment (e.g. nucleus accumbens anterior insula cortices). Adolescents with externalizing diagnoses were significantly less likely to show a normative and significantly more likely to show a deficient reward response than the TD samples. Deficient reward response was associated with elevated CD, MDD, and ADHD symptoms.
Distinguishing the response to reward relative to punishment in TD adolescents via ML indicated notable disruptions in this response in patients with CD and ADHD and associations between reward responsiveness and CD, MDD, and ADHD symptom severity.
尽管存在对诊断可靠性的担忧，但机器学习（ML）已开发出区分患者群体的分类器。本文采用的另一种策略是开发一个功能分类器（超平面）（例如，区分典型发育（TD）青少年对获得奖励与接受惩罚的神经反应），然后确定患有外化和内化病症的青少年的反应功能完整性（奖励反应与超平面的距离）及其与症状群的关联。
299名青少年（平均年龄 = 15.07 ± 2.30岁，117名女性）被分为三组：应用支持向量机（SVM）算法的TD青少年训练样本（n = 65；32名女性），以及两个测试组——TD青少年独立样本（n = 39；14名女性）和患有精神疾病诊断的青少年（重度抑郁症（MDD）、广泛性焦虑症（GAD）、注意力缺陷多动障碍（ADHD）和品行障碍（CD）；n = 195，71名女性）。
SVM ML分析确定了一个超平面，其准确率 = 80.77%，灵敏度 = 78.38%，特异性 = 88.99%，该超平面涉及与奖励和惩罚相关的特征神经区域（例如伏隔核和前岛叶皮质）。与TD样本相比，患有外化诊断的青少年表现出正常奖励反应的可能性显著降低，而表现出奖励反应不足的可能性显著增加。奖励反应不足与CD、MDD和ADHD症状的加重相关。
通过ML区分TD青少年对奖励与惩罚的反应表明，CD和ADHD患者的这种反应存在明显破坏，且奖励反应性与CD、MDD和ADHD症状严重程度之间存在关联。
