Within-disorder heterogeneity complicates mapping the neurobiological features of psychopathology to Diagnostic and Statistical Manual of Mental Disorders conceptualizations. The present study explored the patterns of diagnostic classification errors among disorders with commonly co-occurring features to examine this heterogeneity. Classification analyses were conducted with the University of California, Los Angeles Phenomics Study database using a support-vector classifier to differentiate disorders via whole brain task-based functional connectivity, predicting that model misclassifications would provide insight about brain connectivity characteristics shared across disorders. Whether symptoms and specific brain networks could account for misclassification rates was also explored. The classification model performed better than chance (44% accuracy, p = .01) and revealed that misclassification of schizophrenia (SCZ) as bipolar disorder (BD; 38%) and BD as SCZ (36%) was symmetrical. Attention-deficit/hyperactivity disorder (ADHD) was misclassified as BD at the highest rate (46%) and higher than the converse (17%). SCZ and ADHD were misclassified least (15% SCZ as ADHD and 22% ADHD as SCZ). Considerable variance in misclassification of SCZ as BD (R2 = .83) and BD as SCZ (R2 = .71) could be accounted for by symptoms of both SCZ and BD. Permutation testing revealed disorder- and network-specific effects, with certain networks improving classification accuracy and others hindering it for specific disorders. An approach focused on classification errors replicated known disorder overlap, producing errors in the expected configuration. Further, it identified clinical and neural features within and across diagnostic categories that contribute to disorder misclassification and within-disorder heterogeneity. This approach may facilitate neurobiologically informed phenotypic differentiation within diagnostic groups. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

中文翻译：

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充分利用错误：利用神经影像学数据的机器学习模型生成的错误分类来捕获疾病异质性。


疾病内异质性使将精神病理学的神经生物学特征映射到精神疾病诊断与统计手册的概念化变得复杂。本研究探讨了具有常见同时发生特征的疾病之间的诊断分类错误模式，以检查这种异质性。使用加州大学洛杉矶分校表型组学研究数据库进行分类分析，使用支持向量分类器通过基于全脑任务的功能连接来区分疾病，预测模型错误分类将提供有关疾病之间共享的大脑连接特征的见解。还探讨了症状和特定的大脑网络是否可以解释错误分类率。分类模型的表现优于偶然性 （44% 准确率，p = .01），并揭示了将精神分裂症 （SCZ） 错误分类为双相情感障碍 （BD;38%） 和 BD 错误分类为 SCZ （36%） 是对称的。注意力缺陷/多动障碍 （ADHD） 被错误分类为 BD 的比率最高 （46%），高于相反的 （17%）。SCZ 和 ADHD 的错误分类最少 （15% 的 SCZ 为 ADHD，22% 的 ADHD 为 SCZ）。将 SCZ 错误分类为 BD （R2 = .83） 和 BD 错误分类为 SCZ （R2 = .71） 的相当大的差异可以由 SCZ 和 BD 的症状来解释。排列检验揭示了疾病和网络特异性影响，某些网络提高了分类准确性，而另一些网络则阻碍了特定疾病的分类准确性。一种专注于分类错误的方法复制了已知的疾病重叠，从而在预期配置中产生错误。 此外，它确定了导致疾病错误分类和疾病内异质性的诊断类别内和诊断类别之间的临床和神经特征。这种方法可能有助于诊断组内的神经生物学信息表型分化。（PsycInfo 数据库记录 （c） 2024 APA，保留所有权利）。