Neuroimaging research has uncovered a multitude of neural abnormalities associated with psychopathology, but few prediction-based studies have been conducted during adolescence, and even fewer used neurobiological features that were extracted across multiple neuroimaging modalities. This gap in the literature is critical, as deriving accurate brain-based models of psychopathology is an essential step towards understanding key neural mechanisms and identifying high-risk individuals. As such, we trained adaptive tree-boosting algorithms on multimodal neuroimaging features from the Lifespan Human Connectome Developmental (HCP-D) sample that contained 956 participants between the ages of 8 to 22 years old. Our feature space consisted of 1037 anatomical, 1090 functional, and 192 diffusion MRI features, which were used to derive models that separately predicted internalizing symptoms, externalizing symptoms, and the general psychopathology factor. We found that multimodal models were the most accurate, but all brain-based models of psychopathology yielded out-of-sample predictions that were weakly correlated with actual symptoms (r² < 0.15). White matter microstructural properties, including orientation dispersion indices and intracellular volume fractions, were the most predictive of general psychopathology, followed by cortical thickness and functional connectivity. Spatially, the most predictive features of general psychopathology were primarily localized within the default mode and dorsal attention networks. These results were mostly consistent across all dimensions of psychopathology, except orientation dispersion indices and the default mode network were not as heavily weighted in the prediction of internalizing and externalizing symptoms. Taken with prior literature, it appears that neurobiological features are an important part of the equation for predicting psychopathology but relying exclusively on neural markers is clearly not sufficient, especially among adolescent samples with subclinical symptoms. Consequently, risk factor models of psychopathology may benefit from incorporating additional sources of information that have also been shown to explain individual differences, such as psychosocial factors, environmental stressors, and genetic vulnerabilities.

神经影像学研究发现了多种与精神病理学相关的神经异常，但在青春期期间进行的基于预测的研究很少，使用通过多种神经影像学方式提取的神经生物学特征的研究更少。文献中的这一差距至关重要，因为推导准确的基于大脑的精神病理学模型是理解关键神经机制和识别高风险个体的重要一步。因此，我们根据人类连接组发育 (HCP-D) 样本的多模态神经影像特征训练了自适应树增强算法，该样本包含 956 名年龄在 8 岁到 22 岁之间的参与者。我们的特征空间由 1037 个解剖特征、1090 个功能特征和 192 个扩散 MRI 特征组成，这些特征用于导出分别预测内化症状、外化症状和一般精神病理学因素的模型。我们发现多模态模型是最准确的，但所有基于大脑的精神病理学模型都会产生与实际症状相关性较弱的样​​本外预测（r ² < 0.15）。白质微观结构特性，包括方向分散指数和细胞内体积分数，最能预测一般精神病理学，其次是皮质厚度和功能连接性。在空间上，一般精神病理学最具预测性的特征主要集中在默认模式和背侧注意网络内。这些结果在精神病理学的所有维度上基本一致，除了方向分散指数和默认模式网络在预测内化和外化症状时没有那么重要。 结合之前的文献来看，神经生物学特征似乎是预测精神病理学方程的重要组成部分，但仅仅依靠神经标记显然是不够的，特别是在具有亚临床症状的青少年样本中。因此，精神病理学的风险因素模型可能会受益于纳入其他信息来源，这些信息也已被证明可以解释个体差异，例如心理社会因素、环境压力源和遗传脆弱性。