BACKGROUND Despite the marked interindividual variability in the clinical presentation of schizophrenia, the extent to which individual dimensions of psychopathology relate to the functional variability in brain networks among patients remains unclear. Here, we address this question using network-based predictive modeling of individual psychopathology along 4 data-driven symptom dimensions. Follow-up analyses assess the molecular underpinnings of predictive networks by relating them to neurotransmitter-receptor distribution patterns. METHODS We investigated resting-state functional magnetic resonance imaging data from 147 patients with schizophrenia recruited at 7 sites. Individual expression along negative, positive, affective, and cognitive symptom dimensions was predicted using a relevance vector machine based on functional connectivity within 17 meta-analytic task networks following repeated 10-fold cross-validation and leave-one-site-out analyses. Results were validated in an independent sample. Networks robustly predicting individual symptom dimensions were spatially correlated with density maps of 9 receptors/transporters from prior molecular imaging in healthy populations. RESULTS Tenfold and leave-one-site-out analyses revealed 5 predictive network-symptom associations. Connectivity within theory of mind, cognitive reappraisal, and mirror neuron networks predicted negative, positive, and affective symptom dimensions, respectively. Cognitive dimension was predicted by theory of mind and socioaffective default networks. Importantly, these predictions generalized to the independent sample. Intriguingly, these two networks were positively associated with D1 receptor and serotonin reuptake transporter densities as well as dopamine synthesis capacity. CONCLUSIONS We revealed a robust association between intrinsic functional connectivity within networks for socioaffective processes and the cognitive dimension of psychopathology. By investigating the molecular architecture, this work links dopaminergic and serotonergic systems with the functional topography of brain networks underlying cognitive symptoms in schizophrenia.

中文翻译：

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任务定义的大脑网络的内在连接模式允许个体预测精神分裂症的认知症状维度，并与分子结构有关

背景尽管精神分裂症的临床表现存在显着的个体差异，但精神病理学的个体维度与患者大脑网络功能变异性的相关程度仍不清楚。在这里，我们使用基于网络的个体精神病理学预测模型沿着 4 个数据驱动的症状维度来解决这个问题。后续分析通过将预测网络与神经递质受体分布模式相关联来评估预测网络的分子基础。方法 我们调查了在 7 个地点招募的 147 名精神分裂症患者的静息状态功能磁共振成像数据。沿着消极、积极、情感的个体表达，在重复 10 倍交叉验证和留一个站点分析之后，使用基于 17 个元分析任务网络中的功能连接的相关向量机预测认知症状维度。结果在独立样本中得到验证。稳健地预测个体症状维度的网络与来自健康人群先前分子成像的 9 种受体/转运蛋白的密度图在空间上相关。结果 十倍和留一站点分析揭示了 5 个预测性网络症状关联。心智理论、认知重估和镜像神经元网络中的连通性分别预测了消极、积极和情感症状维度。认知维度由心理理论和社会情感默认网络预测。重要的，这些预测推广到独立样本。有趣的是，这两个网络与 D1 受体和血清素再摄取转运蛋白密度以及多巴胺合成能力呈正相关。结论我们揭示了社会情感过程网络内的内在功能连接与精神病理学的认知维度之间存在强大的关联。通过研究分子结构，这项工作将多巴胺能和血清素能系统与精神分裂症认知症状背后的大脑网络的功能拓扑联系起来。结论我们揭示了社会情感过程网络内的内在功能连接与精神病理学的认知维度之间存在强大的关联。通过研究分子结构，这项工作将多巴胺能和血清素能系统与精神分裂症认知症状背后的大脑网络的功能拓扑联系起来。结论我们揭示了社会情感过程网络内的内在功能连接与精神病理学的认知维度之间存在强大的关联。通过研究分子结构，这项工作将多巴胺能和血清素能系统与精神分裂症认知症状背后的大脑网络的功能拓扑联系起来。