Institute of Neuroscience and Medicine: Brain and Behavior (INM-7), Research Center Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
Institute of Neuroscience and Medicine: Brain and Behavior (INM-7), Research Center Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
Biol Psychiatry. 2021 Feb 1;89(3):308-319. doi: 10.1016/j.biopsych.2020.09.024. Epub 2020 Oct 3.
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.
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.
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 D receptor and serotonin reuptake transporter densities as well as dopamine synthesis capacity.
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.
尽管精神分裂症的临床表现存在明显的个体间变异性，但个体病理维度与患者脑网络功能变异性的关系程度仍不清楚。在这里，我们使用基于网络的个体病理预测模型来解决这个问题，该模型沿着 4 个数据驱动的症状维度进行。后续分析通过将其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。
我们研究了来自 7 个地点的 147 名精神分裂症患者的静息态功能磁共振成像数据。使用基于相关性向量机的方法，根据 17 个元分析任务网络内的功能连接，在经过重复的 10 倍交叉验证和留一站点外分析后，对个体沿着阴性、阳性、情感和认知症状维度的表达进行预测。结果在独立样本中进行了验证。在健康人群的先前分子成像中，与 9 种受体/转运体的密度图具有空间相关性的网络可以稳健地预测个体症状维度。
十倍和留一站点外分析揭示了 5 个预测网络-症状关联。心理理论、认知重评和镜像神经元网络内的连通性分别预测了阴性、阳性和情感症状维度。认知维度由心理理论和社会情感默认网络预测。重要的是，这些预测可以推广到独立样本。有趣的是，这两个网络与 D 受体和 5-羟色胺再摄取转运体密度以及多巴胺合成能力呈正相关。
我们揭示了社会情感过程的网络内固有功能连通性与精神病理学认知维度之间的稳健关联。通过研究分子结构，这项工作将多巴胺能和 5-羟色胺能系统与精神分裂症认知症状的脑网络功能拓扑联系起来。
