Dissecting psychiatric spectrum disorders by generative embedding.
Dissecting psychiatric spectrum disorders by generative embedding.
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DOI：
10.1016/j.nicl.2013.11.002
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发表时间：
2014
影响因子：
4.2
通讯作者：
Stephan, Klaas E.
中科院分区：
文献类型：
--
作者：
Brodersen, Kay H.;Deserno, Lorenz;Schlagenhauf, Florian;Lin, Zhihao;Penny, Will D.;Buhmann, Joachim M.;Stephan, Klaas E.
这项概念验证研究探讨了通过生成嵌入定义精神谱系障碍亚组的可行性，使用动力系统模型从神经影像学数据推断神经元回路机制。为此，我们重新分析了41名诊断为精神分裂症的患者和42名健康对照者的fMRI数据集，这些患者执行了一项数字n-回工作记忆任务。在我们的生成嵌入方法中，我们使用来自视觉-顶叶-前额叶网络的动态因果模型（DCM）的参数估计来定义基于模型的特征空间，以用于监督和无监督学习技术的后续应用。首先，使用线性支持向量机进行分类，我们能够从基于DCM的有效连接估计中预测单个诊断标签，其准确率（78%）明显高于相同区域内的功能连接（62%）或局部活动（55%）。第二，基于变分贝叶斯高斯混合模型的无监督方法提供了两个聚类的证据，这两个聚类映射到患者和对照组，其准确性与监督方法几乎相同（71%）。最后，当仅对患者进行分析时，高斯混合模型表明存在三个患者亚组，每个亚组的特征在于视觉-顶叶-前额叶工作记忆网络的不同架构。重要的是，即使该分析无法获得有关患者临床症状的信息，但三个神经生理学定义的亚组映射到三个临床上不同的亚组，通过阳性和阴性症状量表（PANSS）评估的阴性症状严重程度的显著差异进行区分。总之，这项研究提供了一个具体的例子，精神谱系疾病如何可以分为亚组，定义的神经生理学机制指定的生成模型的网络动力学，如DCM。这些结果证实了我们之前在中风患者中的发现，即与功能连接或区域活动等更传统的测量方法相比，生成嵌入可以显着增强计算方法的可解释性和性能，以进行临床分类。定义精神疾病谱亚组的概念验证研究。我们重新分析了41名精神分裂症患者和42名对照的fMRI数据集。DCM参数高度预测诊断状态（78%的准确性）。无监督聚类的患者提出了三个亚组。这些组在临床上是不同的，其阴性症状的严重程度也不同。
This proof-of-concept study examines the feasibility of defining subgroups in psychiatric spectrum disorders by generative embedding, using dynamical system models which infer neuronal circuit mechanisms from neuroimaging data. To this end, we re-analysed an fMRI dataset of 41 patients diagnosed with schizophrenia and 42 healthy controls performing a numerical n-back working-memory task. In our generative-embedding approach, we used parameter estimates from a dynamic causal model (DCM) of a visual–parietal–prefrontal network to define a model-based feature space for the subsequent application of supervised and unsupervised learning techniques. First, using a linear support vector machine for classification, we were able to predict individual diagnostic labels significantly more accurately (78%) from DCM-based effective connectivity estimates than from functional connectivity between (62%) or local activity within the same regions (55%). Second, an unsupervised approach based on variational Bayesian Gaussian mixture modelling provided evidence for two clusters which mapped onto patients and controls with nearly the same accuracy (71%) as the supervised approach. Finally, when restricting the analysis only to the patients, Gaussian mixture modelling suggested the existence of three patient subgroups, each of which was characterised by a different architecture of the visual–parietal–prefrontal working-memory network. Critically, even though this analysis did not have access to information about the patients' clinical symptoms, the three neurophysiologically defined subgroups mapped onto three clinically distinct subgroups, distinguished by significant differences in negative symptom severity, as assessed on the Positive and Negative Syndrome Scale (PANSS). In summary, this study provides a concrete example of how psychiatric spectrum diseases may be split into subgroups that are defined in terms of neurophysiological mechanisms specified by a generative model of network dynamics such as DCM. The results corroborate our previous findings in stroke patients that generative embedding, compared to analyses of more conventional measures such as functional connectivity or regional activity, can significantly enhance both the interpretability and performance of computational approaches to clinical classification. Proof-of-concept study for defining subgroups in psychiatric spectrum diseases. We re-analysed an fMRI dataset of 41 schizophrenic patients and 42 controls. DCM parameters were highly predictive of diagnostic status (78% accuracy). Unsupervised clustering of patients suggested three subgroups. These groups were clinically distinct and differed in their negative symptom severity.
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影响因子：
3.5
作者：
Dempster EL;Pidsley R;Schalkwyk LC;Owens S;Georgiades A;Kane F;Kalidindi S;Picchioni M;Kravariti E;Toulopoulou T;Murray RM;Mill J
通讯作者：
Mill J
影响因子：
5.7
作者：
Brodersen KH;Haiss F;Ong CS;Jung F;Tittgemeyer M;Buhmann JM;Weber B;Stephan KE
通讯作者：
Stephan KE
影响因子：
1.7
作者：
Costafreda, Sergi G.;Khanna, Akash;Fu, Cynthia H. Y.
通讯作者：
Fu, Cynthia H. Y.
影响因子：
5.7
作者：
David, Olivier;Kiebel, Stefan J.;Friston, Karl J.
通讯作者：
Friston, Karl J.
影响因子：
5.7
作者：
Desseilles, Martin;Schwartz, Sophie;Phillips, Christophe
通讯作者：
Phillips, Christophe