Individualized psychiatric imaging based on inter -subject neural synchronization in movie watching
Individualized psychiatric imaging based on inter -subject neural synchronization in movie watching
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基于电影观看中受试者间神经同步的个体化精神成像
DOI：
10.1016/j.neuroimage.2019.116227
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发表时间：
2020-08-01
期刊：
影响因子：
5.7
通讯作者：
Wang, Jijun
中科院分区：
文献类型：
--
作者：
Yang, Zhi;Wu, Jinfeng;Wang, Jijun
个体的异质性是对尖端神经影像学技术在更好地诊断和早期发现精神疾病方面的繁荣前景的挑战。具有相似临床表现的个体可能由非常不同的病理生理学引起。基于比较组平均值的常规方法提供的信息不足以支持个体化诊断。在这里，我们提出了一个个性化的成像方法，结合自然成像和规范模型。该范式采用具有丰富认知、社会和情感内容的视频片段来唤起健康参与者的同步脑动力学，并建立时空反应规范。通过将个体大脑反应与反应规范进行比较，我们可以使用机器学习技术识别患者。我们应用这种方法来识别包含72名患者和54名健康对照的数据集中的首次发作药物初治精神分裂症患者。与精神分裂症患者相比，视频的某些片段在健康对照组中诱发了更多的同步大脑活动。我们通过对训练集中健康对照者的大脑反应进行平均来建立时空反应规范，并训练分类器根据个体大脑反应与规范之间的差异来识别患者。分类器的性能，然后使用一个独立的测试集进行评估。5折交叉验证的平均准确度为0.71-0.78，取决于参数，如特征的数量和滑动窗口的宽度。这些发现反映了这种方法对个体化诊断的临床工具的潜力。
The individual heterogeneity is a challenge to the prosperous promises of cutting-edge neuroimaging techniques for better diagnosis and early detection of psychiatric disorders. Individuals with similar clinical manifestations may result from very different pathophysiology. Conventional approaches based on comparing group-averages provide insufficient information to support the individualized diagnosis. Here we present an individualized imaging methodology that combines naturalistic imaging and the normative model. This paradigm adopts video clips with rich cognitive, social, and emotional contents to evoke synchronized brain dynamics of healthy participants and builds a spatiotemporal response norm. By comparing individual brain responses with the response norm, we could recognize patients using machine learning techniques. We applied this methodology to recognize first-episode drug-naïve schizophrenia patients in a dataset containing 72 patients and 54 healthy controls. Some segments of the video evoked more synchronized brain activity in the healthy controls than in the schizophrenia patients. We built a spatiotemporal response norm by averaging the brain responses of the healthy controls in a training set, and trained a classifier to recognize patients based on the differences between individual brain responses and the norm. The performance of the classifier was then evaluated using an independent test set. The mean accuracies from a 5-fold cross-validation were 0.71–0.78 depending on the parameters such as the number of features and the width of the sliding windows. These findings reflected the potential of this methodology towards a clinical tool for individualized diagnosis.