Ellis Charles A, Miller Robyn L, Calhoun Vince D
Tri-institutional Center for Translational Research in Neuroimaging and Data Science: Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA 30303 USA.
bioRxiv. 2023 Jan 31:2023.01.29.526110. doi: 10.1101/2023.01.29.526110.
Resting state functional magnetic resonance imaging (rs-fMRI) dynamic functional network connectivity (dFNC) analysis has illuminated brain network interactions across many neuropsychiatric disorders. A common analysis approach involves using hard clustering methods to identify transitory states of brain activity, and in response to this, other methods have been developed to quantify the importance of specific dFNC interactions to identified states. Some of these methods involve perturbing individual features and examining the number of samples that switch states. However, only a minority of samples switch states. As such, these methods actually identify the importance of dFNC features to the clustering of a subset of samples rather than the overall clustering. In this study, we present a novel approach that more capably identifies the importance of each feature to the overall clustering. Our approach uses fuzzy clustering to output probabilities of each sample belonging to states and then measures their Kullback-Leibler divergence after perturbation. We show the viability of our approach in the context of schizophrenia (SZ) default mode network analysis, identifying significant differences in state dynamics between individuals with SZ and healthy controls. We further compare our approach with an existing approach, showing that it captures the effects of perturbation upon most samples. We also find that interactions between the posterior cingulate cortex (PCC) and the anterior cingulate cortex and the PCC and precuneus are important across methods. We expect that our novel explainable clustering approach will enable further progress in rs-fMRI analysis and to other clustering applications.
静息态功能磁共振成像（rs-fMRI）动态功能网络连接性（dFNC）分析揭示了多种神经精神疾病中的脑网络相互作用。一种常见的分析方法是使用硬聚类方法来识别脑活动的瞬时状态，针对这一情况，人们开发了其他方法来量化特定dFNC相互作用对已识别状态的重要性。其中一些方法涉及扰动个体特征并检查状态切换的样本数量。然而，只有少数样本会切换状态。因此，这些方法实际上识别的是dFNC特征对一部分样本聚类的重要性，而非整体聚类的重要性。在本研究中，我们提出了一种更能识别每个特征对整体聚类重要性的新方法。我们的方法使用模糊聚类来输出每个样本属于各状态的概率，然后在扰动后测量它们的库尔贝克-莱布勒散度。我们在精神分裂症（SZ）默认模式网络分析的背景下展示了我们方法的可行性，识别出SZ患者与健康对照者在状态动态方面的显著差异。我们进一步将我们的方法与现有方法进行比较，表明它能捕捉到扰动对大多数样本的影响。我们还发现，后扣带回皮层（PCC）与前扣带回皮层以及PCC与楔前叶之间的相互作用在各种方法中都很重要。我们期望我们新颖的可解释聚类方法将推动rs-fMRI分析及其他聚类应用取得进一步进展。
