Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crespigny Park, London, United Kingdom.
Department of Informatics, University of Sussex, Brighton, United Kingdom.
PLoS One. 2023 Mar 23;18(3):e0282707. doi: 10.1371/journal.pone.0282707. eCollection 2023.
The disconnection hypothesis of schizophrenia proposes that symptoms of the disorder arise as a result of aberrant functional integration between segregated areas of the brain. The concept of metastability characterizes the coexistence of competing tendencies for functional integration and functional segregation in the brain, and is therefore well suited for the study of schizophrenia. In this study, we investigate metastability as a candidate neuromechanistic biomarker of schizophrenia pathology, including a demonstration of reliability and face validity. Group-level discrimination, individual-level classification, pathophysiological relevance, and explanatory power were assessed using two independent case-control studies of schizophrenia, the Human Connectome Project Early Psychosis (HCPEP) study (controls n = 53, non-affective psychosis n = 82) and the Cobre study (controls n = 71, cases n = 59). In this work we extend Leading Eigenvector Dynamic Analysis (LEiDA) to capture specific features of dynamic functional connectivity and then implement a novel approach to estimate metastability. We used non-parametric testing to evaluate group-level differences and a naïve Bayes classifier to discriminate cases from controls. Our results show that our new approach is capable of discriminating cases from controls with elevated effect sizes relative to published literature, reflected in an up to 76% area under the curve (AUC) in out-of-sample classification analyses. Additionally, our new metric showed explanatory power of between 81-92% for measures of integration and segregation. Furthermore, our analyses demonstrated that patients with early psychosis exhibit intermittent disconnectivity of subcortical regions with frontal cortex and cerebellar regions, introducing new insights about the mechanistic bases of these conditions. Overall, these findings demonstrate reliability and face validity of metastability as a candidate neuromechanistic biomarker of schizophrenia pathology.
精神分裂症的断开假说提出，该障碍的症状是由于大脑隔离区域之间异常的功能整合而产生的。亚稳性的概念描述了大脑中功能整合和功能分离竞争趋势的共存，因此非常适合研究精神分裂症。在这项研究中，我们研究了亚稳性作为精神分裂症病理的候选神经机制生物标志物，包括可靠性和表面有效性的证明。使用两个独立的精神分裂症病例对照研究，即人类连接组计划早期精神病（HCPEP）研究（对照组 n=53，非情感性精神病 n=82）和 Cobre 研究（对照组 n=71，病例 n=59），评估了组水平的区分、个体水平的分类、病理生理学相关性和解释力。在这项工作中，我们将主导特征向量动态分析（LEiDA）扩展到捕获动态功能连接的特定特征，然后实现一种新的方法来估计亚稳性。我们使用非参数检验来评估组水平的差异，并使用朴素贝叶斯分类器来区分病例和对照。我们的结果表明，我们的新方法能够以高于文献报道的更高的效果大小区分病例和对照，在样本外分类分析中达到高达 76%的曲线下面积（AUC）。此外，我们的新指标显示，对于整合和分离的度量，解释力为 81-92%。此外，我们的分析表明，早期精神病患者表现出与前额叶皮层和小脑区域的皮质下区域间歇性断开的现象，为这些情况的机制基础提供了新的见解。总的来说，这些发现证明了亚稳性作为精神分裂症病理的候选神经机制生物标志物的可靠性和表面有效性。
