Exploring Dysconnectivity of the Large-scale Neurocognitive Network across Psychiatric Disorders using Spatiotemporal Constrained Nonnegative Matrix Factorization Method
Exploring Dysconnectivity of the Large-scale Neurocognitive Network across Psychiatric Disorders using Spatiotemporal Constrained Nonnegative Matrix Factorization Method
复制标题
使用时空约束非负矩阵分解方法探索跨精神疾病的大规模神经认知网络的脱节性
DOI：
10.1093/cercor/bhab503
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发表时间：
2022
期刊：
影响因子：
3.7
通讯作者：
Hua Zhang
中科院分区：
文献类型：
--
作者：
Ying Li;Weiming Zeng;Jin Deng;Yuhu Shi;Weifang Nie;Sizhe Luo;Hua Zhang
抽象。精神疾病通常具有相似的临床和神经生物学特征。然而，以前关于功能性连接障碍的研究主要集中在单一疾病上，对大脑网络的跨诊断改变仍然知之甚少。因此，本研究提出了一种基于真实的参考信号的时空约束非负矩阵分解（STCNMF）方法，用于提取大规模脑网络，以识别与多种疾病相关的神经认知网络的跨诊断变化。首先从群体参与者的功能磁共振成像（fMRI）数据中挖掘出可用的时间先验信息和空间先验信息，然后将这些先验约束引入到非负矩阵分解目标函数中，以提高其效率。该算法在精神分裂症、双相情感障碍、注意缺陷多动障碍和健康对照的fMRI数据中成功获得了10个静息态功能脑网络，并进一步发现了这些大规模网络的跨诊断变化，包括右额顶网络与默认模式网络之间的连接增强，内侧视觉网络与默认模式网络之间的连接减少，以及一些高度集成的网络节点的存在。此外，每种类型的精神障碍都有其特定的连接特征。这些发现为从脑网络的角度理解多种精神疾病的跨诊断和诊断特异性神经生物学机制提供了新的见解。
Abstract. Psychiatric disorders usually have similar clinical and neurobiological features. Nevertheless, previous research on functional dysconnectivity has mainly focused on a single disorder and the transdiagnostic alterations in brain networks remain poorly understood. Hence, this study proposed a spatiotemporal constrained nonnegative matrix factorization (STCNMF) method based on real reference signals to extract large-scale brain networks to identify transdiagnostic changes in neurocognitive networks associated with multiple diseases. Available temporal prior information and spatial prior information were first mined from the functional magnetic resonance imaging (fMRI) data of group participants, and then these prior constraints were incorporated into the nonnegative matrix factorization objective functions to improve their efficiency. The algorithm successfully obtained 10 resting-state functional brain networks in fMRI data of schizophrenia, bipolar disorder, attention deficit/hyperactivity disorder, and healthy controls, and further found transdiagnostic changes in these large-scale networks, including enhanced connectivity between right frontoparietal network and default mode network, reduced connectivity between medial visual network and default mode network, and the presence of a few hyper-integrated network nodes. Besides, each type of psychiatric disorder had its specific connectivity characteristics. These findings provide new insights into transdiagnostic and diagnosis-specific neurobiological mechanisms for understanding multiple psychiatric disorders from the perspective of brain networks.