Keyvanfard Farzaneh, Nasab Alireza Rahimi, Nasiraei-Moghaddam Abbas
School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran.
Biomedical Engineering Department, Amirkabir University of Technology, Tehran, Iran.
Front Neuroinform. 2023 May 18;17:1175886. doi: 10.3389/fninf.2023.1175886. eCollection 2023.
Functional connectivity (FC) of the brain changes in various brain disorders. Its complexity, however, makes it difficult to obtain a systematic understanding of these alterations, especially when they are found individually and through hypothesis-based methods. It would be easier if the variety of brain connectivity alterations is extracted through data-driven approaches and expressed as variation modules (subnetworks). In the present study, we modified a blind approach to determine inter-group brain variations at the network level and applied it specifically to schizophrenia (SZ) disorder. The analysis is based on the application of independent component analysis (ICA) over the subject's dimension of the FC matrices, obtained from resting-state functional magnetic resonance imaging (rs-fMRI). The dataset included 27 SZ people and 27 completely matched healthy controls (HC). This hypothesis-free approach led to the finding of three brain subnetworks significantly discriminating SZ from HC. The area associated with these subnetworks mostly covers regions in visual, ventral attention, and somatomotor areas, which are in line with previous studies. Moreover, from the graph perspective, significant differences were observed between SZ and HC for these subnetworks, while there was no significant difference when the same parameters (path length, network strength, global/local efficiency, and clustering coefficient) across the same limited data were calculated for the whole brain network. The increased sensitivity of those subnetworks to SZ-induced alterations of connectivity suggested whether an individual scoring method based on their connectivity values can be applied to classify subjects. A simple scoring classifier was then suggested based on two of these subnetworks and resulted in acceptable sensitivity and specificity with an area under the ROC curve of 77.5%. The third subnetwork was found to be a less specific building block (module) for describing SZ alterations. It projected a wider range of inter-individual variations and, therefore, had a lower chance to be considered as a SZ biomarker. These findings confirmed that investigating brain variations from a modular viewpoint can help to find subnetworks that are more sensitive to SZ-induced alterations. Altogether, our study results illustrated the developed method's ability to systematically find brain alterations caused by SZ disorder from a network perspective.
大脑的功能连接性（FC）在各种脑部疾病中会发生变化。然而，其复杂性使得难以对这些改变获得系统性的理解，尤其是当它们是通过基于假设的方法单独发现时。如果通过数据驱动的方法提取各种脑连接性改变并将其表示为变异模块（子网络），将会更容易。在本研究中，我们修改了一种盲法来确定网络水平上的组间脑差异，并将其专门应用于精神分裂症（SZ）疾病。该分析基于对从静息态功能磁共振成像（rs-fMRI）获得的FC矩阵的受试者维度应用独立成分分析（ICA）。数据集包括27名SZ患者和27名完全匹配的健康对照（HC）。这种无假设方法导致发现了三个能显著区分SZ和HC的脑子网。与这些子网相关的区域大多覆盖视觉、腹侧注意和躯体运动区域，这与先前的研究一致。此外，从图的角度来看，这些子网在SZ和HC之间观察到显著差异，而当对全脑网络计算相同有限数据的相同参数（路径长度、网络强度、全局/局部效率和聚类系数）时，没有显著差异。这些子网对SZ诱导的连接性改变的敏感性增加表明，基于其连接性值的个体评分方法是否可用于对受试者进行分类。然后基于其中两个子网提出了一种简单的评分分类器，其在ROC曲线下面积为77.5%时，具有可接受的敏感性和特异性。发现第三个子网是描述SZ改变的特异性较低的构建块（模块）。它呈现出更广泛的个体间变异范围，因此，被视为SZ生物标志物的机会较低。这些发现证实，从模块化观点研究脑差异有助于找到对SZ诱导的改变更敏感的子网。总之，我们的研究结果说明了所开发方法从网络角度系统地发现由SZ疾病引起的脑改变的能力。
