Department of Biomedical Engineering, Acibadem University, Istanbul, Turkey.
School of Psychology, University of Kent, Canterbury, UK.
Sci Rep. 2024 Oct 16;14(1):24314. doi: 10.1038/s41598-024-72199-0.
Individuals suffering from obsessive compulsive disorder (OCD) and schizophrenia (SCZ) frequently exhibit symptoms of cognitive disassociations, which are linked to poor functional integration among brain regions. The loss of functional integration can be assessed using graph metrics computed from functional connectivity matrices (FCMs) derived from neuroimaging data. A healthy brain at rest is known to exhibit small-world features with high clustering coefficients and shorter path lengths in contrast to random networks. The aim of this study was to compare the small-world properties of prefrontal cortical functional networks of healthy subjects with OCD and SCZ patient groups by use of hemodynamic data obtained with functional near infrared spectroscopy (fNIRS). 13 healthy subjects and 47 patients who were clinically diagnosed with either OCD (N = 21) or SCZ (N = 26) completed a Stroop test while their prefrontal cortex (PFC) hemodynamics were monitored with fNIRS. The Stroop test had a block design consisting of neutral, congruent and incongruent stimuli. For each subject and stimuli type, FCMs were derived separately which were then used to compute small world features that included (i) global efficiency (GE), (ii) clustering coefficient (CC), (iii) modularity (Q), and (iv) small-world parameter ( ). Small-world features of patients exhibited random networks which were indicated by higher GE and lower CC values when compared to healthy controls, implying a higher neuronal operational cost.
个体患有强迫症 (OCD) 和精神分裂症 (SCZ) 时常表现出认知脱节的症状，这与大脑区域之间功能整合不良有关。可以使用从神经影像学数据得出的功能连接矩阵 (FCM) 计算的图度量来评估功能整合的丧失。众所周知，健康的大脑在休息时表现出小世界特征，具有较高的聚类系数和较短的路径长度，与随机网络形成对比。本研究的目的是通过使用功能近红外光谱 (fNIRS) 获得的血液动力学数据，比较健康受试者与 OCD 和 SCZ 患者组前额皮质功能网络的小世界特性。13 名健康受试者和 47 名经临床诊断患有 OCD（N=21）或 SCZ（N=26）的患者完成了 Stroop 测试，同时使用 fNIRS 监测其前额皮质 (PFC) 的血液动力学。Stroop 测试采用了由中性、一致和不一致刺激组成的块设计。对于每个受试者和刺激类型，分别推导出 FCM，然后使用它们来计算小世界特征，包括 (i) 全局效率 (GE)、(ii) 聚类系数 (CC)、(iii) 模块度 (Q) 和 (iv) 小世界参数 ( )。与健康对照组相比，患者的小世界特征表现为随机网络，这表明 GE 更高，CC 值更低，这意味着神经元的运行成本更高。
