Zhang Hua, Zeng Weiming, Deng Jin, Shi Yuhu, Zhao Le, Li Ying
Lab of Digital Image and Intelligent Computation, Shanghai Maritime University, Shanghai, China.
College of Mathematics and Informatics, South China Agricultural University, Guangzhou, China.
Front Neurosci. 2021 Dec 3;15:771947. doi: 10.3389/fnins.2021.771947. eCollection 2021.
Resting-state functional MRI (rs-fMRI) has been increasingly applied in the research of brain cognitive science and psychiatric diseases. However, previous studies only focused on specific activation areas of the brain, and there are few studies on the inactivation areas. This may overlook much information that explains the brain's cognitive function. In this paper, we propose a relatively inert network (RIN) and try to explore its important role in understanding the cognitive mechanism of the brain and the study of mental diseases, using adult attention deficit hyperactivity disorder (ADHD) as an example. Here, we utilize methods based on group independent component analysis (GICA) and -test to identify RIN and calculate its corresponding time series. Through experiments, alterations in the RIN and the corresponding activation network (AN) in adult ADHD patients are observed. And compared with those in the left brain, the activation changes in the right brain are greater. Further, when the RIN functional connectivity is introduced as a feature to classify adult ADHD patients from healthy controls (HCs), the classification accuracy rate is 12% higher than that of the original functional connectivity feature. This was also verified by testing on an independent public dataset. These findings confirm that the RIN of the brain contains much information that will probably be neglected. Moreover, this research provides an effective new means of exploring the information integration between brain regions and the diagnosis of mental illness.
静息态功能磁共振成像（rs-fMRI）已越来越多地应用于脑认知科学和精神疾病的研究。然而，以往的研究仅关注大脑的特定激活区域，而对失活区域的研究较少。这可能会忽略许多解释大脑认知功能的信息。在本文中，我们提出了一个相对惰性网络（RIN），并以成人注意力缺陷多动障碍（ADHD）为例，试图探索其在理解大脑认知机制和精神疾病研究中的重要作用。在此，我们利用基于组独立成分分析（GICA）和t检验的方法来识别RIN并计算其相应的时间序列。通过实验，观察到成人ADHD患者的RIN和相应激活网络（AN）的改变。并且与左脑相比，右脑的激活变化更大。此外，当将RIN功能连接性作为一种特征用于从健康对照（HC）中分类成人ADHD患者时，分类准确率比原始功能连接性特征高12%。这也在一个独立的公共数据集上得到了验证。这些发现证实大脑的RIN包含许多可能被忽视的信息。此外，本研究为探索脑区之间的信息整合和精神疾病的诊断提供了一种有效的新手段。
