IEEE Trans Med Imaging. 2018 Nov;37(11):2381-2389. doi: 10.1109/TMI.2018.2821655. Epub 2018 Mar 30.
The human brain is an amazingly complex network. Aberrant activities in this network can lead to various neurological disorders such as multiple sclerosis, Parkinson's disease, Alzheimer's disease, and autism. functional magnetic resonance imaging has emerged as an important tool to delineate the neural networks affected by such diseases, particularly autism. In this paper, we propose a special type of mixed-effects model together with an appropriate procedure for controlling false discoveries to detect disrupted connectivities for developing a neural network in whole brain studies. Results are illustrated with a large data set known as autism brain imaging data exchange which includes 361 subjects from eight medical centers.
人脑是一个令人惊叹的复杂网络。该网络中的异常活动会导致各种神经紊乱，如多发性硬化症、帕金森病、阿尔茨海默病和自闭症。功能磁共振成像已成为描绘受此类疾病影响的神经网络的重要工具，尤其是自闭症。在本文中，我们提出了一种特殊类型的混合效应模型以及一种适当的程序来控制假发现，以检测全脑研究中神经网络的连通性中断。使用一个称为自闭症脑成像数据交换的大型数据集来说明结果，该数据集包括来自 8 个医疗中心的 361 名受试者。
