Networks are a natural way of representing the human brain for studying its structure and function and, as such, have been extensively used. In this framework, case–control studies for understanding autism pertain to comparing samples of healthy and autistic brain networks. In order to understand the biological mechanisms involved in the pathology, it is key to localize the differences on the brain network. Motivated by this question, we hereby propose a general non‐parametric finite‐sample exact statistical framework that allows to test for differences in connectivity within and between prespecified areas inside the brain network, with strong control of the family‐wise error rate. We demonstrate unprecedented ability to differentiate children with non‐syndromic autism from children with both autism and tuberous sclerosis complex using electroencephalography data. The implementation of the method is available in the R package nevada.

中文翻译：

---

网络值数据的多尺度零假设检验：自闭症患者的大脑网络分析

网络是代表人类大脑以研究其结构和功能的自然方式，因此已被广泛使用。在这种框架下，用于了解自闭症的病例对照研究涉及比较健康和自闭症大脑网络的样本。为了了解病理学所涉及的生物学机制，关键在于在大脑网络上定位差异。出于这个问题的动机，我们在此提出一个通用的非参数有限样本精确统计框架，该框架可以测试大脑网络内预定区域之内和之间的连通性差异，并可以有效地控制家庭错误率。我们利用脑电图数据证明了前所未有的能力，能够将非综合症自闭症儿童与自闭症和结节性硬化症儿童区分开。该方法的实现在R包内华达州。