With the release of the multi-site Autism Brain Imaging Data Exchange, many researchers have applied machine learning methods to distinguish between healthy subjects and autistic individuals by using features extracted from resting state functional MRI data. An important part of applying machine learning to this problem is extracting these features. Specifically, whether to include negative correlations between brain region activities as relevant features and how best to define these features. For the second question, the graph theoretical properties of the brain network may provide a reasonable answer. In this study, we investigated the first issue by comparing three different approaches. These included using the positive correlation matrix (comprising only the positive values of the original correlation matrix), the absolute value of the correlation matrix, or the anticorrelation matrix (comprising only the negative correlation values) as the starting point for extracting relevant features using graph theory. We then trained a multi-layer perceptron in a leave-one-site out manner in which the data from a single site was left out as testing data and the model was trained on the data from the other sites. Our results show that on average, using graph features extracted from the anti-correlation matrix led to the highest accuracy and AUC scores. This suggests that anti-correlations should not simply be discarded as they may include useful information that would aid the classification task. We also show that adding the PCA transformation of the original correlation matrix to the feature space leads to an increase in accuracy.

中文翻译：

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反相关在基于图论的自闭症谱系障碍分类中的重要性


随着多站点自闭症脑成像数据交换的发布，许多研究人员已经应用机器学习方法，通过使用从静息状态功能 MRI 数据提取的特征来区分健康受试者和自闭症个体。将机器学习应用于此问题的一个重要部分是提取这些特征。具体来说，是否将大脑区域活动之间的负相关性纳入相关特征以及如何最好地定义这些特征。对于第二个问题，大脑网络的图论特性或许可以提供合理的答案。在本研究中，我们通过比较三种不同的方法来研究第一个问题。其中包括使用正相关矩阵（仅包含原始相关矩阵的正值）、相关矩阵的绝对值或反相关矩阵（仅包含负相关值）作为使用图提取相关特征的起点理论。然后，我们以留一站点的方式训练多层感知器，其中来自单个站点的数据被保留作为测试数据，并且模型根据来自其他站点的数据进行训练。我们的结果表明，平均而言，使用从反相关矩阵中提取的图特征可以获得最高的准确度和 AUC 分数。这表明不应简单地丢弃反相关性，因为它们可能包含有助于分类任务的有用信息。我们还表明，将原始相关矩阵的 PCA 变换添加到特征空间可以提高准确性。