Attention Deficit Hyperactive Disorder (ADHD) is a common behavioral problem affecting children. In this work, we investigate the automatic classification of ADHD subjects using the resting state Functional Magnetic Resonance Imaging (fMRI) sequences of the brain. We show that the brain can be modeled as a functional network, and certain properties of the networks differ in ADHD subjects from control subjects. We compute the pairwise correlation of brain voxels' activity over the time frame of the experimental protocol which helps to model the function of a brain as a network. Different network features are computed for each of the voxels constructing the network. The concatenation of the network features of all the voxels in a brain serves as the feature vector. Feature vectors from a set of subjects are then used to train a PCA-LDA (principal component analysis-linear discriminant analysis) based classifier. We hypothesized that ADHD-related differences lie in some specific regions of the brain and using features only from those regions is sufficient to discriminate ADHD and control subjects. We propose a method to create a brain mask that includes the useful regions only and demonstrate that using the feature from the masked regions improves classification accuracy on the test data set. We train our classifier with 776 subjects and test on 171 subjects provided by The Neuro Bureau for the ADHD-200 challenge. We demonstrate the utility of graph-motif features, specifically the maps that represent the frequency of participation of voxels in network cycles of length 3. The best classification performance (69.59%) is achieved using 3-cycle map features with masking. Our proposed approach holds promise in being able to diagnose and understand the disorder.

翻译：注意缺陷多动障碍（ADHD）是一种影响儿童的常见行为问题。本研究利用静息态功能磁共振成像（fMRI）序列对大脑进行自动分类ADHD受试者的研究。我们证明，大脑可被建模为功能网络，且ADHD受试者与对照受试者的网络某些属性存在差异。我们计算了实验协议时间窗内脑体素活动的成对相关性，这有助于将大脑功能建模为网络。针对构成网络的每个体素，计算了不同的网络特征。将所有体素的网络特征串联起来作为特征向量，随后利用一组受试者的特征向量训练基于PCA-LDA（主成分分析-线性判别分析）的分类器。我们假设ADHD相关差异存在于大脑某些特定区域，仅利用这些区域的特征即可区分ADHD与对照受试者。我们提出了一种方法创建仅包含有用区域的脑掩模，并证明使用掩模区域的特征可提高测试数据集的分类准确率。我们使用The Neuro Bureau为ADHD-200挑战提供的776名受试者数据训练分类器，并在171名受试者上进行测试。我们展示了图模体特征的有效性，特别是表示体素在长度为3的网络环中参与频率的图谱。使用带掩模的三环图谱特征取得了最佳分类性能（69.59%）。我们提出的方法在诊断和理解该障碍方面具有应用前景。