Exploiting the Brain’s Network Structure for Automatic Identification of ADHD Subjects
Exploiting the Brain’s Network Structure for Automatic Identification of ADHD Subjects
复制标题
利用大脑网络结构自动识别多动症受试者
DOI：
10.48550/arxiv.2306.09239
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
M. Shah
中科院分区：
文献类型：
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作者：
Soumyabrata Dey;Ravishankar Rao;M. Shah
注意缺陷多动障碍（ADHD）是一种影响儿童的常见行为问题。在这项工作中，我们研究了使用静息状态脑功能磁共振成像（fMRI）序列对ADHD受试者的自动分类。我们的研究表明，大脑可以被建模为一个功能网络，并且ADHD受试者的某些网络特性与对照组受试者不同。我们在实验协议的时间框架内计算脑体素活动的两两相关性，这有助于模拟大脑作为网络的功能。对于构建网络的每个体素，计算不同的网络特征。大脑中所有体素的网络特征的连接作为特征向量。然后使用来自一组受试者的特征向量来训练基于PCA-LDA（主成分分析-线性判别分析）的分类器。我们假设ADHD相关的差异存在于大脑的某些特定区域，仅使用这些区域的特征就足以区分ADHD和对照组。我们提出了一种方法来创建一个只包含有用区域的脑屏蔽，并证明使用屏蔽区域的特征可以提高测试数据集的分类精度。我们用776个科目训练我们的分类器，并对神经局提供的171个科目进行测试，以应对ADHD-200的挑战。我们展示了图形基序特征的效用，特别是表示体素在长度为3的网络周期中的参与频率的地图。使用带掩蔽的3周期地图特征实现了最佳的分类性能（69.59%）。我们提出的方法有望诊断和理解这种疾病。
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.