Exploiting the brain's network structure in identifying ADHD subjects.
Exploiting the brain's network structure in identifying ADHD subjects.
复制标题
利用大脑的网络结构来识别多动症主体。
DOI：
10.3389/fnsys.2012.00075
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发表时间：
2012
影响因子：
3
通讯作者：
Shah M
中科院分区：
文献类型：
--
作者：
Dey S;Rao AR;Shah M
注意力缺陷多动障碍（ADHD）是一种影响儿童的常见行为问题。在这项工作中，我们调查的自动分类ADHD受试者使用静息状态功能磁共振成像（fMRI）序列的大脑。我们发现，大脑可以被建模为一个功能网络，和某些属性的网络不同，从控制受试者在ADHD的主题。我们在实验方案的时间范围内计算大脑体素活动的成对相关性，这有助于将大脑的功能建模为网络。为构成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级联用作特征向量。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和对照受试者。我们提出了一种方法来创建一个大脑掩模，其中只包括有用的区域，并证明使用的功能，从掩蔽区域提高分类精度的测试数据集。我们用776名受试者训练我们的分类器，并对神经局提供的171名受试者进行ADHD-200挑战测试。我们展示了图形主题功能的实用性，特别是代表长度为3的网络周期中体素参与频率的地图。最好的分类性能（69.59%）是使用3循环地图功能与掩蔽。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 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 brain and using features only from those regions are sufficient to discriminate ADHD and control subjects. We propose a method to create a brain mask which 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.