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.

本研究利用静息状态功能性磁共振成像序列研究了 ADHD 受试者的自动分类，计算脑部体素活动的配对相关性，使用网络特征训练 PCA-LDA 分类器，应用图形模式特征和脑部区域做脑图像，在分类性能，尤其是使用网络长度 3 的循环图提高诊断和了解该疾病的效力。

利用大脑网络结构自动识别 ADHD 患者

Exploiting the Brain's Network Structure for Automatic Identification of  ADHD Subjects

Community detection is a fundamental and important issue in network science,
but there are only a few community detection algorithms based on graph neural
networks, among which unsupervised algorithms are almost blank. By fusing the
high-order modularity information with network features, this paper proposes a
Variational Graph AutoEncoder Reconstruction based community detection VGAER
for the first time, and gives its non-probabilistic version. They do not need
any prior information. We have carefully designed corresponding input features,
decoder, and downstream tasks based on the community detection task and these
designs are concise, natural, and perform well (NMI values under our design are
improved by 59.1% - 565.9%). Based on a series of experiments with wide range
of datasets and advanced methods, VGAER has achieved superior performance and
shows strong competitiveness and potential with a simpler design. Finally, we
report the results of algorithm convergence analysis and t-SNE visualization,
which clearly depicted the stable performance and powerful network modularity
ability of VGAER. Our codes are available at this https URL.

本文提出了一种基于变分图自编码器重构的社区检测方法 VGAER，通过融合高阶模块化信息与网络特征，不需要任何先验信息，设计了对应的输入特征、解码器和下游任务，并在广泛数据集和先进方法的一系列实验中取得了卓越的性能和竞争力。该方法具有更简单的设计，并展现了强大的网络模块化能力。