The accurate identification of an attention deficit hyperactivity disorder (ADHD) subject has remained a challenge for both neuroscience research and clinical diagnosis. Unfortunately, the traditional methods concerning the classification model and feature extraction usually depend on the single-channel model and static measurements (i.e., functional connectivity, FC) in the small, homogenous single-site dataset, which is limited and may cause the loss of intrinsic information in functional MRI (fMRI). In this study, we proposed a new two-stage network structure by combing a separated channel convolutional neural network (SC-CNN) with an attention-based network (SC-CNN-attention) to discriminate ADHD and healthy controls on a large-scale multi-site database (5 sites and n = 1019). To utilize both intrinsic temporal feature and the interactions of temporal dependent in whole-brain resting-state fMRI, in the first stage of our proposed network structure, a SC- CNN is used to learn the temporal feature of each brain region, and an attention network in the second stage is adopted to capture temporal dependent features among regions and extract fusion features. Using a “leave-one-site-out” cross-validation framework, our proposed method obtained a mean classification accuracy of 68.6% on five different sites, which is higher than those reported in previous studies. The classification results demonstrate that our proposed network is robust to data variants and is also replicated across sites. The combination of the SC-CNN with the attention network is powerful to capture the intrinsic fMRI information to discriminate ADHD across multi-site resting-state fMRI data.

中文翻译：

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分离通道注意力卷积神经网络 (SC-CNN-Attention) 在多站点 Rs-fMRI 数据集中识别 ADHD

准确识别注意力缺陷多动障碍 (ADHD) 对象一直是神经科学研究和临床诊断的挑战。不幸的是，传统的分类模型和特征提取方法通常依赖于单通道模型和静态测量（即功能连通性，FC）在小的、同质的单站点数据集中，这是有限的，可能会导致损失功能性 MRI (fMRI) 中的内在信息。在这项研究中，我们通过将分离通道卷积神经网络 (SC-CNN) 与基于注意力的网络 (SC-CNN-attention) 相结合，提出了一种新的两阶段网络结构，以大规模区分 ADHD 和健康对照。多站点数据库（5 个站点，n = 1019）。为了在全脑静息状态 fMRI 中利用固有时间特征和时间依赖的相互作用，在我们提出的网络结构的第一阶段，使用 SC-CNN 来学习每个大脑区域的时间特征，以及注意力第二阶段的网络用于捕获区域之间的时间相关特征并提取融合特征。使用“留一个站点”交叉验证框架，我们提出的方法在五个不同站点上获得了 68.6% 的平均分类准确率，高于之前研究报告的结果。分类结果表明，我们提出的网络对数据变体具有鲁棒性，并且还可以跨站点复制。