Brain functional connectivity networks (FCNs) derived from resting-state functional magnetic resonance imaging (rs-fMRI) data have been widely used to identify altered brain network patterns in attention-deficit/hyperactivity disorder (ADHD). Current graph neural network (GNN) approaches using FCNs predominantly emphasize node features while underutilizing edge information. Moreover, these GNN-based methods also inadequately represent dynamic interdependencies among evolving node features across network layers, limiting their diagnostic performance. We present a graph convolutional network via joint feature learning between nodes and edges (JNEL-GCN) that integrates neuroimaging features for ADHD classification and biomarker discovery. Our framework constructs dual graph representations: 1) a node graph using amplitude of low-frequency fluctuations (ALFF) measures across multiple frequency bands as nodal features, along with functional connectivity (FC) and node feature relationship matrices as edge attributes; and 2) an edge graph derived through line graph theory, enabling the interchange of node and edge roles. By leveraging the dual-graph design, our model implements an alternating feature update mechanism with optimized graph convolution operations, facilitating feature hierarchical learning of node-edge relationships across network layers. Extensive experiments demonstrate remarkable performance, achieving 97.3% accuracy on ADHD200 and 97.1% on ABIDE-I datasets, significantly outperforming current benchmarks. Meanwhile, gradient-based biomarker analysis identifies significant regions in bilateral limbic and default mode networks associated with ADHD, aligning with the findings in existing literature. Therefore, this dual-graph approach advances neuroimaging-based diagnosis by comprehensively capturing dynamic network interactions, while providing interpretable biomarkers for clinical neuroscience applications.

In this study, we apply a graph convolutional network (GCN) in attention deficit hyperactivity disorder (ADHD) classification by using multimodal data. Here, multimodal data is integrated to construct a dual graph for leveraging the modality information. Then, a GCN learning model is performed within an existing binary hypothesis testing (BHT) framework to fulfill the classification task. To preserve specifical topological structure of dual graph, we propose a graph-based feature selection approach to choose typical data (nodes) on the graphs. With these typical data, we design our GCN model, effectively learning the high-level features for identifying ADHD subjects. The experiments show the average accuracy of our method reaches 95.2% on various ADHD-200 datasets. Importantly, this result is achieved only on the right limbic system rather than the whole brain. The limited brain regions used indicate their potential ability as biomarkers. Moreover, biomarkers are found with their special topological structures. On the Peking University dataset, the found biomarkers center around the hippocampus and parahippocampal gyri, which are highly correlated with ADHD disease. Differing from isolated biomarker findings in traditional methods, our method discloses the abnormal circuit for ADHD and is more meaningful for further learning ADHD mechanism.

We propose a subspace-enhanced hypergraph neural network (seHGNN) for classifying anxiety disorder (AD). By leveraging a learnable incidence matrix, seHGNN strengthens the influence of hyperedges in graphs and enhances feature extraction performance of HGNNs. Then, we conduct this model within an existing binary hypothesis testing framework, where multi-modal data on the limbic system is integrated into a hypergraph. Experiments show that the seHGNN achieves a high accuracy of 90.7% for AD classification, surpassing other deep-learning-based methods, especially GNN-based methods. Our seHGNN also successfully identifies discriminative AD biomarkers, consistent with existing reports. This provides strong evidence supporting the effectiveness and interpretability of our proposed method.

Deep learning methods have been extensively employed in the classification of attention deficit hyperactivity disorder (ADHD) in decades. However, these methods either are still with unsatisfactory accuracy or lack the ability to capture relevant biological markers. To address these challenges, we introduce a knowledge distillation architecture within a binary hypothesis testing framework. In detail, we employ an existing AENet as a teacher model and guide a student model in learning high-level features of ADHD disease. More importantly, an attention block is adopted in this student model to obtain attention weights and better quantify the contributions of used brain functional connections. Now, these weights become robust in coping with the individual diversity among ADHD and healthy control groups, making it convenient for biomarker detection. Experiments demonstrate that our method attains an average classification accuracy of 99.5%. ADHD biomarkers are effectively identified and solidly supported by recent reports. This further illustrates the validity of our knowledge distillation approach.

Deep learning methods have been widely applied in Attention Deficit Hyperactivity Disorder (ADHD) classification in the past decade due to their effective learned features. However, these features are lack of neurobiological meanings and hard to be biomarkers. Here, we proposed an attention auto-encoding network with triplet loss (Tri-Att-AENet) for both ADHD classification and biomarker identification. Taking brain functional connectivities (FCs) as material, we introduced an attention encoding subnetwork to obtain the weighted FCs with their weights as attention scores. A triplet loss function was further employed on these scores, providing sufficient evidence for biomarker selection. Meanwhile, the weighted FCs became discriminative to pursue a higher accuracy. Experiments show that our method achieves an average accuracy of 99.6% for classification. The selected FC biomarkers are in accord with reported neurobiological results and well fulfill the task of biomarker identification.

Abstract Deep‐learning methods have been developed in recent years and have achieved dramatic improvements for image denoising. The existing deep‐learning methods can be conducted using two major models: Encoder–decoder and high‐resolution, where the high‐resolution model has superior resolution ability for detail description and restoration. In this study, a high‐resolution‐based network called multiscale residual fusion network (MRF‐Net) is proposed, which employed the spatial and contextual information of images. In detail, dilated convolution layers are used to enlarge the network's receptive field and learned sufficient features in a multiscale feature extracting module. The function of dilated convolution is reinterpreted here and it is viewed as a complex downsampling operation. Therefore, multiscale feature analysis could be performed in the proposed network by dilated convolution. Multilevel feature maps are sequentially obtained through a residual projection module, where considerable contextual and spatial information was collected from the multiscale features. In a residual fusion module, all maps were aggregated to generate a residual image effectively for noise removal. Experiments demonstrated that the MRF‐Net outperformed several state‐of‐the‐art model‐based and deep‐learning methods in both blind and non‐blind image denoising tests. Meanwhile, ablation studies were executed to verify the denoising performance of each module. Moreover, this method exhibited high computational efficiency, thus demonstrating its practicability.

In this paper, a sparse approximation algorithm using eigenvectors of the graph Laplacian is proposed for image denoising, in which the eigenvectors of the graph Laplacian of images are incorporated in the sparse model as basis functions. Here, an eigenvector-based sparse approximation problem is presented under a set of residual error constraints. The corresponding relaxed iterative solution is also provided to efficiently solve such problem in the framework of the double sparsity model. Experiments show that the proposed algorithm can achieve a better performance than some state-of-art denoising methods, especially measured with the SSIM index.