Brain disorder diagnosis via resting-state functional magnetic resonance imaging (rs-fMRI) is usually limited due to the complex imaging features and sample size. For brain disorder diagnosis, the graph convolutional network (GCN) has achieved remarkable success by capturing interactions between individuals and the population. However, there are mainly three limitations: 1) The previous GCN approaches consider the non-imaging information in edge construction but ignore the sensitivity differences of features to non-imaging information. 2) The previous GCN approaches solely focus on establishing interactions between subjects (i.e., individuals and the population), disregarding the essential relationship between features. 3) Multisite data increase the sample size to help classifier training, but the inter-site heterogeneity limits the performance to some extent. This paper proposes a knowledge-aware multisite adaptive graph Transformer to address the above problems. First, we evaluate the sensitivity of features to each piece of non-imaging information, and then construct feature-sensitive and feature-insensitive subgraphs. Second, after fusing the above subgraphs, we integrate a Transformer module to capture the intrinsic relationship between features. Third, we design a domain adaptive GCN using multiple loss function terms to relieve data heterogeneity and to produce the final classification results. Last, the proposed framework is validated on two brain disorder diagnostic tasks. Experimental results show that the proposed framework can achieve state-of-the-art performance.

Obsessive-Compulsive Disorder (OCD) is a hereditary mental illness, and unaffected first-degree relative (UFDR) is also at high risk. This study constructed a framework based on traditional machine learning and deep learning methods to identify OCD and UFDR. Specifically, we propose Multiple Spatial Similarity-Aware Learning (MSSL) models to construct Brain Functional Connectivity Networks (BFCNs) and use Diffusion Structure-Aware Graph Convolutional Network (DSAGCN) for feature learning. Two regularization terms are added to the MSSL model, one is to limit the similarity of functional connectivity of adjacent brain regions, and the other is to constrain the similarity of signals in adjacent brain regions. In this strategy, redundant information can be removed while constructing the interrelationships of various brain regions, which can reflect the interrelationships between brain regions.The graph diffusion is introduced to the DSAGCN model, which can not only solve the edge noise problem caused by the graph structure constructed by the custom adjacency matrix but also enrich the node features by using the position information of the nodes on the reconstructed graph structure.This framework has been validated on our dataset collected from local hospitals, and the experimental results show that our proposed method outperforms the state-of-the-art.

Drug-target interaction (DTI) prediction is vital for drug discovery and repurposing. Hypergraph is utilized in DTI prediction for modeling higher-order relationships in biomedical networks. Although the strategies of modeling hypergraph-based drug-related interactions with multi-channel and utilizing self-supervised learning task to improve DTI prediction performance have been proven promising, current researches fail to effectively model feature interaction across different channels and fully exploit cross-channel information for self-supervised task. In this study, we propose a hypergraph-based self-supervised multi-channel interaction framework HSMI-DTI for DTI prediction. HSMI-DTI aims to extract hypergraph features effectively and model cross-channel correlations, leveraging hierarchical self-supervised learning to uncover the discover correlations between different channels. We compare HSMI-DTI with advanced baselines, and experiment results show our model outperforms existing methods, thereby optimizing DTI prediction performance.

Identifying reproducible and interpretable biomarkers for Alzheimer's disease (AD) detection remains a challenge. AD detection using multi-center datasets can expand the sample size to improve robustness but might lead to a data privacy problem. Moreover, due to the high cost of labeling data, a lot of unlabeled data in each center is not fully utilized. To address this, a hybrid FL (HFL) framework is proposed that not only uses unlabeled data to train deep learning networks, but also achieves data privacy protection. We propose a novel Brain-region Attention Network (BANet), which highlights important regions via attention to represent the region of interest (ROIs).Specifically, we use a brain template to extract ROI signals from the preprocessed structure magnetic resonance imaging (sMRI) data. In addition, we add a self-supervised loss to the current loss to guide the attention map generation to learn the representations from unlabeled data. Finally, we evaluate our method on a multi-center database which is constructed using five AD datasets. The experimental results show that the proposed method performs better than state-of-the-art methods, achieving mean accuracy rates of 85.69 %, 63.34 %, and 69.89 % on the AD vs. NC, MCI vs. NC, and AD vs. MCI respectively. The source code is available for reproducibility at: https://github.com/yuliangCarmelo/HFL .

Multimodal neuroimaging provides complementary information critical for accurate early diagnosis of Alzheimer's disease (AD). However, the inherent variability between multimodal neuroimages hinders the effective fusion of multimodal features. Moreover, achieving reliable and interpretable diagnoses in the field of multimodal fusion remains challenging. To address them, we propose a novel multimodal diagnosis network based on multi-fusion and disease-induced learning (MDL-Net) to enhance early AD diagnosis by efficiently fusing multimodal data. Specifically, MDL-Net proposes a multi-fusion joint learning (MJL) module, which effectively fuses multimodal features and enhances the feature representation from global, local, and latent learning perspectives. MJL consists of three modules, global-aware learning (GAL), local-aware learning (LAL), and outer latent-space learning (LSL) modules. GAL via a self-adaptive Transformer (SAT) learns the global relationships among the modalities. LAL constructs local-aware convolution to learn the local associations. LSL module introduces latent information through outer product operation to further enhance feature representation. MDL-Net integrates the disease-induced region-aware learning (DRL) module via gradient weight to enhance interpretability, which iteratively learns weight matrices to identify AD-related brain regions. We conduct the extensive experiments on public datasets and the results confirm the superiority of our proposed method. Our code will be available at: https://github.com/qzf0320/MDL-Net.

Image fusion technology aims to obtain a comprehensive image containing a specific target or detailed information by fusing data of different modalities. However, many deep learning-based algorithms consider edge texture information through loss functions instead of specifically constructing network modules. The influence of the middle layer features is ignored, which leads to the loss of detailed information between layers. In this article, we propose a multidiscriminator hierarchical wavelet generative adversarial network (MHW-GAN) for multimodal image fusion. First, we construct a hierarchical wavelet fusion (HWF) module as the generator of MHW-GAN to fuse feature information at different levels and scales, which avoids information loss in the middle layers of different modalities. Second, we design an edge perception module (EPM) to integrate edge information from different modalities to avoid the loss of edge information. Third, we leverage the adversarial learning relationship between the generator and three discriminators for constraining the generation of fusion images. The generator aims to generate a fusion image to fool the three discriminators, while the three discriminators aim to distinguish the fusion image and edge fusion image from two source images and the joint edge image, respectively. The final fusion image contains both intensity information and structure information via adversarial learning. Experiments on public and self-collected four types of multimodal image datasets show that the proposed algorithm is superior to the previous algorithms in terms of both subjective and objective evaluation.

Automatic and accurate classification of retinal optical coherence tomography (OCT) images is essential to assist physicians in diagnosing and grading pathological changes in pathologic myopia (PM). Clinically, due to the obvious differences in the position, shape, and size of the lesion structure in different scanning directions, ophthalmologists usually need to combine the lesion structure in the OCT images in the horizontal and vertical scanning directions to diagnose the type of pathological changes in PM. To address these challenges, we propose a novel feature interaction Transformer network (FIT-Net) to diagnose PM using OCT images, which consists of two dual-scale Transformer (DST) blocks and an interactive attention (IA) unit. Specifically, FIT-Net divides image features of different scales into a series of feature block sequences. In order to enrich the feature representation, we propose an IA unit to realize the interactive learning of class token in feature sequences of different scales. The interaction between feature sequences of different scales can effectively integrate different scale image features, and hence FIT-Net can focus on meaningful lesion regions to improve the PM classification performance. Finally, by fusing the dual-view image features in the horizontal and vertical scanning directions, we propose six dual-view feature fusion methods for PM diagnosis. The extensive experimental results based on the clinically obtained datasets and three publicly available datasets demonstrate the effectiveness and superiority of the proposed method. Our code is avaiable at: https://github.com/chenshaobin/FITNet.

In multi-site studies of Alzheimer's disease (AD), the difference of data in multi-site datasets leads to the degraded performance of models in the target sites. The traditional domain adaptation method requires sharing data from both source and target domains, which will lead to data privacy issue. To solve it, federated learning is adopted as it can allow models to be trained with multi-site data in a privacy-protected manner. In this paper, we propose a multi-site federated domain adaptation framework via Transformer (FedDAvT), which not only protects data privacy, but also eliminates data heterogeneity. The Transformer network is used as the backbone network to extract the correlation between the multi-template region of interest features, which can capture the brain abundant information. The self-attention maps in the source and target domains are aligned by applying mean squared error for subdomain adaptation. Finally, we evaluate our method on the multi-site databases based on three AD datasets. The experimental results show that the proposed FedDAvT is quite effective, achieving accuracy rates of 88.75%, 69.51%, and 69.88% on the AD vs. NC, MCI vs. NC, and AD vs. MCI two-way classification tasks, respectively.

Alzheimer's disease (AD) is a neurodegenerative disease with an irreversible and progressive process. To understand the brain functions and identify the biomarkers of AD and early stages of the disease [also known as, mild cognitive impairment (MCI)], it is crucial to build the brain functional connectivity network (BFCN) using resting-state functional magnetic resonance imaging (rs-fMRI). Existing methods have been mainly developed using only a single time-point rs-fMRI data for classification. In fact, multiple time-point data is more effective than a single time-point data in diagnosing brain diseases by monitoring the disease progression patterns using longitudinal analysis. In this article, we utilize multiple rs-fMRI time-point to identify early MCI (EMCI) and late MCI (LMCI), by integrating the fused sparse network (FSN) model with parameter-free centralized (PFC) learning. Specifically, we first construct the FSN framework by building multiple time-point BFCNs. The multitask learning via PFC is then leveraged for longitudinal analysis of EMCI and LMCI. Accordingly, we can jointly learn the multiple time-point features constructed from the BFCN model. The proposed PFC method can automatically balance the contributions of different time-point information via learned specific and common features. Finally, the selected multiple time-point features are fused by a similarity network fusion (SNF) method. Our proposed method is evaluated on the public AD neuroimaging initiative phase-2 (ADNI-2) database. The experimental results demonstrate that our method can achieve quite promising performance and outperform the state-of-the-art methods.

Alzheimer's disease (AD) is a neurodegenerative and non-curable disease, with serious cognitive impairment, such as dementia. Clinically, it is critical to study the disease with multi-source data in order to capture a global picture of it. In this respect, an adaptive ensemble manifold learning (AEML) algorithm is proposed to retrieve multi-source neuroimaging data. Specifically, an objective function based on manifold learning is formulated to impose geometrical constraints by similarity learning. The complementary characteristics of various sources of brain disease data for disorder discovery are investigated by tuning weights from ensemble learning. In addition, a generalized norm is explicitly explored for adaptive sparseness degree control. The proposed AEML algorithm is evaluated by the public AD neuroimaging initiative database. Results obtained from the extensive experiments demonstrate that our algorithm outperforms the traditional methods.

Accurate identification and understanding informative feature is important for early Alzheimer's disease (AD) prognosis and diagnosis. In this paper, we propose a novel discriminative sparse learning method with relational regularization to jointly predict the clinical score and classify AD disease stages using multimodal features. Specifically, we apply a discriminative learning technique to expand the class-specific difference and include geometric information for effective feature selection. In addition, two kind of relational information are incorporated to explore the intrinsic relationships among features and training subjects in terms of similarity learning. We map the original feature into the target space to identify the informative and predictive features by sparse learning technique. A unique loss function is designed to include both discriminative learning and relational regularization methods. Experimental results based on a total of 805 subjects [including 226 AD patients, 393 mild cognitive impairment (MCI) subjects, and 186 normal controls (NCs)] from AD neuroimaging initiative database show that the proposed method can obtain a classification accuracy of 94.68% for AD versus NC, 80.32% for MCI versus NC, and 74.58% for progressive MCI versus stable MCI, respectively. In addition, we achieve remarkable performance for the clinical scores prediction and classification label identification, which has efficacy for AD disease diagnosis and prognosis. The algorithm comparison demonstrates the effectiveness of the introduced learning techniques and superiority over the state-of-the-arts methods.