Autism spectrum disorders (ASDs) typically occur in early childhood and affect brain development. Identifying the diseased regions of interest (ROIs) is crucial for improving early diagnosis. However, most current deep learning algorithms for ASD diagnosis struggle to locate lesion ROIs and fully explore relationships between them, which limits their clinical applicability. To resolve these, a dual attention learning fusion algorithm (DALFA) is proposed to enhance feature extraction and fusion from multimodal data. Specifically, the algorithm first constructs features based on both the functional and structural data of the subjects and applies appropriate methods for feature extraction. After concatenating the extracted features, the dual attention learning is employed to fuse these features across two dimensions, optimizing feature representation. Furthermore, this study introduces a dual attention learning neural network (DALNN) that implements and refines the details of DALFA. We conduct sufficient validation experiments using data from the ABIDE dataset. The classification accuracy of DALNN reaches 83.35%, outperforming existing methods. In addition, DALNN can extract ASD-related ROIs, which is beneficial for guiding early clinical intervention.

Timely risk prediction of Alzheimer's disease (AD) holds significant clinical value. However, the inherent fuzziness of disease information hinders the deeper understanding of AD pathogenesis and limits the effectiveness of current predictive models. This article explores the staged evolutionary patterns of AD by integrating fuzzy graph-based disease modeling and deep learning. First, we use fuzzy graphs to quantify interpathogeny associations through fuzzy memberships. Second, we propose a fuzzy entropy propagation model to mathematically describe AD deterioration as the spread of fuzzy entropy information in fuzzy graphs. Finally, we introduce a novel fuzzy graph evolutionary generative adversarial network (FGE-GAN) for disease risk prediction and pathogeny extraction. In the generator of FGE-GAN, fuzzy graph convolution (FGC) layers are designed based on the mathematical model to capture AD's evolutionary patterns with interpretability. Experiments on multiple brain disease datasets indicate that FGE-GAN outperforms state-of-the-art methods in disease risk prediction. In addition, the extracted multiomics pathogenies provide valuable insights for early intervention. The code is available at: github.com/fmri123456/FGE-GAN.

Coordinated analysis of macro-level neuroimaging data and micro-level genetic information provides a comprehensive understanding of Alzheimer's Disease (AD) pathogenesis. However, existing methods fail to fully explore the critical features and regulatory relationships within biological contexts. To address these limitations, we propose a Multi-level Learning and Interactive Fusion Algorithm (MLLIFA), which integrates large foundation models for disease diagnosis and pathogenic factors extraction. The algorithm first leverages large foundation models to construct high-quality brain region and gene features. Then, two sparse attention mechanisms are employed to extract key information from the constructed features. Finally, an interactive learning module is utilized to uncover potential relationships between features in the biological context, guiding the fusion of multi-omics data. Results on the ADNI dataset demonstrate that MLLIFA achieves classification accuracy of 91.22 % and 90.12 % for AD and EMCI. Additionally, pathogenic brain regions and risk genes are successfully identified. This work not only provides strong support for AD pathogenesis research but also lays a solid foundation for achieving early diagnosis and treatment.

The analysis of multiomics biomedical data has become increasingly critical in clinical decision-making for brain diseases, such as Alzheimer's disease (AD). However, the inherent fuzziness of biomedical information limits the classification performance of existing methods, and current disease models struggle to explore pathogenetic mechanisms. Facing with these issues, this article develops a fuzzy graph-based deep learning method to achieve accurate diagnosis and pathogeny identification for brain diseases. First, fuzzy graphs are constructed to describe the associations between pathogenies using fuzzy memberships. Second, a mathematical model inspired by the fuzzy mechanisms of brain is established, effectively capturing the fuzzy congregation patterns of feature information across brain regions and genes. Finally, a brain-inspired fuzzy graph convolutional network (BI-FGCN) is proposed. In BI-FGCN, white-boxed convolutional operations are designed based on the mathematical model. Experimental results across multiple brain disease datasets demonstrate the superiority of BI-FGCN in AD diagnosis and pathogeny identification. We provide a reliable supporting method for the diagnosis and treatment of brain diseases.

Risk prediction for Alzheimer's Disease (AD) is crucial for delaying disease progression in clinics. However, existing methods face significant challenges in leveraging the high-order and fuzzy associations in biomedical data. To address this limitation, we propose a fuzzy hypergraph-based deep learning framework to explore multi-omics associations and enhance predictive performances efficiently. In specific, we first establish a mathematical model of fuzzy structural entropy propagation. This model represents multi-omics associations by fuzzy hypergraphs, characterizing the progression of AD as the topological evolution of fuzzy hypergraphs. Next, we design a Fuzzy Hypergraph Generative Adversarial Network (FHG-GAN). Particularly, large foundation models are used to generate high-quality feature representations, alleviating data noise and inconsistencies. FHG-GAN captures the evolutionary patterns of diseases using fuzzy hypergraph convolutional layers designed based on the mathematical model, thereby achieving accurate disease risk prediction and pathogeny extraction. Finally, we experimentally demonstrate the superiority of FHG-GAN compared with advanced methods, indicating FHG-GAN's ability to support the early diagnosis and treatment of AD-like diseases. Our code is published on: github.com/fmri123456/FHG-GAN.

As a complex neural network system, the brain regions and genes collaborate to effectively store and transmit information. We abstract the collaboration correlations as the brain region gene community network (BG-CN) and present a new deep learning approach, such as the community graph convolutional neural network (Com-GCN), for investigating the transmission of information within and between communities. The results can be used for diagnosing and extracting causal factors for Alzheimer's disease (AD). First, an affinity aggregation model for BG-CN is developed to describe intercommunity and intracommunity information transmission. Second, we design the Com-GCN architecture with intercommunity convolution and intracommunity convolution operations based on the affinity aggregation model. Through sufficient experimental validation on the AD neuroimaging initiative (ADNI) dataset, the design of Com-GCN matches the physiological mechanism better and improves the interpretability and classification performance. Furthermore, Com-GCN can identify lesioned brain regions and disease-causing genes, which may assist precision medicine and drug design in AD and serve as a valuable reference for other neurological disorders.

Complementary information in multi-omics data are crucial for understanding the pathogenesis of Alzheimer's Disease (AD). However, existing studies face challenges in addressing the high-level noise and heterogeneity in multi-omics data. This article presents a novel approach that combines large foundation models (LFMs) with soft attention mechanisms to enhance, select, and fuse multi-omics features, thereby improving the performance of disease classification. Specifically, we first propose a mathematical model based on soft attention mechanisms. This model employs multi-head attention (MHA) and self-attention (SA) for feature selection, and uses cross-attention (CA) for feature fusion. Then, a multi-way soft attention fusion framework (MSAFF) with LFMs is proposed. In this approach, biomedical LFMs are used to construct low-noise biomedical features. The multi-way soft attention algorithm implements effective feature selection and fusion described in the mathematical model. Experimental results on the public imaging genetics datasets demonstrate the advanced performances of MSAFF in both disease classification and AD-related pathogeny discrimination. This article provides intelligent support for the diagnosis and pathogenesis research of AD. Our code can be accessed at github.com/fmri123456/MSAFF.

The risk prediction of Alzheimer's disease (AD) is crucial for its early prevention and treatment. However, current risk prediction methods face challenges in effectively extracting and fusing multiomics features, particularly overlooking the multilevel evolutionary mechanisms of AD. This article combines biomedical large foundation models with the conditional generative adversarial network (GAN) to mine the evolutionary patterns of AD by considering the regulatory effect of genes on brain lesions. Specifically, we first use biomedical large foundation models to effectively construct high-quality imaging genetic features. Next, a conditional probabilistic state transition mathematical model is constructed to describe AD progression as state transitions of brain regions under genetic regulations. Based on the mathematical model, a conditional probabilistic state transition GAN (CPST-GAN) is proposed. This algorithm can mine the dynamic evolutionary patterns of AD by fusing brain imaging and genetic features to achieve risk prediction of AD. Finally, experiments on the public imaging genetics datasets validate the effectiveness and superiority of CPST-GAN in evolutionary pattern mining and risk prediction of AD. This article not only provides a reliable intelligence algorithm for early intervention of AD but also offers new insights for future research on AD pathogenesis. The code has been published at github.com/fmri123456/CPST-GAN.

Multi-view learning is dedicated to integrating information from different views and improving the generalization performance of models. However, in most current works, learning under different views has significant independency, overlooking common information mapping patterns that exist between these views. This paper proposes a Structure Mapping Generative adversarial network (SM-GAN) framework, which utilizes the consistency and complementarity of multi-view data from the innovative perspective of information mapping. Specifically, based on network-structured multi-view data, a structural information mapping model is proposed to capture hierarchical interaction patterns among views. Subsequently, three different types of graph convolutional operations are designed in SM-GAN based on the model. Compared with regular GAN, we add a structural information mapping module between the encoder and decoder wthin the generator, completing the structural information mapping from the micro-view to the macro-view. This paper conducted sufficient validation experiments using public imaging genetics data in Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. It is shown that SM-GAN outperforms baseline and advanced methods in multi-label classification and evolution prediction tasks.

In the studies of neurodegenerative diseases such as Alzheimer's Disease (AD), researchers often focus on the associations among multi-omics pathogeny based on imaging genetics data. However, current studies overlook the communities in brain networks, leading to inaccurate models of disease development. This paper explores the developmental patterns of AD from the perspective of community evolution. We first establish a mathematical model to describe functional degeneration in the brain as the community evolution driven by entropy information propagation. Next, we propose an interpretable Community Evolutionary Generative Adversarial Network (CE-GAN) to predict disease risk. In the generator of CE-GAN, community evolutionary convolutions are designed to capture the evolutionary patterns of AD. The experiments are conducted using functional magnetic resonance imaging (fMRI) data and single nucleotide polymorphism (SNP) data. CE-GAN achieves 91.67% accuracy and 91.83% area under curve (AUC) in AD risk prediction tasks, surpassing advanced methods on the same dataset. In addition, we validated the effectiveness of CE-GAN for pathogeny extraction. The source code of this work is available at https://github.com/fmri123456/CE-GAN.

Alzheimer's disease (AD) is a neurodegenerative disease with profound pathogenetic causes. Imaging genetic data analysis can provide comprehensive insights into its causes. To fully utilize the multi-level information in the data, this article proposes a hypergraph structural information aggregation model, and constructs a novel deep learning method named hypergraph structural information aggregation generative adversarial networks (HSIA-GANs) for the automatic sample classification and accurate feature extraction. Specifically, HSIA-GAN is composed of generator and discriminator. The generator has three main functions. First, vertex graph and edge graph are constructed based on the input hypergraph to present the low-order relations. Second, the low-order structural information of hypergraph is extracted by the designed vertex convolution layers and edge convolution layers. Finally, the synthetic hypergraph is generated as the input of the discriminator. The discriminator can extract the high-order structural information directly from hypergraph through vertex-edge convolution, fuse the high and low-order structural information, and finalize the results through the full connection (FC) layers. Based on the data acquired from AD neuroimaging initiative, HSIA-GAN shows significant advantages in three classification tasks, and extracts discriminant features conducive to better disease classification.

Predicting disease progression in the initial stage to implement early intervention and treatment can effectively prevent the further deterioration of the condition. Traditional methods for medical data analysis usually fail to perform well because of their incapability for mining the correlation pattern of pathogenies. Therefore, many calculation methods have been excavated from the field of deep learning. In this study, we propose a novel method of influence hypergraph convolutional generative adversarial network (IHGC-GAN) for disease risk prediction. First, a hypergraph is constructed with genes and brain regions as nodes. Then, an influence transmission model is built to portray the associations between nodes and the transmission rule of disease information. Third, an IHGC-GAN method is constructed based on this model. This method innovatively combines the graph convolutional network (GCN) and GAN. The GCN is used as the generator in GAN to spread and update the lesion information of nodes in the brain region-gene hypergraph. Finally, the prediction accuracy of the method is improved by the mutual competition and repeated iteration between generator and discriminator. This method can not only capture the evolutionary pattern from early mild cognitive impairment (EMCI) to late MCI (LMCI) but also extract the pathogenic factors and predict the deterioration risk from EMCI to LMCI. The results on the two datasets indicate that the IHGC-GAN method has better prediction performance than the advanced methods in a variety of indicators.

Imaging genetics provides unique insights into the pathological studies of complex brain diseases by integrating the characteristics of multi-level medical data. However, most current imaging genetics research performs incomplete data fusion. Also, there is a lack of effective deep learning methods to analyze neuroimaging and genetic data jointly. Therefore, this paper first constructs the brain region-gene networks to intuitively represent the association pattern of pathogenetic factors. Second, a novel feature information aggregation model is constructed to accurately describe the information aggregation process among brain region nodes and gene nodes. Finally, a deep learning method called feature information aggregation and diffusion generative adversarial network (FIAD-GAN) is proposed to efficiently classify samples and select features. We focus on improving the generator with the proposed convolution and deconvolution operations, with which the interpretability of the deep learning framework has been dramatically improved. The experimental results indicate that FIAD-GAN can not only achieve superior results in various disease classification tasks but also extract brain regions and genes closely related to AD. This work provides a novel method for intelligent clinical decisions. The relevant biomedical discoveries provide a reliable reference and technical basis for the clinical diagnosis, treatment and pathological analysis of disease.

The roles of brain regions activities and gene expressions in the development of Alzheimer's disease (AD) remain unclear. Existing imaging genetic studies usually has the problem of inefficiency and inadequate fusion of data. This study proposes a novel deep learning method to efficiently capture the development pattern of AD. First, we model the interaction between brain regions and genes as node-to-node feature aggregation in a brain region-gene network. Second, we propose a feature aggregation graph convolutional network (FAGCN) to transmit and update the node feature. Compared with the trivial graph convolutional procedure, we replace the input from the adjacency matrix with a weight matrix based on correlation analysis and consider common neighbor similarity to discover broader associations of nodes. Finally, we use a full-gradient saliency graph mechanism to score and extract the pathogenetic brain regions and risk genes. According to the results, FAGCN achieved the best performance among both traditional and cutting-edge methods and extracted AD-related brain regions and genes, providing theoretical and methodological support for the research of related diseases.

Abstract It is a hot spot in the field of computer application to diagnose complex brain diseases such as Asperger syndrome (AS) using machine learning technology. To identify AS patients and detect lesions, this paper proposes a novel clustering-evolutionary random support vector machine (SVM) ensemble (CERSVME) based on graph theory. Firstly, we extract graph theory indexes from the resting-state functional magnetic resonance imaging (fMRI) data as sample features and construct an ensemble learner by integrating multiple SVM classifiers. Secondly, the base learners with high redundancy and poor classification ability are deleted through clustering evolutions to improve the performance of the model. Then the CERSVME model is used to classify fMRI image of AS patients and healthy controls. According to the classification results, a multi-stage analysis scheme is designed to find the AS-related brain areas. We validate the proposed approach on 135 participants from autism brain imaging data exchange cohort. The highest accuracy reported by the CERSVME reaches 95.24%. More importantly, the diseased brain areas such as middle frontal gyrus, hippocampus and precuneus are found based on their contributions to classification performances of the CERSVME. Our study provides useful assistances for the clinical detection of patients with AS.

Medical imaging technology and gene sequencing technology have long been widely used to analyze the pathogenesis and make precise diagnoses of mild cognitive impairment (MCI). However, few studies involve the fusion of radiomics data with genomics data to make full use of the complementarity between different omics to detect pathogenic factors of MCI. This paper performs multimodal fusion analysis based on functional magnetic resonance imaging (fMRI) data and single nucleotide polymorphism (SNP) data of MCI patients. In specific, first, using correlation analysis methods on sequence information of regions of interests (ROIs) and digitalized gene sequences, the fusion features of samples are constructed. Then, introducing weighted evolution strategy into ensemble learning, a novel weighted evolutionary random forest (WERF) model is built to eliminate the inefficient features. Consequently, with the help of WERF, an overall multimodal data analysis framework is established to effectively identify MCI patients and extract pathogenic factors. Based on the data of MCI patients from the ADNI database and compared with some existing popular methods, the superiority in performance of the framework is verified. Our study has great potential to be an effective tool for pathogenic factors detection of MCI.

Fusion analysis of disease-related multi-modal data is becoming increasingly important to illuminate the pathogenesis of complex brain diseases. However, owing to the small amount and high dimension of multi-modal data, current machine learning methods do not fully achieve the high veracity and reliability of fusion feature selection. In this paper, we propose a genetic-evolutionary random forest (GERF) algorithm to discover the risk genes and disease-related brain regions of early mild cognitive impairment (EMCI) based on the genetic data and resting-state functional magnetic resonance imaging (rs-fMRI) data. Classical correlation analysis method is used to explore the association between brain regions and genes, and fusion features are constructed. The genetic-evolutionary idea is introduced to enhance the classification performance, and to extract the optimal features effectively. The proposed GERF algorithm is evaluated by the public Alzheimer's Disease Neuroimaging Initiative (ADNI) database, and the results show that the algorithm achieves satisfactory classification accuracy in small sample learning. Moreover, we compare the GERF algorithm with other methods to prove its superiority. Furthermore, we propose the overall framework of detecting pathogenic factors, which can be accurately and efficiently applied to the multi-modal data analysis of EMCI and be able to extend to other diseases. This work provides a novel insight for early diagnosis and clinicopathologic analysis of EMCI, which facilitates clinical medicine to control further deterioration of diseases and is good for the accurate electric shock using transcranial magnetic stimulation.

MOTIVATION: The multimodal data fusion analysis becomes another important field for brain disease detection and increasing researches concentrate on using neural network algorithms to solve a range of problems. However, most current neural network optimizing strategies focus on internal nodes or hidden layer numbers, while ignoring the advantages of external optimization. Additionally, in the multimodal data fusion analysis of brain science, the problems of small sample size and high-dimensional data are often encountered due to the difficulty of data collection and the specialization of brain science data, which may result in the lower generalization performance of neural network. RESULTS: We propose a genetically evolved random neural network cluster (GERNNC) model. Specifically, the fusion characteristics are first constructed to be taken as the input and the best type of neural network is selected as the base classifier to form the initial random neural network cluster. Second, the cluster is adaptively genetically evolved. Based on the GERNNC model, we further construct a multi-tasking framework for the classification of patients with brain disease and the extraction of significant characteristics. In a study of genetic data and functional magnetic resonance imaging data from the Alzheimer's Disease Neuroimaging Initiative, the framework exhibits great classification performance and strong morbigenous factor detection ability. This work demonstrates that how to effectively detect pathogenic components of the brain disease on the high-dimensional medical data and small samples. AVAILABILITY AND IMPLEMENTATION: The Matlab code is available at https://github.com/lizi1234560/GERNNC.git.

Alzheimer's disease (AD) has become a severe medical challenge. Advances in technologies produced high-dimensional data of different modalities including functional magnetic resonance imaging (fMRI) and single nucleotide polymorphism (SNP). Understanding the complex association patterns among these heterogeneous and complementary data is of benefit to the diagnosis and prevention of AD. In this paper, we apply the appropriate correlation analysis method to detect the relationships between brain regions and genes, and propose "brain region-gene pairs" as the multimodal features of the sample. In addition, we put forward a novel data analysis method from technology aspect, cluster evolutionary random forest (CERF), which is suitable for "brain region-gene pairs". The idea of clustering evolution is introduced to improve the generalization performance of random forest which is constructed by randomly selecting samples and sample features. Through hierarchical clustering of decision trees in random forest, the decision trees with higher similarity are clustered into one class, and the decision trees with the best performance are retained to enhance the diversity between decision trees. Furthermore, based on CERF, we integrate feature construction, feature selection and sample classification to find the optimal combination of different methods, and design a comprehensive diagnostic framework for AD. The framework is validated by the samples with both fMRI and SNP data from ADNI. The results show that we can effectively identify AD patients and discover some brain regions and genes associated with AD significantly based on this framework. These findings are conducive to the clinical treatment and prevention of AD.

Many watermarking techniques for intellectual property (IP) protection are not resilient to tampering or removal attacks, especially for field programmable gate array (FPGA)-based IP cores. If attacked, the damaged watermarks cannot provide sufficient evidence in front of a court. To address this issue, the authors present a signature restoration scheme. The thought of secret sharing is introduced to share the signature into small watermarks. These watermarks are encoded with Reed-Solomon (RS) codes and embedded into unused lookup tables (LUTs) of used slices. Unlike most of existing techniques, the proposed scheme can restore the signature only by extracting parts of watermarks. So, it is tolerant to some damaged watermarks caused by removal attacks. The experiments show that the proposed scheme incurs no extra hardware resource and timing overhead. The robustness against attacks is much better by comparing to other schemes.