CE-GAN: Community Evolutionary Generative Adversarial Network for Alzheimer's Disease Risk Prediction.

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.

Structure Mapping Generative Adversarial Network for Multi-View Information Mapping Pattern Mining.

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.

Community Graph Convolution Neural Network for Alzheimer's Disease Classification and Pathogenetic Factors Identification.

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.

Hypergraph Structural Information Aggregation Generative Adversarial Networks for Diagnosis and Pathogenetic Factors Identification of Alzheimer's Disease With Imaging Genetic Data.

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.

Pathogeny Detection for Mild Cognitive Impairment via Weighted Evolutionary Random Forest With Brain Imaging and Genetic Data.

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.

Pathogenic Factors Identification of Brain Imaging and Gene in Late Mild Cognitive Impairment.

Mild cognitive impairment (MCI) is a dangerous signal of severe cognitive decline. It can be separated into two steps: early MCI (EMCI) and late MCI (LMCI). As the post-state of MCI and pre-state of Alzheimer's disease (AD), LMCI receives insufficient attention in the field of brain science, causing the internal mechanism of LMCI has not been well understood. To better explore the focus and pathological mechanism of LMCI, a method called genetic evolved random forest (GERF) is applied. Resting functional magnetic resonance imaging (rfMRI) and gene data are obtained from 62 subjects (36 LMCI and 26 normal controls), and Pearson correlation analysis is adopted to perform the multimodal fusion of two types of data to construct fusion features. We identified pathogenic brain regions and genes that are highly related to LMCI using GERF and achieves a good effect. Compared with the normal control (NC) group, the abnormal brain regions of LMCI are PUT.L, PreCG.L, IFGtriang.R, REC.R, DCG.R, PoCG.L, and HES.L, and the pathogenic genes are FHIT, RF00019, FRMD4A, PTPRD, and RBFOX1. More importantly, most of these risk genes and abnormal brain regions have been confirmed to be related to AD and MCI in previous studies. In this study, we mapped them to LMCI with higher accuracies, so as to provide a more robust understanding of the physiological mechanism of MCI.

Detecting Risk Gene and Pathogenic Brain Region in EMCI Using a Novel GERF Algorithm Based on Brain Imaging and Genetic Data.

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.