Classification of Alzheimer's Disease Using Whole Brain Hierarchical Network

Deep Learning for Alzheimer's Disease Prediction: A Comprehensive Review

Alzheimer's disease (AD) is a neurological disorder that significantly impairs cognitive function, leading to memory loss and eventually death. AD progresses through three stages: early stage, mild cognitive impairment (MCI) (middle stage), and dementia. Early diagnosis of Alzheimer's disease is crucial and can improve survival rates among patients. Traditional methods for diagnosing AD through regular checkups and manual examinations are challenging. Advances in computer-aided diagnosis systems (CADs) have led to the development of various artificial intelligence and deep learning-based methods for rapid AD detection. This survey aims to explore the different modalities, feature extraction methods, datasets, machine learning techniques, and validation methods used in AD detection. We reviewed 116 relevant papers from repositories including Elsevier (45), IEEE (25), Springer (19), Wiley (6), PLOS One (5), MDPI (3), World Scientific (3), Frontiers (3), PeerJ (2), Hindawi (2), IO Press (1), and other multiple sources (2). The review is presented in tables for ease of reference, allowing readers to quickly grasp the key findings of each study. Additionally, this review addresses the challenges in the current literature and emphasizes the importance of interpretability and explainability in understanding deep learning model predictions. The primary goal is to assess existing techniques for AD identification and highlight obstacles to guide future research.

DeepKEGG: a multi-omics data integration framework with biological insights for cancer recurrence prediction and biomarker discovery

Deep learning-based multi-omics data integration methods have the capability to reveal the mechanisms of cancer development, discover cancer biomarkers and identify pathogenic targets. However, current methods ignore the potential correlations between samples in integrating multi-omics data. In addition, providing accurate biological explanations still poses significant challenges due to the complexity of deep learning models. Therefore, there is an urgent need for a deep learning-based multi-omics integration method to explore the potential correlations between samples and provide model interpretability. Herein, we propose a novel interpretable multi-omics data integration method (DeepKEGG) for cancer recurrence prediction and biomarker discovery. In DeepKEGG, a biological hierarchical module is designed for local connections of neuron nodes and model interpretability based on the biological relationship between genes/miRNAs and pathways. In addition, a pathway self-attention module is constructed to explore the correlation between different samples and generate the potential pathway feature representation for enhancing the prediction performance of the model. Lastly, an attribution-based feature importance calculation method is utilized to discover biomarkers related to cancer recurrence and provide a biological interpretation of the model. Experimental results demonstrate that DeepKEGG outperforms other state-of-the-art methods in 5-fold cross validation. Furthermore, case studies also indicate that DeepKEGG serves as an effective tool for biomarker discovery. The code is available at https://github.com/lanbiolab/DeepKEGG.

Diagnosis of Alzheimer's disease via optimized lightweight convolution-attention and structural MRI

Alzheimer's disease (AD) poses a substantial public health challenge, demanding accurate screening and diagnosis. Identifying AD in its early stages, including mild cognitive impairment (MCI) and healthy control (HC), is crucial given the global aging population. Structural magnetic resonance imaging (sMRI) is essential for understanding the brain's structural changes due to atrophy. While current deep learning networks overlook voxel long-term dependencies, vision transformers (ViT) excel at recognizing such dependencies in images, making them valuable in AD diagnosis. Our proposed method integrates convolution-attention mechanisms in transformer-based classifiers for AD brain datasets, enhancing performance without excessive computing resources. Replacing multi-head attention with lightweight multi-head self-attention (LMHSA), employing inverted residual (IRU) blocks, and introducing local feed-forward networks (LFFN) yields exceptional results. Training on AD datasets with a gradient-centralized optimizer and Adam achieves an impressive accuracy rate of 94.31% for multi-class classification, rising to 95.37% for binary classification (AD vs. HC) and 92.15% for HC vs. MCI. These outcomes surpass existing AD diagnosis approaches, showcasing the model's efficacy. Identifying key brain regions aids future clinical solutions for AD and neurodegenerative diseases. However, this study focused exclusively on the AD Neuroimaging Initiative (ADNI) cohort, emphasizing the need for a more robust, generalizable approach incorporating diverse databases beyond ADNI in future research.

Hypergraph convolutional network for longitudinal data analysis in Alzheimer's disease

Alzheimer's disease (AD) is an irreversible and progressive neurodegenerative disease. Longitudinal structural magnetic resonance imaging (sMRI) data have been widely used for tracking AD pathogenesis and diagnosis. However, existing methods tend to treat each time point equally without considering the temporal characteristics of longitudinal data. In this paper, we propose a weighted hypergraph convolution network (WHGCN) to use the internal correlations among different time points and leverage high-order relationships between subjects for AD detection. Specifically, we construct hypergraphs for sMRI data at each time point using the K-nearest neighbor (KNN) method to represent relationships between subjects, and then fuse the hypergraphs according to the importance of the data at each time point to obtain the final hypergraph. Subsequently, we use hypergraph convolution to learn high-order information between subjects while performing feature dimensionality reduction. Finally, we conduct experiments on 518 subjects selected from the Alzheimer's disease neuroimaging initiative (ADNI) database, and the results show that the WHGCN can get higher AD detection performance and has the potential to improve our understanding of the pathogenesis of AD.

Graph-Based Fusion of Imaging, Genetic and Clinical Data for Degenerative Disease Diagnosis

Graph learning methods have achieved noteworthy performance in disease diagnosis due to their ability to represent unstructured information such as inter-subject relationships. While it has been shown that imaging, genetic and clinical data are crucial for degenerative disease diagnosis, existing methods rarely consider how best to use their relationships. How best to utilize information from imaging, genetic and clinical data remains a challenging problem. This study proposes a novel graph-based fusion (GBF) approach to meet this challenge. To extract effective imaging-genetic features, we propose an imaging-genetic fusion module which uses an attention mechanism to obtain modality-specific and joint representations within and between imaging and genetic data. Then, considering the effectiveness of clinical information for diagnosing degenerative diseases, we propose a multi-graph fusion module to further fuse imaging-genetic and clinical features, which adopts a learnable graph construction strategy and a graph ensemble method. Experimental results on two benchmarks for degenerative disease diagnosis (Alzheimers Disease Neuroimaging Initiative and Parkinson's Progression Markers Initiative) demonstrate its effectiveness compared to state-of-the-art graph-based methods. Our findings should help guide further development of graph-based models for dealing with imaging, genetic and clinical data.

Deep Hashing Mutual Learning for Brain Network Classification

Recently, clinical phenotypic semantic information has begun to play an important role in some brain network classification methods based on deep learning. However, most current methods only consider the phenotypic semantic information of individual brain networks but ignore the potential phenotypic characteristics among group brain networks. To address this problem, we present a deep hashing mutual learning (DHML)-based brain network classification method. Specifically, we first design a separable CNN-based deep hashing learning to extract individual topological features of brain networks and map them into hash codes. Secondly, we construct a group brain network relationship graph based on the similarity of phenotypic semantic information, in which each node is a brain network, and the properties of the nodes are the individual features extracted in the previous step. Then, we adopt a GCN-based deep hashing learning to extract the group topological features of the brain network and map them to hash codes. Finally, the two deep hashing learning models perform mutual learning by measuring the distribution differences between the hash codes to achieve the interaction of individual and group features. The experimental results on the three commonly used brain atlases (AAL Atlas, Dosenbach160 Atlas, and CC200 Atlas) of the ABIDE I dataset show that our proposed DHML method achieves optimal classification performance compared with some state-of-the-art methods.

Neuroimaging and machine learning for studying the pathways from mild cognitive impairment to alzheimer's disease: a systematic review

BACKGROUND

This systematic review synthesizes the most recent neuroimaging procedures and machine learning approaches for the prediction of conversion from mild cognitive impairment to Alzheimer's disease dementia.

METHODS

We systematically searched PubMed, SCOPUS, and Web of Science databases following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) systematic review guidelines.

RESULTS

Our search returned 2572 articles, 56 of which met the criteria for inclusion in the final selection. The multimodality framework and deep learning techniques showed potential for predicting the conversion of MCI to AD dementia.

CONCLUSION

Findings of this systematic review identified that the possibility of using neuroimaging data processed by advanced learning algorithms is promising for the prediction of AD progression. We also provided a detailed description of the challenges that researchers are faced along with future research directions. The protocol has been registered in the International Prospective Register of Systematic Reviews- CRD42019133402 and published in the Systematic Reviews journal.

A cross-sectional study of explainable machine learning in Alzheimer's disease: diagnostic classification using MR radiomic features

Introduction

Alzheimer's disease (AD) even nowadays remains a complex neurodegenerative disease and its diagnosis relies mainly on cognitive tests which have many limitations. On the other hand, qualitative imaging will not provide an early diagnosis because the radiologist will perceive brain atrophy on a late disease stage. Therefore, the main objective of this study is to investigate the necessity of quantitative imaging in the assessment of AD by using machine learning (ML) methods. Nowadays, ML methods are used to address high dimensional data, integrate data from different sources, model the etiological and clinical heterogeneity, and discover new biomarkers in the assessment of AD.

Methods

In this study radiomic features from both entorhinal cortex and hippocampus were extracted from 194 normal controls (NC), 284 mild cognitive impairment (MCI) and 130 AD subjects. Texture analysis evaluates statistical properties of the image intensities which might represent changes in MRI image pixel intensity due to the pathophysiology of a disease. Therefore, this quantitative method could detect smaller-scale changes of neurodegeneration. Then the radiomics signatures extracted by texture analysis and baseline neuropsychological scales, were used to build an XGBoost integrated model which has been trained and integrated.

Results

The model was explained by using the Shapley values produced by the SHAP (SHapley Additive exPlanations) method. XGBoost produced a f1-score of 0.949, 0.818, and 0.810 between NC vs. AD, MC vs. MCI, and MCI vs. AD, respectively.

Discussion

These directions have the potential to help to the earlier diagnosis and to a better manage of the disease progression and therefore, develop novel treatment strategies. This study clearly showed the importance of explainable ML approach in the assessment of AD.

Machine Learning on Visibility Graph Features Discriminates the Cognitive Event-Related Potentials of Patients with Early Alzheimer's Disease from Healthy Aging

We present a framework for electroencephalography (EEG)-based classification between patients with Alzheimer's Disease (AD) and robust normal elderly (RNE) via a graph theory approach using visibility graphs (VGs). This EEG VG approach is motivated by research that has demonstrated differences between patients with early stage AD and RNE using various features of EEG oscillations or cognitive event-related potentials (ERPs). In the present study, EEG signals recorded during a word repetition experiment were wavelet decomposed into 5 sub-bands (δ,θ,α,β,γ). The raw and band-specific signals were then converted to VGs for analysis. Twelve graph features were tested for differences between the AD and RNE groups, and t-tests employed for feature selection. The selected features were then tested for classification using traditional machine learning and deep learning algorithms, achieving a classification accuracy of 100% with linear and non-linear classifiers. We further demonstrated that the same features can be generalized to the classification of mild cognitive impairment (MCI) converters, i.e., prodromal AD, against RNE with a maximum accuracy of 92.5%. Code is released online to allow others to test and reuse this framework.

RBS-Net: Hippocampus segmentation using multi-layer feature learning with the region, boundary and structure loss

Hippocampus has great influence over the Alzheimer's disease (AD) research because of its essential role as a biomarker in the human brain. Thus the performance of hippocampus segmentation influences the development of clinical research for brain disorders. Deep learning using U-net-like networks becomes prevalent in hippocampus segmentation on Magnetic Resonance Imaging (MRI) due to its efficiency and accuracy. However, current methods lose sufficient detailed information during pooling, which hinders the segmentation results. And weak supervision on the details like edges or positions results in fuzzy and coarse boundary segmentation, causing great differences between the segmentation and ground-truth. In view of these drawbacks, we propose a Region-Boundary and Structure Net (RBS-Net), which consists of a primary net and an auxiliary net. (1) Our primary net focuses on the region distribution of hippocampus and introduces a distance map for boundary supervision. Furthermore the primary net adds a multi-layer feature learning module to compensate the information loss during pooling and strengthen the differences between the foreground and background, improving the region and boundary segmentation. (2) The auxiliary net concentrates on the structure similarity and also utilizes the multi-layer feature learning module, and this parallel task can refine encoders by similarizing the structure of the segmentation and ground-truth. We train and test our network using 5-fold cross-validation on HarP, a public available hippocampus dataset. Experimental results demonstrate that our proposed RBS-Net achieves a Dice of 89.76% in average, outperforming several state-of-the-art hippocampus segmentation methods. Furthermore, in few shot circumstances, our proposed RBS-Net achieves better results in terms of a comprehensive evaluation compared to several state-of-the-art deep learning-based methods. Finally we can observe that visual segmentation results for the boundary and detailed regions are improved by our proposed RBS-Net.

BoF-SVM-based data intelligence model for detecting dementia

Dementia is a brain condition that impairs the cognitive abilities of an individual. Mild cognitive impairment is a mediator phase of healthy and dementia controls. The motivation of this study is to predict dementia using magnetic resonance imaging data, which is significant for the diagnosis of normal control and dementia patients. The proposed model leverages effective methods like Discrete Wavelet Transform, Bag of Features, and Support Vector Machine. The four wavelets haar, Daubechies, symlets, and coiflets are used for image compression. The results of the proposed data intelligence model are promising in terms of accuracy which is 92.32% which is better than the recently proposed models. Also, the proposed data intelligence model is compared with the models which may use curvelet transform, and shearlet transform and with the methods which have gone without using DWT transforms. The comparisons have also been made with the models that have used other prevalent techniques like Principal Component Analysis, Fisher Discriminant Ratio, and Gray Level Co-occurrence Matrix. The outcomes support the usage of each technique in the suggested data intelligence paradigm.

A review on Alzheimer's disease classification from normal controls and mild cognitive impairment using structural MR images

Alzheimer's disease (AD) is an irreversible neurodegenerative brain disorder that degrades the memory and cognitive ability in elderly people. The main reason for memory loss and reduction in cognitive ability is the structural changes in the brain that occur due to neuronal loss. These structural changes are most conspicuous in the hippocampus, cortex, and grey matter and can be assessed by using neuroimaging techniques viz. Positron Emission Tomography (PET), structural Magnetic Resonance Imaging (MRI) and functional MRI (fMRI), etc. Out of these neuroimaging techniques, structural MRI has evolved as the best technique as it indicates the best soft tissue contrast and high spatial resolution which is important for AD detection. Currently, the focus of researchers is on predicting the conversion of Mild Cognitive Impairment (MCI) into AD. MCI represents the transition state between expected cognitive changes with normal aging and Alzheimer's disease. Not every MCI patient progresses into Alzheimer's disease. MCI can develop into stable MCI (sMCI, patients are called non-converters) or into progressive MCI (pMCI, patients are diagnosed as MCI converters). This paper discusses the prognosis of MCI to AD conversion and presents a review of structural MRI-based studies for AD detection. AD detection framework includes feature extraction, feature selection, and classification process. This paper reviews the studies for AD detection based on different feature extraction methods and machine learning algorithms for classification. The performance of various feature extraction methods has been compared and it has been observed that the wavelet transform-based feature extraction method would give promising results for AD classification. The present study indicates that researchers are successful in classifying AD from Normal Controls (N_rmC) but, it still requires a lot of work to be done for MCI/ N_rmC and MCI/AD, which would help in detecting AD at its early stage.

Ensemble Model for Diagnostic Classification of Alzheimer's Disease Based on Brain Anatomical Magnetic Resonance Imaging

Alzheimer's is one of the fast-growing diseases among people worldwide leading to brain atrophy. Neuroimaging reveals extensive information about the brain's anatomy and enables the identification of diagnostic features. Artificial intelligence (AI) in neuroimaging has the potential to significantly enhance the treatment process for Alzheimer's disease (AD). The objective of this study is two-fold: (1) to compare existing Machine Learning (ML) algorithms for the classification of AD. (2) To propose an effective ensemble-based model for the same and to perform its comparative analysis. In this study, data from the Alzheimer's Diseases Neuroimaging Initiative (ADNI), an online repository, is utilized for experimentation consisting of 2125 neuroimages of Alzheimer's disease (n = 975), mild cognitive impairment (n = 538) and cognitive normal (n = 612). For classification, the framework incorporates a Decision Tree (DT), Random Forest (RF), Naïve Bayes (NB), and K-Nearest Neighbor (K-NN) followed by some variations of Support Vector Machine (SVM), such as SVM (RBF kernel), SVM (Polynomial Kernel), and SVM (Sigmoid kernel), as well as Gradient Boost (GB), Extreme Gradient Boosting (XGB) and Multi-layer Perceptron Neural Network (MLP-NN). Afterwards, an Ensemble Based Generic Kernel is presented where Master-Slave architecture is combined to attain better performance. The proposed model is an ensemble of Extreme Gradient Boosting, Decision Tree and SVM_Polynomial kernel (XGB + DT + SVM). At last, the proposed method is evaluated using cross-validation using statistical techniques along with other ML models. The presented ensemble model (XGB + DT + SVM) outperformed existing state-of-the-art algorithms with an accuracy of 89.77%. The efficiency of all the models was optimized using Grid-based tuning, and the results obtained after such process showed significant improvement. XGB + DT + SVM with optimized parameters outperformed all other models with an efficiency of 95.75%. The implication of the proposed ensemble-based learning approach clearly shows the best results compared to other ML models. This experimental comparative analysis improved understanding of the above-defined methods and enhanced their scope and significance in the early detection of Alzheimer's disease.

Improving the Robustness of Loanword Identification in Social Media Texts

As a potential bilingual resource, loanwords play a very important role in many natural language processing tasks. If loanwords in a low-resource language can be identified effectively, the generated donor-receipt word pairs will benefit many cross-lingual NLP tasks. However, most studies on loanword identification mainly focus on formal texts such as news and government documents. Loanword identification in social media texts is still an under-studied field. Since it faces many challenges and can be widely used in several downstream tasks, more efforts should be put on loanword identification in social media texts. In this study, we present a multi-task learning architecture with deep bi-directional RNNs for loanword identification in social media texts, where different task supervision can happen at different layers. The multi-task neural network architecture learns higher order feature representations from word and character sequences along with basic spell error checking (SEC), part-of-speech (POS) tagging and named entity recognition (NER) information. Experimental results on Uyghur loanword identification in social media texts in five donor languages (Chinese, Arabic, Russian, Turkish, and Farsi) show that our method achieves the best performance compared with several strong baseline systems. We also combine the loanword detection results into the training data of neural machine translation for low-resource language pairs. Experiments show that models trained on the extended datasets achieve significant improvements compared with the baseline models in all language pairs.

Triploid genetic algorithm for convolutional neural network-based diagnosis of mild cognitive impairment

The diagnosis of mild cognitive impairment (MCI), which is deemed a formative phase of dementia, may greatly assist clinicians in delaying its headway toward dementia. This article proposes a deep learning approach based on a triploid genetic algorithm, a proposed variant of genetic algorithms, for classifying MCI converts and non-converts using structural magnetic resonance imaging data. It also explores the effect of the choice of activation functions and that of the selection of hyper-parameters on the performance of the model. The proposed work is a step toward automated convolutional neural networks. The performance of the proposed method is measured in terms of accuracy and empirical studies exhibit the preeminence of our proposed method over the existing ones. The proposed model results in a maximum accuracy of 0.97961. Thus, it may contribute to the effective diagnosis of MCI and may prove important in clinical settings.

Functional Brain Network Classification Based on Deep Graph Hashing Learning

Brain network classification using resting-state functional magnetic resonance imaging (rs-fMRI) is an effective analytical method for diagnosing brain diseases. In recent years, brain network classification methods based on deep learning have attracted increasing attention. However, these methods only consider the spatial topological characteristics of the brain network but ignore its proximity relationships in semantic space. To overcome this problem, we propose a novel brain network classification method based on deep graph hashing learning named BNC-DGHL. Specifically, we first extract the deep features of the brain network and then learn a graph hash function based on clinical phenotype labels and the similarity of diagnostic labels. Secondly, we use the learned graph hash function to convert deep features into hash codes, which can maintain the original semantic spatial relationships. Finally, we calculate the distance between hash codes to obtain the predicted category of the brain network. Experimental results on ABIDE I, ABIDE II, and ADHD-200 datasets demonstrate that our method achieves better classification performance of brain diseases compared with some state-of-the-art methods, and the abnormal functional connectivities between brain regions identified may serve as biomarkers associated with related brain diseases.

An enhanced multi-modal brain graph network for classifying neuropsychiatric disorders

It has been proven that neuropsychiatric disorders (NDs) can be associated with both structures and functions of brain regions. Thus, data about structures and functions could be usefully combined in a comprehensive analysis. While brain structural MRI (sMRI) images contain anatomic and morphological information about NDs, functional MRI (fMRI) images carry complementary information. However, efficient extraction and fusion of sMRI and fMRI data remains challenging. In this study, we develop an enhanced multi-modal graph convolutional network (MME-GCN) in a binary classification between patients with NDs and healthy controls, based on the fusion of the structural and functional graphs of the brain region. First, based on the same brain atlas, we construct structural and functional graphs from sMRI and fMRI data, respectively. Second, we use machine learning to extract important features from the structural graph network. Third, we use these extracted features to adjust the corresponding edge weights in the functional graph network. Finally, we train a multi-layer GCN and use it in binary classification task. MME-GCN achieved 93.71% classification accuracy on the open data set provided by the Consortium for Neuropsychiatric Phenomics. In addition, we analyzed the important features selected from the structural graph and verified them in the functional graph. Using MME-GCN, we found several specific brain connections important to NDs.

Extracting ROI-Based Contourlet Subband Energy Feature From the sMRI Image for Alzheimer's Disease Classification

Structural magnetic resonance imaging (sMRI)-based Alzheimer's disease (AD) classification and its prodromal stage-mild cognitive impairment (MCI) classification have attracted many attentions and been widely investigated in recent years. Owing to the high dimensionality, representation of the sMRI image becomes a difficult issue in AD classification. Furthermore, regions of interest (ROI) reflected in the sMRI image are not characterized properly by spatial analysis techniques, which has been a main cause of weakening the discriminating ability of the extracted spatial feature. In this study, we propose a ROI-based contourlet subband energy (ROICSE) feature to represent the sMRI image in the frequency domain for AD classification. Specifically, a preprocessed sMRI image is first segmented into 90 ROIs by a constructed brain mask. Instead of extracting features from the 90 ROIs in the spatial domain, the contourlet transform is performed on each of these ROIs to obtain their energy subbands. And then for an ROI, a subband energy (SE) feature vector is constructed to capture its energy distribution and contour information. Afterwards, SE feature vectors of the 90 ROIs are concatenated to form a ROICSE feature of the sMRI image. Finally, support vector machine (SVM) classifier is used to classify 880 subjects from ADNI and OASIS databases. Experimental results show that the ROICSE approach outperforms six other state-of-the-art methods, demonstrating that energy and contour information of the ROI are important to capture differences between the sMRI images of AD and HC subjects. Meanwhile, brain regions related to AD can also be found using the ROICSE feature, indicating that the ROICSE feature can be a promising assistant imaging marker for the AD diagnosis via the sMRI image. Code and Sample IDs of this paper can be downloaded at https://github.com/NWPU-903PR/ROICSE.git.

LDICDL: LncRNA-Disease Association Identification Based on Collaborative Deep Learning

It has been proved that long noncoding RNA (lncRNA) plays critical roles in many human diseases. Therefore, inferring associations between lncRNAs and diseases can contribute to disease diagnosis, prognosis and treatment. To overcome the limitation of traditional experimental methods such as expensive and time-consuming, several computational methods have been proposed to predict lncRNA-disease associations by fusing different biological data. However, the prediction performance of lncRNA-disease associations identification needs to be improved. In this study, we propose a computational model (named LDICDL) to identify lncRNA-disease associations based on collaborative deep learning. It uses an automatic encoder to denoise multiple lncRNA feature information and multiple disease feature information, respectively. Then, the matrix decomposition algorithm is employed to predict the potential lncRNA-disease associations. In addition, to overcome the limitation of matrix decomposition, the hybrid model is developed to predict associations between new lncRNA (or disease) and diseases (or lncRNA). The ten-fold cross validation and de novo test are applied to evaluate the performance of method. The experimental results show LDICDL outperforms than other state-of-the-art methods in prediction performance.

Regional Brain Fusion: Graph Convolutional Network for Alzheimer's Disease Prediction and Analysis

Alzheimer's disease (AD) has raised extensive concern in healthcare and academia as one of the most prevalent health threats to the elderly. Due to the irreversible nature of AD, early and accurate diagnoses are significant for effective prevention and treatment. However, diverse clinical symptoms and limited neuroimaging accuracy make diagnoses challenging. In this article, we built a brain network for each subject, which assembles several commonly used neuroimaging data simply and reasonably, including structural magnetic resonance imaging (MRI), diffusion-weighted imaging (DWI), and amyloid positron emission tomography (PET). Based on some existing research results, we applied statistical methods to analyze (i) the distinct affinity of AD burden on each brain region, (ii) the topological lateralization between left and right hemispheric sub-networks, and (iii) the asymmetry of the AD attacks on the left and right hemispheres. In the light of advances in graph convolutional networks for graph classifications and summarized characteristics of brain networks and AD pathologies, we proposed a regional brain fusion-graph convolutional network (RBF-GCN), which is constructed with an RBF framework mainly, including three sub-modules, namely, hemispheric network generation module, multichannel GCN module, and feature fusion module. In the multichannel GCN module, the improved GCN by our proposed adaptive native node attribute (ANNA) unit embeds within each channel independently. We not only fully verified the effectiveness of the RBF framework and ANNA unit but also achieved competitive results in multiple sets of AD stages' classification tasks using hundreds of experiments over the ADNI clinical dataset.

A Two-Stage Model for Predicting Mild Cognitive Impairment to Alzheimer's Disease Conversion

Early detection of Alzheimer's disease (AD), such as predicting development from mild cognitive impairment (MCI) to AD, is critical for slowing disease progression and increasing quality of life. Although deep learning is a promising technique for structural MRI-based diagnosis, the paucity of training samples limits its power, especially for three-dimensional (3D) models. To this end, we propose a two-stage model combining both transfer learning and contrastive learning that can achieve high accuracy of MRI-based early AD diagnosis even when the sample numbers are restricted. Specifically, a 3D CNN model was pretrained using publicly available medical image data to learn common medical features, and contrastive learning was further utilized to learn more specific features of MCI images. The two-stage model outperformed each benchmark method. Compared with the previous studies, we show that our model achieves superior performance in progressive MCI patients with an accuracy of 0.82 and AUC of 0.84. We further enhance the interpretability of the model by using 3D Grad-CAM, which highlights brain regions with high-predictive weights. Brain regions, including the hippocampus, temporal, and precuneus, are associated with the classification of MCI, which is supported by the various types of literature. Our model provides a novel model to avoid overfitting because of a lack of medical data and enable the early detection of AD.

Multi-level Functional Connectivity Fusion Classification Framework for Brain Disease Diagnosis

Brain disease diagnosis is a new hotspot in the cross research of artificial intelligence and neuroscience. Quantitative analysis of functional magnetic resonance imaging (fMRI) data can provide valuable biomarkers that contributes to clinical diagnosis, and the analysis of functional connectivity (FC) has become the primary method. However, previous studies mainly focus on brain disease classification based on the low-order FC features, ignoring the potential role of high-order functional relationships among brain regions. To solve this problem, this study proposed a novel multi-level FC fusion classification framework (MFC) for brain disease diagnosis. We firstly designed a deep neural network (DNN) model to extract and learn abstract feature representations for the constructed low-order and high-order FC patterns. Both unsupervised and supervised learning steps were performed during the DNN model training, and the prototype learning was introduced in the supervised fine-tuning to improve the intra-class compactness and inter-class separability of the feature representation. Then, we combined the learned multi-level abstract FC features and trained an ensemble classifier with a hierarchical stacking learning strategy for the brain disease classification. Systematic experiments were conducted on two real large-scale fMRI datasets. Results showed that the proposed MFC model obtained robust classification performance for different preprocessing pipelines, different brain parcellations, and different cross-validation schemes, suggesting the effectiveness and generality of the proposed MFC model. Overall, this study provides a promising solution to combine the informative low-order and high-order FC patterns to further promote the classification of brain diseases.

Prediction of Glioma Grade Using Intratumoral and Peritumoral Radiomic Features From Multiparametric MRI Images

The accurate prediction of glioma grade before surgery is essential for treatment planning and prognosis. Since the gold standard (i.e., biopsy)for grading gliomas is both highly invasive and expensive, and there is a need for a noninvasive and accurate method. In this study, we proposed a novel radiomics-based pipeline by incorporating the intratumoral and peritumoral features extracted from preoperative mpMRI scans to accurately and noninvasively predict glioma grade. To address the unclear peritumoral boundary, we designed an algorithm to capture the peritumoral region with a specified radius. The mpMRI scans of 285 patients derived from a multi-institutional study were adopted. A total of 2153 radiomic features were calculated separately from intratumoral volumes (ITVs)and peritumoral volumes (PTVs)on mpMRI scans, and then refined using LASSO and mRMR feature ranking methods. The top-ranking radiomic features were entered into the classifiers to build radiomic signatures for predicting glioma grade. The prediction performance was evaluated with five-fold cross-validation on a patient-level split. The radiomic signatures utilizing the features of ITV and PTV both show a high accuracy in predicting glioma grade, with AUCs reaching 0.968. By incorporating the features of ITV and PTV, the AUC of IPTV radiomic signature can be increased to 0.975, which outperforms the state-of-the-art methods. Additionally, our proposed method was further demonstrated to have strong generalization performance in an external validation dataset with 65 patients. The source code of our implementation is made publicly available at https://github.com/chengjianhong/glioma_grading.git.

Functional MRI and ApoE4 genotype for predicting cognitive decline in amyloid-positive individuals

Background:

In light of advancements in machine learning techniques, many studies have implemented machine learning approaches combined with data measures to predict and classify Alzheimer’s disease. Studies that predicted cognitive status with longitudinal follow-up of amyloid-positive individuals remain scarce, however.

Objective:

We developed models based on voxel-wise functional connectivity (FC) density mapping and the presence of the ApoE4 genotype to predict whether amyloid-positive individuals would experience cognitive decline after 1 year.

Methods:

We divided 122 participants into cognitive decline and stable cognition groups based on the participants’ change rates in Mini-Mental State Examination scores. In addition, we included 68 participants from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database as an external validation data set. Subsequently, we developed two classification models: the first model included 99 voxels, and the second model included 99 voxels and the ApoE4 genotype as features to train the models by Wide Neural Network algorithm with fivefold cross-validation and to predict the classes in the hold-out test and ADNI data sets.

Results:

The results revealed that both models demonstrated high accuracy in classifying the two groups in the hold-out test data set. The model for FC demonstrated good performance, with a mean F1-score of 0.86. The model for FC combined with the ApoE4 genotype achieved superior performance, with a mean F1-score of 0.90. In the ADNI data set, the two models demonstrated stable performances, with mean F1-scores of 0.77 in the first and second models.

Conclusion:

Our findings suggest that the proposed models exhibited promising accuracy for predicting cognitive status after 1 year in amyloid-positive individuals. Notably, the combination of FC and the ApoE4 genotype increased prediction accuracy. These findings can assist clinicians in predicting changes in cognitive status in individuals with a high risk of Alzheimer’s disease and can assist future studies in developing precise treatment and prevention strategies.

Machine learning methods for predicting progression from mild cognitive impairment to Alzheimer's disease dementia: a systematic review

BACKGROUND

An increase in lifespan in our society is a double-edged sword that entails a growing number of patients with neurocognitive disorders, Alzheimer's disease being the most prevalent. Advances in medical imaging and computational power enable new methods for the early detection of neurocognitive disorders with the goal of preventing or reducing cognitive decline. Computer-aided image analysis and early detection of changes in cognition is a promising approach for patients with mild cognitive impairment, sometimes a prodromal stage of Alzheimer's disease dementia.

METHODS

We conducted a systematic review following PRISMA guidelines of studies where machine learning was applied to neuroimaging data in order to predict whether patients with mild cognitive impairment might develop Alzheimer's disease dementia or remain stable. After removing duplicates, we screened 452 studies and selected 116 for qualitative analysis.

RESULTS

Most studies used magnetic resonance image (MRI) and positron emission tomography (PET) data but also magnetoencephalography. The datasets were mainly extracted from the Alzheimer's disease neuroimaging initiative (ADNI) database with some exceptions. Regarding the algorithms used, the most common was support vector machine with a mean accuracy of 75.4%, but convolutional neural networks achieved a higher mean accuracy of 78.5%. Studies combining MRI and PET achieved overall better classification accuracy than studies that only used one neuroimaging technique. In general, the more complex models such as those based on deep learning, combined with multimodal and multidimensional data (neuroimaging, clinical, cognitive, genetic, and behavioral) achieved the best performance.

CONCLUSIONS

Although the performance of the different methods still has room for improvement, the results are promising and this methodology has a great potential as a support tool for clinicians and healthcare professionals.

Early Detection of Alzheimer's Disease Based on Clinical Trials, Three-Dimensional Imaging Data, and Personal Information Using Autoencoders

Background:

A timely diagnosis of Alzheimer's disease (AD) is crucial to obtain more practical treatments. In this article, a novel approach using Auto-Encoder Neural Networks (AENN) for early detection of AD was proposed.

Method:

The proposed method mainly deals with the classification of multimodal data and the imputation of missing data. The data under study involve the MiniMental State Examination, magnetic resonance imaging, positron emission tomography, cerebrospinal fluid data, and personal information. Natural logarithm was used for normalizing the data. The Auto-Encoder Neural Networks was used for imputing missing data. Principal component analysis algorithm was used for reducing dimensionality of data. Support Vector Machine (SVM) was used as classifier. The proposed method was evaluated using Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Then, 10fold crossvalidation was used to audit the detection accuracy of the method.

Results:

The effectiveness of the proposed approach was studied under several scenarios considering 705 cases of ADNI database. In three binary classification problems, that is AD vs. normal controls (NCs), mild cognitive impairment (MCI) vs. NC, and MCI vs. AD, we obtained the accuracies of 95.57%, 83.01%, and 78.67%, respectively.

Conclusion:

Experimental results revealed that the proposed method significantly outperformed most of the stateoftheart methods.

Spatio-temporal graph convolutional network for diagnosis and treatment response prediction of major depressive disorder from functional connectivity

The pathophysiology of major depressive disorder (MDD) has been explored to be highly associated with the dysfunctional integration of brain networks. It is therefore imperative to explore neuroimaging biomarkers to aid diagnosis and treatment. In this study, we developed a spatiotemporal graph convolutional network (STGCN) framework to learn discriminative features from functional connectivity for automatic diagnosis and treatment response prediction of MDD. Briefly, dynamic functional networks were first obtained from the resting-state fMRI with the sliding temporal window method. Secondly, a novel STGCN approach was proposed by introducing the modules of spatial graph attention convolution (SGAC) and temporal fusion. A novel SGAC was proposed to improve the feature learning ability and special anatomy prior guided pooling was developed to enable the feature dimension reduction. A temporal fusion module was proposed to capture the dynamic features of functional connectivity between adjacent sliding windows. Finally, the STGCN proposed approach was utilized to the tasks of diagnosis and antidepressant treatment response prediction for MDD. Performances of the framework were comprehensively examined with large cohorts of clinical data, which demonstrated its effectiveness in classifying MDD patients and predicting the treatment response. The sound performance suggests the potential of the STGCN for the clinical use in diagnosis and treatment prediction.

ILDMSF: Inferring Associations Between Long Non-Coding RNA and Disease Based on Multi-Similarity Fusion

The dysregulation and mutation of long non-coding RNAs (lncRNAs) have been proved to result in a variety of human diseases. Identifying potential disease-related lncRNAs may benefit disease diagnosis, treatment and prognosis. A number of methods have been proposed to predict the potential lncRNA-disease relationships. However, most of them may give rise to incorrect results due to relying on single similarity measure. This article proposes a novel framework (ILDMSF) by fusing the lncRNA similarities and disease similarities, which are measured by lncRNA-related gene and known lncRNA-disease interaction and disease semantic interaction, and known lncRNA-disease interaction, respectively. Further, the support vector machine is employed to identify the potential lncRNA-disease associations based on the integrated similarity. The leave-one-out cross validation is performed to compare ILDMSF with other state of the art methods. The experimental results demonstrate our method is prospective in exploring potential correlations between lncRNA and disease.

A Gene Rank Based Approach for Single Cell Similarity Assessment and Clustering

Single-cell RNA sequencing (scRNA-seq) technology provides quantitative gene expression profiles at single-cell resolution. As a result, researchers have established new ways to explore cell population heterogeneity and genetic variability of cells. One of the current research directions for scRNA-seq data is to identify different cell types accurately through unsupervised clustering methods. However, scRNA-seq data analysis is challenging because of their high noise level, high dimensionality and sparsity. Moreover, the impact of multiple latent factors on gene expression heterogeneity and on the ability to accurately identify cell types remains unclear. How to overcome these challenges to reveal the biological difference between cell types has become the key to analyze scRNA-seq data. For these reasons, the unsupervised learning for cell population discovery based on scRNA-seq data analysis has become an important research area. A cell similarity assessment method plays a significant role in cell clustering. Here, we present BioRank, a new cell similarity assessment method based on annotated gene sets and gene ranks. To evaluate the performances, we cluster cells by two classical clustering algorithms based on the similarity between cells obtained by BioRank. In addition, BioRank can be used by any clustering algorithm that requires a similarity matrix. Applying BioRank to 12 public scRNA-seq datasets, we show that it is better than or at least as well as several popular similarity assessment methods for single cell clustering.

Identification of early mild cognitive impairment using multi-modal data and graph convolutional networks

BACKGROUND

The identification of early mild cognitive impairment (EMCI), which is an early stage of Alzheimer's disease (AD) and is associated with brain structural and functional changes, is still a challenging task. Recent studies show great promises for improving the performance of EMCI identification by combining multiple structural and functional features, such as grey matter volume and shortest path length. However, extracting which features and how to combine multiple features to improve the performance of EMCI identification have always been a challenging problem. To address this problem, in this study we propose a new EMCI identification framework using multi-modal data and graph convolutional networks (GCNs). Firstly, we extract grey matter volume and shortest path length of each brain region based on automated anatomical labeling (AAL) atlas as feature representation from T1w MRI and rs-fMRI data of each subject, respectively. Then, in order to obtain features that are more helpful in identifying EMCI, a common multi-task feature selection method is applied. Afterwards, we construct a non-fully labelled subject graph using imaging and non-imaging phenotypic measures of each subject. Finally, a GCN model is adopted to perform the EMCI identification task.

RESULTS

Our proposed EMCI identification method is evaluated on 210 subjects, including 105 subjects with EMCI and 105 normal controls (NCs), with both T1w MRI and rs-fMRI data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Experimental results show that our proposed framework achieves an accuracy of 84.1% and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.856 for EMCI/NC classification. In addition, by comparison, the accuracy and AUC values of our proposed framework are better than those of some existing methods in EMCI identification.

CONCLUSION

Our proposed EMCI identification framework is effective and promising for automatic diagnosis of EMCI in clinical practice.

DeTrAs: deep learning-based healthcare framework for IoT-based assistance of Alzheimer patients

Healthcare 4.0 paradigm aims at realization of data-driven and patient-centric health systems wherein advanced sensors can be deployed to provide personalized assistance. Hence, extreme mentally affected patients from diseases like Alzheimer can be assisted using sophisticated algorithms and enabling technologies. Motivated from this fact, in this paper, DeTrAs: Deep Learning-based Internet of Health Framework for the Assistance of Alzheimer Patients is proposed. DeTrAs works in three phases: (1) A recurrent neural network-based Alzheimer prediction scheme is proposed which uses sensory movement data, (2) an ensemble approach for abnormality tracking for Alzheimer patients is designed which comprises two parts: (a) convolutional neural network-based emotion detection scheme and (b) timestamp window-based natural language processing scheme, and (3) an IoT-based assistance mechanism for the Alzheimer patients is also presented. The evaluation of DeTrAs depicts almost 10–20% improvement in terms of accuracy in contrast to the different existing machine learning algorithms.

AIMAFE: Autism spectrum disorder identification with multi-atlas deep feature representation and ensemble learning

BACKGROUND

Autism spectrum disorder (ASD) is a neurodevelopmental disorder that could cause problems in social communications. Clinically, diagnosing ASD mainly relies on behavioral criteria while this approach is not objective enough and could cause delayed diagnosis. Since functional magnetic resonance imaging (fMRI) can measure brain activity, it provides data for the study of brain dysfunction disorders and has been widely used in ASD identification. However, satisfactory accuracy for ASD identification has not been achieved.

NEW METHOD

To improve the performance of ASD identification, we propose an ASD identification method based on multi-atlas deep feature representation and ensemble learning. We first calculate multiple functional connectivity based on different brain atlases from fMRI data of each subject. Then, to get the more discriminative features for ASD identification, we propose a multi-atlas deep feature representation method based on stacked denoising autoencoder (SDA). Finally, we propose multilayer perceptron (MLP) and an ensemble learning method to perform the final ASD identification task.

RESULTS

Our proposed method is evaluated on 949 subjects (including 419 ASDs and 530 typical control (TCs)) from the Autism Brain Imaging Data Exchange (ABIDE) and achieves accuracy of 74.52% (sensitivity of 80.69%, specificity of 66.71%, AUC of 0.8026) for ASD identification.

COMPARISON WITH EXISTING METHODS

Compared with some previously published methods, our proposed method obtains the better performance for ASD identification.

CONCLUSION

The results suggest that our proposed method is efficient to improve the performance of ASD identification, and is promising for ASD clinical diagnosis.

Stratifying patients using fast multiple kernel learning framework: case studies of Alzheimer's disease and cancers

BACKGROUND

Predictive patient stratification is greatly emerging, because it allows us to prospectively identify which patients will benefit from what interventions before their condition worsens. In the biomedical research, a number of stratification methods have been successfully applied and have assisted treatment process. Because of heterogeneity and complexity of medical data, it is very challenging to integrate them and make use of them in practical clinic. There are two major challenges of data integration. Firstly, since the biomedical data has a high number of dimensions, combining multiple data leads to the hard problem of vast dimensional space handling. The computation is enormously complex and time-consuming. Secondly, the disparity of different data types causes another critical problem in machine learning for biomedical data. It has a great need to develop an efficient machine learning framework to handle the challenges.

METHODS

In this paper, we propose a fast-multiple kernel learning framework, referred to as fMKL-DR, that optimise equations to calculate matrix chain multiplication and reduce dimensions in data space. We applied our framework to two case studies, Alzheimer's disease (AD) patient stratification and cancer patient stratification. We performed several comparative evaluations on various biomedical datasets.

RESULTS

In the case study of AD patients, we enhanced significantly the multiple-ROIs approach based on MRI image data. The method could successfully classify not only AD patients and non-AD patients but also different phases of AD patients with AUC close to 1. In the case study of cancer patients, the framework was applied to six types of cancers, i.e., glioblastoma multiforme cancer, ovarian cancer, lung cancer, breast cancer, kidney cancer, and liver cancer. We efficiently integrated gene expression, miRNA expression, and DNA methylation. The results showed that the classification model basing on integrated datasets was much more accurate than classification model basing on the single data type.

CONCLUSIONS

The results demonstrated that the fMKL-DR remarkably improves computational cost and accuracy for both AD patient and cancer patient stratification. We optimised the data integration, dimension reduction, and kernel fusion. Our framework has great potential for mining large-scale cohort data and aiding personalised prevention.

Inferring LncRNA-disease associations based on graph autoencoder matrix completion

Accumulating studies have indicated that long non-coding RNAs (lncRNAs) play crucial roles in large amount of biological processes. Predicting lncRNA-disease associations can help biologist to understand the molecular mechanism of human disease and benefit for disease diagnosis, treatment and prevention. In this paper, we introduce a computational framework based on graph autoencoder matrix completion (GAMCLDA) to identify lncRNA-disease associations. In our method, the graph convolutional network is utilized to encode local graph structure and features of nodes for learning latent factor vectors of lncRNA and disease. Further, the inner product of lncRNA factor vector and disease factor vector is used as decoder to reconstruct the lncRNA-disease association matrix. In addition, the cost-sensitive neural network is utilized to deal with the imbalance between positive and negative samples. The experimental results show GAMLDA outperforms other state-of-the-art methods in prediction performance which is evaluated by AUC value, AUPR value, PPV and F1-score. Moreover, the case study shows our method is the effectively tool for potential lncRNA-disease prediction.

Constructing Disease Similarity Networks Based on Disease Module Theory

Quantifying the associations between diseases is now playing an important role in modern biology and medicine. Actually discovering associations between diseases could help us gain deeper insights into pathogenic mechanisms of complex diseases, thus could lead to improvements in disease diagnosis, drug repositioning, and drug development. Due to the growing body of high-throughput biological data, a number of methods have been developed for computing similarity between diseases during the past decade. However, these methods rarely consider the interconnections of genes related to each disease in protein-protein interaction network (PPIN). Recently, the disease module theory has been proposed, which states that disease-related genes or proteins tend to interact with each other in the same neighborhood of a PPIN. In this study, we propose a new method called ModuleSim to measure associations between diseases by using disease-gene association data and PPIN data based on disease module theory. The experimental results show that by considering the interactions between disease modules and their modularity, the disease similarity calculated by ModuleSim has a significant correlation with disease classification of Disease Ontology (DO). Furthermore, ModuleSim outperforms other four popular methods which are all using disease-gene association data and PPIN data to measure disease-disease associations. In addition, the disease similarity network constructed by MoudleSim suggests that ModuleSim is capable of finding potential associations between diseases.