Mining Alzheimer's disease clinical data: reducing effects of natural aging for predicting progression and identifying subtypes

Introduction

Because Alzheimer's disease (AD) has significant heterogeneity in encephalatrophy and clinical manifestations, AD research faces two critical challenges: eliminating the impact of natural aging and extracting valuable clinical data for patients with AD.

Methods

This study attempted to address these challenges by developing a novel machine-learning model called tensorized contrastive principal component analysis (T-cPCA). The objectives of this study were to predict AD progression and identify clinical subtypes while minimizing the influence of natural aging.

Results

We leveraged a clinical variable space of 872 features, including almost all AD clinical examinations, which is the most comprehensive AD feature description in current research. T-cPCA yielded the highest accuracy in predicting AD progression by effectively minimizing the confounding effects of natural aging.

Discussion

The representative features and pathogenic circuits of the four primary AD clinical subtypes were discovered. Confirmed by clinical doctors in Tangdu Hospital, the plaques (18F-AV45) distribution of typical patients in the four clinical subtypes are consistent with representative brain regions found in four AD subtypes, which further offers novel insights into the underlying mechanisms of AD pathogenesis.

Tensor Completion for Alzheimer's Disease Prediction From Diffusion Tensor Imaging

OBJECTIVE

Alzheimer's disease (AD) is a slowly progressive neurodegenerative disorder with insidious onset. Accurate prediction of the disease progression has received increasing attention. Cognitive scores that reflect patients' cognitive status have become important criteria for predicting AD. Most existing methods consider the relationship between neuroimages and cognitive scores to improve prediction results. However, the inherent structure information in interrelated cognitive scores is rarely considered.

METHOD

In this article, we propose a relation-aware tensor completion multitask learning method (RATC-MTL), in which the cognitive scores are represented as a third-order tensor to preserve the global structure information in clinical scores. We combine both tensor completion and linear regression into a unified framework, which allows us to capture both inter and intra modes correlations in cognitive tensor with a low-rank constraint, as well as incorporate the relationship between biological features and cognitive status by imposing a regression model on multiple cognitive scores.

RESULT

Compared to the single-task and state-of-the-art multi-task algorithms, our proposed method obtains the best results for predicting cognitive scores in terms of four commonly used metrics. Furthermore, the overall performance of our method in classifying AD progress is also the best.

CONCLUSION

Our results demonstrate the effectiveness of the proposed framework in fully exploring the global structure information in cognitive scores.

SIGNIFICANCE

This study introduces a novel concept of leveraging tensor completion to assist in disease diagnoses, potentially offering a solution to the issue of data scarcity encountered in prolonged monitoring scenarios.

An efficient Alzheimer's disease prediction based on MEPC-SSC segmentation and momentum geo-transient MLPs

A decline in cognitive functioning of the brain termed Alzheimer's Disease (AD) is an irremediable progressive brain disorder, which has no corroborated disease-modifying treatment. Therefore, to slow or avoid disease progression, a greater endeavour has been made to develop techniques for earlier detection, particularly at pre-symptomatic stages. To predict AD, several strategies have been developed. Nevertheless, it is still challenging to predict AD by classifying them into AD, Mild Cognitive Impairment (MCI), along with Normal Control (NC) regarding larger features. By utilizing the Momentum Golden Eagle Optimizer-centric Transient Multi-Layer Perceptron network (Momentum GEO-Transient MLP), an effectual AD prediction technique has been proposed to trounce the aforementioned issues. Firstly, the input images are supplied for post-processing. In post-processing, by employing Patch Wise L1 Norm (PWL1N), the image resizing along with noise removal is engendered. Then, by utilizing Truncate Intensity Based Operation (TIBO) from the post-processed images, the unwanted brain parts are taken away. Next, the skull-stripped images are pre-processed. In this, by deploying Carnot Cycle Entropy-centric Global and Local technique (c2EBGAL), the images are normalized along with ameliorated. Afterward, by implementing Modified Emperor Penguins Colony-centered Sparse Subspace Clustering (MEPC-SSC), the pre-processed images are segmented. Then, for extracting the features, the segmented images are utilized; subsequently, the features being extracted are fed to the Momentum GEO-Transient MLPs.For transferring images fromMRI into more compact higher-level features, this system is wielded for fusing features from diverse layers. The parameters, which minimize the computation complexity, are decreased. For AD classification, the proposed technique is analogized to the prevailing methodologies regardingaccuracy, sensitivity, specificity et cetera along with acquired enhanced outcomes. Thus, the proposed system is apt for the AD diagnosis.

Spatio-temporal Tensor Multi-Task Learning for Predicting Alzheimer's Disease in a Longitudinal study

The utilisation of machine learning techniques to predict Alzheimer's Disease (AD) progression will substantially assist researchers and clinicians in establishing effective AD prevention and treatment strategies. In this research, we present a novel Multi-Task Learning (MTL) model for modelling AD progression based on tensor formation from spatio-temporal similarity measures of brain biomarkers. In this model, each patient sample's prediction in the tensor is assigned to a task, with each task sharing a set of latent factors acquired via tensor decomposition. To further improve the performance of the model, we present a novel regularisation term which utilises the convex combination of disease progression to modify longitudinal stability and ensure that two regression models have a minimal variation at successive time points. The model can be utilised to effectively predict AD progression with magnetic resonance imaging (MRI) data and cognitive scores of AD patients at various stages. We conducted extensive experiments to evaluate the performance for the proposed model and algorithm utilising data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Compared to single-task and state-of-the-art multi-task regression techniques, our proposed method has greater accuracy and stability for predicting AD progress in terms of root mean square error, with an average reduction of 2.60 compared to single-task regression methods and 1.17 compared to multi-task regression methods in the Mini-Mental State Examination (MMSE) questionnaire; with an average reduction of 5.08 compared to single-task regression methods and 2.71 compared to multi-task regression methods in the Alzheimer's Disease Assessment Scale-Cognitive subscale (ADAS-Cog).

Low-rank sparse feature selection with incomplete labels for Alzheimer's disease progression prediction

BACKGROUND

How to predict the cognitive performance of Alzheimer's disease (AD) and identify the informative neuroimaging markers is essential for timely treatment and possible delay of the disease. However, incomplete labeled samples and noises in neuroimaging data pose challenges to building reliable and robust prediction models. In this paper, we present a model named Low-rank Sparse Feature Selection with Incomplete Labels (LSFSIL) for predicting cognitive performance and identifying informative neuroimaging markers with MRI data and incomplete cognitive scores.

METHOD

We propose a sparse matrix decomposition method to decompose the incomplete cognitive score matrix into two parts for recovering missing scores and utilizing incomplete labeled data. The former is the recovered cognitive score matrix without missing values. To make the recovered scores close to the real ones, a manifold regularizer is devised to fit the label distribution for capturing the label correlations locally. The latter is a ℓ₁-norm regularized matrix which represents the associated errors. Next, a low-rank regression model that regards the recovered matrix as the target is developed to increase the robustness to noises and outliers. Besides, ℓ_2,1-norm is introduced into the objective function as a sparse regularization to identify the important features.

RESULTS

Experimental results demonstrate that LSFSIL achieves higher performance and outperforms several state-of-the-art feature selection approaches. Moreover, the neuroimaging markers selected by LSFSIL are consistent with the previous AD studies.

CONCLUSIONS

LSFSIL is effective in informative neuroimaging marker identification for cognitive performance prediction with incomplete labeled data.

Artificial intelligence in brain MRI analysis of Alzheimer's disease over the past 12 years: A systematic review

INTRODUCTION

Multiple structural brain changes in Alzheimer's disease (AD) and mild cognitive impairment (MCI) have been revealed on magnetic resonance imaging (MRI). There is a fast-growing effort in applying artificial intelligence (AI) to analyze these data. Here, we review and evaluate the AI studies in brain MRI analysis with synthesis.

METHODS

A systematic review of the literature, spanning the years from 2009 to 2020, was completed using the PubMed database. AI studies using MRI imaging to investigate normal aging, mild cognitive impairment, and AD-dementia were retrieved for review. Bias assessment was completed using the PROBAST criteria.

RESULTS

97 relevant studies were included in the review. The studies were typically focused on the classification of AD, MCI, and normal aging (71% of the reported studies) and the prediction of MCI conversion to AD (25%). The best performance was achieved by using the deep learning-based convolution neural network algorithms (weighted average accuracy 89%), in contrast to 76-86% using Logistic Regression, Support Vector Machines, and other AI methods.

DISCUSSION

The synthesized evidence is paramount to developing sophisticated AI approaches to reliably capture and quantify multiple subtle MRI changes in the whole brain that exemplify the complexity and heterogeneity of AD and brain aging.

Multi-view prediction of Alzheimer's disease progression with end-to-end integrated framework

Alzheimer's disease is a common neurodegenerative brain disease that affects the elderly population worldwide. Its early automatic detection is vital for early intervention and treatment. A common solution is to perform future cognitive score prediction based on the baseline brain structural magnetic resonance image (MRI), which can directly infer the potential severity of disease. Recently, several studies have modelled disease progression by predicting the future brain MRI that can provide visual information of brain changes over time. Nevertheless, no studies explore the intra correlation of these two solutions, and it is unknown whether the predicted MRI can assist the prediction of cognitive score. Here, instead of independent prediction, we aim to predict disease progression in multi-view, i.e., predicting subject-specific changes of cognitive score and MRI volume concurrently. To achieve this, we propose an end-to-end integrated framework, where a regression model and a generative adversarial network are integrated together and then jointly optimized. Three integration strategies are exploited to unify these two models. Moreover, considering that some brain regions, such as hippocampus and middle temporal gyrus, could change significantly during the disease progression, a region-of-interest (ROI) mask and a ROI loss are introduced into the integrated framework to leverage this anatomical prior knowledge. Experimental results on the longitudinal Alzheimer's Disease Neuroimaging Initiative dataset demonstrated that the integrated framework outperformed the independent regression model for cognitive score prediction. And its performance can be further improved with the ROI loss for both cognitive score and MRI prediction.

Prediction of Alzheimer's Disease Progression with Multi-Information Generative Adversarial Network

Alzheimer's disease (AD) is a chronic neurodegenerative disease, and its long-term progression prediction is definitely important. The structural Magnetic Resonance Imaging (sMRI) can be used to characterize the cortical atrophy that is closely coupled with clinical symptoms in AD and its prodromal stages. Many existing methods have focused on predicting the cognitive scores at future time-points using a set of morphological features derived from sMRI. The 3D sMRI can provide more massive information than the cognitive scores. However, very few works consider to predict an individual brain MRI image at future time-points. In this article, we propose a disease progression prediction framework that comprises a 3D multi-information generative adversarial network (mi-GAN) to predict what one's whole brain will look like with an interval, and a 3D DenseNet based multi-class classification network optimized with a focal loss to determine the clinical stage of the estimated brain. The mi-GAN can generate high-quality individual 3D brain MRI image conditioning on the individual 3D brain sMRI and multi-information at the baseline time-point. Experiments are implemented on the Alzheimer's Disease Neuroimaging Initiative (ADNI). Our mi-GAN shows the state-of-the-art performance with the structural similarity index (SSIM) of 0.943 between the real MRI images at the fourth year and the generated ones. With mi-GAN and focal loss, the pMCI vs. sMCI accuracy achieves 6.04% improvement in comparison with conditional GAN and cross entropy loss.

Multi disease-prediction framework using hybrid deep learning: an optimal prediction model

Big data and its approaches are generally helpful for healthcare and biomedical sectors for predicting the disease. For trivial symptoms, the difficulty is to meet the doctors at any time in the hospital. Thus, big data provides essential data regarding the diseases on the basis of the patient's symptoms. For several medical organizations, disease prediction is important for making the best feasible health care decisions. Conversely, the conventional medical care model offers input as structured that requires more accurate and consistent prediction. This paper is planned to develop the multi-disease prediction using the improvised deep learning concept. Here, the different datasets pertain to "Diabetes, Hepatitis, lung cancer, liver tumor, heart disease, Parkinson's disease, and Alzheimer's disease", from the benchmark UCI repository is gathered for conducting the experiment. The proposed model involves three phases (a) Data normalization (b) Weighted normalized feature extraction, and (c) prediction. Initially, the dataset is normalized in order to make the attribute's range at a certain level. Further, weighted feature extraction is performed, in which a weight function is multiplied with each attribute value for making large scale deviation. Here, the weight function is optimized using the combination of two meta-heuristic algorithms termed as Jaya Algorithm-based Multi-Verse Optimization algorithm (JA-MVO). The optimally extracted features are subjected to the hybrid deep learning algorithms like "Deep Belief Network (DBN) and Recurrent Neural Network (RNN)". As a modification to hybrid deep learning architecture, the weight of both DBN and RNN is optimized using the same hybrid optimization algorithm. Further, the comparative evaluation of the proposed prediction over the existing models certifies its effectiveness through various performance measures.

PET Imaging of Tau Pathology and Amyloid-β, and MRI for Alzheimer's Disease Feature Fusion and Multimodal Classification

BACKGROUND

Machine learning is a promising tool for biomarker-based diagnosis of Alzheimer's disease (AD). Performing multimodal feature selection and studying the interaction between biological and clinical AD can help to improve the performance of the diagnosis models.

OBJECTIVE

This study aims to formulate a feature ranking metric based on the mutual information index to assess the relevance and redundancy of regional biomarkers and improve the AD classification accuracy.

METHODS

From the Alzheimer's Disease Neuroimaging Initiative (ADNI), 722 participants with three modalities, including florbetapir-PET, flortaucipir-PET, and MRI, were studied. The multivariate mutual information metric was utilized to capture the redundancy and complementarity of the predictors and develop a feature ranking approach. This was followed by evaluating the capability of single-modal and multimodal biomarkers in predicting the cognitive stage.

RESULTS

Although amyloid-β deposition is an earlier event in the disease trajectory, tau PET with feature selection yielded a higher early-stage classification F1-score (65.4%) compared to amyloid-β PET (63.3%) and MRI (63.2%). The SVC multimodal scenario with feature selection improved the F1-score to 70.0% and 71.8% for the early and late-stage, respectively. When age and risk factors were included, the scores improved by 2 to 4%. The Amyloid-Tau-Neurodegeneration [AT(N)] framework helped to interpret the classification results for different biomarker categories.

CONCLUSION

The results underscore the utility of a novel feature selection approach to reduce the dimensionality of multimodal datasets and enhance model performance. The AT(N) biomarker framework can help to explore the misclassified cases by revealing the relationship between neuropathological biomarkers and cognition.

Dynamic Prediction in Clinical Survival Analysis Using Temporal Convolutional Networks

Accurate prediction of disease trajectories is critical for early identification and timely treatment of patients at risk. Conventional methods in survival analysis are often constrained by strong parametric assumptions and limited in their ability to learn from high-dimensional data. This paper develops a novel convolutional approach that addresses the drawbacks of both traditional statistical approaches as well as recent neural network models for survival. We present Match-Net: a missingness-aware temporal convolutional hitting-time network, designed to capture temporal dependencies and heterogeneous interactions in covariate trajectories and patterns of missingness. To the best of our knowledge, this is the first investigation of temporal convolutions in the context of dynamic prediction for personalized risk prognosis. Using real-world data from the Alzheimer's disease neuroimaging initiative, we demonstrate state-of-the-art performance without making any assumptions regarding underlying longitudinal or time-to-event processes-attesting to the model's potential utility in clinical decision support.