The Latest Articles on Predicting Alzheimer’s Disease Progression

Our staff editors continue to share exciting, interesting, and thought-provoking reading material in the recommended articles series.

This week, we would like to share several latest articles on Predicting Alzheimer’s Disease Progression.

Title: Specific serum autoantibodies predict the development and progression of Alzheimer’s disease with high accuracy
Authors: Liangjuan Fang, Bin Jiao, Xixi Liu, Zhenghong Wang, Peng Yuan, Hui Zhou, Xuewen Xiao, Liqin Cao, Jifeng Guo, Beisha Tang, Lu Shen
Type: Research article
Abstract:
Autoimmunity plays a key role in the pathogenesis of Alzheimer’s disease (AD). However, whether autoantibodies in peripheral blood can be used as biomarkers for AD has been elusive. Serum samples were obtained from 1,686 participants, including 767 with AD, 146 with mild cognitive impairment (MCI), 255 with other neurodegenerative diseases, and 518 healthy controls. Specific autoantibodies were measured using a custom-made immunoassay. Multivariate support vector machine models were employed to investigate the correlation between serum autoantibody levels and disease states. As a result, seven candidate AD-specific autoantibodies were identified, including MAPT, DNAJC8, KDM4D, SERF1A, CDKN1A, AGER, and ASXL1. A classification model with high accuracy (area under the curve (AUC) = 0.94) was established. Importantly, these autoantibodies could distinguish AD from other neurodegenerative diseases and out-performed amyloid and tau protein concentrations in cerebrospinal fluid in predicting cognitive decline (P < 0.001). This study indicated that AD onset and progression are possibly accompanied by an unappreciated serum autoantibody response. Therefore, future studies could optimize its application as a convenient biomarker for the early detection of AD.      
Access this article: https://doi.org/10.1016/j.bbi.2023.11.018

Title: A hierarchical attention-based multimodal fusion framework for predicting the progression of Alzheimer’s disease
Authors: Peixin Lu, Lianting Hu, Alexis Mitelpunkt, Surbhi Bhatnagar, Long Lu, Huiying Liang
Type: Research Article
Abstract:
Early detection and treatment can slow the progression of Alzheimer's Disease (AD), one of the most common neurodegenerative diseases. Recent studies have demonstrated the value of multimodal fusion in early AD detection. However, most approaches to this have failed to consider data modality domains, their relationships, and variations in their relative importance. To address these challenges, we propose a Hierarchical Attention-Based Multimodal Fusion framework (HAMF) that utilizes imaging, genetic and clinical data for early AD detection. In the HAMF model, attention mechanisms are utilized to learn the appropriate weights for each modality and to understand the interaction between modalities through hierarchical attention. HAMF performs better than state-of-the-art methods, achieving an accuracy of 87.2% and an AUC of 0.913, which are superior to unimodal models. By comparing the results of different unimodal and multimodal models, we find that multimodal fusion can improve model performance more than unimodal models and clinical data is the most important modality. Our ablation experiment confirmed the effectiveness of HAMF. Finally, we used SHapley Additive exPlanations (SHAP) to improve the model's interpretability. We provide the model as a guide for future research in the field, and as a framework for generating actional advice and decision support system for clinical practitioners.      
Access this article: https://doi.org/10.1016/j.bspc.2023.105669

Title: Identification of essential plasma protein using manifold regularized sparse group-lasso for prediction of Alzheimer’s disease
Authors: Zhi Ma, Xi Guan, Yiqun Liu, Wei Shao
Type: Research Article
Abstract:      
Accurate diagnose of Alzheimer’s disease (AD), especially in its early stage, is very important for the possible delay and early treatment of the disease. Many researches indicate that the change of the expression level of plasma proteins will lead to the emergence of AD, which creates a new focus on finding essential proteins that affects AD. Nearly all the existing models were constructed on the independent hypothesis, where each candidate protein is treated independently. However, proteomics studies suggest that the essential proteins in cells do not act independently under illness conditions. Instead, they perform biological functions in groups, where each group is composed of proteins with similar functions. Accordingly, we propose a manifold regularized sparse group-lasso (i.e., MSGL) method to identify essential proteins that will incur AD. Specifically, we firstly propose a novel method to calculate the similarity between pairs of proteins with the aid of Gene Ontology, and then use the spectral clustering algorithm to divide the proteins into groups. Next, we adopt a sparse group-lasso model to select proteins according to both intra-group sparsity and inter-group sparsity, which ensures only a small number of proteins in a few of groups will be selected. In addition, we also introduce a manifold-based Laplacian regularizer to preserve the data distribution information. Finally, support vector machine (SVM) is used to classify AD patients. The experimental results show that our method can achieve a classification accuracy of 97.5%, a sensitivity of 97.6%, and specificity of 97.4%, demonstrating great potential in classification of AD patients.      
Access this article: https://doi.org/10.1016/j.displa.2023.102578

Title: A slice selection guided deep integrated pipeline for Alzheimer’s prediction from Structural Brain MRI
Authors: Muhammad Sakib Khan Inan, Nabila Sabrin Sworna, A.K.M. Muzahidul Islam, Salekul Islam, Zulfikar Alom, Mohammad Abdul Azim, Swakkhar Shatabda, Alzheimer’s Disease Neuroimaging Initiative
Type: Research Article
Abstract:
Alzheimer’s disease, a progressive form of dementia, has risen to become the fifth leading cause of death among individuals aged 65 and older. The diagnosis of Alzheimer’s is both time-consuming and costly, involving radiologists and clinical experts at multiple stages, which presents a significant challenge in the medical field. Moreover, cases of Alzheimer’s and dementia often go undiagnosed or misdiagnosed worldwide. To address this issue, medical experts meticulously analyze patients’ structural MRI (sMRI) scans to identify potential abnormalities linked to Alzheimer’s or other forms of dementia. Recognizing the devastating impact of this disease on people’s lives, Artificial Intelligence (AI) researchers have been dedicated to developing automated solutions for early-stage Alzheimer’s diagnosis in recent years, aiming to support medical practitioners in their efforts. Despite the application of various AI-driven solutions that use sMRI data for Alzheimer’s diagnosis, there are still research gaps that need attention. These gaps include the need for guided slice selection and the development of a simpler yet effective integrated pipeline where each stage of the process is fully automated, eliminating the need for medical practitioner intervention. In this study, we propose an integrated automated solution that incorporates a guided machine learning-based selection process using K-Means++ leading to a Gradient Boosting-based method for identifying the 16 most relevant 2-dimensional sMRI slices from 3-dimensional sMRI data. This step is crucial for accurate Alzheimer’s classification. Furthermore, we introduce a deep learning architecture that combines EfficientNetV2S-based transfer learning with densely-learned features in an optimized manner. To evaluate the effectiveness of our proposed deep-integrated architecture, we used two benchmark datasets from ADNI and OASIS, conducting rigorous experimental analysis and validation. The results demonstrated that our integrated architecture outperformed all other experimented architectures, achieving a 20-Fold Cross Validation Accuracy of 83.64% (CN vs AD), 82.69% (CN vs MCIc), and 71.40% (CN vs MCInc) on the ADNI dataset, and 91.54% (CN vs AD) on the OASIS dataset. This signifies the potential of our approach in improving Alzheimer’s diagnosis accuracy and offers hope for early detection and intervention in this debilitating disease.      
Access this article: https://doi.org/10.1016/j.bspc.2023.105773

Title: De-accumulated error collaborative learning framework for predicting Alzheimer’s disease progression
Authors: Hongli Cheng, Shizhong Yuan, Weimin Li, Xiao Yu, Fangyu Liu, Xiao Liu, Tsigabu Teame Bezabih
Type: Research Article
Abstract:      
Alzheimer’s disease (AD) is a chronic neurodegenerative disorder where precise prediction of progression is crucial for improving clinical diagnosis. However, missing data often complicates AD prediction in clinical practice. Existing studies primarily have employed recurrent neural networks to impute missing data, but this approach suffers from the “error accumulation” problem, leading to unsatisfactory prediction results. To address this issue, this study proposes the LSTM-TSGAIN collaborative learning framework, which consists of the following three main aspects: using a generative adversarial imputation method to reduce LSTM prediction errors; collaborative training of the adversarial imputation fusion module and the time series learning module to improve the performance of the model; the model inputs were adjusted to variable lengths to accommodate the differences in the number of visits of different subjects. The effectiveness of the model is demonstrated through experiments using longitudinal data from the ADNI dataset of 1256 subjects, which shows its superiority over state-of-the-art methods.
Access this article: https://doi.org/10.1016/j.bspc.2023.105767