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1. Optimal anti-amyloid-beta therapy for Alzheimer’s disease via a personalized mathematical model

   https://doi.org/10.1371/journal.pcbi.1010481  

   Hao, Wenrui ; Lenhart, Suzanne ; Petrella, Jeffrey R. ( September 2022 , PLOS Computational Biology)

   Jenner, Adrianne (Ed.)

   With the recent approval by the FDA of the first disease-modifying drug for Alzheimer’s Disease (AD), personalized medicine will be increasingly important for appropriate management and counseling of patients with AD and those at risk. The growing availability of clinical biomarker data and data-driven computational modeling techniques provide an opportunity for new approaches to individualized AD therapeutic planning. In this paper, we develop a new mathematical model, based on AD cognitive, cerebrospinal fluid (CSF) and MRI biomarkers, to provide a personalized optimal treatment plan for individuals. This model is parameterized by biomarker data from the AD Neuroimaging Initiative (ADNI) cohort, a large multi-institutional database monitoring the natural history of subjects with AD and mild cognitive impairment (MCI). Optimal control theory is used to incorporate time-varying treatment controls and side-effects into the model, based on recent clinical trial data, to provide a personalized treatment regimen with anti-amyloid-beta therapy. In-silico treatment studies were conducted on the approved treatment, aducanumab, as well as on another promising anti-amyloid-beta therapy under evaluation, donanemab. Clinical trial simulations were conducted over both short-term (78 weeks) and long-term (10 years) periods with low-dose (6 mg/kg) and high-dose (10 mg/kg) regimens for aducanumab, and a single-dose regimen (1400 mg) for donanemab. Results confirm those of actual clinical trials showing a large and sustained effect of both aducanumab and donanemab on amyloid beta clearance. The effect on slowing cognitive decline was modest for both treatments, but greater for donanemab. This optimal treatment computational modeling framework can be applied to other single and combination treatments for both prediction and optimization, as well as incorporate new clinical trial data as it becomes available. 

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

   https://doi.org/10.3233/JAD-210064  

   Shojaie, Mehdi ; Tabarestani, Solale ; Cabrerizo, Mercedes ; DeKosky, Steven T. ; Vaillancourt, David E. ; Loewenstein, David ; Duara, Ranjan ; Adjouadi, Malek ( December 2021 , Journal of Alzheimer's Disease)

   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. 

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3. Ensemble Machine Learning for Alzheimer’s disease Classification from Retinal Vasculature

   Lin, Hely ; Fang, Ruogu ( October 2021 , Biomedical Engineering Society Annual Meeting)

   null (Ed.)

   Introduction: Alzheimer’s disease (AD) causes progressive irreversible cognitive decline and is the leading cause of dementia. Therefore, a timely diagnosis is imperative to maximize neurological preservation. However, current treatments are either too costly or limited in availability. In this project, we explored using retinal vasculature as a potential biomarker for early AD diagnosis. This project focuses on stage 3 of a three-stage modular machine learning pipeline which consisted of image quality selection, vessel map generation, and classification [1]. The previous model only used support vector machine (SVM) to classify AD labels which limited its accuracy to 82%. In this project, random forest and gradient boosting were added and, along with SVM, combined into an ensemble classifier, raising the classification accuracy to 89%. Materials and Methods: Subjects classified as AD were those who were diagnosed with dementia in “Dementia Outcome: Alzheimer’s disease” from the UK Biobank Electronic Health Records. Five control groups were chosen with a 5:1 ratio of control to AD patients where the control patients had the same age, gender, and eye side image as the AD patient. In total, 122 vessel images from each group (AD and control) were used. The vessel maps were then segmented from fundus images through U-net. A t-test feature selection was first done on the training folds and the selected features was fed into the classifiers with a p-value threshold of 0.01. Next, 20 repetitions of 5-fold cross validation were performed where the hyperparameters were solely tuned on the training data. An ensemble classifier consisting of SVM, gradient boosting tree, and random forests was built and the final prediction was made through majority voting and evaluated on the test set. Results and Discussion: Through ensemble classification, accuracy increased by 4-12% relative to the individual classifiers, precision by 9-15%, sensitivity by 2-9%, specificity by at least 9-16%, and F1 score by 712%. Conclusions: Overall, a relatively high classification accuracy was achieved using machine learning ensemble classification with SVM, random forest, and gradient boosting. Although the results are very promising, a limitation of this study is that the requirement of needing images of sufficient quality decreased the amount of control parameters that can be implemented. However, through retinal vasculature analysis, this project shows machine learning’s high potential to be an efficient, more cost-effective alternative to diagnosing Alzheimer’s disease. Clinical Application: Using machine learning for AD diagnosis through retinal images will make screening available for a broader population by being more accessible and cost-efficient. Mobile device based screening can also be enabled at primary screening in resource-deprived regions. It can provide a pathway for future understanding of the association between biomarkers in the eye and brain. 

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4. Learning Multi-Modal Biomarker Representations via Globally Aligned Longitudinal Enrichments

   https://doi.org/10.1609/aaai.v34i01.5426  

   Lu, Lyujian ; Elbeleidy, Saad ; Baker, Lauren Zoe ; Wang, Hua ( June 2020 , Proceedings of the AAAI Conference on Artificial Intelligence)

   Alzheimer's Disease (AD) is a chronic neurodegenerative disease that severely impacts patients' thinking, memory and behavior. To aid automatic AD diagnoses, many longitudinal learning models have been proposed to predict clinical outcomes and/or disease status, which, though, often fail to consider missing temporal phenotypic records of the patients that can convey valuable information of AD progressions. Another challenge in AD studies is how to integrate heterogeneous genotypic and phenotypic biomarkers to improve diagnosis prediction. To cope with these challenges, in this paper we propose a longitudinal multi-modal method to learn enriched genotypic and phenotypic biomarker representations in the format of fixed-length vectors that can simultaneously capture the baseline neuroimaging measurements of the entire dataset and progressive variations of the varied counts of follow-up measurements over time of every participant from different biomarker sources. The learned global and local projections are aligned by a soft constraint and the structured-sparsity norm is used to uncover the multi-modal structure of heterogeneous biomarker measurements. While the proposed objective is clearly motivated to characterize the progressive information of AD developments, it is a nonsmooth objective that is difficult to efficiently optimize in general. Thus, we derive an efficient iterative algorithm, whose convergence is rigorously guaranteed in mathematics. We have conducted extensive experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) data using one genotypic and two phenotypic biomarkers. Empirical results have demonstrated that the learned enriched biomarker representations are more effective in predicting the outcomes of various cognitive assessments. Moreover, our model has successfully identified disease-relevant biomarkers supported by existing medical findings that additionally warrant the correctness of our method from the clinical perspective. 

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5. What’s the cut-point?: a systematic investigation of tau PET thresholding methods

   https://doi.org/10.1186/s13195-022-00986-w  

   Weigand, Alexandra J. ; Maass, Anne ; Eglit, Graham L. ; Bondi, Mark W. ( April 2022 , Alzheimer's Research & Therapy)

   Tau positron emission tomography (PET) is increasing in popularity for biomarker characterization of Alzheimer’s disease (AD), and recent frameworks rely on tau PET cut-points to stage individuals along the AD continuum. Given the lack of standardization in tau PET thresholding methods, this study sought to systematically canvass and characterize existing studies that have derived tau PET cut-points and then directly assess different methods of tau PET thresholding in terms of their concurrent validity.

   First, a literature search was conducted in PubMed to identify studies of AD and related clinical phenotypes that used the Flortaucipir (AV-1451) tau PET tracer to derive a binary cut-point for tau positivity. Of 540 articles screened and 47 full-texts reviewed, 23 cohort studies met inclusion criteria with a total of 6536 participants. Second, we derived and compared tau PET cut-points in a 2 × 2 × 2 design that systematically varied region (temporal meta-ROI and entorhinal cortex), analytic method (receiver operating characteristics and 2 standard deviations above comparison group), and criterion/comparison variable (amyloid-beta negative cognitively unimpaired or cognitively unimpaired only) using a sample of 453 older adults from the Alzheimer’s Disease Neuroimaging Initiative.

   For the systematic review, notable variability in sample characteristics, preprocessing methods, region of interest, and analytic approach were observed, which were accompanied by discrepancy in proposed tau PET cut points. The empirical follow-up indicated the cut-point derived based on 2 standard deviations above a either comparison group in either ROI best differentiated tau positive and negative groups on cerebrospinal fluid phosphorylated tau, Mini-Mental State Examination score, and delayed memory performance.

   Given the impact of discrepant thresholds on tau positivity rates, biomarker staging, and eligibility for future clinical treatment trials, recommendations are offered to select cut-point derivations based on the unique goals and priorities of different studies.

    

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