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1. Optimal control for anti-abeta treatment in Alzheimer’s disease using a reaction–diffusion model

   https://doi.org/10.1098/rsif.2025.1158  

   Lee, Sun; Kao, Chiu-Yen; Li, Zhiyuan; Dan, Tingting; Wu, Guorong; Hao, Wenrui (July 2026, Journal of the Royal Society Interface)

   Abstract Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that severely impairs survival and quality of life. While anti-amyloid beta (Aβ) therapies can slow disease progression, their efficacy depends on personalized dosing that maximizes benefits and minimizes risks, such as amyloid-related imaging abnormalities (ARIA). Mathematical modelling offers a powerful tool for understanding AD dynamics and optimizing treatment, yet most models focus solely on temporal behaviour, overlooking spatial heterogeneity within the brain. In this study, we propose a spatially explicit reaction–diffusion model to describe Aβ plaque dynamics. We formulate an optimal control problem to minimize plaque concentration while balancing therapeutic efficacy and treatment risk. Under reasonable assumptions, we establish well-posedness and uniqueness of the optimal solution. A finite element method (FEM)-based numerical framework is developed to compute personalized treatment strategies. Our model is calibrated using longitudinal Aβ positron emission tomography (PET) data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), enabling estimation of patient-specific parameters, such as growth rate and effective diffusivity. Results show that optimized treatment strategies consistently outperform constant dosing regimens across patient groups, achieving substantial reductions in cumulative amyloid burden while minimizing side effects. This integrated, data-driven framework advances personalized, spatially informed therapeutic optimization for AD. 

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2. Comprehensive Anatomical Staging Predicts Clinical Progression in Mild Cognitive Impairment: A Data-Driven Approach

   https://doi.org/10.3390/ijms26125514  

   Tandon, Raghav; Mei, Yajun; Lah, James J; Mitchell, Cassie S (June 2025, International Journal of Molecular Sciences)

   Alzheimer’s disease (AD) presents significant challenges in clinical practice due to its heterogeneous manifestation and variable progression rates. This work develops a comprehensive anatomical staging framework to predict progression from mild cognitive impairment (MCI) to AD. Using the ADNI database, the scalable Subtype and Stage Inference (s-SuStaIn) model was applied to 118 neuroanatomical features from cognitively normal (n = 504) and AD (n = 346) participants. The framework was validated on 808 MCI participants through associations with clinical progression, CSF and FDG-PET biomarkers, and neuropsychiatric measures, while adjusting for common confounders (age, gender, education, and APOE ε4 alleles). The framework demonstrated superior prognostic accuracy compared to traditional risk assessment (C-index = 0.73 vs. 0.62). Four distinct disease subtypes showed differential progression rates, biomarker profiles (FDG-PET and CSF Aβ42), and cognitive trajectories: Subtype 1, subcortical-first pattern; Subtype 2, executive–cortical pattern; Subtype 3, disconnection pattern; and Subtype 4, frontal–executive pattern. Stage-dependent changes revealed systematic deterioration across diverse cognitive domains, particularly in learning acquisition, visuospatial processing, and functional abilities. This data-driven approach captures clinically meaningful disease heterogeneity and improves prognostication in MCI, potentially enabling more personalized therapeutic strategies and clinical trial design. 

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3. Network Diffusion-Constrained Variational Generative Models for Investigating the Molecular Dynamics of Brain Connectomes Under Neurodegeneration

   https://doi.org/10.3390/ijms26031062  

   Xie, Jiajia; Tandon, Raghav; Mitchell, Cassie S (February 2025, International Journal of Molecular Sciences)

   Alzheimer’s disease (AD) is a complex and progressive neurodegenerative condition with significant societal impact. Understanding the temporal dynamics of its pathology is essential for advancing therapeutic interventions. Empirical and anatomical evidence indicates that network decoupling occurs as a result of gray matter atrophy. However, the scarcity of longitudinal clinical data presents challenges for computer-based simulations. To address this, a first-principles-based, physics-constrained Bayesian framework is proposed to model time-dependent connectome dynamics during neurodegeneration. This temporal diffusion network framework segments pathological progression into discrete time windows and optimizes connectome distributions for biomarker Bayesian regression, conceptualized as a learning problem. The framework employs a variational autoencoder-like architecture with computational enhancements to stabilize and improve training efficiency. Experimental evaluations demonstrate that the proposed temporal meta-models outperform traditional static diffusion models. The models were evaluated using both synthetic and real-world MRI and PET clinical datasets that measure amyloid beta, tau, and glucose metabolism. The framework successfully distinguishes normative aging from AD pathology. Findings provide novel support for the “decoupling” hypothesis and reveal eigenvalue-based evidence of pathological destabilization in AD. Future optimization of the model, integrated with real-world clinical data, is expected to improve applications in personalized medicine for AD and other neurodegenerative diseases. 

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4. Unsupervised Machine Learning to Identify Separable Clinical Alzheimer’s Disease Sub-Populations

   https://doi.org/10.3390/brainsci11080977  

   Prakash, Jayant; Wang, Velda; Quinn, Robert E.; Mitchell, Cassie S. (August 2021, Brain Sciences)

   Heterogeneity among Alzheimer’s disease (AD) patients confounds clinical trial patient selection and therapeutic efficacy evaluation. This work defines separable AD clinical sub-populations using unsupervised machine learning. Clustering (t-SNE followed by k-means) of patient features and association rule mining (ARM) was performed on the ADNIMERGE dataset from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Patient sociodemographics, brain imaging, biomarkers, cognitive tests, and medication usage were included for analysis. Four AD clinical sub-populations were identified using between-cluster mean fold changes [cognitive performance, brain volume]: cluster-1 represented least severe disease [+17.3, +13.3]; cluster-0 [−4.6, +3.8] and cluster-3 [+10.8, −4.9] represented mid-severity sub-populations; cluster-2 represented most severe disease [−18.4, −8.4]. ARM assessed frequently occurring pharmacologic substances within the 4 sub-populations. No drug class was associated with the least severe AD (cluster-1), likely due to lesser antecedent disease. Anti-hyperlipidemia drugs associated with cluster-0 (mid-severity, higher volume). Interestingly, antioxidants vitamin C and E associated with cluster-3 (mid-severity, higher cognition). Anti-depressants like Zoloft associated with most severe disease (cluster-2). Vitamin D is protective for AD, but ARM identified significant underutilization across all AD sub-populations. Identification and feature characterization of four distinct AD sub-population “clusters” using standard clinical features enhances future clinical trial selection criteria and cross-study comparative analysis. 

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5. Bridging language markers and pathology: correlations between digital speech measures and surrogate CSF biomarkers in Alzheimer's disease

   https://doi.org/10.1002/dad2.70466  

   Pang, Yijiang; Chen, Liu; Dodge, Hiroko H; Zhou, Jiayu (July 2026, Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring)

   Abstract INTRODUCTIONDigital language markers show promise in detecting early cognitive impairment related to Alzheimer's disease (AD), yet their relationship with cerebrospinal fluid (CSF) biomarkers of AD pathology remains unclear mainly due to the lack of data with both CSF and language markers. METHODSUsing National Alzheimer's Coordinating Center (NACC) clinical data as anchor variables, language makers in the Internet‐Based Conversational Engagement Randomized Controlled Clinical Trial (I‐CONECT) study were linked to NACC CSF data. Surrogate CSF biomarkers were created for I‐CONECT subjects using machine learning models from common NACC clinical variables. Correlations assessed associations between CSF and language markers. RESULTSLower predicted CSF amyloid beta correlated significantly with reduced syntactic complexity and shorter speech responses. Higher predicted total tau and phosphorylated tau correlated with reduced syntactic complexity. DISCUSSIONThis study demonstrates novel links between language markers and fluid biomarkers, highlighting conversational language as a potential accessible, non‐invasive approach for early detection and monitoring of Alzheimer's pathology. 

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