Corriveau‐Lecavalier, Nick

Abstract Introduction Remote cognitive assessments offer an accessible, scalable way to support clinical characterization and longitudinal monitoring of cognition. This study evaluates the feasibility of implementing Mayo Test Drive (MTD), a remote, self-administered digital cognitive assessment, within a specialized clinic focused on eligibility assessments for anti-amyloid therapies for Alzheimer’s disease (AD). Method Two-hundred and nine ( N =209) patients (M age =71.2, 53% female) were evaluated through the Mayo Clinic Rochester Alzheimer’s Disease Treatment Clinic (ADTC) and had the opportunity to complete MTD, either as a remote, patient-initiated session through a patient-facing healthcare portal or during an in-clinic neuropsychological evaluation. Results Clinical diagnoses included 55.2% mild cognitive impairment (MCI), 31.0% mild dementia, and 11.6% moderate dementia, in addition to 2.2% cognitively unimpaired (CU), as determined by neurology consensus at a multidisciplinary case conference. Among those who initiated MTD ( n =197/209), 97.5% completed a session (70.6% remotely, 26.9% in clinic). Completion rates were high across diagnostic groups and did not significantly differ by clinical severity ( p =.12): CU (100%), MCI (99.1%), mild dementia (97.0%), and moderate dementia (89.5%). Remote completion rates overall were high (97.9%). Among completers, administration setting (remote vs. in-clinic) did not significantly differ by clinical stage, χ²(3)=2.37, p =.49. Reasons for non-initiation included not receiving or opening portal messages and other/unknown factors. Discussion Remote digital cognitive assessment is feasible within the context of clinic eligibility evaluations for AD therapies. The high completion rates of MTD across clinical impairment levels in this setting support development of paths for broader use in cognitive characterization in AD care.

Abstract Patients with atypical variants of Alzheimer's disease (AD) often present at a younger age with predominantly non‐amnestic impairments and a more aggressive disease course. Historically, individuals with atypical presentations have not been included in large‐scale clinical trials, which typically focus on late‐onset, sporadic amnestic‐predominant AD. Consequently, treatment options and research efforts specific to atypical AD remain limited. The emergence of amyloid‐targeting therapies that slow disease progression underscores these challenges, as evidence supporting their efficacy in early‐onset amnestic and non‐amnestic AD variants is scarce. This perspective article argues that atypical AD represents an excellent disease model for clinical trials and proposes strategies to address critical gaps in clinical trial design for this population. Key considerations include optimizing participant selection approaches, establishing syndrome‐specific or surrogate biological and clinical endpoints, and fostering advocacy to enhance early and accurate diagnosis, equitable representation, and outcomes for these populations.

Abstract Alzheimer’s disease (AD) emerges from multi-scale interactions between molecular pathology and disruptions in large-scale brain network dynamics. Understanding how these processes co-evolve and relate to disease stages is essential for advancing complex systems models of aging and AD, and for developing system-informed interventions. However, progress has been limited by a lack of large-scale longitudinal data. To address this, we examined the longitudinal relationship between subsystems of the default mode network (DMN) (posterior DMN, ventral DMN, anterior dorsal DMN) using task-free functional MRI (fMRI) and amyloid positron emission tomography (PET) imaging in a large longitudinal cohort spanning the clinico-biological spectrum of AD (n = 1,451; 2,763 time points) using mixed-effect models. We also assessed whether patterns of DMN connectivity predicted conversion to amyloid positivity, mild cognitive impairment (MCI), and dementia using Cox proportional hazards models. Our findings reveal a dynamic interplay between amyloid accumulation and connectivity within and between DMN subsystems, with both hyper- and hypoconnectivity emerging across DMN subsystems in association with increasing amyloid burden. Importantly, survival models showed that DMN connectivity patterns predicted conversion to critical stages of the disease, including not only conversion to MCI and dementia, but also conversion to amyloid positivity in otherwise clinically unimpaired individuals who were amyloid negative at baseline. These associations were independent of age, APOE4 status, sex, education, and in-scanner motion. These results support a model in which breakdowns in tightly regulated feedback loops governing DMN physiology represent a core systems-level pathophysiology of AD. Notably, this functional dyshomeostasis precedes detectable amyloidosis on imaging. Future studies should focus on the development of robust biomarkers of brain function that can be applied at the individual level, which could in turn help support the development of therapeutic approaches targeting system-level pathophysiology.

Abstract This presentation will first provide an overview of the rationale behind this Perspective Session bringing the Atypical Alzheimer's disease (AD) and Clinical Trials ISTAART Professional Interest Area (PIAs) together. We will then highlight foundational notions about atypical forms of Alzheimer's disease (AD) and critical shortcomings in current clinical trial designs that fail to address the unique needs of these populations. We will first briefly describe the distinct clinical and biological features of atypical AD variants, including posterior cortical atrophy (PCA), logopenic variant primary progressive aphasia (lvPPA), dysexecutive AD (dAD), and behavioral AD (bvAD). These variants are typically characterized by early‐onset symptoms and predominant non‐memory impairments, factors that have contributed to the significant underrepresentation of these patients in large‐scale clinical trials. Special attention will be given to recent FDA‐approved amyloid‐targeting therapies, which have largely focused on the “typical” amnestic, late‐onset presentation of AD, further underscoring the urgent need for inclusive research efforts for the broader phenotypic spectrum of the disease. We will identify critical gaps in clinical trial design for atypical AD and propose early strategic avenues to address these challenges. Key considerations include optimizing enrollment strategies, developing syndrome‐specific biological and clinical endpoints, and fostering advocacy to promote accurate diagnosis, equitable representation, and improved outcomes for these underrepresented populations.

Abstract Background Neuropathologic evaluation is essential for diagnosing and treating dementia, but definitive diagnoses is only available after post‐mortem brain autopsy. Positron emission tomography (PET) offers a potential in vivo proxy for predicting neuropathology. This study proposes a semi‐supervised learning (SSL) model using a single FDG‐PET image to predict underlying neuropathology. Method The SSL model leverages a self‐organized graph structure based on a patient similarity network (PSN) created from 1,495 FDG‐PET images across the aging and neurodegenerative spectrum. Each patient is represented by a node, with edges indicating image similarity derived from principal component analysis features and cosine similarity weights. The PSN structure connects 20 nearest neighbors. Neuropathologic labels were available for 204 autopsied patients. The Poisson Learning Method was employed to propagate labels from these annotated nodes to unlabeled nodes, guided by the PSN structure. FDG‐PET images were collected from clinical and research participants at the Mayo Clinic and processed using an MRI‐free pipeline. Neuropathologic labels were assigned based on widely accepted diagnostic criteria. Leave‐one‐out cross‐validation (LOOCV) was used to assess model performance. Results The confusion matrix demonstrated high accuracy in predicting Alzheimer's disease, frontotemporal lobar degeneration with tau inclusions, and Lewy body disease pathology. A Sankey diagram and label distribution analysis revealed strong correlations between clinical and predicted neuropathologic labels. Conclusion This study highlights the potential of a graph‐based SSL pipeline to predict neuropathological changes using FDG‐PET imaging data, despite limited pathology labels. The findings underscore the value of SSL in bridging the gap between abundant neuroimaging data and scarce neuropathologic labels, contributing to improved dementia diagnosis and understanding.

Objective To determine the clinical features that identify patients with suspected rapidly progressive dementia (RPD) who will develop RPD. Methods Patients with suspected RPD were enrolled and followed at Mayo Clinic (Jacksonville, FL; January 2020 to October 2023) and Washington University (Saint Louis, MO; June 2016 to December 2019). Two dementia specialists independently reviewed clinical data and assigned diagnoses. Patients were diagnosed with RPD if they developed dementia within 1 year or incapacitation within 2 years of symptom onset. The associations between clinical features and causes and outcomes were explored via univariate and multivariate comparisons. Results Of 248 patients with suspected RPD, 185 (74.6%) met criteria for RPD. Patients with RPD were older (62.6 ± 14.5 vs 55.2 ± 18.1 years; p < 0.001), and more frequently diagnosed with Alzheimer's disease or related dementias (OR 3.13, 95% CI 1.40, 7.31) or Creutzfeldt–Jakob disease (OR 4.67, 95% CI 1.38, 15.75). Visual agnosia (15/183; 8.2%), substantial brain atrophy (27/182; 14.8%), and periodic epileptiform discharges (11/151; 7.3%) were exclusively detected in patients with RPD. After controlling for age, intensive care unit admission (OR 4.77, 95% CI 1.61, 14.12), lack of seizures (OR 2.47, 95% CI 1.15, 5.31), abnormal brain magnetic resonance imaging (OR 2.01, 95% CI 0.997, 4.05), and cerebrospinal fluid white blood cell count ≤5 cells/mm 3 (OR 3.30, 95% CI 1.60, 6.82) were also independently associated with RPD. Patients with RPD were more likely to die or develop severe dementia (mean time to outcome, 36.1 months; 95% CI 31.2–41.0). Interpretation Selected clinical features may identify patients with suspected RPD who are likely to continue to decline. Early recognition of these features may improve diagnostic accuracy and inform prognosis. ANN NEUROL 2025;98:764–776