Jones, David T

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 Motor speech disorders (MSDs) are early markers of neurological disease, but expert perceptual analysis is rarely available outside specialized centers. Automated speech analysis offers a scalable alternative, yet prior studies have not systematically compared modeling approaches or assessed clinically relevant metrics in independent datasets. This study compared static acoustic features, articulatory informed Phonet features, and self-supervised pretrained models for binary and multi label MSD classification. We trained and evaluated models on 583 speech samples using speaker level splits. Baseline models included logistic regression and Gated Recurrent Units (GRUs) trained on eGeMAPS and MFCCs. We extracted three types of Phonet derived features and evaluated pretrained HuBERT and SSAST models in frozen, partially fine-tuned, and fully fine-tuned configurations. Binary classification distinguished MSDs from controls, while multi label classification identified six MSD subtypes. Models were assessed using validation AUC, and cut points were tested on two independent datasets. Pretrained and Phonet based models substantially outperformed static acoustic features. In binary classification, HuBERT achieved the highest AUC (0.95), while compact Phonet derived GRUs achieved comparable performance (up to 0.94). These models generalized well to independent datasets, maintaining high sensitivity (0.94) and specificity (0.97). In multi label classification, Phonet models achieved the highest macro average AUC (0.86), but threshold-based subtype performance declined on unseen data. Automated MSD detection is feasible and clinically promising. Binary classification generalized well, whereas multi label classification showed limited threshold stability across datasets.

Abstract Travelling waves are a prominent feature of hippocampal activity, but the mechanisms determining their propagation and influence on the cerebral cortex remain unclear. Using a biophysically-grounded model of neural activity evolving across hippocampal and cortical surfaces, we show that spatial gradients in external in-put or neural excitability are a sufficient mechanism for the emergence of travelling waves along the long axis of the hippocampus. These waves emerge only above a critical gradient threshold, propagate with biologically-plausible velocities and exhibit frequency-dependent reversals in direction across slow and fast theta regimes. When coupled to the cerebral cortex, anterior-to-posterior hippocampal waves selectively reorganise globally synchronous cortical activity into metastable travelling waves, whereas posterior-to-anterior hippocampal waves do not, revealing a directional asymmetry in hippocampal–cortical communication. Conversely, cortical waves aligned with large-scale functional gradients induce structured hippocampal waves, revealing complementary direction specific effects across the two systems. These analyses identify structured excitability gradients as a principle governing wave propagation in the hippocampus and suggest how wave-to-wave interactions may coordinate complex cortical and hippocampal interactions during mnemonic and perceptual processes.

Abstract The growing availability of large-scale biomarker datasets has allowed data-driven methods to characterize Alzheimer’s disease biological heterogeneity. However, most prior studies have focused on cohorts of late-onset amnestic cases, leaving early-onset Alzheimer’s disease underexplored. We aimed to characterize tau-PET-based subtypes through a robust data-driven approach in the Longitudinal Early-Onset Alzheimer’s Disease Study. Baseline [18F]Flortaucipir PET scans from 365 amyloid-PET-positive participants with sporadic early-onset Alzheimer’s disease were quantified in the left and right medial temporal, lateral temporal, occipital, parietal, and frontal cortices. Tau PET values were z-scored against 85 amyloid-PET-negative cognitively normal age-matched participants and fitted into Subtype and Stage Inference (SuStaIn)—an unsupervised clustering algorithm that simultaneously models subtypes and progression from cross-sectional data. The derived subtypes were subsequently characterized by baseline and longitudinal clinical, cognitive, MRI, tau and amyloid PET features. We identified three tau-PET-based subtypes: on average, Subtype 1/Typical (n = 144, 40%) showed a predominant bilateral temporoparietal pattern typical of Alzheimer’s disease. Subtype 2/Left temporal (n = 111, 31%) showed predominant left temporal binding. Subtype 3/Posterior (n = 104, 29%) showed early and permeating occipitoparietal involvement. Subtypes did not differ in demographics or global amyloid burden, but were relatively more enriched for specific clinical presentations: S1/Typical for amnestic presentations, S2/Left Temporal for primary progressive aphasia, and S3/Posterior for posterior cortical atrophy. Baseline tau PET subtypes aligned with cortical atrophy patterns and domain-specific cognitive impairment. When follow-up tau PET scans were fitted to SuStaIn trained on baseline data, 85.6% (n = 172/201) of participants retained the same subtype classification, indicating subtype temporal stability, and progressed within subtypes by 0.56 ± 0.70 SuStaIn stage/year. Longitudinal voxel-wise linear mixed-effects modelling revealed tau accumulation patterns for each subtype in regions relatively spared at baseline: occipital lobe accumulation predominated in S1/Typical, bilateral frontal and right temporal in S2/Left Temporal, and bilateral frontotemporal lobes in S3/Posterior. All subtypes showed longitudinal increases in Clinical Dementia Rating-Sum of Boxes, but with slower worsening in S3/Posterior compared with the other subtypes. Our findings reveal robust subtypes in sporadic early-onset Alzheimer’s disease characterized by distinct spatiotemporal tau patterns that parallel differences in clinical presentations and trajectories of neurodegeneration. These subtypes extend beyond traditional clinical syndromes and support a more nuanced framework for individualized prognosis and care. Incorporating tau PET subtyping into clinical trial design could enable more targeted therapeutic approaches for this younger population.

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.