Deep learning-based brain age models quantify regional deviations from normative aging and may capture structural changes relevant to dementia risk. Plasma phosphorylated tau-217 (p-tau217) is a scalable Alzheimer’s disease biomarker, but its relationship to brain aging and cognition in cognitively unimpaired adults is unclear. In this cross-sectional study, we tested whether brain age patterns serve as indirect pathways linking plasma p-tau217 to cognition in the Aging Brain Cohort (ABC). Neuroimaging data from 518 adults (mean age = 43.7 years, 70.8% female) were analyzed using a validated deep learning brain age model, and decomposed via exploratory factor analysis into six gradients: frontal, dorsal, ventral, left frontotemporal, right frontotemporoparietal, and bilateral parietal. In a parallel mediation model including all six gradients as simultaneous mediators in adults aged ≥60 years (N = 71), a significant specific indirect effect of plasma p-tau217 on Montreal Cognitive Assessment (MoCA) scores was observed through accelerated right frontotemporoparietal aging (β = −0.111, 95% CI [−0.313, −0.010], p = 0.031). No other indirect pathways were significant, and neither the total nor direct effect was significant. These findings suggest a specific brain aging phenotype as a potential intermediate pathway linking tau-related pathology to cognition prior to clinical impairment.
Nicholas Riccardi, Ansley Martin, Dariusz Pytel et al.· npj Dementia· 0 citations
Two-stage models of grammatical encoding posit that sentence production unfolds in two sequential steps: the construction of a hierarchical syntactic structure, followed by its linearization into a sequence suitable for articulation. While widely accepted in psycho- and neurolinguistics, such models lack formal computational implementations. Here, we introduce a novel multinomial processing tree (MPT) model that operationalizes this two-stage framework to explain syntactic error patterns in individuals with aphasia. Drawing on discourse samples annotated for distinct error types, we fit an MPT model to estimate individual abilities at each processing stage. These ability estimates correlated with observed error rates and localized to distinct neural substrates: hierarchical encoding ability was linked to the posterior superior temporal sulcus and parietal cortex, linearization to posterior inferior frontal regions, and omission-related processes to more dorsal frontal regions and the underlying white matter. Our findings support a neurocomputational dissociation between hierarchical and linear stages of grammatical encoding, aligning with prior lesion-symptom mapping and theoretical accounts of expressive agrammatism and paragrammatism. This work represents the first computational instantiation of a two-stage model of syntactic production, bridging formal modeling and lesion analysis to advance our understanding of the architecture and breakdown of grammatical encoding.
Jeremy D Yeaton, Grant M. Walker, Danielle Fahey et al.· Psychonomic Bulletin & Revie...· 0 citations