Biology subjects

Crutch, S. J.

Publications and source records attributed to Crutch, S. J..

3 recordsLinked to original sources

bioRxiv2026

Incomplete letter recognition is limited by cortical and not optical factors: Simulating the visual deficits of dementia in healthy adults

Incomplete letter recognition tasks are frequently used to detect visual deficits arising from neurodegenerative syndromes, including Posterior Cortical Atrophy (PCA; visual-variant Alzheimers disease). A recent development of this approach is the Graded Incomplete Letters Test (GILT), which measures recognition thresholds for letters degraded by removing pixelated sections (decreasing completeness). Although GILT thresholds are strongly elevated in PCA relative to typical adults, the precise cortical visual impairments underlying these deficits are unclear, as is the potential contribution from age-related optical limitations. We compared candidate cortical factors (crowding and global integration) with optical limitations (blur and low contrast) by simulating these factors in typical adults (n=6) viewing incomplete letter stimuli. Participants identified foveally presented letters (12 alternatives), with completeness varied using QUEST. At baseline, thresholds averaged [~]5% completeness. Optical factors were simulated by separately applying blur and lowered contrast. These factors had minimal effect on thresholds, except where blur/contrast levels approached visibility limits, where thresholds rose modestly but remained far below clinical levels in PCA. Cortical factors were simulated by increasing crowding (disruptions from clutter) through peripheral presentation, with global-integration impairments simulated by varying pixel size to alter the distribution of degradation (limiting spatial integration) or degrading letters dynamically with limited-lifetime pixels (limiting temporal integration). These manipulations substantially elevated thresholds, with combined crowding and global-integration impairments increasing thresholds to levels comparable with PCA. We conclude that impaired incomplete letter recognition is driven primarily by cortical rather than optical factors, and that neurodegenerative deficits may reflect the combined impact of multiple cortical limitations.

neuroscience↗

bioRxiv2023

The multidimensional neurocognitive geometry in Alzheimer's disease and posterior cortical atrophy

Background and ObjectivesAlzheimers disease spans heterogeneous typical and atypical phenotypes. Posterior cortical atrophy is one striking example, characterised by prominent impairment in visual and other posterior functions in contrast to typical, amnestic Alzheimers disease. The primary study objective was to establish how the similarities and differences of cognition and brain volumes within Alzheimers disease and posterior cortical atrophy (and by extension other Alzheimers disease variants), can be conceptualised as systematic variations across a transdiagnostic, graded multidimensional space. MethodsThis was a cross-sectional, single-center, observational, cohort study performed at the National Hospital for Neurology & Neurosurgery, London, UK. Data were collected from a cohort of PCA and AD patients, matched for age, disease duration and MMSE scores. There were two sets of outcome measures: (1) scores on a neuropsychological battery containing 22 tests spanning visuoperceptual and visuospatial processing, episodic memory, language, executive functions, calculation, and visuospatial processing; and (2) measures extracted from high-resolution T1-weighted volumetric MRI scans. Principal component analysis was used to extract the transdiagnostic dimensions of phenotypical variation from the detailed neuropsychological data. Voxel-based morphometry was used to examine associations between the PCA-derived clinical phenotypes and the structural measures. ResultsWe enrolled 93 PCA participants (mean: age = 59.9 yrs, MMSE = 21.2; 59/93 female) and 58 AD participants (mean: age = 57.1 yrs, MMSE = 19.7; 22/58 female). The principal component analysis for posterior cortical atrophy (sample adequacy confirmed: Kaiser-Meyer-Olkin = 0.865) extracted three dimensions accounting for 61.0% of variance in patients performance, reflecting general cognitive impairment, visuoperceptual deficits and visuospatial impairments. Plotting Alzheimers disease cases into the posterior cortical atrophy-derived multidimensional space, and vice versa, revealed graded, overlapping variations between cases along these dimensions, with no evidence for categorical-like patient clustering. Likewise, the relationship between brain volumes and scores on the extracted dimensions was overlapping for posterior cortical atrophy and Alzheimers disease cases. DiscussionThese results provide evidence supporting a reconceptualization of clinical and radiological variation in these heterogenous Alzheimers disease phenotypes as being along shared phenotypic continua spanning posterior cortical atrophy and Alzheimers disease, arising from systematic graded variations within a transdiagnostic, multidimensional neurocognitive geometry.

neuroscience↗

bioRxiv2018

Non-Parametric Mixture Modelling and its Application to Disease Progression Modelling

Dementia is characterised by its progressive degeneration of cognitive abilities. In research cohorts, detailed neuropsychological test batteries are often administered to better understand how cognition changes over time. Understanding cognitive changes in dementia is of great importance, particularly in determining how structural changes in the brain may affect cognition and in facilitating earlier detection of symptomatic changes. Disease progression models are often applied to these data to understand how a disease changes over time from cross-sectional data or to disease trajectories from large numbers of individuals. Previous disease progression models used to build longitudinal models from cross-sectional data have focused on brain imaging data; however, these models are not directly applicable to cognitive data. Here we use the novel, non-parametric, Kernel Density Estimation Mixture Modelling (KDEMM) approach and demonstrate accurate modelling of the progression of cognitive test data. We found that using KDEMM resulted in more accurate models of disease progression in simulated data compared to Gaussian Mixture Models (GMMs) for the majority of parameters used to simulate the data. When comparing KDEMM and GMM to cognitive data collected in different Alzheimers Disease subtypes, we found the KDEMM resulted in a model much more in line with clinical phenotype. We anticipate that the KDEMM will be used to integrate cognitive test data, and other non-normally distributed datasets into complex disease progression models.

neuroscience↗