Biology subjects

Wijeratne, P. A.

Publications and source records attributed to Wijeratne, P. A..

4 recordsLinked to original sources

bioRxiv2026

Revealing trajectories of multi-modal voxel-level changes in neurodegenerative diseases using latent event mapping

Neurodegenerative diseases are driven by pathological mechanisms that can be indirectly measured in vivo using multi-modal neuroimaging. However, current computational methods that aim to reconstruct trajectories of voxel-level changes in the brain are either not computationally scalable or fully interpretable, limiting their ability to reveal associations between disease progression and underlying mechanisms. Here we introduce Latent Event Mapping (LEMING), a generative unsupervised modelling technique that learns a latent map of disease events along a common pseudo-timeline of events. We apply LEMING to amyloid PET and structural MRI data from the Alzheimers Disease Neuroimaging Initiative to reveal the first voxel-level trajectories of events in Alzheimers disease. Notably, we show how LEMING can provide new insights into progression-dependent disease mechanisms. We find that acetylcholine receptor density is significantly positively associated with both late-stage amyloid and atrophy events, suggesting that either these receptors are targeted later in disease progression, or that amyloid does not play an active role. This has strong implications for therapeutics that target acetylcholine receptors, particularly for early-stage intervention strategies.

bioinformatics↗

bioRxiv2021

pySuStaIn: a Python implementation of the Subtype and Stage Inference algorithm

Progressive disorders are highly heterogeneous. Symptom-based clinical classification of these disorders may not reflect the underlying pathobiology. Data-driven subtyping and staging of patients has the potential to disentangle the complex spatiotemporal patterns of disease progression. Tools that enable this are in high demand from clinical and treatment-development communities. Here we describe the pySuStaIn software package, a Python-based implementation of the Subtype and Stage Inference (SuStaIn) algorithm. SuStaIn unravels the complexity of heterogeneous diseases by inferring multiple disease progression patterns (subtypes) and individual severity (stages) from cross-sectional data. The primary aims of pySuStaIn are to enable widespread application and translation of SuStaIn via an accessible Python package that supports simple extension and generalization to novel modelling situations within a single, consistent architecture. Code metadataCurrent code version v1.0 Permanent link to code/repository used of this code version https://github.com/ucl-pond/pySuStaIn Legal Code License MIT Code versioning system used git Software code languages, tools, and services used Python Compilation requirements, operating environments & dependencies Linux, Mac, Windows Support email for questions leon.aksman@loni.usc.edu, p.wijeratne@ucl.ac.uk, alexandra.young@kcl.ac.uk

neuroscience↗

bioRxiv2020

Tau-first subtype of Alzheimer's disease consistently identified across in vivo and post mortem studies

Alzheimers disease (AD) is marked by the spread of misfolded amyloid-{beta} and tau proteins throughout the brain. While it is commonly believed that amyloid-{beta} abnormality drives the cascade of AD pathogenesis, several in vivo and post mortem studies indicate that in some subjects localized tau-based neurofibrillary tangles precede amyloid-{beta} pathology. This suggests that there may be multiple distinct subtypes of protein aggregation pathways within AD, with potentially different demographic, cognitive and comorbidity profiles. We investigated this hypothesis, applying data-driven disease progression subtyping models to post mortem immunohistochemistry and in vivo positron emission tomography (PET) and cerebrospinal fluid (CSF) based measures of protein pathologies in two large observational cohorts. We consistently identified both amyloid-first and tau-first AD subtypes, where tau-first subjects had higher levels of soluble TREM2 compared to amyloid-first subjects. Our work provides insight into AD progression that may be valuable for interventional trials targeting amyloid-{beta} and tau.

neuroscience↗

bioRxiv2018

Sequence of cognitive changes associated with development of Alzheimer’s disease in Down syndrome – data driven analysis

ObjectiveIndividuals with Down syndrome (DS) have an extremely high genetic risk for Alzheimers disease (AD) however the course of cognitive decline associated with progression to dementia is ill-defined. Data-driven methods can estimate long-term trends from cross-sectional data while adjusting for variability in baseline ability, which complicates dementia assessment in those with DS.\n\nMethodsWe applied an event-based model to cognitive test data and informant-rated questionnaire data from 283 adults with DS (the largest study of cognitive functioning in DS to date) to estimate the sequence of cognitive decline and individuals disease stage.\n\nResultsDecline in tests of memory, sustained attention / motor coordination, and verbal fluency occurred early, demonstrating that AD in DS follows a similar pattern of change to other forms of AD. Later decline was found for informant measures. Using the resulting staging model, we showed that adults with a clinical diagnosis of dementia and those with APOE 3:4 or 4:4 genotype were significantly more likely to be staged later, suggesting the model is valid.\n\nInterpretationOur results identify tests of memory and sustained attention may be particularly useful measures to track decline in the preclinical/prodromal stages of AD in DS whereas informant-measures may be useful in later stages (i.e. during conversion to dementia, or post-diagnosis). These results have implications for the selection of outcome measures of treatment trials to delay or prevent cognitive decline due to AD in DS. As clinical diagnoses are generally made late into AD progression, early assessment is essential.

neuroscience↗