Biology subjects

Oxtoby, N. P.

Publications and source records attributed to Oxtoby, N. P..

8 recordsLinked to original sources

bioRxiv2024

Unbiased data-driven analysis of five amyloid-beta peptides for biomarker investigations in familial Alzheimer's disease

Structured AbstractO_ST_ABSINTRODUCTIONC_ST_ABSChanges to the relative abundance of amyloid-beta (A{beta}) peptides are hallmarks of Alzheimers disease (AD). iPSC-derived neurons offer a physiological model of A{beta} production. We employed unbiased, data-driven analyses to investigate combinations of A{beta} peptides as AD biomarkers and the relative contribution of peptides to AD pathogenesis. METHODSWe measured A{beta}37, A{beta}38, A{beta}40, A{beta}42 and A{beta}43 in ten iPSC-neuronal cultures from PSEN1 mutation carriers. We combined these data with published cell model data and used linear weighted combinations to 1) distinguish AD from controls, and 2) predict age-at-onset for PSEN1 mutations. RESULTSData-driven approaches distinguished A{beta}42 and A{beta}43 from shorter peptides, providing unbiased evidence for their contribution to disease. Weighted linear combinations of A{beta} peptides outperform A{beta}42/40 and provide insights into relative peptide contribution as biomarkers; the optimal ratio for all data is represented as (21 {middle dot} A{beta}37 + 10 {middle dot} A{beta}38 + 69 {middle dot} A{beta}40)/(94 {middle dot} A{beta}42 + 6 {middle dot} A{beta}43). DISCUSSIONThe algorithm discovered herein can be further refined to improve biomarkers for AD.

neuroscience↗

bioRxiv2022

White matter microstructural abnormality precedes cortical volumetric decline in Alzheimer's disease: evidence from data-driven disease progression modelling

Sequencing the regional progression of neurodegeneration in Alzheimers disease (AD) informs disease mechanisms and facilitates identification and staging of individuals at greatest risk of imminent cognitive decline, which may aid the development of early therapeutic interventions. Previous attempts to sequence neurodegeneration have analysed measures of regional volume and identified the initial sites of atrophy. However, focal microstructural alterations in white matter have also been reported in early AD. Yet, the temporal ordering of abnormality in measures of white matter microstructure relative to grey matter volume has not been established. In this study we used event-based modelling of disease progression (EBM) to provide a data-driven evaluation of the temporal sequence of abnormality in markers of white matter microstructure relative to grey matter volume. Regional microstructural metrics derived from diffusion tensor imaging (DTI) and regional volumes from Freesurfer cortical parcellation were obtained from the Alzheimers disease Neuroimaging Initiative (ADNI) database for 441 amyloid-positive participants (81 AD-dementia, 159 mild cognitive impairment, 201 cognitively normal). The estimated sequence shows a series of abnormalities in markers of white matter microstructure, followed by sequential grey matter volumetric decline, with no overlap between the two. Analysis of positional variance and cross-validation supports the robustness of our findings. These results provide the first data-driven evidence that markers of white matter microstructural degeneration precede those of cortical volumetric decline in the AD cascade. This prompts a re-evaluation of the view that regional volumetric decline can be used to characterise the very earliest stages of AD neurodegeneration. Instead, we suggest that white matter microstructural markers provide an earlier window into AD neurodegeneration. An early staging system of AD neurodegeneration based on measures of brain microstructure may find application in selecting AD subjects with early but minimal brain damage for clinical trials that aim to prevent cognitive decline.

neuroscience↗

bioRxiv2022

S-EBM: Generalising event-based modelling of disease progression for simultaneous events

This study introduces the parsimonious event-based model of disease progression (P-EBM). The P-EBM generalises the event-based model of disease progression (EBM) to allow inference of fewer disease progression stages than the number of input biomarkers. The original EBM is designed to estimate a single distinct biomarker abnormality, termed an event, at each model stage. By allowing multiple events within a common stage, the P-EBM prevents redundant complexity to permit discovery of parsimonious sequences of disease progression - those that contain purely serial events, as in the original EBM, as well as those containing one or more sets of simultaneous events. This study describes P-EBM theory, evaluates its sequence estimation and staging performance and demonstrates its application to derive a parsimonious sequence of disease progression in sporadic Alzheimers disease (AD). Results show that the P-EBM can accurately recover a wider range of sequences than EBM under a range of realistic experimental scenarios, including different numbers of simultaneous events, biomarker disease signals and dataset sizes. The P-EBM sequence successfully highlights redundant biomarkers and stages subjects using fewer biomarkers. In sporadic AD, the P-EBM estimates a shorter sequence than the EBM with substantially higher likelihood which plausibly suggests that some biomarker events appear simultaneously. The P-EBM has potential application for generating new insights into disease evolution and for suggesting efficient biomarker collection strategies for patient staging.

neuroscience↗

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

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↗

bioRxiv2018

Data-driven models of dominantly-inherited Alzheimer’s disease progression

Dominantly-inherited Alzheimers disease is widely hoped to hold the key to developing interventions for sporadic late onset Alzheimers disease. We use emerging techniques in generative data-driven disease-progression modelling to characterise dominantly-inherited Alzheimers disease progression with unprecedented resolution, and without relying upon familial estimates of years until symptom onset (EYO). We retrospectively analysed biomarker data from the sixth data freeze of the Dominantly Inherited Alzheimer Network observational study, including measures of amyloid proteins and neurofibrillary tangles in the brain, regional brain volumes and cortical thicknesses, brain glucose hypometabolism, and cognitive performance from the Mini-Mental State Examination (all adjusted for age, years of education, sex, and head size, as appropriate). Data included 338 participants with known mutation status (211 mutation carriers: 163 PSEN1; 17 PSEN2; and 31 APP) and a baseline visit (age 19-66; up to four visits each, 1{middle dot}1 {+/-} 1{middle dot}9 years in duration; spanning 30 years before, to 21 years after, parental age of symptom onset). We used an event-based model to estimate sequences of biomarker changes from baseline data across disease subtypes (mutation groups), and a differential-equation model to estimate biomarker trajectories from longitudinal data (up to 66 mutation carriers, all subtypes combined). The two models concur that biomarker abnormality proceeds as follows: amyloid deposition in cortical then sub-cortical regions (approximately 24{+/-}11 years before onset); CSF p-tau (17{+/-}8 years), tau and A{beta}42 changes; neurodegeneration first in the putamen and nucleus accumbens (up to 6 {+/-} 2 years); then cognitive decline (7 {+/-} 6 years), cerebral hypometabolism (4 {+/-} 4 years), and further regional neurodegeneration. Our models predicted symptom onset more accurately than EYO: root-mean-squared error of 1{middle dot}35 years versus 5{middle dot}54 years. The models reveal hidden detail on dominantly-inherited Alzheimers disease progression, as well as providing data-driven systems for fine-grained patient staging and prediction of symptom onset with great potential utility in clinical trials.

neuroscience↗

bioRxiv2017

Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with Subtype and Stage Inference

The heterogeneity of neurodegenerative diseases is a key confound to disease understanding and treatment development, as study cohorts typically include multiple phenotypes on distinct disease trajectories. Here we present a new machine learning technique - Subtype and Stage Inference (SuStaIn) - able to uncover data-driven disease phenotypes with distinct temporal progression patterns, from widely available crosssectional patient studies. Results from imaging studies in two neurodegenerative diseases reveal new subgroups and their distinct trajectories of regional neurodegeneration. In genetic frontotemporal dementia, SuStaIn identifies genotypes from imaging alone, validating its ability to identify subtypes, and characterises within-group heterogeneity for the first time. In Alzheimers disease, SuStaIn uncovers three subtypes, uniquely revealing their temporal complexity. SuStaIn provides fine-grained patient stratification, which substantially enhances the ability to predict conversion between diagnostic categories over standard models that ignore subtype (p=7.18x10--4) or temporal stage (p=3.96x10-5). SuStaIn thus offers new promise for enabling disease subtype discovery and precision medicine.

neuroscience↗