Biology subjects

Parker, C. S.

Publications and source records attributed to Parker, C. S..

3 recordsLinked to original sources

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

Not all voxels are created equal: reducing estimation bias in regional NODDI metrics using tissue-weighted means

Neurite orientation dispersion and density imaging (NODDI) estimates microstructural properties of brain tissue relating to the organisation and processing capacity of neurites, which are essential elements for neuronal communication. Descriptive statistics of NODDI tissue metrics are commonly analysed in regions-of-interest (ROI) to identify brain-phenotype associations. Here, the conventional method to calculate the ROI mean weights all voxels equally. However, this produces biased estimates in the presence of CSF partial volume. This study introduces the tissue-weighted mean, which calculates the mean NODDI metric across the tissue within an ROI, utilising the tissue fraction estimate from NODDI to reduce estimation bias. We demonstrate the proposed mean in a study of white matter abnormalities in young onset Alzheimers disease (YOAD). Results show the conventional mean induces significant bias that correlates with CSF partial volume, primarily affecting periventricular regions and more so in YOAD subjects than in healthy controls. Due to the differential extent of bias between healthy controls and YOAD subjects, the conventional mean under- or over-estimated the effect size for group differences in many ROIs. This demonstrates the importance of using the correct estimation procedure when inferring group differences in studies where the extent of CSF partial volume differs between groups. These findings are robust across different acquisition and processing conditions. Bias persists in ROIs at higher image resolution, as demonstrated using data obtained from the third phase of the Alzheimers disease neuroimaging initiative (ADNI); and when performing ROI analysis in template space. This suggests that conventional ROI means of NODDI metrics are biased estimates under most contemporary experimental conditions, the correction of which requires the proposed tissue-weighted mean. The tissue-weighted mean produces accurate estimates of ROI means and group differences when ROIs contain voxels with CSF partial volume. In addition to NODDI, the technique can be applied to other multi-compartment models that account for CSF partial volume, such as the free water elimination method. We expect the technique to help generate new insights into normal and abnormal variation in tissue microstructure of regions typically confounded by CSF partial volume, such as those in individuals with larger ventricles due to atrophy associated with neurodegenerative disease.

neuroscience↗