Biology subjects

McGregor, K.

Publications and source records attributed to McGregor, K..

5 recordsLinked to original sources

bioRxiv2026

Functional Reorganization of Motor Subcircuits in Parkinson's disease

Parkinsons disease disrupts motor control across multiple body parts, yet the neural mechanisms underlying these impairments remain incompletely defined. We compared resting-state functional connectivity in people with mild-to-moderate Parkinsons disease (n = 58) and neurotypical older adults (n = 24), focusing on regions implicated in internally generated (IG) and externally generated (EG) movement pathways. For our analysis, we leveraged the reproducible NeuroMark independent component template and motor effector-specific mapping of primary motor cortex (M1). Our results reveal both increased and decreased connectivity patterns in Parkinsons disease: M1 subregions associated with control of the leg, hand, and larynx showed robust increases in connectivity exclusively with cerebellar territories, particularly Crus II and Lobules VIIIa/VIIIb. The postcentral gyrus (primary somatosensory cortex) showed primarily increased connectivity with cerebellar regions and the insula. In contrast, the caudate nucleus displayed a mixed profile, with increased connectivity to the superior temporal gyrus and decreased connectivity to the superior medial frontal gyrus and cerebellar Crus II. Our motor effector-specific analysis of disease severity scores (MDS-UPDRS) in people with Parkinsons disease revealed mild impairments across all categories (leg, hand, larynx) but disproportionately greater hand-related deficits, suggesting that some of the observed M1 connectivity differences may be influenced by these behavioral asymmetries. These anatomically precise, effector-specific alterations suggest compensatory recruitment of cerebellar circuits in Parkinsons disease and provide a framework for targeting motor subcircuits in rehabilitation, including dance-based interventions.

neuroscience↗

bioRxiv2025

zifalsnm: Zero-Inflated Bayesian factor analysis model with skew-normal priors for modeling microbiome data

BackgroundAdvancements in next-generation sequencing have transformed our understanding of host-microbe interactions, revealing links between microbial composition and chronic conditions such as diabetes, Crohns disease, and others. However, the analysis of microbiome data is complex due to its high-dimension as well as additional statistical characteristics. One primary objective is to achieve effective dimensionality reduction while simultaneously accounting for the datas compositional nature and zero inflation. Existing probabilistic models are often based on the assumption that the log-ratio-transformed compositions are normally distributed. This assumption is problematic because it can fail to capture the significant skewness inherent in these transformed compositions. ResultsWe propose a new model called the Zero-Inflated Factor Analysis Logistic Skew-Normal Multinomial (ZIFA-LSNM) model. ZIFA-LSNM integrates a zero-inflation component to handle excess zeros, employs factor analysis for dimensionality reduction, and, critically, utilizes skew-normal priors on the latent factors to explicitly model data asymmetry. Posterior inference is performed using an efficient variational inference algorithm. Through simulation studies and real data analysis, the ZIFA-LSNM model is shown to have improved performance in parameter recovery and composition estimation compared to its Gaussian-based counterparts. ConclusionThe ZIFA-LSNM model demonstrates that explicitly accounting for skewness in the latent factor structure can substantially improve inference in commonly-observed microbiome data. The proposed model, therefore, offers a flexible and scalable framework allowing for the improved analysis of the complex relationships between microbial communities and human health.

genetics↗

bioRxiv2023

Proportionality-based association metrics in count compositional data

MotivationCompositional data comprise vectors that describe the constituent parts of a whole. Data arising from various -omics platforms such as 16S and RNA-sequencing are compositional in nature. However, correlations between features on raw counts have no meaningful interpretation. Metrics of proportionality were formulated to address this problem. However, there is an inherent bias that arises when calculating these metrics empirically on count-based measures due to variability in read depths. ResultsWe quantify the bias introduced by empirically calculating proportionality-based association metrics in count data. Additionally, we propose a means of estimating these metrics within a logit-normal multinomial model in pursuit of more accurate estimates. The model-based estimates are shown to outperform empirical estimates in simulated data, and are additionally applied to a mouse embryonic stem-cell single-cell sequencing dataset as well as a pediatric-onset multiple sclerosis metagenomic dataset. Availability and ImplementationAn R package is available at https://CRAN.R-project.org/package=countprop. Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2020

Microbial community modelling and diversity estimation using the hierarchical Pitman-Yor process

BackgroundThe human microbiome comprises the microorganisms that inhabit the various locales of the human body and plays a vital role in human health. The composition of a microbial population is often quantified through measures of species diversity, which summarize the number of species along with their relative abundances into a single value. In a microbiome sample there will certainly be species missing from the target population which will affect the diversity estimates. MethodsWe employ a model based on the hierarchical Pitman-Yor (HPY) process to model the species abundance distributions over multiple populations. The model parameters are estimated using a Gibbs sampler. We also derive estimates of species diversity, conditional and unconditional on the observed data, as a function of the HPY parameters Finally, we derive a general formula for the Hill numbers in the HPY context. ResultsWe show that the Gibbs sampler for the HPY model performs well in simulations. We also show that the conditional estimates of diversity from the HPY model improve over naive estimates when species are missing. Similarly the conditional HPY estimates tend to perform better than the naive estimates especially when the number of individuals sampled from a population is small.

bioinformatics↗

bioRxiv2019

MDiNE: A model to estimate differential co-occurrence networks in microbiome studies

MotivationThe human microbiota is the collection of microorganisms colonizing the human body, and plays an integral part in human health. A growing trend in microbiome analysis is to construct a network to estimate the co-occurrence patterns among taxa though precision matrices. Existing methods do not facilitate investigation into how these networks change with respect to covariates. ResultsWe propose a new model called Microbiome Differential Network Estimation (MDiNE) to estimate network changes with respect to a binary covariate. The counts of individual taxa in the samples are modelled through a multinomial distribution whose probabilities depend on a latent Gaussian random variable. A sparse precision matrix over all the latent terms determines the co-occurrence network among taxa. The model fit is obtained and evaluated using Hamiltonian Monte Carlo methods. The performance of our model is evaluated through an extensive simulation study, and is shown to outperform existing methods in terms of estimation of network parameters. We also demonstrate an application of the model to estimate changes in the intestinal microbial network topology with respect to Crohns disease. Availability and ImplementationMDiNE is implemented in a freely available R package: https://github.com/kevinmcgregor/mdine. Supplementary informationA file containing supplemental material has been submitted with this manuscript.

bioinformatics↗