Search bioRxiv⌕ Search

Biology subjects

He, V.

Publications and source records attributed to He, V..

3 recordsLinked to original sources

Multilayer Network Modelling of the Human Reading System

Reading is supported by rapid and flexible coordination of neural activity across distributed brain regions. We have previously shown that left fusiform gyrus (FusG) provides a bridge flexibly linking the visual form analysis required for reading with the language system. Here, we investigate the dynamic organisation of an extending reading network encompassing classical perisylvian language areas and FusG. We do so by applying multilayer network modelling to language fMRI data acquired through the Australian Epilepsy Project, using a paradigm that contrasts reading with visuospatial judgements. The dataset included 201 participants with left dominant language, both with and without seizure disorders. We hypothesised that the relative strength of dynamic inter-actions within this extended language network is associated with reading ability. Time resolved functional connectivity was estimated using a sliding window Pearsons correlation approach, and the resulting connectivity matrices were entered into a multilayer community detection algorithm to quantify spatiotemporal community structure within the reading network. We concentrate our analyses on allegiance, the probability that a pair of regions is assigned to the same community over time. Our results show that community structure within the reading network is characterised by a preference for within hemisphere assignment over cross hemisphere assignment, as well as higher nodal allegiance among left language regions compared with their right hemisphere homologues. As anticipated, within versus between network allegiance followed a similar gradient in both language and attention networks: lowest between left language and right attentional regions, intermediate between the left FusG and each respective network, and highest within-network (left language or right attention). Importantly, as hypothesised, reading ability was associated with FusG-inferior frontal gyrus (IFG) interactions: higher left FusG-left IFG allegiance correlated with better reading performance, whereas increased right FusG-left IFG allegiance correlated with poorer reading. These findings highlight hemispheric asymmetries in the dynamic organisation of the reading system and provide novel evidence linking individual differences in reading ability to network level dynamics. Our findings align with a developmental literature suggesting that as reading proficiency improves, there is a shift from bilateral to unilateral left occipitotemporal engagement.

neuroscience↗

VO: The Vaccine Ontology

With the widespread use of vaccines in research and clinical settings, there is an urgent need to standardize vaccine representation, integrate information across diverse vaccine types, and support computer-assisted reasoning. Accordingly, we have since 2007 developed the community-based Vaccine Ontology (VO), which aligns with the Basic Formal Ontology and adheres to OBO Foundry principles. VO models ontologically vaccines, vaccine components, vaccine immune responses, vaccine investigation studies and other vaccine-related topics. VO represents more than 10,000 vaccines targeting 289 infectious pathogens and cancers in humans and over 30 nonhuman animal species. VO provides mappings to external resources such as RxNorm, CVX, FDA, and USDA. Various VO use cases exist. VO facilitates vaccine standardization in resources such as the VIOLIN vaccine database, ImmPort, and the Vaccine Adjuvant Compendium (VAC). Semantic queries can be made to query VO. VO has been shown to enhance experimental and clinical vaccine data analysis and vaccine literature mining. Overall, VO standardizes vaccine modeling and representation and greatly supports vaccine AI research in the Semantic Web era.

bioinformatics↗

Common pitfalls during model specification in psychophysiological interaction analysis

Psychophysiological interaction (PPI) analysis is a widely used regression method in functional neuroimaging for capturing task-dependent changes in connectivity from a seed region. The present work identifies, and provides corrections for, common methodological pitfalls in PPI analysis that compromise model validity. Firstly, if the seed time series is extracted with prewhitening, the temporal structure of the signal is altered and subsequent deconvolution of prewhitened data becomes suboptimal. Furthermore, prewhitening again during model fitting results in double prewhitening of the seed regressor. Secondly, a failure to mean-centre the task regressor when calculating the interaction term can also lead to model misspecification and potentially spurious inferences. By using simulations and empirical language fMRI data from the Australian Epilepsy Project, we demonstrate the adverse effects of these issues, and how they are resolved when corrected. A systematic review of current practices revealed widespread model misspecification, and underreporting of methods, in published PPI studies. We provide clearer reporting guidelines, and advocate for appropriate methods for handling of prewhitening and mean-centring to ensure the validity of PPI analyses.

neuroscience↗