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Tailby, C.

Publications and source records attributed to Tailby, C..

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↗

Robust-tedana: An automated denoising pipeline for multi-echo fMRI data

Multi-echo functional Magnetic Resonance Imaging (fMRI) data are acquired by recording image volumes at multiple echo times and can be used to improve the separation of neural activity from noise. TE-Dependent ANAlysis (tedana) is an open-source software tailored to denoising of multi-echo fMRI data. The efficacy of denoising can however be inconsistent, often necessitating manual inspection that precludes its application in large-scale studies where processing is ideally fully automated. Here, we introduce Robusttedana, an optimised denoising pipeline that achieves adequate results at both single-subject and group level. Robust-tedana incorporates Marchenko-Pastur Principal Component Analysis (MPPCA) for effective thermal noise reduction, robust independent component analysis for stabilised signal decomposition, and a modified component classification process. We evaluated its performance on Multi-Band Multi-Echo (MBME) language-task fMRI data from the Australian Epilepsy Project (AEP) using objective measures, comparing to conventional fMRI analysis with and without multi-echo-based denoising. Experts manual evaluation was undertaken on a subset of these data to validate the objective measures. The proposed pipeline both mitigates the prevalence of erroneous attenuation of genuine task activation due to instability of single-subject analysis, and increases the magnitude of group-wise effects. Robust-tedana therefore facilitates advanced analysis of MBME fMRI data in an automated pipeline, including for clinical research assessment of individuals.

bioengineering↗

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↗