Biology subjects

Butler, J. S.

Publications and source records attributed to Butler, J. S..

5 recordsLinked to original sources

bioRxiv2026

Assessing the Clinical Utility of Finite Element Analysis Using Post-operative CT-Derived Models: A Material Comparison of Multi-level Spinal Fusion Constructs

Successful instrumented fusion of the lumbar spine is a complex surgical challenge, with positive patient outcomes dependent on careful surgical planning. Material selection is of critical importance to a mechanical construct supporting successful spinal fusion. Therefore, the aims of this study were to (a) evaluate the potential clinical use of finite element analysis (FEA) and (b) conduct a retrospective mechanical analysis of different implant materials in patients having undergone spinal fusion using FEA. Our methodology involved segmenting the spine from post-operative computed tomography (CT) image data from patients with previous spinal fusion. FEA models representing post-surgery cases were developed and different biomechanical loading conditions such as compression, flexion, bending and extension whilst testing pedicle screws of different materials were simulated. Patient specific finite element models were created, and biomechanical analysis were completed for all three patients. Polyetheretherketone (PEEK) constructs typically demonstrated lower peak implant stress when compared to titanium constructs for all spinal fusion levels. Furthermore, increasing the spinal fusion level resulted in significant differences in the maximum von Mises stress within both the bone and the instrumentation, whereas the 2-level fusion exhibited comparable stress levels in the bone irrespective of the instrumentation material. This pilot explores the potential of FEA as a clinical tool for assessing device and bone stresses. In our cohort, different materials can influence the stresses in both the instrumentation and the instrumented vertebrae, suggesting FEA can be useful pre- operative tool with regards to instrument selection and post-operatively to assess instrumentation and bone stresses. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=74 SRC="FIGDIR/small/742715v1_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@1399da7org.highwire.dtl.DTLVardef@19d73e2org.highwire.dtl.DTLVardef@2337c8org.highwire.dtl.DTLVardef@232c90_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2025

Modelling Audio-Visual Reaction Time with Recurrent Mean-Field Networks

Understanding how the brain integrates multisensory information during detection and decision-making remains an active area of research. While many inferences have been drawn about behavioural outcomes, key questions persist regarding both the nature of environmental cues and the internal mechanisms of integration. These complexities make multisensory integration particularly well suited to investigate through mathematical modelling. In this study, we present three models of audio-visual integration within a biologically motivated mean-field recurrent framework. These models extend a non-linear system of differential equations originally developed for unisensory decision-making. The OR and SUM models represent opposing ends of the integration spectrum: the former simulates independent unisensory processing using a winner-take-all (WTA) strategy, while the latter implements a linear summation model for full integration. A third model--the REPEAT Model--incorporates switch and repeat costs observed in multisensory tasks. We simulate 121 participants with varying unisensory evidence accumulation rates, capturing behavioural diversity from modality dominance to balanced integration. Model outputs (reaction time and accuracy) were compared with empirical results from audio-visual detection tasks. We further fit the outputs to a drift diffusion model, allowing comparison between simulated and theoretically optimal multisensory drift rates. The OR and SUM Models reproduced established unisensory response patterns. Drift diffusion analysis revealed suboptimal integration in the OR Model and optimal integration in the SUM Model. However, the SUM Model also produced supra-optimal responses under certain conditions, inconsistent with behavioural data. The REPEAT Model successfully captured the role of priming in sensory repetition effects, distinguishing it from true multisensory integration. Overall, these models highlight how biologically grounded mathematical frameworks can shed light on the mechanisms underlying multisensory integration, particularly the nuanced contributions of modality repetition and integration efficiency.

neuroscience↗

bioRxiv2023

Signatures of time interval reproduction in the human electroencephalogram (EEG).

Accurate timing is essential for coordinating our actions in everyday tasks such as playing music and sport. Although an extensive body of research has examined the human electrophysiological signatures underpinning timing, the specific dynamics of these signals remain unclear. Here, we recorded electroencephalography (EEG) while participants performed a variant of a time interval reproduction task that has previously been administered to macaques, and examined how task performance was predicted by the dynamics of three well known EEG signals: limb-selective motor preparation in the mu/beta band (8-30Hz), the Contingent Negative Variation (CNV) and the Centro-Parietal Positivity (CPP) evidence accumulation signal. In close correspondence with single unit recordings in macaques, contralateral mu/beta signals indicated that participants reproduced intervals by adjusting the starting level and build-up rate of motor preparation to reach a response triggering threshold at the desired time. The CNV showed a highly similar pattern with the exception that its pre-response amplitude was increased for faster reproductions. This pattern of results suggests that, rather than tracing a veridical temporal accumulator as had been suggested in earlier work, the CNV more closely resembles a dynamic anticipatory signal. In contrast, the CPP did not exhibit any relationship with reproduction time suggesting that the evidence accumulation processes guiding perceptual decisions are not involved in generating representations of elapsed time. Our findings highlight close similarities in the dynamics exhibited by intracranial and non-invasive motor preparation signals during interval reproduction while indicating that the CNV traces a functionally distinct process whose precise role remains to be understood.

neuroscience↗

bioRxiv2022

Neural correlates of multisensory enhancement in audiovisual narrative speech perception: a fMRI investigation

This fMRI study investigated the effect of seeing articulatory movements of a speaker while listening to a naturalistic narrative stimulus. It had the goal to identify regions of the language network showing multisensory enhancement under synchronous audiovisual conditions. We expected this enhancement to emerge in regions known to underlie the integration of auditory and visual information such as the posterior superior temporal gyrus as well as parts of the broader language network, including the semantic system. To this end we presented 53 participants with a continuous narration of a story in auditory alone, visual alone, and both synchronous and asynchronous audiovisual speech conditions while recording brain activity using BOLD fMRI. We found multisensory enhancement in an extensive network of regions underlying multisensory integration and parts of the semantic network as well as extralinguistic regions not usually associated with multisensory integration, namely the primary visual cortex and the bilateral amygdala. Analysis also revealed involvement of thalamic brain regions along the visual and auditory pathways more commonly associated with early sensory processing. We conclude that under natural listening conditions, multisensory enhancement not only involves sites of multisensory integration but many regions of the wider semantic network and includes regions associated with extralinguistic sensory, perceptual and cognitive processing.

neuroscience↗

bioRxiv2019

Dissociable neural circuits underlie the resolution of three discrete sources of competition during task-switching.

Humans perform sub-optimally when juggling more than one task, but are nonetheless required to multitask during many daily activities. Rapidly and effectively switching attentional focus between tasks is fundamental to navigating complex environments. Task-switching paradigms in conjunction with neuroimaging have identified brain networks underpinning flexible reallocation of cognitive resources and a core network of neural regions is repeatedly implicated (i.e., posterior parietal, inferior frontal, anterior cingulate, and middle frontal cortex). Performance costs such as reduced accuracy and slowed responses accompany the first execution of a task following a task-switch. These costs stem from three main sources of competition: 1) the need to reconfigure task-rules, 2) the immediate history of motor responding, and 3) whether inputs to be acted upon provide congruent or incongruent information regarding the appropriate motor response, relative to the recently \"switched-away-from\" task. Here, we asked whether both common (domain-general) and non-overlapping (dissociable) neural circuits were involved in resolving these three distinct sources of competition under high-demand task-switching conditions. Dissociable neural circuits were active in resolving each of the three sources of competition. No domain-general regions were implicated in all three. Rather, two regions were common across rule-switching and stimulus incongruence, and five regions to incongruence and response-switching. Each source of conflict elicited activation from many regions including the posterior cingulate, thalamus, and cerebellum, regions not commonly implicated in the task-switching literature. These results suggest that dissociable neural networks are principally responsible for resolving different sources of competition, but with partial interaction of some overlapping domain-general circuitry.

neuroscience↗