Biology subjects

Booth, V.

Publications and source records attributed to Booth, V..

15 recordsLinked to original sources

bioRxiv2026

Rapid switching of in-phase and anti-phase interhemispheric rhythms within a single theta cycle: a mechanistic explanation

The granular retrosplenial cortex (RSG) supports memory consolidation and spatial navigation. In vivo recordings of local field potential (LFP) from RSG reveal coexisting gamma (30-80 Hz) and spline (100-160 Hz) oscillations, coupled to distinct phases of the background theta ([~]8 Hz) rhythm. Remarkably, bilateral recordings reveal that gamma and splines exhibit divergent interhemispheric phase relationships: gamma is in-phase across hemispheres, while splines are anti-phase. Here, using numerical bifurcation analysis of next-generation neural mass models, we show that RSG oscillations within a single hemisphere can be understood as alternation of PING (pyramidal-interneuron gamma) and ING (interneuron gamma) mechanisms in an excitatory-inhibitory network. Theta-frequency external drive to the inhibitory population switches the network between these two rhythms. Next, we build canonical interhemispheric circuits, composed of two such excitatory-inhibitory modules, delay-coupled across hemispheres. These circuits switch between in-phase gamma and anti-phase splines within a single theta cycle, reproducing the cross-spectral statistics of experimental RSG recordings. Finally, we present analytical results for threshold-linear neural mass models, showing cross-hemispheric phase patterning is governed by the ratio of cross-hemispheric delay to local inhibitory delay; we obtain explicit ranges of this ratio for which RSG-like patterning could be expected. Our work establishes that the unique oscillatory profile of RSG can be explained parsimoniously by minimal interhemispheric circuit configurations and provides a framework for experimental interrogation of interhemispheric rhythms.

neuroscience↗

bioRxiv2026

A dynamical circuit model for C. elegans chemotaxis with emergent sharp turns

With its rigorously characterized connectome, the nematode Caenorhabditis elegans is a powerful model organism to study the fundamental roles of neuronal circuits in behavior. However, despite the breadth of research, many questions remain unanswered regarding how these organisms are able to successfully navigate their environment. Here, we present a biologically grounded dynamical circuit model for the investigation of sensory-guided behavior during C. elegans chemotaxis. Our model consists of the chemosensory neuron AWA, interneurons RIM and RIA, motor neurons, including SMDs and RMDs, and body wall muscles that provide proprioceptive feedback through stretch receptors. The connectivity prioritizes functional and dynamical correspondence with the biological circuit over one-to-one anatomical fidelity. After optimization with an evolutionary algorithm, the model locomotes toward a chemical attractant, effectively capturing nematode chemotactic behavior. A key emergent property of the model that contributes to successful chemotaxis is the ability to make sharp turns, which resemble the omega turns of living nematodes. The direction and magnitude of these turns depend on the phase of the ongoing locomotory cycle. The sharp turning behavior is triggered by decreases in the concentration of the attractant. In the model, decreases in attractant concentration levels reduce AWA activity, which in turn triggers disinhibition of RIM activity and subsequent changes in RIA oscillatory activity. The ensuing coordinated changes in downstream motor neurons' activity patterns produce sharp turns, which correct the model worm's path to head toward the attractant source and, after reaching the concentration gradient peak, allow the model worm to remain in its proximity. The proposed framework and its emergent dynamics provide new insights into how circuit-level dynamics may generate key features of C. elegans chemotactic behavior, including omega-like turns. In parallel, it generates experimentally testable hypotheses about how the participating neuronal elements contribute to chemotactic behavior.

neuroscience↗

bioRxiv2026

Impacts of heminode disruption on auditory processing of noisy sound stimuli

Hidden hearing loss (HHL) is an auditory neuropathy characterized by altered auditory nerve responses despite normal hearing thresholds. Recent experimental and computational studies suggest that permanent disruptions to heminode positions in spiral ganglion neuron (SGN) fibers can contribute to these deficits. However, the interaction between heminode disruption and noisy backgrounds ubiquitous in daily listening remains unexplored. This study investigates how background noise affects auditory processing with these peripheral disorders and how deficits propagate to downstream sound localization circuits in the superior olivary complex. We developed computational models of SGN fibers with mild and severe degrees of heminode disruption, subjected to sinusoidal tone stimuli in the presence of background noise with varying spectral characteristics. We analyzed the phase-locking of SGN fiber responses to the stimulus tone and modeled the subsequent effects on interaural time difference (ITD) sensitivity in the medial superior olive (MSO) using a binaural localization network. We found that near-tone-frequency noise disrupted SGN phase locking through cycle-to-cycle variability in spike phases, with effects consistent across tone frequencies. Mild heminode disruption produced frequency-dependent degradation in SGN phase locking, with effects observed only at higher frequencies tested (600-1000 Hz), without reducing overall firing rates. Critically, the effects of noise and heminode disruption were additive, with combined exposure leading to reduced ITD sensitivity and large temporal fluctuations in MSO responses. Severe heminode disruption, which additionally reduced firing rates at the SGN fibers and subsequent stages, produced profound localization deficits across all frequencies tested. Thus, our model results suggest that noisy environments exacerbate auditory deficits from peripheral disorders implicated in HHL and could potentially impair speech intelligibility through degradation in localization ability. This model may be useful for understanding the downstream impacts of SGN neuropathies.

neuroscience↗

bioRxiv2024

Equity in Action: A Four-Year journey towards Gender Parity and Racial Diversity in Biochemistry Hiring

Recruitment of faculty members in academic departments shapes the department for decades in both research and teaching arenas. Having a diverse department is beneficial for undergraduate and graduate students as representation of underrepresented minority groups in the professoriate can inspire a greater diversity of students to pursue higher levels of education or research-focused careers. Increased diversity benefits research directly as diverse teams have been shown to have better ideas and outcomes. In 2020, our department had lower gender diversity than would be expected based on the pool of PhD students and post-doctoral fellows in Canada. Therefore, we altered our hiring process, primarily by redacting applications, for recruitment into entry-level tenure-track faculty positions. With this change in process, female hires increased from 17% in the previous ten years (5 hires) to 80% in the subsequent four years (5 hires) with no substantial change in hiring of racially diverse individuals (50% to 40%). Overall, combined with retirements, the percentage of female faculty in the department went from 25% to 50% and the percentage of racialized faculty went from 38% to 44%. The new hires have met or exceeded expectations of success with respect to grant funding and are on track to meet or exceed expectations for other metrics of success. Thus, our intervention was very successful in increasing the diversity of our department within a short timeframe. We believe that our experience could provide other departments with a template for making substantive change, even in the absence of internal expertise in the area. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=99 SRC="FIGDIR/small/617211v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@164c310org.highwire.dtl.DTLVardef@42585borg.highwire.dtl.DTLVardef@1b6f6eaorg.highwire.dtl.DTLVardef@10ca067_HPS_FORMAT_FIGEXP M_FIG C_FIG

scientific communication and education↗

bioRxiv2024

Mechanisms for dysregulation of excitatory-inhibitory balance underlying allodynia in dorsal horn neural subcircuits

Chronic pain is a wide-spread condition that is debilitating and expensive to manage, costing the United States alone around $600 billion in 2010. In a common type of chronic pain called allodynia, non-painful stimuli produce painful responses with highly variable presentations across individuals. While the specific mechanisms remain unclear, allodynia is hypothesized to be caused by the dysregulation of excitatory-inhibitory (E-I) balance in pain-processing neural circuitry in the dorsal horn of the spinal cord. In this work, we analyze biophysically-motivated subcircuit structures that represent common motifs in neural circuits in layers I-II of the dorsal horn. These circuits are hypothesized to be part of the neural pathways that mediate two different types of allodynia: static and dynamic. We use neural firing rate models to describe the activity of populations of excitatory and inhibitory interneurons within each subcircuit. By accounting for experimentally-observed responses under healthy conditions, we specify model parameters defining populations of subcircuits that yield typical behavior under normal conditions. Then, we implement a sensitivity analysis approach to identify the mechanisms most likely to cause allodynia-producing dysregulation of the subcircuits E-I signaling. We find that disruption of E-I balance generally occurs either due to downregulation of inhibitory signaling so that excitatory neurons are "released" from inhibitory control, or due to upregulation of excitatory neuron responses so that excitatory neurons "escape" their inhibitory control. Which of these mechanisms is most likely to occur, the subcircuit components involved in the mechanism, and the proportion of subcircuits exhibiting the mechanism can vary depending on the subcircuit structure. These results suggest specific hypotheses about diverse mechanisms that may be most likely responsible for allodynia, thus offering predictions for the high interindividual variability observed in allodynia and identifying targets for further experimental studies on the underlying mechanisms of this chronic pain condition. Author summaryWhile chronic pain affects roughly 20% of the US adult population [1], symptoms and presentations of the condition are highly variable across individuals and its causes remain largely unknown. A prevailing hypothesis for the cause of a type of chronic pain called allodynia is that the balance between excitatory and inhibitory signaling pathways between neuron populations in the spinal cord dorsal horn may be disrupted. To help better understand neural mechanisms underlying allodynia, we analyze biologically-motivated mathematical models of subcircuits of neuron populations that are part of the pain processing signaling pathway in the dorsal horn of the spinal cord. We use a novel sensitivity analysis approach to identify mechanisms of subcircuit dysregulation that may contribute to two different types of allodynia. The model results identify specific subcircuit components that are most likely to contribute to each type of allodynia. These mechanisms suggest targets for further experimental study, as well as for pharmacological intervention for better pain treatments.

neuroscience↗

bioRxiv2022

Mapping the Spatial Dynamics of the Human Oral Mucosa in Chronic Inflammatory Disease

The interplay among different cells in a tissue is essential for maintaining homeostasis. Although, disease states have been traditionally attributed to individual cell types, increasing evidence and new therapeutic options have demonstrated the primary role of multicellular functions to understand health and disease, opening new avenues to understand pathogenesis and develop new treatment strategies. We recently described the cellular composition and dynamics of the human oral mucosa; however, the spatial arrangement of cells is needed to better understand a morphologically complex tissue. Here, we link single-cell RNA sequencing, spatial transcriptomics, and high-resolution multiplex fluorescence in situ hybridisation to characterise human oral mucosa in health and oral chronic inflammatory disease. We deconvolved expression for resolution enhancement of spatial transcriptomic data and defined highly specialised epithelial and stromal compartments describing location-specific immune programs. Furthermore, we spatially mapped a rare pathogenic fibroblast population localised in a highly immunogenic region, responsible for lymphocyte recruitment through CXCL8 and CXCL10 and with a possible role in pathological angiogenesis through ALOX5AP. Collectively, our study provides a comprehensive reference for the study of oral chronic disease pathogenesis.

cell biology↗

bioRxiv2021

Modeling Synaptic Effects of Anesthesia and its Cortical Cholinergic Reversal

General anesthetics work through a variety of molecular mechanisms while resulting in the common end point of sedation and loss of consciousness. Generally, the administration of common inhalation anesthetics induces decreases in synaptic excitation while promoting synaptic inhibition. Animal studies have shown that, during anesthesia, exogenously induced increases in acetylcholine-mediated effects in the brain can elicit wakeful-like behavior despite the continued presence of the anesthetic. Less investigated, however, is the question of whether the brains electrophysiological activity is also restored to pre-anesthetic levels and quality by such interventions. Here we apply a computational model of a network composed of excitatory and inhibitory neurons to simulate the network effects of changes in synaptic inhibition and excitation due to anesthesia and its reversal by muscarinic receptor-mediated cholinergic effects. We use a differential evolution algorithm to fit model parameters to match measures of spiking activity, neuronal connectivity, and network dynamics recorded in the visual cortex of rodents during anesthesia with desflurane in vivo. We find that facilitating muscarinic receptor effects of acetylcholine on top of anesthetic-induced synaptic changes predicts reversal of the neurons spiking activity, functional connectivity, as well as pairwise and population interactions. Thus, our model results predict a possible neuronal mechanism for the induced reversal of the effects of anesthesia on post synaptic potentials, consistent with experimental behavioral observations. Author SummaryHere, we apply a computational model of a network composed of excitatory and inhibitory neurons to simulate the network effects of changes in synaptic inhibition and excitation due to anesthesia and we investigate the possibility of its reversal by muscarinic receptor-mediated cholinergic effects. Specifically, we use a differential evolution algorithm to fit model parameters to match dynamics recorded in the visual cortex of rodents during anesthesia with desflurane in vivo. We find that changes of the fitted synaptic parameters in response to the increasing desflurane concentration matched those established by neurophysiology. Further, our results demonstrate that the cellular effects induced by anesthesia can be mitigated by the changes in cellular excitability due to acetylcholine.

neuroscience↗

bioRxiv2021

Conjugates for use in peptide therapeutics: a systematic review and meta-analysis

While peptides can be excellent therapeutics for several conditions, their limited in vivo half-lives have been a major bottleneck in the development of therapeutic peptides. Conjugating the peptide to an inert chemical moiety is a strategy that has repeatedly proven to be successful in extending the half-life of some therapeutics. This systematic review and meta-analysis was conducted to examine the available literature and assess it in an unbiased manner to determine which conjugates, both biological and synthetic, provide the greatest increase in therapeutic peptide half-life. Systematic searches run on PubMed, Scopus and SciFinder databases resulted in 845 studies pertaining to the topic, 16 of these were included in this review after assessment against pre-specified inclusion criteria registered on PROSPERO (#CRD42020222579). The most common reasons for exclusion were non-IV administration and large peptide size. Of the 16 studies that were included, a diverse suite of conjugates that increased half-life from 0.1 h to 33.57 h was identified. Amongst these peptides, the largest increase in half-life was seen when conjugated with glycosaminoglycans. A meta-analysis of studies that contained fatty acid conjugates indicated that acylation contributed to a statistically significant extension of half-life. Additionally, another meta-analysis followed by a sensitivity analysis suggested that conjugation with specifically engineered recombinant peptides might contribute to a more efficient extension of peptide half-life as compared to PEGylation. Moreover, we confirmed that while polyethylene glycol is a good synthetic conjugate, its chain length likely has an impact on its effectiveness in extending half-life. Furthermore, we found that most animal studies do not include as much detail when reporting findings as compared to human studies. Inclusion of additional experimental detail on aspects such as independent assessment and randomization may be an easily accomplished strategy to drive more conjugated peptides towards clinical studies.

pharmacology and toxicology↗

bioRxiv2021

Personalized sleep-wake patterns aligned with circadian rhythm relieve daytime sleepiness

Shift workers and many other groups experience irregular sleep-wake patterns. This can induce excessive daytime sleepiness that decreases productivity and elevates the risk of accidents. However, the degree of daytime sleepiness is not correlated with standard sleep parameters like total sleep time, suggesting other factors are involved. Here, we analyze real-world sleep-wake patterns of shift workers measured by wearables with a newly developed user-friendly computational package that simulates homeostatic sleep pressure - the physiological need for sleep - and the circadian rhythm. This reveals that shift workers who align sleep-wake patterns with their circadian rhythm have lower daytime sleepiness, even if they sleep less. The alignment, quantified by a new parameter, circadian sleep sufficiency, can be increased by dynamically adjusting daily sleep durations according to varying bedtimes. Our computational package provides flexible and personalized real-time sleep-wake patterns for individuals to reduce their daytime sleepiness and could be used with wearable devices to develop smart alarms.

systems biology↗

bioRxiv2020

Contrasting mechanisms for hidden hearing loss: synaptopathy vs myelin defects

Hidden hearing loss (HHL) is an auditory neuropathy characterized by normal hearing thresholds but reduced amplitude of the sound-evoked auditory nerve compound action potential (CAP). It has been proposed that in humans HHL leads to speech discrimination and intelligibility deficits, particularly in noisy environments. Animal models originally indicated that HHL can be caused by moderate noise exposures or aging, and that loss of inner hair cell (IHC) synapses could be its cause. A recent study provided evidence that transient loss of cochlear Schwann cells also causes permanent auditory deficits in mice which have characteristics of HHL. Histological analysis of the cochlea after auditory nerve remyelination showed a permanent disruption of the myelination patterns at the heminode of type I spiral ganglion neuron (SGN) peripheral terminals, suggesting that this defect could be contributing to HHL. To shed light on the mechanisms of different HHL scenarios and to test their impact on type I SGN activity, we constructed a reduced biophysical model for a population of SGN peripheral axons. We found that the amplitudes of simulated sound-evoked SGN CAPs are lower and have greater latencies when the heminodes are disorganized, i.e. they are placed at different distances from the hair cell rather than at the same distance as seen in the normal cochlea. Thus, our model confirms that disruption of the position of the heminode causes desynchronization of SGN spikes leading to a loss of temporal resolution and reduction of the sound-evoked SGN CAP. We also simulated synaptopathy by removing high threshold IHC-SGN synapses and found that the amplitude of simulated sound-evoked SGN CAPs decreases while latencies remain unchanged, corresponding to what has been observed in noise exposed animals. This model can be used to further study the effects of synaptopathy or demyelination on auditory function. Author summaryHidden hearing loss is an auditory disorder caused by noise exposure, aging or peripheral neuropathy which is estimated to affect 12-15% of the worlds population. It is a hidden disorder because subjects have normal hearing thresholds, i.e., the condition cannot be revealed by standard audiological tests, but they report difficulties in understanding speech in noisy environments. Studies on animal models suggest two possible pathogenic mechanisms for hidden hearing loss: (1) loss of synapses between inner hair cells and auditory nerve fibers, and (2) disruption of auditory-nerve myelin. In this study, we constructed a computational model of sound-evoked auditory neuron fiber activity and auditory nerve compound action potential to understand how each one of these mechanisms affects nerve transmission. We show that disruption of auditory-nerve myelin desynchronizes sound-evoked auditory neuron spiking, decreasing the amplitude and increasing the latency of the compound action potential. In addition, elongation of the initial axon segment may cause spike generation failure leading to decreased spiking probability. In contrast, the effect of synapse loss is only to decrease the probability of firing, thus reducing the compound action potential amplitude without disturbing its latency. This model, which accurately represents the in vivo findings, could be useful to make further predictions on the consequences of HHL and extend it to explore the impact of synaptopathy and myelinopathy on hearing.

neuroscience↗

bioRxiv2020

Defining human mesenchymal and epithelial heterogeneity in response to oral inflammatory disease

Human oral soft tissues provide the first barrier of defence against chronic inflammatory disease and hold a remarkable scarless wounding phenotype. Tissue homeostasis requires coordinated actions of epithelial, mesenchymal and immune cells. However, the extent of heterogeneity within the human oral mucosa and how tissue cell types are affected during the course of disease progression is unknown. Using single cell transcriptome profiling we reveal a striking remodelling of the epithelial and mesenchymal niches with a decrease in functional populations that are linked to the aetiology of the disease. Analysis of ligand-receptor interaction pairs identify potential intercellular hubs driving the inflammatory component of the disease. Our work establishes a reference map of the human oral mucosa in health and disease, and a framework for the development of new therapeutic strategies.

cell biology↗

bioRxiv2020

Spatially localized cluster solutions in inhibitory neural networks

Neurons in the inhibitory network of the striatum display cell assembly firing patterns which recent results suggest may consist of spatially compact neural clusters. Previous computational modeling of striatal neural networks has indicated that non-monotonic, distance-dependent coupling may promote spatially localized cluster firing. Here, we identify conditions for the existence and stability of cluster firing solutions in which clusters consist of spatially adjacent neurons in inhibitory neural networks. We consider simple non-monotonic, distance-dependent connectivity schemes in weakly coupled 1-D networks where cells make strong connections with their kth nearest neighbors on each side. Using the phase model reduction of the network system, we prove the existence of cluster solutions where neurons that are spatially close together are also synchronized in the same cluster, and find stability conditions for these solutions. Our analysis predicts the long-term behavior for networks of neurons, and we confirm our results by numerical simulations of biophysical neuron network models. Additionally, we add weaker coupling between closer neighbors as a perturbation to our network connectivity. We analyze the existence and stability of cluster solutions of the perturbed network and validate our results with numerical simulations. Our results demonstrate that an inhibitory network with non-monotonic, distance-dependent connectivity can exhibit cluster solutions where adjacent cells fire together.

neuroscience↗

bioRxiv2017

Investigating circadian rhythmicity in pain sensitivity using a neural circuit model for spinal cord processing of pain

Primary processing of painful stimulation occurs in the dorsal horn of the spinal cord. In this article, we introduce mathematical models of the neural circuitry in the dorsal horn responsible for processing nerve fiber inputs from noxious stimulation of peripheral tissues and generating the resultant pain signal. The differential equation models describe the average firing rates of excitatory and inhibitory interneuron populations, as well as the wide dynamic range (WDR) neurons whose output correlates with the pain signal. The temporal profile of inputs on the different afferent nerve fibers that signal noxious and innocuous stimulation and the excitability properties of the included neuronal populations are constrained by experimental results. We consider models for the spinal cord circuit in isolation and when top-down inputs from higher brain areas that modulate pain processing are included. We validate the models by replicating experimentally observed phenomena of A fiber inhibition of pain and wind-up. We then use the models to investigate mechanisms for the observed phase shift in circadian rhythmicity of pain that occurs with neuropathic pain conditions. Our results suggest that changes in neuropathic pain rhythmicity can occur through dysregulation of inhibition within the dorsal horn circuit.

synthetic biology↗

bioRxiv2017

The Modulation of Pain by Circadian and Sleep-Dependent Processes: A Review of the Experimental Evidence

This proceedings paper is the first in a series of three papers developing mathematical models for the complex relationship between pain and the sleep-wake cycle. Here, we briefly review what is known about the relationship between pain and the sleep-wake cycle in humans and laboratory rodents in an effort to identify constraints for the models. While it is well accepted that sleep behavior is regulated by a daily (circadian) timekeeping system and homeostatic sleep drive, the joint modulation of these two primary biological processes on pain sensitivity has not been considered. Under experimental conditions, pain sensitivity varies across the 24 h day, with highest sensitivity occurring during the evening in humans. Pain sensitivity is also modulated by sleep behavior, with pain sensitivity increasing in response to the build up of homeostatic sleep pressure following sleep deprivation or sleep disruption. To explore the interaction between these two biological processes using modeling, we first compare the magnitude of their effects across a variety of experimental pain studies in humans. To do this comparison, we normalize the results from experimental pain studies relative to the range of physiologicallymeaningful stimulation levels. Following this normalization, we find that the estimated impact of the daily rhythm and of sleep deprivation on experimental pain measurements is surprisingly consistent across different pain modalities. We also review evidence documenting the impact of circadian rhythms and sleep deprivation on the neural circuitry in the spinal cord underlying pain sensation. The characterization of sleep-dependent and circadian influences on pain sensitivity in this review paper is used to develop and constrain the mathematical models introduced in the two companion articles.

neuroscience↗

bioRxiv2017

A Two-Process Model for Circadian and Sleep-dependent Modulation of Pain Sensitivity

Pain sensitivity is strongly modulated by time of day and by prior sleep behavior. These two factors, governed by the circadian rhythm and homeostatic sleep drive, respectively, likewise dictate the timing and duration of sleep. The fields of sleep and circadian research have identified much of the physiology underlying the circadian rhythm and homeostatic sleep drive with mathematical modeling playing an important role in understanding how these two processes interact to affect sleep behavior. We hypothesize that the daily rhythm of pain sensitivity and its sleep-dependent modulation reflect an interaction of the circadian rhythm and homeostatic sleep drive. To investigate this hypothesis, we adapt the formalism of a classic mathematical model for the regulation of sleep behavior by the circadian rhythm and homeostatic sleep drive, called the Two Process model, to simulate the interaction of these two processes on pain sensitivity. To construct the model, we utilize data from experimental reports on the daily rhythmicity of pain sensitivity in humans to define a \"daily pain sensitivity\" function. We decompose this function into two processes: a sleep-dependent process S(t) that follows the homeostatic sleep drive and a circadian process C(t) that is dictated by the circadian rhythm. By simulating different sleep schedules with the original Two Process model, we compute changes in the sleep-dependent process S(t) that modulates pain sensitivity. By combining S(t) with the circadian process C(t), our model predicts resultant changes in the daily pain sensitivity rhythm. We illustrate model predictions for changes in pain sensitivity due to sleep deprivation, sleep restriction and shift work schedules. We believe that this model may be a useful tool for pain management by providing predictions of the variations in pain sensitivity due to changing sleep schedules.

neuroscience↗