Kumar Murty, V. | Search bioRxiv

bioRxiv2026

Unifying transcranial focused ultrasound and transcranial magnetic stimulation effects with calcium-dependent synaptic plasticity theory

Low-intensity transcranial focused ultrasound stimulation (TUS) is an emerging technology that shares features of both established invasive neurostimulation techniques such as deep brain stimulation (DBS) and noninvasive techniques such as transcranial magnetic stimulation (TMS). Like DBS, TUS can target non-superficial brain structures with millimeter-level precision. Like TMS, but unlike DBS, the most important physiological effect of TUS from a clinical perspective is its ability to induce lasting neuroplastic changes (long term potentiation/depression; LTP/LTD) from relatively short stimulation sessions. Thus follows the intriguing possibility that, although TUS and TMS have fundamentally different primary mechanisms of action -- acoustic versus electromagnetic -- they might nevertheless share a common secondary mechanism of plasticity induction through temporally patterned stimulation. A quantitative mathematical theory of this secondary mechanistic pathway could therefore have important explanatory and predictive value in both modalities. Two major challenges to the development of such a theory, however, are: i) experimental results showing contradictory plasticity effects between TUS and TMS for nominally similar stimulation parameters, and ii) the markedly different temporal structures of their stimulation waveforms (ranging from discrete pulses in TMS to continuous sinusoidal oscillations in TUS), even for highly aligned protocol designs such as continuous theta burst (cTB) stimulation. Here we show that a mathematical model of calcium-dependent synaptic plasticity in corticothalamic circuits, already developed extensively for TMS, can indeed provide such a unified description of stimulation effects across these two modalities. Numerical simulations using this model for a range of TUS and TMS protocols reproduced plasticity effects consistent with experimentally observed changes in cortical excitability. In particular, our model addresses both of the above challenges, by i) reconciling apparently contradictory results across modalities for the same stimulation parameters, and ii) introducing a simple algebraic approach, which we term the 'equivalent energy principle', for relating corresponding TMS and TUS waveforms. The ability of the model to account for differing effects across multiple stimulation modalities provides further support for the underlying general theory describing calcium-based regulation of stimulation plasticity effects, which spans multiple scales of system organization -- from ion channel kinetics to neural population activity. Our work also provides a foundation for future bidirectional exchange of new experimental observations and insights between experimental and theoretical TUS and TMS research, including strategies for model-based protocol optimization and discovery of novel plasticity-inducing TUS and TMS paradigms.

neuroscience↗

bioRxiv2023

Analysing the distribution of SARS-CoV-2 infections in schools: integrating model predictions with real world observations

School closures were used as strategies to mitigate transmission in the COVID-19 pandemic. Understanding the nature of SARS-CoV-2 outbreaks and the distribution of infections in classrooms could help inform targeted or precision preventive measures and outbreak management in schools, in response to future pandemics. In this work, we derive an analytical model of Probability Density Function (PDF) of SARS-CoV-2 secondary infections and compare the model with infection data from all public schools in Ontario, Canada between September-December, 2021. The model accounts for major sources of variability in airborne transmission like viral load and dose-response (i.e., the human bodys response to pathogen exposure), air change rate, room dimension, and classroom occupancy. Comparisons between reported cases and the modeled PDF demonstrated the intrinsic overdispersed nature of the real-world and modeled distributions, but uncovered deviations stemming from an assumption of homogeneous spread within a classroom. The inclusion of near-field transmission effects resolved the discrepancy with improved quantitative agreement between the data and modeled distributions. This study provides a practical tool for predicting the size of outbreaks from one index infection, in closed spaces such as schools, and could be applied to inform more focused mitigation measures. Author summaryAt the start of the COVID-19 pandemic, there was huge uncertainty around the risks of SARS-CoV-2 spread in classrooms. In the absence of early predictions surrounding classroom risks, many jurisdictions across countries closed in-person education. There is great interest in adopting a more precision approach to better inform future interventions in the context of airborne virus risks. For this purpose, we need tools that can predict the probability of the size of outbreaks within classrooms along with the impact of interventions including masks, better ventilation, and physical distancing by limiting the number of students per classroom. To this end, we have developed a robust but practical model that yields the probability of secondary infections stemming from index cases occurring within schools on a given day. During model development, the major underlying physical and biological factors that dictate the disease transmission process, both at long-range and close-range, have been accounted for. This enables our model to modify its predictions for different scenarios - and possibly allows its use beyond schools. Finally, the models predictive capability has been verified by comparing its outputs with publicly available data on SARS-CoV-2 diagnoses in Ontario public schools. To our knowledge, this is the first time an analytical model derived from mostly first principles describes real-world infection distributions, satisfactorily. The quantitative match between the theoretical prediction and real-world data offers the proposed model as a possible powerful tool for better-informed precision pandemic mitigation strategies in indoor environments like schools.

biophysics↗