Biology subjects

Hananeia, N.

Publications and source records attributed to Hananeia, N..

3 recordsLinked to original sources

bioRxiv2024

Multi-scale modelling of location- and frequency-dependent synaptic plasticity induced by transcranial magnetic stimulation in the dendrites of pyramidal neurons

BackgroundRepetitive transcranial magnetic stimulation (rTMS) induces long-term changes of synapses, but the mechanisms behind these modifications are not fully understood. Al- though there has been progress in the development of multi-scale modeling tools, no com- prehensive module for simulating rTMS-induced synaptic plasticity in biophysically realistic neurons exists.. ObjectiveWe developed a modelling framework that allows the replication and detailed prediction of long-term changes of excitatory synapses in neurons stimulated by rTMS. MethodsWe implemented a voltage-dependent plasticity model that has been previously established for simulating frequency-, time-, and compartment-dependent spatio-temporal changes of excitatory synapses in neuronal dendrites. The plasticity model can be incorporated into biophysical neuronal models and coupled to electrical field simulations. ResultsWe show that the plasticity modelling framework replicates long-term potentiation (LTP)-like plasticity in hippocampal CA1 pyramidal cells evoked by 10-Hz repetitive magnetic stimulation (rMS). This plasticity was strongly distance dependent and concentrated at the proximal synapses of the neuron. We predicted a decrease in the plasticity amplitude for 5 Hz and 1 Hz protocols with decreasing frequency. Finally, we successfully modelled plasticity in distal synapses upon local electrical theta-burst stimulation (TBS) and predicted proximal and distal plasticity for rMS TBS. Notably, the rMS TBS-evoked synaptic plasticity exhibited robust facilitation by dendritic spikes and low sensitivity to inhibitory suppression. ConclusionThe plasticity modelling framework enables precise simulations of LTP-like cellular effects with high spatio-temporal resolution, enhancing the efficiency of parameter screening and the development of plasticity-inducing rTMS protocols. HighlightsO_LIFirst rigorously validated model of TMS-induced long-term synaptic plasticity in ex- tended neuronal dendrites that goes beyond point-neuron and mean-field modelling C_LIO_LIRobust simulations of experimental data on LTP-like plasticity in the proximal dendrites of CA1 hippocampal pyramidal cells evoked by 10 Hz repetitive magnetic stimulation (rMS) C_LIO_LIReplication of distal synaptic plasticity for a local electrical theta burst stimulation (TBS) protocol C_LIO_LIPrediction of distal and proximal LTP-like plasticity for rMS TBS C_LIO_LI1 Hz rMS does not induce long-term depression C_LI

neuroscience↗

bioRxiv2023

Axon morphology and intrinsic cellular properties determine repetitive transcranial magnetic stimulation threshold for plasticity

Repetitive transcranial magnetic stimulation (rTMS) is a widely used therapeutic tool in neurology and psychiatry, but its cellular and molecular mechanisms are not fully understood. Standardizing stimulus parameters, specifically electric field strength and direction, is crucial in experimental and clinical settings. It enables meaningful comparisons across studies and facilitating the translation of findings into clinical practice. However, the impact of biophysical properties inherent to the stimulated neurons and networks on the outcome of rTMS protocols remains not well understood. Consequently, achieving standardization of biological effects across different brain regions and subjects poses a significant challenge. This study compared the effects of 10 Hz repetitive magnetic stimulation (rMS) in entorhino-hippocampal tissue cultures from mice and rats, providing insights into the impact of the same stimulation protocol on similar neuronal networks under standardized conditions. We observed the previously described plastic changes in excitatory and inhibitory synaptic strength of CA1 pyramidal neurons in both mouse and rat tissue cultures, but a higher stimulation intensity was required for the induction of rMS-induced synaptic plasticity in rat tissue cultures. Through systematic comparison of neuronal structural and functional properties and computational modeling, we found that morphological parameters of CA1 pyramidal neurons alone are insufficient to explain the observed differences between the groups. However, axon morphologies of individual cells played a significant role in determining activation thresholds. Notably, differences in intrinsic cellular properties were sufficient to account for the 10 % higher intensity required for the induction of synaptic plasticity in the rat tissue cultures. These findings demonstrate the critical importance of axon morphology and intrinsic cellular properties in predicting the plasticity effects of rTMS, carrying valuable implications for the development of computer models aimed at predicting and standardizing the biological effects of rTMS.

neuroscience↗

bioRxiv2020

Multi-scale Modeling Toolbox for Single Neuron and Subcellular Activity under (repetitive) Transcranial Magnetic Stimulation

Transcranial Magnetic Stimulation (TMS) is a non-invasive brain stimulation technique widely used in research and clinical applications. However, its mechanism of action and the neural response to TMS are still poorly understood. Multi-scale modeling can complement experimental research and provide a framework between the physical input parameters and the subcellular neural effects of TMS. At the macroscopic level, sophisticated numerical models exist to estimate the induced electric fields in whole-brain volume conductor models. However, multi-scale computational modeling approaches to predict TMS cellular and subcellular responses, crucial to understanding TMS plasticity inducing protocols, are not available so far. Here, we develop a multi-scale Neuron Modeling for TMS toolbox (NeMo-TMS) that enables researchers to easily generate accurate neuron models from morphological reconstructions, couple them to the external electric fields induced by TMS, and to simulate the cellular and subcellular responses of the neurons. Both single-pulse and rTMS protocols can be simulated and results visualized in 3D. We openly share our toolbox and provide example scripts and datasets for the user to explore. NeMo-TMS toolbox (https://github.com/OpitzLab/NeMo-TMS) allows researchers a previously not available level of detail and precision in realistically modeling the physical and physiological effects of TMS.

neuroscience↗