Calcium dependent plasticity applied to repetitive transcranial magnetic stimulation with a neural field model

Finding synaptic couplings from a biophysical model of motor evoked potentials after theta-burst transcranial magnetic stimulation

OBJECTIVE

We aimed to use measured input-output (IO) data to identify the best fitting model for motor evoked potentials.

METHODS

We analyzed existing IO data before and after intermittent and continuous theta-burst stimulation (iTBS & cTBS) from a small group of subjects (18 for each). We fitted individual synaptic couplings and sensitivity parameters using variations of a biophysical model. A best performing model was selected and analyzed.

RESULTS

cTBS gives a broad reduction in MEPs for amplitudes larger than resting motor threshold (RMT). Close to threshold, iTBS gives strong potentiation. The model captures individual IO curves. There is no change to the population average synaptic weights post TBS but the change in excitatory-to-excitatory synaptic coupling is strongly correlated with the experimental post-TBS response relative to baseline.

CONCLUSIONS

The model describes population-averaged and individual IO curves, and their post-TBS change. Variation among individuals is accounted for with variation in synaptic couplings, and variation in sensitivity of neural response to stimulation.

SIGNIFICANCE

The best fitting model could be applied more broadly and validation studies could elucidate underlying biophysical meaning of parameters.

Transcranial magnetic stimulation as a tool to induce and explore plasticity in humans

Activity-dependent synaptic plasticity is the main theoretical framework to explain mechanisms of learning and memory. Synaptic plasticity can be explored experimentally in animals through various standardized protocols for eliciting long-term potentiation and long-term depression in hippocampal and cortical slices. In humans, several non-invasive protocols of repetitive transcranial magnetic stimulation and transcranial direct current stimulation have been designed and applied to probe synaptic plasticity in the primary motor cortex, as reflected by long-term changes in motor evoked potential amplitudes. These protocols mimic those normally used in animal studies for assessing long-term potentiation and long-term depression. In this chapter, we first discuss the physiologic basis of theta-burst stimulation, paired associative stimulation, and transcranial direct current stimulation. We describe the current biophysical and theoretical models underlying the molecular mechanisms of synaptic plasticity and metaplasticity, defined as activity-dependent changes in neural functions that modulate subsequent synaptic plasticity such as long-term potentiation (LTP) and long-term depression (LTD), in the human motor cortex including calcium-dependent plasticity, spike-timing-dependent plasticity, the role of N-methyl-d-aspartate-related transmission and gamma-aminobutyric-acid interneuronal activity. We also review the putative microcircuits responsible for synaptic plasticity in the human motor cortex. We critically readdress the issue of variability in studies investigating synaptic plasticity and propose available solutions. Finally, we speculate about the utility of future studies with more advanced experimental approaches.

Multi-scale modeling toolbox for single neuron and subcellular activity under Transcranial Magnetic Stimulation

BACKGROUND

Transcranial Magnetic Stimulation (TMS) is a widely used non-invasive brain stimulation method. However, its mechanism of action and the neural response to TMS are still poorly understood. Multi-scale modeling can complement experimental research to study the subcellular neural effects of TMS. At the macroscopic level, sophisticated numerical models exist to estimate the induced electric fields. 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.

OBJECTIVE

We develop an open-source multi-scale toolbox Neuron Modeling for TMS (NeMo-TMS) to address this problem.

METHODS

NeMo-TMS generates accurate neuron models from morphological reconstructions, couples them to the external electric fields induced by TMS, and simulates the cellular and subcellular responses of single-pulse and repetitive TMS.

RESULTS

We provide examples showing some of the capabilities of the toolbox.

CONCLUSION

NeMo-TMS toolbox allows researchers a previously not available level of detail and precision in realistically modeling the physical and physiological effects of TMS.

Modeling motor-evoked potentials from neural field simulations of transcranial magnetic stimulation

OBJECTIVE

To develop a population-based biophysical model of motor-evoked potentials (MEPs) following transcranial magnetic stimulation (TMS).

METHODS

We combined an existing MEP model with population-based cortical modeling. Layer 2/3 excitatory and inhibitory neural populations, modeled with neural-field theory, are stimulated with TMS and feed layer 5 corticospinal neurons, which also couple directly but weakly to the TMS pulse. The layer 5 output controls mean motoneuron responses, which generate a series of single motor-unit action potentials that are summed to estimate a MEP.

RESULTS

A MEP waveform was generated comparable to those observed experimentally. The model captured TMS phenomena including a sigmoidal input-output curve, common paired pulse effects (short interval intracortical inhibition, intracortical facilitation, long interval intracortical inhibition) including responses to pharmacological interventions, and a cortical silent period. Changes in MEP amplitude following theta burst paradigms were observed including variability in outcome direction.

CONCLUSIONS

The model reproduces effects seen in common TMS paradigms.

SIGNIFICANCE

The model allows population-based modeling of changes in cortical dynamics due to TMS protocols to be assessed in terms of changes in MEPs, thus allowing a clear comparison between population-based modeling predictions and typical experimental outcome measures.

Less Might Be More: Conduction Failure as a Factor Possibly Limiting the Efficacy of Higher Frequencies in rTMS Protocols

Introduction: rTMS has been proven effective in the treatment of neuropsychiatric conditions, with class A (definite efficacy) evidence for treatment of depression and pain (Lefaucheur et al., 2014). The efficacy in stimulation protocols is, however, quite heterogeneous. Saturation of neuronal firing by HFrTMS without allowing time for recovery may lead to neuronal response failures (NRFs) that compromise the efficacy of stimulation with higher frequencies.

Objectives: To examine the efficacy of different rTMS temporal stimulation patterns focusing on a possible upper stimulation limit related to response failures. Protocol patterns were derived from published clinical studies on therapeutic rTMS for depression and pain. They were compared with conduction failures in cell cultures.

Methodology: From 57 papers using protocols rated class A for depression and pain (Lefaucheur et al., 2014) we extracted Inter-train interval (ITI), average frequency, total duration and total number of pulses and plotted them against the percent improvement on the outcome scale. Specifically, we compared 10 Hz trains with ITIs of 8 s (protocol A) and 26 s (protocol B) in vitro on cultured cortical neurons.

Results: In the in vitro experiments, protocol A with 8-s ITIs resulted in more frequent response failures, while practically no response failures occurred with protocol B (26-s intervals). The HFrTMS protocol analysis exhibited no significant effect of ITIs on protocol efficiency.

Discussion: In the neuronal culture, longer ITIs appeared to allow the neuronal response to recover. In the available human dataset on both depression and chronic pain, data concerning shorter ITIs is does not allow a significant conclusion.

Significance: NRF may interfere with the efficacy of rTMS stimulation protocols when the average stimulation frequency is too high, proposing ITIs as a variable in rTMS protocol efficacy. Clinical trials are necessary to examine effect of shorter ITIs on the clinical outcome in a controlled setting.

Quantitative theory of deep brain stimulation of the subthalamic nucleus for the suppression of pathological rhythms in Parkinson's disease

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is modeled to explore the mechanisms of this effective, but poorly understood, treatment for motor symptoms of drug-refractory Parkinson's disease and dystonia. First, a neural field model of the corticothalamic-basal ganglia (CTBG) system is developed that reproduces key clinical features of Parkinson's disease, including its characteristic 4-8 Hz and 13-30 Hz electrophysiological signatures. Deep brain stimulation of the STN is then modeled and shown to suppress the pathological 13-30 Hz (beta) activity for physiologically realistic and optimized stimulus parameters. This supports the idea that suppression of abnormally coherent activity in the CTBG system is a major factor in DBS therapy for Parkinson's disease, by permitting normal dynamics to resume. At high stimulus intensities, nonlinear effects in the target population mediate wave-wave interactions between resonant beta activity and the stimulus pulse train, leading to complex spectral structure that shows remarkable similarity to that seen in steady-state evoked potential experiments.

Personalized Transcranial Magnetic Stimulation in Psychiatry

Transcranial magnetic stimulation (TMS) is a noninvasive brain stimulation technique that allows for modulating the activity of local neural populations and related neural networks. TMS is touted as a viable intervention to normalize brain activity and alleviate some psychiatric symptoms. However, TMS interventions are known to be only moderately reliable, and the efficacy of such therapies remains to be proven for psychiatric disorders other than depression. We review new opportunities to personalize TMS interventions using neuroimaging and computational modeling, aiming to optimize treatment to suit particular individuals and clinical subgroups. Specifically, we consider the prospect of improving the efficacy of existing TMS interventions by parsing broad diagnostic categories into biologically and clinically homogeneous biotypes. Biotypes can provide distinct treatment targets for optimized TMS interventions. We further discuss the utility of computational models in refining TMS personalization and efficiently establishing optimal cortical targets for distinct biotypes. Personalizing cortical stimulation targets, treatment frequencies, and intensities can improve the therapeutic efficacy of TMS and potentially establish noninvasive brain stimulation as a viable treatment for psychiatric symptoms.

The Dynamics of Neural Fields on Bounded Domains: An Interface Approach for Dirichlet Boundary Conditions

Continuum neural field equations model the large-scale spatio-temporal dynamics of interacting neurons on a cortical surface. They have been extensively studied, both analytically and numerically, on bounded as well as unbounded domains. Neural field models do not require the specification of boundary conditions. Relatively little attention has been paid to the imposition of neural activity on the boundary, or to its role in inducing patterned states. Here we redress this imbalance by studying neural field models of Amari type (posed on one- and two-dimensional bounded domains) with Dirichlet boundary conditions. The Amari model has a Heaviside nonlinearity that allows for a description of localised solutions of the neural field with an interface dynamics. We show how to generalise this reduced but exact description by deriving a normal velocity rule for an interface that encapsulates boundary effects. The linear stability analysis of localised states in the interface dynamics is used to understand how spatially extended patterns may develop in the absence and presence of boundary conditions. Theoretical results for pattern formation are shown to be in excellent agreement with simulations of the full neural field model. Furthermore, a numerical scheme for the interface dynamics is introduced and used to probe the way in which a Dirichlet boundary condition can limit the growth of labyrinthine structures.

Mapping how local perturbations influence systems-level brain dynamics

The human brain exhibits a distinct spatiotemporal organization that supports brain function and can be manipulated via local brain stimulation. Such perturbations to local cortical dynamics are globally integrated by distinct neural systems. However, it remains unclear how local changes in neural activity affect large-scale system dynamics. Here, we briefly review empirical and computational studies addressing how localized perturbations affect brain activity. We then systematically analyze a model of large-scale brain dynamics, assessing how localized changes in brain activity at the different sites affect whole-brain dynamics. We find that local stimulation induces changes in brain activity that can be summarized by relatively smooth tuning curves, which relate a region's effectiveness as a stimulation site to its position within the cortical hierarchy. Our results also support the notion that brain hubs, operating in a slower regime, are more resilient to focal perturbations and critically contribute to maintain stability in global brain dynamics. In contrast, perturbations of peripheral regions, characterized by faster activity, have greater impact on functional connectivity. As a parallel with this region-level result, we also find that peripheral systems such as the visual and sensorimotor networks were more affected by local perturbations than high-level systems such as the cingulo-opercular network. Our findings highlight the importance of a periphery-to-core hierarchy to determine the effect of local stimulation on the brain network. This study also provides novel resources to orient empirical work aiming at manipulating functional connectivity using non-invasive brain stimulation.