Neural field theory of calcium dependent plasticity with applications to transcranial magnetic stimulation

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.

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.

Altered motor cortical plasticity in patients with hepatic encephalopathy: A paired associative stimulation study

OBJECTIVE

Hepatic encephalopathy (HE) is a potentially reversible brain dysfunction caused by liver failure. Altered synaptic plasticity is supposed to play a major role in the pathophysiology of HE. Here, we used paired associative stimulation with an inter-stimulus interval of 25 ms (PAS25), a transcranial magnetic stimulation (TMS) protocol, to test synaptic plasticity of the motor cortex in patients with manifest HE.

METHODS

23 HE-patients and 23 healthy controls were enrolled in the study. Motor evoked potential (MEP) amplitudes were assessed as measure for cortical excitability. Time courses of MEP amplitude changes after the PAS25 intervention were compared between both groups.

RESULTS

MEP-amplitudes increased after PAS25 in the control group, indicating PAS25-induced synaptic plasticity in healthy controls, as expected. In contrast, MEP-amplitudes within the HE group did not change and were lower than in the control group, indicating no induction of plasticity.

CONCLUSIONS

Our study revealed reduced synaptic plasticity of the primary motor cortex in HE.

SIGNIFICANCE

Reduced synaptic plasticity in HE provides a link between pathological changes on the molecular level and early clinical symptoms of the disease. This decrease may be caused by disturbances in the glutamatergic neurotransmission due to the known hyperammonemia in HE patients.

A Connectome-Based, Corticothalamic Model of State- and Stimulation-Dependent Modulation of Rhythmic Neural Activity and Connectivity

Rhythmic activity in the brain fluctuates with behaviour and cognitive state, through a combination of coexisting and interacting frequencies. At large spatial scales such as those studied in human M/EEG, measured oscillatory dynamics are believed to arise primarily from a combination of cortical (intracolumnar) and corticothalamic rhythmogenic mechanisms. Whilst considerable progress has been made in characterizing these two types of neural circuit separately, relatively little work has been done that attempts to unify them into a single consistent picture. This is the aim of the present paper. We present and examine a whole-brain, connectome-based neural mass model with detailed long-range cortico-cortical connectivity and strong, recurrent corticothalamic circuitry. This system reproduces a variety of known features of human M/EEG recordings, including spectral peaks at canonical frequencies, and functional connectivity structure that is shaped by the underlying anatomical connectivity. Importantly, our model is able to capture state- (e.g., idling/active) dependent fluctuations in oscillatory activity and the coexistence of multiple oscillatory phenomena, as well as frequency-specific modulation of functional connectivity. We find that increasing the level of sensory drive to the thalamus triggers a suppression of the dominant low frequency rhythms generated by corticothalamic loops, and subsequent disinhibition of higher frequency endogenous rhythmic behaviour of intracolumnar microcircuits. These combine to yield simultaneous decreases in lower frequency and increases in higher frequency components of the M/EEG power spectrum during states of high sensory or cognitive drive. Building on this, we also explored the effect of pulsatile brain stimulation on ongoing oscillatory activity, and evaluated the impact of coexistent frequencies and state-dependent fluctuations on the response of cortical networks. Our results provide new insight into the role played by cortical and corticothalamic circuits in shaping intrinsic brain rhythms, and suggest new directions for brain stimulation therapies aimed at state-and frequency-specific control of oscillatory brain activity.

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.

Neuronal tuning: Selective targeting of neuronal populations via manipulation of pulse width and directionality

INTRODUCTION

Motor evoked potentials (MEP) in response to anteroposterior transcranial (AP) magnetic stimulation (TMS) are sensitive to the TMS pulse shape. We are now able to isolate distinct pulse properties, such as pulse width and directionality and evaluate them individually. Different pulse shapes induce different effects, likely by stimulating different populations of neurons. This implies that not all neurons respond in the same manner to stimulation, possibly, because individual segments of neurons differ in their membrane properties.

OBJECTIVES

To investigate the effect of different pulse widths and directionalities of TMS on MEP latencies, motor thresholds and plastic aftereffects of rTMS.

METHODS

Using a controllable pulse stimulator TMS (cTMS), we stimulated fifteen subjects with quasi-unidirectional TMS pulses of different pulse durations (40 μs, 80 μs and 120 μs) and determined thresholds and MEP AP latencies. We then compared the effects of 80 μs quasi-unidirectional pulses to those of 80 μs pulses with different pulse directionality characteristics (0.6 and 1.0 M ratios). We applied 900 pulses of the selected pulse shapes at 1 Hz.

RESULTS

The aftereffects of 1 Hz rTMS depended on pulse shape and duration. 40 and 80 μs wide unidirectional pulses induced inhibition, 120 μs wide pulses caused excitation. Bidirectional pulses induced inhibition during the stimulation but had facilitatory aftereffects. Narrower pulse shapes caused longer latencies and higher resting motor thresholds (RMT) as compared to wider pulse shapes.

CONCLUSIONS

We can tune the aftereffects of rTMS by manipulating pulse width and directionality; this may be due to the different membrane properties of the various neuronal segments such as dendrites.

SIGNIFICANCE

To date, rTMS frequency has been the main determinant of the plastic aftereffects. However, we showed that pulse width also plays a major role, probably by recruiting novel neuronal targets.

NFTsim: Theory and Simulation of Multiscale Neural Field Dynamics

A user ready, portable, documented software package, NFTsim, is presented to facilitate numerical simulations of a wide range of brain systems using continuum neural field modeling. NFTsim enables users to simulate key aspects of brain activity at multiple scales. At the microscopic scale, it incorporates characteristics of local interactions between cells, neurotransmitter effects, synaptodendritic delays and feedbacks. At the mesoscopic scale, it incorporates information about medium to large scale axonal ranges of fibers, which are essential to model dissipative wave transmission and to produce synchronous oscillations and associated cross-correlation patterns as observed in local field potential recordings of active tissue. At the scale of the whole brain, NFTsim allows for the inclusion of long range pathways, such as thalamocortical projections, when generating macroscopic activity fields. The multiscale nature of the neural activity produced by NFTsim has the potential to enable the modeling of resulting quantities measurable via various neuroimaging techniques. In this work, we give a comprehensive description of the design and implementation of the software. Due to its modularity and flexibility, NFTsim enables the systematic study of an unlimited number of neural systems with multiple neural populations under a unified framework and allows for direct comparison with analytic and experimental predictions. The code is written in C++ and bundled with Matlab routines for a rapid quantitative analysis and visualization of the outputs. The output of NFTsim is stored in plain text file enabling users to select from a broad range of tools for offline analysis. This software enables a wide and convenient use of powerful physiologically-based neural field approaches to brain modeling. NFTsim is distributed under the Apache 2.0 license.

Low-frequency repetitive transcranial magnetic stimulation inhibits the development of methamphetamine-induced conditioned place preference

The abuse of amphetamine-type stimulants (ATS) has become a global public health issue in recent years, these new-type drugs can cause addiction and serious cognitive impairment. However, there are no effective methods for the prevention and treatment of ATS addiction at present. Repetitive transcranial magnetic stimulation (rTMS) is a painless and non-invasive new therapeutic approach that has been used for the treatment of depression and other neuropsychiatric disorders, but whether it can be used to treat drug addiction is unclear. In the present study, we investigated the possible effects of rTMS on methamphetamine(METH)-induced conditioned place preference (CPP). High-frequency (10 Hz) and low-frequency stimulation patterns (1 Hz) were applied to test the effect of rTMS on METH-induced CPP. The results showed that low-frequency but not high-frequency rTMS could block METH-CPP, accompanied with a downregulation of gamma-aminobutyric acid type B receptor subunit 1 (GABA_BR1) expression in rat dorsolateral striatum. These results suggested that low-frequency rTMS could effectively inhibit the development of METH addiction and shed light on the rTMS as a potential approach for the prevention of drug addiction.

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.

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.

Numerical modelling of plasticity induced by transcranial magnetic stimulation

We use neural field theory and spike-timing dependent plasticity to make a simple but biophysically reasonable model of long-term plasticity changes in the cortex due to transcranial magnetic stimulation (TMS). We show how common TMS protocols can be captured and studied within existing neural field theory. Specifically, we look at repetitive TMS protocols such as theta burst stimulation and paired-pulse protocols. Continuous repetitive protocols result mostly in depression, but intermittent repetitive protocols in potentiation. A paired pulse protocol results in depression at short ( < ∼ 10 ms) and long ( > ∼ 100 ms) interstimulus intervals, but potentiation for mid-range intervals. The model is sensitive to the choice of neural populations that are driven by the TMS pulses, and to the parameters that describe plasticity, which may aid interpretation of the high variability in existing experimental results. Driving excitatory populations results in greater plasticity changes than driving inhibitory populations. Modelling also shows the merit in optimizing a TMS protocol based on an individual's electroencephalogram. Moreover, the model can be used to make predictions about protocols that may lead to improvements in repetitive TMS outcomes.