Neural field theory of synaptic metaplasticity with applications to theta burst stimulation

Assessing the Impact of Transcranial Magnetic Stimulation on Speech Perception in Noise

Healthy aging is associated with reduced speech perception in noise (SPiN) abilities. The etiology of these difficulties remains elusive, which prevents the development of new strategies to optimize the speech processing network and reduce these difficulties. The objective of this study was to determine if sublexical SPiN performance can be enhanced by applying TMS to three regions involved in processing speech: the left posterior temporal sulcus, the left superior temporal gyrus, and the left ventral premotor cortex. The second objective was to assess the impact of several factors (age, baseline performance, target, brain structure, and activity) on post-TMS SPiN improvement. The results revealed that participants with lower baseline performance were more likely to improve. Moreover, in older adults, cortical thickness within the target areas was negatively associated with performance improvement, whereas this association was null in younger individuals. No differences between the targets were found. This study suggests that TMS can modulate sublexical SPiN performance, but that the strength and direction of the effects depend on a complex combination of contextual and individual factors.

Neural Field Continuum Limits and the Structure-Function Partitioning of Cognitive-Emotional Brain Networks

In The cognitive-emotional brain, Pessoa overlooks continuum effects on nonlinear brain network connectivity by eschewing neural field theories and physiologically derived constructs representative of neuronal plasticity. The absence of this content, which is so very important for understanding the dynamic structure-function embedding and partitioning of brains, diminishes the rich competitive and cooperative nature of neural networks and trivializes Pessoa's arguments, and similar arguments by other authors, on the phylogenetic and operational significance of an optimally integrated brain filled with variable-strength neural connections. Riemannian neuromanifolds, containing limit-imposing metaplastic Hebbian- and antiHebbian-type control variables, simulate scalable network behavior that is difficult to capture from the simpler graph-theoretic analysis preferred by Pessoa and other neuroscientists. Field theories suggest the partitioning and performance benefits of embedded cognitive-emotional networks that optimally evolve between exotic classical and quantum computational phases, where matrix singularities and condensations produce degenerate structure-function homogeneities unrealistic of healthy brains. Some network partitioning, as opposed to unconstrained embeddedness, is thus required for effective execution of cognitive-emotional network functions and, in our new era of neuroscience, should be considered a critical aspect of proper brain organization and operation.

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.

Adiabatic dynamic causal modelling

This technical note introduces adiabatic dynamic causal modelling, a method for inferring slow changes in biophysical parameters that control fluctuations of fast neuronal states. The application domain we have in mind is inferring slow changes in variables (e.g., extracellular ion concentrations or synaptic efficacy) that underlie phase transitions in brain activity (e.g., paroxysmal seizure activity). The scheme is efficient and yet retains a biophysical interpretation, in virtue of being based on established neural mass models that are equipped with a slow dynamic on the parameters (such as synaptic rate constants or effective connectivity). In brief, we use an adiabatic approximation to summarise fast fluctuations in hidden neuronal states (and their expression in sensors) in terms of their second order statistics; namely, their complex cross spectra. This allows one to specify and compare models of slowly changing parameters (using Bayesian model reduction) that generate a sequence of empirical cross spectra of electrophysiological recordings. Crucially, we use the slow fluctuations in the spectral power of neuronal activity as empirical priors on changes in synaptic parameters. This introduces a circular causality, in which synaptic parameters underwrite fast neuronal activity that, in turn, induces activity-dependent plasticity in synaptic parameters. In this foundational paper, we describe the underlying model, establish its face validity using simulations and provide an illustrative application to a chemoconvulsant animal model of seizure 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.

Obesity is Associated with Reduced Plasticity of the Human Motor Cortex

Obesity is characterised by excessive body fat and is associated with several detrimental health conditions, including cardiovascular disease and diabetes. There is some evidence that people who are obese have structural and functional brain alterations and cognitive deficits. It may be that these neurophysiological and behavioural consequences are underpinned by altered plasticity. This study investigated the relationship between obesity and plasticity of the motor cortex in people who were considered obese (n = 14, nine males, aged 35.4 ± 14.3 years) or healthy weight (n = 16, seven males, aged 26.3 ± 8.5 years). A brain stimulation protocol known as continuous theta burst transcranial magnetic stimulation was applied to the motor cortex to induce a brief suppression of cortical excitability. The suppression of cortical excitability was quantified using single-pulse transcranial magnetic stimulation to record and measure the amplitude of the motor evoked potential in a peripheral hand muscle. Therefore, the magnitude of suppression of the motor evoked potential by continuous theta burst stimulation was used as a measure of the capacity for plasticity of the motor cortex. Our results demonstrate that the healthy-weight group had a significant suppression of cortical excitability following continuous theta burst stimulation (cTBS), but there was no change in excitability for the obese group. Comparing the response to cTBS between groups demonstrated that there was an impaired plasticity response for the obese group when compared to the healthy-weight group. This might suggest that the capacity for plasticity is reduced in people who are obese. Given the importance of plasticity for human behaviour, our results add further emphasis to the potentially detrimental health effects of obesity.

The After-Effect of Accelerated Intermittent Theta Burst Stimulation at Different Session Intervals

OBJECTIVE

The study aims to investigate the after-effect of three sessions of intermittent theta-burst stimulation (iTBS) on motor cortical excitability. The iTBS was induced over the primary motor cortex (M1) at different time intervals.

METHODS

The study has a crossover design. Sixteen participants were assigned to three groups and received different accelerated iTBS (aiTBS) protocols during each visit: (1) three continuous sessions with no interval (iTBS18000); (2) three iTBS sessions with 10-min intervals (iTBS600 × 3∗10); and (3) three iTBS sessions with 30-min intervals (iTBS600 × 3∗30). As washout period, each visit is separated by at least 7 days. We measured the motor cortical excitability changes and intracortical inhibition.

RESULTS

A dose of 1,800 pulses of aiTBS per day is tolerable. The iTBS1800 led to a reduced cortical excitability; whereas iTBS600 × 3∗10 and iTBS600 × 3∗30 enhanced cortical excitability to a differential extent. After a total dose of 1,800 pulses, iTBS600 × 3∗30 exhibited the longer effect and highest percentage of individuals with enhanced cortical excitability.

CONCLUSION

The results suggest that aiTBS protocols at different time intervals result in different motor cortical excitability after-effects.

Next-generation neural mass and field modeling

The Wilson-Cowan population model of neural activity has greatly influenced our understanding of the mechanisms for the generation of brain rhythms and the emergence of structured brain activity. As well as the many insights that have been obtained from its mathematical analysis, it is now widely used in the computational neuroscience community for building large-scale in silico brain networks that can incorporate the increasing amount of knowledge from the Human Connectome Project. Here, we consider a neural population model in the spirit of that originally developed by Wilson and Cowan, albeit with the added advantage that it can account for the phenomena of event-related synchronization and desynchronization. This derived mean-field model provides a dynamic description for the evolution of synchrony, as measured by the Kuramoto order parameter, in a large population of quadratic integrate-and-fire model neurons. As in the original Wilson-Cowan framework, the population firing rate is at the heart of our new model; however, in a significant departure from the sigmoidal firing rate function approach, the population firing rate is now obtained as a real-valued function of the complex-valued population synchrony measure. To highlight the usefulness of this next-generation Wilson-Cowan style model, we deploy it in a number of neurobiological contexts, providing understanding of the changes in power spectra observed in electro- and magnetoencephalography neuroimaging studies of motor cortex during movement, insights into patterns of functional connectivity observed during rest and their disruption by transcranial magnetic stimulation, and to describe wave propagation across cortex.

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.

Effect of Stimulation Waveform on the Non-linear Entrainment of Cortical Alpha Oscillations

In the past decade, there has been a surge of interest in using patterned brain stimulation to manipulate cortical oscillations, in both experimental and clinical settings. But the relationship between stimulation waveform and its impact on ongoing oscillations remains poorly understood and severely restrains the development of new paradigms. To address some aspects of this intricate problem, we combine computational and mathematical approaches, providing new insights into the influence of waveform of both low and high-frequency stimuli on synchronous neural activity. Using a cellular-based cortical microcircuit network model, we performed numerical simulations to test the influence of different waveforms on ongoing alpha oscillations, and derived a mean-field description of stimulation-driven dynamics to better understand the observed responses. Our analysis shows that high-frequency periodic stimulation translates into an effective transformation of the neurons' response function, leading to waveform-dependent changes in oscillatory dynamics and resting state activity. Moreover, we found that randomly fluctuating stimulation linearizes the neuron response function while constant input moves its activation threshold. Taken together, our findings establish a new theoretical framework in which stimulation waveforms impact neural systems at the population-scale through non-linear interactions.

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.

Melodic Priming of Motor Sequence Performance: The Role of the Dorsal Premotor Cortex

The purpose of this study was to determine whether exposure to specific auditory sequences leads to the induction of new motor memories and to investigate the role of the dorsal premotor cortex (dPMC) in this crossmodal learning process. Fifty-two young healthy non-musicians were familiarized with the sound to key-press mapping on a computer keyboard and tested on their baseline motor performance. Each participant received subsequently either continuous theta burst stimulation (cTBS) or sham stimulation over the dPMC and was then asked to remember a 12-note melody without moving. For half of the participants, the contour of the melody memorized was congruent to a subsequently performed, but never practiced, finger movement sequence (Congruent group). For the other half, the melody memorized was incongruent to the subsequent finger movement sequence (Incongruent group). Hearing a congruent melody led to significantly faster performance of a motor sequence immediately thereafter compared to hearing an incongruent melody. In addition, cTBS speeded up motor performance in both groups, possibly by relieving motor consolidation from interference by the declarative melody memorization task. Our findings substantiate recent evidence that exposure to a movement-related tone sequence can induce specific, crossmodal encoding of a movement sequence representation. They further suggest that cTBS over the dPMC may enhance early offline procedural motor skill consolidation in cognitive states where motor consolidation would normally be disturbed by concurrent declarative memory processes. These findings may contribute to a better understanding of auditory-motor system interactions and have implications for the development of new motor rehabilitation approaches using sound and non-invasive brain stimulation as neuromodulatory tools.

Metaplasticity in human primary somatosensory cortex: effects on physiology and tactile perception

Theta-burst stimulation (TBS) over human primary motor cortex evokes plasticity and metaplasticity, the latter contributing to the homeostatic balance of excitation and inhibition. Our knowledge of TBS-induced effects on primary somatosensory cortex (SI) is limited, and it is unknown whether TBS induces metaplasticity within human SI. Sixteen right-handed participants (6 females, mean age 23 yr) received two TBS protocols [continuous TBS (cTBS) and intermittent TBS (iTBS)] delivered in six different combinations over SI in separate sessions. TBS protocols were delivered at 30 Hz and were as follows: a single cTBS protocol, a single iTBS protocol, cTBS followed by cTBS, iTBS followed by iTBS, cTBS followed by iTBS, and iTBS followed by cTBS. Measures included the amplitudes of the first and second somatosensory evoked potentials (SEPs) via median nerve stimulation, their paired-pulse ratio (PPR), and temporal order judgment (TOJ). Dependent measures were obtained before TBS and at 5, 25, 50, and 90 min following stimulation. Results indicate similar effects following cTBS and iTBS; increased amplitudes of the second SEP and PPR without amplitude changes to SEP 1, and impairments in TOJ. Metaplasticity was observed such that TOJ impairments following a single cTBS protocol were abolished following consecutive cTBS protocols. Additionally, consecutive iTBS protocols altered the time course of effects when compared with a single iTBS protocol. In conclusion, 30-Hz cTBS and iTBS protocols delivered in isolation induce effects consistent with a TBS-induced reduction in intracortical inhibition within SI. Furthermore, cTBS- and iTBS-induced metaplasticity appear to follow homeostatic and nonhomeostatic rules, respectively.