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

Statistical method accounts for microscopic electric field distortions around neurons when simulating activation thresholds

INTRODUCTION

Notwithstanding advances in computational models of neuromodulation, there are mismatches between simulated and experimental activation thresholds. Transcranial Magnetic Stimulation (TMS) of the primary motor cortex generates motor evoked potentials (MEPs). At the threshold of MEP generation, whole-head models predict macroscopic (at millimeter scale) electric fields (50-70 V/m) which are considerably below conventionally simulated cortical neuron thresholds (175-350 V/m).

METHODS

We hypothesize that this apparent contradiction is in part a consequence of electrical field warping by brain microstructure. Classical neuronal models ignore the physical presence of neighboring neurons and microstructure and assume that the macroscopic field directly acts on the neurons. In previous work, we performed advanced numerical calculations considering realistic microscopic compartments (e.g., cells, blood vessels), resulting in locally inhomogeneous (micrometer scale) electric field and altered neuronal activation thresholds. Here we combine detailed neural threshold simulations under homogeneous field assumptions with microscopic field calculations, leveraging a novel statistical approach.

RESULTS

We show that, provided brain-region specific microstructure metrics, a single statistically derived scaling factor between microscopic and macroscopic electric fields can be applied in predicting neuronal thresholds. For the cortical sample considered, the statistical method matches TMS experimental thresholds.

CONCLUSIONS

Our approach can be broadly applied to neuromodulation models, where fully coupled microstructure scale simulations may not be computationally tractable.

Statistical method accounts for microscopic electric field distortions around neurons when simulating activation thresholds

Introduction

Notwithstanding advances in computational models of neuromodulation, there are mismatches between simulated and experimental activation thresholds. Transcranial Magnetic Stimulation (TMS) of the primary motor cortex generates motor evoked potentials (MEPs). At the threshold of MEP generation, whole-head models predict macroscopic (at millimeter scale) electric fields (50-70 V/m) which are considerably below conventionally simulated cortical neuron thresholds (175-350 V/m).

Methods

We hypothesize that this apparent contradiction is in part a consequence of electrical field warping by brain microstructure. Classical neuronal models ignore the physical presence of neighboring neurons and microstructure and assume that the macroscopic field directly acts on the neurons. In previous work, we performed advanced numerical calculations considering realistic microscopic compartments (e.g., cells, blood vessels), resulting in locally inhomogeneous (micrometer scale) electric field and altered neuronal activation thresholds. Here we combine detailed neural threshold simulations under homogeneous field assumptions with microscopic field calculations, leveraging a novel statistical approach.

Results

We show that, provided brain-region specific microstructure metrics, a single statistically derived scaling factor between microscopic and macroscopic electric fields can be applied in predicting neuronal thresholds. For the cortical sample considered, the statistical methods match TMS experimental thresholds.

Conclusions

Our approach can be broadly applied to neuromodulation models, where fully coupled microstructure scale simulations may not be computationally tractable.

Next generation bioelectronic medicine: making the case for non-invasive closed-loop autonomic neuromodulation

The field of bioelectronic medicine has advanced rapidly from rudimentary electrical therapies to cutting-edge closed-loop systems that integrate real-time physiological monitoring with adaptive neuromodulation. Early innovations, such as cardiac pacemakers and deep brain stimulation, paved the way for these sophisticated technologies. This review traces the historical and technological progression of bioelectronic medicine, culminating in the emerging potential of closed-loop devices for multiple disorders of the brain and body. We emphasize both invasive techniques, such as implantable devices for brain, spinal cord and autonomic regulation, while we introduce new prospects for non-invasive neuromodulation, including focused ultrasound and newly developed autonomic neurography enabling precise detection and titration of inflammatory immune responses. The case for closed-loop non-invasive autonomic neuromodulation (incorporating autonomic neurography and splenic focused ultrasound stimulation) is presented through its applications in conditions such as sepsis and chronic inflammation, illustrating its capacity to revolutionize personalized healthcare. Today, invasive or non-invasive closed-loop systems have yet to be developed that dynamically modulate autonomic nervous system function by responding to real-time physiological and molecular signals; it represents a transformative approach to therapeutic interventions and major opportunity by which the bioelectronic field may advance. Knowledge gaps remain and likely contribute to the lack of available closed loop autonomic neuromodulation systems, namely, (1) significant exogenous and endogenous noise that must be filtered out, (2) potential drift in the signal due to temporal change in disease severity and/or therapy induced neuroplasticity, and (3) confounding effects of exogenous therapies (e.g., concurrent medications that dysregulate autonomic nervous system functions). Leveraging continuous feedback and real-time adjustments may overcome many of these barriers, and these next generation systems have the potential to stand at the forefront of precision medicine, offering new avenues for individualized and adaptive treatment.

Circuits and mechanisms for TMS-induced corticospinal waves: Connecting sensitivity analysis to the network graph

Transcranial magnetic stimulation (TMS) is a non-invasive, FDA-cleared treatment for neuropsychiatric disorders with broad potential for new applications, but the neural circuits that are engaged during TMS are still poorly understood. Recordings of neural activity from the corticospinal tract provide a direct readout of the response of motor cortex to TMS, and therefore a new opportunity to model neural circuit dynamics. The study goal was to use epidural recordings from the cervical spine of human subjects to develop a computational model of a motor cortical macrocolumn through which the mechanisms underlying the response to TMS, including direct and indirect waves, could be investigated. An in-depth sensitivity analysis was conducted to identify important pathways, and machine learning was used to identify common circuit features among these pathways. Sensitivity analysis identified neuron types that preferentially contributed to single corticospinal waves. Single wave preference could be predicted using the average connection probability of all possible paths between the activated neuron type and L5 pyramidal tract neurons (PTNs). For these activations, the total conduction delay of the shortest path to L5 PTNs determined the latency of the corticospinal wave. Finally, there were multiple neuron type activations that could preferentially modulate a particular corticospinal wave. The results support the hypothesis that different pathways of circuit activation contribute to different corticospinal waves with participation of both excitatory and inhibitory neurons. Moreover, activation of both afferents to the motor cortex as well as specific neuron types within the motor cortex initiated different I-waves, and the results were interpreted to propose the cortical origins of afferents that may give rise to certain I-waves. The methodology provides a workflow for performing computationally tractable sensitivity analyses on complex models and relating the results to the network structure to both identify and understand mechanisms underlying the response to acute stimulation.

Anatomical measurements and field modeling to assess transcranial magnetic stimulation motor and non-motor effects

OBJECTIVE

Explore how anatomical measurements and field modeling can be leveraged to improve investigations of transcranial magnetic stimulation (TMS) effects on both motor and non-motor TMS targets.

METHODS

TMS motor effects (targeting the primary motor cortex [M1]) were evaluated using the resting motor threshold (rMT), while TMS non-motor effects (targeting the superior temporal gyrus [STG]) were assessed using a pain memory task. Anatomical measurements included scalp-cortex distance (SCD) and cortical thickness (CT), whereas field modeling encompassed the magnitude of the electric field (E) induced by TMS.

RESULTS

Anatomical measurements and field modeling values differed significantly between M1 and STG. For TMS motor effects, rMT was correlated with SCD, CT, and E values at M1 (p < 0.05). No correlations were found between these metrics for the STG and TMS non-motor effects (pain memory; all p-values > 0.05).

CONCLUSION

Although anatomical measurements and field modeling are closely related to TMS motor effects, their relationship to non-motor effects - such as pain memory - appear to be much more tenuous and complex, highlighting the need for further advancement in our use of TMS and virtual lesion paradigms.

Multi-scale modeling to investigate the effects of transcranial magnetic stimulation on morphologically-realistic neuron with depression

Transcranial magnetic stimulation (TMS) is a non-invasive neuromodulation technique to activate or inhibit the activity of neurons, and thereby regulate their excitability. This technique has demonstrated potential in the treatment of neuropsychiatric disorders, such as depression. However, the effect of TMS on neurons with different severity of depression is still unclear, limiting the development of efficient and personalized clinical application parameters. In this study, a multi-scale computational model was developed to investigate and quantify the differences in neuronal responses to TMS with different degrees of depression. The microscale neuronal models we constructed represent the hippocampal CA1 region in rats under normal conditions and with varying severities of depression (mild, moderate, and major depressive disorder). These models were then coupled to a macroscopic TMS-induced E-Fields model of a rat head comprising multiple types of tissue. Our results demonstrate alterations in neuronal membrane potential and calcium concentration across varying levels of depression severity. As depression severity increases, the peak membrane potential and polarization degree of neuronal soma and dendrites gradually decline, while the peak calcium concentration decreases and the peak arrival time prolongs. Concurrently, the electric fields thresholds and amplification coefficient gradually rise, indicating an increasing difficulty in activating neurons with depression. This study offers novel insights into the mechanisms of magnetic stimulation in depression treatment using multi-scale computational models. It underscores the importance of considering depression severity in treatment strategies, promising to optimize TMS therapeutic approaches.

Dosimetry for repetitive transcranial magnetic stimulation: a translational study from Alzheimer's disease patients to controlledin vitroinvestigations

Objective.Recent studies have indicated that repetitive transcranial magnetic stimulation (rTMS) could enhance cognition in Alzheimer's Disease (AD) patients, but to now the molecular-level interaction mechanisms driving this effect remain poorly understood. While cognitive scores have been the primary measure of rTMS effectiveness, employing molecular-based approaches could offer more precise treatment predictions and prognoses. To reach this goal, it is fundamental to assess the electric field (E-field) and the induced current densities (J) within the stimulated brain areas and to translate these values toin vitrosystems specifically devoted in investigating molecular-based interactions of this stimulation.Approach.This paper offers a methodological procedure to guide dosimetric assessment to translate the E-field induced in humans (in a specific pilot study) intoin vitrosettings. Electromagnetic simulations on patients' head models and cellular holders were conducted to characterize exposure conditions and determine necessary adjustments forin vitroreplication of the same dose delivered in humans using the same stimulating coil.Main results.Our study highlighted the levels of E-field andJinduced in the target brain region and showed that the computed E-field andJwere different among patients that underwent the treatment, so to replicate the exposure to thein vitrosystem, we have to consider a range of electric quantities as reference. To match the E-field to the levels calculated in patients' brains, an increase of at least the 25% in the coil feeding current is necessary whenin vitrostimulations are performed. Conversely, to equalize current densities, modifications in the cells culture medium conductivity have to be implemented reducing it to one fifth of its value.Significance.This dosimetric assessment and subsequent experimental adjustments are essential to achieve controlledin vitroexperiments to better understand rTMS effects on AD cognition. Dosimetry is a fundamental step for comparing the cognitive effects with those obtained by stimulating a cellular model at an equal dose rigorously evaluated.

A survey on integral equations for bioelectric modeling

Bioelectric modeling problems, such as electroencephalography, magnetoencephalography, transcranial electrical stimulation, deep brain stimulation, and transcranial magnetic stimulation, among others, can be approached through the formulation and resolution of integral equations of theboundary element method(BEM). Recently, it has been realized that thecharge-based formulationof the BEM is naturally well-suited for the application of thefast multipole method(FMM). The FMM is a powerful algorithm for the computation of many-body interactions and is widely applied in electromagnetic modeling problems. With the introduction of BEM-FMM in the context of bioelectromagnetism, the BEM can now compete with thefinite element method(FEM) in a number of application cases. This survey has two goals: first, to give a modern account of the main BEM formulations in the literature and their integration with FMM, directed to general researchers involved in development of BEM software for bioelectromagnetic applications. Second, to survey different techniques and available software, and to contrast different BEM and FEM approaches. As a new contribution, we showcase that the charge-based formulation is dual to the more common surface potential formulation.

Ultra-high frequency repetitive TMS at subthreshold intensity induces suprathreshold motor response via temporal summation

Objective.The transcranial magnetic stimulation (TMS) coil induces an electric field that diminishes rapidly upon entering the brain. This presents a challenge in achieving focal stimulation of a deep brain structure. Neuronal elements, including axons, dendrites, and cell bodies, exhibit specific time constants. When exposed to repetitive TMS pulses at a high frequency, there is a cumulative effect on neuronal membrane potentials, resulting in temporal summation. This study aims to determine whether TMS pulse train at high-frequency and subthreshold intensity could induce a suprathreshold response.Approach.As a proof of concept, we developed a TMS machine in-house that could consistently output pulses up to 250 Hz, and performed experiments on 22 awake rats to test whether temporal summation was detectable under pulse trains at 100, 166, or 250 Hz.Main results.Results revealed that TMS pulses at 55% maximum stimulator output (MSO, peak dI/dt= 68.5 A/μs at 100% MSO, pulse width = 48μs) did not induce motor responses with either single pulses or pulse trains. Similarly, a single TMS pulse at 65% MSO failed to evoke a motor response in rats; however, a train of TMS pulses at frequencies of 166 and 250 Hz, but not at 100 Hz, successfully triggered motor responses and MEP signals, suggesting a temporal summation effect dependent on both pulse intensities and pulse train frequencies.Significance.We propose that the temporal summation effect can be leveraged to design the next-generation focal TMS system: by sequentially driving multiple coils at high-frequency and subthreshold intensity, areas with the most significant overlapping E-fields undergo maximal temporal summation effects, resulting in a suprathreshold response.

A New Angle on Transcranial Magnetic Stimulation Coil Orientation: A Targeted Narrative Review

Transcranial magnetic stimulation (TMS) is used to treat several neuropsychiatric disorders including depression, where it is effective in approximately one half of patients for whom pharmacological approaches have failed. Treatment response is related to stimulation parameters such as the stimulation frequency, pattern, intensity, location, total number of pulses and sessions applied, and target brain network engagement. One critical but underexplored component of the stimulation procedure is the orientation or yaw angle of the commonly used figure-of-eight TMS coil, which is known to impact neuronal response to TMS. However, coil orientation has remained largely unchanged since TMS was first used to treat depression and continues to be based on motor cortex anatomy, which may not be optimal for the dorsolateral prefrontal cortex treatment site. In this targeted narrative review, we evaluate experimental, clinical, and computational evidence indicating that optimizing coil orientation may improve TMS treatment outcomes. The properties of the electric field induced by TMS, the changes to this field caused by the differing conductivities of head tissues, and the interaction between coil orientation and the underlying cortical anatomy are summarized. We describe evidence that the magnitude and site of cortical activation, surrogate markers of TMS dosing and brain network targeting considered central in clinical response to TMS, are influenced by coil orientation. We suggest that coil orientation should be considered when applying therapeutic TMS and propose several approaches to optimizing this potentially important treatment parameter.

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

Background

Repetitive transcranial magnetic stimulation (rTMS) induces long-term changes of synapses, but the mechanisms behind these modifications are not fully understood. Although there has been progress in the development of multi-scale modeling tools, no comprehensive module for simulating rTMS-induced synaptic plasticity in biophysically realistic neurons exists..

Objective

We developed a modelling framework that allows the replication and detailed prediction of long-term changes of excitatory synapses in neurons stimulated by rTMS.

Methods

We 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.

Results

We 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.

Conclusion

The 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.

Coil Orientation and Stimulation Threshold in Transcranial Magnetic Stimulation (TMS)

Transcranial Magnetic Stimulation (TMS) is used to treat mental disorders and explore brain function via applied electromagnetic fields generated by a high current coil placed on the subject's scalp. While TMS has been in clinical use for decades, and continues to be a rapidly expanding therapeutic modality, there are still many unknowns regarding how stimulation parameters may affect treatment efficacy and how they may be optimized. One key parameter that is readily accessible is coil orientation and its effects on TMS stimulation threshold. In this work, a multi-scale modeling approach addressed the effects of coil orientation on the TMS stimulation threshold for neuronal activation with several TMS coil current pulse shapes and widths. The modeling tool that was used incorporated an anatomically realistic macro scale model of the human brain cortex, and a micro scale neuronal model based on a representative layer 5 pyramidal brain cell. Simulations were performed for the left primary motor cortical area. Coil orientations associated with a minimum stimulation threshold for various current pulses were identified and then validated using data collected and published by another research team. This multi-scale modeling approach is versatile and can potentially be used for various current pulses and TMS coil locations to predict and map optimum TMS coil orientations for a wide range of treatment applications.

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

Introduction

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, is crucial in experimental and clinical settings. It enables meaningful comparisons across studies and facilitates 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.

Methods

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.

Results

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. Although morphologies of mouse and rat CA1 neurons showed no significant differences, simulations confirmed that axon morphologies significantly influence individual cell 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.

Conclusion

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.

intensity- and frequency-specific effects of transcranial alternating current stimulation are explained by network dynamics

Objective. Transcranial alternating current stimulation (tACS) can be used to non-invasively entrain neural activity and thereby cause changes in local neural oscillatory power. Despite its increased use in cognitive and clinical neuroscience, the fundamental mechanisms of tACS are still not fully understood.Approach. We developed a computational neuronal network model of two-compartment pyramidal neurons (PY) and inhibitory interneurons, which mimic the local cortical circuits. We modeled tACS with electric field strengths that are achievable in human applications. We then simulated intrinsic network activity and measured neural entrainment to investigate how tACS modulates ongoing endogenous oscillations.Main results. The intensity-specific effects of tACS are non-linear. At low intensities (<0.3 mV mm-1), tACS desynchronizes neural firing relative to the endogenous oscillations. At higher intensities (>0.3 mV mm-1), neurons are entrained to the exogenous electric field. We then further explore the stimulation parameter space and find that the entrainment of ongoing cortical oscillations also depends on stimulation frequency by following an Arnold tongue. Moreover, neuronal networks can amplify the tACS-induced entrainment via synaptic coupling and network effects. Our model shows that PY are directly entrained by the exogenous electric field and drive the inhibitory neurons.Significance. The results presented in this study provide a mechanistic framework for understanding the intensity- and frequency-specific effects of oscillating electric fields on neuronal networks. This is crucial for rational parameter selection for tACS in cognitive studies and clinical applications.

Induced neural phase precession through exogenous electric fields

The gradual shifting of preferred neural spiking relative to local field potentials (LFPs), known as phase precession, plays a prominent role in neural coding. Correlations between the phase precession and behavior have been observed throughout various brain regions. As such, phase precession is suggested to be a global neural mechanism that promotes local neuroplasticity. However, causal evidence and neuroplastic mechanisms of phase precession are lacking so far. Here we show a causal link between LFP dynamics and phase precession. In three experiments, we modulated LFPs in humans, a non-human primate, and computational models using alternating current stimulation. We show that continuous stimulation of motor cortex oscillations in humans lead to a gradual phase shift of maximal corticospinal excitability by ~90°. Further, exogenous alternating current stimulation induced phase precession in a subset of entrained neurons (~30%) in the non-human primate. Multiscale modeling of realistic neural circuits suggests that alternating current stimulation-induced phase precession is driven by NMDA-mediated synaptic plasticity. Altogether, the three experiments provide mechanistic and causal evidence for phase precession as a global neocortical process. Alternating current-induced phase precession and consequently synaptic plasticity is crucial for the development of novel therapeutic neuromodulation methods.

Estimations of Charge Deposition Onto Convoluted Axon Surfaces Within Extracellular Electric Fields

OBJECTIVE

Biophysical models of neural stimulation are a valuable approach to explaining the mechanisms of neuronal recruitment via applied extracellular electric fields. Typically, the applied electric field is estimated via a macroscopic finite element method solution and then applied to cable models as an extracellular voltage source. However, the field resolution is limited by the finite element size (typically 10's-100's of times greater than average neuronal cross-section). As a result, induced charges deposited onto anatomically realistic curved membrane interfaces are not taken into consideration. However, these details may alter estimates of the applied electric field and predictions of neural tissue activation.

METHODS

To estimate microscopic variations of the electric field, data for intra-axonal space segmented from 3D scanning electron microscopy of the mouse brain genu of corpus callosum were used. The boundary element fast multipole method was applied to accurately compute the extracellular solution. Neuronal recruitment was then estimated via an activating function.

RESULTS

Taking the physical structure of the arbor into account generally predicts higher values of the activating function. The relative integral 2-norm difference is 90% on average when the entire axonal arbor is present. A large fraction of this difference might be due to the axonal body itself. When an isolated physical axon is considered with all other axons removed, the relative integral 2-norm difference between the single-axon solution and the complete solution is 25% on average.

CONCLUSION

Our result may provide an explanation as to why Deep Brain Stimulation experiments typically predict lower activation thresholds than commonly used FEM/Cable model approaches to predicting neuronal responses to extracellular electrical stimulation.

SIGNIFICANCE

These results may change methods for bi-domain neural modeling and neural excitation.

Repetitive transcranial magnetic stimulation (rTMS) triggers dose-dependent homeostatic rewiring in recurrent neuronal networks

Repetitive transcranial magnetic stimulation (rTMS) is a non-invasive brain stimulation technique used to induce neuronal plasticity in healthy individuals and patients. Designing effective and reproducible rTMS protocols poses a major challenge in the field as the underlying biomechanisms of long-term effects remain elusive. Current clinical protocol designs are often based on studies reporting rTMS-induced long-term potentiation or depression of synaptic transmission. Herein, we employed computational modeling to explore the effects of rTMS on long-term structural plasticity and changes in network connectivity. We simulated a recurrent neuronal network with homeostatic structural plasticity among excitatory neurons, and demonstrated that this mechanism was sensitive to specific parameters of the stimulation protocol (i.e., frequency, intensity, and duration of stimulation). Particularly, the feedback-inhibition initiated by network stimulation influenced the net stimulation outcome and hindered the rTMS-induced structural reorganization, highlighting the role of inhibitory networks. These findings suggest a novel mechanism for the lasting effects of rTMS, i.e., rTMS-induced homeostatic structural plasticity, and highlight the importance of network inhibition in careful protocol design, standardization, and optimization of stimulation.

A fast direct solver for surface-based whole-head modeling of transcranial magnetic stimulation

When modeling transcranial magnetic stimulation (TMS) in the brain, a fast and accurate electric field solver can support interactive neuronavigation tasks as well as comprehensive biophysical modeling. We formulate, test, and disseminate a direct (i.e., non-iterative) TMS solver that can accurately determine global TMS fields for any coil type everywhere in a high-resolution MRI-based surface model with ~ 200,000 or more arbitrarily selected observation points within approximately 5 s, with the solution time itself of 3 s. The solver is based on the boundary element fast multipole method (BEM-FMM), which incorporates the latest mathematical advancement in the theory of fast multipole methods-an FMM-based LU decomposition. This decomposition is specific to the head model and needs to be computed only once per subject. Moreover, the solver offers unlimited spatial numerical resolution. Despite the fast execution times, the present direct solution is numerically accurate for the default model resolution. In contrast, the widely used brain modeling software SimNIBS employs a first-order finite element method that necessitates additional mesh refinement, resulting in increased computational cost. However, excellent agreement between the two methods is observed for various practical test cases following mesh refinement, including a biophysical modeling task. The method can be readily applied to a wide range of TMS analyses involving multiple coil positions and orientations, including image-guided neuronavigation. It can even accommodate continuous variations in coil geometry, such as flexible H-type TMS coils. The FMM-LU direct solver is freely available to academic users.

Induced neural phase precession through exogeneous electric fields

The gradual shifting of preferred neural spiking relative to local field potentials (LFPs), known as phase precession, plays a prominent role in neural coding. Correlations between the phase precession and behavior have been observed throughout various brain regions. As such, phase precession is suggested to be a global neural mechanism that promotes local neuroplasticity. However, causal evidence and neuroplastic mechanisms of phase precession are lacking so far. Here we show a causal link between LFP dynamics and phase precession. In three experiments, we modulated LFPs in humans, a non-human primate, and computational models using alternating current stimulation. We show that continuous stimulation of motor cortex oscillations in humans lead to a gradual phase shift of maximal corticospinal excitability by ~90°. Further, exogenous alternating current stimulation induced phase precession in a subset of entrained neurons (~30%) in the non-human primate. Multiscale modeling of realistic neural circuits suggests that alternating current stimulation-induced phase precession is driven by NMDA-mediated synaptic plasticity. Altogether, the three experiments provide mechanistic and causal evidence for phase precession as a global neocortical process. Alternating current-induced phase precession and consequently synaptic plasticity is crucial for the development of novel therapeutic neuromodulation methods.

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.

A fast direct solver for surface-based whole-head modeling of transcranial magnetic stimulation

Background

When modeling transcranial magnetic stimulation (TMS) in the brain, a fast and accurate electric field solver can support interactive neuronavigation tasks as well as comprehensive biophysical modeling.

Objective

We formulate, test, and disseminate a direct (i.e., non-iterative) TMS solver that can accurately determine global TMS fields for any coil type everywhere in a high-resolution MRI-based surface model with ~200,000 or more arbitrarily selected observation points within approximately 5 sec, with the solution time itself of 3 sec.

Method

The solver is based on the boundary element fast multipole method (BEM-FMM), which incorporates the latest mathematical advancement in the theory of fast multipole methods - an FMM-based LU decomposition. This decomposition is specific to the head model and needs to be computed only once per subject. Moreover, the solver offers unlimited spatial numerical resolution.

Results

Despite the fast execution times, the present direct solution is numerically accurate for the default model resolution. In contrast, the widely used brain modeling software SimNIBS employs a first-order finite element method that necessitates additional mesh refinement, resulting in increased computational cost. However, excellent agreement between the two methods is observed for various practical test cases following mesh refinement, including a biophysical modeling task.

Conclusion

The method can be readily applied to a wide range of TMS analyses involving multiple coil positions and orientations, including image-guided neuronavigation. It can even accommodate continuous variations in coil geometry, such as flexible H-type TMS coils. The FMM-LU direct solver is freely available to academic users.

Intensity- and frequency-specific effects of transcranial alternating current stimulation are explained by network dynamics

Transcranial alternating current stimulation (tACS) can be used to non-invasively entrain neural activity, and thereby cause changes in local neural oscillatory power. Despite an increased use in cognitive and clinical neuroscience, the fundamental mechanisms of tACS are still not fully understood. Here, we develop a computational neuronal network model of two-compartment pyramidal neurons and inhibitory interneurons which mimic the local cortical circuits. We model tACS with electric field strengths that are achievable in human applications. We then simulate intrinsic network activity and measure neural entrainment to investigate how tACS modulates ongoing endogenous oscillations. First, we show that intensity-specific effects of tACS are non-linear. At low intensities (<0.3 mV/mm), tACS desynchronizes neural firing relative to the endogenous oscillations. At higher intensities (>0.3 mV/mm), neurons are entrained to the exogenous electric field. We then further explore the stimulation parameter space and find that entrainment of ongoing cortical oscillations also depends on frequency by following an Arnold tongue. Moreover, neuronal networks can amplify the tACS induced entrainment via excitation-inhibition balance. Our model shows that pyramidal neurons are directly entrained by the exogenous electric field and drive the inhibitory neurons. Our findings can thus provide a mechanistic framework for understanding the intensity- and frequency- specific effects of oscillating electric fields on neuronal networks. This is crucial for rational parameters selection for tACS in cognitive studies and clinical applications.

Microglial Cytokines Mediate Plasticity Induced by 10 Hz Repetitive Magnetic Stimulation

Microglia, the resident immune cells of the CNS, sense the activity of neurons and regulate physiological brain functions. They have been implicated in the pathology of brain diseases associated with alterations in neural excitability and plasticity. However, experimental and therapeutic approaches that modulate microglia function in a brain region-specific manner have not been established. In this study, we tested for the effects of repetitive transcranial magnetic stimulation (rTMS), a clinically used noninvasive brain stimulation technique, on microglia-mediated synaptic plasticity; 10 Hz electromagnetic stimulation triggered a release of plasticity-promoting cytokines from microglia in mouse organotypic brain tissue cultures of both sexes, while no significant changes in microglial morphology or microglia dynamics were observed. Indeed, substitution of tumor necrosis factor α (TNFα) and interleukin 6 (IL6) preserved synaptic plasticity induced by 10 Hz stimulation in the absence of microglia. Consistent with these findings, in vivo depletion of microglia abolished rTMS-induced changes in neurotransmission in the mPFC of anesthetized mice of both sexes. We conclude that rTMS affects neural excitability and plasticity by modulating the release of cytokines from microglia.SIGNIFICANCE STATEMENT Repetitive transcranial magnetic stimulation (rTMS) is a noninvasive brain stimulation technique that induces cortical plasticity. Despite its wide use in neuroscience and clinical practice (e.g., depression treatment), the cellular and molecular mechanisms of rTMS-mediated plasticity remain not well understood. Herein, we report an important role of microglia and plasticity-promoting cytokines in synaptic plasticity induced by 10 Hz rTMS in organotypic slice cultures and anesthetized mice, thereby identifying microglia-mediated synaptic adaptation as a target of rTMS-based interventions.

Repetitive transcranial magnetic stimulation (rTMS) triggers dose-dependent homeostatic rewiring in recurrent neuronal networks

Repetitive transcranial magnetic stimulation (rTMS) is a non-invasive brain stimulation technique used to induce neuronal plasticity in healthy individuals and patients. Designing effective and reproducible rTMS protocols poses a major challenge in the field as the underlying biomechanisms remain elusive. Current clinical protocol designs are often based on studies reporting rTMS-induced long-term potentiation or depression of synaptic transmission. Herein, we employed computational modeling to explore the effects of rTMS on long-term structural plasticity and changes in network connectivity. We simulated a recurrent neuronal network with homeostatic structural plasticity between excitatory neurons, and demonstrated that this mechanism was sensitive to specific parameters of the stimulation protocol (i.e., frequency, intensity, and duration of stimulation). The feedback-inhibition initiated by network stimulation influenced the net stimulation outcome and hindered the rTMS-induced homeostatic structural plasticity, highlighting the role of inhibitory networks. These findings suggest a novel mechanism for the lasting effects of rTMS, i.e., rTMS-induced homeostatic structural plasticity, and highlight the importance of network inhibition in careful protocol design, standardization, and optimization of stimulation.

A Review of AI Cloud and Edge Sensors, Methods, and Applications for the Recognition of Emotional, Affective and Physiological States

Affective, emotional, and physiological states (AFFECT) detection and recognition by capturing human signals is a fast-growing area, which has been applied across numerous domains. The research aim is to review publications on how techniques that use brain and biometric sensors can be used for AFFECT recognition, consolidate the findings, provide a rationale for the current methods, compare the effectiveness of existing methods, and quantify how likely they are to address the issues/challenges in the field. In efforts to achieve the key goals of Society 5.0, Industry 5.0, and human-centered design better, the recognition of emotional, affective, and physiological states is progressively becoming an important matter and offers tremendous growth of knowledge and progress in these and other related fields. In this research, a review of AFFECT recognition brain and biometric sensors, methods, and applications was performed, based on Plutchik's wheel of emotions. Due to the immense variety of existing sensors and sensing systems, this study aimed to provide an analysis of the available sensors that can be used to define human AFFECT, and to classify them based on the type of sensing area and their efficiency in real implementations. Based on statistical and multiple criteria analysis across 169 nations, our outcomes introduce a connection between a nation's success, its number of Web of Science articles published, and its frequency of citation on AFFECT recognition. The principal conclusions present how this research contributes to the big picture in the field under analysis and explore forthcoming study trends.

Transcranial direct current stimulation for gait recovery following stroke: A systematic review of current literature and beyond

Background

Over the last decade, transcranial direct current stimulation (tDCS) has set promise contributing to post-stroke gait rehabilitation. Even so, results are still inconsistent due to low sample size, heterogeneity of samples, and tDCS design differences preventing comparability. Nonetheless, updated knowledge in post-stroke neurophysiology and stimulation technologies opens up opportunities to massively improve treatments.

Objective

The current systematic review aims to summarize the current state-of-the-art on the effects of tDCS applied to stroke subjects for gait rehabilitation, discuss tDCS strategies factoring individual subject profiles, and highlight new promising strategies.

Methods

MEDLINE, SCOPUS, CENTRAL, and CINAHL were searched for stroke randomized clinical trials using tDCS for the recovery of gait before 7 February 2022. In order to provide statistical support to the current review, we analyzed the achieved effect sizes and performed statistical comparisons.

Results

A total of 24 records were finally included in our review, totaling n = 651 subjects. Detailed analyses revealed n = 4 (17%) studies with large effect sizes (≥0.8), n = 6 (25%) studies with medium ones (≥0.5), and n = 6 (25%) studies yielding low effects sizes (≤ 0.2). Statistically significant negative correlations (rho = −0.65, p = 0.04) and differences (p = 0.03) argued in favor of tDCS interventions in the sub-acute phase. Finally, significant differences (p = 0.03) were argued in favor of a bifocal stimulation montage (anodal M1 ipsilesional and cathodal M1 contralesional) with respect to anodal ipsilesional M1.

Conclusion

Our systematic review highlights the potential of tDCS to contribute to gait recovery following stroke, although also the urgent need to improve current stimulation strategies and subject-customized interventions considering stroke severity, type or time-course, and the use of network-based multifocal stimulation approaches guided by computational biophysical modeling.

Systematic review registration

PROSPERO: CRD42021256347.

Dosing Transcranial Magnetic Stimulation of the Primary Motor and Dorsolateral Prefrontal Cortices With Multi-Scale Modeling

Transcranial magnetic stimulation (TMS) can depolarize cortical neurons through the intact skin and skull. The characteristics of the induced electric field (E-field) have a major impact on specific outcomes of TMS. Using multi-scale computational modeling, we explored whether the stimulation parameters derived from the primary motor cortex (M1) induce comparable macroscopic E-field strengths and subcellular/cellular responses in the dorsolateral prefrontal cortex (DLPFC). To this aim, we calculated the TMS-induced E-field in 16 anatomically realistic head models and simulated the changes in membrane voltage and intracellular calcium levels of morphologically and biophysically realistic human pyramidal cells in the M1 and DLPFC. We found that the conventional intensity selection methods (i.e., motor threshold and fixed intensities) produce variable macroscopic E-fields. Consequently, it was challenging to produce comparable subcellular/cellular responses across cortical regions with distinct folding characteristics. Prospectively, personalized stimulation intensity selection could standardize the E-fields and the subcellular/cellular responses to repetitive TMS across cortical regions and individuals. The suggested computational approach points to the shortcomings of the conventional intensity selection methods used in clinical settings. We propose that multi-scale modeling has the potential to overcome some of these limitations and broaden our understanding of the neuronal mechanisms for TMS.