scholarly journals A Multi-Scale Computational Model of the effects of TMS on Motor Cortex

2016 ◽  
Author(s):  
Hyeon Seo ◽  
Natalie Schaworonkow ◽  
Sung Chan Jun ◽  
Jochen Triesch
Keyword(s):  
Transcranial Magnetic Stimulation ◽  
Computational Model ◽  
Electric Fields ◽  
Action Potentials ◽  
Magnetic Stimulation ◽  
Pyramidal Neurons ◽  
Pyramidal Cells ◽  
Cell Types ◽  
Cortical Layer ◽  
Multi Scale

AbstractThe detailed biophysical mechanisms through which transcranial magnetic stimulation (TMS) activates cortical circuits are still not fully understood. Here we present a multi-scale computational model to describe and explain the activation of different cell types in motor cortex due to transcranial magnetic stimulation. Our model determines precise electric fields based on an individual head model derived from magnetic resonance imaging and calculates how these electric fields activate morphologically detailed models of different neuron types. We predict detailed neural activation patterns for different coil orientations consistent with experimental findings. Beyond this, our model allows us to predict activation thresholds for individual neurons and precise initiation sites of individual action potentials on the neurons’ complex morphologies. Specifically, our model predicts that cortical layer 3 pyramidal neurons are generally easier to stimulate than layer 5 pyramidal neurons, thereby explaining the lower stimulation thresholds observed for I-waves compared to D-waves. It also predicts differences in the regions of activated cortical layer 5 and layer 3 pyramidal cells depending on coil orientation. Finally, it predicts that under standard stimulation conditions, action potentials are mostly generated at the axon initial segment of corctial pyramidal cells, with a much less important activation site being the part of a layer 5 pyramidal cell axon where it crosses the boundary between grey matter and white matter. In conclusion, our computational model offers a detailed account of the mechanisms through which TMS activates different cortical cell types, paving the way for more targeted application of TMS based on individual brain morphology in clinical and basic research settings.

scholarly journals A multi-scale computational model of the effects of TMS on motor cortex

F1000Research ◽  
2016 ◽  
Vol 5 ◽  
pp. 1945 ◽  
Author(s):  
Hyeon Seo ◽  
Natalie Schaworonkow ◽  
Sung Chan Jun ◽  
Jochen Triesch
Keyword(s):  
Transcranial Magnetic Stimulation ◽  
Computational Model ◽  
Electric Fields ◽  
Action Potentials ◽  
Magnetic Stimulation ◽  
Pyramidal Neurons ◽  
Pyramidal Cells ◽  
Cell Types ◽  
Cortical Layer ◽  
Multi Scale

The detailed biophysical mechanisms through which transcranial magnetic stimulation (TMS) activates cortical circuits are still not fully understood. Here we present a multi-scale computational model to describe and explain the activation of different cell types in motor cortex due to transcranial magnetic stimulation. Our model determines precise electric fields based on an individual head model derived from magnetic resonance imaging and calculates how these electric fields activate morphologically detailed models of different neuron types. We predict detailed neural activation patterns for different coil orientations consistent with experimental findings. Beyond this, our model allows us to predict activation thresholds for individual neurons and precise initiation sites of individual action potentials on the neurons’ complex morphologies. Specifically, our model predicts that cortical layer 3 pyramidal neurons are generally easier to stimulate than layer 5 pyramidal neurons, thereby explaining the lower stimulation thresholds observed for I-waves compared to D-waves. It also predicts differences in the regions of activated cortical layer 5 and layer 3 pyramidal cells depending on coil orientation. Finally, it predicts that under standard stimulation conditions, action potentials are mostly generated at the axon initial segment of corctial pyramidal cells, with a much less important activation site being the part of a layer 5 pyramidal cell axon where it crosses the boundary between grey matter and white matter. In conclusion, our computational model offers a detailed account of the mechanisms through which TMS activates different cortical cell types, paving the way for more targeted application of TMS based on individual brain morphology in clinical and basic research settings.

scholarly journals A multi-scale computational model of the effects of TMS on motor cortex

F1000Research ◽  
2017 ◽  
Vol 5 ◽  
pp. 1945 ◽  
Author(s):  
Hyeon Seo ◽  
Natalie Schaworonkow ◽  
Sung Chan Jun ◽  
Jochen Triesch
Keyword(s):  
Motor Cortex ◽  
Computational Model ◽  
Electric Fields ◽  
Pyramidal Cell ◽  
Action Potentials ◽  
Pyramidal Neurons ◽  
Pyramidal Cells ◽  
Cell Types ◽  
Cortical Layer ◽  
Multi Scale

The detailed biophysical mechanisms through which transcranial magnetic stimulation (TMS) activates cortical circuits are still not fully understood. Here we present a multi-scale computational model to describe and explain the activation of different pyramidal cell types in motor cortex due to TMS. Our model determines precise electric fields based on an individual head model derived from magnetic resonance imaging and calculates how these electric fields activate morphologically detailed models of different neuron types. We predict neural activation patterns for different coil orientations consistent with experimental findings. Beyond this, our model allows us to calculate activation thresholds for individual neurons and precise initiation sites of individual action potentials on the neurons’ complex morphologies. Specifically, our model predicts that cortical layer 3 pyramidal neurons are generally easier to stimulate than layer 5 pyramidal neurons, thereby explaining the lower stimulation thresholds observed for I-waves compared to D-waves. It also shows differences in the regions of activated cortical layer 5 and layer 3 pyramidal cells depending on coil orientation. Finally, it predicts that under standard stimulation conditions, action potentials are mostly generated at the axon initial segment of cortical pyramidal cells, with a much less important activation site being the part of a layer 5 pyramidal cell axon where it crosses the boundary between grey matter and white matter. In conclusion, our computational model offers a detailed account of the mechanisms through which TMS activates different cortical pyramidal cell types, paving the way for more targeted application of TMS based on individual brain morphology in clinical and basic research settings.

scholarly journals A multi-scale computational model of the effects of TMS on motor cortex

F1000Research ◽  
2017 ◽  
Vol 5 ◽  
pp. 1945 ◽  
Author(s):  
Hyeon Seo ◽  
Natalie Schaworonkow ◽  
Sung Chan Jun ◽  
Jochen Triesch
Keyword(s):  
Motor Cortex ◽  
Computational Model ◽  
Electric Fields ◽  
Pyramidal Cell ◽  
Action Potentials ◽  
Pyramidal Neurons ◽  
Pyramidal Cells ◽  
Cell Types ◽  
Cortical Layer ◽  
Multi Scale

The detailed biophysical mechanisms through which transcranial magnetic stimulation (TMS) activates cortical circuits are still not fully understood. Here we present a multi-scale computational model to describe and explain the activation of different pyramidal cell types in motor cortex due to TMS. Our model determines precise electric fields based on an individual head model derived from magnetic resonance imaging and calculates how these electric fields activate morphologically detailed models of different neuron types. We predict neural activation patterns for different coil orientations consistent with experimental findings. Beyond this, our model allows us to calculate activation thresholds for individual neurons and precise initiation sites of individual action potentials on the neurons’ complex morphologies. Specifically, our model predicts that cortical layer 3 pyramidal neurons are generally easier to stimulate than layer 5 pyramidal neurons, thereby explaining the lower stimulation thresholds observed for I-waves compared to D-waves. It also shows differences in the regions of activated cortical layer 5 and layer 3 pyramidal cells depending on coil orientation. Finally, it predicts that under standard stimulation conditions, action potentials are mostly generated at the axon initial segment of cortical pyramidal cells, with a much less important activation site being the part of a layer 5 pyramidal cell axon where it crosses the boundary between grey matter and white matter. In conclusion, our computational model offers a detailed account of the mechanisms through which TMS activates different cortical pyramidal cell types, paving the way for more targeted application of TMS based on individual brain morphology in clinical and basic research settings.

scholarly journals Spontaneous Ultraslow Na+ Fluctuations in the Neonatal Mouse Brain

Cells ◽  
2019 ◽  
Vol 9 (1) ◽  
pp. 102 ◽  
Author(s):  
Lisa Felix ◽  
Daniel Ziemens ◽  
Gerald Seifert ◽  
Christine Rose
Keyword(s):  
Action Potentials ◽  
Pyramidal Neurons ◽  
Network Formation ◽  
Pyramidal Cells ◽  
Cell Types ◽  
Neonatal Mouse ◽  
Tissue Slices ◽  
Low Frequencies ◽  
Mouse Hippocampus ◽  
First Time

In the neonate forebrain, network formation is driven by the spontaneous synchronized activity of pyramidal cells and interneurons, consisting of bursts of electrical activity and intracellular Ca2+ oscillations. By employing ratiometric Na+ imaging in tissue slices obtained from animals at postnatal day 2–4 (P2–4), we found that 22% of pyramidal neurons and 43% of astrocytes in neonatal mouse hippocampus also exhibit transient fluctuations in intracellular Na+. These occurred at very low frequencies (~2/h), were exceptionally long (~8 min), and strongly declined after the first postnatal week. Similar Na+ fluctuations were also observed in the neonate neocortex. In the hippocampus, Na+ elevations in both cell types were diminished when blocking action potential generation with tetrodotoxin. Neuronal Na+ fluctuations were significantly reduced by bicuculline, suggesting the involvement of GABAA-receptors in their generation. Astrocytic signals, by contrast, were neither blocked by inhibition of receptors and/or transporters for different transmitters including GABA and glutamate, nor of various Na+-dependent transporters or Na+-permeable channels. In summary, our results demonstrate for the first time that neonatal astrocytes and neurons display spontaneous ultraslow Na+ fluctuations. While neuronal Na+ signals apparently largely rely on suprathreshold GABAergic excitation, astrocytic Na+ signals, albeit being dependent on neuronal action potentials, appear to have a separate trigger and mechanism, the source of which remains unclear at present.

scholarly journals Simulation of transcranial magnetic stimulation in head model with morphologically-realistic cortical neurons

2018 ◽  
Author(s):  
Aman Aberra ◽  
Boshuo Wang ◽  
Warren M Grill ◽  
Angel V Peterchev
Keyword(s):  
Transcranial Magnetic Stimulation ◽  
Electric Fields ◽  
Cortical Neurons ◽  
Magnetic Stimulation ◽  
Brain Activity ◽  
Pyramidal Cells ◽  
Human Head ◽  
Mechanistic Explanation ◽  
Head Model ◽  
Neural Activation

Transcranial magnetic stimulation (TMS) enables non-invasive modulation of brain activity with both clinical and research applications, but fundamental questions remain about the neural types and elements it activates and how stimulation parameters affect the neural response. We integrated detailed neuronal models with TMS-induced electric fields in the human head to quantify the effects of TMS on cortical neurons. TMS activated with lowest intensity layer 5 pyramidal cells at their intracortical axonal terminations in the superficial gyral crown and lip regions. Layer 2/3 pyramidal cells and inhibitory basket cells may be activated too, whereas direct activation of layers 1 and 6 was unlikely. Neural activation was largely driven by the field magnitude, contrary to theories implicating the field component normal to the cortical surface. Varying the induced current's direction caused a waveform-dependent shift in the activation site and provided a mechanistic explanation for experimentally observed differences in thresholds and latencies of muscle responses. This biophysically-based simulation provides a novel method to elucidate mechanisms and inform parameter selection of TMS and other forms of cortical stimulation.

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

2020 ◽  
Author(s):  
Sina Shirinpour ◽  
Nicholas Hananeia ◽  
James Rosado ◽  
Christos Galanis ◽  
Andreas Vlachos ◽  
...  
Keyword(s):  
Transcranial Magnetic Stimulation ◽  
Electric Fields ◽  
Magnetic Stimulation ◽  
Numerical Models ◽  
Repetitive Transcranial Magnetic Stimulation ◽  
Single Pulse ◽  
Neuron Models ◽  
Multi Scale ◽  
Scale Modeling ◽  
Multi Scale Modeling

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

scholarly journals Control of impulsivity by Gi-protein signalling in layer-5 pyramidal neurons of the anterior cingulate cortex

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Bastiaan van der Veen ◽  
Sampath K. T. Kapanaiah ◽  
Kasyoka Kilonzo ◽  
Peter Steele-Perkins ◽  
Martin M. Jendryka ◽  
...  
Keyword(s):  
Gene Expression ◽  
Anterior Cingulate Cortex ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
Pyramidal Neurons ◽  
Treatment Options ◽  
Pyramidal Cells ◽  
Cingulate Cortex ◽  
Cell Types ◽  
Anterior Cingulate

AbstractPathological impulsivity is a debilitating symptom of multiple psychiatric diseases with few effective treatment options. To identify druggable receptors with anti-impulsive action we developed a systematic target discovery approach combining behavioural chemogenetics and gene expression analysis. Spatially restricted inhibition of three subdivisions of the prefrontal cortex of mice revealed that the anterior cingulate cortex (ACC) regulates premature responding, a form of motor impulsivity. Probing three G-protein cascades with designer receptors, we found that the activation of Gi-signalling in layer-5 pyramidal cells (L5-PCs) of the ACC strongly, reproducibly, and selectively decreased challenge-induced impulsivity. Differential gene expression analysis across murine ACC cell-types and 402 GPCRs revealed that - among Gi-coupled receptor-encoding genes - Grm2 is the most selectively expressed in L5-PCs while alternative targets were scarce. Validating our approach, we confirmed that mGluR2 activation reduced premature responding. These results suggest Gi-coupled receptors in ACC L5-PCs as therapeutic targets for impulse control disorders.

Single-unit analysis of different hippocampal cell types during classical conditioning of rabbit nictitating membrane response

1983 ◽  
Vol 50 (5) ◽  
pp. 1197-1219 ◽  
Author(s):  
T. W. Berger ◽  
P. C. Rinaldi ◽  
D. J. Weisz ◽  
R. F. Thompson
Keyword(s):  
Classical Conditioning ◽  
Pyramidal Cell ◽  
Action Potentials ◽  
Pyramidal Cells ◽  
Cell Types ◽  
Firing Frequency ◽  
Spontaneous Firing ◽  
Nictitating Membrane ◽  
Nictitating Membrane Response ◽  
Firing Characteristics

Extracellular single-unit recordings from neurons in the CA1 and CA3 regions of the dorsal hippocampus were monitored during classical conditioning of the rabbit nictitating membrane response. Neurons were classified as different cell types using response to fornix stimulation (i.e., antidromic or orthodromic activation) and spontaneous firing characteristics as criteria. Results showed that hippocampal pyramidal neurons exhibit learning-related neural plasticity that develops gradually over the course of classical conditioning. The learning-dependent pyramidal cell response is characterized by an increase in frequency of firing within conditioning trials and a within-trial pattern of discharge that correlates strongly with amplitude-time course of the behavioral response. In contrast, pyramidal cell activity recorded from control animals given unpaired presentations of the conditioned and unconditioned stimulus (CS and UCS) does not show enhanced discharge rates with repeated stimulation. Previous studies of hippocampal cellular electrophysiology have described what has been termed a theta-cell (19-21, 45), the activity of which correlates with slow-wave theta rhythm generated in the hippocampus. Neurons classified as theta-cells in the present study exhibit responses during conditioning that are distinctly different than pyramidal cells. theta-Cells respond during paired conditioning trials with a rhythmic bursting; the between-burst interval occurs at or near 8 Hz. In addition, two different types of theta-cells were distinguishable. One type of theta-cell increases firing frequency above pretrial levels while displaying the theta bursting pattern. The other type decreases firing frequency below pretrial rates while showing a theta-locked discharge. In addition to pyramidal and theta-neurons, several other cell types recorded in or near the pyramidal cell layer could be distinguished. One cell type was distinctive in that it could be activated with a short, invariant latency following fornix stimulation, but spontaneous action potentials of such neurons could not be collided with fornix shock-induced action potentials. These neurons exhibit a different profile of spontaneous firing characteristics than those of antidromically identified pyramidal cells. Nevertheless, neurons in this noncollidable category display the same learning-dependent response as pyramidal cells. It is suggested that the noncollidable neurons represent a subpopulation of pyramidal cells that do not project an axon via the fornix but project, instead, to other limbic cortical regions.(ABSTRACT TRUNCATED AT 400 WORDS)

scholarly journals Bayesian modeling of BAC firing as a mechanism for apical amplification in neocortical pyramidal neurons

2019 ◽  
Author(s):  
Jim W. Kay ◽  
W. A. Phillips ◽  
Jaan Aru ◽  
Bruce P. Graham ◽  
Matthew E. Larkum
Keyword(s):  
Action Potentials ◽  
Pyramidal Neurons ◽  
Pyramidal Cells ◽  
Single Site ◽  
Biophysical Model ◽  
Cellular Mechanisms ◽  
Context Sensitive ◽  
Dendritic Shaft ◽  
Current Task ◽  
Integration Sites

AbstractPyramidal cells in layer 5 of the neocortex have two distinct integration sites. These cells integrate inputs to basal dendrites in the soma while integrating inputs to the tuft in a site at the top of the apical trunk. The two sites communicate by action potentials that backpropagate to the apical site and by backpropagation-activated calcium spikes (BAC firing) that travel from the apical to the somatic site. Six key messages arise from the probabilistic information-theoretic analyses of BAC firing presented here. First, we suggest that pyramidal neurons with BAC firing could convert the odds in favour of the presence of a feature given the basal data into the odds in favour of the presence of a feature given the basal data and the apical input, by a simple Bayesian calculation. Second, the strength of the cell’s response to basal input can be amplified when relevant to the current context, as specified by the apical input, without corrupting the message that it sends. Third, these analyses show rigorously how this apical amplification depends upon communication between the sites. Fourth, we use data on action potentials from a very detailed multi-compartmental biophysical model to study our general model in a more realistic setting, and demonstrate that it describes the data well. Fifth, this form of BAC firing meets criteria for distinguishing modulatory from driving interactions that have been specified using recent definitions of multivariate mutual information. Sixth, our general decomposition can be extended to cases where, instead of being purely driving or purely amplifying, apical and basal inputs can be partly driving and partly amplifying to various extents. These conclusions imply that an advance beyond the assumption of a single site of integration within pyramidal cells is needed, and suggest that the evolutionary success of neocortex may depend upon the cellular mechanisms of context-sensitive selective amplification hypothesized here.Author summaryThe cerebral cortex has a key role in conscious perception, thought, and action, and is predominantly composed of a particular kind of neuron: the pyramidal cells. The distinct shape of the pyramidal neuron with a long dendritic shaft separating two regions of profuse dendrites allows them to integrate inputs to the two regions separately and combine the results non-linearly to produce output. Here we show how inputs to this more distant site strengthen the cell’s output when it is relevant to the current task and environment. By showing that such neurons have capabilities that transcend those of neurons with the single site of integration assumed by many neuroscientists, this ‘splitting of the neuronal atom’ offers a radically new viewpoint from which to understand the evolution of the cortex and some of its many pathologies. This also suggests that approaches to artificial intelligence using neural networks might come closer to something analogous to real intelligence, if, instead of basing them on processing elements with a single site of integration, they were based on elements with two sites, as in cortex.

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