79 research outputs found
Estimation of the motor threshold for near-rectangular stimuli using the Hodgkin-Huxley model
The motor threshold measurement is a standard in preintervention probing in TMS experiments. We aim to predict the motor threshold for near-rectangular stimuli to efficiently determine the motor threshold size before any experiments take place. Estimating the behavior of large-scale networks requires dynamically accurate and efficient modeling. We utilized a Hodgkin–Huxley (HH) type model to evaluate motor threshold values and computationally validated its function with known true threshold data from 50 participants trials from state-of-the-art published datasets. For monophasic, bidirectional, and unidirectional rectangular stimuli in posterior-anterior or anterior-posterior directions as generated by the cTMS device, computational modeling of the HH model captured the experimentally measured population-averaged motor threshold values at high precision (maximum error ≤ 8%). The convergence of our biophysically based modeling study with experimental data in humans reveals that the effect of the stimulus shape is strongly correlated with the activation kinetics of the voltage-gated ion channels. The proposed method can reliably predict motor threshold size using the conductance-based neuronal models and could therefore be embedded in new generation neurostimulators. Advancements in neural modeling will make it possible to enhance treatment procedures by reducing the number of delivered magnetic stimuli to participants
Transcranial magnetic stimulation of the brain: What is stimulated? – A consensus and critical position paper
Copyright © 2022 The Author(s) and International Federation of Clinical Neurophysiology. Transcranial (electro)magnetic stimulation (TMS) is currently the method of choice to non-invasively induce neural activity in the human brain. A single transcranial stimulus induces a time-varying electric field in the brain that may evoke action potentials in cortical neurons. The spatial relationship between the locally induced electric field and the stimulated neurons determines axonal depolarization. The induced electric field is influenced by the conductive properties of the tissue compartments and is strongest in the superficial parts of the targeted cortical gyri and underlying white matter. TMS likely targets axons of both excitatory and inhibitory neurons. The propensity of individual axons to fire an action potential in response to TMS depends on their geometry, myelination and spatial relation to the imposed electric field and the physiological state of the neuron. The latter is determined by its transsynaptic dendritic and somatic inputs, intrinsic membrane potential and firing rate. Modeling work suggests that the primary target of TMS is axonal terminals in the crown top and lip regions of cortical gyri. The induced electric field may additionally excite bends of myelinated axons in the juxtacortical white matter below the gyral crown. Neuronal excitation spreads ortho- and antidromically along the stimulated axons and causes secondary excitation of connected neuronal populations within local intracortical microcircuits in the target area. Axonal and transsynaptic spread of excitation also occurs along cortico-cortical and cortico-subcortical connections, impacting on neuronal activity in the targeted network. Both local and remote neural excitation depend critically on the functional state of the stimulated target area and network. TMS also causes substantial direct co-stimulation of the peripheral nervous system. Peripheral co-excitation propagates centrally in auditory and somatosensory networks, but also produces brain responses in other networks subserving multisensory integration, orienting or arousal. The complexity of the response to TMS warrants cautious interpretation of its physiological and behavioural consequences, and a deeper understanding of the mechanistic underpinnings of TMS will be critical for advancing it as a scientific and therapeutic tool.Aman S. Aberra was supported by a U. S. A. National Science Foundation Graduate Research Fellowship (No. DGF 1106401). Andrea Antal has been supported by a grant of the Federal Ministry of Education and Research (BMBF) of Germany (Grant 01GP2124B) and by a grant of the Lower Saxony Ministry of Science and Culture (Grant 76251-12-7/19 ZN 3456). Marco Davare has been supported by a BBSRC responsive mode grant. Klaus Funke has been supported by a grant of the Federal Ministry of Education and Research (BMBF) of Germany (Grant 01EE1403B) as part of the German Center for Brain Stimulation (GCBS) and by the Deutsche Forschungsgemeinschaft (DFG) (Grants FU256/3-2; 122679504–SFB874). Mark Hallett is supported by the NINDS Intramural Program. Anke N. Karabanov holds a 4-year Sapere Aude Fellowship which is sponsored by the Independent Research Fund Denmark (Grant Nr. 0169-00027B). The sponsor had no direct involvement in the collection, analysis and interpretation of data and in the writing of the manuscript. Giacomo Koch has been supported by na EU grant H2020-EU.1.2.2. - FET Proactive (Neurotwin ID: 101017716). Sabine Meunier is Emeritus Research Director at INSERM, this has no direct involvement in the collection, analysis and interpretation of data and in the writing of the manuscript. Carlo Miniussi has been supported by a grant of the Caritro Foundation, Italy. Walter Paulus received grants from the Deutsche Forschungsgemeinschaft and BMBF. Angel V. Peterchev was supported by grants from the U. S. A. National Institutes of Health (Grants Nos. R01NS117405, R01NS088674, RF1MH114268, R01MH111865). Traian Popa has been supported by the Defitech Foundation and NIBS-iCog grant from the Swiss National Science Foundation. Hartwig R. Siebner holds a 5-year professorship in precision medicine at the Faculty of Health Sciences and Medicine, University of Copenhagen which is sponsored by the Lundbeck Foundation (Grant Nr. R186-2015-2138). The salary for Janine Kesselheim (PhD project) has been covered by a project grant “Biophysically adjusted state-informed cortex stimulation” (BASICS) funded by a synergy grant from Novo Nordisk Foundation (PI: Hartwig R Siebner, Interdisciplinary Synergy Program 2014; grant number NNF14OC001). Axel Thielscher has been supported by grants of the Lundbeck foundation (R118-A11308, R244-2017-196 and R313-2019-622). Yoshikazu Ugawa has been supported in part by grants from the Research Project Grant-in-aid for Scientific Research from the Ministry of Education, Culture, Sports, Science and Technology (Grants 15H05881, 16H05322, 19H01091, 20K07866). Ulf Ziemann received grants from the German Ministry of Education and Research (BMBF), European Research Council (ERC), and German Research Foundation (DFG)
Threshold estimation with transcranial magnetic stimulation : algorithm comparison
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Noise analysis and active noise control strategy of transcranial magnetic stimulation device
Effect of coil orientation on strength-duration time constant with controllable pulse parameter transcranial magnetic stimulation
Erratum to ‘Electroconvulsive stimulation increases astrocyte marker GFAP in mouse hippocampus regardless of chronic social defeat stress’ [12 (2) (March–April 2019) 543]
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