TMS-evoked Oscillations Research Articles

Transferability of cathodal tDCS effects from the primary motor to the prefrontal cortex: A multimodal TMS-EEG study

Neurophysiological effects of transcranial direct current stimulation (tDCS) have been extensively studied over the primary motor cortex (M1). Much less is however known about its effects over non-motor areas, such as the prefrontal cortex (PFC), which is the neuronal foundation for many high-level cognitive functions and involved in neuropsychiatric disorders. In this study, we, therefore, explored the transferability of cathodal tDCS effects over M1 to the PFC. Eighteen healthy human participants (11 males and 8 females) were involved in eight randomized sessions per participant, in which four cathodal tDCS dosages, low, medium, and high, as well as sham stimulation, were applied over the left M1 and left PFC. After-effects of tDCS were evaluated via transcranial magnetic stimulation (TMS)-electroencephalography (EEG), and TMS-elicited motor evoked potentials (MEP), for the outcome parameters TMS-evoked potentials (TEP), TMS-evoked oscillations, and MEP amplitude alterations. TEPs were studied both at the regional and global scalp levels. The results indicate a regional dosage-dependent nonlinear neurophysiological effect of M1 tDCS, which is not one-to-one transferable to PFC tDCS. Low and high dosages of M1 tDCS reduced early positive TEP peaks (P30, P60), and MEP amplitudes, while an enhancement was observed for medium dosage M1 tDCS (P30). In contrast, prefrontal low, medium and high dosage tDCS uniformly reduced the early positive TEP peak amplitudes. Furthermore, for both cortical areas, regional tDCS-induced modulatory effects were not observed for late TEP peaks, nor TMS-evoked oscillations. However, at the global scalp level, widespread effects of tDCS were observed for both, TMS-evoked potentials and oscillations. This study provides the first direct physiological comparison of tDCS effects applied over different brain areas and therefore delivers crucial information for future tDCS applications.

Comparison of synchrosqueezing transform to alternative methods for time-frequency analysis of TMS-evoked EEG oscillations

BackgroundTranscranial magnetic stimulation combined with electroencephalography (TMS-EEG) has become a powerful tool to assess cortical properties, such as cortical oscillation. An accurate and robust time–frequency analysis tool with high resolution would facilitate the understanding of TMS-evoked oscillations. MethodsThe synchrosqueezing transform (SST), the Hilbert-Huang transform (HHT) and the Morlet wavelet transform (MWT) were used to analyze TMS-evoked oscillations. Firstly, we generated simulation data, and compared the performance of three methods to analyze the time–frequency characteristics of simulation data; then, we collected TMS-EEG data from normal people (NOR), patients with minimally conscious state (MCS) and vegetative state (VS). SST was used to analysis time–frequency characteristics of TMS-evoked oscillations in different states of consciousness. In addition, relative power (RP) and spectral entropy (SeEn) were calculated based on SST results. ResultsSimulation results showed that SST detected rhythm characteristics of instantaneous signal and background signal more completely. Results of NOR group showed that SST could more accurately detect TMS-evoked oscillations with high frequency resolution than HHT and MWT. The main frequency of TMS-evoked oscillations for DOC patients (MCS: 11.73 ± 1.94 Hz; VS: 2.06 ± 0.25 Hz) was different from that of NOR (22.79 ± 1.42 Hz) based on SST. RP and SeEn further verified the differences of the main frequency of TMS-evoked oscillations between NOR and DOC patients. ConclusionsThe results suggest that SST is a robust analytical method and outperforms HHT and MWT in studying TMS-evoked oscillations. The main frequency of TMS-evoked oscillations of DOC was lower than that of NOR.

Dynamic reorganization of TMS-evoked activity in subcortical stroke patients

Since early days after stroke, the brain undergoes a complex reorganization to allow compensatory mechanisms that promote functional recovery. However, these mechanisms are still poorly understood and there is urgent need to identify neurophysiological markers of functional recovery after stroke. Here we aimed to track longitudinally the time-course of cortical reorganization by measuring for the first time EEG cortical activity evoked by TMS pulses in patients with subcortical stroke.Thirteen patients in the sub-acute phase of ischemic subcortical stroke with motor symptoms completed the longitudinal study, being evaluated within 20 days and after 40, 60 and 180 days after stroke onset. For each time-point, EEG cortical activity evoked by single TMS pulses was assessed over the motor and parietal cortex of the affected and unaffected hemisphere. We evaluated global TMS-evoked activity and TMS-evoked oscillations in different frequency bands. These measurements were paralleled with clinical and behavioral assessment.We found that motor cortical activity measured by TMS-EEG varied across time in the affected hemisphere. An increase of TMS-evoked activity was evident at 40 days after stroke onset. Moreover, stroke patients showed a significant increase in TMS-evoked alpha oscillations, as highlighted performing analysis in the time-frequency domain. Notably, these changes indicated that crucial mechanisms of cortical reorganization occur in this short-time window. These changes coincided with the clinical improvement. TMS-evoked alpha oscillatory activity recorded at baseline was associated to better functional recovery at 40 and 60 days' follow-up evaluations, suggesting that the power of the alpha rhythm can be considered a good predictor of motor recovery. This study demonstrates that cortical activity increases dynamically in the early phases of recovery after stroke in the affected hemisphere. These findings point to TMS-evoked alpha oscillatory activity as a potential neurophysiological markers of stroke recovery and could be helpful to determine the temporal window in which neuromodulation should be potentially able to drive neuroplasticity in an effective functional direction.

Subthalamic stimulation and levodopa modulate cortical reactivity in Parkinson's patients

BackgroundThe effects of deep brain stimulation of the subthalamic nucleus (DBS-STN) and L-dopa (LD) on cortical activity in Parkinson's disease (PD) are poorly understood. ObjectivesBy combining transcranial magnetic stimulation (TMS) and electroencephalography (EEG) we explored the effects of STN-DBS, either alone or in combination with L-Dopa (LD), on TMS-evoked cortical activity in a sample of implanted PD patients. MethodsPD patients were tested in three clinical conditions: i) LD therapy with STN-DBS turned on (ON/ON condition); ii) without LD therapy with STN-DBS turned on (OFF/ON condition); iii) without LD therapy with STN-DBS turned off (OFF/OFF condition). TMS pulses were delivered over left M1 while simultaneously acquiring EEG. Eight age-matched healthy volunteers (HC) were tested as a control group. ResultsSTN-DBS enhanced early global TMS-evoked activity (∼45–80ms) and high-alpha TMS-evoked oscillations (11–13 Hz) as compared to OFF/OFF condition, independently from concomitant LD therapy. LD intake (ON/ON condition) produced a further increase of late TMS-evoked activity (∼80–130ms) and beta TMS-evoked oscillations (13–30 Hz), as compared to OFF/OFF and OFF/ON conditions, that normalized reactivity as compared to HC range of values. ConclusionsOur data reveal that bilateral STN-DBS and LD therapy induce a modulation of specific cortical components and specific ranges of frequency. These findings demonstrate that STN-DBS and LD therapy may have synergistic effects on motor cortical activity.

Cortical inhibition of distinct mechanisms in the dorsolateral prefrontal cortex is related to working memory performance: A TMS–EEG study

Paired-pulse transcranial magnetic stimulation combined with electroencephalography (TMS–EEG) is a method for studying cortical inhibition from the dorsolateral prefrontal cortex (DLPFC). However, little is known about the mechanisms underlying TMS-evoked cortical potentials (TEPs) from this region, let alone inhibition of these components. The aim of this study was to assess cortical inhibition of distinct TEPs and oscillations in the DLPFC using TMS–EEG and to investigate the relationship of these mechanisms to working memory. 30 healthy volunteers received single and paired (interstimulus interval = 100 msec) TMS to the left DLPFC. Variations in long-interval cortical inhibition (LICI) of different TEP peaks (N40, P60, N100) and different TMS-evoked oscillations (alpha, lower beta, upper beta, gamma) were compared between individuals. Variation in N100 slope following single pulse TMS, another putative marker of inhibition, was also compared with LICI of each measure. Finally, these measures were correlated with performance of a working memory task. LICI resulted in significant suppression of all TEP peaks and TMS-evoked oscillations (all p < .05). There were no significant correlations between LICI of different TEP peaks or TMS-evoked oscillations with the exception of P60 and N100. Variation in N100 slope correlated with LICI of N40 and beta oscillations. In addition, LICI of P60 and N100 were differentially correlated with working memory performance. The results suggest that both the LICI paradigm and N100 following single pulse TMS reflect complementary methods for assessing GABAB-mediated cortical inhibition in the DLPFC. Furthermore, these measures demonstrate the importance of prefrontal GABAB-mediated inhibitory control for working memory performance.

Removing artefacts from TMS-EEG recordings using independent component analysis: Importance for assessing prefrontal and motor cortex network properties

IntroductionThe combination of transcranial magnetic stimulation and electroencephalography (TMS-EEG) is emerging as a powerful tool for causally investigating cortical mechanisms and networks. However, various artefacts contaminate TMS-EEG recordings, particularly over regions such as the dorsolateral prefrontal cortex (DLPFC). The aim of this study was to substantiate removal of artefacts from TMS-EEG recordings following stimulation of the DLPFC and motor cortex using independent component analysis (ICA). Methods36 healthy volunteers (30.8±9years, 9 female) received 75 single TMS pulses to the left DLPFC or left motor cortex while EEG was recorded from 57 electrodes. A subset of 9 volunteers also received 50 sham pulses. The large TMS artefact and early muscle activity (−2 to ~15ms) were removed using interpolation and the remaining EEG signal was processed in two separate ICA runs using the FastICA algorithm. Five sub-types of TMS-related artefacts were manually identified: remaining muscle artefacts, decay artefacts, blink artefacts, auditory-evoked potentials and other noise-related artefacts. The cause of proposed blink and auditory-evoked potentials was assessed by concatenating known artefacts (i.e. voluntary blinks or auditory-evoked potentials resulting from sham TMS) to the TMS trials before ICA and evaluating grouping of resultant independent components (ICs). Finally, we assessed the effect of removing specific artefact types on TMS-evoked potentials (TEPs) and TMS-evoked oscillations. ResultsOver DLPFC, ICs from proposed muscle and decay artefacts correlated with TMS-evoked muscle activity size, whereas proposed TMS-evoked blink ICs combined with voluntary blinks and auditory ICs with auditory-evoked potentials from sham TMS. Individual artefact sub-types characteristically distorted each measure of DLPFC function across the scalp. When free of artefact, TEPs and TMS-evoked oscillations could be measured following DLPFC stimulation. Importantly, characteristic TEPs following motor cortex stimulation (N15, P30, N45, P60, N100) could be recovered from artefactual data, corroborating the reliability of ICA-based artefact correction. ConclusionsVarious different artefacts contaminate TMS-EEG recordings over the DLPFC and motor cortex. However, these artefacts can be removed with apparent minimal impact on neural activity using ICA, allowing the study of TMS-evoked cortical network properties.

Dorsolateral prefrontal cortex network properties are altered in schizophrenia: a TMS-EEG study

Event Abstract Back to Event Dorsolateral prefrontal cortex network properties are altered in schizophrenia: a TMS-EEG study Nigel Rogasch1*, Tarek Rajji2, Lisa C. Tran2, Neil Bailey1, Bernadette M. Fitzgibbon1, Zafiris J. Daskalakis2 and Paul Fitzgerald1 1 Monash University, Monash Alfred Psychiatry Research Centre, Australia 2 Centre for Addiction and Mental Health, University of Toronto, Temerty Centre for Therapeutic Brain Intervention, Canada Background: Dysfunctional dorsolateral prefrontal cortex (DLPFC) activation during working memory is a consistent finding in schizophrenia (SCZ). However, determining whether these deficits reflect aberrant DLPFC network properties or impaired attention, motivation or sensory integration has proven difficult. The aim of this study was to assess DLPFC function evoked by non-invasive transcranial magnetic stimulation (TMS) and working memory performance in people with and without SCZ. Methods: 19 volutneers with SCZ and 20 healthy controls received single TMS pulses to the left DLPFC while electroencephalography was recorded. Several indices of TMS-evoked cortical function were measured at the DLPFC and across the scalp including TMS-evoked cortical potentials such as the N100 (a putative marker of cortical inhibition) and TMS-evoked cortical oscillations. Working memory was assessed using the Sternberg letter recognition task with 5 and 7 letters. Results: The N100 slope and amplitude were reduced over the DLPFC of SCZ participants compared with controls, whereas latter TMS-evoked potentials (P180) were increased. TMS-evoked oscillations (13-45 Hz) at the DLPFC were also reduced, as was propagation of gamma (31-45 Hz) oscillations to left parietal cortex and upper beta (21-30 Hz) oscillations to contralateral DLPFC. SCZ participants with low working memory capacity displayed significantly reduced TMS-evoked gamma oscillations over DLPFC compared with other SCZ participants and controls. Discussion: Intrinsic DLPFC network properties such as cortical inhibition and the ability to entrain high frequency oscillations in local and distant cortical regions are altered in SCZ. A reduced capability of the DLPFC to generate gamma oscillations may contribute to SCZ-related working memory deficits. Keywords: Schizophrenia, working memory, Transcranial Magnetic Stimulation, gamma oscillations, plasticity Conference: ACNS-2013 Australasian Cognitive Neuroscience Society Conference, Clayton, Melbourne, Australia, 28 Nov - 1 Dec, 2013. Presentation Type: Oral Topic: Executive Processes Citation: Rogasch N, Rajji T, Tran LC, Bailey N, Fitzgibbon BM, Daskalakis ZJ and Fitzgerald P (2013). Dorsolateral prefrontal cortex network properties are altered in schizophrenia: a TMS-EEG study. Conference Abstract: ACNS-2013 Australasian Cognitive Neuroscience Society Conference. doi: 10.3389/conf.fnhum.2013.212.00158 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 15 Oct 2013; Published Online: 25 Nov 2013. * Correspondence: Mr. Nigel Rogasch, Monash University, Monash Alfred Psychiatry Research Centre, Melbourne, Victoria, 3004, Australia, nigel.rogasch@monash.edu Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Nigel Rogasch Tarek Rajji Lisa C Tran Neil Bailey Bernadette M Fitzgibbon Zafiris J Daskalakis Paul Fitzgerald Google Nigel Rogasch Tarek Rajji Lisa C Tran Neil Bailey Bernadette M Fitzgibbon Zafiris J Daskalakis Paul Fitzgerald Google Scholar Nigel Rogasch Tarek Rajji Lisa C Tran Neil Bailey Bernadette M Fitzgibbon Zafiris J Daskalakis Paul Fitzgerald PubMed Nigel Rogasch Tarek Rajji Lisa C Tran Neil Bailey Bernadette M Fitzgibbon Zafiris J Daskalakis Paul Fitzgerald Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Time–frequency spectral analysis of TMS-evoked EEG oscillations by means of Hilbert–Huang transform

A single pulse of Transcranial Magnetic Stimulation (TMS) generates electroencephalogram (EEG) oscillations that are thought to reflect intrinsic properties of the stimulated cortical area and its fast interactions with other cortical areas. Thus, a tool to decompose TMS-evoked oscillations in the time–frequency domain on a millisecond timescale and on a broadband frequency range may help to understand information transfer across cortical oscillators. Some recent studies have employed algorithms based on the Wavelet Transform (WT) to study TMS-evoked EEG oscillations in healthy and pathological conditions. However, these methods do not allow to describe TMS-evoked EEG oscillations with high resolution in time and frequency domains simultaneously. Here, we first develop an algorithm based on Hilbert–Huang Transform (HHT) to compute statistically significant time–frequency spectra of TMS-evoked EEG oscillations on a single trial basis. Then, we compared the performances of the HHT-based algorithm with the WT-based one by applying both of them to a set of simulated signals. Finally, we applied both algorithms to real TMS-evoked potentials recorded in healthy or schizophrenic subjects. We found that the HHT-based algorithm outperforms the WT-based one in detecting the time onset of TMS-evoked oscillations in the classical EEG bands. These results suggest that the HHT-based algorithm may be used to study the communication between different cortical oscillators on a fine time scale.