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Understanding how spontaneous brain activity influences the response to neurostimulation is crucial for the development of neurotherapeutics and brain-computer interfaces. Localized brain activity is suggested to influence the response to neurostimulation, but whether fast-fluctuating (i.e., tens of milliseconds) large-scale brain dynamics also have any such influence is unknown. By stimulating the prefrontal cortex using combined transcranial magnetic stimulation (TMS) and electroencephalography, we examined how dynamic global brain state patterns, as defined by microstates, influence the magnitude of the evoked brain response. TMS applied during what resembled the canonical Microstate C was found to induce a greater evoked response for up to 80 ms compared with other microstates. This effect was found in a repeated experimental session, was absent during sham stimulation, and was replicated in an independent dataset. Ultimately, ongoing and fast-fluctuating global brain states, as probed by microstates, may be associated with intrinsic fluctuations in connectivity and excitation-inhibition balance and influence the neurostimulation outcome. We suggest that the fast-fluctuating global brain states be considered when developing any related paradigms., Competing Interests: D.M.B. has received in-kind equipment support for investigator-initiated studies from MagVenture; he has received research support and in-kind equipment support for an investigator-initiated study from BrainsWay and is the site principal investigator for three sponsor-initiated studies for BrainsWay; he has received medication supplies for an investigator-initiated trial from Indivior; and he has participated on an advisory board for Janssen. Z.J.D. has received research and equipment in-kind support for an investigator-initiated study through BrainsWay and MagVenture. A.K., P.D., M.D.G., S.M., and F.F. report no biomedical financial interests or potential competing financial interests., (Copyright © 2024 the authors.)

Importance: Social and economic costs of depression are exacerbated by prolonged periods spent identifying treatments that would be effective for a particular patient. Thus, a tool that reliably predicts an individual patient's response to treatment could significantly reduce the burden of depression., Objective: To estimate how accurately an outcome of escitalopram treatment can be predicted from electroencephalographic (EEG) data on patients with depression., Design, Setting, and Participants: This prognostic study used a support vector machine classifier to predict treatment outcome using data from the first Canadian Biomarker Integration Network in Depression (CAN-BIND-1) study. The CAN-BIND-1 study comprised 180 patients (aged 18-60 years) diagnosed with major depressive disorder who had completed 8 weeks of treatment. Of this group, 122 patients had EEG data recorded before the treatment; 115 also had EEG data recorded after the first 2 weeks of treatment., Interventions: All participants completed 8 weeks of open-label escitalopram (10-20 mg) treatment., Main Outcomes and Measures: The ability of EEG data to predict treatment outcome, measured as accuracy, specificity, and sensitivity of the classifier at baseline and after the first 2 weeks of treatment. The treatment outcome was defined in terms of change in symptom severity, measured by the Montgomery-Åsberg Depression Rating Scale, before and after 8 weeks of treatment. A patient was designated as a responder if the Montgomery-Åsberg Depression Rating Scale score decreased by at least 50% during the 8 weeks and as a nonresponder if the score decrease was less than 50%., Results: Of the 122 participants who completed a baseline EEG recording (mean [SD] age, 36.3 [12.7] years; 76 [62.3%] female), the classifier was able to identify responders with an estimated accuracy of 79.2% (sensitivity, 67.3%; specificity, 91.0%) when using only the baseline EEG data. For a subset of 115 participants who had additional EEG data recorded after the first 2 weeks of treatment, use of these data increased the accuracy to 82.4% (sensitivity, 79.2%; specificity, 85.5%)., Conclusions and Relevance: These findings demonstrate the potential utility of EEG as a treatment planning tool for escitalopram therapy. Further development of the classification tools presented in this study holds the promise of expediting the search for optimal treatment for each patient.

Background: Cholinergic dysfunction is increasingly assumed to be involved in the pathophysiology of schizophrenia. Short-latency afferent inhibition (SAI) is a transcranial magnetic stimulation (TMS) paradigm that has been shown to assay central cholinergic activity from the motor cortex (M1). Recently, we established a method to index SAI from the dorsolateral prefrontal cortex (DLPFC), an area implicated in the pathophysiology of schizophrenia. We investigated SAI in M1 and DLPFC in schizophrenia. We hypothesized that modulation of N100 on TMS-evoked potentials (TEPs) from the DLPFC would be attenuated in patients with schizophrenia compared to healthy controls., Methods: SAI was examined in 12 patients, whose age was matched to controls, using TMS combined with electroencephalography (EEG). SAI was recorded with TMS applied to left M1 (M1-SAI) and DLPFC (DLPFC-SAI). For group comparison, we used the SAI data of healthy participants in our previous study., Results: In patients, N100 TEP was significantly attenuated with DLPFC-SAI, whereas P180 TEP was significantly increased with M1-SAI. Between patients and controls, there were significant differences in modulation of P180 TEP by M1-SAI (t22 = -2.748, P = .012; patients > controls) and N100 TEP by DLPFC-SAI (t22 = 5.456, P < .0001; patients < controls). Further, modulation of N100 TEP by DLPFC-SAI significantly correlated with executive function (r = -.740, P = .006, N = 12)., Conclusion: Our findings suggest that DLPFC-SAI but not M1-SAI were reduced in patients with schizophrenia and this was linked to deficits in cognition. This may reflect prefrontal cholinergic deficits and represent a biomarker for cholinergic and executive dysfunction in patients with schizophrenia., (© The Author(s) 2017. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Cortical inhibition (CI) occurs largely through GABA receptor-mediated inhibitory neurotransmission, which can be modulated by cholinergic, dopaminergic, and glutamatergic inputs. Transcranial magnetic stimulation (TMS) can be used to index CI through a paradigm known as long-interval CI (LICI). When TMS is combined with electroencephalography (EEG), LICI can index GABA receptor-mediated inhibitory neurotransmission in the dorsolateral prefrontal cortex (DLPFC). We conducted a hypothesis-driven pharmacological study to assess the role of cholinergic, dopaminergic, GABAergic, and glutamatergic neurotransmission on LICI from the DLPFC using TMS-EEG. In this randomized controlled, double-blind crossover within-subject study, 12 healthy participants received five sessions of LICI to the DLPFC in a random order, each preceded by the administration of placebo or one of the four active drugs. LICI was assessed after each drug administration and compared to LICI after placebo. Relative to placebo, baclofen resulted in a significant increase in LICI, while rivastigmine resulted in a significant decrease in LICI. Dextromethorphan and L-DOPA did not result in a significant change in LICI relative to placebo. Our study confirms that LICI in the DLPFC is largely mediated by GABA B receptor-mediated inhibitory neurotransmission and also suggests that cholinergic modulation decreases LICI in the DLPFC. Such findings may help guide future work examining the neurophysiological impact of these neurotransmitters in healthy and diseased states.

Subsequent to global initiatives in mapping the human brain and investigations of neurobiological markers for brain disorders, the number of multi-site studies involving the collection and sharing of large volumes of brain data, including electroencephalography (EEG), has been increasing. Among the complexities of conducting multi-site studies and increasing the shelf life of biological data beyond the original study are timely standardization and documentation of relevant study parameters. We present the insights gained and guidelines established within the EEG working group of the Canadian Biomarker Integration Network in Depression (CAN-BIND). CAN-BIND is a multi-site, multi-investigator, and multi-project network supported by the Ontario Brain Institute with access to Brain-CODE, an informatics platform that hosts a multitude of biological data across a growing list of brain pathologies. We describe our approaches and insights on documenting and standardizing parameters across the study design, data collection, monitoring, analysis, integration, knowledge-translation, and data archiving phases of CAN-BIND projects. We introduce a custom-built EEG toolbox to track data preprocessing with open-access for the scientific community. We also evaluate the impact of variation in equipment setup on the accuracy of acquired data. Collectively, this work is intended to inspire establishing comprehensive and standardized guidelines for multi-site studies.

Over 350 million people worldwide suffer from depression, a third of whom are medication-resistant. Seizure therapy remains the most effective treatment in depression, even when many treatments fail. The utility of seizure therapy is limited due to its cognitive side effects and stigma. The biological targets of seizure therapy remain unknown, hindering design of new treatments with comparable efficacy. Seizures impact the brains temporal dynamicity observed through electroencephalography. This dynamicity reflects richness of information processing across distributed brain networks subserving affective and cognitive processes. We investigated the hypothesis that seizure therapy impacts mood (depressive symptoms) and cognition by modulating brain temporal dynamicity. We obtained resting-state electroencephalography from 34 patients (age = 46.0 ± 14.0, 21 females) receiving two types of seizure treatments-electroconvulsive therapy or magnetic seizure therapy. We used multi-scale entropy to quantify the complexity of the brain's temporal dynamics before and after seizure therapy. We discovered that reduction of complexity in fine timescales underlined successful therapeutic response to both seizure treatments. Greater reduction in complexity of fine timescales in parieto-occipital and central brain regions was significantly linked with greater improvement in depressive symptoms. Greater increase in complexity of coarse timescales was associated with greater decline in cognition including the autobiographical memory. These findings were region and timescale specific. That is, change in complexity in occipital regions (e.g. O2 electrode or right occipital pole) at fine timescales was only associated with change in depressive symptoms, and not change in cognition, and change in complexity in parieto-central regions (e.g. Pz electrode or intra and transparietal sulcus) at coarser timescale was only associated with change in cognition, and not depressive symptoms. Finally, region and timescale specific changes in complexity classified both antidepressant and cognitive response to seizure therapy with good (80%) and excellent (95%) accuracy, respectively. In this study, we discovered a novel biological target of seizure therapy: complexity of the brain resting state dynamics. Region and timescale dependent changes in complexity of the brain resting state dynamics is a novel mechanistic marker of response to seizure therapy that explains both the antidepressant response and cognitive changes associated with this treatment. This marker has tremendous potential to guide design of the new generation of antidepressant treatments., (© The Author (2017). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Ordering information is a critical process underlying several cognitive functions, especially working memory. Theta phase-gamma amplitude coupling is regarded as a neurophysiological representation of ordering information during working memory performance. However, direct evidence has been lacking in humans. Seventy healthy subjects performed the N-back task, a working memory task that tests ordering information at 3 different levels of difficulties and with 3 different types of trials. Using electroencephalography (EEG) during N-back performance, theta-gamma coupling was assessed during response trials. Multivariate general linear model (GLM) and discriminant analysis were used to assess coupling and theta and gamma power across the N-back conditions and the trial types. During the N-back trials that required ordering of information, N-back condition had independent effects on coupling and on theta and gamma power, with equal contributions among these 3 variables. Theta-gamma coupling contribution declined significantly on the trials that did not require ordering and was intermediate on trials that favored but not necessarily required ordering. Our findings demonstrate for the first time the role of theta-gamma coupling as a mechanism that supports ordering information. They also highlight the potential of using theta-gamma coupling as a neurophysiological marker of brain function in health or disease states., (© The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

Concurrent recording of electroencephalography (EEG) during transcranial magnetic stimulation (TMS) is an emerging and powerful tool for studying brain health and function. Despite a growing interest in adaptation of TMS-EEG across neuroscience disciplines, its widespread utility is limited by signal processing challenges. These challenges arise due to the nature of TMS and the sensitivity of EEG to artifacts that often mask TMS-evoked potentials (TEP)s. With an increase in the complexity of data processing methods and a growing interest in multi-site data integration, analysis of TMS-EEG data requires the development of a standardized method to recover TEPs from various sources of artifacts. This article introduces TMSEEG, an open-source MATLAB application comprised of multiple algorithms organized to facilitate a step-by-step procedure for TMS-EEG signal processing. Using a modular design and interactive graphical user interface (GUI), this toolbox aims to streamline TMS-EEG signal processing for both novice and experienced users. Specifically, TMSEEG provides: (i) targeted removal of TMS-induced and general EEG artifacts; (ii) a step-by-step modular workflow with flexibility to modify existing algorithms and add customized algorithms; (iii) a comprehensive display and quantification of artifacts; (iv) quality control check points with visual feedback of TEPs throughout the data processing workflow; and (v) capability to label and store a database of artifacts. In addition to these features, the software architecture of TMSEEG ensures minimal user effort in initial setup and configuration of parameters for each processing step. This is partly accomplished through a close integration with EEGLAB, a widely used open-source toolbox for EEG signal processing. In this article, we introduce TMSEEG, validate its features and demonstrate its application in extracting TEPs across several single- and multi-pulse TMS protocols. As the first open-source GUI-based pipeline for TMS-EEG signal processing, this toolbox intends to promote the widespread utility and standardization of an emerging technology in brain research.

The concurrent combination of transcranial magnetic stimulation (TMS) with electroencephalography (TMS-EEG) is a powerful technology for characterizing and modulating brain networks across developmental, behavioral, and disease states. Given the global initiatives in mapping the human brain, recognition of the utility of this technique is growing across neuroscience disciplines. Importantly, TMS-EEG offers translational biomarkers that can be applied in health and disease, across the lifespan, and in humans and animals, bridging the gap between animal models and human studies. However, to utilize the full potential of TMS-EEG methodology, standardization of TMS-EEG study protocols is needed. In this article, we review the principles of TMS-EEG methodology, factors impacting TMS-EEG outcome measures, and the techniques for preventing and correcting artifacts in TMS-EEG data. To promote the standardization of this technique, we provide comprehensive guides for designing TMS-EEG studies and conducting TMS-EEG experiments. We conclude by reviewing the application of TMS-EEG in basic, cognitive and clinical neurosciences, and evaluate the potential of this emerging technology in brain research.

Combined transcranial magnetic stimulation and electroencephalography (TMS-EEG) enables noninvasive neurophysiological investigation of the human cortex. A TMS paradigm of short-latency afferent inhibition (SAI) is characterized by attenuation of the motor-evoked potential (MEP) and modulation of N100 of the TMS-evoked potential (TEP) when TMS is delivered to motor cortex (M1) following median nerve stimulation. SAI is a marker of cholinergic activity in the motor cortex; however, the SAI has not been tested from the prefrontal cortex. We aimed to explore the effect of SAI in dorsolateral prefrontal cortex (DLPFC). SAI was examined in 12 healthy subjects with median nerve stimulation and TMS delivered to M1 and DLPFC at interstimulus intervals (ISIs) relative to the individual N20 latency. SAI in M1 was tested at the optimal ISI of N20 + 2 ms. SAI in DLPFC was investigated at a range of ISI from N20 + 2 to N20 + 20 ms to explore its temporal profile. For SAI in M1, the attenuation of MEP amplitude was correlated with an increase of TEP N100 from the left central area. A similar spatiotemporal neural signature of SAI in DLPFC was observed with a marked increase of N100 amplitude. SAI in DLPFC was maximal at ISI N20 + 4 ms at the left frontal area. These findings establish the neural signature of SAI in DLPFC. Future studies could explore whether DLPFC-SAI is neurophysiological marker of cholinergic dysfunction in cognitive disorders., (Copyright © 2016 the American Physiological Society.)

Cortical inhibition (CI) is measured by transcranial magnetic stimulation (TMS) combined with electromyography (EMG) through long-interval CI (LICI) and cortical silent period (CSP) paradigms. Recently, we illustrated that LICI can be measured from the dorsolateral prefrontal cortex (DLPFC) through combined TMS with electroencephalography (EEG). We further demonstrated that LICI had different effects on cortical oscillations in the DLPFC compared with motor cortex. The purpose of this study was to establish the validity and reliability of TMS-EEG indices of CI and to replicate our previous findings in an extended sample. The validity of TMS-EEG was examined by evaluating its relationship to standard EMG measures of LICI and the CSP in the left motor cortex in 36 and 16 subjects, respectively. Test-retest reliability was examined in 14 subjects who returned for a repeat session within 7 days of the first session. LICI was applied to the left DLPFC in 30 subjects to compare LICI in the DLPFC with that in the motor cortex. In the motor cortex, EEG measures of LICI correlated with EMG measures of LICI and CSP. All indices of LICI showed high test-retest reliability in motor cortex and DLPFC. Gamma and beta oscillations were significantly inhibited in the DLPFC but not in the motor cortex, confirming previous findings in an extended sample. These findings demonstrate that indexing LICI through TMS combined with EEG is a valid and reliable method to evaluate inhibition from motor and prefrontal regions.

Several studies have demonstrated that cortical inhibition (CI) can be recorded by paired transcranial magnetic stimulation (TMS) of the motor cortex and recorded by surface electromyography (EMG). However, recording CI from other cortical regions that are more closely associated with the pathophysiology of some neurological and psychiatric disorders (eg, dorsolateral prefrontal cortex (DLPFC) in schizophrenia) was previously unattainable. This study, therefore, was designed to investigate whether CI could be measured directly from the motor cortex and DLPFC by combining TMS with electroencephalography (EEG). Long-interval CI (LICI) is a TMS paradigm that was used to index CI in the motor cortex and DLPFC in healthy subjects. In the motor cortex, LICI resulted in significant suppression (32.8+/-30.5%) of mean cortical evoked activity on EEG, which was strongly correlated with LICI recorded by EMG. In the DLPFC, LICI resulted in significant suppression (30.1+/-26.9%) of mean cortical evoked activity and also correlated with LICI in the motor cortex. These data suggest that CI can be recorded by combining TMS with EEG and may facilitate future research attempting to ascertain the role of CI in the pathophysiology of several neurological and psychiatric disorders.

The cortical response to transcranial magnetic stimulation (TMS) has notable inter-trial variability. One source of this variability can be the influence of the phase and power of pre-stimulus neuronal oscillations on single-trial TMS responses. Here, we investigate the effect of brain oscillatory activity on TMS response in 49 distinct healthy participants (64 datasets) who had received single-pulse TMS over the left dorsolateral prefrontal cortex. Across all frequency bands of theta (4–7 Hz), alpha (8–13 Hz), and beta (14–30 Hz), there was no significant effect of pre-TMS phase on single-trial cortical evoked activity. After high-powered oscillations, whether followed by a TMS pulse or not, the subsequent activity was larger than after low-powered oscillations. We further defined a measure, corrected_effect, to enable us to investigate brain responses to the TMS pulse disentangled from the power of ongoing (spontaneous) oscillations. The corrected_effect was significantly different from zero (meaningful added effect of TMS) only in theta and beta bands. Our results suggest that brain state prior to stimulation might play some role in shaping the subsequent TMS-EEG response. Specifically, our findings indicate that the power of ongoing oscillatory activity, but not phase, can influence brain responses to TMS. Aligning the TMS pulse with specific power thresholds of an EEG signal might therefore reduce variability in neurophysiological measurements and also has the potential to facilitate more robust therapeutic effects of stimulation. [ABSTRACT FROM AUTHOR]

Identifying genuine cortical stimulation-elicited electroencephalography (EEG) is crucial for improving the validity and reliability of neurophysiology using transcranial magnetic stimulation (TMS) combined with EEG. In this study, we evaluated the spatiotemporal profiles of single-pulse TMS-elicited EEG response administered to the left dorsal prefrontal cortex (DLPFC) in 28 healthy participants, employing active and sham stimulation conditions. We hypothesized that the early component of TEP would be activated in active stimulation compared with sham stimulation. We specifically analyzed the (1) stimulus response, (2) frequency modulation, and (3) phase synchronization of TMS–EEG data at the sensor level and the source level. Compared with the sham condition, the active condition induced a significant increase in TMS-elicited EEG power in the 30–60 ms time interval in the stimulation area at the sensor level. Furthermore, in the source-based analysis, the active condition induced significant increases in TMS-elicited response in the 30–60 ms compared with the sham condition. Collectively, we found that the active condition could specifically activate the early component of TEP compared with the sham condition. Thus, the TMS–EEG method that was applied to the DLPFC could detect the genuine neurophysiological cortical responses by properly handling potential confounding factors such as indirect response noises. [ABSTRACT FROM AUTHOR]

Background: Long-term potentiation (LTP) depends on glutamatergic neurotransmission and is modulated by cholinergic, dopaminergic and GABAergic inputs. Paired associative stimulation (PAS) is a neurostimulation paradigm that, when combined with electroencephalography (EEG), assesses LTP-like activity (PAS-induced LTP) in the dorsolateral prefrontal cortex (DLPFC). Thus, we conducted a study to assess the role of cholinergic, dopaminergic, GABAergic and glutamatergic neurotransmission on PAS-induced LTP in the DLPFC. We hypothesized that increasing the dopaminergic tone with L-DOPA and the cholinergic tone with rivastigmine will enhance PAS-induced LTP, while increasing the GABAergic tone with baclofen and inhibiting glutamatergic neurotransmission with dextromethorphan will reduce it compared to placebo. Methods: In this randomized controlled, double-blind cross-over within-subject study, 12 healthy participants received five sessions of PAS to the DLPFC in a random order, each preceded by the administration of placebo or one of the four active drugs. PAS-induced LTP was assessed after each drug administration and compared to PAS-induced LTP after placebo. Results: As predicted, L-DOPA and rivastigmine resulted in enhanced PAS-induced LTP in the DLPFC and dextromethorphan inhibited it compared to placebo. In contrast, baclofen did not significantly suppress PAS-induced LTP compared to placebo. Conclusions: This study provides a novel approach to study DLPFC neuroplasticity and its modulation in patients with brain disorders that are associated with abnormalities in these neurochemical systems. This study was based on a single dose administration of each drug. Given that these drugs are typically administered chronically, future studies should assess the effects of chronic administration. [ABSTRACT FROM AUTHOR]

Working memory deficits are common among individuals with Alzheimer's dementia (AD) or mild cognitive impairment (MCI). Yet, little is known about the mechanisms underlying these deficits. Theta-gamma coupling--the modulation of high-frequency gamma oscillations by low-frequency theta oscillations--is a neurophysiologic process underlying working memory. We assessed the relationship between theta-gamma coupling and working memory deficits in AD and MCI. We hypothesized that: (1) individuals with AD would display the most significant working memory impairments followed by MCI and finally healthy control (HC) participants; and (2) there would be a significant association between working memory performance and theta-gamma coupling across all participants. Ninety-eight participants completed the N-back working memory task during an electroencephalography (EEG) recording: 33 with AD (mean ± SD age: 76.5 ± 6.2), 34 with MCI (mean ± SD age: 74.8 ± 5.9) and 31 HCs (mean ± SD age: 73.5 ± 5.2). AD participants performed significantly worse than control and MCI participants on the 1- and 2-back conditions. Regarding theta-gamma coupling, AD participants demonstrated the lowest level of coupling followed by the MCI and finally control participants on the 2-back condition. Finally, a linear regression analysis demonstrated that theta-gamma coupling (b = 0.69, p < 0.001) was the most significant predictor of 2-back performance. Our results provide evidence for a relationship between altered theta-gamma coupling and working memory deficits in individuals with AD and MCI. They also provide insight into a potential mechanism underlying working memory impairments in these individuals. [ABSTRACT FROM AUTHOR]

Combining transcranial magnetic stimulation (TMS) with electroencephalography (EEG) allows for the assessment of various neurophysiological processes in the human cortex. One of these paradigms, short-latency afferent inhibition (SAI), is thought to be a sensitive measure of cholinergic activity. In a previous study, we demonstrated the temporal pattern of this paradigm from both the motor (M1) and dorsolateral prefrontal cortex (DLPFC) using simultaneous TMS-EEG recording. The SAI paradigm led to marked modulations at N100. In this study, we aimed to investigate the age-related effects on TMS-evoked potentials (TEPs) with the SAI from M1 and the DLPFC in younger (18-59 years old) and older (≥60 years old) participants. Older participants showed significantly lower N100 modulation in M1-SAI as well as DLPFC-SAI compared to the younger participants. Furthermore, the modulation of N100 by DLPFC-SAI in the older participants correlated with executive function as measured with the Trail making test. This paradigm has the potential to non-invasively identify cholinergic changes in cortical regions related to cognition in older participants. [ABSTRACT FROM AUTHOR]

Over 350 million people worldwide suffer from depression, a third of whom are medication-resistant. Seizure therapy remains the most effective treatment in depression, even when many treatments fail. The utility of seizure therapy is limited due to its cognitive side effects and stigma. The biological targets of seizure therapy remain unknown, hindering design of new treatments with comparable efficacy. Seizures impact the brains temporal dynamicity observed through electroencephalography. This dynamicity reflects richness of information processing across distributed brain networks subserving affective and cognitive processes. We investigated the hypothesis that seizure therapy impacts mood (depressive symptoms) and cognition by modulating brain temporal dynamicity. We obtained resting-state electroencephalography from 34 patients (age = 46.0 ± 14.0, 21 females) receiving two types of seizure treatments-electroconvulsive therapy or magnetic seizure therapy. We used multi-scale entropy to quantify the complexity of the brain's temporal dynamics before and after seizure therapy. We discovered that reduction of complexity in fine timescales underlined successful therapeutic response to both seizure treatments. Greater reduction in complexity of fine timescales in parieto-occipital and central brain regions was significantly linked with greater improvement in depressive symptoms. Greater increase in complexity of coarse timescales was associated with greater decline in cognition including the autobiographical memory. These findings were region and timescale specific. That is, change in complexity in occipital regions (e.g. O2 electrode or right occipital pole) at fine timescales was only associated with change in depressive symptoms, and not change in cognition, and change in complexity in parieto-central regions (e.g. Pz electrode or intra and transparietal sulcus) at coarser timescale was only associated with change in cognition, and not depressive symptoms. Finally, region and timescale specific changes in complexity classified both antidepressant and cognitive response to seizure therapy with good (80%) and excellent (95%) accuracy, respectively. In this study, we discovered a novel biological target of seizure therapy: complexity of the brain resting state dynamics. Region and timescale dependent changes in complexity of the brain resting state dynamics is a novel mechanistic marker of response to seizure therapy that explains both the antidepressant response and cognitive changes associated with this treatment. This marker has tremendous potential to guide design of the new generation of antidepressant treatments. [ABSTRACT FROM AUTHOR]

Concurrent recording of electroencephalography (EEG) during transcranial magnetic stimulation (TMS) is an emerging and powerful tool for studying brain health and function. Despite a growing interest in adaptation of TMS-EEG across neuroscience disciplines, its widespread utility is limited by signal processing challenges. These challenges arise due to the nature of TMS and the sensitivity of EEG to artifacts that often mask TMS-evoked potentials (TEP)s. With an increase in the complexity of data processing methods and a growing interest in multi-site data integration, analysis of TMS-EEG data requires the development of a standardized method to recover TEPs from various sources of artifacts. This article introduces TMSEEG, an open-source MATLAB application comprised of multiple algorithms organized to facilitate a step-by-step procedure for TMS-EEG signal processing. Using a modular design and interactive graphical user interface (GUI), this toolbox aims to streamline TMS-EEG signal processing for both novice and experienced users. Specifically, TMSEEG provides: (i) targeted removal of TMS-induced and general EEG artifacts; (ii) a step-by-step modular workflow with flexibility to modify existing algorithms and add customized algorithms; (iii) a comprehensive display and quantification of artifacts; (iv) quality control check points with visual feedback of TEPs throughout the data processing workflow; and (v) capability to label and store a database of artifacts. In addition to these features, the software architecture of TMSEEG ensures minimal user effort in initial setup and configuration of parameters for each processing step. This is partly accomplished through a close integration with EEGLAB, a widely used open-source toolbox for EEG signal processing. In this article, we introduce TMSEEG, validate its features and demonstrate its application in extracting TEPs across several single- and multi-pulse TMS protocols. As the first open-source GUI-based pipeline for TMS-EEG signal processing, this toolbox intends to promote the widespread utility and standardization of an emerging technology in brain research. [ABSTRACT FROM AUTHOR]

Combined transcranial magnetic stimulation and electroencephalography (TMS-EEG) enables noninvasive neurophysiological investigation of the human cortex. A TMS paradigm of short-latency afferent inhibition (SAI) is characterized by attenuation of the motor-evoked potential (MEP) and modulation of N100 of the TMS-evoked potential (TEP) when TMS is delivered to motor cortex (M1) following median nerve stimulation. SAI is a marker of cholinergic activity in the motor cortex; however, the SAI has not been tested from the prefrontal cortex. We aimed to explore the effect of SAI in dorsolateral prefrontal cortex (DLPFC). SAI was examined in 12 healthy subjects with median nerve stimulation and TMS delivered to M1 and DLPFC at interstimulus intervals (ISIs) relative to the individual N20 latency. SAI in M1 was tested at the optimal ISI of N20 + 2 ms. SAI in DLPFC was investigated at a range of ISI from N20 + 2 to N20 + 20 ms to explore its temporal profile. For SAI in M1, the attenuation of MEP amplitude was correlated with an increase of TEP N100 from the left central area. A similar spatiotemporal neural signature of SAI in DLPFC was observed with a marked increase of N100 amplitude. SAI in DLPFC was maximal at ISI N20 + 4 ms at the left frontal area. These findings establish the neural signature of SAI in DLPFC. Future studies could explore whether DLPFC-SAI is neurophysiological marker of cholinergic dysfunction in cognitive disorders. [ABSTRACT FROM AUTHOR]

Using TMS and EEG, Radhu et al. reveal reductions in long-interval cortical inhibition specific to the dorsolateral prefrontal cortex in patients with schizophrenia compared to healthy subjects and individuals with obsessive-compulsive disorder (OCD). The absence of such changes in OCD confirms that reductions in inhibition are not a generalized deficit associated with severe psychopathology.Abnormal gamma-aminobutyric acid inhibitory neurotransmission is a key pathophysiological mechanism underlying schizophrenia. Transcranial magnetic stimulation can be combined with electroencephalography to index long-interval cortical inhibition, a measure of GABAergic receptor-mediated inhibitory neurotransmission from the frontal and motor cortex. In previous studies we have reported that schizophrenia is associated with inhibitory deficits in the dorsolateral prefrontal cortex compared to healthy subjects and patients with bipolar disorder. The main objective of the current study was to replicate and extend these initial findings by evaluating long-interval cortical inhibition from the dorsolateral prefrontal cortex in patients with schizophrenia compared to patients with obsessive-compulsive disorder. A total of 111 participants were assessed: 38 patients with schizophrenia (average age: 35.71 years, 25 males, 13 females), 27 patients with obsessive-compulsive disorder (average age: 36.15 years, 11 males, 16 females) and 46 healthy subjects (average age: 33.63 years, 23 females, 23 males). Long-interval cortical inhibition was measured from the dorsolateral prefrontal cortex and motor cortex through combined transcranial magnetic stimulation and electroencephalography. In the dorsolateral prefrontal cortex, long-interval cortical inhibition was significantly reduced in patients with schizophrenia compared to healthy subjects (P = 0.004) and not significantly different between patients with obsessive-compulsive disorder and healthy subjects (P = 0.5445). Long-interval cortical inhibition deficits in the dorsolateral prefrontal cortex were also significantly greater in patients with schizophrenia compared to patients with obsessive-compulsive disorder (P = 0.0465). There were no significant differences in long-interval cortical inhibition across all three groups in the motor cortex. These results demonstrate that long-interval cortical inhibition deficits in the dorsolateral prefrontal cortex are specific to patients with schizophrenia and are not a generalized deficit that is shared by disorders of severe psychopathology. [ABSTRACT FROM AUTHOR]

Schizophrenia (SCZ) is a debilitating mental illness with an elusive pathophysiology. Over the last decade, theories emphasizing cortical dysfunction have received increasing attention to explain the heterogeneous symptoms experienced in SCZ. Transcranial magnetic stimulation (TMS) is a noninvasive form of brain stimulation that is particularly suited to probing the fidelity of specific excitatory and inhibitory neuronal populations in conscious humans. In this study, we review the contribution of TMS in assessing inhibitory and excitatory neuronal populations and their long-range connections in SCZ. In addition, we discuss insights from combined TMS and electroencephalography into the functional consequences of impaired excitation/inhibition on cortical oscillations in SCZ. [ABSTRACT FROM PUBLISHER]

Recently, deep brain stimulation (DBS) has been evaluated as an experimental therapy for treatment-resistant depression. Although there have been encouraging results in open-label trials, about half of the patients fail to achieve meaningful benefit. Although progress has been made in understanding the neurobiology of MDD, the ability to characterize differences in brain dynamics between those who do and do not benefit from DBS is lacking. In this study, we investigated EEG resting-state data recorded from 12 patients that have undergone DBS surgery. Of those, six patients were classified as responders to DBS, defined as an improvement of 50% or more on the 17-item Hamilton Rating Scale for Depression (HAMD-17). We compared hemispheric frontal theta and parietal alpha power asymmetry and synchronization asymmetry between responders and non-responders. Hemispheric power asymmetry showed statistically significant differences between responders and non-responders with healthy controls showing an asymmetry similar to responders but opposite to non-responders. This asymmetry was characterized by an increase in frontal theta in the right hemisphere relative to the left combined with an increase in parietal alpha in the left hemisphere relative to the right in non-responders compared with responders. Hemispheric mean synchronization asymmetry showed a statistically significant difference between responders and non-responders in the theta band, with healthy controls showing an asymmetry similar to responders but opposite to non-responders. This asymmetry resulted from an increase in frontal synchronization in the right hemisphere relative to the left combined with an increase in parietal synchronization in the left hemisphere relative to the right in non-responders compared with responders. Connectivity diagrams revealed long-range differences in frontal/central-parietal connectivity between the two groups in the theta band. This pattern was observed irrespective of whether EEG data were collected with active DBS or with the DBS stimulation turned off, suggesting stable functional and possibly structural modifications that may be attributed to plasticity. [ABSTRACT FROM AUTHOR]

Objective: Long-interval cortical inhibition (LICI) can be recorded from motor and non-motor regions of the cortex through combined transcranial magnetic stimulation (TMS) with electroencephalography (EEG). This study aimed to evaluate additional dimensions of LICI characteristics over an extended time-frequency and spatial domain. This was done by introducing two alternative measures of LICI signal amplitude: the Discrete Fourier Transform (DFT) and the Hilbert transform (HT). Both approaches estimate signal amplitude not taking into account the phase. In both cases LICI was measured as the difference between the unconditioned and conditioned activity evoked by the test pulse. Finally, we evaluated whether the topographical patterns of single and paired responses differed beyond the expected variations in amplitude. Materials and Methods: LICI was delivered as single and paired pulses to the motor cortex (MC) and dorsolateral prefrontal cortex (DLPFC) in 33 healthy subjects with TMS-EEG. Results: Significant differences (p<0.0001) between the unconditioned and conditioned evoked activity were found for both the DLPFC and MC using both methods (i.e., DFT and HT) after correcting for multiple comparisons in the time-frequency domain. The influence of inhibition was found to be significantly larger in space and time than previously considered. Single and paired conditions differ in intensity, but also in their topographic pattern (i.e., the specific spatiotemporal configuration of active sources). Conclusion: Similar results were found by both DFT and HT. The effect of inhibition across the cortex was also found to be complex and extended. In particular, it was found that LICI may be measured with high sensitivity in areas that were relatively distant from the stimulation site, which may have important practical applications. The analysis presented in this study overcomes some limitations of previous studies and could serve as a key reference for future studies examining TMS-indices of inhibition/excitation in healthy and diseased states. [ABSTRACT FROM AUTHOR]

Background: Psychopathic offenders inevitably violate interpersonal norms and frequently resort to aggressive and criminal behaviour. The affective and cognitive deficits underlying these behaviours have been linked to abnormalities in functional interhemispheric connectivity. However, direct neurophysiological evidence for dysfunctional connectivity in psychopathic offenders is lacking. Methods: We used transcranial magnetic stimulation combined with electroencephalography to examine interhemispheric connectivity in the dorsolateral and motor cortex in a sample of psychopathic offenders and healthy controls. We also measured intracortical inhibition and facilitation over the left and right motor cortex to investigate the effects of local cortical processes on interhemispheric connectivity. Results: We enrolled 17 psychopathic offenders and 14 controls in our study. Global abnormalities in right to left functional connectivity were observed in psychopathic offenders compared with controls. Furthermore, in contrast to controls, psychopathic offenders showed increased intracortical inhibition in the right, but not the left, hemisphere. Limitations: The relatively small sample size limited the sensitivity to show that the abnormalities in interhemispheric connectivity were specifically related to the dorsolateral prefrontal cortex in psychopathic offenders. Conclusion: To our knowledge, this study provides the first neurophysiological evidence for abnormal interhemispheric connectivity in psychopathic offenders and may further our understanding of the disruptive antisocial behaviour of these offenders. [ABSTRACT FROM AUTHOR]

Neuroplasticity and long-term potentiation (LTP) in the dorsolateral prefrontal cortex (DLPFC) are considered important mechanisms in learning and memory, and their disruption may be related to the pathophysiology of several neuropsychiatric disorders. Paired associative stimulation (PAS) is a brain stimulation paradigm that produces enhanced activity in the human motor cortex that may be related to LTP. In a group of 15 healthy participants, we report on the potentiation of cortical-evoked activity in the human DLPFC using the combination of PAS and electroencephalography. In contrast, a PAS control condition did not result in potentiation in another group of nine healthy participants. We also demonstrate that PAS-induced potentiation of cortical-evoked activity is characterized by anatomical specificity that is largely confined to the site of stimulation. Finally, we show that PAS results in potentiation of θ- and γ-activity and θ-phase-γ-amplitude coupling. These neurophysiological indices may be related to working memory, an important function of the DLPFC. To our knowledge, this is the first report of potentiation of cortical-evoked activity in the DLPFC. As this potentiation may be related to LTP, our findings provide a model through which neuroplasticity in health and disease states in the frontal cortex can be studied. [ABSTRACT FROM AUTHOR]

Long-interval cortical inhibition (LICI) refers to suppression of neuronal activity following paired-pulse transcranial magnetic stimulation (TMS) with interstimulus intervals (ISIs) between 50 and 200 ms. LICI can be measured either from motor-evoked potentials (MEPs) in small hand muscles or directly from the cortex using concurrent electroencephalography (EEG). However, it remains unclear whether EEG inhibition reflects similar mechanisms to MEP inhibition. Eight healthy participants received single- and paired-pulse TMS (ISI = 100 ms) over the motor cortex. MEPs were measured from a small hand muscle (first dorsal interosseus), whereas early (P30, P60) and late (N100) TMS-evoked cortical potentials (TEPs) were measured over the motor cortex using EEG. Conditioning and test TMS intensities were altered, and modulation of LICI strength was measured using both methods. LICI of MEPs and both P30 and P60 TEPs increased in strength with increasing conditioning intensities and decreased with increasing test intensities. LICI of N100 TEPs remained unchanged across all conditions. In addition, MEP and P30 LICI strength correlated with the slope of the N100 evoked by the conditioning pulse. LICI of early and late TEP components was differentially modulated with altered TMS intensities, suggesting independent underlying mechanisms. LICI of P30 is consistent with inhibition of cortical excitation similar to MEPs, whereas LICI of N100 may reflect presynaptic autoinhibition of inhibitory interneurons. The N100 evoked by the conditioning pulse is consistent with the mechanism responsible for LICI, most likely GABAB-mediated inhibition of cortical activity. [ABSTRACT FROM AUTHOR]

Several lines of evidence suggest that deficits in γ-aminobutyric acid (GABA) inhibitory neurotransmission are implicated in the pathophysiology of schizophrenia, bipolar disorder, major depressive disorder and obsessive-compulsive disorder. Cortical inhibition refers to a neurophysiological process, whereby GABA inhibitory interneurons selectively attenuate pyramidal neurons. Transcranial magnetic stimulation (TMS) represents a noninvasive technique to measure cortical inhibition, excitability and plasticity in the cortex. These measures were traditionally specific to the motor cortex, which is an important limitation when nonmotor neurophysiological processes are of primary interest. Recently, TMS has been combined with electro encephalography (EEG) to derive such measurements directly from the cortex. This review focuses on neurophysiological studies related to inhibitory and excitatory TMS paradigms, linking dysfunctional GABAergic neurotransmission to disease states. We review evidence that suggests cortical inhibition deficits among psychiatric populations and demonstrate how each disorder has a specific neurophysiological response to treatment. We conclude by discussing the future directions of TMS combined with EEG, demonstrating the potential to identify biological markers of neuropsychiatric disorders. [ABSTRACT FROM AUTHOR]

Previous studies have shown that patients with schizophrenia and bipolar disorder have deficits in cortical inhibition. Through the combination of interleaved transcranial magnetic stimulation and electroencephalography, we have recently reported on methods in which cortical inhibition can be measured from the dorsolateral prefrontal cortex, a cortical region that is more closely associated with the pathophysiology of schizophrenia. Furthermore, it is possible to index cortical inhibition of specific oscillatory frequencies including the gamma band (30–50 Hz) whose modulation has been related to higher order cortical processing. In this study, we show that patients with schizophrenia have significant deficits of cortical inhibition of gamma oscillations in the dorsolateral prefrontal cortex compared to healthy subjects and patients with bipolar disorder, while no deficits are demonstrated in the motor cortex. These results suggest that the lack of inhibition of gamma oscillations in the dorsolateral prefrontal cortex may represent an important frontal neurophysiological deficit, which may be responsible for the spectrum of deficits commonly found in schizophrenia. [ABSTRACT FROM PUBLISHER]

Gamma (γ)-oscillations (30–50 Hz) represent important electrophysiological measures, which are generated through the execution of higher order cognitive tasks (eg, working memory) in the dorsolateral prefrontal cortex (DLPFC). By contrast, cortical inhibition (CI) refers to a neurophysiological process in which GABAergic inhibitory interneurons selectively suppress the activation of other neurons in the cortex. Recently, abnormalities in both CI and γ-oscillations have been associated with various neuropsychiatric disorders including schizophrenia. Animal research suggests that suppression of γ-oscillations is, in part, mediated through GABAergic inhibitory neurotransmission. However, no such evidence has been demonstrated in human, largely because of technological limitations. Recently, we reported on novel methods permitting the recording of CI from the DLPFC through transcranial magnetic stimulation (TMS) combined with electroencephalography (EEG). The aim of this study was to examine the effects of GABAergic inhibitory neurotransmission on γ-oscillations by combining TMS with EEG. Long interval cortical inhibition (LICI), a paired TMS paradigm, was used to index GABAB receptor mediated inhibitory neurotransmission in the motor cortex and DLPFC of healthy individuals. γ-Oscillations were significantly inhibited by LICI (38.1±26.5%; p0.013) in the DLPFC but not in the motor cortex. These results provide neurophysiological evidence to demonstrate γ-oscillations are inhibited by LICI in the DLPFC but not in the motor cortex. Such specificity suggests that the modulation of γ-oscillations may represent an important neurophysiological process that may, in part, be responsible for optimal DLPFC functioning in healthy human subjects.Neuropsychopharmacology (2009) 34, 1543–1551; doi:10.1038/npp.2008.211; published online 26 November 2008 [ABSTRACT FROM AUTHOR]

Schizophrenia (SCZ) is a serious mental disorder, and its pathogenesis is complex. Recently, the glutamate hypothesis and the excitatory/inhibitory (E/I) imbalance hypothesis have been proposed as new pathological hypotheses for SCZ. Combined transcranial magnetic stimulation (TMS) and electroencephalography (EEG) is a non-invasive novel method that enables us to investigate the cortical activity in humans, and this modality is a suitable approach to evaluate these hypotheses. In this study, we systematically reviewed TMS-EEG studies that investigated the cortical dysfunction of SCZ to examine the emerging hypotheses for SCZ. The following search terms were set in this systematic review: (TMS or 'transcranial magnetic stimulation') and (EEG or electroencephalog*) and (schizophrenia). We inspected the articles written in English that examined humans and were published by March 2020 via MEDLINE, Embase, PsycINFO, and PubMed. The initial search generated 379 studies, and 14 articles were finally identified. The current review noted that patients with SCZ demonstrated the E/I deficits in the prefrontal cortex, whose dysfunctions were also associated with cognitive impairment and clinical severity. Moreover, TMS-induced gamma activity in the prefrontal cortex was related to positive symptoms, while theta/delta band activities were associated with negative symptoms in SCZ. Thus, this systematic review discusses aspects of the pathophysiological neural basis of SCZ that are not explained by the traditional dopamine hypothesis exclusively, based on the findings of previous TMS-EEG research, mainly in terms of the E/I imbalance hypothesis. In conclusion, TMS-EEG neurophysiology can be applied to establish objective biomarkers for better diagnosis as well as to develop new therapeutic strategies for patients with SCZ. [ABSTRACT FROM AUTHOR]

Electroconvulsive therapy (ECT) is a highly effective neuromodulatory intervention for treatment-resistant major depressive disorder (MDD). Presently, however, understanding of its neurophysiological effects remains incomplete. In the present study, we utilised resting-state electroencephalography (RS-EEG) to explore changes in functional connectivity, network topology, and spectral power elicited by an acute open-label course of ECT in a cohort of 23 patients with treatment-resistant MDD. RS-EEG was recorded prior to commencement of ECT and again within 48 h following each patient's final treatment session. Our results show that ECT was able to enhance connectivity within lower (delta and theta) frequency bands across subnetworks largely confined to fronto-central channels, while, conversely, more widespread subnetworks of reduced connectivity emerged within faster (alpha and beta) bands following treatment. Graph-based topological analyses revealed changes in measures of functional segregation (clustering coefficient), integration (characteristic path length), and small-world architecture following ECT. Finally, post-treatment enhancement of delta and theta spectral power was observed, which showed a positive association with the number of ECT sessions received. Overall, our findings indicate that RS-EEG can provide a sensitive measure of dynamic neural activity following ECT and highlight network-based analyses as a promising avenue for furthering mechanistic understanding of the effects of convulsive therapies. [ABSTRACT FROM AUTHOR]