Evoked EEG Responses to TMS Targeting Regions Outside the Primary Motor Cortex and Their Test-Retest Reliability.

Transcranial magnetic stimulation (TMS)-evoked electroencephalography (EEG) potentials (TEPs) provide unique insights into cortical excitability and connectivity. However, confounding EEG signals from auditory and somatosensory co-stimulation complicate TEP interpretation. Our optimized sham procedure established with TMS of primary motor cortex (Gordon in JAMA 245:118708, 2021) differentiates direct cortical EEG responses to TMS from those caused by peripheral sensory inputs. Using this approach, this study aimed to investigate TEPs and their test-retest reliability when targeting regions outside the primary motor cortex, specifically the left angular gyrus, supplementary motor area, and medial prefrontal cortex. We conducted three identical TMS-EEG sessions one week apart involving 24 healthy participants. In each session, we targeted the three areas separately using a figure-of-eight TMS coil for active TMS, while a second coil away from the head produced auditory input for sham TMS. Masking noise and electric scalp stimulation were applied in both conditions to achieve matched EEG responses to peripheral sensory inputs. High test-retest reliability was observed in both conditions. However, reliability declined for the 'cleaned' TEPs, resulting from the subtraction of evoked EEG response to the sham TMS from those to the active, particularly for latencies > 100 ms following the TMS pulse. Significant EEG differences were found between active and sham TMS at latencies < 90 ms for all targeted areas, exhibiting distinct spatiotemporal characteristics specific to each target. In conclusion, our optimized sham procedure effectively reveals EEG responses to direct cortical activation by TMS in brain areas outside primary motor cortex. Moreover, we demonstrate the impact of peripheral sensory inputs on test-retest reliability of TMS-EEG responses.

Untangling TMS-EEG responses caused by TMS versus sensory input using optimized sham control and GABAergic challenge.

The combination of transcranial magnetic stimulation (TMS) and electroencephalography (EEG) elegantly probes the excitability and connectivity of the human brain. However, TMS-EEG signals inevitably also contain sensory-evoked responses caused by TMS-associated auditory and somatosensory inputs, constituting a substantial confounding factor. Here we applied our recently established optimized SHAM protocol (Gordon et al., Neuroimage 2021:118708) to disentangle TMS-EEG responses caused by TMS vs. sensory input. One unresolved question is whether these responses superimpose without relevant interaction, a requirement for their disaggregation by the optimized SHAM approach. We applied in 20 healthy subjects a pharmacological intervention using a single oral dose of 20 mg of diazepam, a positive modulator of GABAA receptors. Diazepam decreased the amplitudes of the P60 and P150 components specifically in the ACTIVE TMS and/or the ACTIVE TMS minus SHAM conditions but not in the SHAM condition, pointing to a response caused by TMS. In contrast, diazepam suppressed the amplitude of the N100 component indiscriminately in the ACTIVE TMS and SHAM conditions but not in the ACTIVE TMS minus SHAM condition, pointing to a response caused by sensory input. Moreover, diazepam suppressed the beta-band response observed in the motor cortex specifically after ACTIVE TMS and ACTIVE TMS minus SHAM. These findings demonstrate a lack of interaction of TMS-EEG responses caused by TMS vs. sensory input and validate optimized SHAM-controlled TMS-EEG as an appropriate approach to untangle these TMS-EEG responses. This knowledge will enable the proficient use of TMS-EEG to probe the physiology of the human cortex. KEY POINTS: Optimized SHAM disentangles TMS-EEG responses caused by TMS vs. sensory input. Diazepam differentially modulates TMS-EEG responses caused by TMS vs. sensory input. Diazepam modulation of P60 and P150 indicate TMS-EEG responses caused by TMS. Diazepam modulation of N100 indicate a TMS-EEG response caused by sensory input.

Targeting motor cortex high-excitability states defined by functional connectivity with real-time EEG-TMS.

We tested previous post-hoc findings indicating a relationship between functional connectivity (FC) in the motor network and corticospinal excitability (CsE), in a real-time EEG-TMS experiment in healthy participants. We hypothesized that high FC between left and right motor cortex predicts high CsE. FC was quantified in real-time by single-trial phase-locking value (stPLV), and TMS single pulses were delivered based on the current FC. CsE was indexed by motor-evoked potential (MEP) amplitude in a hand muscle. Possible confounding factors (pre-stimulus µ-power and phase, interstimulus interval) were evaluated post hoc. MEPs were significantly larger during high FC compared to low FC. Post hoc analysis revealed that the FC condition showed a significant interaction with µ-power in the stimulated hemisphere. Further, inter-stimulus interval (ISI) interacted with high vs. low FC conditions. In summary, FC was confirmed to be predictive of CsE, but should not be considered in isolation from µ-power and ISI. Moreover, FC was complementary to µ-phase in predicting CsE. Motor network FC is another marker of real-time accessible CsE beyond previously established markers, in particular phase and power of the µ rhythm, and may help define a more robust composite biomarker of high/low excitability states of human motor cortex.

Corticospinal excitability is highest at the early rising phase of sensorimotor µ-rhythm.

Alpha oscillations are thought to reflect alternating cortical states of excitation and inhibition. Studies of perceptual thresholds and evoked potentials have shown the scalp EEG negative phase of the oscillation to correspond to a short-lasting low-threshold and high-excitability state of underlying visual, somatosensory, and primary motor cortex. The negative peak of the oscillation is assumed to correspond to the state of highest excitability based on biophysical considerations and considerable effort has been made to improve the extraction of a predictive signal by individually optimizing EEG montages. Here, we investigate whether it is the negative peak of sensorimotor µ-rhythm that corresponds to the highest corticospinal excitability, and whether this is consistent between individuals. In 52 adult participants, a standard 5-channel surface Laplacian EEG montage was used to extract sensorimotor µ-rhythm during transcranial magnetic stimulation (TMS) of primary motor cortex. Post-hoc trials were sorted from 800 TMS-evoked motor potentials (MEPs) according to the pre-stimulus EEG (estimated instantaneous phase) and MEP amplitude (as an index of corticospinal excitability). Different preprocessing transformations designed to improve the accuracy by which µ-alpha phase predicts excitability were also tested. By fitting a sinusoid to the MEP amplitudes, sorted according to pre-stimulus EEG-phase, we found that excitability was highest during the early rising phase, at a significant delay with respect to the negative peak by on average 45° or 10 ms. The individual phase of highest excitability was consistent across study participants and unaffected by two different EEG-cleaning methods that utilize 64 channels to improve signal quality by compensating for individual noise level and channel covariance. Personalized transformations of the montage did not yield better prediction of excitability from µ-alpha phase. The relationship between instantaneous phase of a brain oscillation and fluctuating cortical excitability appears to be more complex than previously hypothesized. In TMS of motor cortex, a standard surface Laplacian 5-channel EEG montage is effective in extracting a predictive signal and the phase corresponding to the highest excitability appears to be consistent between individuals. This is an encouraging result with respect to the clinical potential of therapeutic personalized brain interventions in the motor system. However, it remains to be investigated, whether similar results can be obtained for other brain areas and brain oscillations targeted with EEG and TMS.

Predicting motor behavior: an efficient EEG signal processing pipeline to detect brain states with potential therapeutic relevance for VR-based neurorehabilitation.

Virtual reality (VR)-based motor therapy is an emerging approach in neurorehabilitation. The combination of VR with electroencephalography (EEG) presents further opportunities to improve therapeutic efficacy by personalizing the paradigm. Specifically, the idea is to synchronize the choice and timing of stimuli in the perceived virtual world with fluctuating brain states relevant to motor behavior. Here, we present an open source EEG single-trial based classification pipeline that is designed to identify ongoing brain states predictive of the planning and execution of movements. 9 healthy volunteers each performed 1080 trials of a repetitive reaching task with an implicit two-alternative forced choice, i.e., use of the right or left hand, in response to the appearance of a visual target. The performance of the EEG decoding pipeline was assessed with respect to classification accuracy of right vs. left arm use, based on the EEG signal at the time of the stimulus. Different features, feature extraction methods, and classifiers were compared at different time windows; the number and location of informative EEG channels and the number of calibration trials needed were also quantified, as well as any benefits from individual-level optimization of pipeline parameters. This resulted in a set of recommended parameters that achieved an average 83.3% correct prediction on never-before-seen testing data, and a state-of-the-art 77.1% in a real-time simulation. Neurophysiological plausibility of the resulting classifiers was assessed by time-frequency and event-related potential analyses, as well as by Independent Component Analysis topographies and cortical source localization. We expect that this pipeline will facilitate the identification of relevant brain states as prospective therapeutic targets in closed-loop EEG-VR motor neurorehabilitation.

Longitudinal associations between different measures of socioeconomic status and health behavior among adolescents. Data from a wealthy Italian region.

We investigate the association between socio-economic status and unhealthy behaviors among adolescents. By using different measures of socio-economic status, we capture both subjective aspects, as operationalized by perceived family affluence, and objective aspects, such as parents' educational levels and family affluence scale. We use data from a sample of 11,623 adolescents who participated in the Health Behavior in School-aged Children (HBSC) study in 2007, 2010, and 2014 in the Lombardy region of Italy. Results show that all of our measures of socio-economic status are correlated with unhealthy behaviors among adolescents. In particular, perceiving a family affluence below average is significantly correlated with a higher probability of reporting all of the unhealthy behaviors included in our analysis. Having at least one parent with university education significantly decreases the odds of being obese or overweight, having an unbalance diet, being physically inactive, and reporting sedentary behaviors. However, adolescents with at least one university educated parent are more likely to make use of cannabis. When controlling for all of our SES measures simultaneously, we find that family affluence scale is no longer significant in determining adolescents' behaviors. Our findings suggest that, when focusing on health inequalities among adolescents, self-perceptions and non-material dimensions of SES have more explanatory power than its material dimensions.

Recording brain responses to TMS of primary motor cortex by EEG - utility of an optimized sham procedure.

INTRODUCTION: Electroencephalography (EEG) is increasingly used to investigate brain responses to transcranial magnetic stimulation (TMS). A relevant issue is that TMS is associated with considerable auditory and somatosensory stimulation, causing peripherally evoked potentials (PEPs) in the EEG, which contaminate the direct cortical responses to TMS (TEPs). All previous attempts to control for PEPs suffer from significant limitations. OBJECTIVE/HYPOTHESIS: To design an optimized sham procedure to control all sensory input generated by subthreshold real TMS targeting the hand area of the primary motor cortex (M1), enabling reliable separation of TEPs from PEPs. METHODS: In 23 healthy (16 female) subjects, we recorded EEG activity evoked by an optimized sham TMS condition which masks and matches auditory and somatosensory co-stimulation during the real TMS condition: auditory control was achieved by noise masking and by using a second TMS coil that was placed on top of the real TMS coil and produced a calibrated sound pressure level. Somatosensory control was obtained by electric stimulation (ES) of the scalp with intensities sufficient to saturate somatosensory input. ES was applied in both the sham and real TMS conditions. Perception of auditory and somatosensory inputs in the sham and real TMS conditions were compared by psychophysical testing. Transcranially evoked EEG signal changes were identified by subtraction of EEG activity in the sham condition from EEG activity in the real TMS condition. RESULTS: Perception of auditory and somatosensory inputs in the sham vs. real TMS conditions was comparable. Both sham and real TMS evoked a series of similar EEG signal deflections and induced broadband power increase in oscillatory activity. Notably, the present procedure revealed EEG potentials and a transient increase in beta band power at the site of stimulation that were only present in the real TMS condition. DISCUSSION: The results validate the effectiveness of our optimized sham approach. Despite the presence of typical responses attributable to sensory input, the procedure provided evidence for direct cortical activation by subthreshold TMS of M1. The findings are relevant for future TMS-EEG experiments that aim at measuring regional brain target engagement controlled by an optimized sham procedure.

Artifacts in EEG-Based BCI Therapies: Friend or Foe?

EEG-based brain-computer interfaces (BCI) have promising therapeutic potential beyond traditional neurofeedback training, such as enabling personalized and optimized virtual reality (VR) neurorehabilitation paradigms where the timing and parameters of the visual experience is synchronized with specific brain states. While BCI algorithms are often designed to focus on whichever portion of a signal is most informative, in these brain-state-synchronized applications, it is of critical importance that the resulting decoder is sensitive to physiological brain activity representative of various mental states, and not to artifacts, such as those arising from naturalistic movements. In this study, we compare the relative classification accuracy with which different motor tasks can be decoded from both extracted brain activity and artifacts contained in the EEG signal. EEG data were collected from 17 chronic stroke patients while performing six different head, hand, and arm movements in a realistic VR-based neurorehabilitation paradigm. Results show that the artifactual component of the EEG signal is significantly more informative than brain activity with respect to classification accuracy. This finding is consistent across different feature extraction methods and classification pipelines. While informative brain signals can be recovered with suitable cleaning procedures, we recommend that features should not be designed solely to maximize classification accuracy, as this could select for remaining artifactual components. We also propose the use of machine learning approaches that are interpretable to verify that classification is driven by physiological brain states. In summary, whereas informative artifacts are a helpful friend in BCI-based communication applications, they can be a problematic foe in the estimation of physiological brain states.

Phase Synchronicity of µ-Rhythm Determines Efficacy of Interhemispheric Communication Between Human Motor Cortices.

The theory of communication through coherence predicts that effective connectivity between nodes in a distributed oscillating neuronal network depends on their instantaneous excitability state and phase synchronicity (Fries, 2005). Here, we tested this prediction by using state-dependent millisecond-resolved real-time electroencephalography-triggered dual-coil transcranial magnetic stimulation (EEG-TMS) (Zrenner et al., 2018) to target the EEG-negative (high-excitability state) versus EEG-positive peak (low-excitability state) of the sensorimotor µ-rhythm in the left (conditioning) and right (test) motor cortex (M1) of 16 healthy human subjects (9 female, 7 male). Effective connectivity was tested by short-interval interhemispheric inhibition (SIHI); that is, the inhibitory effect of the conditioning TMS pulse given 10-12 ms before the test pulse on the test motor-evoked potential. We compared the four possible combinations of excitability states (negative peak, positive peak) and phase relations (in-phase, out-of-phase) of the µ-rhythm in the conditioning and test M1 and a random phase condition. Strongest SIHI was found when the two M1 were in phase for the high-excitability state (negative peak of the µ-rhythm), whereas the weakest SIHI occurred when they were out of phase and the conditioning M1 was in the low-excitability state (positive peak). Phase synchronicity contributed significantly to SIHI variation, with stronger SIHI in the in-phase than out-of-phase conditions. These findings are in exact accord with the predictions of the theory of communication through coherence. They open a translational route for highly effective modification of brain connections by repetitive stimulation at instants in time when nodes in the network are phase synchronized and excitable.SIGNIFICANCE STATEMENT The theory of communication through coherence predicts that effective connectivity between nodes in distributed oscillating brain networks depends on their instantaneous excitability and phase relation. We tested this hypothesis in healthy human subjects by real-time analysis of brain states by electroencephalography in combination with transcranial magnetic stimulation of left and right motor cortex. We found that short-interval interhemispheric inhibition, a marker of interhemispheric effective connectivity, was maximally expressed when the two motor cortices were in phase for a high-excitability state (the trough of the sensorimotor µ-rhythm). We conclude that findings are consistent with the theory of communication through coherence. They open a translational route to highly effectively modify brain connections by repetitive stimulation at instants in time of phase-synchronized high-excitability states.

Phase of sensorimotor µ-oscillation modulates cortical responses to transcranial magnetic stimulation of the human motor cortex.

KEY POINTS: Oscillatory brain activity coordinates the response of cortical neurons to synaptic inputs in a phase-dependent manner. Larger motor-evoked responses are obtained in a hand muscle when transcranial magnetic stimulation (TMS) is synchronized to the phase of the sensorimotor µ-rhythm. In this study we further showed that TMS applied at the negative vs. positive peak of the µ-rhythm is associated with higher absolute amplitude of the evoked EEG potential at 100 ms after stimulation. This demonstrates that cortical responses are sensitive to excitability fluctuation with brain oscillations Our results indicate that brain state-dependent stimulation is a new useful technique for the investigation of stimulus-related cortical dynamics. ABSTRACT: Oscillatory brain activity coordinates the response of cortical neurons to synaptic inputs in a phase-dependent manner. Transcranial magnetic stimulation (TMS) of the human primary motor cortex elicits larger motor-evoked potentials (MEPs) when applied at the negative vs. positive peak of the sensorimotor µ-rhythm recorded with EEG, demonstrating that this phase represents a state of higher excitability of the cortico-spinal system. Here, we investigated the influence of the phase of the µ-rhythm on cortical responses to TMS as measured by EEG. We tested different stimulation intensities above and below resting motor threshold (RMT), and a realistic sham TMS condition. TMS at 110% RMT applied at the negative vs. positive peak of the µ-rhythm was associated with higher absolute amplitudes of TMS-evoked potentials at 70 ms (P70) and 100 ms (N100). Enhancement of the N100 was confirmed with negative peak-triggered 90% RMT TMS, while phase of the µ-rhythm did not influence evoked responses elicited by sham TMS. These findings extend the idea that TMS applied at the negative vs. positive peak of the endogenous µ-oscillation recruits a larger portion of neurons as a function of stimulation intensity. This further corroborates that brain oscillations determine fluctuations in cortical excitability and establishes phase-triggered EEG-TMS as a sensitive tool to investigate the effects of brain oscillations on stimulus-related cortical dynamics.

Different oscillatory entrainment of cortical networks during motor imagery and neurofeedback in right and left handers.

Volitional modulation and neurofeedback of sensorimotor oscillatory activity is currently being evaluated as a strategy to facilitate motor restoration following stroke. Knowledge on the interplay between this regional brain self-regulation, distributed network entrainment and handedness is, however, limited. In a randomized cross-over design, twenty-one healthy subjects (twelve right-handers [RH], nine left-handers [LH]) performed kinesthetic motor imagery of left (48 trials) and right finger extension (48 trials). A brain-machine interface turned event-related desynchronization in the beta frequency-band (16-22 Hz) during motor imagery into passive hand opening by a robotic orthosis. Thereby, every participant subsequently activated either the dominant (DH) or non-dominant hemisphere (NDH) to control contralateral hand opening. The task-related cortical networks were studied with electroencephalography. The magnitude of the induced oscillatory modulation range in the sensorimotor cortex was independent of both handedness (RH, LH) and hemispheric specialization (DH, NDH). However, the regional beta-band modulation was associated with different alpha-band networks in RH and LH: RH presented a stronger inter-hemispheric connectivity, while LH revealed a stronger intra-hemispheric interaction. Notably, these distinct network entrainments were independent of hemispheric specialization. In healthy subjects, sensorimotor beta-band activity can be robustly modulated by motor imagery and proprioceptive feedback in both hemispheres independent of handedness. However, right and left handers show different oscillatory entrainment of cortical alpha-band networks during neurofeedback. This finding may inform neurofeedback interventions in future to align them more precisely with the underlying physiology.

Effects of the Selective α5-GABAAR Antagonist S44819 on Excitability in the Human Brain: A TMS-EMG and TMS-EEG Phase I Study.

Alpha-5 gamma-aminobutyric acid type A receptors (α5-GABAARs) are located extrasynaptically, regulate neuronal excitability through tonic inhibition, and are fundamentally important for processes such as plasticity and learning. For example, pharmacological blockade of α5-GABAAR in mice with ischemic stroke improved recovery of function by normalizing exaggerated perilesional α5-GABAAR-dependent tonic inhibition. S44819 is a novel competitive selective antagonist of the α5-GABAAR at the GABA-binding site. Pharmacological modulation of α5-GABAAR-mediated tonic inhibition has never been investigated in the human brain. Here, we used transcranial magnetic stimulation (TMS) to test the effects of a single oral dose of 50 and 100 mg of S44819 on electromyographic (EMG) and electroencephalographic (EEG) measures of cortical excitability in 18 healthy young adults in a randomized, double-blinded, placebo-controlled, crossover phase I study. A dose of 100 mg, but not 50 mg, of S44819 decreased active motor threshold, the intensity needed to produce a motor evoked potential of 0.5 mV, and the amplitude of the N45, a GABAAergic component of the TMS-evoked EEG response. The peak serum concentration of 100 mg S44819 correlated directly with the decrease in N45 amplitude. Short-interval intracortical inhibition, a TMS-EMG measure of synaptic GABAAergic inhibition, and other components of the TMS-evoked EEG response remained unaffected. These findings provide first time evidence that the specific α5-GABAAR antagonist S44819 reached human cortex to impose an increase in cortical excitability. These data warrant further development of S44819 in a human clinical trial to test its efficacy in enhancing recovery of function after ischemic stroke. SIGNIFICANCE STATEMENT: The extrasynaptic α-5 gamma-aminobutyric acid type A receptor (α5-GABAAR) regulates neuronal excitability through tonic inhibition in the mammalian brain. Tonic inhibition is important for many fundamental processes such as plasticity and learning. Pharmacological modulation of α5-GABAAR-mediated tonic inhibition has never been investigated in the human brain. This study demonstrates that S44819, a selective α5-GABAAR antagonist, increases cortical excitability in healthy human subjects, as indicated by specific markers of transcranial magnetic stimulation-induced muscle and brain responses measured by electromyography and electroencephalography. Our findings imply that tonic inhibition in human cortex can be modified effectively and that this modification can be quantified with noninvasive brain stimulation methods. The actions of S44819 may be suitable to improve plasticity and learning.

The impact of GABAergic drugs on TMS-induced brain oscillations in human motor cortex.

Brain responses to transcranial magnetic stimulation (TMS) as measured with electroencephalography (EEG) have so far been assessed either by TMS-evoked EEG potentials (TEPs), mostly reflecting phase-locked neuronal activity, or time-frequency-representations (TFRs), reflecting oscillatory power arising from a mixture of both evoked (i.e., phase-locked) and induced (i.e., non-phase-locked) responses. Single-pulse TMS of the human primary motor cortex induces a specific pattern of oscillatory changes, characterized by an early (30-200 ms after TMS) synchronization in the α- and ß-bands over the stimulated sensorimotor cortex and adjacent lateral frontal cortex, followed by a late (200-400 ms) α- and ß-desynchronization over the stimulated and contralateral sensorimotor cortex. As GABAergic inhibition plays an important role in shaping oscillatory brain activity, we sought here to understand if GABAergic inhibition contributes to these TMS-induced oscillations. We tested single oral doses of alprazolam, diazepam, zolpidem (positive modulators of the GABAA receptor), and baclofen (specific GABAB receptor agonist). Diazepam and zolpidem enhanced, and alprazolam tended to enhance while baclofen decreased the early α-synchronization. Alprazolam and baclofen enhanced the early ß-synchronization. Baclofen enhanced the late α-desynchronization, and alprazolam, diazepam and baclofen enhanced the late ß-desynchronization. The observed GABAergic drug effects on TMS-induced α- and ß-band oscillations were not explained by drug-induced changes on corticospinal excitability, muscle response size, or resting-state EEG power. Our results provide first insights into the pharmacological profile of TMS-induced oscillatory responses of motor cortex.

Corticomuscular Coherence: a Novel Tool to Assess the Pyramidal Tract Dysfunction in Spinocerebellar Ataxia Type 2.

Clinical signs of corticospinal tract dysfunction are a common feature of spinocerebellar ataxia type 2 (SCA2) patients. The objective of this study is to assess dysfunction of the corticospinal tract in SCA2 using corticomuscular coherence. Testing corticomuscular coherence and rating of ataxia severity and non-ataxia symptoms were performed in 19 SCA2 patients and 24 age-matched controls. Central motor conduction times (CMCT) to upper and lower right limbs were obtained for the SCA2 group using Transcraneal magnetic stimulation (TMS). SCA2 patients exhibited a significant reduction of corticomuscular coherence for lower limbs, but not for upper limbs. This difference remained significant, even when excluding those individuals with clinical signs of corticospinal tract dysfunction. Corticomuscular coherence for lower limbs correlated inversely with CMCT to tibialis anterior muscle. Corticomuscular coherence could be a valuable electrophysiological tool to assess the corticospinal tract involvement in SCA2, even in the absence of clinical signs of corticospinal tract dysfunction.

TMS-EEG signatures of GABAergic neurotransmission in the human cortex.

Combining transcranial magnetic stimulation (TMS) and electroencephalography (EEG) constitutes a powerful tool to directly assess human cortical excitability and connectivity. TMS of the primary motor cortex elicits a sequence of TMS-evoked EEG potentials (TEPs). It is thought that inhibitory neurotransmission through GABA-A receptors (GABAAR) modulates early TEPs (<50 ms after TMS), whereas GABA-B receptors (GABABR) play a role for later TEPs (at ∼100 ms after TMS). However, the physiological underpinnings of TEPs have not been clearly elucidated yet. Here, we studied the role of GABAA/B-ergic neurotransmission for TEPs in healthy subjects using a pharmaco-TMS-EEG approach. In Experiment 1, we tested the effects of a single oral dose of alprazolam (a classical benzodiazepine acting as allosteric-positive modulator at α1, α2, α3, and α5 subunit-containing GABAARs) and zolpidem (a positive modulator mainly at the α1 GABAAR) in a double-blind, placebo-controlled, crossover study. In Experiment 2, we tested the influence of baclofen (a GABABR agonist) and diazepam (a classical benzodiazepine) versus placebo on TEPs. Alprazolam and diazepam increased the amplitude of the negative potential at 45 ms after stimulation (N45) and decreased the negative component at 100 ms (N100), whereas zolpidem increased the N45 only. In contrast, baclofen specifically increased the N100 amplitude. These results provide strong evidence that the N45 represents activity of α1-subunit-containing GABAARs, whereas the N100 represents activity of GABABRs. Findings open a novel window of opportunity to study alteration of GABAA-/GABAB-related inhibition in disorders, such as epilepsy or schizophrenia.

Detecting tones in complex auditory scenes.

In everyday life, our auditory system is bombarded with many signals in complex auditory scenes. Limited processing capacities allow only a fraction of these signals to enter perceptual awareness. This magnetoencephalography (MEG) study used informational masking to identify the neural mechanisms that enable auditory awareness. On each trial, participants indicated whether they detected a pair of sequentially presented tones (i.e., the target) that were embedded within a multi-tone background. We analysed MEG activity for 'hits' and 'misses', separately for the first and second tones within a target pair. Comparing physically identical stimuli that were detected or missed provided insights into the neural processes underlying auditory awareness. While the first tone within a target elicited a stronger early P50m on hit trials, only the second tone evoked a negativity at 150 ms, which may index segregation of the tone pair from the multi-tone background. Notably, a later sustained deflection peaking around 300 and 500 ms (P300m) was the only component that was significantly amplified for both tones, when they were detected pointing towards its key role in perceptual awareness. Additional Dynamic Causal Modelling analyses indicated that the negativity at 150 ms underlying auditory stream segregation is mediated predominantly via changes in intrinsic connectivity within auditory cortices. By contrast, the later P300m response as a signature of perceptual awareness relies on interactions between parietal and auditory cortices. In conclusion, our results suggest that successful detection and hence auditory awareness of a two-tone pair within complex auditory scenes relies on recurrent processing between auditory and higher-order parietal cortices.

Characterization of GABAB-receptor mediated neurotransmission in the human cortex by paired-pulse TMS-EEG.

GABAB-receptor (GABABR) mediated inhibition is important in regulating neuronal excitability. The paired-pulse transcranial magnetic stimulation (TMS) protocol of long-interval intracortical inhibition (LICI) likely reflects this GABABergic inhibition. However, this view is based on indirect evidence from electromyographic (EMG) studies. Here we combined paired-pulse TMS with simultaneous electroencephalography (paired-pulse TMS-EEG) and pharmacology to directly investigate mechanisms of LICI at the cortical level. We tested the effects of a conditioning stimulus (CS100) applied 100ms prior to a test stimulus (TS) over primary motor cortex on TS-evoked EEG-potentials (TEPs). Healthy subjects were given a single oral dose of baclofen, a GABABR agonist, or diazepam, a positive modulator at GABAARs, in a placebo-controlled, pseudo-randomized double-blinded crossover study. LICI was quantified as the difference between paired-pulse TEPs (corrected for long-lasting EEG responses by the conditioning pulse) minus single-pulse TEPs. LICI at baseline (i.e. pre-drug intake) was characterized by decreased P25, N45, N100 and P180 and increased P70 TEP components. Baclofen resulted in a trend towards the enhancement of LICI of the N45 and N100, and significantly enhanced LICI of the P180. In contrast, diazepam consistently suppressed LICI of late potentials (i.e. N100, P180), without having an effect on LICI of earlier (i.e. P25, N45 and P70) potentials. These findings demonstrate for the first time directly at the system level of the human cortex that GABABR-mediated cortical inhibition contributes to LICI, while GABAAR-mediated inhibition occludes LICI. Paired-pulse TMS-EEG allows investigating cortical GABABR-mediated inhibition more directly and specifically than hitherto possible, and may thus inform on network abnormalities caused by disordered inhibition, e.g. in patients with schizophrenia or epilepsy.

Measuring the accuracy of ICA-based artifact removal from TMS-evoked potentials.

BACKGROUND: The analysis and interpretation of transcranial magnetic stimulation (TMS)-evoked potentials (TEPs) relies on successful cleaning of the artifacts, which typically mask the early (0-30 ms) TEPs. Independent component analysis (ICA) is possibly the single most utilized methodology to clean these signals. OBJECTIVE: ICA-based cleaning is reliable provided that the input data are composed of independent components. Differently, in case the underlying components are to some extent dependent, ICA algorithms may yield erroneous estimates of the components, resulting in incorrectly cleaned data. We aim to ascertain whether TEP signals are suited for ICA. METHODS: We present a systematic analysis of how the properties of simulated artifacts imposed on measured artifact-free TEPs affect the ICA results. The variability of the artifact waveform over the recorded trials is varied from deterministic to stochastic. We measure the accuracy of ICA-based cleaning for each level of variability. RESULTS: Our findings indicate that, when the trial-to-trial variability of an artifact component is small, which can result in dependencies between underlying components, ICA-based cleaning biases towards eliminating also non-artifactual TEP data. We also show that the variability can be measured using the ICA-derived components, which in turn allows us to estimate the cleaning accuracy. CONCLUSION: As TEP artifacts tend to have small trial-to-trial variability, one should be aware of the possibility of eliminating brain-derived EEG when applying ICA-based cleaning strategies. In practice, after ICA, the artifact component variability can be measured, and it predicts to some extent the cleaning reliability, even when not knowing the clean ground-truth data.

Blindly separated spontaneous network-level oscillations predict corticospinal excitability.

Objective.The corticospinal responses of the motor network to transcranial magnetic stimulation (TMS) are highly variable. While often regarded as noise, this variability provides a way of probing dynamic brain states related to excitability. We aimed to uncover spontaneously occurring cortical states that alter corticospinal excitability.Approach.Electroencephalography (EEG) recorded during TMS registers fast neural dynamics-unfortunately, at the cost of anatomical precision. We employed analytic Common Spatial Patterns technique to derive excitability-related cortical activity from pre-TMS EEG signals while overcoming spatial specificity issues.Main results.High corticospinal excitability was predicted by alpha-band activity, localized adjacent to the stimulated left motor cortex, and suggesting a travelling wave-like phenomenon towards frontal regions. Low excitability was predicted by alpha-band activity localized in the medial parietal-occipital and frontal cortical regions.Significance.We established a data-driven approach for uncovering network-level neural activity that modulates TMS effects. It requires no prior anatomical assumptions, while being physiologically interpretable, and can be employed in both exploratory investigation and brain state-dependent stimulation.

Corticomuscular coherence is tuned to the spontaneous rhythmicity of speech at 2-3 Hz.

Human speech features rhythmicity that frames distinctive, fine-grained speech patterns. Speech can thus be counted among rhythmic motor behaviors that generally manifest characteristic spontaneous rates. However, the critical neural evidence for tuning of articulatory control to a spontaneous rate of speech has not been uncovered. The present study examined the spontaneous rhythmicity in speech production and its relationship to cortex-muscle neurocommunication, which is essential for speech control. Our MEG results show that, during articulation, coherent oscillatory coupling between the mouth sensorimotor cortex and the mouth muscles is strongest at the frequency of spontaneous rhythmicity of speech at 2-3 Hz, which is also the typical rate of word production. Corticomuscular coherence, a measure of efficient cortex-muscle neurocommunication, thus reveals behaviorally relevant oscillatory tuning for spoken language.