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The combination of Transcranial Magnetic Stimulation (TMS) with Electroencephalography (EEG) exposes the brain's global response to localized and abrupt stimulations. However, large electric artifacts are induced in the EEG by the TMS, obscuring crucial stages of the brain's response. Artifact removal is commonly performed by data processing techniques. However, an experimentally verified physical model for the origin and structure of the TMS-induced discharge artifacts, by which these methods can be justified or evaluated, is still lacking. We re-examine the known contribution of the skin in creating the artifacts, and outline a detailed model for the relaxation of the charge accumulated at the electrode-gel-skin interface due to the TMS pulse. We then experimentally validate implications set forth by the model. We find that the artifacts decay like a power law in time rather than the commonly assumed exponential. In fact, the skin creates a power-law decay of order 1 at each electrode, which is turned into a power law of order 2 by the reference electrode. We suggest an artifact removal method based on the model which can be applied from times after the pulse as short as 2 milliseconds onwards to expose the full EEG from the brain. The method can separate the capacitive discharge artifacts from those resulting from cranial muscle activation, demonstrating that the capacitive effect dominates at short times. Overall, our insight into the physical process allows us to accurately access TMS-evoked EEG responses that directly follow the TMS pulse, possibly opening new opportunities in TMS-EEG research., Competing Interests: The authors have declared that no competing interests exist.

Objective: The combination of transcranial magnetic stimulation (TMS) and electroencephalography (EEG) has been hampered by the large artifact that the TMS generates in the EEG. Using TMS with EEG necessitates a sophisticated artifact-resistant EEG system that can acquire reliable signals in the crucial several tens of milliseconds immediately following the TMS pulse. Here, we demonstrate the use of a novel artifact removal algorithm together with a 24-bit EEG system to achieve similar recordings as those obtained with the dedicated TMS-compatible EEG system., Methods: This setup was used to compare TMS-evoked responses between a group of healthy controls and a group of patients with schizophrenia, a condition in which effective neural connectivity is thought to be compromised., Results: We observe differences in TMS-evoked responses between the two groups, similar to those recently reported in a study that used a dedicated TMS-compatible EEG system., Conclusions: The standard 24-bit EEG system combined with an artifact removal algorithm produces results similar to the dedicated TMS-compatible system., Significance: This paves the way for more researchers and clinicians to use TMS-evoked responses for research and diagnosis of a wide spectrum of disorders.

Transcranial Magnetic Stimulation (TMS) is rapidly gaining acceptance as a non-invasive probe into brain functionality. We utilize TMS to study the connectivity of a simple motor network in patients of schizophrenia (N=19), and in healthy control subjects (N=9). TMS was used in an externally paced finger tapping task, perturbing the internal network oscillations invoked by the finger motion as it keeps pace with a metronome. TMS perturbations were synchronized to the metronome and applied to the network at the level of the primary motor cortex (M1). Contrary to initial expectations, TMS did not affect the sensorimotor synchronization of subjects with schizophrenia or their tapping accuracy. TMS did cause extreme deviations in the finger's trajectory, and altered the timing perceptions of subjects with schizophrenia. Additionally, it invoked high-level deficiencies related to attention and volition in the form of lapses, implying that the connectivity between modules in the brain that underlie motor control, sensorimotor synchronization, timing perception and awareness of action, can be disrupted by TMS in subjects with schizophrenia, but not in healthy subjects. The ability to disrupt high level network functions with perturbations to the lower level of M1 supports models describing deficits in connectivity of distributed networks in the brains of schizophrenia patients. It also demonstrates the use of TMS to probe connectivity between components of such networks.

We study the interplay between motor programs and their timing in the brain by using precise pulses of transcranial magnetic stimulation (TMS) applied to the primary motor cortex. The movement of the finger performing a tapping task is periodically perturbed in synchronization with a metronome. TMS perturbation can profoundly affect both the finger trajectory and its kinematics, but the tapping accuracy itself is surprisingly not affected. The motion of the finger during the TMS perturbation can be categorized into two abnormal behaviors that subjects were unaware of: a doubling of the frequency of the tap and a stalling of the finger for half the period. More stalls occurred as the tapping frequency increased. In addition, an enhancement of the velocity of the finger on its way up was observed. We conclude that the timing process involved in controlling the tapping movement is separate from the motor processes in charge of execution of the motor commands. We speculate that the TMS is causing a release of the motor plan ahead of time into activation mode. The observed doubles and stalls are then the result of an indirect interaction in the brain, making use of an existing motor plan to correct the preactivation and obtain the temporal goal of keeping the beat.

Although previous research in alcohol dependent populations identified alterations within local structures of the addiction 'reward' circuitry, there is limited research into global features of this network, especially in early recovery. Transcranial magnetic stimulation (TMS) is capable of non-invasively perturbing the brain network while electroencephalography (EEG) measures the network response. The current study is the first to apply a TMS inhibitory paradigm while utilising network science (graph theory) to quantify network anomalies associated with alcohol dependence. Eleven individuals with alcohol-dependence (ALD) in early recovery and 16 healthy controls (HC) were administered 75 single pulses and 75 paired-pulses (inhibitory paradigm) to both the left and right prefrontal cortex (PFC). For each participant, Pearson cross-correlation was applied to the EEG data and correlation matrices constructed. Global network measures (mean degree, clustering coefficient, local efficiency and global efficiency) were extracted for comparison between groups. Following administration of the inhibitory paired-pulse TMS to the left PFC, the ALD group exhibited altered mean degree, clustering coefficient, local efficiency and global efficiency compared to HC. Decreases in local efficiency increased the prediction of being in the ALD group, while all network metrics (following paired-pulse left TMS) were able to adequately discriminate between the groups. In the ALD group, reduced mean degree and global clustering was associated with increased severity of past alcohol use. Our study provides preliminary evidence of altered network topology in patients with alcohol dependence in early recovery. Network anomalies were predictive of high alcohol use and correlated with clinical features of alcohol dependence. Further research using this novel brain mapping technique may identify useful network biomarkers of alcohol dependence and recovery. [ABSTRACT FROM AUTHOR]

The electroencephalogram (EEG) of schizophrenia patients is known to exhibit a reduction of signal-to-noise ratio and of phase locking, as well as a facilitation of excitability, in response to a variety of external stimuli. Here, we demonstrate these effects in transcranial magnetic stimulation (TMS)-evoked potentials and in the resting-state EEG. To ensure veracity, we used 3 weekly sessions and analyzed both resting-state and TMS-EEG data. For the TMS responses, our analysis verifies known results. For the resting state, we introduce the methodology of mean-normalized variation to the EEG analysis (quartile-based coefficient of variation), which allows for a comparison of narrow-band EEG amplitude fluctuations to narrow-band Gaussian noise. This reveals that amplitude fluctuations in the delta, alpha, and beta bands of healthy controls are different from those in schizophrenia patients, on time scales of tens of seconds. We conclude that the EEG-measured cortical activity patterns of schizophrenia patients are more similar to noise, both in alpha- and beta-resting state and in TMS responses. Our results suggest that the ability of neuronal populations to form stable, locally, and temporally correlated activity is reduced in schizophrenia, a conclusion, that is, in accord with previous experiments on TMS-EEG and on resting-state EEG. [ABSTRACT FROM AUTHOR]