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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.

Electroencephalographic (EEG) analysis has emerged as a powerful tool for brain state interpretation and diagnosis, but not for the diagnosis of mental disorders; this may be explained by its low spatial resolution or depth sensitivity. This paper concerns the diagnosis of schizophrenia using EEG, which currently suffers from several cardinal problems: it heavily depends on assumptions, conditions and prior knowledge regarding the patient. Additionally, the diagnostic experiments take hours, and the accuracy of the analysis is low or unreliable. This article presents the "TFFO" (Time-Frequency transformation followed by Feature-Optimization), a novel approach for schizophrenia detection showing great success in classification accuracy with no false positives. The methodology is designed for single electrode recording, and it attempts to make the data acquisition process feasible and quick for most patients.

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.

The rubber-hand illusion (RHI), an illusion in which tactile sensations are referred to a synthetic alien limb, is enhanced in schizophrenia patients. Somatosensory evoked responses of the illusion were compared between schizophrenia patients and normal control subjects. Schizophrenia patients had significant alterations in long latency evoked responses during the illusion. These findings support the hypothesis of alterations in associative higher-level neuronal activity in schizophrenia. The findings support previous results pointing to alterations in associative brain regions in schizophrenia.

Deficits in processing contextual information are one of the main features of cognitive dysfunction in schizophrenia, but the neurophysiologic substrate underlying this dysfunction is poorly understood. We used ERPs to investigate local contextual processing in schizophrenic patients. Local context was defined as the occurrence of a short predictive series of stimuli occurring before delivery of a target event. Response times of predicted targets were faster in controls compared to patients. Schizophrenia patients failed to generate the P3b latency shift between predicted and random targets that was observed in controls and demonstrated a prominent reduction of the peak of an early latency context dependent positivity. The current study provides evidence of contextual processing deficits in schizophrenia patients by demonstrating alteration in the behavioral and neural correlates of local contextual processing. [ABSTRACT FROM AUTHOR]

Presents an electroencephalograph study of functional connectivity and working memory in schizophrenia. Importance of connectivity for simulation of working memory; Failure of schizophrenics to activate the neural networks of the fronto-temporal regions; Findings supporting a 'disconnection syndrome' for schizophrenia.