Repetitive transcranial magnetic stimulation dissociates working memory manipulation from retention functions in the prefrontal, but not posterior parietal, cortex

Understanding the contributions of the prefrontal cortex (PFC) to working memory is central to understanding the neural bases of high-level cognition. One question that remains controversial is whether the same areas of the dorsolateral PFC (dlPFC) that participate in the manipulation of information in working memory also contribute to its short-term retention (STR). We evaluated this question by first identifying, with functional magnetic resonance imaging (fMRI), brain areas involved in manipulation. Next, these areas were targeted with repetitive transcranial magnetic stimulation (rTMS) while subjects performed tasks requiring only the STR or the STR plus manipulation of information in working memory. fMRI indicated that manipulation-related activity was independent of retention-related activity in both the PFC and superior parietal lobule (SPL). rTMS, however, yielded a different pattern of results. Although rTMS of the dlPFC selectively disrupted manipulation, rTMS of the SPL disrupted manipulation and STR to the same extent. rTMS of the postcentral gyrus (a control region) had no effect on performance. The implications of these results are twofold. In the PFC, they are consistent with the view that this region contributes more importantly to the control of information in working memory than to its STR. In the SPL, they illustrate the importance of supplementing the fundamentally correlational data from neuroimaging with a disruptive method, which affords stronger inference about structure-function relations

The spectral features of EEG responses to transcranial magnetic stimulation of the primary motor cortex depend on the amplitude of the motor evoked potentials

Transcranial magnetic stimulation (TMS) of the primary motor cortex (M1) can excite both cortico-cortical and cortico-spinal axons resulting in TMS-evoked potentials (TEPs) and motor-evoked potentials (MEPs), respectively. Despite this remarkable difference with other cortical areas, the influence of motor output and its amplitude on TEPs is largely unknown. Here we studied TEPs resulting from M1 stimulation and assessed whether their waveform and spectral features depend on the MEP amplitude. To this aim, we performed two separate experiments. In experiment 1, single-pulse TMS was applied at the same supra-threshold intensity on primary motor, prefrontal, premotor and parietal cortices and the corresponding TEPs were compared by means of local mean field power and time-frequency spectral analysis. In experiment 2 we stimulated M1 at resting motor threshold in order to elicit MEPs characterized by a wide range of amplitudes. TEPs computed from high-MEP and low-MEP trials were then compared using the same methods applied in experiment 1. In line with previous studies, TMS of M1 produced larger TEPs compared to other cortical stimulations. Notably, we found that only TEPs produced by M1 stimulation were accompanied by a late event-related desynchronization (ERD-peaking at ~300 ms after TMS), whose magnitude was strongly dependent on the amplitude of MEPs. Overall, these results suggest that M1 produces peculiar responses to TMS possibly reflecting specific anatomo-functional properties, such as the re-entry of proprioceptive feedback associated with target muscle activation

TMS-Induced Cortical Potentiation during Wakefulness Locally Increases Slow Wave Activity during Sleep

BACKGROUND: Sleep slow wave activity (SWA) is thought to reflect sleep need, increasing in proportion to the length of prior wakefulness and decreasing during sleep. However, the process responsible for SWA regulation is not known. We showed recently that SWA increases locally after a learning task involving a circumscribed brain region, suggesting that SWA may reflect plastic changes triggered by learning. METHODOLOGY/PRINCIPAL FINDINGS: To test this hypothesis directly, we used transcranial magnetic stimulation (TMS) in conjunction with high-density EEG in humans. We show that 5-Hz TMS applied to motor cortex induces a localized potentiation of TMS-evoked cortical EEG responses. We then show that, in the sleep episode following 5-Hz TMS, SWA increases markedly (+39.1±17.4%, p<0.01, n = 10). Electrode coregistration with magnetic resonance images localized the increase in SWA to the same premotor site as the maximum TMS-induced potentiation during wakefulness. Moreover, the magnitude of potentiation during wakefulness predicts the local increase in SWA during sleep. CONCLUSIONS/SIGNIFICANCE: These results provide direct evidence for a link between plastic changes and the local regulation of sleep need

Sleep and Plasticity in Schizophrenia

Schizophrenia is a devastating mental illness with a worldwide prevalence of approximately 1%. Although the clinical features of the disorder were described over one hundred years ago, its neurobiology is still largely elusive despite several decades of research. Schizophrenia is associated with marked sleep disturbances and memory impairment. Above and beyond altered sleep architecture, sleep rhythms including slow waves and spindles are disrupted in schizophrenia. In the healthy brain, these rhythms reflect and participate in plastic processes during sleep. This chapter discusses evidence that schizophrenia patients exhibit dysfunction of sleep-mediated plasticity on a behavioral, cellular, and molecular level and offers suggestions on how the study of sleeping brain activity can shed light on the pathophysiological mechanisms of the disorder

Bayesian Optimization of Machine Learning Classification of Resting-State EEG Microstates in Schizophrenia: A Proof-of-Concept Preliminary Study Based on Secondary Analysis

Resting-state electroencephalography (EEG) microstates reflect sub-second, quasi-stable states of brain activity. Several studies have reported alterations of microstate features in patients with schizophrenia (SZ). Based on these findings, it has been suggested that microstates may represent neurophysiological biomarkers for the classification of SZ. To explore this possibility, machine learning approaches can be employed. Bayesian optimization is a machine learning approach that selects the best-fitted machine learning model with tuned hyperparameters from existing models to improve the classification. In this proof-of-concept preliminary study based on secondary analysis, 20 microstate features were extracted from 14 SZ patients and 14 healthy controls&rsquo; EEG signals. These parameters were then ranked as predictors based on their importance, and an optimized machine learning approach was applied to evaluate the performance of the classification. SZ patients had altered microstate features compared to healthy controls. Furthermore, Bayesian optimization outperformed conventional multivariate analyses and showed the highest accuracy (90.93%), AUC (0.90), sensitivity (91.37%), and specificity (90.48%), with reliable results using just six microstate predictors. Altogether, in this proof-of-concept study, we showed that machine learning with Bayesian optimization can be utilized to characterize EEG microstate alterations and contribute to the classification of SZ patients

To Alfred Deakin

To access publisher full text version of this article. Please click on the hyperlink in Additional Links fieldTranscranial Magnetic Stimulation (TMS) is a non-invasive method of stimulating the brain that is increasingly being used in neuropsychiatric research and clinical psychiatry. This review examines the role of TMS in schizophrenia research as a diagnostic and a therapeutic resource. After a brief overview of TMS, we describe the application of TMS to schizophrenia in studies of cortical excitability and inhibition, and we discuss the potential confounding role of neuroleptic medications. Based on these studies, it appears that some impairment of cortical inhibition may be present in schizophrenic subjects. We then review attempts to employ TMS for treating different symptoms of schizophrenia. Some encouraging results have been obtained, such as the reduction of auditory hallucinations after slow TMS over auditory cortex and an improvement of psychotic symptoms after high frequency TMS over left prefrontal cortex. However, these results need to be confirmed using better placebo conditions. Future studies are likely to employ TMS in combination with functional brain imaging to examine the effects produced by the stimulated area on activity in other brain regions. Such studies may reveal impaired effective connectivity between specific brain areas, which could identify these regions as targets for selective stimulation with therapeutic doses of TMS