Stroke Recovery-Related Changes in Cortical Reactivity Based on Modulation of Intracortical Inhibition.

Background: Cortical excitation/inhibition dynamics have been suggested as a key mechanism occurring after stroke. Their supportive or maladaptive role in the course of recovery is still not completely understood. Here, we used transcranial magnetic stimulation (TMS)-electroencephalography coupling to study cortical reactivity and intracortical GABAergic inhibition, as well as their relationship to residual motor function and recovery longitudinally in patients with stroke.

Functional segregation within the dorsal frontoparietal network: a multimodal dynamic causal modeling study.

Discrimination and integration of motion direction requires the interplay of multiple brain areas. Theoretical accounts of perception suggest that stimulus-related (i.e.

Sedation Modulates Frontotemporal Predictive Coding Circuits and the Double Surprise Acceleration Effect.

Two important theories in cognitive neuroscience are predictive coding (PC) and the global workspace (GW) theory. A key research task is to understand how these two theories relate to one another, and particularly, how the brain transitions from a predictive early state to the eventual engagement of a brain-scale state (the GW). To address this question, we present a source-localization of EEG responses evoked by the local-global task-an experimental paradigm that engages a predictive hierarchy, which encompasses the GW.

Placing meta-stable states of consciousness within the predictive coding hierarchy: The deceleration of the accelerated prediction error.

While many studies have linked prediction errors and event related potentials at a single processing level, few consider how these responses interact across levels. In response, we present a factorial analysis of a multi-level oddball task - the local-global task - and we explore it when participants are sedated versus recovered. We found that the local and global levels in fact interact.

Riemannian Geometry Applied to Detection of Respiratory States From EEG Signals: The Basis for a Brain-Ventilator Interface.

Goal: During mechanical ventilation, patient-ventilator disharmony is frequently observed and may result in increased breathing effort, compromising the patient's comfort and recovery. This circumstance requires clinical intervention and becomes challenging when verbal communication is difficult. In this study, we propose a brain-computer interface (BCI) to automatically and noninvasively detect patient-ventilator disharmony from electroencephalographic (EEG) signals: a brain-ventilator interface (BVI).