How do we prepare to stop ourselves in the future? Here, we used scalp EEG to test the hypothesis that people prepare to stop by putting parts of their motor system (specifically, here, sensorimotor cortex) into a suppressed state ahead of time. On each trial, participants were cued to prepare to stop one hand and then initiated a bimanual movement. On a minority of trials, participants were instructed to stop the cued hand while continuing quickly with the other. We used a guided multivariate source separation method to examine oscillatory power changes in presumed sensorimotor cortical areas. We observed that, when people prepare to stop a hand, there were above-baseline beta band power increases (12-24 Hz) in contralateral cortex up to a second earlier. This increase in beta band power in the proactive period was functionally relevant because it predicted, trial by trial, the degree of selectivity with which participants subsequently stopped a response but did not relate to movement per se. Thus, preparing to stop particular response channels corresponds to increased beta power from contralateral (sensorimotor) cortex, and this relates specifically to subsequent stopping. These results provide a high temporal resolution and frequency-specific electrophysiological signature of the preparing-to-stop state that is pertinent to future studies of mitigating provocation, including in clinical disorders. The results also highlight the utility of guided multivariate source separation for revealing the cortical dynamics underlying both movement and response suppression.
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http://dx.doi.org/10.1162/jocn_a_01373 | DOI Listing |
Phys Eng Sci Med
January 2025
Amrita School of Artificial Intelligence, Amrita Vishwa Vidyapeetham, Bangalore, India.
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Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, PR China.
Evidence suggests that attenuated mismatch negative (MMN) waves have a close link to auditory verbal hallucinations (AVH) and their clinical outcomes, especially impaired neural oscillations such as θ, β representing attentional control. In current study, thirty patients with schizophrenia and AVH (SZ) and twenty-nine healthy controls (HC) underwent multi-feature MMN paradigm measurements including frequency and duration deviant stimuli (fMMN and dMMN). Clinical symptoms and MMN paradigm were followed up among SZ group after 8-week treatment.
View Article and Find Full Text PDFBiology (Basel)
January 2025
School of Instrumentation Science and Optoelectronic Engineering, Beihang University, Beijing 100191, China.
Neural oscillations observed during semantic processing embody the function of brain language processing. Precise parameterization of the differences in these oscillations across various semantics from a time-frequency perspective is pivotal for elucidating the mechanisms of brain language processing. The superlet transform and cluster depth test were used to compute the time-frequency representation of oscillatory difference (ODTFR) between neural activities recorded by optically pumped magnetometer-based magnetoencephalography (OPM-MEG) during processing congruent and incongruent Chinese semantics.
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January 2025
Department of Rehabilitation Sciences, KU Leuven, B-3001, Leuven, Belgium.
Electroencephalogram (EEG) during pinprick stimulation has the potential to unveil neural mechanisms underlying sensorimotor impairments post-stroke. A proof-of-concept study explored event-related peak pinprick amplitude and oscillatory responses in healthy controls and in people with acute and subuacute motor and sensorimotor stroke, their relationship, and to what extent EEG somatosensory responses can predict sensorimotor impairment. In this study, 26 individuals participated, 10 people with an acute and early subacute sensorimotor stroke, 6 people with an acute and early subacute motor stroke, and 10 age-matched controls.
View Article and Find Full Text PDFFront Syst Neurosci
January 2025
International research center for Cognitive Applied Neuroscience (IrcCAN), Università Cattolica del Sacro Cuore, Milan, Italy.
This study examines the impact of positive and negative feedback on recall of past decisions, focusing on behavioral performance and electrophysiological (EEG) responses. Participants completed a decision-making task involving 10 real-life scenarios, each followed by immediate positive or negative feedback. In a recall phase, participants' accuracy (ACC), errors (ERRs), and response times (RTs) were recorded alongside EEG data to analyze brain activity patterns related to recall.
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