Cognitive deficits in schizophrenia have been widely reported. Neurophysiological and neuropsychological assessments have been conducted to study these impairments. Event-related potentials (ERPs) are relevant markers of cognitive deficits in schizophrenia, and reductions in specific ERP components have been found. The MATRICS Consensus Cognitive Battery (MCCB) was developed to obtain a consensus battery for the assessment of cognitive deficits in schizophrenia. Here, we aimed to study modulations of several ERP components in first episode psychosis (FEP). We also examined neuropsychological deficits using the MCCB, and correlations between ERP and MCCB impairments. Thirty-eight FEP patients were compared to thirty-eight healthy controls. The following ERP components were examined: P1, N1, MMN, P2, early-P3 and late-P3. We used an auditory three-stimulus oddball paradigm, with standard (60%), target (20%) and distractor (20%) stimuli. FEP patients showed significantly lower amplitudes of P2, early-P3 and late-P3 components. FEP patients also showed significant deficits in all the MCCB cognitive domains. Finally, correlational analyses found strong associations between amplitudes of P2, early-P3 and late-P3 components and MCCB tests for attention and speed of processing. These findings indicate that deficits in late auditory ERP components are present in FEP, whereas early components are preserved. These reductions in late ERP components were related to attentional deficits in FEP as assessed by MCCB. These findings indicate that MCCB is a valid battery for studying cognitive impairments in the initial stages of schizophrenia, and highlight the utility of converging neurophysiological and neuropsychological measures to examine attentional impairments in schizophrenia.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1016/j.jocn.2016.10.023 | DOI Listing |
J Pain Res
January 2025
Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan.
Purpose: Spinal cord stimulation (SCS) is pivotal in treating chronic intractable pain. To elucidate the mechanism of action among conventional and current novel types of SCSs, a stable and reliable electrophysiology model in the consensus animals to mimic human SCS treatment is essential. We have recently developed a new in vivo implantable pulsed-ultrahigh-frequency (pUHF) SCS platform for conducting behavioral and electrophysiological studies in rats.
View Article and Find Full Text PDFJ Food Sci Technol
February 2025
DÖHLER Food, İstanbul, Turkey.
Unlabelled: In this study, the changes in the physicochemical properties, color stability, and amino acid composition of cemen paste (CP) produced by adjusting to different pH levels (3.0, 4.0, 5.
View Article and Find Full Text PDFClin Neurophysiol Pract
December 2024
NeuRAL Lab, Abbott Neuromodulation, Plano, TX 75024, USA.
Objective: This study aims to investigate the sources of later response in epidural spinal recordings (ESRs) obtained from implanted leads during spinal cord stimulation, a topic has not been widely studied in previous research.
Methods: Two patients with lower back and lower extremity pain underwent SCS implantation with intraoperative neuromonitoring (IONM). The timing of extracted peaks in ESRs and intramuscular electromyography (EMG) recordings were analyzed and compared to a Monte Carlo simulation for synchronization analysis.
J Clin Med Res
January 2025
Department of Rehabilitation Medicine, Affiliated Hospital of Guilin Medical University, Guilin 541001, Guangxi, China.
Background: Transcranial static magnetic stimulation (tSMS) as a new noninvasive brain stimulation (NIBS) technique is gradually gaining widespread attention. This study aims to investigate the effects of tSMS on the excitability of the somatosensory cortex in healthy adults.
Methods: Forty healthy volunteers were recruited and randomly assigned to either the intervention group (tSMS) or the control group (sham), with 20 participants in each.
Neuroimage
January 2025
Department of Computer Science, University of Innsbruck, Technikerstrasse 21a, Innsbruck, 6020, Austria. Electronic address:
The objective of this study is to assess the potential of a transformer-based deep learning approach applied to event-related brain potentials (ERPs) derived from electroencephalographic (EEG) data. Traditional methods involve averaging the EEG signal of multiple trials to extract valuable neural signals from the high noise content of EEG data. However, this averaging technique may conceal relevant information.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!