scholarly journals Hyper-Activated Brain Resting-State Network and Mismatch Negativity Deficit in Schizophrenia With Auditory Verbal Hallucination Revealed by an Event-Related Potential Evidence

2020 ◽  
Vol 11 ◽  
Author(s):  
Qiaoling Sun ◽  
Yehua Fang ◽  
Xuemei Peng ◽  
Yongyan Shi ◽  
Jinhong Chen ◽  
...  
2015 ◽  
Author(s):  
Jorge Rudas ◽  
Darwin Martínez ◽  
Javier Guaje ◽  
Athena Demertzi ◽  
Lizette Heine ◽  
...  

2002 ◽  
Vol 11 (1) ◽  
pp. 42-49
Author(s):  
Devin L. McCaslin ◽  
Lawrence L. Feth ◽  
Gary P. Jacobson ◽  
Pamela J. Mishler

This investigation was conducted to determine whether an exogenous event-related potential called the mismatch negativity (MMN) would change systematically in response to frequency-modulated signals with varying temporal properties. Both N1 and P2 waveforms were recorded for 50-ms frequency-modulated signals from normal hearing listeners. The standard stimuli for this investigation were continuous sweep tones with center frequencies of 1000 Hz that traversed a frequency range of 200 Hz in a single step. The rare stimuli were signals that traversed the same frequency range in two, four, six, or eight discrete steps. Results suggest that for the 10 participants, 1) the mean MMN peak-to-peak amplitude and mean area decreased significantly with decreases in step duration, 2) MMN area amplitude was the best indicator of psychophysical performance for the two magnitude measures, and 3) MMN onsets and peak latencies did not show either a significant increase or decrease in latency as step duration decreased.


2009 ◽  
Vol 5 (4S_Part_1) ◽  
pp. P27-P28
Author(s):  
Katell Mevel ◽  
Brigitte Landeau ◽  
Florence Mézenge ◽  
Nicolas Villain ◽  
Marine Fouquet ◽  
...  

2017 ◽  
Vol 8 (1) ◽  
Author(s):  
Takuya Ito ◽  
Kaustubh R. Kulkarni ◽  
Douglas H. Schultz ◽  
Ravi D. Mill ◽  
Richard H. Chen ◽  
...  

2018 ◽  
Vol 40 (4) ◽  
pp. 1062-1081 ◽  
Author(s):  
Liqun Kuang ◽  
Xie Han ◽  
Kewei Chen ◽  
Richard J. Caselli ◽  
Eric M. Reiman ◽  
...  

2014 ◽  
Vol 36 (1) ◽  
pp. 199-212 ◽  
Author(s):  
Hugo-Cesar Baggio ◽  
Bàrbara Segura ◽  
Roser Sala-Llonch ◽  
Maria-José Marti ◽  
Francesc Valldeoriola ◽  
...  

2021 ◽  
Vol 15 ◽  
Author(s):  
Nadine D. Herzog ◽  
Tim P. Steinfath ◽  
Ricardo Tarrasch

Sustained attention is the ability to continually concentrate on task-relevant information, even in the presence of distraction. Understanding the neural mechanisms underlying this ability is critical for comprehending attentional processes as well as neuropsychiatric disorders characterized by attentional deficits, such as attention deficit hyperactivity disorder (ADHD). In this study, we aimed to investigate how trait-like critical oscillations during rest relate to the P300 evoked potential—a biomarker commonly used to assess attentional deficits. We measured long−range temporal correlations (LRTC) in resting-state EEG oscillations as index for criticality of the signal. In addition, the attentional performance of the subjects was assessed as reaction time variability (RTV) in a continuous performance task following an oddball paradigm. P300 amplitude and latencies were obtained from EEG recordings during this task. We found that, after controlling for individual variability in task performance, LRTC were positively associated with P300 amplitudes but not latencies. In line with previous findings, good performance in the sustained attention task was related to higher P300 amplitudes and earlier peak latencies. Unexpectedly, we observed a positive relationship between LRTC in ongoing oscillations during rest and RTV, indicating that greater criticality in brain oscillations during rest relates to worse task performance. In summary, our results show that resting-state neuronal activity, which operates near a critical state, relates to the generation of higher P300 amplitudes. Brain dynamics close to criticality potentially foster a computationally advantageous state which promotes the ability to generate higher event-related potential (ERP) amplitudes.


Sign in / Sign up

Export Citation Format

Share Document