Scheduled service maintenance on November 22


On Friday, November 22, 2024, between 06:00 CET and 18:00 CET, GIN services will undergo planned maintenance. Extended service interruptions should be expected. We will try to keep downtimes to a minimum, but recommend that users avoid critical tasks, large data uploads, or DOI requests during this time.

We apologize for any inconvenience.

Here you can find the data to the study: Brodhun C, Borelli E, Weiss T (2023). Neural correlates of word processing influenced by painful primes. PLOS ONE.

Thomas Weiss b464465b8d 'datacite.yml' ändern hace 1 año
LICENSE c9c8d67f29 Initial commit hace 1 año
README.md e087d39a60 'README.md' ändern hace 1 año
datacite.yml b464465b8d 'datacite.yml' ändern hace 1 año

README.md

Data_for_Broduhn_et_al_PLOS_ONE_2023

Here you can find the data to the study: Brodhun C, Borelli E, Weiss T (2023). Neural correlates of word processing influenced by painful primes. PLOS ONE. The data contain raw EEG data and EEG log files as well as trigger information for Brain Vision Analyzer (compatible with EEGLAB), and subjective parameters about pain and control conditions for 48 individuals. Meta information about data structure are included. Detailed information about methods and data analysis are presented in the above-mentioned paper.

datacite.yml
Title Data_for_Brodhun_et_al_PLOS_ONE_2023
Authors Brodhun,Christoph;Clinical Psychology, Friedrich Schiller University Jena, Germany
Borelli,Eleonora;Dept. Med. Surg.Sci., University of Modena and Reggio Emilia, Modena, Italy
Weiss,Thomas;Clinical Psychology, Friedrich Schiller University Jena, Germany
Description Here you can find the data to the study: Brodhun C, Borelli E, Weiss T (2023). Neural correlates of word processing influenced by painful primes. PLOS ONE. Abstract: The administration of painful primes has been shown to influence the perception of successively presented semantic stimuli. Painful primes lead to more negative valence ratings of pain-related, negative, and positive words than no prime. This effect was greater for pain-related than negative words. The identities of this effect’s neural correlates remain unknown. In this EEG experiment, 48 healthy subjects received noxious electrical stimuli of moderate intensity. During this priming, they were presented with adjectives of variable valence (pain-related, negative, positive, and neutral). The triggered event-related potentials were analyzed during N1 (120–180 ms), P2 (170–260 ms), P3 (300–350 ms), N400 (370–550 ms), and two late positive complex components (LPC1 [650–750 ms] and LPC2 [750–1000 ms]). Larger eventrelated potentials were found for negative and pain-related words compared to positive words in later components (N400, LPC1, and LPC2), mainly in the frontal regions. Early components (N1, P2, and P3) were less affected by the word category but were by the prime condition (painful vs. no stimulation). Later components (LPC1, LPC2) were not affected by the prime condition. An interaction effect involving prime and word category was found on the behavioral level but not the electrophysiological level. This finding indicates that the interaction effect does not directly translate from the behavioral to the electrophysiological level. Possible reasons for this discrepancy are discussed. The data contain raw EEG data and EEG log files as well as trigger information for Brain Vision Analyzer (compatible with EEGLAB), and subjective parameters about pain and control conditions for 48 individuals. Meta information about data structure are included. Detailed information about methods and data analysis are presented in the above-mentioned paper.
License Creative Commons CC0 1.0 Public Domain Dedication (https://creativecommons.org/publicdomain/zero/1.0/)
References Brodhun C, Borelli E, Weiss T (2023) Neural correlates of word processing influenced by painful primes. 10.1371/journal.pone.0295148 [10.1371/journal.pone.0295148] (IsSupplementTo)
Funding
Keywords Neuroscience
EEG
pain
priming
word processing
event-related potentials
Resource Type Dataset