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      An extremely fast neural mechanism to detect emotional visual stimuli: A two-experiment study

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          Abstract

          Defining the brain mechanisms underlying initial emotional evaluation is a key but unexplored clue to understanding affective processing. Event-related potentials (ERPs), especially suited for investigating this issue, were recorded in two experiments (n = 36 and n = 35). We presented emotionally negative (spiders) and neutral (wheels) silhouettes homogenized regarding their visual parameters. In Experiment 1, stimuli appeared at fixation or in the periphery (200 trials per condition and location), the former eliciting a N40 (39 milliseconds) and a P80 (or C1: 80 milliseconds) component, and the latter only a P80. In Experiment 2, stimuli were presented only at fixation (500 trials per condition). Again, an N40 (45 milliseconds) was observed, followed by a P100 (or P1: 105 milliseconds). Analyses revealed significantly greater N40-C1P1 peak-to-peak amplitudes for spiders in both experiments, and ANCOVAs showed that these effects were not explained by C1P1 alone, but that processes underlying N40 significantly contributed. Source analyses pointed to V1 as an N40 focus (more clearly in Experiment 2). Sources for C1P1 included V1 (P80) and V2/LOC (P80 and P100). These results and their timing point to low-order structures (such as visual thalamic nuclei or superior colliculi) or the visual cortex itself, as candidates for initial evaluation structures.

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          Statistical power analyses using G*Power 3.1: tests for correlation and regression analyses.

          Franz Faul, Edgar Erdfelder, Axel Buchner … (2009)
          G*Power is a free power analysis program for a variety of statistical tests. We present extensions and improvements of the version introduced by Faul, Erdfelder, Lang, and Buchner (2007) in the domain of correlation and regression analyses. In the new version, we have added procedures to analyze the power of tests based on (1) single-sample tetrachoric correlations, (2) comparisons of dependent correlations, (3) bivariate linear regression, (4) multiple linear regression based on the random predictor model, (5) logistic regression, and (6) Poisson regression. We describe these new features and provide a brief introduction to their scope and handling.
          Article 0 comments Cited 5647 times – based on 0 reviews     Review now
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            FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data

            Robert Oostenveld, Pascal Fries, Eric Maris … (2012)
            This paper describes FieldTrip, an open source software package that we developed for the analysis of MEG, EEG, and other electrophysiological data. The software is implemented as a MATLAB toolbox and includes a complete set of consistent and user-friendly high-level functions that allow experimental neuroscientists to analyze experimental data. It includes algorithms for simple and advanced analysis, such as time-frequency analysis using multitapers, source reconstruction using dipoles, distributed sources and beamformers, connectivity analysis, and nonparametric statistical permutation tests at the channel and source level. The implementation as toolbox allows the user to perform elaborate and structured analyses of large data sets using the MATLAB command line and batch scripting. Furthermore, users and developers can easily extend the functionality and implement new algorithms. The modular design facilitates the reuse in other software packages.
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              Removing electroencephalographic artifacts by blind source separation.

              T P Jung, S Makeig, C. Humphries … (2000)
              Eye movements, eye blinks, cardiac signals, muscle noise, and line noise present serious problems for electroencephalographic (EEG) interpretation and analysis when rejecting contaminated EEG segments results in an unacceptable data loss. Many methods have been proposed to remove artifacts from EEG recordings, especially those arising from eye movements and blinks. Often regression in the time or frequency domain is performed on parallel EEG and electrooculographic (EOG) recordings to derive parameters characterizing the appearance and spread of EOG artifacts in the EEG channels. Because EEG and ocular activity mix bidirectionally, regressing out eye artifacts inevitably involves subtracting relevant EEG signals from each record as well. Regression methods become even more problematic when a good regressing channel is not available for each artifact source, as in the case of muscle artifacts. Use of principal component analysis (PCA) has been proposed to remove eye artifacts from multichannel EEG. However, PCA cannot completely separate eye artifacts from brain signals, especially when they have comparable amplitudes. Here, we propose a new and generally applicable method for removing a wide variety of artifacts from EEG records based on blind source separation by independent component analysis (ICA). Our results on EEG data collected from normal and autistic subjects show that ICA can effectively detect, separate, and remove contamination from a wide variety of artifactual sources in EEG records with results comparing favorably with those obtained using regression and PCA methods. ICA can also be used to analyze blink-related brain activity.
              Article 0 comments Cited 774 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Luis Carretié:
                ORCID: https://orcid.org/0000-0001-7375-6739
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: ResourcesRole: SoftwareRole: SupervisionRole: Writing – original draft
                Uxía Fernández-Folgueiras:
                ORCID: https://orcid.org/0000-0001-5535-0559
                Role: InvestigationRole: MethodologyRole: SoftwareRole: Writing – review & editing
                Dominique Kessel:
                ORCID: https://orcid.org/0000-0001-8275-9923
                Role: Formal analysisRole: MethodologyRole: SoftwareRole: Writing – review & editing
                Guzmán Alba:
                ORCID: https://orcid.org/0000-0001-9910-4886
                Role: InvestigationRole: Writing – review & editing
                Estrella Veiga-Zarza:
                ORCID: https://orcid.org/0000-0001-8951-9121
                Role: InvestigationRole: Writing – review & editing
                Manuel Tapia: Role: ResourcesRole: Writing – review & editing
                Fátima Álvarez:
                ORCID: https://orcid.org/0000-0002-3985-9053
                Role: InvestigationRole: MethodologyRole: SoftwareRole: Writing – review & editing
                Yansong Li: Role: Editor
                Journal
                Journal ID (nlm-ta): PLoS One
                Journal ID (iso-abbrev): PLoS One
                Journal ID (publisher-id): plos
                Title: PLOS ONE
                Publisher: Public Library of Science (San Francisco, CA USA )
                ISSN (Electronic): 1932-6203
                Publication date (Electronic): 21 June 2024
                Publication date Collection: 2024
                Volume: 19
                Issue: 6
                Electronic Location Identifier: e0299677
                Affiliations
                [001] Facultad de Psicología, Universidad Autónoma de Madrid, Madrid, Spain
                Nanjing University, CHINA
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                https://orcid.org/0000-0001-7375-6739
                https://orcid.org/0000-0001-5535-0559
                https://orcid.org/0000-0001-8275-9923
                https://orcid.org/0000-0001-9910-4886
                https://orcid.org/0000-0001-8951-9121
                https://orcid.org/0000-0002-3985-9053
                Article
                Publisher ID: PONE-D-24-05631
                DOI: 10.1371/journal.pone.0299677
                PMC ID: 11192326
                PubMed ID: 38905211
                SO-VID: 940a3d7d-864d-4235-baaa-2a7fd99c6d85
                Copyright © © 2024 Carretié et al
                License:

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                Date received : 14 February 2024
                Date accepted : 3 May 2024
                Page count
                Figures: 5, Tables: 4, Pages: 22
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100004837, Ministerio de Ciencia e Innovación;
                Award ID: PID2021-124420NB-I00
                Award Recipient :
                ORCID: https://orcid.org/0000-0001-7375-6739
                This work was supported by Ministerio de Ciencia e Innovación [MICINN; grant number PID2021-124420NB-I00).
                Categories
                Subject: Research Article
                Subject: Biology and Life Sciences
                Subject: Psychology
                Subject: Emotions
                Subject: Social Sciences
                Subject: Psychology
                Subject: Emotions
                Subject: Research and Analysis Methods
                Subject: Bioassays and Physiological Analysis
                Subject: Electrophysiological Techniques
                Subject: Brain Electrophysiology
                Subject: Electroencephalography
                Subject: Event-Related Potentials
                Subject: Biology and Life Sciences
                Subject: Physiology
                Subject: Electrophysiology
                Subject: Neurophysiology
                Subject: Brain Electrophysiology
                Subject: Electroencephalography
                Subject: Event-Related Potentials
                Subject: Biology and Life Sciences
                Subject: Neuroscience
                Subject: Neurophysiology
                Subject: Brain Electrophysiology
                Subject: Electroencephalography
                Subject: Event-Related Potentials
                Subject: Biology and Life Sciences
                Subject: Neuroscience
                Subject: Brain Mapping
                Subject: Electroencephalography
                Subject: Event-Related Potentials
                Subject: Medicine and Health Sciences
                Subject: Clinical Medicine
                Subject: Clinical Neurophysiology
                Subject: Electroencephalography
                Subject: Event-Related Potentials
                Subject: Research and Analysis Methods
                Subject: Imaging Techniques
                Subject: Neuroimaging
                Subject: Electroencephalography
                Subject: Event-Related Potentials
                Subject: Biology and Life Sciences
                Subject: Neuroscience
                Subject: Neuroimaging
                Subject: Electroencephalography
                Subject: Event-Related Potentials
                Subject: Biology and Life Sciences
                Subject: Anatomy
                Subject: Brain
                Subject: Visual Cortex
                Subject: Medicine and Health Sciences
                Subject: Anatomy
                Subject: Brain
                Subject: Visual Cortex
                Subject: Biology and Life Sciences
                Subject: Neuroscience
                Subject: Cognitive Science
                Subject: Cognitive Psychology
                Subject: Perception
                Subject: Sensory Perception
                Subject: Vision
                Subject: Biology and Life Sciences
                Subject: Psychology
                Subject: Cognitive Psychology
                Subject: Perception
                Subject: Sensory Perception
                Subject: Vision
                Subject: Social Sciences
                Subject: Psychology
                Subject: Cognitive Psychology
                Subject: Perception
                Subject: Sensory Perception
                Subject: Vision
                Subject: Biology and Life Sciences
                Subject: Neuroscience
                Subject: Sensory Perception
                Subject: Vision
                Subject: Research and Analysis Methods
                Subject: Bioassays and Physiological Analysis
                Subject: Electrophysiological Techniques
                Subject: Membrane Electrophysiology
                Subject: Electrode Recording
                Subject: Research and Analysis Methods
                Subject: Bioassays and Physiological Analysis
                Subject: Electrophysiological Techniques
                Subject: Brain Electrophysiology
                Subject: Electroencephalography
                Subject: Biology and Life Sciences
                Subject: Physiology
                Subject: Electrophysiology
                Subject: Neurophysiology
                Subject: Brain Electrophysiology
                Subject: Electroencephalography
                Subject: Biology and Life Sciences
                Subject: Neuroscience
                Subject: Neurophysiology
                Subject: Brain Electrophysiology
                Subject: Electroencephalography
                Subject: Biology and Life Sciences
                Subject: Neuroscience
                Subject: Brain Mapping
                Subject: Electroencephalography
                Subject: Medicine and Health Sciences
                Subject: Clinical Medicine
                Subject: Clinical Neurophysiology
                Subject: Electroencephalography
                Subject: Research and Analysis Methods
                Subject: Imaging Techniques
                Subject: Neuroimaging
                Subject: Electroencephalography
                Subject: Biology and Life Sciences
                Subject: Neuroscience
                Subject: Neuroimaging
                Subject: Electroencephalography
                Subject: Biology and Life Sciences
                Subject: Anatomy
                Subject: Head
                Subject: Scalp
                Subject: Medicine and Health Sciences
                Subject: Anatomy
                Subject: Head
                Subject: Scalp
                Subject: Biology and Life Sciences
                Subject: Anatomy
                Subject: Brain
                Subject: Amygdala
                Subject: Medicine and Health Sciences
                Subject: Anatomy
                Subject: Brain
                Subject: Amygdala
                Custom metadata
                Data Availability Data described in the paper are available at the Open Science Framework ( https://osf.io/9bc2y).

                ScienceOpen disciplines: Uncategorized
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