scholarly journals Spatio-Temporal Pattern of Appraising Social and Emotional Relevance: Evidence from Event-Related Brain Potentials

2017 ◽  
Author(s):  
Annekathrin Schacht ◽  
Pascal Vrtička
Keyword(s):  
Social Information ◽  
Emotional Valence ◽  
Stimulus Dimension ◽  
Stimulus Onset ◽  
Fmri Data ◽  
Brain Potentials ◽  
Social Content ◽  
Social And Emotional ◽  
Relevant Stimulus Dimension ◽  
Spatio Temporal

Social information is highly intrinsically relevant for the human species because of its direct link to guiding physiological responses and behavior. Accordingly, extant functional magnetic resonance imaging (fMRI) data suggest that social content may form a unique stimulus dimension. It remains largely unknown, however, how neural activity underlying social (versus nonsocial) information processing temporally unfolds, and how such social information appraisal may interact with the processing of other stimulus characteristics, particularly emotional meaning. Here, we presented complex visual scenes differing in both social (versus nonsocial) and emotional relevance (positive, negative, neutral) intermixed with scrambled versions of these pictures to N= 24 healthy young adults. Event-related brain potentials (ERPs) to intact pictures were examined for gaining insight to the dynamics of appraisal of both dimensions, implemented within the brain. Our main finding is an early interaction between social and emotional relevance due to enhanced amplitudes of early ERP components to emotionally positive pictures of social compared to nonsocial content, presumably reflecting rapid allocation of attention and counteracting an overall negativity bias. Importantly, our ERP data show high similarity with previously observed fMRI data using the same stimuli, and source estimations located the ERP effects in overlapping occipito-temporal brain areas. Our new data suggest that relevance detection may occur already as early as around 100 ms after stimulus onset and may combine relevance checks not only examining intrinsic pleasantness/emotional valence, but also social content as a unique, highly relevant stimulus dimension.

scholarly journals Characterizing pinprick-evoked brain potentials before and after experimentally induced secondary hyperalgesia

2015 ◽  
Vol 114 (5) ◽  
pp. 2672-2681 ◽  
Author(s):  
Emanuel N. van den Broeke ◽  
André Mouraux ◽  
Antonia H. Groneberg ◽  
Doreen B. Pfau ◽  
Rolf-Detlef Treede ◽  
...  
Keyword(s):  
Event Related Potentials ◽  
Stimulus Onset ◽  
Secondary Hyperalgesia ◽  
Brain Potentials ◽  
Nociceptive Pathways ◽  
Before And After ◽  
Pain Ratings ◽  
Related Potentials ◽  
Positive Complex ◽  
Stimulation Of

Secondary hyperalgesia is believed to be a key feature of “central sensitization” and is characterized by enhanced pain to mechanical nociceptive stimuli. The aim of the present study was to characterize, using EEG, the effects of pinprick stimulation intensity on the magnitude of pinprick-elicited brain potentials [event-related potentials (ERPs)] before and after secondary hyperalgesia induced by intradermal capsaicin in humans. Pinprick-elicited ERPs and pinprick-evoked pain ratings were recorded in 19 healthy volunteers, with mechanical pinprick stimuli of varying intensities (0.25-mm probe applied with a force extending between 16 and 512 mN). The recordings were performed before (T0) and 30 min after (T1) intradermal capsaicin injection. The contralateral noninjected arm served as control. ERPs elicited by stimulation of untreated skin were characterized by 1) an early-latency negative-positive complex peaking between 120 and 250 ms after stimulus onset (N120-P240) and maximal at the vertex and 2) a long-lasting positive wave peaking 400–600 ms after stimulus onset and maximal more posterior (P500), which was correlated to perceived pinprick pain. After capsaicin injection, pinprick stimuli were perceived as more intense in the area of secondary hyperalgesia and this effect was stronger for lower compared with higher stimulus intensities. In addition, there was an enhancement of the P500 elicited by stimuli of intermediate intensity, which was significant for 64 mN. The other components of the ERPs were unaffected by capsaicin. Our results suggest that the increase in P500 magnitude after capsaicin is mediated by facilitated mechanical nociceptive pathways.

Comparison of Early Cortical Networks in Efficient and Inefficient Visual Search: An Event-Related Potential Study

2003 ◽  
Vol 15 (7) ◽  
pp. 1039-1051 ◽  
Author(s):  
Ute Leonards ◽  
Julie Palix ◽  
Christoph Michel ◽  
Vicente Ibanez
Keyword(s):  
Event Related Potentials ◽  
Temporal Correlation ◽  
Temporal Analysis ◽  
Search Display ◽  
Stimulus Onset ◽  
Event Related Potential ◽  
Time Range ◽  
Cortical Networks ◽  
Related Potentials ◽  
Spatio Temporal

Functional magnetic resonance imaging studies have indicated that efficient feature search (FS) and inefficient conjunction search (CS) activate partially distinct frontoparietal cortical networks. However, it remains a matter of debate whether the differences in these networks reflect differences in the early processing during FS and CS. In addition, the relationship between the differences in the networks and spatial shifts of attention also remains unknown. We examined these issues by applying a spatio-temporal analysis method to high-resolution visual event-related potentials (ERPs) and investigated how spatio-temporal activation patterns differ for FS and CS tasks. Within the first 450 msec after stimulus onset, scalp potential distributions (ERP maps) revealed 7 different electric field configurations for each search task. Configuration changes occurred simultaneously in the two tasks, suggesting that contributing processes were not significantly delayed in one task compared to the other. Despite this high spatial and temporal correlation, two ERP maps (120–190 and 250–300 msec) differed between the FS and CS. Lateralized distributions were observed only in the ERP map at 250–300 msec for the FS. This distribution corresponds to that previously described as the N2pc component (a negativity in the time range of the N2 complex over posterior electrodes of the hemisphere contralateral to the target hemifield), which has been associated with the focusing of attention onto potential target items in the search display. Thus, our results indicate that the cortical networks involved in feature and conjunction searching partially differ as early as 120 msec after stimulus onset and that the differences between the networks employed during the early stages of FS and CS are not necessarily caused by spatial attention shifts.

scholarly journals Feature integration within discrete time windows

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Leila Drissi-Daoudi ◽  
Adrien Doerig ◽  
Michael H. Herzog
Keyword(s):  
Computational Model ◽  
Discrete Time ◽  
Time Windows ◽  
Temporal Integration ◽  
Sensory Information ◽  
Stimulus Onset ◽  
Feature Integration ◽  
Spatio Temporal ◽  
The Individual ◽  
Over Time

Abstract Sensory information must be integrated over time to perceive, for example, motion and melodies. Here, to study temporal integration, we used the sequential metacontrast paradigm in which two expanding streams of lines are presented. When a line in one stream is offset observers perceive all other lines to be offset too, even though they are straight. When more lines are offset the offsets integrate mandatorily, i.e., observers cannot report the individual offsets. We show that mandatory integration lasts for up to 450 ms, depending on the observer. Importantly, integration occurs only when offsets are presented within a discrete window of time. Even stimuli that are in close spatio-temporal proximity do not integrate if they are in different windows. A window of integration starts with stimulus onset and integration in the next window has similar characteristics. We present a two-stage computational model based on discrete time windows that captures these effects.

scholarly journals Deep Transformation Method for Discriminant Analysis of Multi-Channel Resting State fMRI

2019 ◽  
Vol 33 ◽  
pp. 2556-2563 ◽  
Author(s):  
Abhay M S Aradhya ◽  
Aditya Joglekar ◽  
Sundaram Suresh ◽  
M. Pratama
Keyword(s):  
Resting State ◽  
Cognitive Disorders ◽  
Feature Space ◽  
Resting State Fmri ◽  
Transformation Method ◽  
Brain Network ◽  
Fmri Data ◽  
Functional Activities ◽  
Temporal Correlations ◽  
Spatio Temporal

Analysis of resting state - functional Magnetic Resonance Imaging (rs-fMRI) data has been a challenging problem due to a high homogeneity, large intra-class variability, limited samples and difference in acquisition technologies/techniques. These issues are predominant in the case of Attention Deficit Hyperactivity Disorder (ADHD). In this paper, we propose a new Deep Transformation Method (DTM) that extracts the discriminant latent feature space from rsfMRI and projects it in the subsequent layer for classification of rs-fMRI data. The hidden transformation layer in DTM projects the original rs-fMRI data into a new space using the learning policy and extracts the spatio-temporal correlations of the functional activities as a latent feature space. The subsequent convolution and decision layers transform the latent feature space into high-level features and provide accurate classification. The performance of DTM has been evaluated using the ADHD200 rs-fMRI benchmark data with crossvalidation. The results show that the proposed DTM achieves a mean classification accuracy of 70.36% and an improvement of 8.25% on the state of the art methodologies was observed. The improvement is due to concurrent analysis of the spatio-temporal correlations between the different regions of the brain and can be easily extended to study other cognitive disorders using rs-fMRI. Further, brain network analysis has been studied to identify the difference in functional activities and the corresponding regions behind cognitive symptoms in ADHD.

Localization of Latent Epileptic Activities Using Spatio-Temporal Independent Component Analysis of fMRI Data

2006 ◽  
Vol 19 (1-2) ◽  
pp. 21-28 ◽  
Author(s):  
Huafu Chen ◽  
Dezhong Yao ◽  
Guangming Lu ◽  
Zhiqiang Zhang ◽  
Qiaoli Hu
Keyword(s):  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Fmri Data ◽  
Spatio Temporal

scholarly journals Localization of Latent Epileptic Activities Using Spatio-Temporal Independent Component Analysis of fMRI Data

2007 ◽  
Vol 19 (4) ◽  
pp. 223-223
Author(s):  
Huafu Chen ◽  
Dezhong Yao ◽  
Guangming Lu ◽  
Zhiqiang Zhang ◽  
Qiaoli Hu
Keyword(s):  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Fmri Data ◽  
Spatio Temporal

Classification of Spatio-Temporal fMRI Data in the Spiking Neural Network

2018 ◽  
Vol 8 (6) ◽  
pp. 2670 ◽  
Author(s):  
Shaznoor Shakira Saharuddin ◽  
Norhanifah Murli ◽  
Muhammad Azani Hasibuan
Keyword(s):  
Neural Network ◽  
Fmri Data ◽  
Spiking Neural Network ◽  
Spatio Temporal

scholarly journals Investigations into resting-state connectivity using independent component analysis

2005 ◽  
Vol 360 (1457) ◽  
pp. 1001-1013 ◽  
Author(s):  
Christian F Beckmann ◽  
Marilena DeLuca ◽  
Joseph T Devlin ◽  
Stephen M Smith
Keyword(s):  
Independent Component Analysis ◽  
Resting State ◽  
Temporal Structure ◽  
Low Frequency ◽  
Component Analysis ◽  
Independent Component ◽  
Fmri Data ◽  
Analytical Technique ◽  
Spatio Temporal ◽  
Resting State Connectivity

Inferring resting-state connectivity patterns from functional magnetic resonance imaging (fMRI) data is a challenging task for any analytical technique. In this paper, we review a probabilistic independent component analysis (PICA) approach, optimized for the analysis of fMRI data, and discuss the role which this exploratory technique can take in scientific investigations into the structure of these effects. We apply PICA to fMRI data acquired at rest, in order to characterize the spatio-temporal structure of such data, and demonstrate that this is an effective and robust tool for the identification of low-frequency resting-state patterns from data acquired at various different spatial and temporal resolutions. We show that these networks exhibit high spatial consistency across subjects and closely resemble discrete cortical functional networks such as visual cortical areas or sensory–motor cortex.

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