Positive Classification Advantage: Tracing the Time Course Based on Brain Oscillation

Abstract

The present study aimed to explore the modulation of frequency bands (alpha, beta, theta) underlying the positive facial expressions classification advantage within different post-stimulus time intervals (100–200 ms, 200–300 ms, 300–400 ms). For this purpose, we recorded electroencephalogram (EEG) activity during an emotion discrimination task for happy, sad and neutral faces. The correlation between the non-phase-locked power of frequency bands and reaction times (RTs) was assessed. The results revealed that beta played a major role in positive classification advantage (PCA) within the 100–200 and 300–400 ms intervals, whereas theta was important within the 200–300 ms interval. We propose that the beta band modulated the neutral and emotional face classification process, and that the theta band modulated for happy and sad face classification.

Gender Differences in Global Functional Connectivity During Facial Emotion Processing: A Visual MMN Study

“…Moreover, the MMN is more sensitive to the deviant feature located on the lower half-field to avoid low-level processing of facial features (Czigler et al, 2004 ). In addition, some previous studies indicates that even a schematic face made from simple line fragments triggered the face-sensitive N170 and MMN (Sagiv and Bentin, 2001 ; Kreegipuu et al, 2013 ; Liu et al, 2013 ; Xu et al, 2013 ; Yan et al, 2018 ). All the facial stimuli were presented with an exposure duration of 150 ms and an inter-stimulus interval of 450 ms, with a pseudo-random selection process in each block.…”

The Influence of Self-Referential Processing on Attentional Orienting in Frontoparietal Networks

“…The present experimental design was based on a mixed block/event-related paradigm that allows for the more complete utilization of the blood-oxygen-level-dependent (BOLD) signal which, in turn, enables a deeper interpretation of how brain regions function on multiple timescales (Petersen and Dubis, 2012 ). As in previous studies (Friston et al, 1999 ; Yan et al, 2017 ), we presented alternating blocks of experimental trials with the cue condition as well as blocks for baseline measures. The congruence trials were presented in a pseudorandom event-related distribution within the experimental blocks.…”

The neurophysiological architecture of semantic dementia: spectral dynamic causal modelling of a neurodegenerative proteinopathy

“…With particular reference to the semantic appraisal network and SD, striking convergence of core semantic network elements has been demonstrated when task-free and task-directed connectivity patterns are compared directly, albeit with additional extra-temporal connectivity during task-based processing 38 . Furthermore, changes in resting-state network connectivity have been directly correlated with semantic deficits in SD 29 , 43 , 50 , 51 . Considered more broadly, semantic processing is likely to be a major constituent of the ‘default mode’ operation of the resting brain, maintaining readiness to respond appropriately to objects in the environment that impinge on homeostatic and other self-referential processes 38 , 45 .…”

“…Rather than addressing a particular semantic task or deficit, our goal was to identify changes in intrinsic network architecture (evident in the resting brain) in SD that could potentially affect various active, task directed processes during semantic cognition. We targeted a small number of regions in the anterior temporal and inferior frontal lobes that have been consistently shown to be core to the neural network primarily targeted by pathogenic protein spread in SD 2 , 4 , 24 , 28 , 29 , 31 – 33 , 43 , 44 , 50 . Although the role of inter-hemispheric protein spread in SD is unclear 24 , 28 , as both cerebral hemispheres become affected in tandem with evolution of the disease, we separately explored key commissural connections linking the semantic appraisal networks in each hemisphere.…”