NCA changes our perspectives on emotional processing
Brain systems supporting the processing of emotion involve deep brain structures (amygdala, thalamus, insula, anterior cingulate cortex, and cerebellum) and fairly superficial cortical areas (prefrontal cortex, temporal and visual cortices). The cerebellum is a recent addition to our view of the emotion-related distributed circuitry. NCA’s very recent findings provide for the first time the temporal component of emotional processing within the cerebellum. It is indicated that: (i) arousal, valence, and their interaction are processed in parallel within anatomically distinct cerebellar lobules, (ii) these processes unfold at well-defined latencies relative to stimulus onset following a temporal hierarchy, and (iii) cerebellar responses are organized into an early prioritization of high arousal, followed by an unpleasant valence effect, and later a pleasant valence by high arousal interaction (Styliadis et al., 2015a).
Distinct cerebellar lobules process arousal, valence and their interaction in parallel following a temporal hierarchy (Styliadis et al., 2015a)
NCA’s research focuses on understanding the functions of the individual amygdala sub-divisions by unmasking their contributions to the processing of valence, arousal or their interaction effect. The results reveal contrasting though parallel roles for laterobasal (LB) and centromedial (CM) sub-divisions of human amygdala in mediating the effects of unpleasant stimuli and for interweaving the effects of pleasure by high arousal respectively. It is indicated that there exists a distinct functional specificity of amygdala anatomical sub-divisions in the emotional processing (Styliadis et al., 2014).
Amygdala responses to valence and its interaction by arousal (Styliadis et al., 2014)
NCA’s research demonstrates evidence for gender differences in the way pleasant and unpleasant stimuli of high and low arousal are processed. Females show greater negativity than males on negative components (N100 and N200) upon viewing emotional stimuli. This effect is further modulated by a gender by valence interaction on the N100 of Pz electrode; females exhibit greater negativity than males but only for the unpleasant stimuli. Arousal effects are observed early on in the processing (N100) on frontal and central electrodes, and these effects are also modulated by gender; high arousing pictures evoke more negative response in females as compared to males (Lithari et al., 2010).
Gender differences across arousal and valence dimensions (Lithari et al., 2010)
NCA develops a hybrid methodology for the rejection of ocular artifacts
The NCA team proposes a hybrid methodology that combines the main advantages of regression and Blind Source Separation (BSS) techniques for rejecting ocular artifacts from electroencephalographic (EEG) and magnetoencephalographic (MEG) signals. Given that the artifactual independent components (ICs) extracted by a BSS method include more ocular and less cerebral activity than the contaminated EEG signals, a regression algorithm is applied to the ICs rather than directly to the recorded signals. The performance of the proposed technique was compared with two automatic techniques; a regression technique based on Least Mean Square (LMS) algorithm and a BSS-based artifact rejection technique called wavelet-ICA (W-ICA) on the artificially contaminated data. For comparison, two metrics were used to assess the different methods’ performance: the first quantified how successful each technique was in removing the ocular artifacts from the EEG recordings, and the second one quantified how much each technique distorted the ongoing brain activity in both time and frequency domains. Confirming our main hypothesis, results have shown that the artifactual ICs contained more ocular and less cerebral activity (p < 0.04) (artifact to signal ratio (ASR) = 1.83 ± 3.65) in contrast to the contaminated electrode signals (ASR = 0.69 ± 3.40). Our results reveal that NCA’s REG-ICA removes the ocular artifacts more successfully than the BSS-based artifact rejection technique called wavelet-ICA (W-ICA) (p < 0.01) or the regression technique based on Least Mean Square (LMS) algorithm (p < 0.01). It also distorts less the brain activity in the time domain when compared to W-ICA and LMS. In the frequency domain, it distorts the brain activity less than the W-ICA in all frequency bands, and less than the LMS for the delta, beta, and gamma bands.