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Spatial and temporal cortical variability track with age and affective experience during emotion regulation in youth.

ABSTRACT: Variability is a fundamental feature of human brain activity that is particularly pronounced during development. However, developmental neuroimaging research has only recently begun to move beyond characterizing brain function exclusively in terms of magnitude of neural activation to incorporate estimates of variability. No prior neuroimaging study has done so in the domain of emotion regulation. We investigated how age and affective experiences relate to spatial and temporal variability in neural activity during emotion regulation. In the current study, 70 typically developing youth aged 8 to 17 years completed a cognitive reappraisal task of emotion regulation while undergoing functional MRI. Estimates of spatial and temporal variability during regulation were calculated across a network of brain regions, defined a priori, and were then related to age and affective experiences. Results showed that increasing age was associated with reduced spatial and temporal variability in a set of frontoparietal regions (e.g., dorsomedial prefrontal cortex, superior parietal lobule) known to be involved in effortful emotion regulation. In addition, youth who reported less negative affect during regulation had less spatial variability in the ventrolateral prefrontal cortex, which has previously been linked to cognitive reappraisal. We interpret age-related reductions in spatial and temporal variability as implying neural specialization. These results suggest that the development of emotion regulation is undergirded by a process of neural specialization and open a host of possibilities for incorporating neural variability into the study of emotion regulation development. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

SUBMITTER: Guassi Moreira JF

PROVIDER: S-EPMC6716618 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

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Spatial and temporal cortical variability track with age and affective experience during emotion regulation in youth.

Guassi Moreira João F JF McLaughlin Katie A KA Silvers Jennifer A JA

Developmental psychology 20190901 9

Variability is a fundamental feature of human brain activity that is particularly pronounced during development. However, developmental neuroimaging research has only recently begun to move beyond characterizing brain function exclusively in terms of magnitude of neural activation to incorporate estimates of variability. No prior neuroimaging study has done so in the domain of emotion regulation. We investigated how age and affective experiences relate to spatial and temporal variability in neur ...[more]

PMID: 31464495

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Project description:IntroductionRecently, the microstate analysis method has been widely used to investigate the temporal and spatial dynamics of electroencephalogram (EEG) signals. However, most studies have focused on EEG at resting state, and few use microstate analysis to study emotional EEG. This paper aims to investigate the temporal and spatial patterns of EEG in emotional states, and the specific neurophysiological significance of microstates during the emotion cognitive process, and further explore the feasibility and effectiveness of applying the microstate analysis to emotion recognition.MethodsWe proposed a KLGEV-criterion-based microstate analysis method, which can automatically and adaptively identify the optimal number of microstates in emotional EEG. The extracted temporal and spatial microstate features then served as novel feature sets to improve the performance of EEG emotion recognition. We evaluated the proposed method on two publicly available emotional EEG datasets: the SJTU Emotion EEG Dataset (SEED) and the Database for Emotion Analysis using Physiological Signals (DEAP).ResultsFor the SEED dataset, 10 microstates were identified using the proposed method. These temporal and spatial features were fed into AutoGluon, an open-source automatic machine learning model, yielding an average three-class accuracy of 70.38% (±8.03%) in subject-dependent emotion recognition. For the DEAP dataset, the method identified 9 microstates. The average accuracy in the arousal dimension was 74.33% (±5.17%) and 75.49% (±5.70%) in the valence dimension, which were competitive performance compared to some previous machine-learning-based studies. Based on these results, we further discussed the neurophysiological relationship between specific microstates and emotions, which broaden our knowledge of the interpretability of EEG microstates. In particular, we found that arousal ratings were positively correlated with the activity of microstate C (anterior regions of default mode network) and negatively correlated with the activity of microstate D (dorsal attention network), while valence ratings were positively correlated with the activity of microstate B (visual network) and negatively correlated with the activity of microstate D (dorsal attention network).DiscussionIn summary, the findings in this paper indicate that the proposed KLGEV-criterion-based method can be employed to research emotional EEG signals effectively, and the microstate features are promising feature sets for EEG-based emotion recognition.

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Spatial and temporal cortical variability track with age and affective experience during emotion regulation in youth.

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Spatial and temporal cortical variability track with age and affective experience during emotion regulation in youth.

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