Dataset Information

Recovery from emotion recognition impairment after temporal lobectomy.

ABSTRACT: Mesial temporal lobe epilepsy (MTLE) can be associated with emotion recognition impairment that can be particularly severe in patients with early onset seizures (1-3). Whereas, there is growing evidence that memory and language can improve in seizure-free patients after anterior temporal lobectomy (ATL) (4), the effects of surgery on emotional processing are still unknown. We used functional magnetic resonance imaging (fMRI) to investigate short-term reorganization of networks engaged in facial emotion recognition in MTLE patients. Behavioral and fMRI data were collected from six patients before and after ATL. During the fMRI scan, patients were asked to make a gender decision on fearful and neutral faces. Behavioral data demonstrated that two patients with early onset right MTLE were impaired in fear recognition while fMRI results showed they lacked specific activations for fearful faces. Post-ATL behavioral data showed improved emotion recognition ability, while fMRI demonstrated the recruitment of a functional network for fearful face processing. Our results suggest that ATL elicited brain plasticity mechanisms allowing behavioral and fMRI improvement in emotion recognition.

SUBMITTER: Benuzzi F

PROVIDER: S-EPMC4047513 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Recovery from emotion recognition impairment after temporal lobectomy.

Benuzzi Francesca F Zamboni Giovanna G Meletti Stefano S Serafini Marco M Lui Fausta F Baraldi Patrizia P Duzzi Davide D Rubboli Guido G Tassinari Carlo Alberto CA Nichelli Paolo Frigio PF

Frontiers in neurology 20140606

Mesial temporal lobe epilepsy (MTLE) can be associated with emotion recognition impairment that can be particularly severe in patients with early onset seizures (1-3). Whereas, there is growing evidence that memory and language can improve in seizure-free patients after anterior temporal lobectomy (ATL) (4), the effects of surgery on emotional processing are still unknown. We used functional magnetic resonance imaging (fMRI) to investigate short-term reorganization of networks engaged in facial ...[more]

PMID: 24936197

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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.

Dataset Information

Recovery from emotion recognition impairment after temporal lobectomy.

Publications

Recovery from emotion recognition impairment after temporal lobectomy.

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