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Studies on differences in brain structure and function according to sex are reported to contribute to differences in behavior and cognition. However, few studies have investigated brain structures or used tractography to investigate gender differences in pain sensitivity. The identification of tracts involved in sex-based structural differences that show pain sensitivity has remained elusive to date. Here, we attempted to demonstrate the sex differences in pain sensitivity and to clarify its relationship with brain structural connectivity. In this study, pain behavior test and brain diffusion tensor imaging (DTI) were performed in male and female rats and tractography was performed on the whole brain using fiber tracking software. We selected eight brain regions related to pain and performed a tractography analysis of these regions. Fractional anisotropy (FA) measurements using automated tractography revealed sex differences in the anterior cingulate cortex (ACC)-, prefrontal cortex (PFC)-, and ventral posterior thalamus-related brain connections. In addition, the results of the correlation analysis of pain sensitivity and DTI tractography showed differences in mean, axial, and radial diffusivities, as well as FA. This study revealed the potential of DTI for exploring circuits involved in pain sensitivity. The behavioral and functional relevance’s of measures derived from DTI tractography is demonstrated by their relationship with pain sensitivity.

Background: Major depressive disorder (MDD) is a mood disorder associated with disruptions in emotional control. Previous studies have investigated abnormal regional activity and connectivity within the fronto-limbic circuit. However, condition-specific connectivity changes and their association with the pathophysiology of MDD remain unexplored. This study investigated effective connectivity in the fronto-limbic circuit induced by negative emotional processing from patients with MDD. Methods: Thirty-four unmedicated female patients with MDD and 28 healthy participants underwent event-related functional magnetic resonance imaging at 7T while viewing emotionally negative and neutral images. Brain regions whose dynamics are driven by experimental conditions were identified by using statistical parametric mapping. Effective connectivity among regions of interest was then estimated by using dynamic causal modeling. Results: Patients with MDD had lower activation of the orbitofrontal cortex (OFC) and higher activation of the parahippocampal gyrus (PHG) than healthy controls (HC). In association with these regional changes, we found that patients with MDD did not have significant modulatory connections from the primary visual cortex (V1) to OFC, whereas those connections of HC were significantly positively modulated during negative emotional processing. Regarding the PHG activity, patients with MDD had greater modulatory connection from the V1, but reduced negative modulatory connection from the OFC, compared with healthy participants. Conclusions: These results imply that disrupted effective connectivity among regions of the OFC, PHG, and V1 may be closely associated with the impaired regulation of negative emotional processing in the female patients with MDD.

Emotions are experienced differently by individuals, and thus, it is important to account for individuals' experienced emotions to understand their physiological responses to emotional stimuli. The present study investigated the physiological responses to a fear-inducing stimulus and examined whether these responses can predict experienced fear. A total of 230 participants were presented with neutral and fear-inducing film clips, after which they self-rated their experienced emotions. Physiological measures (skin conductance level and response: SCL, SCR, heart rate: HR, pulse transit time: PTT, fingertip temperature: FT, and respiratory rate: RR) were recorded during the stimuli presentation. We examined the correlations between the physiological measures and the participants' experienced emotional intensity, and performed a multiple linear regression to predict fear intensity based on the physiological responses. Of the participants, 92.5% experienced the fear emotion, and the average intensity was 5.95 on a 7-point Likert scale. Compared to the neutral condition, the SCL, SCR, HR, and RR increased significantly during the fear-inducing stimulus presentation whereas FT and PTT decreased significantly. Fear intensity correlated positively with SCR and HR and negatively with SCL, FT, PTT, and RR. The multiple linear regression demonstrated that fear intensity was predicted by a combination of SCL, SCR, HR, FT, and RR. Our findings indicate that the physiological responses to experiencing fear are associated with cholinergic, sympathetic, and α-adrenergic vascular activation as well as myocardial β-sympathetic excitation, and support the use of multimodal physiological signals for quantifying emotions.

Background and aims Individuals with problematic hypersexual behavior (PHB) are unable to control their sexual cravings, regardless of other situational factors. This inability to control cravings is a common trait in patients with neurological pathologies related to response inhibition. Until recently, however, it was unclear whether individuals with PHB have decreased inhibition and altered neural responses in the brain regions associated with inhibition compared to healthy control individuals, especially in the presence of distracting sexual stimuli. In this study, we examined the neural and psychological underpinnings of inhibition in individuals with PHB. Methods Thirty individuals with PHB and 30 healthy subjects underwent functional magnetic resonance imaging while performing a modified go/no-go task with neutral or sexual backgrounds used as distractors. Results Individuals with PHB showed poorer response inhibition than healthy subjects, especially when sexual distractors were present. Further, compared to healthy control subjects, individuals with PHB showed decreased activation in the right inferior frontal gyrus (IFG) and reduced functional connectivity between the IFG and the pre-supplementary motor area (preSMA) when response inhibition was required. Finally, the reduced activation and connectivity were more pronounced in the presence of sexual distractors than in the presence of neutral distractors. Discussion These findings suggest that individuals with PHB show reduced ability to inhibit responses that might be related to lower IFG activation and IFG-preSMA connectivity during response inhibition. Our results provide insights into the neurobiological underpinnings of poor response inhibition in individuals with PHB.

Background Although emotion-specific autonomic responses based on the discrete theory of emotion have been widely studied, studies on the reliability of physiological responses to emotional stimuli are limited. In this study, we aimed to assess the reliability of physiological changes induced by the six basic emotions (happiness, sadness, anger, fear, disgust, and surprise) that were measured during 10 weekly repeated experiments. Methods Twelve college students participated, and in each experiment, physiological signals were collected before and while participants were watching emotion-provoking film clips. Additionally, the participants self-evaluated the emotions that they experienced during the film presentation at the end of each emotional stimulus. To avoid adaptation of participants to identical stimuli during repeated measurements, we used 10 different film clips for each emotion, and thus a total of 60 film clips over 10 weeks were used. Physiological features, such as skin conductance level (SCL), fingertip temperature (FT), heart rate (HR), and blood volume pulse (BVP), were extracted from the physiological signals. Two reliability indices, Cronbach’s alpha and intraclass correlation coefficient, were calculated from the physiological features to assess internal consistency and interrater reliability, respectively. Results We found that SCL, HR, and BVP measured during the emotion-provoking phase over the 10 weekly sessions were more reliable than those assessed at baseline. Furthermore, SCL, HR, and BVP from the emotion-provoking phase exhibited excellent internal consistency and interrater reliability. Conclusions Our findings suggest that these features can be used as reliable physiological indices in emotion studies. The results also support the significance of physiological signals as meaningful indicators for emotion recognition in HCI (human computer interface) area.

Additional file 1: Table S1. Physiological responses and their reliability indices from the baseline and emotion-provoking phases of the happiness condition. Table S2. Physiological responses and their reliability indices from the baseline and emotion-provoking phases of the sadness condition. Table S3. Physiological responses and their reliability indices from the baseline and emotion-provoking phases of the anger condition. Table S4. Physiological responses and their reliability indices from the baseline and emotion-provoking phases of the fear condition. Table S5. Physiological responses and their reliability indices from the baseline and emotion-provoking phases of the disgust condition. Table S6. Physiological responses and their reliability indices from the baseline and emotion-provoking phases of the surprise condition.

This study suggests an effective and valid method of inducing emotions in Korean college students and teenagers. While 605 subjects were experiencing different types of emotions, multimodal emotional responses (physiological signals and facial, vocal, thermal and EEG signals) were acquired. Emotional response data were categorized by self-reports and partially by raters. This is the first Korean multimodal emotional database incomparable to databases in terms of its scale. For this reason, the developed database can be used for emotion research in the future.

Accumulating evidence suggests a relationship between problematic hypersexual behavior (PHB) and diminished executive control. Clinical studies have demonstrated that individuals with PHB exhibit high levels of impulsivity; however, relatively little is known regarding the neural mechanisms underlying impaired executive control in PHB. This study investigated the neural correlates of executive control in individuals with PHB and healthy controls using event-related functional magnetic resonance imaging (fMRI). Twenty-three individuals with PHB and 22 healthy control participants underwent fMRI while performing a Stroop task. Response time and error rates were measured as surrogate indicators of executive control. Individuals with PHB exhibited impaired task performance and lower activation in the right dorsolateral prefrontal cortex (DLPFC) and inferior parietal cortex relative to healthy controls during the Stroop task. In addition, blood oxygen level-dependent responses in these areas were negatively associated with PHB severity. The right DLPFC and inferior parietal cortex are associated with higher-order cognitive control and visual attention, respectively. Our findings suggest that individuals with PHB have diminished executive control and impaired functionality in the right DLPFC and inferior parietal cortex, providing a neural basis for PHB.

Neuroimaging studies on the characteristics of individuals with Internet gaming disorder (IGD) have been accumulating due to growing concerns regarding the psychological and social problems associated with Internet use. However, relatively little is known about the brain characteristics underlying IGD, such as the associated functional connectivity and structure. The aim of this study was to investigate alterations in gray matter (GM) volume and functional connectivity during resting state in individuals with IGD using voxel-based morphometry and a resting-state connectivity analysis. The participants included 20 individuals with IGD and 20 age- and sex-matched healthy controls. Resting-state functional and structural images were acquired for all participants using 3 T magnetic resonance imaging. We also measured the severity of IGD and impulsivity using psychological scales. The results show that IGD severity was positively correlated with GM volume in the left caudate (p

Objective: With the wide and rapid expansion of computers and smartphones, Internet use has become an essential part of life and an important tool that serves various purposes. Despite the advantages of Internet use, psychological and behavioral problems, including Internet addiction, have been reported. In response to growing concern, researchers have focused on the characteristics of Internet addicts. However, relatively little is known about the behavioral and neural mechanisms that underlie Internet addiction, especially with respect to risky decision making, which is an important domain frequently reported in other types of addictions. Method: To examine the neural characteristics of decision making in Internet addicts, Internet addicts and healthy controls were scanned while they performed a financial decision-making task. Results: Relative to healthy controls, Internet addicts showed (1) more frequent risky decision making; (2) greater activation in the dorsal anterior cingulate cortex and the left caudate nucleus, which are brain regions involved in conflict monitoring and reward, respectively; and (3) less activation in the ventrolateral prefrontal cortex, an area associated with cognitive control/regulation. Conclusion: These findings suggest that risky decision making may be an important behavioral characteristic of Internet addiction and that altered brain function in regions associated with conflict monitoring, reward and cognitive control/regulation might be critical biological risk factors for Internet addiction.

Neuroimaging studies on the characteristics of hypersexual disorder have been accumulating, yet alternations in brain structures and functional connectivity in individuals with problematic hypersexual behavior (PHB) has only recently been studied. This study aimed to investigate gray matter deficits and resting-state abnormalities in individuals with PHB using voxel-based morphometry and resting-state connectivity analysis. Seventeen individuals with PHB and 19 age-matched healthy controls participated in this study. Gray matter volume of the brain and resting-state connectivity were measured using 3T magnetic resonance imaging. Compared to healthy subjects, individuals with PHB had significant reductions in gray matter volume in the left superior temporal gyrus (STG) and right middle temporal gyrus. Individuals with PHB also exhibited a decrease in resting-state functional connectivity between the left STG and left precuneus and between the left STG and right caudate. The gray matter volume of the left STG and its resting-state functional connectivity with the right caudate both showed significant negative correlations with the severity of PHB. The findings suggest that structural deficits and resting-state functional impairments in the left STG might be linked to PHB and provide new insights into the underlying neural mechanisms of PHB.

Objective: The aim of this study is to identify the relationship between personality and psychological responses induced by emotional stimuli (happiness, sadness, anger, boring and stress) for children. Background: Many researches are interested in assertion that there is close correlation between personality and emotion. The relationship between personality and emotion needs to be studied in view of the extended integration, not in view of respective property, because personality is deeply ingrained, and the relatively enduring patterns of thought, feeling and behavior and emotion can take advantage of individual differences in sensitivities to situational cues and predispositions to emotional state. In particular, studies on the personality and emotion for children are necessary in that childhood is an important period for formation of their personality and emotion expression and regulation. Method: Prior to the experiment, we made parents of 94 children rate personalities of their children, based on Korean Personality Inventory for Children (K-PIC). Results of 64 children without missing answers to all questions were analyzed. 64 children were exposed to five emotional stimuli and were asked to report the classification and intensity of their experienced emotion. Results: Children were classified into two groups of the lower 25% and higher 25% scores in twenty sub-scales of K-PIC, and psychological responses to five emotional stimuli between two groups were compared. Accuracy of emotion experienced by emotional stimuli showed a significant difference between the two groups, the lower and higher scores in Hyperactivity and Adjustment. Also, there was a significant difference in the intensity of experienced emotions between the two groups in Intellectual Screening and Psychosis. Conclusion: Our result has shown that hyperactivity, adjustment, intellectual screening and psychosis influence the accuracy and intensity of emotional responses. Application: This study can offer a guideline to overcome methodological limitation of emotion studies for children and help researcher basically understand and recognize human emotion in HCI.

Objective: In this study, we used resting-state fMRI data to map differences in functional connectivity between a comprehensive set of 8 distinct cortical and subcortical brain regions in healthy controls and Internet addicts. We also investigated the relationship between resting state connectivity strength and the level of psychopathology (ex. score of internet addiction scale and score of Barratt impulsiveness scale). Background: There is a lot of evidence of relationship between Internet addiction and impaired inhibitory control. Clinical evidence suggests that Internet addicts have a high level of impulsivity as measured by behavioral task of response inhibition and a self report questionnaire. Method: 15 Internet addicts and 15 demographically similar non-addicts participated in the current resting-state fMRI experiment. For the connectivity analysis, regions of interests (ROIs) were defined based on the previous studies of addictions. Functional connectivity assessment for each subject was obtained by correlating time-series across the ROIs, resulting in 8x8 matrixs for each subject. Within-group, functional connectivity patterns were observed by entering the z maps of the ROIs of each subject into second-level one sample t test. Two sample t test was also performed to examine between group differences. Results: Between group, the analysis revealed that the connectivity in between the orbito frontal cortex and inferior parietal cortex, between orbito frontal cortex and putamen, between the orbito frontal cortex and anterior cingulate cortex, between the insula and anterior cingulate cortex, and between amydgala and insula was significantly stronger in control group than in the Internet addicts, while the connectivity in between the orbito frontal cortex and insula showed stronger negative correlation in the Internet addicts relative to control group (p Conclusion: This study found that Internet addicts had declined connectivity strength in the orbitofrontal cortex (OFC) and other regions (e.g., ACC, IPC, and insula) during resting-state. It may reflect deficits in the OFC function to process information from different area in the corticostriatal reward network. Application: The results might help to develop theoretical modeling of Internet addiction for Internet addiction discrimination.

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Facial feature extraction and tracking are essential steps in human-robot-interaction (HRI) field such as face recognition, gaze estimation, and emotion recognition. Active shape model (ASM) is one of the successful generative models that extract the facial features. However, applying only ASM is not adequate for modeling a face in actual applications, because positions of facial features are unstably extracted due to limitation of the number of iterations in the ASM fitting algorithm. The unaccurate positions of facial features decrease the performance of the emotion recognition. In this paper, we propose real-time facial feature extraction and tracking framework using ASM and LK optical flow for emotion recognition. LK optical flow is desirable to estimate time-varying geometric parameters in sequential face images. In addition, we introduce a straightforward method to avoid tracking failure caused by partial occlusions that can be a serious problem for tracking based algorithm. Emotion recognition experiments with k-NN and SVM classifier shows over 95% classification accuracy for three emotions: "joy", "anger", and "disgust".

Background: Very little is known about the neural circuitry underlying anger processing among alcoholics. The purpose of this study was to examine the altered brain activity of alcoholic individuals during transient anger emotion. Methods: Using functional magnetic resonance imaging (fMRI), 18 male patients diagnosed with alcohol dependence in an inpatient alcohol treatment facility and 16 social drinkers with similar demographics were scanned during the viewing of anger-provoking film clips. Results: While there was no significant difference in the level of experienced anger between alcohol-dependent patients and non-alcoholic controls, significantly greater activation was observed in the bilateral dorsal anterior cingulate cortex (dACC) and the right precentral gyrus among alcoholic patients compared to the normal controls. Conclusions: In summary, specific brain regions were identified that are associated with anger among patients with alcohol dependency.

In biomedical engineering application, mental/physical health monitoring using biosensors has been lately noticed because bio-signal acquisition by non-invasive sensors is relatively simple as well as bio-signal is less sensitive to social/cultural difference. In particular, although it is known that they are significantly correlated with human emotional state, whether the signals by various emotions are stable remains unknown. In this study, we examined the consistency of physiological responses induced by six basic emotions, happiness, sadness, anger, fear, disgust and surprise using an experiment that was repeated 10 times. Twelve college subjects participated in this experiment. For emotion induction, sixty different emotional stimuli were selected in a pilot experiment. Heart Rate (HR), Skin Conductance Level (SCL), mean of Skin Temperature (meanSKT), and mean of Photoplethysmograph (meanPPG) were measured before the presentation of stimuli as a baseline and during the presentation of the stimuli as emotional state. The results showed that physiological responses during emotional states for the 10 times the experiment was repeated were stable and consistent compared to the baseline. In particular, we could identify that physiological features such as SCL, HR, and PPG are very reliable. Our results suggest that bio-signals by six emotions are consistent over time regardless of various stimuli. This means that physiological responses are reliable and biosensors are useful tool for emotion recognition.

Recently, the most popular research in the field of emotion recognition on human-computer interaction is to recognize human's feeling using various physiological signals. In the psychophysiological research, it is known that there is strong correlation between human emotion state and physiological reaction. In this study, seven kinds of emotion (happiness, sadness, anger, fear, disgust, surprise, stress) are evoked by audio-visual film clips as stimulation, and then autonomic nervous system responses as physiological signals are measured as the reaction of stimulation. In addition that, seven different emotions will be classified by the proposed classification methodology using physiological signals. We introduce a classification methodology on instance-based learning with feature selection that dwells upon the usage of evolutionally inspired optimization technique of Genetic Algorithms (GAs). In classification problems, it becomes important to carefully select prototypes and establish a subset of features in order to achieve a sound performance of a classifier. The study offers a complete algorithmic framework and demonstrates the effectiveness of the approach for the classification of seven emotions. Numerical experiments show that a suitable selection of prototypes and a substantial reduction of the feature space could be accomplished and the classifier formed in this manner is characterized by high classification accuracy for the seven emotions based on physiological signals.

Objective: The aim of this study is to investigate facial temperature changes induced by facial expression and emotional state in order to recognize a persons emotion using facial thermal images. Background: Facial thermal images have two advantages compared to visual images. Firstly, facial temperature measured by thermal camera does not depend on skin color, darkness, and lighting condition. Secondly, facial thermal images are changed not only by facial expression but also emotional state. To our knowledge, there is no study to concurrently investigate these two sources of facial temperature changes. Method: 231 students participated in the experiment. Four kinds of stimuli inducing anger, fear, boredom, and neutral were presented to participants and the facial temperatures were measured by an infrared camera. Each stimulus consisted of baseline and emotion period. Baseline period lasted during 1min and emotion period 1~3min. In the data analysis, the temperature differences between the baseline and emotion state were analyzed. Eyes, mouth, and glabella were selected for facial expression features, and forehead, nose, cheeks were selected for emotional state features. Results: The temperatures of eyes, mouth, glanella, forehead, and nose area were significantly decreased during the emotional experience and the changes were significantly different by the kind of emotion. The result of linear discriminant analysis for emotion recognition showed that the correct classification percentage in four emotions was 62.7% when using both facial expression features and emotional state features. The accuracy was slightly but significantly decreased at 56.7% when using only facial expression features, and the accuracy was 40.2% when using only emotional state features. Conclusion: Facial expression features are essential in emotion recognition, but emotion state features are also important to classify the emotion. Application: The results of this study can be applied to human-computer interaction system in the work places or the automobiles.

Objective: This study classified three different emotional states(boredom, pain, and surprise) using physiological signals. Background: Emotion recognition studies have tried to recognize human emotion by using physiological signals. It is important for emotion recognition to apply on human-computer interaction system for emotion detection. Method: 122 college students participated in this experiment. Three different emotional stimuli were presented to participants and physiological signals, i.e., EDA(Electrodermal Activity), SKT(Skin Temperature), PPG(Photoplethysmogram), and ECG (Electrocardiogram) were measured for 1 minute as baseline and for 1~1.5 minutes during emotional state. The obtained signals were analyzed for 30 seconds from the baseline and the emotional state and 27 features were extracted from these signals. Statistical analysis for emotion classification were done by DFA(discriminant function analysis) (SPSS 15.0) by using the difference values subtracting baseline values from the emotional state. Results: The result showed that physiological responses during emotional states were significantly differed as compared to during baseline. Also, an accuracy rate of emotion classification was 84.7%. Conclusion: Our study have identified that emotions were classified by various physiological signals. However, future study is needed to obtain additional signals from other modalities such as facial expression, face temperature, or voice to improve classification rate and to examine the stability and reliability of this result compare with accuracy of emotion classification using other algorithms. Application: This could help emotion recognition studies lead to better chance to recognize various human emotions by using physiological signals as well as is able to be applied on human-computer interaction system for emotion recognition. Also, it can be useful in developing an emotion theory, or profiling emotion-specific physiological responses as well as establishing the basis for emotion recognition system in human-computer interaction.

Recently, emotion recognition systems based on physiological signals have introduced in humancomputer interaction researches. The aim of this study is to classify seven emotions (happiness, sadness, anger, fear, disgust, surprise, and stress) by machine learning algorithms using physiological signals. 12 college students participated in this experiment over 10 times. Total 70 emotional stimuli (10 emotional stimuli per each emotion) had been tested their suitability and effectiveness prior to experiment. Physiological signals, i.e. EDA, ECG, PPG, and SKT were acquired and were analyzed. Physiological signals were obtained prior to the presentation of emotional stimuli and while emotional stimuli were presented to participants. 28 features were extracted the acquired signals and analyzed for 30 seconds from the baseline and the emotional states. For emotion recognition, the data which is subtracted baseline values from the emotional state applied to 5 machine learning algorithm, i.e. FLD, CART, SOMs, Naive Bayes and SVM. The result showed that an accuracy of emotion classification by SVM was highest and lowest by FLD. This means that SVM is the best emotion recognition algorithm in this study. Our result can help emotion recognition studies lead to better chance to recognize not only basic emotion but also user’s various emotions, e.g., boredom, frustration, love, pain, etc., by using physiological signals. Also, it is able to be applied on many human-computer interaction devices for emotion detection.

Objective: The aim of this study is to compare results of emotion recognition by several algorithms which classify three different emotional states(happiness, neutral, and surprise) using physiological features. Background: Recent emotion recognition studies have tried to detect human emotion by using physiological signals. It is important for emotion recognition to apply on human-computer interaction system for emotion detection. Method: 217 students participated in this experiment. While three kinds of emotional stimuli were presented to participants, ANS responses(EDA, SKT, ECG, RESP, and PPG) as physiological signals were measured in twice first one for 60 seconds as the baseline and 60 to 90 seconds during emotional states. The obtained signals from the session of the baseline and of the emotional states were equally analyzed for 30 seconds. Participants rated their own feelings to emotional stimuli on emotional assessment scale after presentation of emotional stimuli. The emotion classification was analyzed by Linear Discriminant Analysis(LDA, SPSS 15.0), Support Vector Machine (SVM), and Multilayer perceptron(MLP) using difference value which subtracts baseline from emotional state. Results: The emotional stimuli had 96% validity and 5.8 point efficiency on average. There were significant differences of ANS responses among three emotions by statistical analysis. The result of LDA showed that an accuracy of classification in three different emotions was 83.4%. And an accuracy of three emotions classification by SVM was 75.5% and 55.6% by MLP. Conclusion: This study confirmed that the three emotions can be better classified by LDA using various physiological features than SVM and MLP. Further study may need to get this result to get more stability and reliability, as comparing with the accuracy of emotions classification by using other algorithms. Application: This could help get better chances to recognize various human emotions by using physiological signals as well as be applied on human-computer interaction system for recognizing human emotions.

Objective: This study is to review three representative psychological perspectives that explain scientific construct of emotion, that are the discrete emotion model, appraisal model, and dimensional model. Background: To develop emotion sensitive interface is the fusion area of emotion and scientific technology, it is necessary to have a balanced mixture of both the scientific theory of emotion and practical engineering technology. Extensional theories of the emotional structure can provide engineers with relevant knowledge in functional application of the systems. Method: To achieve this purpose, firstly, literature review on the basic emotion model and the circuit model of discrete emotion model as well as representative theories was done. Secondly, review on the classical and modern theories of the appraisal model emphasizing cognitive appraisal in emotion provoking events was conducted. Lastly, a review on dimensional theories describing emotion by dimensions and representative theories was conducted. Results: The paper compared the three models based on the prime points of the each model. In addition, this paper also made a comment on a need for a comprehensive model an alternative to each model, which is componential model by Scherer(2001) describing numerous emotional aspects. Conclusion: However, this review suggests a need for an evolved comprehensive model taking consideration of social context effect and discrete neural circuit while pinpointing the limitation of componential model. Application: Insight obtained by extensive scientific research in human emotion can be valuable in development of emotion sensitive interface and emotion recognition technology.

This study aimed to investigate the differences in brain functions during verbal working memory between individuals with alcohol use disorders (AUD) and normal controls. fMRI was used to scan brain activations associated with verbal working memory while participants performed 2-back and 0-back tasks. A total of 21 young male college students participated in the study. Eleven of those who clinically met the criteria for AUD were assigned to the AUD group, whereas ten demographically similar subjects who were social drinkers but not AUD were assigned to the normal control group. The AUD group showed less activation in bilateral frontal and precentral, left superior temporal, left superior parietal, and left cerebellar cortex during the 2-back task relative to 0-back task compared to the normal control group. In contrast, the control group showed less activation only in the right uncus than the AUD group. These results suggest that subjects with AUD present abnormality in brain functioning during verbal working memory.

This study was conducted to whether the administration of mexiletine, a Na+ channel blocker, impacts the recovery from demyelination. Under anesthesia, 0.1% ethidium bromide was injected into the dorsal funiculus (T3), followed by a mexiletine or saline treatment. Motor evoked potential (MEP) recordings and luxol fast blue stainings were performed at one, seven, 14, and 21 days post-operatively. Conduction was delayed during demyelination, but the mexiletine-injected group demonstrated shortened latencies and reductions in the demyelination area when compared to the control. These results suggest that systemic mexiletine plays a positive role in protecting neural tissues from demyelination.

Recent advances in optical imaging techniques have made it possible to monitor neural activity and provided powerful tools to reveal the spatiotemporal patterns of neural activity. We used optical imaging to determine whether nerve injury affects excitability of the sensory cortex. Male Sprague-Dawley rats were subjected to neuropathic surgery consisting of a tight ligation and transection of the left tibial and sural nerves while under pentobarbital anesthesia. The rats were reanesthetized with urethane two weeks post-operatively, and the exposed cortex surfaces were stained with a voltage-sensitive dye (di-2-ANEPEQ). After electrical stimulation of the receptive field, optical signals from the cerebral cortex were recorded using an optical imaging system. Increased optical intensity and an enlarged area of activation were observed in the cerebral cortex of neuropathic rats during electrical stimulation compared to normal or sham-operated rats. Higher electric stimulation resulted in more intensity and a larger area of activation in neuropathic rats. These results suggest that cortical excitability, resulting from peripheral stimulation, may be affected by nerve injury, which indicates a degree of neural plasticity.