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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.

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.

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".

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.

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: 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 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.

Functional deficits after spinal cord injury have originated not only from the direct physical damage itself, but from the secondary biochemical and pathological changes. Apoptotic cell death has been seen around the periphery of an injured site and has been known to ultimately progress to necrosis and infarction. We have initiated the present study focusing on the role of apoptosis in the secondary injury of the brain after acute spinal cord injury (SCI), and conducted a series of experiments, the study examining the morphological changes in the brain following the spinal injury. Under pentobarbital anesthesia, male Sprague–Dawley rats were subjected to SCI model. Rats were laminectomized and SCI was induced using NYU spinal impactor at T9 segment. The behavioral test was performed. Electrophysiologically, motor evoked potentials (MEPs) were recorded. The animals were subjected to morphological study at 12, 24, 48, 72 h, and 1 week, postoperatively. Locomotor deficits were observed after SCI, and changes in the amplitudes and latencies of the MEPs were observed. The morphological changes were evidenced by terminal TUNEL staining and Calbindin-D 28K immunohistochemistry. The TUNEL-positive cells were located at the brain motor cortex after SCI. TUNEL-positive cells were seldom found 4 h after injury. In addition, Calbindin-D 28K immunoreactive neurons were observed in the motor cortex after injury. These results suggest that apoptosis may play an important role in the pathophysiology of the brain motor cortex following acute spinal cord injury and functions that were deteriorated after SCI may be related to these electrophysiological and morphological changes.

The most crucial feature of human computer interaction is computers and computer-based applications to infer the emotional states of humans or others human agents based on covert and/or overt signals of those emotional states. In emotion recognition, bio-signals reflect sequences of neural activity induced by emotional events and also, have many technical advantages. The aim of this study is to classify six emotions (joy, sadness, anger, fear, surprise, and neutral) that human have often experienced in real life from multi-channel bio-signals using machine learning algorithms. We have measured physiological responses of three-hundred participants for acquisition of bio-signals such as electrodermal activity, electrocardiograph, skin temperature, and photoplethysmograph during six emotions induction. Also, for emotion classification, we have extracted eighteen features from the signals and performed emotion classification using five algorithms, linear discriminant analysis, Naive Bayes, classification and regression tree, self-organization map and support vector machine. The used algorithms were evaluated by only training, 10-fold cross-validation and repeated random sub-sampling validation. We have obtained recognition accuracy from 42.4 to 100% for only training and 39.2 to 53.9% for testing. Also, the result for testing showed that an accuracy of emotion recognition by Naive Bayes and linear discriminant analysis were highest (53.9%, 52.7%) and was lowest by support vector machine (39.2%). This means that Naive Bayes is the best emotion recognition algorithm for basic emotions. To apply to real system, we have to discuss in the view point of testing and this means that it needs to apply various methodologies for the accuracy improvement of emotion recognition in the future analysis.

Emotions are complex processes involving multiple response channels, including physiological systems, facial expressions and voices. Bio-signals reflect sequences of neural activity, which result in changes in autonomic and neuroendocrine systems induced by emotional events. Therefore in human-computer interaction researches, one of the most current interesting topics in emotion recognition is to recognize human's feeling using bio-signals. The aim of this study is to classify emotions (joy, sadness, anger, fear, surprise, and neutral) that human have often experienced in real life from multichannel bio-signals using machine learning algorithms. We have measured physiological responses of three-hundred participants for acquisition of bio-signals such as electrodermal activity, electrocardiograph, skin temperature, and photoplethysmo-graph during six emotions induction. Also, for emotion classification, we have extracted eighteen features from the signals and performed emotion classification using four algorithms, linear discriminant analysis, Naive Bayes, classification and regression tree and support vector machine. The used algorithms were evaluated by only training, 10-fold cross-validation and repeated random sub-sampling validation. We have obtained recognition accuracy from 56.4 to 100% for only training and 39.2 to 53.9% for testing. Also, the result for testing showed that an accuracy of emotion recognition by Naive Bayes was highest (53.9%) and lowest by support vector machine (39.2%). This means that Naive Bayes is the best emotion recognition algorithm for basic emotions. This result can be helpful to provide the basis for the emotion recognition technique in human-computer interaction.

It has been well documented that there is opioid resistance in neuropathic pain. This indicates that the endogenous opioid system may not be involved effectively in modulating neuropathic pain. The present study sought to determine if activation of the descending pain inhibition system might produce analgesia in the animal neuropathic model we developed. Under ketamine anesthesia, male Sprague-Dawley rats were chronically implanted with stimulating electrodes in the ventral periaqueductal gray matter (PAG) and both the tibial and sural nerves of the sciatic nerve branches were severed. Pain sensitivity was measured with a von Frey filament and acetone applied to the sensitive area for 1 week postoperatively. Rats with neuropathic pain syndrome after transection of the tibial and sural nerves were tested as to the analgesic effects of ventral PAG stimulation for an additional two weeks. Electrical stimulation of the ventral PAG turned out to be highly effective in alleviating neuropathic pain. Mechanical allodynia and cold allodynia were reduced by PAG stimulation. Naloxone reversed the antiallodynic effects of ventral PAG stimulation. These results suggest that activation of the descending pain inhibition system including the ventral PAG reduces neuropathic pain syndrome and that opiates are involved in this system.

We have previously demonstrated that electrical stimulation of the ventral periaqueductal gray matter (PAG) produced analgesia in neuropathic pain in rats. Opioids were also shown to be involved in analgesic effects. This study sought to determine whether opiates microinjected into the ventral PAG produce analgesia. Male Sprague-Dawley rats were chronically implanted with a guide cannula in the PAG under pentobarbital anesthesia and both the tibial and sural nerves were completely cut. Pain sensitivity was postoperatively measured with a von Frey filament and acetone applied to the sensitive area for I week. Opioids such as [D-Ala 2 ,N-MePhe 4 ,Gly(ol) 5 ]-enkephalin (DAMGO) and [D-Pen 2 ,D-Pen 5 ].-enkephalin (DPDPE) were injected into the PAG. DAMGO, a μ-opioid agonist, and DPDPE, a δ-opioid agonist, were highly effective in reducing neuropathic pain. These effects were reversed by naloxone. These results suggest that the neurons in the ventral PAG are activated by opioids to produce analgesia and that specific opioid receptors are involved in the descending pain inhibition system from the PAG.

The average cerebellar volume of Korean men (135.19 cm3) is larger than that of Korean women (123.06 cm3), and that of subjects in their twenties (134.28 cm3) is larger than that of subjects in their forties (121.83 cm3). Atrophy of the cerebellum is more markedly observed in men than in women. There is a relation between body weight and cerebellar volume for men, but not for women.

The purpose of this study is to identify optimal algorithm for emotion classification which classify seven different emotional states (happiness, sadness, anger, fear, disgust, surprise, and stress) using physiological features. Skin temperature, photoplethysmography, electrodermal activity and electrocardiogram are recorded and analyzed as physiological signals. The emotion stimuli used to induce a participant's emotion are evaluated for their suitability and effectiveness. For classification problems of seven emotions, the design involves two main phases. At the first phase, Particle Swarm Optimization selects P % of patterns to be treated as prototypes of seven emotional categories. At the second phase, the PSO is instrumental in the formation of a core set of features that constitute a collection of the most meaningful and highly discriminative elements of the original feature space. The study offers a complete algorithmic framework and demonstrates the effectiveness of the approach for a collection of selected data sets.

Recently, the one of main topic of emotion recognition or classification research is to recognize human's feeling or emotion using various physiological signals. It is one of the core processes to implement emotional intelligence in human computer interaction (HCI) research. The purpose of this study was to identify the best algorithm being able to discriminate negative emotions, such as sadness, fear, surprise, and stress using physiological features. Electrodermal activity (EDA), electrocardiogram (ECG), skin temperature (SKT), and photoplethysmography (PPG) are recorded and analyzed as physiological signals. And emotional stimuli used in this study are audio-visual film clips which have examined for their appropriateness and effectiveness through preliminary experiment. For classification of negative emotions, five machine learning algorithms, i.e., LDF, CART, SOM, Naive Bayes and SVM are used. Result of emotion classification shows that an accuracy of emotion classification by SVM (100.0%) was the highest and by LDA (50.7%) was the lowest. CART showed emotion classification accuracy of 84.0%, SOM was 51.2% and Naive Bayes was 76.2%. This can be helpful to provide the basis for the emotion recognition technique in HCI.

The purpose of this study is to establish a reference for image acquisition for completing a standard brain for diverse Korean population, and to develop database management system that saves and manages acquired brain images and personal information of subjects. 3D MP-RAGE (Magnetization Prepared Rapid Gradient Echo) technique which has excellent Signal to Noise Ratio (SNR) and Contrast to Noise Ratio (CNR) as well as reduces image acquisition time was selected for anatomical image acquisition, and parameter values were obtained for the optimal image acquisition. Using these standards, image data of 121 young adults (early twenties) were obtained and stored in the system. System was designed to obtain, save, and manage not only anatomical image data but also subjects' basic demographic factors, medical history, handedness inventory, state-trait anxiety inventory, A-type personality inventory, self-assessment depression inventory, mini-mental state examination, intelligence test, and results of personality test via a survey questionnaire. Additionally this system was designed to have functions of saving, inserting, deleting, searching, and printing image data and personal information of subjects, and to have accessibility to them as well as automatic connection setup with ODBC. This newly developed system may have major contribution to the completion of a standard brain for diverse Korean population since it can save and manage their image data and personal information.

We investigated the effects of ChunDoSunBup (CDSB) Qi-training, one of the Korean popular Qi-training systems, on EEG patterns, activation coefficients and state anxiety in 13 trainees with open eyes. CDSB Qi-training procedure consists of 3 stages: sound exercise, reciting Chunmoon, which is similar to a mantra; haeng-gong, a kind of body motion; and meditation. Compared to the control state (resting state before Qi-training), subjects reported less state anxiety, their activation coefficients decreased significantly during sound exercise and meditation in the occipital regions. Mean relative power and changes of mean absolute power of alpha wave increased significantly during sound exercise and meditation in the occipital regions. These results suggest that sound exercise and meditation in ChunDoSunBup Qi-training may reduce activation of the visual cortex and influence the thalamus and other functions of the brain. These could reduce anxiety levels and modulate the psychological, neurological, and physiological functions in man.

Purpose: This study purposed to measure the standard volume of the cerebellum of normal Koreans who were in their 20s and 40s, and we also wished to find out the difference in the volume of the cerebellum according to gender and age. Materials and Methods: This study collected MR brain images from 118 people in their 20s (males: 58, females: 60) and 100 people in their 40s (males: 41, females: 59), for a total of 218 people. For each of sagittal, coronal and axial sections, the cerebellar part of the images was segmented using automatic and manual methods, and the volume was then measured. In order to observe differences according to gender and age and also to observe the interactive effect between gender and age, a two-way ANOVA test was performed using gender (2 levels) and age (2 levels) as independent variables. Results: The average volume of the cerebellum of Koreans in their 20s was 133.74 () and that of Koreans in their 40s was 121.83 (). The average volume of the cerebellum of male Koreans in their 20s and 40s was 134.55 () and that of female Koreans was 123.06 (). The volume of the cerebellum was significantly larger in Koreans in their 20s than those volumes of the cerebellum of Koreans in their 40s, and the cerebellum volumes in male Koreans were larger than those of the Korean females. Moreover, the reduction of the volume of the cerebellum with age was significantly larger in male Koreans than in the Korean females. Conclusion: According to the results of measuring the volume of the cerebellum for normal Koreans in their 20s and 40s, the volume was significantly different according to gender and age, and the reduction of the volume of the cerebellum with age was significantly larger in men than in women.

Stimulation of the periaqueductal gray matter (PAG) is known to modulate nociception at the spinal level. Several studies have suggested that nociception may also be modulated via ascending projections from the PAG. To study this hypothesis, the descending pathway was selectively disrupted immediately caudal to the PAG in 28 rats. Twenty-eight additional rats served as non-lesioned controls. All animals were chronically implanted with a stimulating electrode in the PAG, and antinociception was assessed using tests involving spinally and supraspinally mediated responses (tail-flick and hot-plate tests, respectively). Significantly fewer lesioned than non-lesioned rats showed stimulation-produced analgesia (SPA) in the tail-flick test (4 of 28 vs 14 of 28, respectively). In contrast, no significant difference in the incidence of SPA occurred between lesioned and non-lesioned rats in the hot-plate test. These findings demonstrate that nociception can be modulated at the supraspinal, as well as spinal, level.