Kyongsik Yun

Interpersonal body and neural synchronization as a marker of implicit social interaction

One may have experienced his or her footsteps unconsciously synchronize with the footsteps of a friend while walking together, or heard an audiences clapping hands naturally synchronize into a steady rhythm. However, the mechanisms of body movement synchrony and the role of this phenomenon in implicit interpersonal interactions remain unclear. We aimed to evaluate unconscious body movement synchrony changes as an index of implicit interpersonal interaction between the participants, and also to assess the underlying neural correlates and functional connectivity among and within the brain regions. We found that synchrony of both fingertip movement and neural activity between the two participants increased after cooperative interaction. These results suggest that the increase of interpersonal body movement synchrony via interpersonal interaction can be a measurable basis of implicit social interaction. The paradigm provides a tool for identifying the behavioral and the neural correlates of implicit social interaction.

Noninvasive remote activation of the ventral midbrain by transcranial direct current stimulation of prefrontal cortex

The midbrain lies deep within the brain and has an important role in reward, motivation, movement and the pathophysiology of various neuropsychiatric disorders such as Parkinson’s disease, schizophrenia, depression and addiction. To date, the primary means of acting on this region has been with pharmacological interventions or implanted electrodes. Here we introduce a new noninvasive brain stimulation technique that exploits the highly interconnected nature of the midbrain and prefrontal cortex to stimulate deep brain regions. Using transcranial direct current stimulation (tDCS) of the prefrontal cortex, we were able to remotely activate the interconnected midbrain and cause increases in participants’ appraisals of facial attractiveness. Participants with more enhanced prefrontal/midbrain connectivity following stimulation exhibited greater increases in attractiveness ratings. These results illustrate that noninvasive direct stimulation of prefrontal cortex can induce neural activity in the distally connected midbrain, which directly effects behavior. Furthermore, these results suggest that this tDCS protocol could provide a promising approach to modulate midbrain functions that are disrupted in neuropsychiatric disorders.

Beta-frequency Eeg activity increased during transcranial direct current stimulation

Transcranial direct current stimulation (tDCS) is a technique for noninvasively stimulating specific cortical regions of the brain with small (<2 mA) and constant direct current on the scalp. tDCS has been widely applied, not only for medical treatment, but also for cognitive and somatosensory function enhancement, motor learning improvement, and social behavioral change. However, the mechanism that underlies the effect of tDCS is unclear. In this study, we performed simultaneous electroencephalogram (EEG) monitoring during tDCS to understand the dynamic electrophysiological changes throughout the stimulation. A total of 10 healthy individuals participated in this experiment. We recorded EEGs with direct current stimulation, as well as during a 5-min resting state before and after the stimulation. All participants kept their eyes closed during the experiment. Anode and cathode patches of tDCS were placed on the left and the right dorsolateral prefrontal cortex, respectively. In addition, an EEG electrode was placed on the medial prefrontal cortex. The beta-frequency power increased promptly after starting the stimulation. The significant beta-power increase was maintained during the stimulation. Other frequency bands did not show any significant changes. The results indicate that tDCS of the left dorsolateral prefrontal cortex changed the brain to a ready state for efficient cognitive functioning by increasing the beta-frequency power. This is the first attempt to simultaneously stimulate the cortex and record the EEG and then systematically analyze the prestimulation, during-stimulation, and poststimulation EEG data.

Modulation of theta phase synchronization in the human electroencephalogram during a recognition memory task

To the extent that recognition memory relies on interactions among widely distributed neural assemblies across the brain, phase synchronization between brain rhythms may play an important role in meditating those interactions. As the theta rhythm is known to modulate in power during the recognition memory process, we aimed to determine how the phase synchronization of the theta rhythms across the brain changes with recognition memory. Fourteen human participants performed a visual object recognition task in a virtual reality environment. Electroencephalograms were recorded from the scalp of the participants while they either recognized objects that had been presented previously or identified new objects. From the electroencephalogram recordings, we analyzed the phase-locking value of the theta rhythms, which indicates the degree of phase synchronization. We found that the overall phase-locking value recorded during the recognition of previously viewed objects was greater than that recorded during the identification of new objects. Specifically, the theta rhythms became strongly synchronized between the frontal and the left parietal areas during the recognition of previously viewed objects. These results suggest that the recognition memory process may involve an interaction between the frontal and the left parietal cortical regions mediated by theta phase synchronization.

Mathematically Gifted Adolescents Have Deficiencies in Social Valuation and Mentalization

Many mathematically gifted adolescents are characterized as being indolent, underachieving and unsuccessful despite their high cognitive ability. This is often due to difficulties with social and emotional development. However, research on social and emotional interactions in gifted adolescents has been limited. The purpose of this study was to observe differences in complex social strategic behaviors between gifted and average adolescents of the same age using the repeated Ultimatum Game. Twenty-two gifted adolescents and 24 average adolescents participated in the Ultimatum Game. Two adolescents participate in the game, one as a proposer and the other as a responder. Because of its simplicity, the Ultimatum Game is an apt tool for investigating complex human emotional and cognitive decision-making in an empirical setting. We observed strategic but socially impaired offers from gifted proposers and lower acceptance rates from gifted responders, resulting in lower total earnings in the Ultimatum Game. Thus, our results indicate that mathematically gifted adolescents have deficiencies in social valuation and mentalization.

On the Same Wavelength: Face-to-Face Communication Increases Interpersonal Neural Synchronization

Understanding neural mechanisms of social interaction is important for understanding human social nature and for developing treatments for social deficits related to disorders such as autism. However, conventional cognitive and behavioral neuroscience has concentrated on developing novel experimental paradigms and investigating human–computer interactions, rather than studying interpersonal interaction per se. To fully understand neural mechanisms of human interpersonal interaction, we will likely have to investigate human behavior and neural processes in face-to-face social interaction rather than human–computer interaction. Recently, simultaneous EEG or functional near-infrared spectroscopy (fNIRS) has been used to record brain activity of two participants in a face-to-face setting (i.e., hyperscanning) to investigate human social interaction in a more naturalistic context (Jiang et al., 2012; Yun et al., 2012).

Different Gain/Loss Sensitivity and Social Adaptation Ability in Gifted Adolescents during a Public Goods Game

Gifted adolescents are considered to have high IQs with advanced mathematical and logical performances, but are often thought to suffer from social isolation or emotional mal-adaptation to the social group. The underlying mechanisms that cause stereotypic portrayals of gifted adolescents are not well known. We aimed to investigate behavioral performance of gifted adolescents during social decision-making tasks to assess their affective and social/non-social cognitive abilities. We examined cooperation behaviors of 22 gifted and 26 average adolescents during an iterative binary public goods (PG) game, a multi-player social interaction game, and analyzed strategic decision processes that include cooperation and free-riding. We found that the gifted adolescents were more cooperative than average adolescents. Particularly, comparing the strategies for the PG game between the two groups, gifted adolescents were less sensitive to loss, yet were more sensitive to gain. Additionally, the behavioral characteristics of average adolescents, such as low trust of the group and herding behavior, were not found in gifted adolescents. These results imply that gifted adolescents have a high cognitive ability but a low ability to process affective information or to adapt in social groups compared with average adolescents. We conclude that gain/loss sensitivity and the ability to adapt in social groups develop to different degrees in average and gifted adolescents.

Transcranial Alternating Current Stimulation (tACS) Mechanisms and Protocols

Perception, cognition and consciousness can be modulated as a function of oscillating neural activity, while ongoing neuronal dynamics are influenced by synaptic activity and membrane potential. Consequently, transcranial alternating current stimulation (tACS) may be used for neurological intervention. The advantageous features of tACS include the biphasic and sinusoidal tACS currents, the ability to entrain large neuronal populations, and subtle control over somatic effects. Through neuromodulation of phasic, neural activity, tACS is a powerful tool to investigate the neural correlates of cognition. The rapid development in this area requires clarity about best practices. Here we briefly introduce tACS and review the most compelling findings in the literature to provide a starting point for using tACS. We suggest that tACS protocols be based on functional brain mechanisms and appropriate control experiments, including active sham and condition blinding.

Classification of Suicide Attempts through a Machine Learning Algorithm Based on Multiple Systemic Psychiatric Scales

Classification and prediction of suicide attempts in high-risk groups is important for preventing suicide. The purpose of this study was to investigate whether the information from multiple clinical scales has classification power for identifying actual suicide attempts. Patients with depression and anxiety disorders (N = 573) were included, and each participant completed 31 self-report psychiatric scales and questionnaires about their history of suicide attempts. We then trained an artificial neural network classifier with 41 variables (31 psychiatric scales and 10 sociodemographic elements) and ranked the contribution of each variable for the classification of suicide attempts. To evaluate the clinical applicability of our model, we measured classification performance with top-ranked predictors. Our model had an overall accuracy of 93.7% in 1-month, 90.8% in 1-year, and 87.4% in lifetime suicide attempts detection. The area under the receiver operating characteristic curve (AUROC) was the highest for 1-month suicide attempts detection (0.93), followed by lifetime (0.89), and 1-year detection (0.87). Among all variables, the Emotion Regulation Questionnaire had the highest contribution, and the positive and negative characteristics of the scales similarly contributed to classification performance. Performance on suicide attempts classification was largely maintained when we only used the top five ranked variables for training (AUROC; 1-month, 0.75, 1-year, 0.85, lifetime suicide attempts detection, 0.87). Our findings indicate that information from self-report clinical scales can be useful for the classification of suicide attempts. Based on the reliable performance of the top five predictors alone, this machine learning approach could help clinicians identify high-risk patients in clinical settings.

Improved target recognition response using collaborative brain-computer interfaces

The advantage of using collaborative brain-computer interfaces in improving human response in visual target recognition tests was investigated. We used a public EEG dataset created from recordings made using a 32-channel EEG system by Delorme et al. (2004) to compare the classification accuracy using one, two, and three EEG signal sets from different subjects. Fourteen participants performed a go/no-go categorization task on images that were presented very briefly, with the target images of natural photos of animals and distractor images of photos that did not contain animals. First, we compared the EEG responses evoked by the target and distractor images, and it was determined that the P300 (i.e., a positive deflection in voltage with a latency of 300 ms) response evoked by the target images was significantly higher than that evoked by the distractor images. Second, we calculated and compared the classification accuracy using one, two, and three EEG signal sets. We used a linear support vector machine with 5-fold cross validation. Compared to the results obtained from single brain prediction (79.4%), the overall accuracy of two- and three-brains prediction was higher (89.3% and 88.7%, respectively). Furthermore, the time required to achieve 90% accuracy was significantly less when using EEGs from two and three brains (100 ms) than when using one brain (230 ms). These results provide evidence to support the hypothesis that one can achieve higher levels of perceptual and cognitive performance by leveraging the power of multiple brains through collaborative brain-computer interfaces.