近赤外線スベクトロスコピィを用いた小児期自閉スペクトラム症の前頭前野における血液動態反応の低下

Background: Functional neuroimaging studies suggest that prefrontal cortex dysfunction is present in people with autism spectrum disorder (ASD). Near-infrared spectroscopy is a noninvasive optical tool for examining oxygenation and hemodynamic changes in the cerebral cortex by measuring changes in oxygenated hemoglobin. Methods: Twelve drug-naïve male participants, aged 7-15 years and diagnosed with ASD according to DSM-5 criteria, and 12 age- and intelligence quotient (IQ)-matched healthy control males participated in the present study after giving informed consent. Relative concentrations of oxyhemoglobin were measured with frontal probes every 0.1 s during the Stroop color-word task, using 24-channel near-infrared spectroscopy. Results: Oxyhemoglobin changes during the Stroop color-word task in the ASD group were significantly smaller than those in the control group at channels 12 and 13, located over the dorsolateral prefrontal cortex (FDR-corrected P: 0.0021-0.0063). Conclusion: The results suggest that male children with ASD have reduced prefrontal hemodynamic responses, measured with near-infrared spectroscopy.博士（医学）・乙第1442号・令和元年12月5日© The Author(s) 2019. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/ publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated

Reduced prefrontal hemodynamic response in pediatric autism spectrum disorder measured with near-infrared spectroscopy.

Background: Functional neuroimaging studies suggest that prefrontal cortex dysfunction is present in people with autism spectrum disorder (ASD). Near-infrared spectroscopy is a noninvasive optical tool for examining oxygenation and hemodynamic changes in the cerebral cortex by measuring changes in oxygenated hemoglobin. Methods: Twelve drug-naïve male participants, aged 7-15 years and diagnosed with ASD according to DSM-5 criteria, and 12 age- and intelligence quotient (IQ)-matched healthy control males participated in the present study after giving informed consent. Relative concentrations of oxyhemoglobin were measured with frontal probes every 0.1 s during the Stroop color-word task, using 24-channel near-infrared spectroscopy. Results: Oxyhemoglobin changes during the Stroop color-word task in the ASD group were significantly smaller than those in the control group at channels 12 and 13, located over the dorsolateral prefrontal cortex (FDR-corrected P: 0.0021-0.0063). Conclusion: The results suggest that male children with ASD have reduced prefrontal hemodynamic responses, measured with near-infrared spectroscopy.博士（医学）・乙第1442号・令和元年12月5日© The Author(s) 2019. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/ publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.identifier:Child and adolescent psychiatry and mental health Vol.13 Article No.29 (2019 Jun)identifier:17532000identifier:http://ginmu.naramed-u.ac.jp/dspace/handle/10564/3698identifier:Child and adolescent psychiatry and mental health, 13: Article No.2

The Validation of Automated Social Skills Training in Members of the General Population Over 4 Weeks: Comparative Study

BackgroundSocial skills training by human trainers is a well-established method of teaching appropriate social and communication skills and strengthening social self-efficacy. Specifically, human social skills training is a fundamental approach to teaching and learning the rules of social interaction. However, it is cost-ineffective and offers low accessibility, since the number of professional trainers is limited. A conversational agent is a system that can communicate with a human being in a natural language. We proposed to overcome the limitations of current social skills training with conversational agents. Our system is capable of speech recognition, response selection, and speech synthesis and can also generate nonverbal behaviors. We developed a system that incorporated automated social skills training that completely adheres to the training model of Bellack et al through a conversational agent. ObjectiveThis study aimed to validate the training effect of a conversational agent–based social skills training system in members of the general population during a 4-week training session. We compare 2 groups (with and without training) and hypothesize that the trained group’s social skills will improve. Furthermore, this study sought to clarify the effect size for future larger-scale evaluations, including a much larger group of different social pathological phenomena. MethodsFor the experiment, 26 healthy Japanese participants were separated into 2 groups, where we hypothesized that group 1 (system trained) will make greater improvement than group 2 (nontrained). System training was done as a 4-week intervention where the participants visit the examination room every week. Each training session included social skills training with a conversational agent for 3 basic skills. We evaluated the training effect using questionnaires in pre- and posttraining evaluations. In addition to the questionnaires, we conducted a performance test that required the social cognition and expression of participants in new role-play scenarios. Blind ratings by third-party trainers were made by watching recorded role-play videos. A nonparametric Wilcoxson Rank Sum test was performed for each variable. Improvement between pre- and posttraining evaluations was used to compare the 2 groups. Moreover, we compared the statistical significance from the questionnaires and ratings between the 2 groups. ResultsOf the 26 recruited participants, 18 completed this experiment: 9 in group 1 and 9 in group 2. Those in group 1 achieved significant improvement in generalized self-efficacy (P=.02; effect size r=0.53). We also found a significant decrease in state anxiety presence (P=.04; r=0.49), measured by the State-Trait Anxiety Inventory (STAI). For ratings by third-party trainers, speech clarity was significantly strengthened in group 1 (P=.03; r=0.30). ConclusionsOur findings reveal the usefulness of the automated social skills training after a 4-week training period. This study confirms a large effect size between groups on generalized self-efficacy, state anxiety presence, and speech clarity

Eye-movement analysis on facial expression for identifying children and adults with neurodevelopmental disorders

Experienced psychiatrists identify people with autism spectrum disorder (ASD) and schizophrenia (Sz) through interviews based on diagnostic criteria, their responses, and various neuropsychological tests. To improve the clinical diagnosis of neurodevelopmental disorders such as ASD and Sz, the discovery of disorder-specific biomarkers and behavioral indicators with sufficient sensitivity is important. In recent years, studies have been conducted using machine learning to make more accurate predictions. Among various indicators, eye movement, which can be easily obtained, has attracted much attention and various studies have been conducted for ASD and Sz. Eye movement specificity during facial expression recognition has been studied extensively in the past, but modeling taking into account differences in specificity among facial expressions has not been conducted. In this paper, we propose a method to detect ASD or Sz from eye movement during the Facial Emotion Identification Test (FEIT) while considering differences in eye movement due to the facial expressions presented. We also confirm that weighting using the differences improves classification accuracy. Our data set sample consisted of 15 adults with ASD and Sz, 16 controls, and 15 children with ASD and 17 controls. Random forest was used to weight each test and classify the participants as control, ASD, or Sz. The most successful approach used heat maps and convolutional neural networks (CNN) for eye retention. This method classified Sz in adults with 64.5% accuracy, ASD in adults with up to 71.0% accuracy, and ASD in children with 66.7% accuracy. Classifying of ASD result was significantly different (p<.05) by the binomial test with chance rate. The results show a 10% and 16.7% improvement in accuracy, respectively, compared to a model that does not take facial expressions into account. In ASD, this indicates that modeling is effective, which weights the output of each image

The Validation of Automated Social Skills Training in Members of the General Population Over 4 Weeks: Comparative Study

Background: Social skills training by human trainers is a well-established method of teaching appropriate social and communication skills and strengthening social self-efficacy. Specifically, human social skills training is a fundamental approach to teaching and learning the rules of social interaction. However, it is cost-ineffective and offers low accessibility, since the number of professional trainers is limited. A conversational agent is a system that can communicate with a human being in a natural language. We proposed to overcome the limitations of current social skills training with conversational agents. Our system is capable of speech recognition, response selection, and speech synthesis and can also generate nonverbal behaviors. We developed a system that incorporated automated social skills training that completely adheres to the training model of Bellack et al through a conversational agent. Objective: This study aimed to validate the training effect of a conversational agent?based social skills training system in members of the general population during a 4-week training session. We compare 2 groups (with and without training) and hypothesize that the trained group’s social skills will improve. Furthermore, this study sought to clarify the effect size for future larger-scale evaluations, including a much larger group of different social pathological phenomena. Methods: For the experiment, 26 healthy Japanese participants were separated into 2 groups, where we hypothesized that group 1 (system trained) will make greater improvement than group 2 (nontrained). System training was done as a 4-week intervention where the participants visit the examination room every week. Each training session included social skills training with a conversational agent for 3 basic skills. We evaluated the training effect using questionnaires in pre- and posttraining evaluations. In addition to the questionnaires, we conducted a performance test that required the social cognition and expression of participants in new role-play scenarios. Blind ratings by third-party trainers were made by watching recorded role-play videos. A nonparametric Wilcoxson Rank Sum test was performed for each variable. Improvement between pre- and posttraining evaluations was used to compare the 2 groups. Moreover, we compared the statistical significance from the questionnaires and ratings between the 2 groups. Results: Of the 26 recruited participants, 18 completed this experiment: 9 in group 1 and 9 in group 2. Those in group 1 achieved significant improvement in generalized self-efficacy (P=.02; effect size r=0.53). We also found a significant decrease in state anxiety presence (P=.04; r=0.49), measured by the State-Trait Anxiety Inventory (STAI). For ratings by third-party trainers, speech clarity was significantly strengthened in group 1 (P=.03; r=0.30). Conclusions: Our findings reveal the usefulness of the automated social skills training after a 4-week training period. This study confirms a large effect size between groups on generalized self-efficacy, state anxiety presence, and speech clarity

Automatic evaluation-feedback system for automated social skills training

Social skills training (SST), which is a rehabilitation program for improving daily interpersonal communication, has been used for more than 40 years. Although such training’s demand is increasing, its accessibility is limited due to the lack of experienced trainers. To tackle this issue, automated SST systems have been studied for years. An evaluation-feedback pipeline of social skills is a crucial component of an SST system. Unfortunately, research that considers both the evaluation and feedback parts of automation remains insufficient. In this paper, we collected and analyzed the characteristics of a human?human SST dataset that consisted of 19 healthy controls, 15 schizophreniacs, 16 autism spectrum disorder (ASD) participants, and 276 sessions with score labels of six clinical measures. From our analysis of this dataset, we developed an automated SST evaluation-feedback system under the supervision of professional, experienced SST trainers. We identified their preferred or most acceptable feedback methods by running a user-study on the following conditions: with/without recorded video of the role-plays of users and different amounts of positive and corrective feedback. We confirmed a reasonable performance of our social-skill-score estimation models as our system’s evaluation part with a maximum Spearman’s correlation coefficient of 0.68. For the feedback part, our user-study concluded that people understood more about what aspects they need to improve by watching recorded videos of their own performance. In terms of the amount of feedback, participants most preferred a 2-positive/1-corrective format. Since the average amount of feedback preferred by the participants nearly equaled that from experienced trainers in human?human SSTs, our result suggests the practical future possibilities of an automated evaluation-feedback system that complements SSTs done by professional trainers

Table_1_Feasibility of a wrist-worn wearable device for estimating mental health status in patients with mental illness.DOCX

ObjectReal-world data from wearable devices has the potential to understand mental health status in everyday life. We aimed to investigate the feasibility of estimating mental health status using a wrist-worn wearable device (Fitbit Sense) that measures movement using a 3D accelerometer and optical pulse photoplethysmography (PPG).MethodsParticipants were 110 patients with mental illnesses from different diagnostic groups. The study was undertaken between 1 October 2020 and 31 March 2021. Participants wore a Fitbit Sense on their wrist and also completed the State–Trait Anxiety Inventory (STAI), Positive and Negative Affect Schedule (PANAS), and EuroQol 5 dimensions 5-level (EQ-5D-5L) during the study period. To determine heart rate (HR) variability (HRV), we calculated the sdnn (standard deviation of the normal-to-normal interval), coefficient of variation of R-R intervals, and mean HR separately for each sleep stage and the daytime. The association between mental health status and HR and HRV was analyzed.ResultsThe following significant correlations were found in the wake after sleep onset stage within 3 days of mental health status assessment: sdnn, HR and STAI scores, HR and PANAS scores, HR and EQ-5D-5L scores. The association between mental health status and HR and HRV was stronger the closer the temporal distance between mental health status assessment and HR measurement.ConclusionA wrist-worn wearable device that measures PPG signals was feasible for use with patients with mental illness. Resting state HR and HRV could be used as an objective assessment of mental health status within a few days of measurement.</p

Image_1_Feasibility of a wrist-worn wearable device for estimating mental health status in patients with mental illness.JPEG

ObjectReal-world data from wearable devices has the potential to understand mental health status in everyday life. We aimed to investigate the feasibility of estimating mental health status using a wrist-worn wearable device (Fitbit Sense) that measures movement using a 3D accelerometer and optical pulse photoplethysmography (PPG).MethodsParticipants were 110 patients with mental illnesses from different diagnostic groups. The study was undertaken between 1 October 2020 and 31 March 2021. Participants wore a Fitbit Sense on their wrist and also completed the State–Trait Anxiety Inventory (STAI), Positive and Negative Affect Schedule (PANAS), and EuroQol 5 dimensions 5-level (EQ-5D-5L) during the study period. To determine heart rate (HR) variability (HRV), we calculated the sdnn (standard deviation of the normal-to-normal interval), coefficient of variation of R-R intervals, and mean HR separately for each sleep stage and the daytime. The association between mental health status and HR and HRV was analyzed.ResultsThe following significant correlations were found in the wake after sleep onset stage within 3 days of mental health status assessment: sdnn, HR and STAI scores, HR and PANAS scores, HR and EQ-5D-5L scores. The association between mental health status and HR and HRV was stronger the closer the temporal distance between mental health status assessment and HR measurement.ConclusionA wrist-worn wearable device that measures PPG signals was feasible for use with patients with mental illness. Resting state HR and HRV could be used as an objective assessment of mental health status within a few days of measurement.</p