Papers - KISHIMOTO Taishiro

Effects of Psychotropics on the Microbiome in Patients with Depression and Anxiety: Considerations in a Naturalistic Clinical Setting.

Tomizawa Y, Kurokawa S, Ishii D, Miyaho K, Ishii C, Sanada K, Fukuda S, Mimura M, Kishimoto T

The international journal of neuropsychopharmacology (The international journal of neuropsychopharmacology)  24 ( 2 ) 97 - 107 2020.09

ISSN  1461-1457

　View Summary

© The Author(s) 2020. Published by Oxford University Press on behalf of CINP. BACKGROUND: The antibacterial effects of psychotropics may be part of their pharmacological effects when treating depression. However, limited studies have focused on gut microbiota in relation to prescribed medication. METHOD: We longitudinally investigated the relationship between patients' prescribed medications and intestinal bacterial diversity in a naturalistic treatment course for patients with major depressive disorders and anxiety disorders. Patients were recruited and their stool was collected at 3 time points during their usual psychiatric treatments. Gut microbiota were analyzed using 16S rRNA gene sequencing. We examined the impact of psychotropics (i.e., antidepressants, anxiolytics, antipsychotics) on their gut microbial diversity and functions. RESULTS: We collected 246 stool samples from 40 patients. Despite no differences in microbial diversity between medication groups at the baseline, over the course of treatment, phylogenic diversity whole-tree diversity decreased in patients on antipsychotics compared with patients without (P = .027), and beta diversity followed this trend. Based on a fixed-effect model, antipsychotics predicted microbial diversity; the higher doses correlated with less diversity based on the Shannon index and phylogenic diversity whole tree (estimate = -0.00254, SE = 0.000595, P < .0001; estimate = -0.02644, SE = 0.00833, P = .002, respectively). CONCLUSION: Antipsychotics may play a role in decreasing the alpha diversity of the gut microbiome among patients with depression and anxiety, and our results indicate a relationship with medication dosage. Future studies are warranted and should consider patients' types and doses of antipsychotics in order to further elucidate the mechanisms of gut-brain interactions in psychiatric disorders.

• Access to Document (DOI)

A study on energy resolution of CANDLES detector

B. T. Khai, S. Ajimura, W. M. Chan, K. Fushimi, R. Hazama, H. Hiraoka, T. Iida, K. Kanagawa, H. Kino, T. Kishimoto, T. Maeda, K. Nakajima, M. Nomachi, I. Ogawa, T. Ohata, K. Suzuki, Y. Takemoto, Y. Takihira, Y. Tamagawa, M. Tozawa, M. Tsuzuki, S. Umehara, S. Yoshida

 2020.09

　View Summary

In a neutrino-less double-beta-decay ($0\nu\beta\beta$) experiment, an
irremovable two-neutrino double-beta-decay ($2\nu\beta\beta$) background
surrounds the Q-value of the double beta decay isotope. The energy resolution
must be improved to differentiate between $0\nu\beta\beta$ and $2\nu\beta\beta$
events. CAlcium fluoride for studies of Neutrino and Dark matters by Low Energy
Spectrometer (CANDLES) discerns the $0\nu\beta\beta$ of $^{48}$Ca using a
CaF$_2$ scintillator as the detector and source. Photomultiplier tubes (PMTs)
collect scintillation photons. Ideally, the energy resolution should equal the
statistical fluctuation of the number of photoelectrons. At the Q-value of
$^{48}$Ca, the current energy resolution (2.6%) exceeds this fluctuation
(1.6%). Because of CaF$_2$'s long decay constant of 1000 ns, a signal
integration in 4000 ns is used to calculate the energy. The baseline
fluctuation ($\sigma_{\rm baseline}$) is accumulated in the signal integration,
degrading the energy resolution. Therefore, this paper studies $\sigma_{\rm
baseline}$ in the CANDLES detector, which has a severe effect (1%) at the
Q-value of $^{48}$Ca. To avoid $\sigma_{\rm baseline}$, photon counting can be
used to obtain the number of photoelectrons in each PMT; however, a significant
photoelectron signal overlapping probability in each PMT causes missing
photoelectrons in counting and reduces the energy resolution. "Partial photon
counting" reduces $\sigma_{\rm baseline}$ and minimizes photoelectron loss. We
thus obtain improved energy resolutions of 4.5--4.0% at 1460.8 keV
($\gamma$-ray of $^{40}$K), and 3.3--2.9% at 2614.5 keV ($\gamma$-ray of
$^{208}$Tl). The energy resolution at the Q-value shows an estimated
improvement of 2.2%, with improved detector sensitivity by factor 1.09 for the
$0\nu\beta\beta$ half-life of $^{48}$Ca.


• Access to Document (DOI)

Prescription patterns in patients with schizophrenia in Japan: First-quality indicator data from the survey of “Effectiveness of Guidelines for Dissemination and Education in psychiatric treatment (EGUIDE)” project

Ichihashi K., Hori H., Hasegawa N., Yasuda Y., Yamamoto T., Tsuboi T., Iwamoto K., Kishimoto T., Horai T., Yamada H., Sugiyama N., Nakamura T., Tsujino N., Nemoto K., Oishi S., Usami M., Katsumoto E., Yamamori H., Tomita H., Suwa T., Furihata R., Inagaki T., Fujita J., Onitsuka T., Miura K., Matsumoto J., Ohi K., Matsui Y., Takaesu Y., Hashimoto N., Iga J., Ogasawara K., Yamada H., Watanabe K., Inada K., Hashimoto R.

Neuropsychopharmacology Reports (Neuropsychopharmacology Reports)  40 ( 3 ) 281 - 286 2020.09

　View Summary

© 2020 The Authors. Neuropsychopharmacology Reports published by John Wiley & Sons Australia, Ltd on behalf of the Japanese Society of NeuropsychoPharmacology. Background: Guideline for Pharmacological Therapy for Schizophrenia was published by the Japanese Society of Neuropsychopharmacology in 2015. “Effectiveness of Guidelines for Dissemination and Education in psychiatric treatment (EGUIDE)” project aimed to standardize medical practice using quality indicators (QIs) as indices to evaluate the quality of medical practice. In this study, we have reported the quality indicator values of prescription before the beginning of the guideline lectures in the EGUIDE project to ascertain the baseline status of treating patients with schizophrenia. Methods: A cross-sectional, retrospective case record survey was conducted, involving 1164 patients with schizophrenia at the time of discharge. We checked all types and dosage of psychotropic drugs. Results: Forty-three percent of patients had antipsychotic polypharmacy, and substantial concomitant medication was observed (antidepressants; 8%, mood stabilizers: 37%, anxiolytics or hypnotics: 68%). Conclusions: In the results obtained in this study, we plant to report changes in the effectiveness of education in the EGUIDE project near the future.

• Access to Document (DOI)

Using speech recognition technology to investigate the association between timing-related speech features and depression severity

Yamamoto M., Takamiya A., Sawada K., Yoshimura M., Kitazawa M., Liang K.C., Fujita T., Mimura M., Kishimoto T.

PLoS ONE (PLoS ONE)  15 ( 9 September ) e0238726 2020.09

ISSN  1932-6203

　View Summary

© 2020 Yamamoto et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Background There are no reliable and validated objective biomarkers for the assessment of depression severity. We aimed to investigate the association between depression severity and timing-related speech features using speech recognition technology. Method Patients with major depressive disorder (MDD), those with bipolar disorder (BP), and healthy controls (HC) were asked to engage in a non-structured interview with research psychologists. Using automated speech recognition technology, we measured three timing-related speech features: speech rate, pause time, and response time. The severity of depression was assessed using the Hamilton Depression Rating Scale 17-item version (HAMD-17). We conducted the current study to answer the following questions: 1) Are there differences in speech features among MDD, BP, and HC? 2) Do speech features correlate with depression severity? 3) Do changes in speech features correlate with within-subject changes in depression severity? Results We collected 1058 data sets from 241 individuals for the study (97 MDD, 68 BP, and 76 HC). There were significant differences in speech features among groups; depressed patients showed slower speech rate, longer pause time, and longer response time than HC. All timing-related speech features showed significant associations with HAMD-17 total scores. Longitudinal changes in speech rate correlated with changes in HAMD-17 total scores. Conclusions Depressed individuals showed longer response time, longer pause time, and slower speech rate than healthy individuals, all of which were suggestive of psychomotor retardation. Our study suggests that speech features could be used as objective biomarkers for the assessment of depression severity.

• Access to Document (DOI)

Safety of psychotropic medications in people with COVID-19: evidence review and practical recommendations

Ostuzzi Giovanni, Papola Davide, Gastaldon Chiara, Schoretsanitis Georgios, Bertolini Federico, Amaddeo Francesco, Cuomo Alessandro, Emsley Robin, Fagiolini Andrea, Imperadore Giuseppe, Kishimoto Taishiro, Michencigh Giulia, Nose Michela, Purgato Marianna, Serdar Dursun, Stubbs Brendon, Taylor David, Thornicroft Graham, Ward Philip B., Hiemke Christoph, Correll Christoph U., Barbui Corrado

BMC MEDICINE 18 ( 1 )  2020.07

ISSN  1741-7015

• Access to Document (DOI)

Safety of psychotropic medications in people with COVID-19: evidence review and practical recommendations.

Ostuzzi G, Papola D, Gastaldon C, Schoretsanitis G, Bertolini F, Amaddeo F, Cuomo A, Emsley R, Fagiolini A, Imperadore G, Kishimoto T, Michencigh G, Nosé M, Purgato M, Dursun S, Stubbs B, Taylor D, Thornicroft G, Ward PB, Hiemke C, Correll CU, Barbui C

BMC medicine 18 ( 1 ) 215 2020.07

• Access to Document (DOI)

Safety of psychotropic medications in people with COVID-19: Evidence review and practical recommendations

Ostuzzi G., Papola D., Gastaldon C., Schoretsanitis G., Bertolini F., Amaddeo F., Cuomo A., Emsley R., Fagiolini A., Imperadore G., Kishimoto T., Michencigh G., Nosé M., Purgato M., Serdar D., Stubbs B., Taylor D., Thornicroft G., Ward P.B., Hiemke C., Correll C.U., Barbui C.

BMC Medicine (BMC Medicine)  18 ( 1 )  2020.07

　View Summary

© 2020 The Author(s). Background: The novel coronavirus pandemic calls for a rapid adaptation of conventional medical practices to meet the evolving needs of such vulnerable patients. People with coronavirus disease (COVID-19) may frequently require treatment with psychotropic medications, but are at the same time at higher risk for safety issues because of the complex underlying medical condition and the potential interaction with medical treatments. Methods: In order to produce evidence-based practical recommendations on the optimal management of psychotropic medications in people with COVID-19, an international, multi-disciplinary working group was established. The methodology of the WHO Rapid Advice Guidelines in the context of a public health emergency and the principles of the AGREE statement were followed. Available evidence informing on the risk of respiratory, cardiovascular, infective, hemostatic, and consciousness alterations related to the use of psychotropic medications, and drug-drug interactions between psychotropic and medical treatments used in people with COVID-19, was reviewed and discussed by the working group. Results: All classes of psychotropic medications showed potentially relevant safety risks for people with COVID-19. A set of practical recommendations was drawn in order to inform frontline clinicians on the assessment of the anticipated risk of psychotropic-related unfavorable events, and the possible actions to take in order to effectively manage this risk, such as when it is appropriate to avoid, withdraw, switch, or adjust the dose of the medication. Conclusions: The present evidence-based recommendations will improve the quality of psychiatric care in people with COVID-19, allowing an appropriate management of the medical condition without worsening the psychiatric condition and vice versa.

• Access to Document (DOI)

Modulation of hippocampal functional connectivity and depressive symptom improvement following ECT

Takamiya A., Kishimoto T., Hirano J., Nakajima K., Katayama N., Kikuchi T., Yamagata B., Nakagawa A., Mimura M.

EUROPEAN PSYCHIATRY 63   S373 - S373 2020.07

ISSN  0924-9338

Speech quality feature analysis for classification of depression and dementia patients

Sumali B., Mitsukura Y., Liang K.C., Yoshimura M., Kitazawa M., Takamiya A., Fujita T., Mimura M., Kishimoto T.

Sensors (Switzerland) (Sensors (Switzerland))  20 ( 12 ) 1 - 17 2020.06

ISSN  14248220

　View Summary

© MDPI AG. All rights reserved. Loss of cognitive ability is commonly associated with dementia, a broad category of progressive brain diseases. However, major depressive disorder may also cause temporary deterioration of one’s cognition known as pseudodementia. Differentiating a true dementia and pseudodementia is still difficult even for an experienced clinician and extensive and careful examinations must be performed. Although mental disorders such as depression and dementia have been studied, there is still no solution for shorter and undemanding pseudodementia screening. This study inspects the distribution and statistical characteristics from both dementia patient and depression patient, and compared them. It is found that some acoustic features were shared in both dementia and depression, albeit their correlation was reversed. Statistical significance was also found when comparing the features. Additionally, the possibility of utilizing machine learning for automatic pseudodementia screening was explored. The machine learning part includes feature selection using LASSO algorithm and support vector machine (SVM) with linear kernel as the predictive model with age-matched symptomatic depression patient and dementia patient as the database. High accuracy, sensitivity, and specificity was obtained in both training session and testing session. The resulting model was also tested against other datasets that were not included and still performs considerably well. These results imply that dementia and depression might be both detected and differentiated based on acoustic features alone. Automated screening is also possible based on the high accuracy of machine learning results.

• Access to Document (DOI)

Effects of Psychotropics on the Microbiome in Patients With Depression and Anxiety: a Naturalistic Prospective Study

Kurokawa Shunya, Tomizawa Yoshihiro, Ishii Daiki, Miyaho Katsuma, Sanada Kenji, Mimura Masaru, Kishimoto Taishiro

BIOLOGICAL PSYCHIATRY 87 ( 9 ) S279 - S280 2020.05

ISSN  0006-3223

Examination of Language Characteristics Among Patients With Alzheimer's Disease Using Natural Language Processing

Momota Yuki, Isa Shimpei, Eguchi Yoko, Kitazawa Momoko, Mimura Masaru, Kishimoto Taishiro

BIOLOGICAL PSYCHIATRY 87 ( 9 ) S285 - S285 2020.05

ISSN  0006-3223

Gut microbiota and majore depressive disorder: A systematic review and meta-analysis

Sanada K., Nakajima S., Kurokawa S., Barceló-Soler A., Ikuse D., Hirata A., Yoshizawa A., Tomizawa Y., Salas-Valero M., Noda Y., Mimura M., Iwanami A., Kishimoto T.

Journal of Affective Disorders (Journal of Affective Disorders)  266   1 - 13 2020.04

ISSN  01650327

　View Summary

© 2020 Elsevier B.V. Background: Growing attention has been paid to the field of gut microbiota for mental disorders over the last decade. However, to our knowledge, no studies have conducted systematic reviews on the association between gut microbiota and major depressive disorder (MDD) in both interventional and non-interventional studies. Methods: We conducted a systematic review and meta-analysis of 16 studies (10 observational [701 participants] and six interventional trials [302 participants]) examining gut microbiota in patients with MDD. The primary outcome measures were differences in the profile of microbiota inthe observational studies, and symptom changes for depression between pre- and post-intervention with probiotics in the interventional trials. Results: In the observational studies, significant reductions in several taxa at the family and genus levels were observed in patients with MDD compared to non-depressed controls. In the interventional studies with probiotics, a significant improvement was found in depressive symptomatology compared to controls (SMD = .-1.62, 95% CI = .-2.73 to -0.51, p< 0.01). Limitations: Lack of consideration of the effects of diet and pharmacotherapy was a possible limitation. Conclusions: Our results indicate that several taxa at the family and genus levels, specifically family Prevotellaceae, genus Coprococcus,and Faecalibacterium, were decreased in MDD compared to non-depressed controls in observational studies, and depressive symptoms were improved compared to controls in interventional studies with probiotics. Due to the limited number of studies, further studies considering diet and pharmacotherapy are needed to explore the relationships between gut microbiota and MDD in humans.

• Access to Document (DOI)

Evaluating the severity of depressive symptoms using upper body motion captured by RGB-depth sensors and machine learning in a clinical interview setting: A preliminary study

Horigome T., Sumali B., Kitazawa M., Yoshimura M., Liang K.c., Tazawa Y., Fujita T., Mimura M., Kishimoto T.

Comprehensive Psychiatry (Comprehensive Psychiatry)  98   152169 2020.04

ISSN  0010440X

　View Summary

© 2020 The Authors Background: Mood disorders have long been known to affect motor function. While methods to objectively assess such symptoms have been used in experiments, those same methods have not yet been applied in clinical practice because the methods are time-consuming, labor-intensive, or invasive. Methods: We videotaped the upper body of each subject using a Red-Green-Blue-Depth (RGB-D) sensor during a clinical interview setting. We then examined the relationship between depressive symptoms and body motion by comparing the head motion of patients with major depressive disorders (MDD) and bipolar disorders (BD) to the motion of healthy controls (HC). Furthermore, we attempted to predict the severity of depressive symptoms by using machine learning. Results: A total of 47 participants (HC, n = 16; MDD, n = 17; BD, n = 14) participated in the study, contributing to 144 data sets. It was found that patients with depression move significantly slower compared to HC in the 5th percentile and 50th percentile of motion speed. In addition, Hamilton Depression Rating Scale (HAMD)-17 scores correlated with 5th percentile, 50th percentile, and mean speed of motion. Moreover, using machine learning, the presence and/or severity of depressive symptoms based on HAMD-17 scores were distinguished by a kappa coefficient of 0.37 to 0.43. Limitations: Limitations include the small number of subjects, especially the number of severe cases and young people. Conclusions: The RGB-D sensor captured some differences in upper body motion between depressed patients and controls. If much larger samples are accumulated, machine learning may be useful in identifying objective measures for depression in the future.

• Access to Document (DOI)

The energy calibration system for CANDLES using (n, γ) reaction

T. Iida, K. Mizukoshi, T. Ohata, T. Uehara, T. Batpurev, W. M. Chan, K. Fushimi, R. Hazama, M. Ishikawa, H. Kakubata, K. Kanagawa, S. Katagiri, B. T. Khai, T. Kishimoto, X. Li, T. Maeda, K. Matsuoka, K. Morishita, M. Moser, K. Nakajima, M. Nomachi, I. Ogawa, M. Shokati, K. Suzuki, Y. Takemoto, Y. Takihira, Y. Tamagawa, K. Tetsuno, V. T. T. Trang, S. Umehara, S. Yoshida

 2020.03

　View Summary

CAlcium fluoride for the study of Neutrinos and Dark matters by Low-energy
Spectrometer (CANDLES) searches for neutrino-less double beta decay of
$^{48}$Ca using a CaF$_2$ scintillator array. A high Q-value of $^{48}$Ca at
4,272 keV enabled us to achieve very low background condition, however, at the
same it causes difficulties in calibrating the detector's Q-value region
because of the absence of a standard high-energy $\gamma$-ray source.
Therefore, we have developed a novel calibration system based on $\gamma$-ray
emission by neutron capture on $^{28}$Si, $^{56}$Fe and $^{58}$Ni nuclei. In
the paper, we report the development of the new calibration system as well as
the results of energy calibration in CANDLES up to 9 MeV.


• Access to Document (DOI)

Predicting individual remission after electroconvulsive therapy based on pretreatment structural MRI: A machine learning approach

Takamiya A, Liang KC, Nishikata S, Tarumi R, Sawada K, Kurokawa S, Hirano J, Yamagata B, Mimura M, Kishimoto T

J ECT (Journal of ECT)  36 ( 3 ) 205 - 210 2020.02

Research paper (scientific journal), Accepted,  ISSN  1095-0680

　View Summary

Objective To identify important clinical or imaging features predictive of an individual's response to electroconvulsive therapy (ECT) by utilizing a machine learning approach. Methods Twenty-seven depressed patients who received ECT were recruited. Clinical demographics and pretreatment structural magnetic resonance imaging (MRI) data were used as candidate features to build models to predict remission and post-ECT Hamilton Depression Rating Scale scores. Support vector machine and support vector regression with elastic-net regularization were used to build models using (i) only clinical features, (ii) only MRI features, and (iii) both clinical and MRI features. Consistently selected features across all individuals were identified through leave-one-out cross-validation. Results Compared with models that include only clinical variables, the models including MRI data improved the prediction of ECT remission: the prediction accuracy improved from 70% to 93%. Features selected consistently across all individuals included volumes in the gyrus rectus, the right anterior lateral temporal lobe, the cuneus, and the third ventricle, as well as 2 clinical features: psychotic features and family history of mood disorder. Conclusions Pretreatment structural MRI data improved the individual predictive accuracy of ECT remission, and only a small subset of features was important for prediction.

• Access to Document (DOI)

Evaluating depression with multimodal wristband-type wearable device: screening and assessing patient severity utilizing machine-learning

70. Tazawa Y, Liang KC, Yoshimura M, Kitazawa M, Kaise Y, Takamiya A, Kishi A, Horigome T, Mitsukura Y, Mimura M, Kishimoto T

Heliyon (Heliyon)  6 ( 2 ) e03274 2020.02

Research paper (scientific journal), Accepted,  ISSN  24058440

　View Summary

© 2020 Objective: We aimed to develop a machine learning algorithm to screen for depression and assess severity based on data from wearable devices. Methods: We used a wearable device that calculates steps, energy expenditure, body movement, sleep time, heart rate, skin temperature, and ultraviolet light exposure. Depressed patients and healthy volunteers wore the device continuously for the study period. The modalities were compared hourly between patients and healthy volunteers. XGBoost was used to build machine learning models and 10-fold cross-validation was applied for the validation. Results: Forty-five depressed patients and 41 healthy controls participated, creating a combined 5,250 days' worth of data. Heart rate, steps, and sleep were significantly different between patients and healthy volunteers in some comparisons. Similar differences were also observed longitudinally when patients' symptoms improved. Based on seven days' data, the model identified symptomatic patients with 0.76 accuracy and predicted Hamilton Depression Rating Scale-17 scores with a 0.61 correlation coefficient. Skin temperature, sleep time-related features, and the correlation of those modalities were the most significant features in machine learning. Limitations: The small number of subjects who participated in this study may have weakened the statistical significance of the study. There are differences in the demographic data among groups although we performed a correction for multiple comparisons. Validation in independent datasets was not performed, although 10-fold cross validation with the internal data was conducted. Conclusion: The results indicated that utilizing wearable devices and machine learning may be useful in identifying depression as well as assessing severity.

• Access to Document (DOI)

Autism Spectrum Disorder’s Severity Prediction Model Using Utterance Features for Automatic Diagnosis Support

Sakishita M., Ogawa C., Tsuchiya K., Iwabuchi T., Kishimoto T., Kano Y.

Studies in Computational Intelligence (Studies in Computational Intelligence)  843   83 - 95 2020

ISSN  1860949X

　View Summary

© Springer Nature Switzerland AG 2020. Diagnoses of autism spectrum disorder (ASD) are difficult due to difference of interviewers and environments, etc. We show relations between utterance features and ASD severity scores, which were manually given by clinical psychologists. These scores are based on the Autism Diagnostic Observation Schedule (ADOS), which is the standard metrics for symptom evaluation for subjects who are suspected as ASD. We built our original corpus where we transcribed voice records of our ADOS evaluation experiment movies. Our corpus is the world largest as speech/dialog of ASD subjects, and there has been no such ADOS corpus available in Japanese language as far as we know. We investigated relationships between ADOS scores (severity) and our utterance features, automatically estimated their scores using support vector regression (SVR). Our average estimation errors were around error rates that human ADOS experts are required not to exceed. Because our detailed analysis for each part of the ADOS test (“puzzle toy assembly + story telling” part and the “depiction of a picture” part) shows different error rates, effectiveness of our features would depend on the contents of the records. Our entire results suggest a new automatic way to assist humans’ diagnosis, which could help supporting language rehabilitation for individuals with ASD in future.

• Access to Document (DOI)

Large-Scale Dialog Corpus Towards Automatic Mental Disease Diagnosis

Sakishita M., Kishimoto T., Takinami A., Eguchi Y., Kano Y.

Studies in Computational Intelligence (Studies in Computational Intelligence)  843   111 - 118 2020

Research paper (international conference proceedings),  ISSN  1860949X

　View Summary

© Springer Nature Switzerland AG 2020. Recently, the number of people who are diagnosed as mental diseases is increasing. Efficient and objective diagnosis is important to start medical treatments in earlier stages. However, mental disease diagnosis is difficult to quantify criteria, because it is performed through conversations with patients, not by physical surveys. We aim to automate mental disease diagnosis in order to resolve these issues. We recorded conversations between psychologists and subjects to build our diagnosis speech corpus. Our subjects include healthy persons, people with mental diseases of depression, bipolar disorder, schizophrenia, anxiety and dementia. All of our subjects are diagnosed by doctors of psychiatry. Then we made accurate transcription manually, adding utterance time stamps, linguistic and non-linguistic annotations. Using our corpus, we performed feature analysis to find characteristics for each disease. We also tried automatic mental disease diagnosis by machine learning, while the number of sample data is few because we were still in our pilot study phase. We will increase the number of subjects in future.

• Access to Document (DOI)

Neuronal network mechanisms associated with depressive symptom improvement following electroconvulsive therapy

Takamiya A., Kishimoto T., Hirano J., Nishikata S., Sawada K., Kurokawa S., Yamagata B., Kikuchi T., Mimura M.

Psychological Medicine (Psychological Medicine)  51 ( 16 ) 1 - 8 2020

ISSN  00332917

　View Summary

Copyright © The Author(s) 2020. Published by Cambridge University Press. BackgroundElectroconvulsive therapy (ECT) is the most effective antidepressant treatment for severe depression. Although recent structural magnetic resonance imaging (MRI) studies have consistently reported ECT-induced hippocampal volume increases, most studies did not find the association of the hippocampal volume changes with clinical improvement. To understand the underlying mechanisms of ECT action, we aimed to identify the longitudinal effects of ECT on hippocampal functional connectivity (FC) and their associations with clinical improvement.MethodsResting-state functional MRI was acquired before and after bilateral ECT in 27 depressed individuals. A priori hippocampal seed-based FC analysis and a data-driven multivoxel pattern analysis (MVPA) were conducted to investigate FC changes associated with clinical improvement. The statistical threshold was set at cluster-level false discovery rate-corrected p < 0.05.ResultsDepressive symptom improvement after ECT was positively associated with the change in the right hippocampus-ventromedial prefrontal cortex FC, and negatively associated with the right hippocampus-superior frontal gyrus FC. MVPA confirmed the results of hippocampal seed-based analyses and identified the following additional clusters associated with clinical improvement following ECT: the thalamus, the sensorimotor cortex, and the precuneus.ConclusionsECT-induced change in the right frontotemporal connectivity and thalamocortical connectivity, and changes in the nodes of the default mode network were associated with clinical improvement. Modulation of these networks may explain the underlying mechanisms by which ECT exert its potent and rapid antidepressant effect.

• Access to Document (DOI)

Autism Spectrum Disorder's Severity Prediction System Using Utterance Features

Sakishita Masahito, Ogawa Chihiro, Tsuchiya Kenji J., Iwabuchi Toshiki, Kishimoto Taishiro, Kano Yoshinobu

Transactions of the Japanese Society for Artificial Intelligence (The Japanese Society for Artificial Intelligence)  35 ( 3 ) B - J45_1-11 2020

ISSN  13460714

　View Summary

<p>In recent years, population with autism spectrum disorder (ASD) are growing explosively, and diagnosis of ASD is difficult due to difference of interviewers and environments, etc. We show relations between utterance features and ASD severity scores, which were manually given by a clinical psychologist. These scores are of the Autism Diagnostic Observation Schedule (ADOS), which is one of the standard metrics for symptom evaluation for subjects who are suspected as ASD. We built our original corpus where we transcribed voice records of our ADOS evaluation experiment movies. Our corpus is the world largest as speech/dialog of ASD subjects, and there has been no such ADOS corpus available in Japanese language as far as we know. We investigated relationships between ADOS scores (severity) and utterance features we defined. Our system automatically estimated their scores using support vector regression (SVR). Our average estimation errors were around error rates that human ADOS experts are required not to exceed. Because our detailed analysis for each part of the ADOS test ("puzzle toy assembly + story telling" part and the "depiction of a picture" part) shows different error rates, effectiveness of our features would depend on the contents of the records. By comparing an ADOS score prediction result of adults and adults with that of children, we showed common features of ADOS scores between children and adults. Our entire results suggest a new automatic way to assist humans' diagnosis, which could help supporting language rehabilitation for patients with ASD in future.</p>

• Access to Document (DOI)