Bun, Shogyoku

写真a

Affiliation

School of Medicine, Department of Neuropsychiatry (Shinanomachi)

Position

Project Associate Professor (Non-tenured)
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Papers 【 Display / hide 】

  • Amyloid-β prediction machine learning model using source-based morphometry across neurocognitive disorders

    Momota Y., Bun S., Hirano J., Kamiya K., Ueda R., Iwabuchi Y., Takahata K., Yamamoto Y., Tezuka T., Kubota M., Seki M., Shikimoto R., Mimura Y., Kishimoto T., Tabuchi H., Jinzaki M., Ito D., Mimura M.

    Scientific Reports 14 ( 1 )  2024.12

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    Previous studies have developed and explored magnetic resonance imaging (MRI)-based machine learning models for predicting Alzheimer’s disease (AD). However, limited research has focused on models incorporating diverse patient populations. This study aimed to build a clinically useful prediction model for amyloid-beta (Aβ) deposition using source-based morphometry, using a data-driven algorithm based on independent component analyses. Additionally, we assessed how the predictive accuracies varied with the feature combinations. Data from 118 participants clinically diagnosed with various conditions such as AD, mild cognitive impairment, frontotemporal lobar degeneration, corticobasal syndrome, progressive supranuclear palsy, and psychiatric disorders, as well as healthy controls were used for the development of the model. We used structural MR images, cognitive test results, and apolipoprotein E status for feature selection. Three-dimensional T1-weighted images were preprocessed into voxel-based gray matter images and then subjected to source-based morphometry. We used a support vector machine as a classifier. We applied SHapley Additive exPlanations, a game-theoretical approach, to ensure model accountability. The final model that was based on MR-images, cognitive test results, and apolipoprotein E status yielded 89.8% accuracy and a receiver operating characteristic curve of 0.888. The model based on MR-images alone showed 84.7% accuracy. Aβ-positivity was correctly detected in non-AD patients. One of the seven independent components derived from source-based morphometry was considered to represent an AD-related gray matter volume pattern and showed the strongest impact on the model output. Aβ-positivity across neurological and psychiatric disorders was predicted with moderate-to-high accuracy and was associated with a probable AD-related gray matter volume pattern. An MRI-based data-driven machine learning approach can be beneficial as a diagnostic aid.

  • Decreased short-latency afferent inhibition in individuals with mild cognitive impairment: A TMS-EEG study

    Mimura Y., Tobari Y., Nakajima S., Takano M., Wada M., Honda S., Bun S., Tabuchi H., Ito D., Matsui M., Uchida H., Mimura M., Noda Y.

    Progress in Neuro-Psychopharmacology and Biological Psychiatry 132 2024.06

    ISSN  02785846

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    TMS combined with EEG (TMS-EEG) is a tool to characterize the neurophysiological dynamics of the cortex. Among the TMS paradigms, short-latency afferent inhibition (SAI) allows the investigation of inhibitory effects mediated by the cholinergic system. The aim of this study was to compare cholinergic function in the DLPFC between individuals with mild cognitive impairment (MCI) and healthy controls (HC) using TMS-EEG with the SAI paradigm. In this study, 30 MCI and 30 HC subjects were included. The SAI paradigm consisted of 80 single pulse TMS and 80 SAI stimulations applied to the left DLPFC. N100 components, global mean field power (GMFP) and total power were calculated. As a result, individuals with MCI showed reduced inhibitory effects on N100 components and GMFP at approximately 100 ms post-stimulation and on β-band activity at 200 ms post-stimulation compared to HC. Individuals with MCI showed reduced SAI, suggesting impaired cholinergic function in the DLPFC compared to the HC group. We conclude that these findings underscore the clinical applicability of the TMS-EEG method as a powerful tool for assessing cholinergic function in individuals with MCI.

  • Relationship Between Life Satisfaction and Psychological Characteristics Among Community-Dwelling Oldest-old: Focusing on Erikson's Developmental Stages and the Big Five Personality Traits

    Kida H., Niimura H., Eguchi Y., Suzuki K., Shikimoto R., Bun S., Takayama M., Mimura M.

    American Journal of Geriatric Psychiatry 32 ( 6 ) 724 - 735 2024.06

    ISSN  10647481

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    Objective: To clarify the relationship between life satisfaction and the psychological characteristics of the oldest-old, and explore the factors for achieving mental health and longevity. Design: This cross-sectional study conducted questionnaire surveys and face-to-face interviews as part of a larger prospective cohort study. Setting: Arakawa Ward, a district in Tokyo, Japan. Participants: A total of 247 oldest-old individuals from two age groups, 85+ (aged 85–87 years) and 95+ (aged 95 years or older). Measurements: Life satisfaction was assessed using the Satisfaction with Life Scale (SWLS), developmental stages of the elderly (Erikson's 8th and 9th stages, i.e., ego integrity, and gerotranscendence), and the Big Five personality traits (extraversion, agreeableness, openness, conscientiousness, and neuroticism) using the NEO-Five Factor Inventory. Multiple regression analyses were performed to examine the relationship between the SWLS scores and each assessment, controlling for age, sex, education, activities of daily living, depressive symptoms, and cognitive function. Results: The SWLS scores of 85+ were positively correlated with scores of ego integrity, extraversion, and conscientiousness. Contrastingly, the SWLS scores of 95+ were positively correlated with gerotranscendence scores. Conclusions: Psychological characteristics associated with the level of life satisfaction among community-dwelling oldest-old individuals were identified, but a causal relationship between these factors and life satisfaction was not established. Ego integrity, extraversion, conscientiousness, and gerotranscendence may be associated with enhanced life satisfaction and mental health in the oldest-old. Further, the factors associated with life satisfaction in the 85+ and 95+ age groups varied, suggesting that life satisfaction among the oldest-old has different foundations in different age groups.

  • Live two-way video versus face-to-face treatment for depression, anxiety, and obsessive-compulsive disorder: A 24-week randomized controlled trial

    Kishimoto T., Kinoshita S., Kitazawa M., Hishimoto A., Asami T., Suda A., Bun S., Kikuchi T., Sado M., Takamiya A., Mimura M., Sato Y., Takemura R., Nagashima K., Nakamae T., Abe Y., Kanazawa T., Kawabata Y., Tomita H., Abe K., Hongo S., Kimura H., Sato A., Kida H., Sakuma K., Funayama M., Sugiyama N., Hino K., Amagai T., Takamiya M., Kodama H., Goto K., Fujiwara S., Kaiya H., Nagao K.

    Psychiatry and Clinical Neurosciences 78 ( 4 ) 220 - 228 2024.04

    ISSN  13231316

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    Aim: Live two-way video, easily accessible from home via smartphones and other devices, is becoming a new way of providing psychiatric treatment. However, lack of evidence for real-world clinical setting effectiveness hampers its approval by medical insurance in some countries. Here, we conducted the first large-scale pragmatic, randomized controlled trial to determine the effectiveness of long-term treatment for multiple psychiatric disorders via two-way video using smartphones and other devices, which are currently the primary means of telecommunication. Methods: This randomized controlled trial compared two-way video versus face-to-face treatment for depressive disorder, anxiety disorder, and obsessive-compulsive disorder in the subacute/maintenance phase during a 24-week period. Adult patients with the above-mentioned disorders were allocated to either a two-way video group (≥50% video sessions) or a face-to-face group (100% in-person sessions) and received standard treatment covered by public medical insurance. The primary outcome was the 36-Item Short-Form Health Survey Mental Component Summary (SF-36 MCS) score. Secondary outcomes included all-cause discontinuation, working alliance, adverse events, and the severity rating scales for each disorder. Results: A total of 199 patients participated in this study. After 24 weeks of treatment, two-way video treatment was found to be noninferior to face-to-face treatment regarding SF-36 MCS score (48.50 vs 46.68, respectively; p < 0.001). There were no significant differences between the groups regarding most secondary end points, including all-cause discontinuation, treatment efficacy, and satisfaction. Conclusion: Two-way video treatment using smartphones and other devices, was noninferior to face-to-face treatment in real-world clinical settings. Modern telemedicine, easily accessible from home, can be used as a form of health care.

  • Social impact of brain fog and analysis of risk factors: Long COVID in Japanese population

    Shigematsu L., Kimura R., Terai H., Mimura Y., Ito D., Bun S., Namkoong H., Asakura T., Chubachi S., Masaki K., Ohgino K., Miyata J., Kawada I., Ishii M., Takemura R., Ueda S., Yoshiyama T., Kokuto H., Kusumoto T., Oashi A., Miyawaki M., Saito F., Tani T., Ishioka K., Takahashi S., Nakamura M., Sato Y., Fukunaga K.

    Annals of Clinical and Translational Neurology  2024

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    Objective: To reveal the clinical features and assess risk factors linked to brain fog and its societal implications, including labor productivity, providing valuable insights for the future care of individuals who have experienced coronavirus disease 2019 (COVID-19). Methods: We analyzed a comprehensive cohort dataset comprising 1,009 patients with COVID-19 admitted to Japanese hospitals. To assess brain fog, we analyzed patients who responded to a questionnaire indicating symptoms such as memory impairment and poor concentration. Results: The prevalence of brain fog symptoms decreased 3 months posthospitalization but remained stable up to 12 months. Neurological symptoms such as taste and smell disorders and numbness at hospitalization correlated with a higher frequency of identifying brain fog as a long COVID manifestation. Our findings indicated that advanced age, female sex, a high body mass index, oxygen required during hospitalization, chronic obstructive pulmonary disease, asthma, and elevated C-reactive protein and elevated D-dimer levels were risk factors in patients exhibiting brain fog. Additionally, we demonstrated the negative impact of brain fog on labor productivity by presenteeism scores. Interpretations: This study clarified the clinical characteristics of patients experiencing brain fog as a long COVID manifestation, specifically emphasizing neurological symptoms during hospitalization and their correlation with brain fog. Additionally, the study identified associated risk factors for its onset and revealed that the emergence of brain fog was linked to a decline in labor productivity.

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