Hirano, Jinichi



School of Medicine, Department of Neuropsychiatry (Shinanomachi)


Assistant Professor/Senior Assistant Professor


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 (Scientific Reports)  14 ( 1 ) 7633 2024.12

    ISSN  2045-2322

     View Summary

    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.

  • Segmentation and Volume Estimation of the Habenula Using Deep Learning in Patients With Depression

    Kyuragi Y., Oishi N., Hatakoshi M., Hirano J., Noda T., Yoshihara Y., Ito Y., Igarashi H., Miyata J., Takahashi K., Kamiya K., Matsumoto J., Okada T., Fushimi Y., Nakagome K., Mimura M., Murai T., Suwa T.

    Biological Psychiatry Global Open Science (Biological Psychiatry Global Open Science)  4 ( 4 ) 100314 2024.07

     View Summary

    Background: The habenula is involved in the pathophysiology of depression. However, its small structure limits the accuracy of segmentation methods, and the findings regarding its volume have been inconsistent. This study aimed to create a highly accurate habenula segmentation model using deep learning, test its generalizability to clinical magnetic resonance imaging, and examine differences between healthy participants and patients with depression. Methods: This multicenter study included 382 participants (patients with depression: N = 234, women 47.0%; healthy participants: N = 148, women 37.8%). A 3-dimensional residual U-Net was used to create a habenula segmentation model on 3T magnetic resonance images. The reproducibility and generalizability of the predictive model were tested on various validation cohorts. Thereafter, differences between the habenula volume of healthy participants and that of patients with depression were examined. Results: A Dice coefficient of 86.6% was achieved in the derivation cohort. The test-retest dataset showed a mean absolute percentage error of 6.66, indicating sufficiently high reproducibility. A Dice coefficient of >80% was achieved for datasets with different imaging conditions, such as magnetic field strengths, spatial resolutions, and imaging sequences, by adjusting the threshold. A significant negative correlation with age was observed in the general population, and this correlation was more pronounced in patients with depression (p < 10−7, r = −0.59). Habenula volume decreased with depression severity in women even when the effects of age and scanner were excluded (p = .019, η2 = 0.099). Conclusions: Habenula volume could be a pathophysiologically relevant factor and diagnostic and therapeutic marker for depression, particularly in women.

  • Clinical characteristics and potential association to Parkinson’s disease and dementia with Lewy bodies in patients with major depressive disorder who received maintenance electroconvulsive therapy: a retrospective chart review study

    Kudo S., Uchida T., Nishida H., Takamiya A., Kikuchi T., Yamagata B., Mimura M., Hirano J.

    BMC Psychiatry (BMC Psychiatry)  23 ( 1 ) 243 2023.12

     View Summary

    Background: Maintaining remission after electroconvulsive therapy (ECT) is clinically relevant in patients with depression, and maintenance ECT has been introduced in patients who fail to maintain remission after ECT. However, the clinical characteristics and biological background of patients who receive maintenance ECT are poorly understood. Thus, this study aimed to examine the clinical background of patients who underwent maintenance ECT. Methods: Patients with major depressive disorder who underwent ECT followed by maintenance ECT (mECT group) and those who did not (acute ECT [aECT] group) were included. Clinical characteristics, including the results of neuroimaging examinations for Parkinson’s disease (PD) and dementia with Levy body (DLB) such as myocardial 123I-metaiodobenzylguanidine (MIBG) scintigraphy and dopamine transporter imaging single-photon emission computerized tomography (DaT-SPECT), were compared between the groups. Results: In total, 13 and 146 patients were included in the mECT and aECT groups, respectively. Compared to the aECT group, the mECT group showed a significantly higher prevalence of melancholic features (92.3% vs. 27.4%, p < 0.001) and catatonic features (46.2% vs. 9.6%, p = 0.002). Overall, 8 of the 13 patients in the mECT group and 22 of the 146 patients in the aECT group underwent neuroimaging examinations for PD/DLB. The rate of patients examined is significantly higher in the mECT group than in the aECT group (61.5% vs. 11.2%, p < 0.001). Among the groups examined, 7/8 patients in the mECT group and 16/22 patients in the aECT group showed relevant neuroimaging findings for PD/DLB; the positive rate was not significantly different between the two groups (87.5% vs. 72.7%, p = 0.638). Conclusions: Patients who receive acute and maintenance ECT may have underlying neurodegenerative diseases, including PD/DLB. Investigating the neurobiology of patients who receive maintenance ECT is important for developing appropriate treatments for depression.

  • Development of medical device software for the screening and assessment of depression severity using data collected from a wristband-type wearable device: SWIFT study protocol

    Kishimoto T., Kinoshita S., Kikuchi T., Bun S., Kitazawa M., Horigome T., Tazawa Y., Takamiya A., Hirano J., Mimura M., Liang K.C., Koga N., Ochiai Y., Ito H., Miyamae Y., Tsujimoto Y., Sakuma K., Kida H., Miura G., Kawade Y., Goto A., Yoshino F.

    Frontiers in Psychiatry (Frontiers in Psychiatry)  13   1025517 2022.12

    ISSN  1664-0640

     View Summary

    Introduction: Few biomarkers can be used clinically to diagnose and assess the severity of depression. However, a decrease in activity and sleep efficiency can be observed in depressed patients, and recent technological developments have made it possible to measure these changes. In addition, physiological changes, such as heart rate variability, can be used to distinguish depressed patients from normal persons; these parameters can be used to improve diagnostic accuracy. The proposed research will explore and construct machine learning models capable of detecting depressive episodes and assessing their severity using data collected from wristband-type wearable devices. Methods and analysis: Patients with depressive symptoms and healthy subjects will wear a wristband-type wearable device for 7 days; data on triaxial acceleration, pulse rate, skin temperature, and ultraviolet light will be collected. On the seventh day of wearing, the severity of depressive episodes will be assessed using Structured Clinical Interview for DSM-5 (SCID-5), Hamilton Depression Rating Scale (HAMD), and other scales. Data for up to five 7-day periods of device wearing will be collected from each subject. Using wearable device data associated with clinical symptoms as supervisory data, we will explore and build a machine learning model capable of identifying the presence or absence of depressive episodes and predicting the HAMD scores for an unknown data set. Discussion: Our machine learning model could improve the clinical diagnosis and management of depression through the use of a wearable medical device. Clinical trial registration: [https://jrct.niph.go.jp/latest-detail/jRCT1031210478], identifier [jRCT1031210478].

  • Intergenerational concordance of brain structure between depressed mothers and their never-depressed daughters

    Minami F., Hirano J., Ueda R., Takamiya A., Yamagishi M., Kamiya K., Mimura M., Yamagata B.

    Psychiatry and Clinical Neurosciences (Psychiatry and Clinical Neurosciences)  76 ( 11 ) 579 - 586 2022.11

    ISSN  13231316

     View Summary

    Aim: Parents have significant genetic and environmental influences, which are known as intergenerational effects, on the cognition, behavior, and brain of their offspring. These intergenerational effects are observed in patients with mood disorders, with a particularly strong association of depression between mothers and daughters. The main purpose of our study was to investigate female-specific intergenerational transmission patterns in the human brain among patients with depression and their never-depressed offspring. Methods: We recruited 78 participants from 34 families, which included remitted parents with a history of depression and their never-depressed biological offspring. We used source-based and surface-based morphometry analyses of magnetic resonance imaging data to examine the degree of associations in brain structure between four types of parent-offspring dyads (i.e. mother-daughter, mother-son, father-daughter, and father-son). Results: Using independent component analysis, we found a significant positive correlation of gray matter structure between exclusively the mother-daughter dyads within brain regions located in the default mode and central executive networks, such as the bilateral anterior cingulate cortex, posterior cingulate cortex, precuneus, middle frontal gyrus, middle temporal gyrus, superior parietal lobule, and left angular gyrus. These similar observations were not identified in other three parent-offspring dyads. Conclusions: The current study provides biological evidence for greater vulnerability of daughters, but not sons, in developing depression whose mothers have a history of depression. Our findings extend our knowledge on the pathophysiology of major psychiatric conditions that show sex biases and may contribute to the development of novel interventions targeting high-risk individuals.

display all >>

Papers, etc., Registered in KOARA 【 Display / hide

Reviews, Commentaries, etc. 【 Display / hide

  • 【革新脳と関連プロジェクトから見えてきた新しい脳科学】ヒト疾患研究 a)精神疾患 精神疾患とイメージング研究

    平野 仁一, 三村 將

    生体の科学 ((公財)金原一郎記念医学医療振興財団)  73 ( 5 ) 452 - 453 2022.10

    ISSN  0370-9531

     View Summary


  • 機能神経外科からみる高次脳機能 電気刺激療法と認知機能障害

    工藤 駿, 高宮 彰紘, 平野 仁一, 三村 將

    神経心理学 (日本神経心理学会)  38 ( 3 ) 216 - 221 2022.09

    ISSN  0911-1085

     View Summary


  • Prolonged Post-Electroconvulsive Therapy Delirium Controlled with Donepezil

    Takamiya A., Sawada K., Hirano J., Mimura M., Kishimoto T.

    Journal of ECT (Journal of ECT)  35 ( 3 ) E29 - E30 2019.09

    ISSN  10950680

  • 【ニューロモデュレーション治療の可能性】精神疾患に対するニューロモデュレーション

    高宮 彰紘, 岸本 泰士郎, 平野 仁一, 山縣 文, 三村 將

    精神科 ((有)科学評論社)  34 ( 6 ) 551 - 556 2019.06

    ISSN  1347-4790

  • 【免疫と精神神経疾患】うつ病

    平野 仁一

    精神科 ((有)科学評論社)  27 ( 4 ) 234 - 240 2015.10

    ISSN  1347-4790

display all >>

Research Projects of Competitive Funds, etc. 【 Display / hide

  • 認知症早期診断に向けた人工知能によるあたらしい網膜イメージングシステムの開発


    Grants-in-Aid for Scientific Research, Grant-in-Aid for Scientific Research (C), No Setting

     View Summary


  • 電気けいれん療法の治療効果発現におけるγ帯域神経活動が与える影響の解明


    MEXT,JSPS, Grant-in-Aid for Scientific Research, Grant-in-Aid for Scientific Research (C), Principal investigator

     View Summary

    慶應義塾大学病院脳波室直下に3テスラのMRIが存在したため、高密度脳波計Geodesic EEG System400で取得した脳波データにおいて従来の皿電極 脳波計では明らかでなかった電磁波による周期的ノイズが混入した。この電磁波によるノイズ対策のためにさまざまな解析技法を用いてノイズ除去を試みたが、ノイズの除去が困難であった。慶應義塾大学病院内で精神神経科外来等代替えのデータ取得場所も検討したが、電子機器の影響を受けてノイズが著しく混入してしまう状況であった。このため、計測機器の変更等研究計画の変更が必要な状況とである。このため、現時点において当初予定されていた被験者登録がなされていない状況である。
    一方で、本研究の主旨である電気けいれん療法の作用機序解明に関連して、過去に慶應義塾大学にてうつ病に対して電気けいれん療法を受療した症例を対象として、電気けいれん療法の治療効果予測を機械学習の手法を用いてい行い、うつ病エピソードの罹患期間、重症度等が治療効果に関係することを明らかとした(Individual Prediction of Remission Based on Clinical Features Following Electroconvulsive Therapy: A Machine Learning Approach)。また、電気けいれん療法に置いて重要な麻酔薬選択において、吸入麻酔薬であるセボフルレンは、静脈麻酔薬であるチオペンタールに比して、総刺激回数、治療回数、不全発作数が多くなることを明らかとした。(Impact of Sevoflurane and Thiopental Used Over the Course of Electroconvulsive Therapy: Propensity Score Matching Analysis. )
    慶應義塾大学病院脳波室の直下に3テスラのMRIが存在したたたため、高密度脳波計Geodesic EEG System400で取得した脳波データでは従来の皿電極脳 波計では明らかでなかった電磁波による周期的ノイズが混入した。この電磁波によるノイズ対策のためにさまざまな解析技法を用いてノイズ除去を試みたが、ノイズの除去が困難であった。慶應義塾内で代替えのデータ取得場所も検討したが、電子機器の影響を受けてノイズが著しく混入してしまう。このため、計測機器の変更等研究計画の変更が必要な状況となっている。このため、現時点において当初予定されていた被験者登録がなされていない状況である。
    高密度脳波計Geodesic EEG System400でノイズを低減した脳波データ取得が脳波室以外での脳波取得を含めて困難と判断し、脳波計を簡易脳波計等に変更する等の研究計画の変更での対応を検討している。

  • 人工知能による高齢者の不安全運転の予測モデルの確立と神経基盤の解明


    Grants-in-Aid for Scientific Research, Grant-in-Aid for Scientific Research (B), No Setting

     View Summary

    その間、これまでに蓄積できた実車データ、脳画像データ、神経心理データの一部を用いて、健常高齢者の白質構造の異常と危険運転の関係性についての論文を発表した。前回発表した論文内容をさらに支持するかたちで、背側注意ネットワークを構成する右の上縦束の白質繊維の構造異常が不安全運転と大きく関与していることが示された。論文は、「White matter alterations in the dorsal attention network contribute to a high risk of unsafe driving in healthy older people」という題名で、2022年の7月にPsychiatry and Clinical Neuroscience Reportにアクセプトされた。
    ・Yamamoto Y, Yamagata B., et al. White matter alterations in the dorsal attention network contribute to a high risk of unsafe driving in healthy older people. Psychiatry Clin. Neurosci. Rep. 2022;1:e45. https://doi.org/10.1002/pcn5.45

  • The mechanisms of electroconvulsive therapy: a multimodal longitudinal study.


    MEXT,JSPS, Grant-in-Aid for Scientific Research, HIRANO Jinichi, Grant-in-Aid for Scientific Research (C), Principal investigator

     View Summary

    Twenty-five cases were registered. No adverse event was admitted. Increasing the hippocampal volume after electroconvulsive therapy might be specific and play a central role in the treatment mechanism of electroconvulsive therapy. Like previous studies, we also identified white matter changes in diffusion tensor imaging and functional connectivity changes on resting-state functional MRI.


Courses Taught 【 Display / hide











display all >>