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Published Papers 【 display / non-display 】

• Depressive mixed state and anxious distress as risk factors for suicidal behavior during major depressive episodes

  Ota, K; Shinzato, H; Otsuka, N; Zamami, Y; Kurihara, K; Futenma, K; Kondo, T; Takaesu, Y

  SCIENTIFIC REPORTS ( Scientific Reports )  15 ( 1 ) 11918 - 11918   2025.04 [ Peer Review Accepted ]

  Type of publication: Research paper (scientific journal)

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  Accurately assessing and predicting suicidal behavior in patients with depression are challenging for researchers and clinicians. We examined various risk factors for suicidal behavior during major depressive episodes (MDE), especially focusing on depressive mixed state (DMX) and anxious distress (AD). We recruited 187 patients with MDE and divided them into two groups-with and without suicidal behavior-defined as the cut-off score of 1 or more on the suicidal behavior sub-item in the quick inventory of depressive symptomatology-self report. The presence of DMX was defined as a total score of 13 or more on the self-administered 8-item questionnaire for DMX. We used multivariate logistic regression analysis with the presence or absence of suicidal behavior as a dependent variable for investigating factors associated with suicidal behavior. The with suicidal behavior group was younger and indicated a greater proportion of past suicide attempts, AD, and DMX than the without suicidal behavior group. Logistic regression analysis revealed that AD (P = 0.020) and DMX (P = 0.018) were significantly associated with suicidal behavior. AD and DMX may promote suicidal behavior during MDE. These two psychopathological features should be carefully monitored and intensively treated for the prevention of suicide-related events.

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• Dissecting heterogeneity in cortical thickness abnormalities in major depressive disorder: a large-scale ENIGMA MDD normative modelling study.

  Bayer JMM, van Velzen LS, Pozzi E, Davey C, Han LKM, Bauduin SEEC, Bauer J, Benedetti F, Berger K, Bonnekoh LM, Brosch K, Bülow R, Couvy-Duchesne B, Cullen KR, Dannlowski U, Dima D, Dohm K, Evans JW, Fu CHY, Fuentes-Claramonte P, Godlewska BR, Goltermann J, Gonul A, Goya-Maldonado R, Grabe HJ, Groenewold NA, Grotegerd D, Gruber O, Hahn T, Hall GB, Hamilton J, Harrison BJ, Hatton SN, Hermesdorf M, Hickie IB, Ho TC, Jahanshad N, Jansen A, Jamieson AJ, Kamishikiryo T, Kircher T, Klimes-Dougan B, Krämer B, Kraus A, Krug A, Leehr EJ, Leenings R, Li M, McIntosh A, Medland SE, Meinert S, Melloni E, Mwangi B, Nenadić I, Okada G, Oudega M, Portella MJ, Rodríguez E, Romaniuk L, Rosa PG, Sacchet MD, Salvador R, Sämann PG, Shinzato H, Sim K, Simulionyte E, Soares JC, Stein DJ, Stein F, Stolicyn A, Straube B, Strike LT, Teutenberg L, Thomas-Odenthal F, Thomopoulos SI, Usemann P, van der Wee NJA, Völzke H, Wagenmakers M, Walter M, Whalley HC, Whittle S, Winter NR, Wittfeld K, Wu M, Yang TT, Zarate CA, Zunta-Soares GB, Thompson PM, Veltman DJ, Marquand AF, Schmaal L

  bioRxiv : the preprint server for biology     2025.03 [ Peer Review Accepted ]

  Type of publication: Research paper (scientific journal)

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• The 12-item self-report Questionnaire for Difficulty in Social Communication as a simultaneous prescreening of autism spectrum and social anxiety

  Teruya, M; Kurihara, K; Ishibashi, T; Ota, K; Shinzato, H; Takaesu, Y; Kondo, T

  PSYCHIATRY AND CLINICAL NEUROSCIENCES REPORTS ( Psychiatry and Clinical Neurosciences Reports )  4 ( 1 ) e70084   2025.03 [ Peer Review Accepted ]

  Type of publication: Research paper (scientific journal)

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  AIM: Young patients with social communication difficulties are often diagnosed with autism spectrum disorder (ASD), social communication disorder (SCD), or social anxiety disorder (SAD). This study aimed to develop a questionnaire, especially focusing on the prescreening of SAD complicated by ASD/SCD. METHODS: The 12-item self-report Questionnaire for Difficulty in Social Communication (DISC-12) was developed and analyzed using exploratory factor analysis in 94 patients with ASD/SCD (35 with SAD, 59 without). An additional 17 patients with only SAD were included. Convergent validity was assessed via correlations with the Autism Spectrum Quotient (AQ) and Liebowitz Social Anxiety Scale (LSAS). DISC-12 scores and demographics were compared across ASD/SCD, ASD/SCD + SAD, and SAD groups. Receiver operating characteristic (ROC) analysis of DISC-12 subscales distinguished autistic traits from social anxiety. RESULTS: Factor analysis revealed a three-factor model for the DISC-12, comprising nonassertiveness, poor empathy, and interpersonal hypersensitivity. DISC-12 showed significant correlations with the AQ (r = 0.412, p < 0.001) and LSAS (r = 0.429, p < 0.001). Patients with ASD/SCD had higher Poor Empathy scores, while SAD patients had higher Interpersonal Hypersensitivity scores than the other groups. ROC analysis indicated that Poor Empathy and Interpersonal Hypersensitivity subscale scores effectively differentiated ASD/SCD from patients with SAD and vice versa. CONCLUSION: DISC-12 is a rapid and effective prescreening tool for identifying both ASD and social anxiety, particularly in young patients with self-reported difficulties in social communication.

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• Comprehensive evaluation of pipelines for classification of psychiatric disorders using multi-site resting-state fMRI datasets

  Yuji Takahara, Yuto Kashiwagi, Tomoki Tokuda, Junichiro Yoshimoto, Yuki Sakai, Ayumu Yamashita, Toshinori Yoshioka, Hidehiko Takahashi, Hiroto Mizuta, Kiyoto Kasai, Akira Kunimitsu, Naohiro Okada, Eri Itai, Hotaka Shinzato, Satoshi Yokoyama, Yoshikazu Masuda, Yuki Mitsuyama, Go Okada, Yasumasa Okamoto, Takashi Itahashi, Haruhisa Ohta, Ryu-ichiro Hashimoto, Kenichiro Harada, Hirotaka Yamagata, Toshio Matsubara, Koji Matsuo, Saori C. Tanaka, Hiroshi Imamizu, Koichi Ogawa, Sotaro Momosaki, Mitsuo Kawato, Okito Yamashita

  Neural Networks ( Elsevier BV )    107335 - 107335   2025.02 [ Peer Review Accepted ]

  Type of publication: Research paper (scientific journal)

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• Predicting Antidepressant Treatment Response From Cortical Structure on MRI: A Mega-Analysis From the ENIGMA-MDD Working Group

  Poirot, MG; Boucherie, DE; Caan, MWA; Goya-Maldonado, R; Belov, V; Corruble, E; Colle, R; Couvy-Duchesne, B; Kamishikiryo, T; Shinzato, H; Ichikawa, N; Okada, G; Okamoto, Y; Harrison, B; Davey, CG; Jamieson, AJ; Cullen, KR; Basgöze, Z; Klimes-Dougan, B; Mueller, BA; Benedetti, F; Poletti, S; Melloni, EMT; Ching, CRK; Zeng, LL; Radua, J; Han, LKM; Jahanshad, N; Thomopoulos, SI; Pozzi, E; Veltman, DJ; Schmaal, L; Thompson, PM; Ruhe, HG; Reneman, L; Schrantee, A

  HUMAN BRAIN MAPPING ( Human Brain Mapping )  46 ( 1 ) e70053   2025.01 [ Peer Review Accepted ]

  Type of publication: Research paper (scientific journal)

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  Accurately predicting individual antidepressant treatment response could expedite the lengthy trial-and-error process of finding an effective treatment for major depressive disorder (MDD). We tested and compared machine learning-based methods that predict individual-level pharmacotherapeutic treatment response using cortical morphometry from multisite longitudinal cohorts. We conducted an international analysis of pooled data from six sites of the ENIGMA-MDD consortium (n = 262 MDD patients; age = 36.5 ± 15.3 years; 154 (59%) female; mean response rate = 57%). Treatment response was defined as a ≥ 50% reduction in symptom severity score after 4-12 weeks post-initiation of antidepressant treatment. Structural MRI was acquired before, or < 14 days after, treatment initiation. The cortex was parcellated using FreeSurfer, from which cortical thickness and surface area were measured. We tested several machine learning pipeline configurations, which varied in (i) the way we presented the cortical data (i.e., average values per region of interest, as a vector containing voxel-wise cortical thickness and surface area measures, and as cortical thickness and surface area projections), (ii) whether we included clinical data, and the (iii) machine learning model (i.e., gradient boosting, support vector machine, and neural network classifiers) and (iv) cross-validation methods (i.e., k-fold and leave-one-site-out) we used. First, we tested if the overall predictive performance of the pipelines was better than chance, with a corrected 10-fold cross-validation permutation test. Second, we compared if some machine learning pipeline configurations outperformed others. In an exploratory analysis, we repeated our first analysis in three subpopulations, namely patients (i) from a single site, (ii) with comparable response rates, and (iii) showing the least (first quartile) and the most (fourth quartile) treatment response, which we call the extreme (non-)responders subpopulation. Finally, we explored the effect of including subcortical volumetric data on model performance. Overall, performance predicting antidepressant treatment response was not significantly better than chance (balanced accuracy = 50.5%; p = 0.66) and did not vary with alternative pipeline configurations. Exploratory analyses revealed that performance across models was only significantly better than chance in the extreme (non-)responders subpopulation (balanced accuracy = 63.9%, p = 0.001). Including subcortical data did not alter the observed model performance. Cortical structural MRI alone could not reliably predict individual pharmacotherapeutic treatment response in MDD. None of the used machine learning pipeline configurations outperformed the others. In exploratory analyses, we found that predicting response in the extreme (non-)responders subpopulation was feasible on both cortical data alone and combined with subcortical data, which suggests that specific MDD subpopulations may exhibit response-related patterns in structural data. Future work may use multimodal data to predict treatment response in MDD.

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Other Papers 【 display / non-display 】

Other Papers 【 display / non-display 】

• Assessment and risk factors for depressive mixed states

  新里輔鷹, 新里輔鷹

  日本臨床精神神経薬理学会プログラム・抄録集   33rd   2023

   

  J-GLOBAL

• Longitudinal reliability of Brain Network Marker for Major Depressive Disorder and its association with clinical status

  新里輔鷹, 新里輔鷹, 岡田剛, 吉岡利福, 吉岡利福, 山下歩, 板井江梨, 上敷領俊晴, 横山仁史, 光山祐生, 増田慶一, 川人光男, 川人光男, 山下宙人, 酒井雄希, 酒井雄希, 岡本泰昌

  日本生物学的精神医学会(Web)   45th   2023

   

  J-GLOBAL

• Can functional brain imaging techniques be used to diagnose psychiatric disorders?

  新里輔鷹, 岡田剛, 岡本泰昌

  日本生物学的精神医学会(Web)   44th   2022

   

  J-GLOBAL

• 統合失調症認知機能簡易評価尺度日本語版(BACS-J)を用いた,アルコール依存症患者の認知機能の評価

  栗原 雄大, 前上里 泰史, 新城 架乃, 石橋 孝勇, 新里 輔鷹, 甲田 宗良, 中井 美紀, 大鶴 卓, 近藤 毅

  精神神経学雑誌 ( (公社)日本精神神経学会 )  ( 2019特別号 ) S750 - S750   2019.06

   

• 抑うつ性混合状態と自閉スペクトラム症との関連

  新里 輔鷹, 栗原 雄大, 石橋 孝勇, 甲田 宗良, 中村 明文, 近藤 毅

  精神神経学雑誌 ( (公社)日本精神神経学会 )  ( 2019特別号 ) S594 - S594   2019.06

   

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Grant-in-Aid for Scientific Research 【 display / non-display 】

Grant-in-Aid for Scientific Research 【 display / non-display 】

• Quantification and biological backgrounds of depressive mixed state

  Grant-in-Aid for Scientific Research(C)

  Project Year: 2017.04  -  2021.03 

  Direct: 3,600,000 (YEN)  Overheads: 4,680,000 (YEN)  Total: 1,080,000 (YEN)