琉球大学 · 医学部 · 講師

研究キーワードresting-state fMRI・major depressive disorder・cortical thickness・treatment response prediction・normative modeling・psychiatric classification・social communication screening・suicidal behavior

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• Dissecting heterogeneity in cortical thickness abnormalities in major depressive disorder: a large-scale ENIGMA MDD normative modelling study.2025 · 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
• The 12-item self-report Questionnaire for Difficulty in Social Communication as a simultaneous prescreening of autism spectrum and social anxiety2025 · 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.
• Predicting Antidepressant Treatment Response From Cortical Structure on MRI: A Mega-Analysis From the ENIGMA-MDD Working Group2025 · 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.
• Comprehensive evaluation of pipelines for classification of psychiatric disorders using multi-site resting-state fMRI datasets2025 · 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

• 縦断的MRIによる混合性うつ病及び双極性障害の評価と合理的治療方針の確立2022 · 若手研究
• 抑うつ性混合状態の定量的診断と生物学的背景の検討2019 · 本年度は、研究者らが開発した抑うつ性混合状態（depressive mixed state: DMX）の定量的な自記式評価票（DMX-12）を公表し、その症候学的構造を明らかにするとともに、本評価票を指標として一般的なうつ病エピソードにみられるDMXの実態を明らかにした（Shinzato et al, Neuropsychiatr Dis Treat, 2019）。DMX-12は「内発的な不安定さ」「脆弱な応答性」「破壊的感情/行動」の3つの症候クラスターにより構成され、うつ病の重症度が高く潜在的に双極性を有する若年患者がDMXを呈しやすいことが示唆された。破壊的感情/行動クラスターはカテゴリカル診断である混合性うつ病（mixed depression：MD）や混合性の特徴（mixed features specifier：MF）の識別に有用であった。 特に、receiver operating characteristic（ROC）解析により、DMX-12の中の過剰反応、内的緊張、思考促迫・混雑、衝動性、易刺激性、攻撃性、危険行為、不快気分の8症状の総スコアが同一のカットオフ値をもってMD、MFを高い感度と特異性で識別できることから、一定の重症度を持つDMXのスクリーニングに有用となる可能性が示唆されており、現在、これらの成果を投稿中である。 また、DMX-12を用いてDMXと自閉スペクトラム症（autism spectrum disorder：ASD）および自殺行動リスクとの関連についても検討し、MDはASDや自殺関連行動との関連が深く、ASDのうつ病エピソードにおいては「転導性」「気分易変」「衝動性」などの内発的な不安定さを特徴とすることが判明した（新里他，日本精神神経学会，2019）。

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