Iwama, Seitaro

写真a

Affiliation

Faculty of Science and Technology, Department of Biosciences and Informatics (Yagami)

Position

Assistant Professor (Non-tenured)/Research Associate (Non-tenured)/Instructor (Non-tenured)

Related Websites
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Career 【 Display / hide 】

  • 2021.04
    -
    2023.03

    Japan Society for the Promotion of Science, 特別研究員（DC1）

  • 2021.08
    -
    2023.03

    Graduate School of Science and Technology, Keio University, Research Assistant

  • 2023.04
    -
    Present

    Keio University, Faculty of Science and Technology Department of Biosciences and Infomatics, Assistant Professor (Non-tenured)

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Academic Background 【 Display / hide 】

  • 2015.04
    -
    2019.03

    Keio University, Faculty of Science and Technology, Department of Biosciences and Infomatics

  • 2019.04
    -
    2020.09

    Keio University, Graduate School of Science and Technology, 基礎理工学専攻

  • 2020.09
    -
    2023.03

    Keio University, Graduate School of Science and Technology, 基礎理工学専攻

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Papers 【 Display / hide 】

  • Neurofeedback-induced desynchronization of sensorimotor rhythm elicits pre-movement downregulation of intracortical inhibition that shortens simple reaction time in humans: a double-blind sham-controlled randomized study

    Yoshihito Muraoka, Seitaro Iwama, Junichi Ushiba

    Imaging Neuroscience Accepted 2024.10

    Accepted

  • EEG decoding with spatiotemporal convolutional neural network for visualization and closed‐loop control of sensorimotor activities: A simultaneous EEG‐fMRI study

    Seitaro Iwama, Shohei Tsuchimoto, Nobuaki Mizuguchi, Junichi Ushiba

    Human Brain Mapping (Wiley)  45 ( 9 )  2024.06

    Lead author, Accepted,  ISSN  1065-9471

    　View Summary

    Abstract

    Closed‐loop neurofeedback training utilizes neural signals such as scalp electroencephalograms (EEG) to manipulate specific neural activities and the associated behavioral performance. A spatiotemporal filter for high‐density whole‐head scalp EEG using a convolutional neural network can overcome the ambiguity of the signaling source because each EEG signal includes information on the remote regions. We simultaneously acquired EEG and functional magnetic resonance images in humans during the brain‐computer interface (BCI) based neurofeedback training and compared the reconstructed and modeled hemodynamic responses of the sensorimotor network. Filters constructed with a convolutional neural network captured activities in the targeted network with spatial precision and specificity superior to those of the EEG signals preprocessed with standard pipelines used in BCI‐based neurofeedback paradigms. The middle layers of the trained model were examined to characterize the neuronal oscillatory features that contributed to the reconstruction. Analysis of the layers for spatial convolution revealed the contribution of distributed cortical circuitries to reconstruction, including the frontoparietal and sensorimotor areas, and those of temporal convolution layers that successfully reconstructed the hemodynamic response function. Employing a spatiotemporal filter and leveraging the electrophysiological signatures of the sensorimotor excitability identified in our middle layer analysis would contribute to the development of a further effective neurofeedback intervention.

  • Rapid-IAF: Rapid Identification of Individual Alpha Frequency in EEG Data Using Sequential Bayesian Estimation

    Seitaro Iwama, Junichi Ushiba

    IEEE Transactions on Neural Systems and Rehabilitation Engineering (Institute of Electrical and Electronics Engineers (IEEE))  32   915 - 922 2024

    Lead author, Accepted,  ISSN  1534-4320

  • High-density scalp electroencephalogram dataset during sensorimotor rhythm-based brain-computer interfacing

    Seitaro Iwama, Masumi Morishige, Midori Kodama, Yoshikazu Takahashi, Ryotaro Hirose, Junichi Ushiba

    Scientific Data 10 ( 385 ) 1 - 7 2023.06

    Accepted

    　View Summary

    Real-time functional imaging of human neural activity and its closed-loop feedback enable voluntary control of targeted brain regions. In particular, a brain-computer interface (BCI), a direct bridge of neural activities and machine actuation is one promising clinical application of neurofeedback. Although a variety of studies reported successful self-regulation of motor cortical activities probed by scalp electroencephalogram (EEG), it remains unclear how neurophysiological, experimental conditions or BCI designs influence variability in BCI learning. Here, we provide the EEG data during using BCIs based on sensorimotor rhythm (SMR), consisting of 4 separate datasets. All EEG data were acquired with a high-density scalp EEG setup containing 128 channels covering the whole head. All participants were instructed to perform motor imagery of right-hand movement as the strategy to control BCIs based on the task-related power attenuation of SMR magnitude, that is event-related desynchronization. This dataset would allow researchers to explore the potential source of variability in BCI learning efficiency and facilitate follow-up studies to test the explicit hypotheses explored by the dataset.

  • Thirty-minute motor imagery exercise aided by EEG sensorimotor rhythm neurofeedback enhances morphing of sensorimotor cortices: a double-blind sham-controlled study.

    Midori Kodama*, Seitaro Iwama*, Masumi Morishige, Junichi Ushiba

    Cerebral cortex (New York, N.Y. : 1991) (Oxford University Press (OUP))  in press ( 11 ) 6573 - 6584 2023.01

    Lead author, Accepted,  ISSN  1047-3211

    　View Summary

    Neurofeedback training using electroencephalogram (EEG)-based brain-computer interfaces (BCIs) combined with mental rehearsals of motor behavior has demonstrated successful self-regulation of motor cortical excitability. However, it remains unclear whether the acquisition of skills to voluntarily control neural excitability is accompanied by structural plasticity boosted by neurofeedback. Here, we sought short-term changes in cortical structures induced by 30 min of BCI-based neurofeedback training, which aimed at the regulation of sensorimotor rhythm (SMR) in scalp EEG. When participants performed kinesthetic motor imagery of right finger movement with online feedback of either event-related desynchronisation (ERD) of SMR magnitude from the contralateral sensorimotor cortex (SM1) or those from other participants (i.e. placebo), the learning rate of SMR-ERD control was significantly different. Although overlapped structural changes in gray matter volumes were found in both groups, significant differences revealed by group-by-group comparison were spatially different; whereas the veritable neurofeedback group exhibited sensorimotor area-specific changes, the placebo exhibited spatially distributed changes. The white matter change indicated a significant decrease in the corpus callosum in the verum group. Furthermore, the learning rate of SMR regulation was correlated with the volume changes in the ipsilateral SM1, suggesting the involvement of interhemispheric motor control circuitries in BCI control tasks.

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

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Reviews, Commentaries, etc. 【 Display / hide 】

  • ブレイン・マシン・インターフェースからみた脳科学とリハビリテーション

    岩間 清太朗, 牛場 潤一

    理学療法-臨床・研究・教育 30 ( 1 ) 3 - 6 2023.09

    Lead author

  • Motor Learning and ʻObject-based Learningʼ ̶from the Perspective of Neuroscience̶ ̶

    Seitaro Iwama*, Masatoshi Kokubo*, Junichi Ushiba, (*: equally contributed)

    The KeMCO Review 1 2023.04

    Lead author

  • Brain-machine interface and neurorehabilitation

    Junichi Ushiba, Seitaro Iwama

    医学のあゆみ 275 ( 12, 13 ) 1240 - 1245 2020.12

  • Mechanisms, Evidences, and Meta-analysis in Brain-Machine Interface Based Motor Exercise

    Junichi Ushiba, Seitaro Iwama, Meigen Liu

    The Japanese Journal of Rehabilitation Medicine (Japanese Association of Rehabilitation Medicine)  57 ( 10 ) 956 - 964 2020.10

    ISSN  1881-3526

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

  • 次世代先端分野探索研究(新任者研究推進費)

    2023.06
    -
    2024.03

    慶應義塾先端科学技術研究センター, No Setting

  • 感覚運動ネットワークの再編成を誘導する標的定位型ニューロフィードバック法の開発

    2021.04
    -
    2024.03

    日本学術振興会, 科学研究費助成事業 特別研究員奨励費, 特別研究員奨励費, No Setting

    　View Summary

    本研究の目的は、運動関連脳領域の活動パタンから同定される感覚運動ネットワークを標的とした神経機能修飾技術の概念実証である。上肢運動機能に関連する脳内ネットワークの機能変化を誘導するニューロフィードバック法を開発するため、本年度は非侵襲な脳活動計測法である頭皮脳波から運動に関する情報のデコーディング技術について検討を進めた。
    半球間の位相同期性が感覚運動処理過程におけるひとつの介入焦点であることを、文献調査および今年度取得した健常成人30名のデータから見出した。また、補足運動野は従前の生理学研究から、運動計画の出力と両手運動の制御への関与が報告されている。この領域の興奮性と、接続する領域である一次運動野を一過的に調整し、その後に生じる行動課題パフォーマンスの変化を検討可能と着想した。
    そこで、不安定な両手運動の代表例である逆位相の両手運動に着目し、実験系の構築と予備検討を実施した。逆位相とは右手と左手で異なる指を動員することを指し、半球間の干渉により自発的に同じ指を動員する順位相へ転移する。ネットワークの再編成にともない、両手の独立性が向上するかを検証するため、行動学的に指の運動を記録するためのアクションカメラ映像、キーボードの入力タイミング記録を頭皮脳波計測下で行う実験系を構築した。指のタッピング運動に起因する体動を最小限にし、信号品質を担保するため、あごのせ台や体動に由来する信号を効果的に除去する独立成分分析を導入した。これにより、ハードウェアとソフトウェア、2つの観点から信号品質を改善するアプローチを実施したため、複数の被験者で安定的に行動課題中の頭皮脳波を計測し、脳波を実時間処理しフィードバックするシステムの構築が完了した。

  • Ushioda Memorial Fund (The Keio University Doctorate Student Grant-in-Aid Program)

    2021.04
    -
    2022.03

    Keio University, Principal investigator

  • Brain-Machine Interfaceを用いたヒト-機械相互学習過程の評価

    2020.04
    -
    2021.03

    2020年度AIPチャレンジ, Principal investigator

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Awards 【 Display / hide 】

  • 若手研究奨励賞

    2022.08, Motor Control 研究会

  • 奨励表彰（第8回サイエンス・インカレ）

    2019.03, 文部科学省

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Courses Taught 【 Display / hide 】

  • TOPICS IN BIOSCIENCES AND INFORMATICS 2

    2024

  • MATHEMATICS FOR LIFE SCIENCES

    2024

  • LABORATORY IN SCIENCE

    2024

  • BASIC LABORATORY COURSE IN BIOSCIENCES

    2024

  • ADVANCED LABORATORY COURSE IN BIOSCIENCES AND INFORMATICS B

    2024

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Courses Previously Taught 【 Display / hide 】

  • Mathematics for life sciences

    Keio University

    2023.09
    -
    Present

  • Comprehensive exercise for biosciences & informatics

    Keio University

    2023.09
    -
    Present

  • Laboratory in science

    Keio University

    2023.04
    -
    Present

  • Advanced laboratory course in biosciences and informatics

    Keio University

    2023.04
    -
    Present

  • Topics in biosciences and informatics

    Keio University

    2023.04
    -
    Present

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