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論文
- タイトル
- タイトル（英）
- M/EEG source localization for both subcortical and cortical sources using a convolutional neural network with a realistic head conductivity model
- 参照URL
- https://researchmap.jp/noboruusuda/published_papers/51278934
- 著者
- 著者（英）
- Hikaru Yokoyama,Naotsugu Kaneko,Noboru Usuda,Tatsuya Kato,Hui Ming Khoo,Ryohei Fukuma,Satoru Oshino,Naoki Tani,Haruhiko Kishima,Takufumi Yanagisawa,Kimitaka Nakazawa
- 担当区分
- 概要
- 概要（英）
- While electroencephalography (EEG) and magnetoencephalography (MEG) are well-established noninvasive methods in neuroscience and clinical medicine, they suffer from low spatial resolution. Electrophysiological source imaging (ESI) addresses this by noninvasively exploring the neuronal origins of M/EEG signals. Although subcortical structures are crucial to many brain functions and neuronal diseases, accurately localizing subcortical sources of M/EEG remains particularly challenging, and the feasibility is still a subject of debate. Traditional ESIs, which depend on explicitly defined regularization priors, have struggled to set optimal priors and accurately localize brain sources. To overcome this, we introduced a data-driven, deep learning-based ESI approach without the need for these priors. We proposed a four-layered convolutional neural network (4LCNN) designed to locate both subcortical and cortical sources underlying M/EEG signals. We also employed a sophisticated realistic head conductivity model using the state-of-the-art segmentation method of ten different head tissues from individual MRI data to generate realistic training data. This is the first attempt at deep learning-based ESI targeting subcortical regions. Our method showed excellent accuracy in source localization, particularly in subcortical areas compared to other methods. This was validated through M/EEG simulations, evoked responses, and invasive recordings. The potential for accurate source localization of the 4LCNNs demonstrated in this study suggests future contributions to various research endeavors such as the clinical diagnosis, understanding of the pathophysiology of various neuronal diseases, and basic brain functions.
- 出版者・発行元
- 出版者・発行元（英）
- AIP Publishing
- 誌名
- 誌名（英）
- APL Bioengineering
- 巻
- 8
- 号
- 4
- 開始ページ
- 終了ページ
- 出版年月
- 2024年10月28日
- 査読の有無
- 招待の有無
- 掲載種別
- 研究論文（学術雑誌）
- ISSN
- DOI URL
- https://doi.org/10.1063/5.0226457
- 共同研究・競争的資金等の研究課題
研究者
臼田 升
(ウスダ ノボル)