| Presentation | 2021-03-04 Evaluation of effect of source noise on magnetoencephalography source estimation using a structured sparse model Kai Miyazaki, Shun Nirasawa, Kazuaki Akamatsu, Yoichi Miyawaki, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | Magnetoencephalography (MEG) is a method to acquire human brain activity at a high temporal resolution, but its spatial resolution is insufficient to examine active cortical locations. Previous studies have shown that source estimation methods identify active cortical locations with reasonable accuracy and are thus used in various applications of human functional neuroimaging. However, our recent studies suggest that the combination of the source estimation and multivariate pattern analysis (for example, neural decoding) produces “information spreading,” a false positive phenomenon in terms of the identification of informative cortical areas. To resolve this problem, we proposed the application of grouped automatic relevance determination (gARD) that implements functional parcellation of the human brain, showing its better performance to analyze task-relevant brain activity than conventional source estimations. In this study, we further examined the effect of task-irrelevant components such as spontaneous activity on source estimation accuracy and the extent of suppression of information spreading. Results showed that gARD achieved better performance in suppressing information spreading and identifying informative cortical locations while showing source estimation performance equivalent to the conventional method under the noise condition close to real data. These results suggest that our method might be useful for identifying task-relevant source activity and analyzing the corresponding information representation under a mixture of a variety of task-irrelevant cortical activity. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | MEG source estimation / structured sparse model / gARD / information spreading / source noise |
| Paper # | NC2020-56 |
| Date of Issue | 2021-02-24 (NC) |
| Conference Information | |
| Committee | NC / MBE |
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| Conference Date | 2021/3/3(3days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Neuro Computing, Medical Engineering, etc. |
| Chair | Kazuyuki Samejima(Tamagawa Univ) / Takashi Watanabe(Tohoku Univ.) |
| Vice Chair | Rieko Osu(Waseda Univ.) / Ryuhei Okuno(Setsunan Univ.) |
| Secretary | Rieko Osu(NTT) / Ryuhei Okuno(ATR) |
| Assistant | Ken Takiyama(TUAT) / Nobuhiko Wagatsuma(Toho Univ.) / Akihiro Karashima(Tohoku Inst. of Tech.) / Jun Akazawa(Meiji Univ. of Integrative Medicine) |
| Paper Information | |
| Registration To | Technical Committee on Neurocomputing / Technical Committee on ME and Bio Cybernetics |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Evaluation of effect of source noise on magnetoencephalography source estimation using a structured sparse model |
| Sub Title (in English) | |
| Keyword(1) | MEG source estimation |
| Keyword(2) | structured sparse model |
| Keyword(3) | gARD |
| Keyword(4) | information spreading |
| Keyword(5) | source noise |
| 1st Author's Name | Kai Miyazaki |
| 1st Author's Affiliation | The University of Electro-Communications(UEC) |
| 2nd Author's Name | Shun Nirasawa |
| 2nd Author's Affiliation | The University of Electro-Communications(UEC) |
| 3rd Author's Name | Kazuaki Akamatsu |
| 3rd Author's Affiliation | The University of Electro-Communications(UEC) |
| 4th Author's Name | Yoichi Miyawaki |
| 4th Author's Affiliation | The University of Electro-Communications(UEC) |
| Date | 2021-03-04 |
| Paper # | NC2020-56 |
| Volume (vol) | vol.120 |
| Number (no) | NC-403 |
| Page | pp.pp.77-82(NC), |
| #Pages | 6 |
| Date of Issue | 2021-02-24 (NC) |