一般口演（Oral）

Oral New Technologies 一般口演新技術

7月27日（土）15:20～15:35　第10会場（万代島ビル　6F　会議室） 3O-10a2-1 シンチレーションを用いた神経活動の遠隔操作 Takanori Matsubara（松原崇紀）^1,2，Takayuki Yanagida（柳田健之）³，Noriaki Kawaguchi（河口範明）³，Takashi Nakano（中野高志）⁴，Jun-ichiro Yoshimoto（吉本潤一郎）⁴，Shin-ichiro Horigane（堀金慎一郎）⁵，Shuhei Ueda（上田修平）⁵，Sayaka Takemoto-Kimura（竹本ー木村さやか）⁵，Akihiro Yamanaka（山中章弘）^1,2,6，Takayuki Yamashita（山下貴之）^1,2,6,7 ¹名古屋大環境医神経系２ ²名古屋大院医 ³奈良先端大物質創成 ⁴奈良先端大情報科 ⁵名古屋大環境医神経系１ ⁶JST, CREST ⁷JSTさきがけ Recent advances in material science in close conjunction with neuroscience have made it possible to achieve remote/wireless optogenetic control of neuronal activities in living animals. Light-sensitive proteins called opsins used for such studies are activated only by visible light, which has a low tissue penetration depth. Therefore, for stimulating a large population of neurons in the deep brain, it would be advantageous to employ up-converting phosphors which emit visible light in response to more tissue-penetrating near-infrared (NIR) light (Chen et al., Science 359: 679-684, 2018; Miyazaki et al., Cell Reports, 26: 1033-1043, 2019.). However, the tissue penetration depth of NIR is limited to several millimetres and NIR illumination can cause abrupt heat generation in body surface. To overcome these issues, we utilize inorganic scintillators that exhibit visible luminescence called scintillation when excited by X-ray. We show that a certain yellow-emitting scintillator can efficiently activate red-shifted opsins upon X-irradiation. With these scintillator/opsin combinations, we successfully activated and inhibited dopamine neurons in the ventral tegmental area of freely moving mice to wirelessly drive related behaviours under mild X-irradiation. The scintillator crystal incubated with dissociated hippocampal neurons had no negative effects on cell viability, suggesting bio-compatibility of the material. These results demonstrate the potential of scintillation to be used for distant control of cellular functions in the brain and other organ systems of living animals including humans.		7月27日（土）15:35～15:50　第10会場（万代島ビル　6F　会議室） 3O-10a2-2 皮質刺激皮質誘発電位(CCEP)における律動性反応 Takuro Nakae（中江卓郎）^1,2，Riki Matsumoto（松本理器）³，Masaya Togo（十河正弥）⁴，Hirofumi Takeyama（武山博文）⁷，Katsuya Kobayashi（小林勝哉）⁴，Akihiro Shimotake（下竹昭寛）⁵，Masao Matsuhashi（松橋眞生）⁵，Nobutaka Mukae（迎伸孝）⁸，Yukihiro Yamao（山尾幸広）¹，Takayuki Kikuchi（菊池隆幸）¹，Kazumichi Yoshida（吉田和道）^1,6，Takeharu Kunieda（國枝武治）⁶，Akio Ikeda（池田昭夫）⁵，Susumu Miyamoto（宮本享）¹ ¹京都大院医脳病態生理　脳神経外科 ²滋賀県立総合病院　脳神経外科 ³神戸大院医内科学講座　神経内科学分野 ⁴京都大院医脳病態生理　神経内科 ⁵京都大院医　てんかん運動異常生理学講座 ⁶愛媛大院医　脳神経外科 ⁷京都大院医　呼吸管理睡眠制御学講座 ⁸九州大院医　脳神経外科 Introduction: Human brain operates with oscillatory activity in different frequency bands such as mu rhythm and frontal midline theta. We observed some rhythmic pattern in the averaged waveforms in the CCEP investigation (called ""oscillatory response"" here), which is originally a method to trace connectivity by single electrical pulse stimulation. We report on the distribution of the oscillatory responses in CCEP. Methods: Subjects are 15 intractable epilepsy patients who underwent chronic implantation of subdural electrodes for presurgical evaluation. Single-pulse stimuli (0.3 ms, 8 ~ 10 mA, 1 Hz, 30 × 2 trials) were applied through two adjacent electrodes (47.1 ± 8.8 stimulus sites per patient). CCEP was recorded from all of the implanted subdural electrodes (89.6 ± 16.0 electrodes per patient). An oscillatory response was detected by visual inspection on all of the averaged waveform with a constant criteria. If rhythms with different frequency were observed in the same electrode, only the largest one was collected. Results: Totally, 2370 oscillatory responses (5.3 %) were detected in 45129 observations in 15 subjects, including 1087 alpha-band, 908 beta-band and 1235 theta-band responses. Theta rhythm was observed predominantly in the frontal lobe. Alpha rhythm was observed frequently in the postcentral gyrus, the inferior temporal gyrus and the precuneal cortex. Beta rhythm was often observed around the precentral gyrus. When we compare the frequency of oscillatory responses with that of the resting rhythm at each electrode, alpha around the postcentral gyrus and beta around the precentral gyrus showed the concordance between the two. The frequency and localization was consistent with that of mu rhythm. Time-frequency representation analysis revealed that single pulse stimulation seemed to evoke the mu rhythm with its phase locked to the stimulus. Conclusion: Oscillatory CCEP responses were evoked predominantly in the perirolandic area, the precuneal cortex and the frontal lobe. Especially for the perirolandic area, we observed phase resetting of the physiological oscillation (mu rhythm) by exogenous input. This unique oscillatory CCEP may reveal new functional aspects which traditional CCEP evaluation could not assess. Although its sensitivity and specificity was not high, combining this finding with other intrinsic brain activities would be of clinical use for functional brain mapping.

7月27日（土）15:50～16:05　第10会場（万代島ビル　6F　会議室） 3O-10a2-3 高汎用性ハイブリッド手法による行動軌跡からの状態推定と特徴抽出 Kotaro Kimura（木村幸太郎）^1,2，Shuhei J. Yamazaki（山崎修平）^1,2，Takuma Kitanishi（北西卓磨）³，Ken Yoda（依田憲）⁴，Akinori Takahashi（高橋晃周）⁵，Azusa Kamikouchi（上川内あずさ）⁶，Shizuko Hiryu（飛龍志津子）⁷，Takuya Maekawa（前川卓也）⁸ ¹名古屋市大院システム自然科学 ²大阪大院理生物科学 ³大阪市大医 ⁴名古屋大院環境 ⁵極地研 ⁶名古屋大院理 ⁷同志社大生命医科学 ⁸大阪大院情報科学 Animal behavior is the final and integrated output of the brain activity. Thus, recording and analyzing behavior is critical to understand the underlying brain function. While recording animal behavior has become easier than ever with the development of compact and inexpensive devices, detailed behavioral data analysis requires sufficient previous knowledge and/or high content data such as video images of animal postures, which makes it difficult for most of the animal behavioral data to be efficiently analyzed to understand brain function. Here, we report a versatile method using a hybrid supervised/unsupervised machine learning approach to efficiently estimate behavioral states and to extract important behavioral features only from low-content animal trajectory data. As proof of principle experiments, we analyzed trajectory data of worms, fruit flies, rats, and bats in the laboratories, and penguins and flying seabirds in the wild, which were recorded with various methods and span a wide range of spatiotemporal scales—from mm to 1000 km in space and from sub-seconds to days in time. We estimated several states during behavior and comprehensively extracted characteristic features from a behavioral state and/or a specific experimental condition. Physiological and genetic experiments in worms revealed that the extracted behavioral features reflected specific neural or gene activities. Thus, our method provides a versatile and unbiased way to extract behavioral features from simple trajectory data to understand brain function.		7月27日（土）16:05～16:20　第10会場（万代島ビル　6F　会議室） 3O-10a2-4 行動状態依存的な皮質ネットワークダイナミクス解析のためのマウス用バーチャルリアリティシステム開発 Nobuhiro Nakai（中井信裕），Yukiko Sekine（関根友紀子），Masaaki Sato（佐藤正晃），Toru Takumi（内匠透）理研CBS 精神生物学 The cortex consists of functional network modules which display dynamic activity patterns elicited intrinsically or by external stimuli. In behaving animals, multimodal sensory cues are thought to be integrated in the cortex and to contribute to incentive for action or volition. However, the underlying flow of information processed in the cortex is still unclear at the network level. Currently, the cortical functional network is studied mainly in sedated resting animals, for example, by functional MRI, but this technique is difficult to be used for actively behaving animals. To overcome this limitation, we monitored the cortical network dynamics using transcranial cortex-wide calcium imaging in mice behaving in virtual reality (VR). In our VR system, a mouse was head-fixed on a spherical treadmill and interactive VR scenes coupled to the rotation of the treadmill were projected onto a semi-translucent spherical screen. The subject mouse moved in an open field-like virtual arena. The behavior in VR was simultaneously recorded with cortical activity imaged by calcium imaging. Pair-wise correlation of activity computed between functional cortical modules was used to represent the state of cortical network dynamics. We found that highly-patterned cortical network dynamics co-occurred with transitions between two distinct behavioral states (i.e. from resting to walking and vice versa) at a temporal scale of seconds. The network patterns could be classified into the two corresponding behavioral states with 90% accuracy by using a support vector machine-based machine learning classifier, indicating that the patterns of network dynamics provide cortical fingerprints that can predict animal's behavioral states. Our study demonstrates that highly-patterned cortical network dynamics in mice has high predictive power for their behavioral states, as revealed by our machine learning-based analytical approach.