| 特別講演 |
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| 2022年6月30日 14:00～15:00 沖縄コンベンションセンター 劇場棟 第1会場
座長：山中 章弘（名古屋大学環境医学研究所）
1SL01
Rapid eye movement sleep is initiated by basolateral amygdala dopamine signaling in mice
*櫻井 武(1)
1. 筑波大学医学医療系/国際統合睡眠医科学研究機構
*Takeshi Sakurai(1)
1. Faculty of Medicine/WPI-IIIS
Keyword: rapid eye movement sleep, amygdala, dopamine
The sleep cycle is characterized by alternating non-rapid eye movement (NREM) and rapid eye movement (REM) sleeps. The mechanisms by which this cycle is generated have been incompletely understood. Monoaminergic neurons in the locus coeruleus, the tuberomammillary nucleus, and the dorsal raphe share similar firing patterns, with rapid firing during wakefulness, slow and intermittent firing during NREM sleep, and almost no firing during REM sleep. In contrast, dopaminergic (DA) neurons in the ventral tegmental area (VTA) demonstrated different firing patterns from monoaminergic neurons, and DA was absent in most commonly used models of sleep/wakefulness regulation.
We found that a transient increase of dopamine (DA) in the basolateral amygdala (BLA) during NREM sleep terminates NREM sleep and initiates REM sleep. DA acted on dopamine receptor D2 (Drd2)-expressing neurons in the BLA to induce the NREM-to-REM transition. Tracing studies suggest that DAVTA populations sending projections to the BLA and NAc are distinct. The transient DA signaling led to long-lasting activation of the amygdala, which might be necessary for the stability of REM sleep.
This mechanism also plays a role in the cataplectic attacks-a pathological intrusion of REM sleep into wakefulness-in narcoleptics. These results show a critical role of DA signaling in the BLA in initiating REM sleep and provide a neuronal basis for sleep cycle generation.
Neuroimaging and intracranial recording studies have shown amygdala activation during REM sleep in humans, and in narcoleptic patients, cataplexy is often occurred by positive emotion, and amygdala activity increases in cataplexy. We found that DA levels in the BLA transiently increased prior to cataplexy attacks in narcoleptic mice, but not in wild-type mice. Positive emotion might induce a transient increase of DA in the BLA in narcoleptics, but not in wild type, mimicking the DA dynamics that trigger NREM-to-REM transitions. These observations showed the importance of transient DA signaling in the BLA in gating REM sleep by disinhibiting amygdala neurons that send innervations to REM-regulatory regions. This study might shed light on the mechanism of cataplexy, as well as on the pathophysiology of REM sleep disorders, such as REM sleep behavior disorder, and on diseases involving abnormal DA signaling, such as Parkinson's disease. | | 2022年7月2日 14:00～15:00 沖縄コンベンションセンター 劇場棟 第1会場
座長：村松 里衣子（国立精神・神経医療研究センター神経研究所）
3SL01
突然変異動物を用いた脳神経系の発生および疾患の研究
Research on brain development and disorders by utilizing spontaneous mutant animals
*星野 幹雄(1)
1. 国立精神神経医療研究センター 神経研究所
*Mikio Hoshino(1)
1. National Center of Neurlogy and Psyochiatry, National Instiute of Neuroscience
Keyword: brain, development, mutant, epilepsy
The central nervous system is a complex and elaborate structure that is created by precise genetic programming. If the developmental process is impaired, it can cause or lead to brain dysfunction such as mental illnesses and epilepsy. However, the molecular mechanism of normal development as well as the pathology remain largely unexplored. One research method is to analyze knock-out mice for certain genes that seem to be important. However, in those cases, we can only examine genes whose functions are predicted to some extent. On the other hand, studies using spontaneous mutants that exhibit phenotypes of interest may identify unpredicted genes, which occasionally leads to unexpected breakthroughs.
We previously analyzed a mouse mutant that lacked the cerebellum and identified the Ptf1a (Pancreas transcription factor 1a) as a causative gene, which had been previously known as an essential gene for pancreas formation. After extensive analyses, we found that PTF1a protein acts as a fate determinant for cerebellar inhibitory neurons. At that time, the mechanism of neuronal fate determination in the cerebellum was barely known, therefore the study of this mutant led to a breakthrough. Moreover, we revealed that PTF1a is also involved in the development of climbing fiber neurons in the inferior olivary nucleus, some neurons in the cochlear nucleus, and also in the sex differentiation of the brain.
Recently, we have been working on Ihara Epileptic Rat (IER), a mutant rat model of epilepsy. Neither the causative gene nor the mechanism by which IER causes epileptic seizures had been clarified until recently. By the linkage analyses followed by sequencing, we identified Down syndrome cell adhesion molecule-like 1(Dscaml1) as the responsible gene. We clarified the pathological mechanism for seizures in IER, and found a candidate causative DSCAML1 mutation in human epilepsy. The mutated DSCML1 protein is non-functional because it cannot localize at the cell membrane probably due to its misfolding. Knock-in mice that mimic the human-type mutation in Dscaml1 exhibit symptoms similar to those of IER and the epilepsy patient. Treatment with some pharmacological chaperons to restore the misfolding of the mutant protein will also be assessed in this talk. Thus, research on spontaneous mutant animals is fun, interesting and worth trying. | | 2022年7月2日 15:00～16:00 沖縄コンベンションセンター 劇場棟 第1会場
座長：銅谷 賢治（沖縄科学技術大学院神経計算ユニット）
3SL02
Toward Embodied Intelligence with Deep Predictive Learning and Real Robots
*尾形 哲也(1)
1. 早稲田大学／産業技術総合研究所
*Tetsuya Ogata Ogata(1)
1. AIST-Waseda University
Keyword: Deep Predictive Learning, Embodied Intelligence, AIREC, Predictive Coding
Deep learning has become a very powerful tool in image processing and natural language processing and so on. However, there are still significant barriers for the real-world applications such as robot systems. One of the natural robotic applications of deep learning is "image recognition". For example, it can recognize the grasping area of various objects. However, the burden of labeling is significant and, of course, image learning does not guarantee successful object grasping because it depends on its physical properties such as friction, center of gravity and so on. Many researchers are focusing on the deep reinforcement learning. However, trial-and-error learning in the real robots is extremely costly. The Sim2Real approach is one of the solutions of this problem, but it is not easy either. Our “deep predictive learning” is based on the premise that learning of real-world systems is incomplete. It modifies the internal state of the model and generates the motions to the real environment to reduce the prediction error in real time. This approach is essential for autonomous and embodied agents and would be related to the concept of “predictive coding.” The authors used this deep predictive learning to realize a towel folding task with a humanoid robot in 2016. Based on these results, we have conducted joint research with several industrial companies, and some of them have been commercialized. Finally, I will introduce our moonshot project supported by the Japan Cabinet Office of the smart robot named “AIREC (AI-driven Robot for Embrace and Care).” Generally, robots can perform various tasks according by changing the programs, but in current real application, dedicated hardware needs to be designed for each task. In the future, the intelligent technologies such as deep learning is expected to extend the original generalization capabilities of robots. For example, the individual functions of a smartphone are not as powerful as those of a "dedicated" device. However, packing these functions into a single device created novel values in the world. We plan to promote this project by introducing our deep predictive learning framework to train the general-purpose robot, AIREC with various tasks. | | 2022年7月3日 11:00～12:00 沖縄コンベンションセンター 劇場棟 第1会場
座長：森口 茂樹（東北大学 大学院薬学研究科・医薬品開発研究センター）
4SL01
精神疾患解明のための多階層シナプス研究
Multi-scale synaptic analysis for psychiatric research
*林 朗子(1)
1. 理化学研究所
*Akiko Hayashi-Takagi(1)
1. RIKEN CBS
Keyword: dendritic spine, schizophrenia, 2-photon imaging
The spatiotemporal sequence of neuronal firing is crucial for information processing, and how thousands of synaptic inputs drive the firing remains a central question in neuroscience. Despite the accumulating evidence that has suggested a change in synaptic density and strength as possible pathophysiology of various psychiatric disorders, it is unknown whether the synaptopathy is underlying the pathogenesis of the disorders or a secondary consequence. In other words, it is entirely unclear what synaptic changes give rise to what alternation of neuronal computation and subsequent behavioral outcomes. Thus, we interrogated mouse models for schizophrenia by multi-scale synapse analyses, in which dendrite and somatic events are simultaneously assessed during precise stimulation of identified spines. Together with the behavioral analysis, the computational modeling, and the quantification of the human neurons from schizophrenia, we offer a whole new concept for synaptic integration, dendritic computation, and network dynamics as pathogenesis for schizophrenia and related disorders. The second half of my talk is the development of a novel synaptic tool and a microscope for it. Because, no matter how beautifully we can illuminate the spine morphology and how accurately we can quantify the synaptic integration, the links between synapse and brain function remain correlational. Thus, we established AS-PaRac1, which is unique not only because it can specifically label and manipulate the recently potentiated dendritic spine. With use of AS-PaRac1, we developed an activity-dependent simultaneous labeling of the presynaptic bouton and the potentiated spines to establish "functional connectomics" in a synaptic resolution. When we applied this new imaging method for psychiatric model mice, we identified a completely new functional neural circuit in the model animals. This novel tool of "functional connectomics" with multi-scale and multi-modal synaptic analysis could open up new areas of psychiatric research. | |
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