Special Session
My laboratory investigates social cognition and emotion from psychological and neurobiological perspectives. These processes are of utmost importance in our everyday lives, and also prominently dysfunctional in many psychiatric disorders. One focus of our work has been to understand autism from a multimodal perspective. I will present our ongoing work using eyetracking, fMRI, and single-unit recordings in autistic people. Some of these studies can now be extended to much larger samples to investigate individual differences, for instance using webcam-based eyetracking over the internet. We hope that this work will not only inform mechanisms behind social cognition, but eventually contribute to diagnostic criteria and permit a more biologically-based classification of psychiatric illness.
References :
1. Idiosyncratic Brain Activation Patterns Are Associated with Poor Social Comprehension in Autism
2. Deconstructing Theory-of-Mind Impairment in High-Functioning Adults with Autism
3. Personality beyond taxonomy
4. Single-Neuron Correlates of Atypical Face Processing in Autism
5. Atypical gaze patterns in autistic adults are heterogeneous across but reliable within individuals
山田洋 (筑波大学 医学医療系)
Hiroshi Yamada (Tsukuba University, Institute of Medicine)
[Website]
経済学的な選択行動を説明する生物学的なモデルの構築
Constructing a biologically viable model that explains economic choice
Humans economic choice has traditionally been widely examined in the field of economics. In the field, prospect theory explains the typical cognitive characteristics related to human economic choice, which provides a mathematical model to objectively evaluate human’s reward valuations under risk. However, how single neuron activity processes this economic decision-makings remained still unclear. Here, I explain our research we have conducted using macaque monkeys, aiming to construct a neural prospect theory model. First, I explain that we examined how similar decision making behaviors between macaque monkeys and humans by applying prospect theory model to their gambling behavior, occasionally with reinforcement learning model (Reference 1). Then, I explain our study that elucidated the neural mechanism behind the prospect theory model in monkeys (Reference 2). In our experiment, we measured neuronal activity in reward circuitry, the frontal and striatum regions, and found that neural signals were correlated with the subjective valuation of gamble as predicted by prospect theory model. Neurons in various regions in the reward circuitry process information that reflects subjective valuation of gamble in a distributed manner. Moreover, we conducted decoding analysis that mathematically combines neuronal activity patterns observed in various areas of the reward system, and successfully replicated the monkey’s internal subjective valuation of gamble (reward gain and reward probability) predicted from monkey’s choice behavior. Thus, our neural and behavioral model provides a unified theoretical and neurobiological framework that explain economic choice in primates.
References :
１． Dynamic prospect theory: Two core decision theories coexist in the gambling behavior of monkeys and humans. Tymula A, Wang X, Imaizumi Y, Kawai T, Kunimatsu J, Matsumoto M, Yamada H*. Sci Adv. 2023 May 19;9(20):eade7972. doi: 10.1126/sciadv.ade7972.
２． A neuronal prospect theory model in the brain reward circuitry. Imaizumi Y, Tymula A, Tsubo Y, Matsumoto M, Yamada H*. Nat Commun. 2022 Oct 4;13(1):5855.
Minmin Luo (Chinese Institute for Brain Research, Beijing)
[Website]
A Corticoamygdala Circuit for Reward Devaluation
Reward devaluation adaptively controls reward intake. It has been elusive how cortical circuits causally encode reward devaluation in healthy and depressed states. Here I will present our recent findings that that the neural pathway from the anterior cingulate cortex (ACC) to the basolateral amygdala (BLA) controls reward devaluation and depression using a dynamic inhibition code. Fiber photometry and imaging of ACC pyramidal neurons reveal reward inhibition, which attenuates during the satiation process and becomes more blunted in depression mice. Ablating or inhibiting these neurons desensitizes reward devaluation, causes reward intake increase and ultimate obesity, and ameliorates depression, whereas activating the cells sensitizes reward devaluation, suppresses reward consumption, and produces depression-like behaviors. Among many anatomically defined ACC neuron subpopulations, the BLA-projecting one bidirectionally controls reward devaluation and depression-like behaviors. Our data thus indicate that a corticoamygdalar circuit encodes reward devaluation via blunted inhibition, and suggests enhanced inhibition of this circuit as a therapeutic approach to treating depression.
References :
1. Yuan, Z., Qi, Z., Wang, R., Cui, Y., An, S., Wu, G., … & Luo, M. (2023). A corticoamygdalar pathway controls reward devaluation and depression using dynamic inhibition code. Neuron. https://www.cell.com/neuron/pdf/S0896-6273(23)00633-5.pdf
Topic Session
山田真希子（国立研究開発法人量子科学技術研究開発機構量子生命科学研究所）
Makiko Yamada (Institute for Quantum Life Science, National Institutes for Quantum Science and Technology)
[Website]
ポジティブ・イリュージョンの脳メカニズムと身体性：ウェルビーイングへの影響の探索
Understanding Positive Illusions: Exploring Brain Mechanisms and Embodiment for Enhanced Well-Being
Cognitive biases, encompassing cognitive distortions and illusions, have been the subject of extensive research within cognitive science, social psychology, and behavioral economics. These biases often give rise to what is commonly termed “irrationality” in behavioral economics, manifesting as flawed judgments and illogical reasoning. However, it’s essential to acknowledge that cognitive biases also exhibit adaptive qualities, contributing to self-serving interpretations that can promote mental and physical well-being.
In this symposium, my presentation will delve into the realm of “positive illusions,” which represent favorably biased self-perceptions. I will offer a comprehensive examination of their psychological effects, exploring the intricate interplay between positive illusions, embodied cognition, and the underlying brain functional mechanisms that shape our perceptions, thoughts, and well-being.
浅井智久(株式会社国際電気通信基礎技術研究所，認知神経科学研究室)
Tomohisa Asai (ATR, Department of Cognitive Neuroscience)
[Website]
ニューロフィードバック学習が神経活動の幾何学的構造を明らかにする
Neurofeedback reveals neural geometry
Recent neurotechnology has developed various methods for neurofeedback (NF), in which participants observe their own neural activity to be regulated in an ideal direction. Since NF training is not always easy for participants, a learnable protocol is required. Firstly, I introduce our EEG-NF systems which detect global neural states (microstate templates) in real-time. The results clearly indicate their improved learnability for participants. Secondly, how participants could learn through the “externalizing machine” is a critical question of interest. Our results imply a neural geometry in which participants’ neuro-cognitive state is moving. We have tried to depict a neural manifold that represents the EEG dynamics. Finally, the above-mentioned policy could be applicable to fMRI as long as both share the same neural source. The neural manifold in fMRI, samely defined in EEG, produces on-manifold attractors as well (polarized pair states on antipodal points). These results emphasize the dynamic nature of the human brain in terms of the mind-brain problem.
References:
1. Asai, T., Kashihara, S., Chiyohara, S., Hiromitsu, K., & Imamizu, H. (2023).
Spatio-temporal “global” neurodynamics of the human brain in continuous and discrete picture: Simple statistics meet on-manifold microstates as multi-level cortical attractors. bioRviv.
doi: https://doi.org/10.1101/2023.07.13.548951
https://www.biorxiv.org/content/10.1101/2023.07.13.548951v2.article-metrics
Asai, T., Hamamoto, T., Kashihara, S., & Imamizu, H. (2022). Real-Time Detection and Feedback of Canonical Electroencephalogram Microstates: Validating a Neurofeedback System as a Function of Delay. Frontiers in Systems Neuroscience, 16, 786200.
https://www.frontiersin.org/articles/10.3389/fnsys.2022.786200/full
牧野浩史（南洋理工大学 医学部）
Hiroshi Makino (Lee Kong Chian School of Medicine, Nanyang Technological University)
[Website]
知能システムの学習について
Learning in intelligence systems
Artificial intelligence (AI) and neuroscience could mutually be beneficial. While AI research could provide new theories and hypotheses about how the brain solves computational challenges, neuroscience could introduce new algorithms and network architectures to endow machines with human- or animal-like cognitive abilities. Although collaborations between the two fields have seen a resurgence in recent years, direct comparisons between artificially and biologically intelligent systems remain scarce. I will share our recent efforts to understand representation learning in both these systems. By training head-restrained mice and artificial deep reinforcement learning (RL) agents on the same tasks and analyzing task representations in their respective neural networks, we discovered that the representation learning in the mouse cortex shared key features of deep RL algorithms. A systematic hyperparameter search by evaluating thousands of deep RL models revealed that behaviorally optimized AI models better recapitulated neural representation patterns observed in the biological system. These results highlight the remarkable similarities in representations between the two systems and demonstrate the utilities of such comparative approaches, which may define new research trajectories in the fields of AI and neuroscience.
References:
1. Makino H (2023). Arithmetic value representation for hierarchical behavior composition. Nature Neuroscience, 26(1): pp. 140-149.
2. Suhaimi A, Lim AWH, Chia XW, Li C and Makino H (2022). Representation learning in the artificial and biological neural networks underlying sensorimotor integration. Science Advances, 8(22): pp. eabn0984.
Oliver Tüscher (Leibniz Institute for Resilience Research (LIR), the AG Tüscher and Clinical Investigation Center (CIC))
[Website]
Neural network mechanisms of resilience and vulnerability
In a world exposed to multiple crises at an increasing pace, securing mental health by well-functioning and stable complex systems at different neurobiological levels is of utmost importance. At the LIR, we examine resilience to psychological stressors at different intra- and inter-individual levels – reaching from symptom networks of mental distress to cellular networks – by looking through the lens of multidisciplinary research into resilience of complex systems. Thereby, we aim at identifying network characteristics that are associated with resilient responses, that is, the maintenance or fast regain of system functioning during or after stressor exposure. In this talk, we will focus on potential neural network mechanisms of resilience (and vulnerability) and discuss recent translational data (rodent and human) on potential neurocognitive mechanisms of resilience and vulnerability to stress-related mental disorders.
References:
https://pubmed.ncbi.nlm.nih.gov/35094045/
https://pubmed.ncbi.nlm.nih.gov/33755019/
https://pubmed.ncbi.nlm.nih.gov/25158686/
藤野正寛 (NTTコミュニケーション科学基礎研究所)
Masahiro Fujino (NTT Communication Science Laboratories)
[Website]
ありのままの気づきの生理・神経基盤
Physiological and neural basis of mindful awareness
Mindfulness is the receptive attention and awareness of present moment experiences, including sensation, emotion, and thoughts, without reacting, judging, or inhibiting. This state of mindful awareness is considered crucial for enhancing well-being. However, it is difficult to determine whether individuals have achieved this state of awareness, for themselves or external observers like clinical psychologists. Therefore, we have been conducting research to elucidate the physiological and neural basis of mindful awareness. In this presentation, I classify mindfulness meditation into two types: focused attention and open monitoring meditations. Open monitoring meditation is believed to be particularly associated with mindful awareness [1]. I then present studies that investigate the impacts of each type of meditation on autonomic nervous system activity, cortisol levels [2, 3]., and the functional connectivity of the striatum [4]. Additionally, I discuss the idea that the state of mindful awareness may be associated with reduced selective attention and a decreased connection between the self and the experiences unfolding in the present moment.
References:
- Lutz, A., Slagter, H. A., Dunne, J. D. & Davidson, R. J. (2008). Attention regulation and monitoring in meditation. Trends in Cognitive Sciences, 12, 163–169.
- Ooishi, Y., Fujino, M., Inoue, V., Nomura, M., & Kitagawa, N. (2021). Differential effects of focused attention and open monitoring meditation on autonomic cardiac modulation and cortisol secretion. Frontiers in Physiology, 12, 675899. https://doi.org/10.3389/fphys.2021.675899
- Fujino, M., Ueda, Y., Inoue, V., Sanders, J. G., Murphy-Shigematsu, S., and Nomura, M. (2019). Development of instructions of short-term focused attention, insight, and compassion meditation for use in psychological experiments. Japanese Journal of Mindfuluness, 4, 10–33.
- Fujino, M., Ueda, Y., Mizuhara, H., Saiki, J., & Nomura, M. (2018). Open monitoring meditation reduces the involvement of brain regions related to memory function. Scientific Reports, 8, 9968. https://doi.org/10.1038/s41598-018-28274-4
宮崎勝彦 (沖縄科学技術大学院大学 神経計算ユニット)
Katsuhiko Miyazaki (Okinawa Institute of Science and Technology, Neural Computation Unit)
[Website]
楽観と悲観をめぐるセロトニン機序解明
Elucidation of the mechanism of serotonin over optimism and pessimism
Being patient to obtain future rewards is an adaptive behavior based on anticipation of future rewards. We have previously reported the following results from studies in rats and mice that demonstrate a causal relationship between serotonergic neural activity in the dorsal raphe nucleus (DRN) and waiting behavior for future rewards. (1) Rat serotonergic neurons sustainedly enhanced their activity during reward waiting behavior and decreased when they gave up waiting (Miyazaki et al., 2011). (2) Local pharmacological inhibition of the DRN serotonergic neural activity impaired the rats’ patience for waiting for delayed rewards (Miyazaki et al., 2012). (3) Optogenetic activation of serotonergic neurons in the DRN enhanced the patience of mice in waiting for both the conditioned reinforcer tone and food reward (Miyazaki et al., 2014). (4) Serotonin stimulation promoted waiting most effectively when the probability of reward delivery is high, but timing of delivery is uncertain (Miyazaki et al., 2018). (5) When the delay time is constant and the timing of rewards is predictable (low temporal uncertainty), waiting is promoted only by serotonin stimulation in the orbitofrontal cortex (OFC). On the other hand, when the timing of rewards is difficult to predict (high temporal uncertainty), serotonin stimulation not only in the OFC but also in the medial prefrontal cortex (mPFC) promotes waiting (Miyazaki et al., 2020). From these results, we hypothesize that serotonin works to regulate optimism (confidence in the future) and pessimism (giving up on the future) toward achieving the goal. I will show neural activity of DRN serotonin neurons, the OFC, and the mPFC and discuss how serotonin system regulates optimism and pessimism.
References:
1. Miyazaki K, Miyazaki KW, Sivori G, Yamanaka A,Tanaka KF, Doya K (2020) Serotonergic projections to the orbitofrontal and medial prefrontal cortices differentially modulate waiting for future rewards. Science Advances 6:eabc7246. DOI: 10.1126/sciadv.abc7246
2. Miyazaki K, Miyazaki KW, Yamanaka A, Tokuda T, Tanaka KF, Doya K (2018) Reward probability and timing uncertainty alter the effect of dorsal raphe serotonin neurons on patience. Nature Communications 9:2048. DOI: 10.1038/s41467-018-04496-y
3. Miyazaki KW, Miyazaki K, Tanaka KF, Yamanaka A, Takahashi A, Tabuchi S, Doya K (2014) Optogenetic activation of dorsal raphe serotonin neurons enhances patience for future rewards. Current Biology 24:2033-2040. DOI: 10.1016/j.cub.2014.07.041
4. Miyazaki KW, Miyazaki K, Doya K (2012) Activation of dorsal raphe serotonin neurons is necessary for waiting for delayed rewards. Journal of Neuroscience 32:10451-10457. DOI: 10.1523/JNEUROSCI.0915-12.2012
5. Miyazaki K, Miyazaki KW, Doya K (2011) Activation of dorsal raphe serotonin neurons underlies waiting for delayed rewards. Journal of Neuroscience 31:469-479. DOI: 10.1523/JNEUROSCI.3714-10.2011