Department of Neural Computation for Decision-Making (NCD) researches and develops experimental and analytical methods based on computational model of neural circuits to elucidate the mechanisms of human learning and decision making.<\/p>\n
We also aim to apply our findings to to solve social, economic, and health problems.<\/p>\n
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<\/div>\nResearch theme<\/h4>\n1<\/span>Neural mechanisms of decision-making in human<\/b><\/h5>\n\nWe are conducting the researches aiming to reveal the neural mechanisms of decision-making in human. We have revealed how algorithms of reinforcement learning, which is a candidate computational model of learning of animals, are implemented in human brain, using fMRI experiments. We have published findings on neural mechanisms of delay discount (paper 1-3). Recently, we have revealed mechanism of obsessive-compulsive disorder in terms of problematic behaviors as a result of dysfucntion of time-scale of learning (paper 4) and researches aiming to reveal neural mechanisms potentially causing problematic or healthy behaviors in adolescence and older age.<\/p>\n
References<\/strong><\/p>\n\n- Tanaka SC Doya K Okada G Ueda K Okamoto Y Yamawaki S. Prediction of immediate and future rewards differentially recruits cortico-basal ganglia loops.Nature Neuroscience<\/i> 7(8)<\/strong>. 887-893 2004. doi:10.1038\/nn1279<\/a><\/li>\n
- Tanaka SC Schweighofer N Asahi S Shishida K Okamoto Y Yamawaki S Doya K. Serotonin Differentially Regulates Short- and Long-Term Prediction of Rewards in the Ventral and Dorsal Striatum.PLoS ONE<\/i> 2(12)<\/strong>, e1333 2007. doi:10.1371\/journal.pone.0001333<\/a><\/li>\n
- Tanaka SC Shishida K Schweighofer N Okamoto Y Yamawaki S Doya K. Serotonin affects association of aversive outcomes to past actions.The Journal of Neuroscience<\/i> 29(50)<\/strong>, 15669-74 2009. doi:10.1523\/JNEUROSCI.2799-09.2009<\/a><\/li>\n
- Sakai Y Sakai Y Abe Y Narumoto J & Tanaka SC. Memory trace imbalance in reinforcement and punishment systems can reinforce implicit choices leading to obsessive-compulsive behavior.Cell reports<\/i> 40(9)<\/strong>, 111275 2022. https:\/\/doi.org\/10.1016\/j.celrep.2022.111275<\/a><\/li>\n<\/ol>\n\n
\u9589\u3058\u308b<\/a><\/p>\n<\/div>\n<\/div>\n2<\/span>Investigations of attention using electroencephalogram in human<\/b><\/h5>\n\nWe are developing a task assessing attentional state of human and conducting studies using subconscious information, including brain activation data measured through EEG and fMRI and other physiological data such as heart rate. We are developing training methods of metacognition on attentional state, based on subconscious information stated above and technologies to find mind-wandering using EEG.<\/p>\n
References<\/strong><\/p>\n\n- Kawashima I Kumano H. Prediction of Mind-Wandering with Electroencephalogram and Non-linear Regression Modeling. Front Hum Neurosci<\/i>. 11 365 2017. doi:10.3389\/fnhum.2017.00365<\/a><\/li>\n
- Kawashima I Nagahama T Kumano H Momose K Tanaka S C. Pavlovian-based neurofeedback enhances meta-awareness of mind-wandering. Neural Networks<\/i>. 158 239-248 2023.<\/li>\n<\/ol>\n\n
\u9589\u3058\u308b<\/a><\/p>\n<\/div>\n<\/div>\n3<\/span>Multivariate analysis on large human brain imaging database<\/b><\/h5>\n\nWe have analyzed large database including various measures of individual differences of human, including neuroimaging data, physiological data, and data of clinical rating scale, questionnaires, and cognitive tasks. We aim to reveal phenomenon occurring in life periods accompanying significant behavioral changes, such as adolescence and older age, through multi-modal data including neuroimaging data. We intend that these investigations will solve problems specific to these life periods in the future. We have published a paper applying machine learning to large database including data of adolescents and their parents. Our laboratory has multiple large neuroimaging and behavioral database of healthy participants, patients, and older people.<\/p>\n
References<\/strong><\/p>\n\n- Takagi Y Okada Y Ando S Yahata N Morita K Koshiyama D Kawakami S Sawada K Koike S Endo K Yamasaki S Nishida A Kasai K Tanaka SC. Intergenerational transmission of the patterns of functional and structural brain networks.iScience<\/i> 24<\/strong>(7), 102708 2021. https:\/\/doi.org\/10.1016\/j.isci.2021.102708<\/a><\/li>\n
- Takagi Y Hirayama JI Tanaka SC. State-unspecific patterns of whole-brain functional connectivity from resting and multiple task states predict stable individual traits.Neuroimage<\/i> 201<\/strong>, 116036 2019. doi:10.1016\/j.neuroimage.2019.116036<\/a><\/li>\n
- Tanaka SC Yamashita A Yahata N et al. A multi-site multi-disorder resting-state magnetic resonance image database.Scientific Data<\/i> 8(1)<\/strong>, 227 2021. doi:10.1038\/s41597-021-01004-8<\/a><\/li>\n<\/ol>\n
We are conducting the researches aiming to reveal the neural mechanisms of decision-making in human. We have revealed how algorithms of reinforcement learning, which is a candidate computational model of learning of animals, are implemented in human brain, using fMRI experiments. We have published findings on neural mechanisms of delay discount (paper 1-3). Recently, we have revealed mechanism of obsessive-compulsive disorder in terms of problematic behaviors as a result of dysfucntion of time-scale of learning (paper 4) and researches aiming to reveal neural mechanisms potentially causing problematic or healthy behaviors in adolescence and older age.<\/p>\n
References<\/strong><\/p>\n \u9589\u3058\u308b<\/a><\/p>\n<\/div>\n<\/div>\n We are developing a task assessing attentional state of human and conducting studies using subconscious information, including brain activation data measured through EEG and fMRI and other physiological data such as heart rate. We are developing training methods of metacognition on attentional state, based on subconscious information stated above and technologies to find mind-wandering using EEG.<\/p>\n References<\/strong><\/p>\n \u9589\u3058\u308b<\/a><\/p>\n<\/div>\n<\/div>\n We have analyzed large database including various measures of individual differences of human, including neuroimaging data, physiological data, and data of clinical rating scale, questionnaires, and cognitive tasks. We aim to reveal phenomenon occurring in life periods accompanying significant behavioral changes, such as adolescence and older age, through multi-modal data including neuroimaging data. We intend that these investigations will solve problems specific to these life periods in the future. We have published a paper applying machine learning to large database including data of adolescents and their parents. Our laboratory has multiple large neuroimaging and behavioral database of healthy participants, patients, and older people.<\/p>\n References<\/strong><\/p>\n\n
2<\/span>Investigations of attention using electroencephalogram in human<\/b><\/h5>\n
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3<\/span>Multivariate analysis on large human brain imaging database<\/b><\/h5>\n
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