@article{oai:repo.qst.go.jp:00049052,
 author = {Yamanaka, Ko and Hori, Yukiko and Minamimoto, Takafumi and Yamada, Hiroshi and Matsumoto, Naoyuki and Enomoto, Kazuki and Aosaki, Toshihiko and M.Graybiel, Ann and Kimura, Minoru and 堀 由紀子 and 南本 敬史},
 journal = {Journal of Neural Transmission},
 month = {Mar},
 note = {The thalamus provides a massive input to the striatum, but despite accumulating evidence, the functions of this system remain unclear. It is known, however, that the centromedian (CM) and parafascicular (Pf) nuclei of the thalamus can strongly influence particular striatal neuron subtypes, notably including the cholinergic interneurons of the striatum (CINs), key regulators of striatal function. Here, we highlight the thalamostriatal system through the CM-Pf to striatal CINs. We consider how, by virtue of the direct synaptic connections of the CM and PF, their neural activity contributes to the activity of CINs and striatal projection neurons (SPNs). CM-Pf neurons are strongly activated at sudden changes in behavioral context, such as switches in action-outcome contingency or sequence of behavioral requirements, suggesting that their activity may represent change of context operationalized as associability. Striatal CINs, on the other hand, acquire and loose responses to external events associated with particular contexts. In light of this physiological evidence, we propose a hypothesis of the CM-Pf-CINs system, suggesting that it augments associative learning by generating an associability signal and promotes reinforcement learning guided by reward prediction error signals from dopamine-containing neurons. We discuss neuronal circuit and synaptic organizations based on in vivo/in vitro studies that we suppose to underlie our hypothesis. Possible implications of CM-Pf-CINs dysfunction (or degeneration) in brain diseases are also discussed by focusing on Parkinson's disease.},
 pages = {501--513},
 title = {Roles of centromedian parafascicular nuclei of thalamus and cholinergic interneurons in the dorsal striatum in associative learning of environmental events},
 volume = {3},
 year = {2017}
}