期刊
JOURNAL OF NEUROPHYSIOLOGY
卷 110, 期 9, 页码 2027-2036出版社
AMER PHYSIOLOGICAL SOC
DOI: 10.1152/jn.00115.2013
关键词
LTP; striatum; theta; plasticity; learning
资金
- Office of Naval Research Multidisciplinary University Research Initiative Grant [N00014-10-1-0198, NIAAA R01]
Long-term potentiation (LTP) of excitatory afferents to the dorsal striatum likely occurs with learning to encode new skills and habits, yet corticostriatal LTP is challenging to evoke reliably in brain slice under physiological conditions. Here we test the hypothesis that stimulating striatal afferents with theta-burst timing, similar to recently reported in vivo temporal patterns corresponding to learning, evokes LTP. Recording from adult mouse brain slice extracellularly in 1 mM Mg2+, we find LTP in dorsomedial and dorsolateral striatum is preferentially evoked by certain theta-burst patterns. In particular, we demonstrate that greater LTP is produced using moderate intraburst and high theta-range frequencies, and that pauses separating bursts of stimuli are critical for LTP induction. By altering temporal pattern alone, we illustrate the importance of burst-patterning for LTP induction and demonstrate that corticostriatal long-term depression is evoked in the same preparation. In accord with prior studies, LTP is greatest in dorsomedial striatum and relies on N-methyl-D-aspartate receptors. We also demonstrate a requirement for both G(q)- and G(s/olf)-coupled pathways, as well as several kinases associated with memory storage: PKC, PKA, and ERK. Our data build on previous reports of activity-directed plasticity by identifying effective values for distinct temporal parameters in variants of theta-burst LTP induction paradigms. We conclude that those variants which best match reports of striatal activity during learning behavior are most successful in evoking dorsal striatal LTP in adult brain slice without altering artificial cerebrospinal fluid. Future application of this approach will enable diverse investigations of plasticity serving striatal-based learning.
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