National Institute on Drug Abuse - Intramural Research Program
Featured Paper of the Month – February 2017
Published in Biological Psychiatry by Li, Sufang; Yang, Yihong; Hoffmann, Ewa; Tyndale, Rachel F; Stein, Elliot A
Variation in the CYP2A6 gene alters the rate of nicotine metabolic inactivation and is associated with smoking behaviors and cessation success rates. The underlying neurobiological mechanisms of this genetic influence are unknown…
Hot Off the Press! – January 2017
Barbera, Giovanni; Liang, Bo; Zhang, Lifeng; Gerfen, Charles R; Culurciello, Eugenio; Chen, Rong; Li, Yun; Lin, Da-Ting
Neuron, 92 (1), pp. 202–213, 2016, ISSN: 1097-4199 (Electronic); 0896-6273 (Linking).
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Hot Off the Press! – January 2017
Tejeda, Hugo A; Wu, Jocelyn; Kornspun, Alana R; Pignatelli, Marco; Kashtelyan, Vadim; Krashes, Michael J; Lowell, Brad B; Carlezon, William Jr A; Bonci, Antonello
Nat Neurosci, 20 (3), pp. 438–448, 2017, ISSN: 1546-1726 (Electronic); 1097-6256 (Linking).
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Reviews To Read – January 2017
Su, Tsung-Ping; Su, Tzu-Chieh; Nakamura, Yoki; Tsai, Shang-Yi
Trends Pharmacol Sci, 37 (4), pp. 262–278, 2016, ISSN: 1873-3735 (Electronic); 0165-6147 (Linking).
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Featured Paper of the Month – January 2017
Published in Cell Reports by Yau, Hau-Jie; Wang, Dong V; Tsou, Jen-Hui; Chuang, Yi-Fang; Chen, Billy T; Deisseroth, Karl; Ikemoto, Satoshi; Bonci, Antonello
The ventral tegmental area (VTA) receives phenotypically distinct innervations from the pedunculopontine tegmental nucleus (PPTg). While PPTg-to-VTA inputs are thought to play a critical role in stimulus-reward learning, direct evidence linking PPTg-to-VTA phenotypically distinct inputs in the learning process remains lacking…
Featured Paper of the Month – December 2016
Published in Scientific Reports by Root, David H; Wang, Hui-Ling; Liu, Bing; Barker, David J; Mod, Laszlo; Szocsics, Peter; Silva, Afonso C; Magloczky, Zsofia; Morales, Marisela
The rodent ventral tegmental area (VTA) and substantia nigra pars compacta (SNC) contain dopamine neurons intermixed with glutamate neurons (expressing vesicular glutamate transporter 2; VGluT2), which play roles in reward and aversion. However, identifying the neuronal compositions of the VTA and SNC in higher mammals has remained challenging…
Featured Paper of the Month – November 2016
Published in The Journal of Neuroscience by Warren, Brandon L; Mendoza, Michael P; Cruz, Fabio C; Leao, Rodrigo M; Caprioli, Daniele; Rubio, Javier F; Whitaker, Leslie R; McPherson, Kylie B; Bossert, Jennifer M; Shaham, Yavin; Hope, Bruce T
In operant learning, initial reward-associated memories are thought to be distinct from subsequent extinction-associated memories. Memories formed during operant learning are thought to be stored in “neuronal ensembles.” Thus, we hypothesize that different neuronal ensembles encode reward- and extinction-associated memories…
Hot Off the Press! – January 2017
Barbera, Giovanni; Liang, Bo; Zhang, Lifeng; Gerfen, Charles R; Culurciello, Eugenio; Chen, Rong; Li, Yun; Lin, Da-Ting
Neuron, 92 (1), pp. 202–213, 2016, ISSN: 1097-4199 (Electronic); 0896-6273 (Linking).
[Read More]
Featured Paper of the Month – October 2016
Published in The Journal of Biological Chemistry by Stolzenberg, Sebastian; Quick, Matthias; Zhao, Chunfeng; Gotfryd, Kamil; Khelashvili, George; Gether, Ulrik; Loland, Claus J; Javitch, Jonathan A; Noskov, Sergei; Weinstein, Harel; Shi, Lei
Neurotransmitter:sodium symporters (NSSs) terminate neurotransmission by Na(+)-dependent reuptake of released neurotransmitters. Previous studies suggested that Na(+)-binding reconfigures dynamically coupled structural elements in an allosteric interaction network (AIN) responsible for function-related conformational changes, but the intramolecular pathway of this mechanism has remained uncharted…
Featured Paper of the Month – September 2016
Published in Elife by Sadacca, Brian F; Jones, Joshua L; Schoenbaum, Geoffrey
Midbrain dopamine neurons have been proposed to signal reward prediction errors as defined in temporal difference (TD) learning algorithms. While these models have been extremely powerful in interpreting dopamine activity, they typically do not use value derived through inference in computing errors…
