1月18日,，美國物理學家組織網(wǎng)報道，美國科學家繪制出了迄今最完整的大腦神經(jīng)相互作用以增強從學習到服藥等行為的圖譜,，有望為科學家們治療成癮開辟新道路,。相關研究發(fā)表在1月18日出版的《自然》雜志上。
哈佛大學分子和細胞生物學副教授瑙石哥·烏騏達領導的科研團隊,，在多年研究名為獎賞預測失誤的腦部活動過程中得到了上述結(jié)果,。此前，科學家們認為,，預測失誤是學習的關鍵組成部分,，也是巴胺神經(jīng)元放電以對一個意想不到的“獎賞”做出反應以增強導致這種報償行為的產(chǎn)物。
但烏騏達和哈佛大學以及貝斯以色列女執(zhí)事醫(yī)療中心的同事在最新研究中卻指出,，“獎賞”預測失誤實際上是兩類神經(jīng)元(一種依靠多巴胺的神經(jīng)元以及一種使用神經(jīng)傳遞素GABA的抑制性神經(jīng)元)之間復雜相互作用的產(chǎn)物,。烏騏達表示：“此前，人們都不知道GABA神經(jīng)元與獎賞和懲罰循環(huán)有何關系,。我們的最新研究表明，GABA神經(jīng)元抑制了多巴胺神經(jīng)元,，它們雙管齊下來計算獎賞失誤,。”
研究多巴胺或GABA神經(jīng)元面臨的挑戰(zhàn)在于，這兩種細胞會相互混合進入大腦內(nèi)一個比較小的區(qū)域,，使研究人員很難確切地知道他們正在觀察的是哪種細胞,，烏騏達團隊最終找到了巧妙的辦法解決了這一難題,。
科學家們對老鼠的兩組神經(jīng)元(一組用于研究多巴胺神經(jīng)元；一組用于研究GABA神經(jīng)元)進行了遺傳修改,，使得當這些神經(jīng)元被激光脈沖照射時會放電,，一旦研究人員確定他們正在測量正確類型的神經(jīng)元，他們就使用電極來測量這些神經(jīng)元是否放電以及什么時候會放電以對期望的以及實際的獎賞做出反應,。結(jié)果表明,，當多巴胺神經(jīng)元放電發(fā)出獎賞預測失誤信號時，GABA神經(jīng)元會發(fā)出一個期望的獎賞信號,。因此,，GABA神經(jīng)元幫助多巴胺神經(jīng)元計算獎賞預測失誤。
烏騏達表示,，這項研究發(fā)現(xiàn)非常重要,，因為它讓我們可以采用全新的角度來理解如何對行為進行強化或者通過正常的腦部功能；或者通過破壞這兩類神經(jīng)元相互作用的方式,。烏騏達說：“這是一種新的看待成癮的方式,。基于這一理論,，我們能研發(fā)出新的治療成癮的理論,。”（生物谷 Bioon.com）
doi:10.1038/nature10754
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Neuron-type-specific signals for reward and punishment in the ventral tegmental area
Jeremiah Y. Cohen,, Sebastian Haesler,Linh Vong, Bradford B. Lowell & Naoshige Uchida
Dopamine has a central role in motivation and reward. Dopaminergic neurons in the ventral tegmental area (VTA) signal the discrepancy between expected and actual rewards (that is, reward prediction error), but how they compute such signals is unknown. We recorded the activity of VTA neurons while mice associated different odour cues with appetitive and aversive outcomes. We found three types of neuron based on responses to odours and outcomes: approximately half of the neurons (type I, 52%) showed phasic excitation after reward-predicting odours and rewards in a manner consistent with reward prediction error coding; the other half of neurons showed persistent activity during the delay between odour and outcome that was modulated positively (type II, 31%) or negatively (type III, 18%) by the value of outcomes. Whereas the activity of type I neurons was sensitive to actual outcomes (that is, when the reward was delivered as expected compared to when it was unexpectedly omitted), the activity of type II and type III neurons was determined predominantly by reward-predicting odours. We ‘tagged’ dopaminergic and GABAergic neurons with the light-sensitive protein channelrhodopsin-2 and identified them based on their responses to optical stimulation while recording. All identified dopaminergic neurons were of type I and all GABAergic neurons were of type II. These results show that VTA GABAergic neurons signal expected reward, a key variable for dopaminergic neurons to calculate reward prediction error.
