Functional Neural Networks Underlying Latent Inhibition

潜在抑制下的功能神经网络

• 批准号: 7615923
• 负责人: Frank Puga
• 金额: $ 3.13万
• 依托单位: UNIVERSITY OF TEXAS AT AUSTIN
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-29 至 2009-09-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7615923
• 关键词: Affect; Attenuated; Auditory; Behavior; Behavioral; Behavioral Model; Behavioral Paradigm; Behavioral inhibition; Biological Neural Networks; Brain; Brain Mapping; Brain region; Control Groups; Disruption; Enhancers; Equation; Exposure to; Hippocampus (Brain); Histocytochemistry; Individual; Investigation; Learning; Lesion; Maps; Mediating; Memory; Mental disorders; Metabolic; Methylene blue; Modeling; Neurophysiology - biologic function; Nucleus Accumbens; Numbers; Output; Phase; Play; Relative (related person); Research; Research Personnel; Role; Schizophrenia; Sensory Process; Stimulus; System; Testing; Theoretical model; Training; attenuation; base; classical conditioning; conditioned fear; cytochrome c oxidase; entorhinal cortex; improved; insight; memory retention; network models; neuromechanism; relating to nervous system; response; sensory gating

DESCRIPTION (provided by applicant): Functional brain networks underlying a learning effect can provide insight into the neural basis of learned behavior and into the disruption of normal learning functions in the brain. Latent inhibition is a learning effect in which repeated exposure to a stimulus results in the inhibition of an appropriate behavioral response when that familiar stimulus is paired with some consequence. The latent inhibition effect has been demonstrated in many behavioral paradigms, including Pavlovian conditioning, and is disrupted in individuals with schizophrenia. The potential of the latent inhibition paradigm as a model of the behavioral deficits associated with schizophrenia has prompted the investigation of the neural mechanisms underlying the effect. Lesion studies have identified single brain regions, such as the nucleus accumbens and the entorhinal cortex, that play an important role in the latent inhibition paradigm; however, neural functions that mediate associative learning are likely dependent on interactions between and within brain systems. Thus, we ask the question what are the underlying functional neural networks in latent inhibition? We will approach this question by first completing a comprehensive metabolic map of brain regions involved with latent inhibition using cytochrome oxidase histochemistry. We will then use structural equation modeling to determine causal influences in an anatomical network of regions involved in latent inhibition. We plan to compare our functional network model to other theoretical models of latent inhibition and schizophrenia. We will also investigate how functional neural networks are affected when we change the optimal training parameters to attenuate the latent inhibition effect. Lastly, we also look at changes in the functional neural network when an attenuated latent inhibition effect is facilitated with the metabolic enhance methylene blue, which has previously been shown to improve memory retention in our lab. At the completion of these studies, we expect to have a better understanding of the neural mechanisms underlying latent inhibition learning. We will gain insight to changes in metabolic capacity associated with sensory gating in normal, attenuated, and facilitated latent inhibition. The proposed research is expected to provide an understanding of the neural basis of the behavioral deficits observed in schizophrenia and in therapuetic approaches through the use of metabolic enhancers such as metheylene blue.

描述（由申请人提供）：作为学习效应基础的功能性大脑网络可以提供对学习行为的神经基础和大脑中正常学习功能的破坏的洞察。潜在抑制是一种学习效应，其中重复暴露于刺激导致当熟悉的刺激与某些后果配对时适当的行为反应受到抑制。潜伏抑制效应已在许多行为范式中得到证实，包括巴甫洛夫条件反射，并在精神分裂症患者中被破坏。潜在的抑制范式作为精神分裂症相关的行为缺陷的模型的潜力，促使调查的神经机制的影响。损伤研究已经确定了单一的大脑区域，如丘脑核和内嗅皮层，在潜在抑制范式中发挥重要作用;然而，介导联想学习的神经功能可能依赖于大脑系统之间和内部的相互作用。因此，我们提出了这样一个问题：潜在抑制中的潜在功能神经网络是什么？我们将首先利用细胞色素氧化酶组织化学完成一个涉及潜伏性抑制的脑区的综合代谢图来处理这个问题。然后，我们将使用结构方程模型来确定潜在抑制所涉及的区域的解剖网络中的因果影响。我们计划将我们的功能网络模型与潜在抑制和精神分裂症的其他理论模型进行比较。我们还将研究当我们改变最佳训练参数以减弱潜在抑制效应时，功能性神经网络是如何受到影响的。最后，我们还研究了功能神经网络的变化，当代谢增强剂亚甲蓝促进减弱的潜在抑制作用时，代谢增强剂亚甲蓝先前已被证明可以改善我们实验室的记忆力。这些研究的完成，我们期望有一个更好的理解潜在的抑制学习的神经机制。我们将深入了解正常、减弱和易化潜伏抑制中与感觉门控相关的代谢能力变化。这项拟议的研究有望通过使用代谢增强剂（如亚甲蓝）来了解精神分裂症和治疗方法中观察到的行为缺陷的神经基础。