Functional Neural Networks Underlying Latent Inhibition

描述(申请人提供)：学习效应背后的功能性大脑网络可以提供对学习行为的神经基础的洞察，以及大脑正常学习功能的破坏。潜在抑制是一种学习效应，当熟悉的刺激伴随着某种后果时，反复暴露在刺激下会导致适当的行为反应被抑制。这种潜在的抑制效应已经在包括巴甫洛夫条件反射在内的许多行为范式中被证明，并在精神分裂症患者中被破坏。潜在抑制范式作为精神分裂症相关行为缺陷的模型的潜力促使人们研究这种效应背后的神经机制。损伤研究已经确定了单个大脑区域，如伏隔核和内嗅觉皮质，它们在潜在抑制范式中发挥着重要作用；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：潜在抑制的潜在功能神经网络是什么？我们将通过使用细胞色素氧化酶组织化学首先完成与潜在抑制相关的大脑区域的全面代谢图来解决这个问题。然后，我们将使用结构方程模型来确定潜在抑制相关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其他潜在抑制和精神分裂症的理论模型进行比较。我们还将研究当我们改变最佳训练参数以减弱潜在的抑制效应时，函数神经网络是如何受到影响的。最后，我们还观察了当代谢增强亚甲基蓝促进减弱的潜在抑制效应时，功能神经网络的变化，这已经在我们的实验室中被证明可以改善记忆保持。在这些研究完成后，我们希望对潜在抑制学习的神经机制有更好的理解。我们将深入了解在正常的、减弱的和促进的潜伏抑制中与感觉门控相关的代谢能力的变化。这项拟议的研究有望提供对精神分裂症患者行为缺陷的神经基础的理解，以及通过使用代谢增强剂如亚甲基蓝进行治疗的方法。

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.