Functional Neural Networks Underlying Latent Inhibition

描述（由申请人提供）：学习效应背后的功能性大脑网络可以为学习行为的神经基础和大脑正常学习功能的破坏提供见解。潜伏抑制是一种学习效应，当熟悉的刺激伴随着某种后果时，反复接触刺激会导致适当的行为反应被抑制。潜伏抑制效应已经在许多行为范式中得到证实，包括巴甫洛夫条件反射，并且在精神分裂症患者中被破坏。潜在抑制范式作为与精神分裂症相关的行为缺陷模型的潜力促使了对其影响的神经机制的研究。病变研究已经确定了单个大脑区域，如伏隔核和内嗅皮层，在潜在抑制范式中发挥重要作用；然而，调节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，我们要问的问题是潜伏抑制的潜在功能神经网络是什么？我们将通过首先使用细胞色素氧化酶组织化学完成与潜在抑制有关的脑区域的综合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涉及潜在抑制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优训练参数以减弱潜在抑制效应时对功能神经网络的影响。最后，我们还研究了当代谢增强亚甲基蓝促进潜在抑制效应减弱时功能性神经网络的变化，这在我们的实验室中已经被证明可以改善记忆保留。在完成这些研究后，我们期望对潜在抑制学习的神经机制有更好的了解。我们将深入了解正常、减弱和促进潜在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中观察到的行为缺陷的神经基础的理解，并通过使用代谢增强剂（如甲二烯蓝）提供治疗方法。

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.