Predictive Coding Abnormalities in Psychosis: EEG and fMRI

描述(申请人提供)：精神分裂症患者约占所有患者的1%。它的特点是妄想和幻觉，导致高发病率和死亡率。这些症状可能是由对感觉的误解和误解造成的，这些误解和误解是由于基本无法预测感觉和适应与预期的偏差造成的。如果预测编码机制功能失调，本应预测但没有预测的感觉可能会呈现不适当的突显，并导致错觉图式的构建来解释异常体验。这些预测错误对社会和患者来说都是代价高昂的。预测编码模型假设高阶大脑区域发展和维持可预测感觉刺激的表示，通过减少对这些刺激的感觉皮质反应和增加对违反预测模式的刺激的感觉反应来提高神经活动的效率。在这样的模型中，初级感觉皮质的作用是 编码和传递感觉“预测错误”，即主要对那些偏离大脑对预期刺激的表示的刺激做出反应。在基于语境的预测编码中，大脑获取并记住感觉输入的规则时间和空间模式的语境。在基于动作的预测编码中，感觉事件的可预测性源于这样一个事实，即感觉事件是一个人自己行为的可预测结果，不需要学习或记忆。从概念上讲，基于动作的预测编码模型包括整个动物界描述的传出复制/推论放电前向模型系统，在该模型系统中，即将到来的运动计划的“传出副本”从运动传递到感觉皮质，在那里它产生即将到来的运动行为的预期感觉后果的“推论放电”表示。在我们之前工作的基础上，我们建议询问可预测性和性能监控如何影响基于动作的预测编码，以及它们如何影响精神分裂症患者的感觉/知觉加工。我们将使用分别和同时获得的EEG和fMRI来询问基于动作的预测编码的传出拷贝和必然的放电成分的细节。使用EEG，我们可以评估运动动作之前的瞬间活动，或传出副本。利用功能磁共振成像，我们可以评估必然放电机制的抑制作用的空间精确度。通过结合EEG和fMRI，我们可以了解预测编码的功能神经解剖学基础及其在精神分裂症中的异常。最后，我们正在采用一种范式，并使用一种可以翻译到SZ动物模型的方法，这种方法最终可以用来识别参与预测编码的电路的特定元素，涉及负责预测编码的TE神经机制的传输器，甚至是构成精神病经验和行为的基因。

DESCRIPTION (provided by applicant): Schizophrenia affects ~1% of all people. It is characterized by delusions and hallucinations resulting in high morbidity and mortality. These symptoms may be underpinned by misperceptions and misinterpretations of sensations that result from a basic inability to predict sensations and adapt to deviations from the expected. If predictive coding mechanisms are dysfunctional, sensations that should have been predicted, but were not, may take on inappropriate salience and lead to the construction of delusional schema to explain aberrant experience. These errors of prediction are costly to society and the patient. Predictive coding models posit that higher order brain areas develop and maintain representations of predictable sensory stimuli, increasing efficiency of neural activity by reducing sensory cortical responses to these stimuli and increasing sensory responses to stimuli that violate the predicted pattern. In such models, the role of primary sensory cortices is to encode and transmit sensory "prediction errors," that is, to respond mainly to those stimuli that deviate from the brain's representations of expected stimuli. In context-based predictive coding, the brain acquires and remembers the context of regular temporal and spatial patterns of sensory input. In action-based predictive coding, the predictability of sensory events derives from the fact that sensory events are a predictable consequence of one's own actions, and do not need to be learned or remembered. Conceptually, action-based predictive coding models subsume efference-copy/corollary discharge forward model systems described across the animal kingdom in which an "efference copy" of an impending motor plan is transmitted from motor to sensory cortex where it generates a "corollary discharge" representation of the expected sensory consequences of the imminent motor act. Building on our prior work, we propose to ask how predictability and performance monitoring affect action-based predictive coding and how they affect sensory/perceptual processing in schizophrenia. We will use EEG and fMRI, acquired separately and simultaneously, to interrogate details of efference copy and corollary discharge components of action-based predictive coding. Using EEG, we can assess the split-second activity preceding a motor act, or the efference copy. Using fMRI, we can assess the spatial precision of the suppressive action of the corollary discharge mechanism. By integrating EEG and fMRI, we can understand the functional neuroanatomical basis of predictive coding and its abnormalities in schizophrenia. Finally, we are adopting a paradigm and using methods that are translatable to animal models of SZ and that can ultimately be used to identify specific elements of circuits involved in predictive coding, transmitters involved in te neural mechanisms responsible for predictive coding, and even genes that underlie psychotic experiences and behavior.