The internal states of neural circuits: data analysis, modeling, and disease

描述（由申请人提供）：认知行为产生于外部刺激驱动的神经活动与内部活动模式或“大脑状态”之间的相互作用，这些模式或“大脑状态”与动机、意图和经验有关。从历史上看，神经科学的研究几乎完全集中在刺激驱动的活动，忽视了内部状态的影响。显然，要发展 认知功能的理论和理解精神疾病，这是内部状态的障碍，必须发展一种新的神经科学，测量和模拟外部和内部产生的神经活动，并试图揭示它们之间的相互作用。一些重大的挑战阻碍了对内部状态的研究。首先，为了分析实验数据，通常在许多试验中取平均值。然而，如果内部状态在试验之间不同，则该过程将破坏数据中的状态依赖性神经反应。第二，内部状态之间的区别可能只在于它们处理信息的方式，换句话说，它们的动态。第三，到目前为止，神经科学领域还没有深入了解内部状态之间的转换是如何产生的，并且仍然局限于只在适当的时候发生。本申请通过提供以下方法来应对这些挑战：1）推断状态之间的转换时间，以便可以阐明状态特定的活动; 2）在实验数据中发现状态依赖的动态;以及3）确定用于维持内部状态和状态之间转换的机制。内部状态的研究将支持神经科学的基本目标，即发展神经回路水平的理论， 计算和认知功能。然而，这一应用也冲击了两个假设的发展，精神疾病，从而可能导致新的方法来诊断和治疗。首先，疾病可能会发展，因为特定状态的计算出了差错，导致症状，例如，在精神分裂症的情况下，将内部产生的自发活动误认为外部刺激（即幻觉）。第二，也是完全新颖的，精神病现象可能不是从无序状态本身产生的，而是从无序状态转换或“转换病”产生的。例如，在强迫症中，与强迫相关的状态（例如，需要检查门是否锁好）可能完全正常，但病理结果是未能适当地过渡到新状态。本申请中提出的工具将允许健康动物和疾病模型之间的数据比较，从而可以深入了解特定状态的计算如何失败并导致疾病。此外，通过探索过渡可能变得紊乱的潜在机制，该应用程序可以提供对过渡病发展的见解。虽然本申请中开发的分析方法最初将应用于动物数据，但将来它们可以扩展到分析人类大脑活动的非侵入性测量，如fMRI或EEG。如果一个健全的理论，正常和异常的内部状态和状态转换的发展，这样的分析可以帮助诊断精神疾病。

DESCRIPTION (provided by applicant): Cognitive behaviors arise from an interplay between neural activity driven by external stimuli and internal activity patterns or "brain states" that relect motivation, intention and experience. Historically, neuroscience re- search has focused almost exclusively on stimulus-driven activity, ignoring the impact of internal state. Clearly, to develop theories of cognitive function and to understand psychiatric illnesses, which are disorders of internal state, a new neuroscience must be developed that measures and models both externally and internally generated neural activity and seeks to reveal the interactions between them. Several significant challenges have prevented the study of internal state. First, to analyze experimental data, it is typical to average across many trials. However, if the internal state differs from trial to trial, this procedure will destroy the state-dependent neural responses in data. Second, internal states may differ from each other only by the way that they process information in time, in other words, by their dynamics. Third, to date, the neuroscience field has no insight into how transitions between internal states arise and remain limited to only occur at appropriate times. This application meets these challenges by offering methods for 1) inferring the transition times between states so that state-specific activity can be elucidated; 2) discovering state-dependent dynamics in experimental data; and 3) determining the mechanisms for maintenance of internal state and transitioning between states. The study of internal state will support the basic neuroscience goal of developing circuit level theories neural computation and cognitive function. However, this application also impinges upon two hypotheses for the development of psychiatric disease and thus may lead to new approaches to diagnosis and treatment. First, illness may develop because state-specific computations have gone awry leading to symptoms such as, in the case of schizophrenia, the misidentification of internally generated spontaneous activity as external stimuli (that is, hallucinations). Second, and completely novel, psychiatric phenomena may arise, not from disordered states per se, but rather from disordered state transitions, or "transitionopathies". In obsessive-compulsive disorder, for example, a state associated with a compulsion (for example, the need to check that the door is locked) maybe completely nor- mal, but pathology results from the failure to transition appropriately to a new state. The tools presented in this application will permit the comparison of data between healthy animals and disease models, and thus may give insight into how state-specific computations can fail and lead to disease. Additionally, by exploring potential mechanisms by which transitioning may become disordered, this application may offer insight into the development of transitionopathy. While the analysis methods developed in this application will initially be applied to data from animals, in the future they could be extended toward the analysis of noninvasive measures of brain activity in humans such as fMRI or EEG. If a robust theory of normal and abnormal internal states and state transitioning is developed, such analyses could aid in the diagnosis of psychiatric disease.