Linking Large-Scale Dysconnectivity in Schizophrenia to Cortical Circuit Function at the Individual Level through Computational Modeling and Multimodal Neuroimaging

通过计算模型和多模态神经影像将精神分裂症的大规模连接失调与个体水平的皮质回路功能联系起来

• 批准号: 10009445
• 负责人: John David Murray
• 金额: $ 41.42万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-15 至 2022-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10009445
• 关键词: Acute; Age; Architecture; Area; Behavior; Behavioral; Biological; Biological Process; Biophysics; Brain; Brain region; Categories; Clinical; Cognition; Cognitive; Cognitive deficits; Communication; Computer Models; Data; Data Set; Development; Dimensions; Disease; Disease model; Disinhibition; Dose; Equilibrium; Face; Foundations; Functional Magnetic Resonance Imaging; Functional disorder; Goals; Health; Heterogeneity; Human; Impaired cognition; Individual; Individual Differences; Ketamine; Link; Magnetic Resonance Imaging; Measures; Modeling; NMDA receptor antagonist; Neurobiology; Neurocognitive; Neurons; Neurosciences; Noise; Outcome; Patients; Pattern; Performance; Pharmacology; Physiological; Physiology; Population; Process; Property; Psychiatry; Recurrence; Research; Research Domain Criteria; Rest; Sampling; Schizophrenia; Sensory; Severities; Short-Term Memory; Signal Transduction; Structure; Symptoms; Synapses; System; Testing; Variant; Work; association cortex; base; behavior measurement; blind; clinical application; clinical effect; clinical predictors; clinically relevant; cognitive function; cognitive performance; computational neuroscience; computer framework; connectome; design; functional outcomes; improved; in vivo; individual patient; individual variation; insight; interdisciplinary approach; multimodal data; multimodality; myelination; neurobiological mechanism; neuroimaging; neuroimaging marker; neuropathology; neurophysiology; neuropsychiatry; next generation; novel; novel therapeutics; personalized medicine; physiologic model; programs; relating to nervous system; success; therapy design; treatment response

PROJECT SUMMARY There is an acute need in neuropsychiatric research to characterize the dimensional heterogeneity across patients within a categorical disorder such as schizophrenia (SCZ). It is unknown how clinically relevant individual differences in SCZ, such as the severity of cognitive deficits, relate to differences in underlying neural disturbances. Cognitive impairments in SCZ are hypothesized to involve widespread “dysconnectivity,” i.e., abnormal communication or interactions among brain regions in large-scale cortical networks. Noninvasive neuroimaging has revolutionized our understanding of systems-level connectivity disturbances in SCZ, yet the underlying cellular-level mechanisms remain unclear. A leading hypothesis for neuropathology in SCZ proposes disruptions in the balance between excitation (E) and inhibition (I) in cortical circuitry. This mechanism is supported by pharmacological models of SCZ which are hypothesized to induce synaptic disinhibition in cortex through antagonism of NMDA receptors. An emerging approach to bridge this explanatory gap—between neuroimaging observations and underlying biological processes—is to harness computational models of large-scale brain circuits that incorporate key features of neuronal and synaptic dynamics, thereby allowing mechanistic examination of how cellular-level disruptions propagate upward to produce systems-level dysfunction. The overarching goal of this “Computational Psychiatry” proposal is to develop a biophysically-based modeling framework that captures large-scale cortical dynamics at the individual-subject level, apply it to characterize dysconnectivity in SCZ and pharmacological manipulation, and relate model parameters to cognitive function. In Aim 1, we will develop and validate a model fitting framework that optimizes synaptic parameters in the model to match a subject’s personalized resting-state functional connectivity pattern, constrained by their own structural connectivity. Model parameters govern neurobiologically important synaptic properties such as E/I balance. To develop and apply this framework, we will leverage two existing, state-of-the-art multimodal neuroimaging datasets. The first dataset, from the Human Connectome Project (HCP), is from a large number of subjects, and will be used to characterize neural variation in the healthy population. The second dataset, collected at Yale and harmonized with HCP pipelines, is from patients with SCZ, and from matched healthy controls administered a subanesthetic dose of the NMDA receptor antagonist ketamine. In Aim 2, we will extend the model in two targeted directions that are grounded in known neurobiology and related to cortical dysfunction in SCZ: heterogeneity in local recurrent strength across the cortical hierarchy; and network-specific long-range interactions, which may be net-inhibitory. These extensions will be fit quantitatively at the individual level in both datasets. In Aim 3, we will relate model parameters to performance in working memory, a neurocognitive function central to cognitive deficits in SCZ, as well as to other non-imaging measures. This Computational Psychiatry research program advances our understanding of cortical disturbances in SCZ, illuminates individual variation in health and in SCZ, and establishes an extensible framework for Computational Psychiatry to link synapse-level hypotheses with human neuroimaging. Moreover, by combining clinical neuroimaging, pharmacology, and computational neuroscience, this study proposes a framework that can inform rational development of novel, personalized treatments designed to target specific neural disturbances.

项目总结 在神经精神病学研究中迫切需要表征患者在一个 绝对性障碍，如精神分裂症(SCZ)。目前尚不清楚SCZ的个体差异在临床上是如何相关的，如 由于认知缺陷的严重程度，与潜在神经障碍的差异有关。SCZ中的认知障碍是 假设涉及广泛的“连接障碍”，即大脑区域之间的异常交流或相互作用 大规模的皮质网络。非侵入性神经成像彻底改变了我们对系统级连接的理解 SCZ中的干扰，然而潜在的细胞水平的机制仍然不清楚。神经病理学的主要假说 在SCZ中，他提出了皮质回路中兴奋(E)和抑制(I)之间平衡的破坏。这一机制 是由SCZ的药理学模型支持的，该模型被假设通过 NMDA受体的拮抗作用。一种新的方法来弥合这一解释差距--神经成像观察之间的差距 和潜在的生物过程-是利用包含关键字的大规模大脑电路的计算模型 神经元和突触动力学的特征，从而允许对细胞水平的破坏如何进行机械检查 向上传播以产生系统层面的功能障碍。这一“计算精神病学”提案的首要目标是 是开发一种基于生物物理学的建模框架，以捕捉个体受试者的大规模皮质动力学 水平，应用它来表征SCZ和药物操作的连接障碍，并将模型参数与 认知功能。在目标1中，我们将开发和验证一个模型拟合框架，该框架可以优化突触参数 该模型匹配受试者的个性化静息状态功能连接模式，受其自身结构的限制 连通性。模型参数控制着神经生物学上重要的突触属性，如E/I平衡。发展和发展 应用这个框架，我们将利用两个现有的、最先进的多模式神经成像数据集。第一个数据集， 来自人类连接组计划(HCP)，来自大量的受试者，并将被用于表征神经 健康人群中的变异。第二个数据集是在耶鲁大学收集的，与HCP管道相协调，来自患者 使用SCZ，以及来自匹配的健康对照组，给予麻醉下剂量的NMDA受体拮抗剂氯胺酮。 在目标2中，我们将在两个目标方向上扩展该模型，这两个方向以已知的神经生物学为基础，与皮质相关 SCZ的功能障碍：跨皮质的局部复发强度的异质性；以及网络特异性的长程 相互作用，这可能是网络抑制。这些扩展将在两个数据集中的单个级别进行定量匹配。 在目标3中，我们将把模型参数与工作记忆的表现联系起来，工作记忆是认知的核心神经认知功能 SCZ的缺陷，以及其他非成像措施。这项计算精神病学研究计划推进了我们的 了解SCZ中的皮质障碍，阐明健康和SCZ中的个体差异，并建立 计算精神病学的可扩展框架，将突触水平的假设与人类神经成像联系起来。此外， 通过结合临床神经成像、药理学和计算神经科学，这项研究提出了一个框架 可以为合理开发针对特定神经障碍的新型个性化治疗提供信息。

项目成果

Toward Mapping Neurobehavioral Heterogeneity of Psychedelic Neurobiology in Humans.

绘制人类迷幻神经生物学的神经行为异质性。

• DOI: 10.1016/j.biopsych.2022.10.021
• 发表时间: 2023
• 期刊: Biological psychiatry
• 影响因子: 10.6
• 作者: Moujaes,Flora;Preller,KatrinH;Ji,JieLisa;Murray,JohnD;Berkovitch,Lucie;Vollenweider,FranzX;Anticevic,Alan
• 通讯作者: Anticevic,Alan

Biophysical Modeling of Large-Scale Brain Dynamics and Applications for Computational Psychiatry.

• DOI: 10.1016/j.bpsc.2018.07.004
• 发表时间: 2018-09
• 期刊: Biological psychiatry. Cognitive neuroscience and neuroimaging
• 作者: Murray JD;Demirtaş M;Anticevic A
• 通讯作者: Anticevic A

Mapping brain-behavior space relationships along the psychosis spectrum.

• DOI: 10.7554/elife.66968
• 发表时间: 2021-07-20
• 期刊: eLife
• 影响因子: 7.7
• 作者: Ji JL;Helmer M;Fonteneau C;Burt JB;Tamayo Z;Demšar J;Adkinson BD;Savić A;Preller KH;Moujaes F;Vollenweider FX;Martin WJ;Repovš G;Cho YT;Pittenger C;Murray JD;Anticevic A
• 通讯作者: Anticevic A

Transcriptomics Inform Hierarchical Neuroimaging Features Relevant for Psychosis Spectrum Symptoms.

转录组学为与精神病谱系症状相关的分层神经影像学特征提供信息。

• DOI: 10.1016/j.biopsych.2020.05.016
• 发表时间: 2020
• 期刊: Biological psychiatry
• 影响因子: 10.6
• 作者: Ji,JieLisa;Burt,JoshuaB;Anticevic,Alan
• 通讯作者: Anticevic,Alan

Refining the Empirical Constraints on Computational Models of Spatial Working Memory in Schizophrenia.

• DOI: 10.1016/j.bpsc.2020.05.003
• 发表时间: 2020-09
• 期刊: Biological psychiatry. Cognitive neuroscience and neuroimaging
• 作者: Gold JM;Bansal S;Anticevic A;Cho YT;Repovš G;Murray JD;Hahn B;Robinson BM;Luck SJ
• 通讯作者: Luck SJ