An integrative computational interrogation of circuit dysfunction inschizophrenia via neural timescales

通过神经时间尺度对精神分裂症中的回路功能障碍进行综合计算询问

• 批准号: 10704693
• 负责人: Guillermo Horga
• 金额: $ 72.2万
• 依托单位: NEW YORK STATE PSYCHIATRIC INSTITUTE DBA RESEARCH FOUNDATION FOR MENTAL HYGIENE, INC
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-15 至 2027-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10704693
• 关键词: Address; Anxiety Disorders; Architecture; Automobile Driving; Biological; Biological Markers; Biophysics; Brain region; Cells; Clinical; Cognition; Cognitive; Cognitive deficits; Complement; Complex; Computer Models; Data; Data Reporting; Data Set; Development; Developmental Process; Diagnosis; Disease; Disparate; Distal; Equilibrium; Etiology; Exhibits; Foundations; Functional Magnetic Resonance Imaging; Functional disorder; General Population; Genetic; Genotype; Human; Hybrids; Impairment; Individual; Interneurons; Link; Longevity; Measures; Mediating; Mediator; Memory impairment; Mental disorders; Methods; Mood Disorders; Neurocognitive; Neurocognitive Deficit; Neurons; Pathway interactions; Patient Self-Report; Patients; Perception; Performance; Phenotype; Population; Process; Psychoses; Public Health; Recurrence; Reporting; Rest; Risk; Role; Schizophrenia; Selection for Treatments; Severities; Shapes; Short-Term Memory; Specificity; Testing; Validation; Work; age related; behavioral phenotyping; biobank; biophysical model; brain based; candidate marker; cell cortex; cell type; cognitive function; comparison control; connectome; connectome data; course development; early onset; genetic risk factor; genetic variant; genome wide association study; hippocampal pyramidal neuron; human disease; indexing; interest; large datasets; large scale data; multimodal data; neural; novel; operation; phenomenological models; psychotic symptoms; response; risk variant; schizophrenia risk; secondary analysis; social stigma; theories; trait

SUMMARY/ABSTRACT Schizophrenia is a devastating and burdensome illness the mechanisms of which remain elusive. Contributing to their elusiveness are a highly complex set of genetic factors, proposed etiological and pathophysiological pathways, and phenotypic manifestations. To address this complexity, we propose a hybrid method combining data-driven approaches to large-scale multimodal datasets and theory-driven computational approaches in order to provide a theoretically constrained framework bridging genetics, development, circuit function, cognition, and phenomenology of schizophrenia. To that end, and in response to ‘Notice of Special Interest regarding the Use of Human Connectome Data (HCP) for Secondary Analysis’, we will use data from up to 64,000 individuals, including healthy individuals and patients with schizophrenia and other disorders, from various HCP-related projects as well as the UK Biobank. We specifically propose measuring intrinsic neural timescales (INT) from resting-state fMRI data as a theory-driven index of excitation/inhibition (E/I) imbalance in cortical microcircuits. First, extending our prior work we aim to confirm and further characterize INT alterations in schizophrenia (widespread trait-like INT reductions and local hierarchy-dependent INT modulations in relation to psychotic symptoms) and to test their specificity relative to other disorders. Second, we will evaluate the developmental trajectories of INT and characterize the genetic profile of this fMRI measure and its overlap with the genetic profile for schizophrenia risk. Third, given the role of E/I ratio in cortical microcircuits in supporting working-memory computations, we will examine the relationship between INT and working-memory activation and performance. We will further seek to establish INT as a circuit-level mediator of polygenic risk for schizophrenia on cognitive deficits. Throughout, we will use well-powered, rigorous, state-of-the-art fMRI and statistical data-driven methods suitable for large-scale studies and HCP-style fMRI sequences, including cross- validation and tests of generalizability. Together with a strong theoretical foundation and using biophysical modeling to complement fMRI analyses, this hybrid—theory- and data-driven—approach will facilitate an integrated understanding of the circuit-level mechanisms bridging distal genetic-risk factors and proximal manifestations of schizophrenia. In particular, the combination of cutting-edge cell-type enrichment analyses of GWAS (which in schizophrenia have suggested converging enrichment in excitatory and inhibitory cortical cells) and biophysical modeling at the level of cortical microcircuits of interacting excitatory and inhibitory cellular populations will provide an interpretation of disparate data in terms of convergent cell- and circuit-level pathways. In doing so, this project will validate a theoretically informative, interpretable, translatable, and scalable resting-state fMRI measure—INT—that may be relevant across several disorders and, which additionally owing to its high reliability and ease of acquisition, has high potential as a candidate biomarker.

摘要/摘要 精神分裂症是一种毁灭性和抛光的疾病，其机制仍然难以捉摸。贡献 对它们的兴趣是一组高度复杂的遗传因素，该因素是病因和病理生理学 途径和表型表现。为了解决这种复杂性，我们提出了一种混合方法组合 数据驱动的大规模多模式数据集和理论驱动的计算方法的方法 为了提供理论上约束的框架桥接遗传学，开发，电路函数， 精神分裂症的认知和现象学。为此，以及回应“特殊关注的通知” 有关人类连接数据（HCP）进行次要分析的使用”，我们将使用最多数据 有64,000名来自健康的个体和精神分裂症患者和其他疾病的患者 各种与HCP相关的项目以及英国生物库。我们特别提出测量内在中性的 静止状态fMRI数据的时间表（INT）是理论驱动的兴奋/抑制索引（E/I）不平衡的指标 皮质微电路。首先，扩展我们的先前工作，我们旨在确认并进一步表征INT更改 在精神分裂症中 与精神病症状有关）并与其他疾病相比测试其特异性。第二，我们将评估 INT的发育轨迹和表征该功能磁共振成像测量的遗传特征及其重叠 具有精神分裂症风险的遗传特征。第三，鉴于E/I比在皮质微电路中的作用 支持工作记忆计算，我们将检查INT与工作记忆之间的关系 激活和性能。我们将进一步寻求建立INT作为电路级别的多基因风险的中介 精神分裂症认知缺陷。在整个过程中，我们将使用功率良好，严格，最先进的fMRI和 统计数据驱动的方法适用于大规模研究和HCP风格的fMRI序列，包括交叉 验证和通用性测试。以及强大的理论基础并使用生物物理 建模以补充fMRI分析，这种混合动力（理论和数据驱动）将有助于 对电路级机制的综合理解，桥接识别遗传风险因素和近端 精神分裂症的表现。特别是，尖端的细胞类型富集分析的结合 GWAS（在精神分裂症中，这表明兴奋性和抑制性皮质会融合富集 细胞）和在相互作用兴奋性和抑制性的皮质微电路水平上的生物物理模型 细胞种群将根据收敛的细胞和电路级别提供不同数据的解释 途径。这样，该项目将验证理论上的信息，可解释，可翻译，并且 可扩展的休息状态fMRI测量 - Int-可能与几种疾病有关 此外，由于其高可靠性和易于获取的易用性，作为候选生物标志物具有很高的潜力。