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: 10585148
• 负责人: Guillermo Horga
• 金额: $ 75.71万
• 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/10585148
• 关键词: Address; Base of the Brain; Biological Markers; Cells; Cognition; Cognitive deficits; Complement; Complex; Data; Data Reporting; Development; Diagnosis; Disease; Distal; Etiology; Foundations; Functional Magnetic Resonance Imaging; Functional disorder; Genetic; Genotype; Human; Hybrids; Individual; Link; Measures; Mediator of activation protein; Methods; Neurocognitive; Pathway interactions; Patient Self-Report; Performance; Phenotype; Population; Public Health; Rest; Role; Schizophrenia; Selection for Treatments; Short-Term Memory; Specificity; Testing; Validation; Work; behavioral phenotyping; biobank; biophysical model; candidate marker; cell type; connectome data; genetic risk factor; genome wide association study; indexing; individual patient; interest; large scale data; multimodal data; novel; phenomenological models; psychotic symptoms; relating to nervous system; response; 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.

总结/摘要 精神分裂症是一种毁灭性的和沉重的疾病，其机制仍然难以捉摸。贡献 他们的难以捉摸是一个高度复杂的遗传因素，提出病因和病理生理学 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， 大规模多模态数据集的数据驱动方法和理论驱动计算方法 为了提供一个理论上受约束的框架，桥接遗传学、发育、电路功能， 认知和精神分裂症的现象学。为此，为了回应“特别关注通知 关于使用人类连接组数据（HCP）进行二次分析，我们将使用 64，000人，包括健康人和精神分裂症及其他疾病患者， 各种HCP相关项目以及英国生物库。我们特别建议测量内在神经元 从静息态fMRI数据的时间尺度（INT）作为理论驱动的兴奋/抑制（E/I）不平衡的指标， 皮层微电路首先，扩展我们先前的工作，我们的目标是确认和进一步表征INT改变 在精神分裂症中（广泛的特质样INT降低和局部等级依赖性INT调节， 与精神病症状的关系），并测试其相对于其他疾病的特异性。第二，我们将评估 INT的发展轨迹，并描述这种功能磁共振成像测量的遗传特征及其重叠 精神分裂症风险的基因图谱第三，考虑到E/I比率在大脑皮层微回路中的作用， 支持工作记忆计算，我们将研究INT和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作为多基因风险的回路水平介导者， 精神分裂症的认知缺陷在整个过程中，我们将使用强大的，严格的，最先进的功能磁共振成像， 统计数据驱动的方法，适用于大规模的研究和HCP风格的功能磁共振成像序列，包括交叉， 验证和测试的普遍性。加上强大的理论基础，并利用生物物理 作为对功能磁共振成像分析的补充，这种混合理论和数据驱动的方法将有助于 对桥接远端遗传风险因素和近端 精神分裂症的症状特别是，结合尖端的细胞类型富集分析， GWAS（在精神分裂症中，这表明兴奋性和抑制性皮层的会聚富集， 细胞）和生物物理建模在皮层微电路的水平上相互作用的兴奋和抑制 细胞群将在会聚的细胞和电路水平上提供对不同数据的解释 途径。在这样做的过程中，这个项目将验证一个理论上信息丰富，可解释，可翻译， 可扩展的静息状态fMRI测量-INT-可能与几种疾病相关， 此外，由于其高可靠性和易于获得，具有作为候选生物标志物的高潜力。