An integrative computational interrogation of circuit dysfunction inschizophrenia via neural timescales

摘要/摘要 精神分裂症是一种毁灭性的、负担沉重的疾病，其发病机制仍然难以捉摸。贡献 他们难以捉摸的是一组高度复杂的遗传因素，提出了病因和病理生理学 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 大规模多模式数据集的数据驱动方法和理论驱动的计算方法 为了提供一个理论上受限的框架，连接遗传学、发育、电路功能、 精神分裂症的认知和现象学。为此目的，并回应“特别关注通知” 关于使用人类连接组数据(HCP)进行二次分析，我们将使用以下数据 64,000人，包括健康人和精神分裂症及其他障碍患者，来自 与HCP相关的各种项目以及英国生物库。我们特别建议测量固有神经 静息状态fMRI数据的时间尺度(INT)作为兴奋/抑制(E/I)失衡的理论驱动指标 大脑皮层微电路。首先，扩展我们之前的工作，我们的目标是确认和进一步表征INT蚀变 在精神分裂症中(广泛的特质样INT减少和局部等级依赖的INT调制 与精神病症状的关系)，并测试它们相对于其他障碍的特异性。第二，我们将评估 INT的发育轨迹和表征这一fMRI测量及其重叠的遗传学特征 有精神分裂症风险的基因特征。第三，考虑到E/I比在大脑皮质微回路中的作用 为了支持工作记忆计算，我们将检查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作为电路级的多基因风险的调解人 患有认知缺陷的精神分裂症。在整个过程中，我们将使用强大的、严格的、最先进的fMRI和 适用于大规模研究和HCP风格的fMRI序列的统计数据驱动方法，包括交叉 通用性的验证和测试。具有坚实的理论基础，并使用生物物理学 建模是对功能磁共振分析的补充，这种混合理论和数据驱动的方法将有助于 对连接远端遗传风险因素和近端遗传风险因素的电路水平机制的综合理解 精神分裂症的表现。特别是，结合尖端细胞类型富集性分析 Gwas(在精神分裂症患者中发现兴奋性和抑制性皮质的聚集性丰富 细胞)和在皮层微电路水平上相互作用的兴奋和抑制的生物物理模型 蜂窝种群将在汇聚的单元和电路级别上提供对不同数据的解释 小路。在这样做的过程中，这个项目将验证一个理论上信息量大、可解释、可翻译和 可扩展的静息状态fMRI测量-INT-可能与几种疾病相关，并且 此外，由于其高度的可靠性和易获得性，具有作为候选生物标志物的高潜力。

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.