Neuroimaging Dimensions at the Extremes of the Schizophrenia Spectrum

项目摘要 精神分裂症（SZ）是一种严重的精神健康障碍，目前使用抗精神病药物治疗， 有严重的副作用，在约30%的患者中无效，并且不能作为预防性治疗。与 仅在美国，其负担估计为3432亿美元，迫切需要改善早期识别 SZ的策略和改善功能的治疗。更好地理解潜在的神经生物学 是实现这些目标的关键。在全球范围内，大规模的合作神经成像工作的进展能够 生成SZ脑异常的可复制结果，并评估临床和混杂因素的贡献 变量我们之前在ENIGMA（通过荟萃分析增强神经影像遗传学）SZ中的工作 一个工作组确定了主要的灰质缺陷，如皮质下体积较小， SZ皮质然而，我们的研究结果也强调了混杂变量对此类临床研究的重要性。 神经影像学数据，如抗精神病药物或疾病慢性化。与此相反， (SZT)工作组，研究自我报告亚临床精神病特征的功能良好的健康个体， 最近发现，较高的SZT与较厚的皮质有关。此外，SZT的皮质厚度分布 与SZ的皮质变薄呈负相关。基于这些发现，我们认为， 在SZ频谱连续体的极端处的神经异常的综合表征可能不 这只会进一步加深我们对低负债（SZT）和高负债（SZ）的理解，但也可能 对SZ（功能）和临床个体的风险和恢复力预测具有更广泛的意义。 精神病高危人群这一创新的全球举措将率先整合ENIGMA SZT和SZ的数据 接受结构MRI（sMRI）、详细弥散张量成像（DTI）和静息状态功能MRI的队列 （rsfMRI）来解决SZ研究中的以下关键问题： SZT和SZ的签名？这些特征如何与症状严重程度和整体功能相关？可以 基于这些多模态神经成像的SZT和SZ可靠性的标签噪声降低维度测量 措施预测功能不佳？充分利用全球数据和专家团队，从最少24人到可能更好 超过100个队列，我们将解决有关SZ频谱的成像，临床和预测问题， 前所未有的力量。该项目将采用标准化的图像分析，质量保证和统计 使用来自相同个体的多模态神经成像和临床数据进行跨队列的分析程序。 这将产生SZT和SZ的重复FC和SC签名，确定神经成像之间的关系 SZT和SZ的标志物，以及与症状维度和功能的关系。它会把机器- 基于学习的模型，其可用于生成基于成像的、维度的生物标志物， 治疗目标、临床结果的预测因子或高危人群转化为精神病的预测因子。

PROJECT SUMMARY Schizophrenia (SZ) is a severe mental health disorder currently treated with antipsychotic drugs which often have serious side effects, are ineffective in ~30% of patients, and are not useful as a preventive treatment. With its burden estimated at $343.2 billion in the US alone, there is a pressing need to improve early identification strategies for SZ and treatments that improve functioning. A better understanding of the underlying neurobiology is key to achieving these goals. Advances in global, large-scale, collaborative neuroimaging efforts are able to generate replicable findings on brain abnormalities in SZ and assess contributions of clinical and confounding variables. Our prior work within the ENIGMA (Enhancing Neuroimaging Genetics through Meta-Analysis) SZ working group identified predominantly gray matter deficiencies such as smaller subcortical volumes and thinner cortex in SZ. However, our findings also highlighted the importance of confounding variables on such clinical neuroimaging data, such as antipsychotic medication or disease chronicity. In contrast, the ENIGMA Schizotypy (SZT) working group, studying well-functioning healthy individuals who self-report subclinical psychotic traits, recently found that higher SZT was associated with thicker cortex. Moreover, the cortical thickness profile in SZT was inversely related with the profile of cortical thinning in SZ. Based on these findings, we posit that a comprehensive characterization of neural abnormalities at the extremes of the SZ spectrum continuum may not only further our understanding of the low liability (SZT) and high liability (SZ) ends of the spectrum but may also have wider implications for the prediction of risk and resilience in SZ (functioning) and in individuals at clinical high-risk for psychosis. This innovative global initiative will be first to integrate data across ENIGMA SZT and SZ cohorts with structural MRI (sMRI), detailed diffusion tensor imaging (DTI), and resting-state functional MRI (rsfMRI) to address the following key questions in SZ research: what are the functional and structural connectivity signatures of SZT and SZ? How are these signatures related to symptom severity, and global functioning? Can label-noise reduced dimensional measures of SZT and SZ liability based on these multimodal neuroimaging measures predict poor functioning? Leveraging global data and expert teams from minimally 24 to possibly well over 100 cohorts, we will tackle imaging, clinical, and predictive questions about the SZ spectrum with unprecedented power. This project will employ standardized image analyses, quality assurance, and statistical analysis procedures across cohorts with multimodal neuroimaging and clinical data from the same individuals. This will yield replicated FC and SC signatures of SZT and SZ, determine relationships between neuroimaging markers of SZT and SZ, and relationships with symptom dimensions and functioning. It will deliver machine- learning based models that can be used to generate imaging-based, dimensional, biomarkers that may serve as treatment targets, predictors of clinical outcome, or predictors of conversion to psychosis in at-risk populations.