Neuroimaging Dimensions at the Extremes of the Schizophrenia Spectrum

精神分裂症谱系极端的神经影像维度

• 批准号: 10753887
• 负责人: Gemma Modinos
• 金额: $ 77.75万
• 依托单位: UNIVERSITY OF CALIFORNIA-IRVINE
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-15 至 2028-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10753887
• 关键词: Address; Adolescence; Adult; Antipsychotic Agents; Autopsy; Biological Markers; Biology; Brain; Brain Diseases; Brain imaging; Brain region; Characteristics; Chronic; Clinical; Clinical Data; Complex; Confounding Factors (Epidemiology); Data; Development; Diagnosis; Diffusion; Diffusion Magnetic Resonance Imaging; Dimensions; Disease; Early identification; Early treatment; Functional Magnetic Resonance Imaging; Future; General Population; Genetic; Goals; Health; Image; Image Analysis; Individual; Inherited; Interneurons; Joints; Label; Magnetic Resonance Imaging; Measures; Mental disorders; Meta-Analysis; Modeling; Neurobiology; Neuropil; Noise; Outcome; Pathway interactions; Patient Self-Report; Patients; Pattern; Personality Traits; Pharmaceutical Preparations; Pharmacotherapy; Populations at Risk; Prefrontal Cortex; Prevention; Preventive treatment; Procedures; Psychoses; Research; Rest; Risk; Role; Schizophrenia; Severities; Shapes; Standardization; Statistical Data Interpretation; Structure; Symptoms; Synapses; Testing; Thick; Thinness; Work; adverse outcome; brain abnormalities; brain circuitry; clinical high risk for psychosis; cohort; cost; data integration; functional improvement; functional outcomes; gray matter; improved; innovation; machine learning method; machine learning model; multimodal neuroimaging; multimodality; neural; neuroimaging; neuroimaging marker; novel; predict clinical outcome; psychotic; psychotic symptoms; quality assurance; resilience; risk prediction; schizophrenia spectrum disorder; side effect; trait; working group

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.

项目摘要 精神分裂症（SZ）是一种严重的心理健康障碍，目前接受抗精神病药治疗，通常 具有严重的副作用，在约30％的患者中无效，并且没有用作预防治疗。和 仅在美国，它的负担估计为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方法。更好地了解基本神经生物学 是实现这些目标的关键。全球，大规模合作的神经影像学努力的进步能够 产生有关SZ大脑异常的可复制发现，并评估临床和混杂的贡献 变量。我们在谜中的先前工作（通过荟萃分析增强了神经影像学）SZ 工作组确定了主要是灰质的缺陷，例如较小的皮层体积和较薄 SZ的皮质。但是，我们的发现还强调了混淆变量对这种临床的重要性 神经影像学数据，例如抗精神病药物或疾病慢性。相比之下，谜 （SZT）工作组，研究自我报告亚临床精神病特征的能力良好的健康个体， 最近发现，较高的SZT与较厚的皮质有关。此外，SZT的皮质厚度谱 与SZ中皮质稀疏的特征成反比。基于这些发现，我们认为 SZ光谱连续体极端神经异常的全面表征可能不会 只有我们对频谱的低负债（SZT）和高负债（SZ）的理解，但也可以 对SZ（功能）和临床中个人的风险和弹性的预测具有更大的影响 高危精神病。这项创新的全球计划将首先集成跨越Enigma SZT和SZ的数据 与结构MRI（SMRI），详细的扩散张量成像（DTI）和静止状态功能MRI的队列 （RSFMRI）解决SZ研究中的以下关键问题：功能和结构连接性是什么 SZT和SZ的签名？这些签名与症状严重程度和全球功能有何关系？能 标签噪声降低了基于这些多模式神经影像学的SZT和SZ责任的尺寸衡量标签 措施预测功能不佳？利用全球数据和专家团队从最低限度到24到可能很好 超过100个队列，我们​​将解决有关SZ Spectrum的成像，临床和预测性问题 空前的力量。该项目将采用标准化的图像分析，质量保证和统计 来自同一个体的多模式神经影像学和临床数据的跨人群的分析程序。 这将产生SZT和SZ的复制FC和SC签名，确定神经成像之间的关系 SZT和SZ的标记，以及与症状维度和功能的关系。它将提供机器 - 基于学习的模型，可用于生成基于成像的尺寸，生物标志物，可以用作 治疗目标，临床结果的预测因素或高危人群中精神病的预测因素。