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.

项目总结 精神分裂症(SZ)是一种严重的精神健康障碍，目前用抗精神病药物治疗，通常 有严重的副作用，在约30%的患者中无效，作为预防治疗无效。使用 据估计，仅在美国就有3432亿美元的负担，迫切需要改善早期识别 针对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。全球、大规模、协作的神经成像工作的进展能够 在深圳产生可复制的脑异常发现，并评估临床和混淆的贡献 变量。我们之前在Enigma(通过Meta分析增强神经成像遗传学)SZ中的工作 工作组确定了主要的灰质缺陷，如皮质下体积较小和较薄 深圳的大脑皮层。然而，我们的发现也强调了混杂变量对此类临床研究的重要性。 神经影像数据，如抗精神病药物或疾病慢性化。相比之下，谜团分裂型 (SZT)工作组，研究自我报告亚临床精神病特征的功能正常的健康人， 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的皮质变薄程度呈负相关。基于这些发现，我们假设 SZ谱连续体两端的神经异常的综合特征可能不 只有进一步了解频谱的低负债(SZT)和高负债(SZ)端，还可以 对SZ(功能)和临床个体的风险和弹性的预测有更广泛的影响 精神错乱的高风险人群。这一创新的全球计划将是第一个在SZT和SZ之间集成数据的创新计划 结构磁共振成像(SMRI)、详细扩散张量成像(DTI)和静息状态功能磁共振成像的队列 (RsfMRI)来解决SZ研究中的以下关键问题：什么是功能和结构连接 SZT和SZ的签名？这些特征与症状严重程度和全球功能有何关系？能 基于这些多模式神经成像的SZT和SZ倾向性的标签噪声降维测量 衡量标准预测功能不佳？利用全球数据和专家团队，从最低24人到尽可能好 超过100个队列，我们将解决有关SZ频谱的成像、临床和预测性问题 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 使用来自同一个体的多模式神经成像和临床数据跨队列进行分析。 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的标志物及其与症状维度和功能的关系。它将交付机器- 基于学习的模型，可用于生成基于成像的、维度的生物标记物，这些标志物可用作 在高危人群中，治疗目标、临床结果的预测因素或转化为精神病的预测因素。