Longitudinal neuroimaging and neurocognitive assessment of risk and protective factors across the schizophrenia spectrum

精神分裂症谱系风险和保护因素的纵向神经影像和神经认知评估

基本信息

批准号：
10319171
负责人：
ERIN A. HAZLETT
金额：
$ 99.89万
依托单位：
ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-03-06 至 2024-12-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10319171
关键词：
Age Antipsychotic Agents Attenuated Brain Brain region Brodmann&apos s area Clinical Clinical assessments Cognition Cognitive deficits Coupled Data Deterioration Diagnosis Diagnostic Diffusion Magnetic Resonance Imaging Disease Etiology Event Functional Magnetic Resonance Imaging Genetic Goals Hospitalization Impairment Individual Investigation Link Longitudinal Studies Machine Learning Magnetic Resonance Imaging Measures Modeling Neurobiology Neurocognition Neurocognitive Neuropsychology Onset of illness Outcome Pathogenesis Patients Pattern Performance Personality Disorders Pharmaceutical Preparations Phenotype Prefrontal Cortex Psychoses Research Domain Criteria Rest Risk Risk Assessment Risk Factors Scanning Schizophrenia Schizotypal Personality Disorder Severities Short-Term Memory Structure Symptoms Temporal Lobe Testing Time Treatment Factor Ursidae Family base causal model early onset follow-up frontal lobe functional outcomes improved longitudinal course longitudinal design morphogens multimodal neuroimaging multimodality neural circuit neuroimaging novel strategies predictive modeling protective factors resilience schizophrenia-spectrum disorder social deficits white matter

项目摘要

PROJECT SUMMARY Schizotypal personality disorder (SPD) is similar to schizophrenia (SZ), but with fewer and attenuated abnormalities, thus representing an important yet understudied intermediate SZ-spectrum phenotype. Examination of abnormalities in SPD will provide information regarding etiology, genetics, treatment and risk factors associated with psychosis. Although individuals with SPD demonstrate marked temporal lobe abnormalities that resemble SZ, we hypothesize that relative “sparing” or “functional enhancement” in the frontal lobes (e.g., dorsolateral prefrontal cortex), may protect these individuals from frank psychosis and the severe social and cognitive deficits typically observed in SZ. Studying SPD is powerful as antipsychotic medication and hospitalization confounds observed in SZ are not present. Moreover, there is no study examining neurobiological changes in the SZ-spectrum that incorporates individuals with SPD using a longitudinal design as proposed here. This novel approach will help disentangle potential risk and protective factors for psychosis in the SZ spectrum. This is the first longitudinal study to utilize multimodal MR imaging and Research Domain Criteria (RDoC) approaches in SZ-spectrum disorders to identify aberrant neural circuitry along a continuum from healthy controls (HCs) to SPD to SZ and examine changes in these measures in relationship to impairments in symptom severity, neurocognition and functional outcome. We propose studying three groups (80 in each) of demographically matched and rigorously diagnosed individuals (age 18- 40): HCs (no Axis I or personality disorder), unmedicated individuals with SPD (and no Axis I disorder), and early-onset (first 2 years of illness) SZ patients at baseline, 9-, and 18-month follow-up. Measures assessing frontal and temporal lobe integrity include multimodal MR imaging (structural MRI, DTI, resting-state fMRI, and task-based fMRI with a nonverbal event related working-memory task; baseline and 18-months) and neuropsychological assessment (all three timepoints). We will utilize dynamic causal modeling to test competing neurobiological models involving abnormal frontotemporal connectivity in the SZ-spectrum and machine learning approaches to integrate multimodal neuroimaging, neurocognitive, and clinical assessment data. We focus on three specific aims: (1) Investigate the longitudinal course of frontal-temporal lobe/circuitry abnormalities in the SZ-spectrum using multimodal MR imaging; (2) Investigate the longitudinal course of neurocognition, clinical, and functional outcome in the SZ spectrum; (3) Determine which factor or combination of factors differentiate groups in the SZ-spectrum to identify those that are associated with risk for and protection from SZ using machine learning.

项目概要精神分裂型人格障碍（SPD）与精神分裂症（SZ）相似，但发病较少且程度较轻异常，因此代表了一种重要但尚未研究的中间 SZ 谱表型。 SPD 异常检查将提供有关病因、遗传学、治疗和风险的信息尽管 SPD 患者表现出明显的颞叶特征。与 SZ 类似的异常，我们将其称为“保留”或“功能增强” 额叶（例如背外侧前额叶皮层）可以保护这些人免受明显的精神病和研究 SPD 具有强大的抗精神病药作用。此外，尚无在深圳观察到的药物治疗和住院混淆的研究。使用 SZ 谱检查 SPD 个体的神经生物学变化这里提出的纵向设计将有助于消除潜在的风险和保护。 SZ 谱中影响精神病的因素这是第一个利用多模态 MR 成像的纵向研究。和研究领域标准（RDoC）方法在 SZ 谱疾病中识别异常神经沿着从健康对照 (HC) 到 SPD 到 SZ 的连续体的电路，并检查这些措施的变化我们提出与症状严重程度、神经认知和功能结果损害的关系。研究三组（每组 80 人）人口统计匹配且经过严格诊断的个体（年龄 18- 40)：HC（无轴 I 或人格障碍）、未接受药物治疗的 SPD 个体（无轴 I 障碍），以及早发（患病前 2 年）的 SZ 患者在基线、9 个月和 18 个月的随访中进行措施评估。额叶和颞叶完整性包括多模态 MR 成像（结构 MRI、DTI、静息态 fMRI 和基于任务的功能磁共振成像（fMRI），具有非语言事件相关的工作记忆任务（基线和 18 个月）；神经心理学评估（所有三个时间点）。我们将利用动态因果模型进行测试。涉及 SZ 频谱中异常额颞叶连接的竞争性神经生物学模型整合多模式神经影像、神经认知和临床评估的机器学习方法我们专注于三个具体目标：（1）研究额颞叶/电路的纵向过程。使用多模态 MR 成像检测 SZ 谱异常；(2) 研究 SZ 谱的纵向过程； (3) 确定哪个因素或组合的因素区分 SZ 谱中的群体，以确定与以下风险相关的因素：使用机器学习来保护 SZ。