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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.

项目总结