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

• 批准号: 10381940
• 负责人: ERIN A. HAZLETT
• 金额: $ 20.68万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 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/10381940
• 关键词: Acoustics; Activities of Daily Living; Administrative Supplement; Anisotropy; Artificial Intelligence; Clinical; Cognitive; Collaborations; Computers; Data; Diffusion Magnetic Resonance Imaging; Disease; Emotional; Goals; Grain; Grant; Graph; Human; Impairment; Individual; Inferior; Interview; Language; Lead; Linguistics; Longitudinal Studies; Machine Learning; Magnetic Resonance Imaging; Measures; Metaphor; Methods; Modeling; Multimodal Imaging; Natural Language Processing; Neurobiology; Neurocognitive; Parents; Patients; Performance; Phenotype; Production; Psychoses; Quality of life; Risk; Risk Assessment; Risk Factors; Sampling; Schizophrenia; Schizotypal Personality Disorder; Scientist; Semantics; Short-Term Memory; Social Adjustment; Source; Speech; Structure; Symptoms; Techniques; Temporal Lobe; Testing; Thick; Time; analytical method; blood oxygenation level dependent response; brain abnormalities; cohort; frontal lobe; functional disability; functional outcomes; indexing; innovation; longitudinal course; medical schools; multimodality; neuroimaging; novel; preservation; processing speed; protective factors; schizophrenia-spectrum disorder; social; social cognition; syntax; white matter

ABSTRACT The parent R01 project is a longitudinal study examining risk and protective factors in the schizophrenia (SZ) spectrum—from healthy controls (HCs) to individuals with schizotypal personality disorder (SPD) to recent-onset SZ patients (80 per group)—using MRI and neurocognitive approaches. It tests a neurobiological model which posits that individuals with SPD—an intermediate phenotype—have protective factors against developing threshold psychosis, such as preservation of frontal lobe and less severe temporal lobe abnormalities compared to SZ that lead to milder cognitive and social impairments. Examining natural language processing (NLP) as proposed in this supplement is in line with the scope and aims of the parent R01 project and may inform the key neurobiological model being tested. Moreover, examining NLP using novel measures of semantics and syntax in association with measures from the parent R01 of frontal and temporal white matter integrity/connectivity assessed with diffusion tensor imaging and cognitive domains such as processing speed and working memory is innovative. Speech and language provide a rich source of data on human thought, including semantic and emotional content, semantic coherence (i.e. flow of meaning), and syntactic structure and complexity (i.e. usage of parts of speech). There is a critical gap in our understanding of the linguistic mechanisms that underlie thought disorder in SZ spectrum. The use of automated linguistic analytic methods has been limited to only a few studies focused on discriminating SZ patients from HCs and predicting psychosis. Together with our colleagues with expertise in NLP at Icahn School of Medicine at Mount Sinai, we will use advanced computational speech analytic approaches to identify the linguistic basis of language production along a spectrum from normal to thought disordered. We will use optimal interviewing techniques1 to obtain open-ended 30-45 minute narratives from the large (N = 240) English-speaking sample in the parent R01 study, with a range of language disturbances across the spectrum ranging from none/subtle to severe. NLP techniques including Latent Semantic Analysis2 (LSA) and part-of-speech (POS) tagging3,4 will be conducted using artificial intelligence to examine semantic and syntactic language features to include in our overall neurobiological model. These analyses yield fine-grained indices of speech and language that may more accurately capture thought disorder. Three specific aims will assess (1) semantic coherence in language production using LSA2 and examine its association with positive symptoms and functional impairment across the spectrum; (2) syntactic complexity in language production using POS tagging3,4 and measure acoustic features to examine their association with negative symptoms and functional impairment; and (3) the relationship between language and speech features (semantic, syntactic, and acoustic) with putative white matter integrity assessed using diffusion tensor imaging.

抽象的 家长R01项目是一项纵向研究，研究了精神分裂症的风险和受保护因素 （SZ）频谱 - 从健康对照（HC）到精神分裂型人格障碍（SPD）的个体到 最近发作的SZ患者（每组80例） - 使用MRI和神经认知方法。它测试神经生物学 模型认为，具有SPD的个体（一种中间表型）具有保护因素 开发阈值精神病，例如保存额叶和不太严重的临时叶 与导致米勒认知和社会障碍的SZ相比，异常。检查自然 该补充剂中提出的语言处理（NLP）符合父母的范围和目的 R01项目，可能会告知正在测试的关键神经生物学模型。而且，使用小说检查NLP 语义和语法的度量与额叶和临时性的母体R01的度量相关 通过扩散张量成像和认知领域评估的白质完整性/连通性（例如 处理速度和工作记忆是创新的。 语音和语言提供了有关人类思想的丰富数据来源，包括语义和情感 内容，语义连贯性（即含义流）以及句法结构和复杂性（即零件的使用 我们对思想基础的语言机制的理解存在一个危险的差距 SZ频谱中的疾病。自动语言分析方法的使用仅限于少数 研究的重点是将SZ患者与HCS区分开并预测精神病。 与我们的同事在西奈山的伊坎医学院的NLP专业知识一起，我们将使用 先进的计算语音分析方法来识别语言生产的语言基础 沿着从正常到思想无序的频谱。我们将使用最佳采访技术1获得 来自大型（n = 240）英语样本的开放式30-45分钟的叙述在父母R01中 研究，整个频谱中的语言灾难范围从无/微妙到严重。 NLP 将进行包括潜在语义分析2（LSA）和言论部分（POS）标记的技术3,4 使用人工智能检查语义和句法语言特征，以包括我们的整体 神经生物学模型。这些分析产生的言语和语言索引，可能会更多 准确捕获思想障碍。 三个具体目标将使用LSA2评估（1）语言生产的语义连贯性，并检查其 与整个光谱的积极症状和功能障碍相关； （2）句法复杂性 使用POS标记的语言生产3,4并测量声学特征，以检查其与 负面症状和功能障碍； （3）语言和语音特征之间的关系 （语义，句法和声学），使用扩散张量成像评估了假定的白质完整性。