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：
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
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/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

项目摘要

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.

抽象的父项目 R01 是一项纵向研究，检查精神分裂症的风险和保护因素（SZ）谱——从健康对照（HC）到精神分裂型人格障碍（SPD）个体最近发病的精神分裂症患者（每组 80 名）——使用 MRI 和神经认知方法。该模型假设患有 SPD（一种中间表型）的个体具有针对发展精神病阈值，例如保留额叶和不太严重的颞叶与 SZ 相比，这些异常会导致较轻的认知和社交障碍。本补充中提出的语言处理（NLP）符合家长的范围和目标 R01 项目可能会为正在测试的关键神经生物学模型提供信息，此外，还可以使用新颖的方法来检查 NLP。与来自额叶和颞叶的父 R01 的测量相关的语义和句法测量通过扩散张量成像和认知领域评估白质完整性/连接性，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维的丰富数据源，包括语义和情感内容、语义连贯性（即意义的流动）以及句法结构和复杂性（即部分的使用）我们对思想背后的语言机制的理解存在着严重的差距。自动语言分析方法的使用仅限于少数。研究重点是区分 SZ 患者与 HC 并预测精神病。与我们在西奈山伊坎医学院拥有 NLP 专业知识的同事一起，我们将使用先进的计算语音分析方法来识别语言产生的语言基础我们将使用最佳访谈技巧1来获得从正常到思维混乱的范围。来自父 R01 中大型（N = 240）英语样本的 30-45 分钟开放式叙述研究中存在一系列从无/微妙到严重的语言障碍。将使用包括潜在语义分析2（LSA）和词性（POS）标记3,4在内的技术使用人工智能来检查语义和句法语言特征，以包含在我们的整体中这些分析产生了更细粒度的言语和语言指数。准确捕捉思维混乱。三个具体目标将评估 (1) 使用 LSA2 的语言生成中的语义连贯性并检查其 (2) 句法复杂性使用词性标记 3,4 进行语言生成，并测量声学特征以检查其与阴性症状和功能障碍；以及（3）语言和言语特征之间的关系。（语义、句法和声学），并使用扩散张量成像评估假定的白质完整性。