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

摘要 父母R01项目是一项纵向研究，检查精神分裂症的危险因素和保护因素 (SZ)从健康对照（HC）到典型人格障碍（SPD）个体， 近期发作的SZ患者（每组80例）-使用MRI和神经认知方法。它测试一种神经生物学 该模型假定患有SPD（一种中间表型）的个体具有保护因素， 出现阈值精神病，如保留额叶和不太严重的颞叶 与SZ相比，这些异常导致较轻的认知和社会障碍。检查自然 本附录中提出的语言处理（NLP）符合母公司的范围和目标 R01项目，并可能告知正在测试的关键神经生物学模型。此外，使用新的NLP 语义和句法的测量与来自额叶和颞叶的父R01的测量相关联 通过弥散张量成像和认知领域评估白质完整性/连通性， 处理速度和工作记忆是创新的。 语音和语言为人类思维提供了丰富的数据来源，包括语义和情感 内容、语义连贯性（即意义的流动）、句法结构和复杂性（即部件的使用 演讲）。在我们对构成思维基础的语言机制的理解上，存在着一个关键的缺口 SZ频谱中的无序。自动化语言分析方法的使用仅限于少数几种 研究集中于区分SZ患者和HC患者并预测精神病。 我们将与西奈山伊坎医学院在NLP方面具有专业知识的同事一起， 先进的计算语音分析方法，以确定语言产生的语言基础 沿着一个从正常到思维紊乱的光谱。我们将使用最佳的面试技巧1， 来自母研究R01中大量（N = 240）英语样本的开放式30 - 45分钟叙述 研究中，语言障碍的范围从无/轻微到严重。NLP 将采用潜在语义分析（LSA）和词性标注（POS）等技术 使用人工智能来检查语义和句法语言特征， 神经生物学模型这些分析产生了细粒度的语音和语言指数， 准确地捕捉思维障碍。 三个具体的目标将评估（1）语义连贯性的语言生产使用LSA 2，并检查其 与阳性症状和功能障碍的关系;（2）句法复杂性， 使用POS标记的语言产生3，4并测量声学特征，以检查它们与 阴性症状和功能障碍;（3）语言和言语特征之间的关系 （语义、句法和声学）与使用扩散张量成像评估的推定白色物质完整性。

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.