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

摘要 家长R01项目是一项纵向研究，旨在检查精神分裂症患者的风险和保护因素 (SZ)谱--从健康对照组(HCS)到分裂型人格障碍(SPD)患者，再到 新近发病的SZ患者(每组80例)-使用MRI和神经认知方法。它测试了一种神经生物学 该模型假设患有SPD(一种中间表型)的个体具有保护因素 发展为临界性精神病，如额叶保留和较轻的颞叶 与SZ相比，异常会导致较轻的认知和社交障碍。审视自然 本补编建议的语言处理(NLP)与家长的范围和目标是一致的 R01项目，并可能通知正在测试的关键神经生物学模型。此外，使用小说来审查NLP 与来自额叶和颞叶的父R01的度量相关联的语义和句法的度量 使用扩散张量成像和认知域评估白质完整性/连通性 处理速度和工作记忆都是创新的。 言语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(即意义的流动)、句法结构和复杂性(即部分的使用 讲话)。我们对构成思维基础的语言机制的理解存在着严重的差距 SZ谱中的无序。自动化语言分析方法的使用仅限于少数几种 研究的重点是区分SZ患者和HCS患者并预测精神病。 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 识别语言产生的语言基础的高级计算言语分析方法 沿着从正常到思维混乱的光谱。我们将使用最佳面试技术1来获得 来自父母R01中说英语的大样本(N=240)的30-45分钟的开放式叙述 研究中，语言障碍的范围从无/轻微到严重不等。NLP 将进行包括潜在语义分析2(LSA)和词性标注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，以包括在我们的整体 神经生物学模型。这些分析产生的语音和语言的细粒度索引可能会更多 准确捕捉思维障碍。 三个具体的目标将评估(1)在使用LSA2的语言产生中的语义连贯并考察其 与阳性症状和功能损害之间的联系；(2)句法复杂性 使用词性标记3、4产生语言，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)，使用扩散张量成像评估推测的脑白质完整性。

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.