Autism Center of Excellence Network: Neurodevelopmental Biomarkers of Late Diagnosis in Female and Gender Diverse Autism

项目摘要 许多患有自闭症谱系障碍(ASD)的人都很晚-或者从未被诊断(LDX)。初始数据链接女性 性爱到LDX。性别多样性在自闭症患者中的比例也过高，并与LDX有关。自闭症患者被分配 出生时女性(ASDaF)和性别多样性(ASDgd)的人经历了更多的精神疾病 共病，在ASDgd的情况下，自杀。低密度脂蛋白与抑郁、焦虑和 自我伤害，并限制获得支持，增加了滥用的脆弱性。及时诊断(Dx)的障碍 ASDAF可能涉及个体水平的生物行为差异，包括较少不寻常的限制性/重复性 行为(RRB)，以及在社会动机、执行功能和/或智力方面的优势。背景因素， 例如，青少年-顺性-男性对ASD的概念化在转诊模式和临床上占优势 诊断，也起着关键作用。我们的目标是集成定性、定量和人工智能方法 确定LDX的上下文和生物行为预测因素，从而开发出可行的筛查方法 对那些有LDX风险的人进行测量。为了阐明LDX(第一个ASD Dx&gt；12y)的机制，我们将建立在三个遗产的基础上 我们长达十年的纵向ACE网络：1)性别平衡、表型深刻的纵向队列 自闭症青少年和青壮年；2)在ASD-性别自我报告中验证的性别表征方法 量表(GSR)-量化性别认同(二进制和非二进制)特征，超越指定的性别；以及3) 与自闭症合作研究人员合作，让社区参与者参与制定自我报告 工具-自闭症特征自我评估(SAAT)-捕捉自闭症患者的生活经历，包括 强项。我们将招募一个性别平衡的社区自闭症患者样本(16-30岁)来增强 我们的ACE纵向队列包括两个关键亚组：LDX和ASDgd个体。我们将使用故意 在指定的性别、性别和性别多样性、族裔-种族认同以及 患有自闭症的LDX患者。使用混合方法，我们将识别LDX的标记并检查 性别和性别在Dx时机和幸福结果中的相互作用。性别、性别和民族的多样性 由临床医生、自我倡导者、自闭症患者和父母组成的利益相关者团队，他们都是专业人士和/或曾居住过 LDX ASD的经验将指导我们：1.确定性别、性别、认知和行为差异 在及时型(TDX)和LDX自闭症患者之间。2.制定并验证自我报告的ASD筛查措施 为有LDX风险的青少年/成年人提供诊断接入点。3.开发个性化的方法来 用于分类诊断时间(LDX与TDX)和预测QOL指数的生物行为标记提取， 使用创新的人工智能方法将多模式神经成像数据与表型集成 信息。我们将通过加快青少年识别和临床实践来改进研究和临床实践 患有自闭症的成年人，传统上在诊断过程中被遗漏或误导。这项工作将 加快ASD Dx在社区中的应用，以获得适当的支持并改善结果。

Project Summary Many people with autism spectrum disorder (ASD) are late- or never diagnosed (LDx). Initial data links female sex to LDx. Gender diversity is also overrepresented in ASD and associated with LDx. Autistic people assigned female at birth (ASDaF) and those who are gender diverse (ASDgd) experience increased psychiatric comorbidity, and, in the case of ASDgd, suicidality. LDx is associated with increased depression, anxiety and self-harm and limits access to supports, increasing vulnerability to abuse. Obstacles to timely diagnosis (Dx) of ASDaF may involve individual-level biobehavioral differences, including fewer unusual restricted/repetitive behaviors (RRBs), and strengths in social motivation, executive function and/or intelligence. Contextual factors, such as the predominance of young-cisgender-male conceptualizations of ASD in referral patterns and clinical diagnosis, also play a key role. Our goal is to integrate qualitative, quantitative, and artificial intelligence methods to identify contextual and biobehavioral predictors of LDx, leading to the development of a practicable screening measure for those at LDx risk. To illuminate mechanisms of LDx (1st ASD Dx > 12y) we will build on three legacies of our decade-long longitudinal ACE Network: 1) a sex-balanced, deeply phenotyped, longitudinal cohort of autistic youth & young adults; 2) a gender characterization method validated in ASD—the Gender Self-Report Scale (GSRS)—to quantify gender identity (binary and nonbinary) characteristics beyond assigned sex; and 3) a collaboration with autistic co-researchers to engage community-based participants to develop a self-report tool—the Self-Assessment of Autistic Traits (SAAT)—that captures the lived experience of ASD, including strengths. We will recruit a sex-balanced community-based sample of autistic people (ages 16-30y) to augment our longitudinal ACE cohort with two critical subgroups: LDx and ASDgd individuals. We will use intentional sampling and equitable inclusion across assigned sex, gender and gender diversity, ethno-racial identity, and LDx individuals with ASD. Using a mixed-methods approach, we will identify markers of LDx and examine the interplay between sex and gender in Dx timing and well-being outcomes. A sex, gender, and ethnoracially diverse stakeholder team of clinicians, self-advocates, autistic people, and parents, all with professional and/or lived experience with LDx ASD, will guide us as we: 1. Identify sex, gender, cognitive, and behavioral differences between timely (TDx) and LDx autistic people. 2. Develop and validate a self-report ASD screening measure as a diagnostic access point for adolescents/adults at risk for LDx. 3. Develop a personalized approach to biobehavioral marker extraction for classification of diagnostic timing (LDx vs. TDx) and prediction of QoL indices, using an innovative artificial intelligence approach to integrate multimodal neuroimaging data with phenotypic information. We will improve research and clinical practice by accelerating identification of adolescents and adults with ASD who have traditionally been missed or misdirected in the diagnostic process. This work will accelerate ASD Dx in the community, allowing for appropriate supports and improved outcomes.