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）的人都是晚期-或从未诊断（LDx）。初始数据链接女性 性别LDx性别多样性在ASD中也比例过高，并与LDx相关。自闭症患者被分配到 出生时女性（ASDaF）和性别多样性（ASDgd）的人的精神疾病发生率增加 合并症，以及在ASDgd的情况下，自杀。LDx与抑郁、焦虑和 自我伤害和限制获得支持，增加了遭受虐待的脆弱性。及时诊断（Dx）的障碍 ASDaF可能涉及个体水平的生物行为差异，包括较少的不寻常的限制/重复 行为（RRB），以及社会动机，执行功能和/或智力的优势。背景因素， 例如，在转诊模式和临床上， 诊断，也起到关键作用。我们的目标是整合定性、定量和人工智能方法 确定LDx的背景和生物行为预测因子，从而开发出一种可行的筛查方法， 对于那些有LDx风险的人。为了阐明LDx（第一ASD Dx > 12 y）的机制，我们将建立三个遗产 我们长达十年的纵向ACE网络：1）性别平衡，深度表型，纵向队列， 自闭症青年和年轻人; 2）在ASD中验证的性别表征方法-性别自我报告 量表（GSRS）-量化性别认同（二元和非二元）特征，超出指定的性别;以及3） 与自闭症合作研究人员合作，让社区参与者开发自我报告 工具-自闭症特征的自我评估（SAAT）-捕捉ASD的生活经验，包括 长处.我们将招募一个性别平衡的社区自闭症患者样本（年龄16- 30岁）， 我们的纵向ACE队列有两个关键亚组：LDx和ASDgd个体。我们将使用故意 在指定性别、性别和性别多样性、民族-种族身份之间进行抽样和公平包容， ASD患者的LDx。使用混合方法的方法，我们将确定LDx的标志物，并检查 Dx时间和健康结局中性别之间的相互作用。一个性别，性别和种族多样的 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，所有人都有专业和/或生活经验， LDx ASD的经验，将指导我们：1.识别性别、认知和行为差异 及时（TDx）和LDx自闭症患者之间的差异。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/成人提供诊断接入点。3.制定个性化的方法， 用于诊断时间分类（LDx与TDx）和QoL指数预测的生物行为标记提取， 使用创新的人工智能方法将多模态神经成像数据与表型 信息.我们将通过加速青少年的识别来改善研究和临床实践， 患有ASD的成年人，他们在诊断过程中传统上被遗漏或误导。这项工作将 在社区中加速ASD Dx，以获得适当的支持和改善的结果。

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.