Zhang Chen, Paolozza Angelina, Tseng Po-He, Reynolds James N, Munoz Douglas P, Itti Laurent
Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, United States.
Center for Neuroscience Studies, Queen's University, Kingston, ON, Canada.
Front Neurol. 2019 Feb 18;10:80. doi: 10.3389/fneur.2019.00080. eCollection 2019.
Fetal alcohol spectrum disorders (FASD) is one of the most common causes of developmental disabilities and neurobehavioral deficits. Despite the high-prevalence of FASD, the current diagnostic process is challenging and time- and money- consuming, with underreported profiles of the neurocognitive and neurobehavioral impairments because of limited clinical capacity. We assessed children/youth with FASD from a multimodal perspective and developed a high-performing, low-cost screening protocol using a machine learning framework. Participants with FASD and age-matched typically developing controls completed up to six assessments, including saccadic eye movement tasks (prosaccade, antisaccade, and memory-guided saccade), free viewing of videos, psychometric tests, and neuroimaging of the corpus callosum. We comparatively investigated new machine learning methods applied to these data, toward the acquisition of a quantitative signature of the neurodevelopmental deficits, and the development of an objective, high-throughput screening tool to identify children/youth with FASD. Our method provides a comprehensive profile of distinct measures in domains including sensorimotor and visuospatial control, visual perception, attention, inhibition, working memory, academic functions, and brain structure. We also showed that a combination of four to six assessments yields the best FASD vs. control classification accuracy; however, this protocol is expensive and time consuming. We conducted a cost/benefit analysis of the six assessments and developed a high-performing, low-cost screening protocol based on a subset of eye movement and psychometric tests that approached the best result under a range of constraints (time, cost, participant age, required administration, and access to neuroimaging facility). Using insights from the theory of value of information, we proposed an optimal annual screening procedure for children at risk of FASD. We developed a high-capacity, low-cost screening procedure under constrains, with high expected monetary benefit, substantial impact of the referral and diagnostic process, and expected maximized long-term benefits to the tested individuals and to society. This annual screening procedure for children/youth at risk of FASD can be easily and widely deployed for early identification, potentially leading to earlier intervention and treatment. This is crucial for neurodevelopmental disorders, to mitigate the severity of the disorder and/or frequency of secondary comorbidities.
胎儿酒精谱系障碍（FASD）是发育障碍和神经行为缺陷的最常见原因之一。尽管FASD的患病率很高，但目前的诊断过程具有挑战性，且耗费时间和金钱，由于临床能力有限，神经认知和神经行为损伤的报告情况不足。我们从多模态角度评估了患有FASD的儿童/青少年，并使用机器学习框架开发了一种高效、低成本的筛查方案。患有FASD的参与者和年龄匹配的发育正常的对照组完成了多达六项评估，包括眼球跳动任务（顺向扫视、反向扫视和记忆引导扫视）、视频自由观看、心理测量测试以及胼胝体的神经成像。我们比较研究了应用于这些数据的新机器学习方法，以获取神经发育缺陷的定量特征，并开发一种客观、高通量的筛查工具来识别患有FASD的儿童/青少年。我们的方法提供了在感觉运动和视觉空间控制、视觉感知、注意力、抑制、工作记忆、学术功能和脑结构等领域不同测量的综合概况。我们还表明，四项至六项评估的组合产生了最佳的FASD与对照组分类准确率；然而，该方案昂贵且耗时。我们对六项评估进行了成本效益分析，并基于眼球运动和心理测量测试的一个子集开发了一种高效、低成本的筛查方案，该方案在一系列限制条件（时间、成本、参与者年龄、所需管理以及神经成像设备的可用性）下接近最佳结果。利用信息价值理论的见解，我们为有FASD风险的儿童提出了一种最佳年度筛查程序。我们在限制条件下开发了一种高容量、低成本的筛查程序，具有高预期货币效益、转诊和诊断过程的重大影响以及对受测个体和社会的预期最大长期效益。这种针对有FASD风险的儿童/青少年的年度筛查程序可以轻松且广泛地部署用于早期识别，有可能导致更早的干预和治疗。这对于神经发育障碍至关重要，以减轻疾病的严重程度和/或继发性合并症的发生频率。
