Motor Skills as Moderators of Core Symptoms in Autism Spectrum Disorders: Preliminary Data From an Exploratory Analysis With Artificial Neural Networks
Motor Skills as Moderators of Core Symptoms in Autism Spectrum Disorders: Preliminary Data From an Exploratory Analysis With Artificial Neural Networks
复制标题
DOI：
10.3389/fpsyg.2018.02683
复制
发表时间：
2019-01-09
影响因子：
3.8
通讯作者：
Muratori, Filippo
中科院分区：
文献类型：
--
作者：
Fulceri, Francesca;Grossi, Enzo;Muratori, Filippo
运动障碍已被广泛观察到自闭症谱系障碍（ASD）的儿童，和运动问题，目前报告为相关的功能支持ASD的诊断，在目前的精神疾病诊断和统计手册（DSM-5）。关于这个问题的研究报告了不同运动领域的障碍，包括粗运动区和细运动区以及协调、姿势控制和站立平衡。然而，他们未能清楚地说明运动障碍是否与ASD的人口统计学和发育特征有关。评估运动技能的不同方法和分析参与者临床特征的异质性都被认为是结果差异的贡献者。然而，变量之间的关系的非线性可能解释了传统的分析无法抓住核心问题，这表明“单一症状的方法分析”应该被克服。人工神经网络（ANN）是受人脑功能过程启发的计算自适应系统，特别适用于解决非线性问题。本研究旨在应用人工神经网络揭示运动技能和临床变量之间关系的整个频谱。在一所三级保健大学医院招募了32名ASD男性儿童[平均年龄：48.5个月（SD：8.8）;年龄范围：30-60个月]。多学科综合诊断评估与运动技能的标准化评估组合，皮博迪运动发育量表-第二版。通过ANN进行探索性分析。研究结果显示，运动技能差是ASD学龄前儿童的常见临床特征，与高水平的重复行为和低水平的表达性语言有关。此外，还发现运动、认知和社交技能之间存在不明显的趋势。总之，运动异常在学龄前ASD患者中普遍存在，损伤程度可以告知临床医生ASD核心症状的严重程度。了解ASD儿童的运动障碍可能有助于阐明神经生物学基础，并最终指导定制治疗的发展。
Motor disturbances have been widely observed in children with autism spectrum disorder (ASD), and motor problems are currently reported as associated features supporting the diagnosis of ASD in the current Diagnostic and Statistical Manual of Mental Disorders (DSM-5). Studies on this issue reported disturbances in different motor domains, including both gross and fine motor areas as well as coordination, postural control, and standing balance. However, they failed to clearly state whether motor impairments are related to demographical and developmental features of ASD. Both the different methodological approaches assessing motor skills and the heterogeneity in clinical features of participants analyzed have been implicated as contributors to variance in findings. However, the non-linearity of the relationships between variables may account for the inability of the traditional analysis to grasp the core problem suggesting that the "single symptom approach analysis" should be overcome. Artificial neural networks (ANNs) are computational adaptive systems inspired by the functioning processes of the human brain particularly adapted to solving non-linear problems. This study aimed to apply the ANNs to reveal the entire spectrum of the relationship between motor skills and clinical variables. Thirty-two male children with ASD [mean age: 48.5 months (SD: 8.8); age range: 30-60 months] were recruited in a tertiary care university hospital. A multidisciplinary comprehensive diagnostic evaluation was associated with a standardized assessment battery for motor skills, the Peabody Developmental Motor Scale-Second Edition. Exploratory analyses were performed through the ANNs. The findings revealed that poor motor skills were a common clinical feature of preschoolers with ASD, relating both to the high level of repetitive behaviors and to the low level of expressive language. Moreover, unobvious trends among motor, cognitive and social skills have been detected. In conclusion, motor abnormalities in preschoolers with ASD were widespread, and the degree of impairment may inform clinicians about the severity of ASD core symptoms. Understanding motor disturbances in children with ASD may be relevant to clarify neurobiological basis and ultimately to guide the development of tailored treatments.