Carati Elisa, Angotti Marida, Pignataro Veronica, Grossi Enzo, Parmeggiani Antonia
IRCCS Istituto delle Scienze Neurologiche di Bologna, U.O.C. Neuropsichiatria dell'Età Pediatrica, Bologna 40138, Italy; Dipartimento di Scienze Mediche e Chirurgiche (DIMEC), Alma Mater Studiorum, Università di Bologna, Bologna 40138, Italy.
IRCCS Istituto delle Scienze Neurologiche di Bologna, U.O.C. Neuropsichiatria dell'Età Pediatrica, Bologna 40138, Italy.
Res Dev Disabil. 2024 Dec;155:104881. doi: 10.1016/j.ridd.2024.104881. Epub 2024 Nov 21.
Restrictive repetitive behaviors (RRBs) and sensory processing disorders are core symptoms of autism spectrum disorder (ASD). Their relationship is reported, but existing data are conflicting as to whether they are related but distinct, or different aspects of the same phenomenon.
This study investigates this relationship using artificial neural networks (ANN) analysis and an innovative data mining analysis known as Auto Contractive Map (Auto-CM), which allows to discover hidden trends and associations among complex networks of variables (e.g. biological systems).
The Short Sensory Profile and the Repetitive Behavior Scale-Revised were administered to 45 ASD children's caregivers (M 78 %; F 22 %; mean age 6 years). Questionnaires' scores, clinical and demographic data were collected and analyzed applying Auto-CM, and a connectivity map was drawn.
The main associations shown by the resulting maps confirm the known relationship between RBBs and sensory abnormalities, and support the existence of sensory phenotypes, and important links between RRBs and sleep disturbance in ASD.
Our study demonstrates the usefulness of ANNs application and its easy handling to research RBBs and sensory abnormalities in ASD, with the aim to achieve better individualized rehabilitation technique and improve early diagnosis.
PAPER'S CONTRIBUTION: Restricted, repetitive patterns of behaviors and interests and alteration of sensory elaboration are core symptoms of ASD; their impact on patients' quality of life is known. This study introduces two main novelties: 1) the simultaneous and comparative use of two parent questionnaires (SSP and RBS-R) utilized for RRBs and alteration of sensory profile; 2) the application of ANNs to this kind of research. ANNs are adaptive models particularly suited for solving non-linear problems. While they have been widely used in the medical field, they have not been applied yet to the analysis of RRBs and sensory abnormalities in general, much less in children with ASD. The application of Auto Contractive Map (Auto-CM), a fourth generation ANNs analysis, to a dataset previously explored using classical statistical models, confirmed and expanded the associations emerged between SSP and RBS-R subscales and demographic-clinical variables. In particular, the Low Energy subscale has proven to be the central hub of the system; interesting links have emerged between the subscale Self-Injurious Behaviors and the variable intellectual disability and between sleep disturbance and various RRBs. Expanding research in this area aims to guide and modulate an emerging targeted and personalized rehabilitation therapy.
限制性重复行为（RRBs）和感觉加工障碍是自闭症谱系障碍（ASD）的核心症状。它们之间的关系已有报道，但现有数据在它们是相关但不同，还是同一现象的不同方面存在冲突。
本研究使用人工神经网络（ANN）分析和一种称为自动收缩映射（Auto-CM）的创新数据挖掘分析方法来研究这种关系，该方法能够发现变量复杂网络（如生物系统）之间隐藏的趋势和关联。
对45名ASD儿童的照料者（男性占78%；女性占22%；平均年龄6岁）进行了《简短感觉概况量表》和《重复行为量表修订版》的评估。收集问卷得分、临床和人口统计学数据，并应用Auto-CM进行分析，绘制了连接图。
生成的图谱显示的主要关联证实了RRBs与感觉异常之间的已知关系，支持了感觉表型的存在，以及RRBs与ASD睡眠障碍之间的重要联系。
我们的研究证明了ANN应用的有效性及其在研究ASD中RRBs和感觉异常方面的易操作性，旨在实现更好的个性化康复技术并改善早期诊断。
受限的、重复的行为和兴趣模式以及感觉加工的改变是ASD的核心症状；它们对患者生活质量的影响是已知的。本研究引入了两个主要创新点：1）同时并比较使用用于RRBs和感觉概况改变的两份家长问卷（SSP和RBS-R）；2）将ANN应用于此类研究。ANN是特别适合解决非线性问题的自适应模型。虽然它们已在医学领域广泛使用，但尚未应用于一般RRBs和感觉异常的分析，更不用说在ASD儿童中的分析了。将第四代ANN分析方法自动收缩映射（Auto-CM）应用于先前使用经典统计模型探索的数据集，证实并扩展了SSP和RRRBS-R子量表与人口统计学-临床变量之间出现的关联。特别是，低能量子量表已被证明是该系统的中心枢纽；自伤行为子量表与智力残疾变量之间以及睡眠障碍与各种RRBs之间出现了有趣的联系。在这一领域开展更多研究旨在指导和调整新兴的有针对性的个性化康复治疗。
