Parenting Self-Efficacy Scale for Autism Spectrum Disorder: Evidence of Content Validity

Silva, Mariana Rodrigo do Vale Costa e; Alves, Rauni Jandé Roama; Ciasca, Sylvia Maria

doi:10.4236/psych.2022.134034

Cited by 2 publications

(1 citation statement)

References 23 publications

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“…The prevalence of ASD has been increasing in recent years [2], yet the pathological causes of the emergence and development of ASD remain unexplained. Currently, the diagnosis of individuals with ASD is based mainly on behavioural descriptions of symptoms and clinical observations, which are prone to subjectivity [3] [4]. There is an urgent need for reliable biomarkers for the early and objective diagnosis of patients with ASD [5] [6] [7] [8].…”

Section: Introductionmentioning

confidence: 99%

Consistent and Specific Multi-View Functional Brain Networks Fusion for Autism Spectrum Disorder Diagnosis

Zhang¹,

Wang²,

Zhang³

et al. 2023

JAMP

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Functional brain networks (FBN) based on resting-state functional magnetic resonance imaging (rs-fMRI) have become an important tool for exploring underlying organization patterns in the brain, which can provide an objective basis for brain disorders such as autistic spectrum disorder (ASD). Due to its importance, researchers have proposed a number of FBN estimation methods. However, most existing methods only model a type of functional connection relationship between brain regions-of-interest (ROIs), such as partial correlation or full correlation, which is difficult to fully capture the subtle connections among ROIs since these connections are extremely complex. Motivated by the multi-view learning, in this study we propose a novel Consistent and Specific Multi-view FBNs Fusion (CSMF) approach. Concretely, we first construct multi-view FBNs (i.e., multiple types of FBNs modelling various relationships among ROIs), and then these FBNs are decomposed into a consistent representation matrix and their own specific matrices which capture their common and unique information, respectively. Lastly, to obtain a better brain representation, it is fusing the consistent and specific representation matrices in the latent representation spaces of FBNs, but not directly fusing the original FBNs. This potentially makes it more easily to find the comprehensively brain connections. The experimental results of ASD identification on the ABIDE datasets validate the effectiveness of our proposed method compared to several state-of-the-art methods. Our proposed CSMF method achieved 72.8% and 76.67% classification performance on the ABIDE dataset.

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