Review of Progress in Diagnostic Studies of Autism Spectrum Disorder Using Neuroimaging

The Dynamical Biomarkers in Functional Connectivity of Autism Spectrum Disorder Based on Dynamic Graph Embedding

Autism spectrum disorder (ASD) is a neurological and developmental disorder and its early diagnosis is a challenging task. The dynamic brain network (DBN) offers a wealth of information for the diagnosis and treatment of ASD. Mining the spatio-temporal characteristics of DBN is critical for finding dynamic communication across brain regions and, ultimately, identifying the ASD diagnostic biomarker. We proposed the dgEmbed-KNN and the Aggregation-SVM diagnostic models, which use the spatio-temporal information from DBN and interactive information among brain regions represented by dynamic graph embedding. The classification accuracies show that dgEmbed-KNN model performs slightly better than traditional machine learning and deep learning methods, while the Aggregation-SVM model has a very good capacity to diagnose ASD using aggregation brain network connections as features. We discovered over- and under-connections in ASD at the level of dynamic connections, involving brain regions of the postcentral gyrus, the insula, the cerebellum, the caudate nucleus, and the temporal pole. We also found abnormal dynamic interactions associated with ASD within/between the functional subnetworks, including default mode network, visual network, auditory network and saliency network. These can provide potential DBN biomarkers for ASD identification.

Exploring Early Childhood Autism Spectrum Disorders: A Comprehensive Review of Diagnostic Approaches in Young Children

Autism spectrum disorders (ASD) encompass a diverse range of developmental disabilities characterized by pervasive deficiencies in socialization, communication, and the manifestation of behavioral issues. This review aims to clarify the diagnostic criteria for ASD, explore available diagnostic tools, evaluate biomedical examinations facilitating ASD diagnosis, and discuss the approach to screening assessments for ASD. ASDs are prevalent conditions, with a globally estimated prevalence of 7.6 cases per 100 (one in 132), based on a comprehensive review of epidemiological studies. The precise cause of autism and other ASDs remains unknown; however, genetic and environmental factors likely contribute. Common signs and symptoms shared among individuals with autism include restricted repetitive behaviors and learning disabilities. Standardized methods, such as the Diagnostic and Statistical Manual, Fifth Edition (DSM-5), diagnostic instruments, a thorough medical history, multiple physical examinations, laboratory investigations, neuroimaging, and screening tests, play a pivotal role in early autism diagnosis and intervention. ASD is a lifelong neurodevelopmental condition characterized by multiple deficits. Early detection is anticipated to have a positive impact on outcomes.