Autism spectrum disorder (ASD) is a brain condition characterized by diverse signs and symptoms that appear in early childhood. ASD is also associated with communication deficits and repetitive behavior in affected individuals. Various ASD detection methods have been developed, including neuroimaging modalities and psychological tests. Among these methods, magnetic resonance imaging (MRI) imaging modalities are of paramount importance to physicians. Clinicians rely on MRI modalities to diagnose ASD accurately. The MRI modalities are non-invasive methods that include functional (fMRI) and structural (sMRI) neuroimaging methods. However, diagnosing ASD with fMRI and sMRI for specialists is often laborious and time-consuming; therefore, several computer-aided design systems (CADS) based on artificial intelligence (AI) have been developed to assist specialist physicians. Conventional machine learning (ML) and deep learning (DL) are the most popular schemes of AI used for diagnosing ASD. This study aims to review the automated detection of ASD using AI. We review several CADS that have been developed using ML techniques for the automated diagnosis of ASD using MRI modalities. There has been very limited work on the use of DL techniques to develop automated diagnostic models for ASD. A summary of the studies developed using DL is provided in the Supplementary Appendix. Then, the challenges encountered during the automated diagnosis of ASD using MRI and AI techniques are described in detail. Additionally, a graphical comparison of studies using ML and DL to diagnose ASD automatically is discussed. We suggest future approaches to detecting ASDs using AI techniques and MRI neuroimaging.

Autism spectrum disorder (ASD) is a multifactorial neurodevelopmental disorder characterized by impairments in two main areas: social/communication skills and repetitive behavioral patterns. The prevalence of ASD has increased in the past two decades, however, it is not known whether the evident rise in ASD prevalence is due to changes in diagnostic criteria or an actual increase in ASD cases. Due to the complexity and heterogeneity of ASD, symptoms vary in severity and may be accompanied by comorbidities such as epilepsy, attention deficit hyperactivity disorder (ADHD), and gastrointestinal (GI) disorders. Identifying biomarkers of ASD is not only crucial to understanding the biological characteristics of the disorder, but also as a detection tool for its early screening. Hence, this review gives an insight into the main areas of ASD biomarker research that show promising findings. Finally, it covers success stories that highlight the importance of precision medicine and the current challenges in ASD biomarker discovery studies.

Autism spectrum disorder (ASD) refers to a heterogeneous group of complex neurodevelopmental disorders characterized by social skill and communication deficits, along with stereotyped repetitive behavior. miRNAs, small non-coding RNAs that have been recognized as critical regulators of gene expression, play a key role in the neurodevelopmental transcriptional networks of the human brain. Previous investigations have proven that circulating miRNAs open up new possibilities for the emerging roles of diagnostic and prognostic biomarkers in human disorders and diseases. Biomarker development has been progressively becoming more recognized as a cornerstone in medical diagnosis, paving the way to drug discoveries and limiting the progression of various diseases. Due to the complexity of ASD, considerable endeavors have either unsuccessfully identified biomarkers for the disorder or have not yet been established. Cell-free circulating miRNAs in biofluids are extraordinarily stable and considered to represent the next-generation of clinical, non-invasive, biomarkers for many pathologies including neurological and neurodevelopmental disorders. Here, we conducted a review of all peer-reviewed articles addressing the circulating profiles of miRNAs, mostly performed in serum and saliva samples in individuals with ASD.

Autism spectrum disorder (ASD) is a multifaced neurodevelopmental disorder that becomes apparent during early childhood development. The complexity of ASD makes clinically diagnosing the condition difficult. Consequently, by identifying the biomarkers associated with ASD severity and combining them with clinical diagnosis, one may better factionalize within the spectrum and devise more targeted therapeutic strategies. Currently, there are no reliable biomarkers that can be used for precise ASD diagnosis. Consequently, our pilot experimental cohort was subdivided into three groups: healthy controls, individuals those that express severe symptoms of ASD, and individuals that exhibit mild symptoms of ASD. Using next-generation sequencing, we were able to identify several circulating non-coding RNAs (cir-ncRNAs) in plasma. To the best of our knowledge, this study is the first to show that miRNAs, piRNAs, snoRNAs, Y-RNAs, tRNAs, and lncRNAs are stably expressed in plasma. Our data identify cir-ncRNAs that are specific to ASD. Furthermore, several of the identified cir-ncRNAs were explicitly associated with either the severe or mild groups. Hence, our findings suggest that cir-ncRNAs have the potential to be utilized as objective diagnostic biomarkers and clinical targets.

Background: Recent studies indicated that seeded fibril formation and toxicity of α-synuclein (α-syn) play a main role in the pathogenesis of certain diseases including Parkinson’s disease (PD), multiple system atrophy, and dementia with Lewy bodies. Therefore, examination of compounds that abolish the process of seeding is considered a key step towards therapy of several synucleinopathies. Methods: Using biophysical, biochemical and cell-culture-based assays, assessment of eleven compounds, extracted from Chinese medicinal herbs, was performed in this study for their effect on α-syn fibril formation and toxicity caused by the seeding process. Results: Salvianolic acid B and dihydromyricetin were the two compounds that strongly inhibited the fibril growth and neurotoxicity of α-syn. In an in-vitro cell model, these compounds decreased the insoluble phosphorylated α-syn and aggregation. Also, in primary neuronal cells, these compounds showed a reduction in α-syn aggregates. Both compounds inhibited the seeded fibril growth with dihydromyricetin having the ability to disaggregate preformed α-syn fibrils. In order to investigate the inhibitory mechanisms of these two compounds towards fibril formation, we demonstrated that salvianolic acid B binds predominantly to monomers, while dihydromyricetin binds to oligomeric species and to a lower extent to monomers. Remarkably, these two compounds stabilized the soluble non-toxic oligomers lacking β-sheet content after subjecting them to proteinase K digestion. Conclusions: Eleven compounds were tested but only two showed inhibition of α-syn aggregation, seeded fibril formation and toxicity in vitro. These findings highlight an essential beginning for development of new molecules in the field of synucleinopathies treatment.

Coronavirus 2019 (COVID-19) is an infectious respiratory disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that mainly affects the lungs. COVID-19 symptoms include the presence of fevers, dry coughs, fatigue, sore throat, headaches, diarrhea, and a loss of taste or smell. However, it is understood that SARS-CoV-2 is neurotoxic and neuro-invasive and could enter the central nervous system (CNS) via the hematogenous route or via the peripheral nerve route and causes encephalitis, encephalopathy, and acute disseminated encephalomyelitis (ADEM) in COVID-19 patients. This review discusses the possibility of SARS-CoV-2-mediated Multiple Sclerosis (MS) development in the future, comparable to the surge in Parkinson’s disease cases following the Spanish Flu in 1918. Moreover, the SARS-CoV-2 infection is associated with a cytokine storm. This review highlights the impact of these modulated cytokines on glial cell interactions within the CNS and their role in potentially prompting MS development as a secondary disease by SARS-CoV-2. SARS-CoV-2 is neurotropic and could interfere with various functions of neurons leading to MS development. The influence of neuroinflammation, microglia phagocytotic capabilities, as well as hypoxia-mediated mitochondrial dysfunction and neurodegeneration, are mechanisms that may ultimately trigger MS development.