Editorial: Autism spectrum disorder within neurodevelopmental disorders: Catching heterogeneity, specificity, and comorbidity in clinical phenotypes and neurobiological bases

From these statements derives the inspiring idea of creating the present special issue, in order to give a collection of contributions under the frame of "heterogeneity", "specificity" and "comorbidity" at clinical and neurobiological level towards better understanding of etiology and treatment. Indeed, the scope of the research topic has been to shed light on ASD, either at clinical or neurobiological level, keeping in mind the frame of NDDs. This multi-disciplinary effort, aimed at bringing together contributions ranging basic science to sophisticated latent variable modelling approaches aim at clarification of distinct aspect of clinical phenotype, thus paving the way to clinical translatability The special issue has been taken into consideration from several research groups and finally consists of 20 papers (2 review papers and 18 original studies). In this editorial, we will discuss different themes that we have identified across the contributions: (1) Genetic variations and biological implications; (2) Neuroimaging and electrophysiological application; (3) Characterization of clinical phenotype. We conclude with a discussion on future directions in the field.As far as of genetic variations and biological implications, it can be asserted that "synaptopathies", involve disruption to genes expressed at the synapse and account for between 0.5 -2% of autism cases. Phelan McDermid Syndrome (PMS, also known as 22q13 deletion syndrome) and NRXN1 deletions (NRXN1ds) are two synaptopathies associated with autism. PMS often incorporates disruption to the SHANK3 gene, implicated in excitatory postsynaptic scaffolding, whereas the NRXN1 gene encodes neurexin-1, both implicated in trans-synaptic signalling in the brain. Cooke and collaborators (2022) describe the Synaptic Gene (SynaG) study from AIMS2-TRIALS project, which adopts a gene-first approach and comprehensively assesses these two syndromic forms of autism.In the second paper focused on PMS, Isenstein and colleagues (2022), reported a habituation electrophysiological study (EEG-ERP) on PMS subjects to understand hyporesponsiviness in this clinical population. This study suggests that while neural response and habituation is generally preserved in PMS, genotypic and phenotypic characteristics may drive some variability.Autism is often associated with potential risk factors that may alter the expression of certain receptors; for example, Liu and collaborators (2021), in a study of in vitro human neuron progenitor cells, demonstrated that maternal diabetes may be correlated with the onset of ASD. Indeed, they showed that hyperglycemia, due to maternal diabetes, induces a suppression of oxytocin receptor that contributes to social deficits in offspring, through a process of oxidative stress and epigenetic methylation. Along a similar line of research Mariggio and colleagues (2021) focused on genetic expression of dopaminergic system (DS), possibly involved in the pathophysiology of ASD and Attention-Deficit/Hyperactivity Disorder (ADHD). Specifically, single nucleotide polymorphisms (SNPs) DRD1 and DRD2 dopamine receptors might be considered as potential risk factors for ASD and ADHD, thus being only DRD2-12 (rs7131465) significantly associated with a higher risk for the ASD/ADHD overlap.Among biological risk factors also potential involvement of the immune system in the etiopathogenesis of ASD has been investigated. Specifically De Giacomo and collaborators (2021) investigate the levels of immunological markers in peripheral blood of children with ASD founding that regulatory B cells and T cells were decreased in ASD subjects having a possible role in ASD pathophysiologyIn this special issue neuroimaging and electrophysiological application have been particularly used to study the effects of treatment and to understand how alterations in the brain correlate with clinical aspects of the autistic phenotype.Both functional and structural MRI methodologies are represented in the studies included. Lan and colleagues (2022), based on resting state data, reveal a possible relationship between atypical visual attention and poor learning ability in subjects with ASD, while the fMRI entropy connectivity method used by Yu and collaborators (2021) reveal that a combination of abnormal top-down and bottom-up information processing accelerates the deoptimization of brain networks and potentially affects cognitive activities in patients with ASD. Lan and collaborators (2021) reported a spontaneous activity changes in terms of fMRI acquisition, in the visual and language-related brain regions in ASD population, while correlation with multiple clinical indexes did not appeared significant. Though in smaller sample sizes cohorts, preliminary interesting results are reported in structural MRI studies, especially in terms on structure-function connections. A correlation with clinical indexes (in terms of ADOS and ADI-R scores) has been reported in Lucibello and collaborators (2021), where cortical thickness and gyrification alterations in ASD preschoolers are reported. In a longitudinal cohort of subjects at risk for autism, Godel and colleagues (2021) detected a regional structural MRI index of gray-white matter contrast (GWC), founding that early onset of ASD symptoms (i.e. prior to 18-months) was specifically associated with slower GWC rates of change during the second year of life, in areas related to central executive network. In the paper from Chen and collaborators (2021), significant differences between the ASD group and TD group in surface area, cortical volume and cortical thickness were found and correlation with language, considering two ASD subgroups according to their response to speech ABR results, were detected, too. Interestingly, two papers report (Sun et al. 2022;Yu et al. 2021) the application of neuroimaging/electrophysiology respectively serving as treatment monitoring or rehab-intervention. In particular Yu and collaborators (2021) reported a correlation between reduction of socio-communicative symptoms and Default Mode Network activity in 3-6 years ASD children after 12-week mini-basketball training program. Another article in our Research Topic, by Sun and colleagues (2022), proposed a trial through tDCS stimulation in the dorsolateral prefrontal cortex area of children with ASD (4-12 years) e analyzed EEG mismatch negativity response in pre and post-treatment; even though a trend was detected, they did not found significant differences between ASD and healthy subjects, while a meaningful correlation of mismatch negativity response and symptomatology was detected.Functional Intrared-Spectroscopy (fNIRS) technique has been recently reported as an efficient tool to investigate brain activity in autism field (Zhang and Roeyers 2019); in this special issue, Conti and collaborators (2022) systematically reviewed 13 papers applying fNIRS focused on young subjects (preschoolers) with ASD or infants at high risk of developing ASD, either in resting-state or task-evoked conditions. Findings confirm that fNIRS application can represent a promising tool potentially detecting autism traits, even in this very young population.Four articles in this Research Topic are focused on characterization of ASD phenotype and related clinical implications. In particular, Operto and collaborators (2021), compared adaptive skills, emotional/behavioral problems, and parental stress among children with different severity levels of Autism Spectrum Disorder (ASD) symptoms, reporting a strong role of adaptive behavior in modulating the presence of internalizing problems, thus suggesting the importance of taking it into account in rehabilitation program and family support.Beyond emotional/behavioral problems, DSM5 (APA 2013) introduced the possibility of double diagnosing ASD and ADHD conditions and existing literature suggest a shared neurobiology between the two conditions (Di Martino et al. 2013). Interestingly, Aiello and collaborators (2021), tried to disentangle the clinical phenotype and specificity of the two co-occurring conditions in relation to autism traits (C-AQ- (Auyeung et al. 2008)) and empathy (C-EQ (Auyeung et al. 2009)), by comparing children with ASD with and without comorbid ADHD with children presenting ADHD only and children with typical development. The reliability of the C-AQ and C-EQ as behavioral markers to differentiate ASD (regardless of comorbid ADHD) from an ADHD condition and TD was confirmed.Considering the dimensional approach to autism spectrum, it is of interest the detection of autistic traits in general population, too. In this frame, Vaiouli and colleagues (2021) used regression models to establish cross-sectional associations between autistic traits, alexithymia, and social-emotional difficulties in 275 young adults (e.g. college students), thus providing evidence of the influence of different alexithymic facets on the relationship between autistic traits and social-emotional challenges in young adults.Research on ASD parents is another issue of great interest in the field, thus often representing a direct window on the broader autism phenotype (Sucksmith et al. 2013) and related shared neurobiological bases, too (Billeci et al. 2016). In the paper from Uljarevic and collaborators (2021), the relationship between social motivation in ASD children and their parents was investigated, through administration of Social Responsiveness Scale (SRS). The study established that low social motivation in children with ASD may be driven, in part, by lower social motivation in one or both parents.First-degree relatives of ASD individuals may show mild deficits in cognitive flexibility as reported by Cheng and collaborators (2021). Indeed, the authors investigated first degree relatives of ASD individuals, either at clinical in terms of BAPQ (Piven et al. 1997) and CFI (Dennis and Vander Wal 2010) and neuroeletrophysiological level (ERP), reporting cognitive flexi

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   Empathy and emotion recognition in people with autism, first-degree relatives, and controls · 2013

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   Big data approaches to decomposing heterogeneity across the autism spectrum · 2019

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