Background: Neurodevelopmental disorders (NDDs) encompass an inclusive group of conditions that appear during the developmental period but continue to persist in adulthood, ranging from particular difficulties to a global impairment of social, cognitive, and emotional functioning. The developmental trajectories associated with these conditions are highly heterogeneous. This study aimed to analyze and compare developmental and adaptive profiles of preschool-aged children with different NDDs to better characterize their developmental trajectories. Methods: We analyzed data from the initial global evaluation of 196 children with NDDs (aged 20 to 71 months), enrolled in three subgroups: 108 with autism spectrum disorder (ASD), 52 with language disorder (LD), and 36 with mixed specific developmental disorder (MSDD). A comprehensive neuropsychiatric evaluation was performed using standardized tools (Griffiths-III, ADOS-2, VABS-II, and ADI-R), and the parents completed the DP-3 and the CBCL 1½-5. Results: Our results showed that all NDDs exhibited poor psychomotor skills, with children with ASD being the most impaired, although their profiles were comparable to those of MSDD in communication and motor areas. CBCL’s pervasive developmental problem scale has been shown to provide relevant information for distinguishing children with ASD. Furthermore, DP-3 and VABS-II measure highly differentiated developmental profiles of each diagnostic group. Conclusions: Our results highlighted the importance of including parents’/caregivers’ perspectives in defining children’s functioning and the possibility of using DP-3 as a screening tool for different neurodevelopmental disorders.

Background/Objectives: Atypical sensory processing is increasingly recognized as a transdiagnostic dimension of neurodevelopmental disorders (NDDs), with critical implications for emotional and behavioral regulation. This study aimed to identify distinct sensory profiles in preschool children with NDDs and to examine their associations with emotional–behavioral and cognitive/developmental functioning. Methods: A total of 263 children (aged 21–71 months) diagnosed with autism spectrum disorder (ASD), language disorder (LD), or other NDDs (ONDD) were recruited. Sensory processing was assessed using the SPM-P, emotional–behavioral functioning was assessed via the CBCL 1½–5, and cognitive/developmental levels were assessed through standardized instruments. Latent profile analysis (LPA) was conducted to identify sensory subtypes. Group comparisons and multinomial logistic regression were used to examine profile characteristics and predictors of profile membership. Results: Three sensory profiles emerged: (1) Multisystemic Sensory Dysfunction (20.1%), characterized by pervasive sensory and emotional difficulties, primarily observed in ASD; (2) Typical Sensory Processing (44.9%), showing normative sensory and emotional functioning, predominantly LD; and (3) Mixed Subclinical Sensory Processing (35%), with subclinical-range scores across multiple sensory and emotional domains, spanning all diagnoses. Higher cognitive functioning and fewer internalizing symptoms significantly predicted membership in the typical profile. A gradient of symptom severity was observed across profiles, with the Multisystemic group showing the most pronounced emotional–behavioral impairments. Conclusions: Distinct sensory–emotional phenotypes were identified across diagnostic categories, supporting a dimensional model of neurodevelopment. Sensory profiles were strongly associated with emotional functioning, independently of diagnostic status. Early sensory assessment may therefore offer clinically meaningful insights into emotional vulnerability and inform targeted interventions in preschool populations with NDDs.

The increasing prevalence of Autism Spectrum Disorder (ASD) has intensified research efforts aimed at clarifying its neurobiological underpinnings. Electroencephalography (EEG) has enabled the identification of functional alterations in neuronal networks, contributing to the characterization of ASD-related brain dynamics and supporting the investigation of links between neural processes and behavioral impairments. In recent years, Artificial Intelligence (AI) methods have been increasingly applied to EEG analysis, allowing the extraction of complex, high-dimensional features. However, the limited interpretability of many AI-based models represents a major barrier to their clinical translation. To address this issue, Explainable Artificial Intelligence (XAI) approaches have emerged as promising tools to enhance model transparency and neurobiological interpretability. This systematic review examined studies explicitly applying XAI techniques to EEG or event-related potential data from individuals with ASD. A comprehensive literature search was conducted across multiple electronic databases up to November 2025. Studies were included if they involved ASD populations, electrophysiological data, and AI-based analytical approaches with explicit explainability components. Due to substantial methodological heterogeneity, a qualitative narrative synthesis was performed. Eleven studies met the inclusion criteria. Overall, included articles highlighted partially overlapping electrophysiological patterns involving spectral alterations, functional connectivity, and network organization; however, some studies also revealed marked heterogeneity in study design and limited clinical characterization. Consequently, they should be interpreted with caution, as the field remains at a preliminary stage. This review outlines current trends, methodological limitations, and key gaps in XAI-driven EEG research in ASD, and discusses future directions toward clinically meaningful and interpretable neurophysiological biomarkers. The review protocol was registered in PROSPERO (CRD420251231630).

Background/Objectives: Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by impairments in social interaction and communication, as well as by repetitive behaviors, with a rising global prevalence. Concurrently, the use of Assisted Reproductive Technologies (ART) has increased among couples experiencing infertility. This study aimed to compare the frequency of ART-conceived children between those diagnosed with ASD and those with other neurodevelopmental disorders (nASD), and to examine differences in prenatal, perinatal and medical histories of ART- and spontaneously (non-ART)-conceived children within an ASD group. Methods: We retrospectively analyzed data from 507 children with a neurodevelopmental disorders (NDDs) diagnosis, classified into ASD (n = 234) and nASD (n = 273) groups. Subsequent analyses focused on the ASD group, further divided into an ART and non-ART group according to the conception mode. Results: ART-conceived children were more frequent in the ASD group than in the nASD group. Moreover, within ASD, ART was significantly associated with potential risk factors such as twin pregnancy, cesarean delivery, low birth weight and parental age. Logistic Binary Regression confirmed these results, suggesting that ART co-occurs with a cluster of perinatal and familial risk factors. Conclusions: Our results indicate that ART is not an independent causal exposure; however, given the retrospective design and the absence of a general population control group, causal inference cannot be drawn. The observed association with ASD appears to be mediated by perinatal and parental variables. These findings underscore the importance of improving obstetric management and care, and ensuring early developmental monitoring for ART-conceived children.