Affective computing has become increasingly relevant in child-robot interaction (CRI), particularly in social robotics, emotion recognition, engagement assessment, and autism-related interventions. This systematic review with a critical and integrative synthesis analyzes 105 included studies to examine how affect is sensed, represented, processed, expressed, and evaluated in CRI. The literature search was conducted in IEEE Xplore, Web of Science, Scopus, and PubMed, following a systematic screening process guided by the review objectives. A descriptive and structured narrative synthesis was conducted considering publication characteristics, robot platform and morphology, target population, sensing modalities and observed affect-relevant features, affective constructs and representation models, computational and control mechanisms, robot affective expression, evaluation strategies, and remaining research gaps. The findings show a strong emphasis on ASD-related contexts, visually observable and behavioral features, facial emotion recognition, body movement analysis, and engagement assessment. The review also identifies important limitations, including reliance on camera-based affect recognition, comparatively limited use of physiological and other complementary sensing modalities, unclear alignment between robot roles and interaction strategies, insufficient reporting of robot emotional expressiveness and control mechanisms, and limited attention to explainability, data governance, and long-term ethical implications. Based on these findings, an integrative taxonomy of affective computing in CRI is proposed, comprising six interconnected dimensions: interaction context; sensing modalities and observed features; affective constructs and representation models; computational and control mechanisms; robot affective expression; and evaluation and adaptation strategies. Rather than treating these dimensions as entirely novel categories, the taxonomy consolidates and extends previously fragmented classifications into a child-centered representation of the affective interaction process. Overall, this review argues that affective CRI should move beyond automatic emotion recognition toward multimodal, embodied, developmentally appropriate, explainable, and ethically grounded robot interaction.

Recent evidence suggests a link between autism spectrum disorder (ASD) and early-onset Alzheimer's disease and related dementias (AD/ADRD), yet the mechanisms underlying this association remain poorly understood. This narrative review investigates whether early management of ASD may reduce the risk of developing AD/ADRD by targeting shared biological pathways. A literature search was conducted using PubMed and included English-language studies published from 1996 to 2026. Eligible studies addressed ASD, AD/ADRD, neuroinflammation, aging, or dementia, and included systematic reviews, meta-analyses, narrative reviews, clinical trials, randomized controlled trials, and translational animal studies. Studies lacking discussion of mechanisms relevant to ASD, neuroinflammation, aging, or dementia were excluded. Search terms included AD, dementia, ASD, inflammation, pregnenolone, omega-3, prednisolone, celecoxib, minocycline, N-acetylcysteine, sulforaphane, histamine receptor agonists, and GABA. Additional studies were identified through reference list screening. Of 170 records identified, 133 met the inclusion criteria and formed the basis for the review. Findings indicate that neuroinflammation is a central mechanism linking ASD-related comorbidities with established AD/ADRD risk factors. The evidence suggests that reducing neuroinflammation, particularly through interventions targeting microglial activation during childhood or early adulthood, may lower the likelihood of early-onset AD/ADRD in individuals with ASD. Although current evidence is primarily theoretical and indirect, it supports further investigation of anti-inflammatory strategies as potential preventive interventions. The review concludes by proposing future empirical studies to test whether early neuroinflammation-targeted treatment in ASD can reduce the incidence of early-onset AD/ADRD.

Neurodevelopmental and psychiatric conditions often originate in youth and can lead to adverse social and functional outcomes. It is unclear whether these associations are driven by genetic liability to these conditions, with possible developmental differences. We examined multivariable associations between polygenic scores (PGS) for attention-deficit hyperactivity disorder (ADHD), autism spectrum disorder (ASD), schizophrenia, bipolar disorder, depression, anxiety and three outcomes: peer problems, educational attainment/employment and suicidality, in childhood, adolescence and young-adulthood. Data were analysed from the Avon Longitudinal Study of Parents and Children; outcomes at ages 10-13, 16 and 24-25 years. ADHD PGS was associated with peer problems (OR = 1.13, p = 0.020; OR = 1.21, p < 0.0001; OR = 1.14, p = 0.010) and lower educational attainment/employment in childhood, adolescence and young-adulthood(OR = 1.40, p < 0.0001; OR = 1.62, p < 0.0001; OR = 1.29, p = 0.005 respectively) and suicidality in childhood (OR = 1.13, p = 0.030). ASD PGS was associated with peer problems and suicidality in childhood (OR = 1.24, p < 0.0001; OR = 1.24, p = <0.0001) and greater educational attainment in childhood and adolescence (OR = 0.88, p = 0.004; OR = 0.77, p = 0.007). Depression PGS was associated with peer problems in childhood and young-adulthood (OR = 1.18, p = 0.003; OR = 1.21, p < 0.0001), and poorer educational attainment/employment and suicidality in adolescence (OR = 1.39, p = 0.004; OR = 1.28, p < 0.0001) and young-adulthood (OR = 1.26, p = 0.017; OR = 1.42, p < 0.0001). We did not find strong evidence of associations for schizophrenia, bipolar disorder or anxiety PGS. These findings suggest that genetic liability to neurodevelopmental and psychiatric conditions are associated with a range of social/functional outcomes, although the strength and direction of association vary by PGS, outcome and development stage. This highlights the importance of examining genetic liability to multiple conditions simultaneously and of a developmental perspective.

Predictive processing has been proposed as a framework for symptom development in autism (ASD) and schizophrenia (SSD) spectrum disorders, with ASD being associated with an overweighting of (low-level) sensory evidence whereas SSD is characterized by an overweighting of (high-level) prior beliefs, both resulting in altered prediction error processing. This study investigated these hypotheses and their electrophysiological correlates in subclinical expressions of ASD and SSD during language processing. Participants completed an auditory comprehension task that manipulated the precision of high-level semantic prior beliefs and low-level sensory evidence. Hierarchical Bayesian belief-updating modeling and electroencephalography were used to examine whether imbalances in the weighting of prior beliefs and sensory evidence were reflected in altered processing of semantic prediction errors, characterized by mean N400 amplitudes. Computational modeling revealed that increasing schizotypal traits were associated with significant overweighting of prior beliefs, while autistic traits did not show a significant shift. Linear mixed-effect models of mean N400 amplitudes indicated that schizotypy-related overweighting of semantic prior beliefs was reflected in a reduced semantic prediction error signal, indexed by smaller N400 differences between high predictability sentences and both low predictability and high predictability mismatch sentences. A similar but weaker and less distinct pattern emerged for increasing autistic traits. These findings provide converging computational and electrophysiological support for overweighting of semantic prior beliefs with increasing subclinical schizotypy. In contrast, increasing autistic traits were not associated with overweighting of sensory evidence, with electrophysiological results instead suggesting subtle alterations in the weighting of semantic prior beliefs.

Profound autism spectrum disorder (ASD) is frequently attributable to single-gene mutations, with SCN2A, encoding the voltage-gated sodium channel NaV1.2, among the most penetrant. Cortico-striatal circuitry is a key node implicated in ASD, yet how SCN2A deficiency alters human neural circuits remains unclear. Using a human cortico-striatal assembloid model, we show that autism-causing heterozygous SCN2A protein-truncating variants (PTVs) impair long-range cortical axonal projections, reduce striatal spine density, and attenuate excitatory cortico-striatal synaptic transmission. Genotype-defined assembloids reveal contributions from both impaired cortical projections and intrinsic striatal vulnerability. Paradoxically, despite these connectivity deficits, SCN2A-deficient neurons exhibit increased intrinsic excitability, and assembloids show elevated spontaneous network firing, suggesting a potentially distinctive feature of human neural models. Notably, canine adenovirus type 2-mediated delivery of human SCN2A rescued cellular and circuit deficits. Collectively, our study unveils human circuit dysfunction caused by SCN2A deficiency and supports the therapeutic potential of targeted gene replacement for SCN2A-related ASD.

Recent evidence in a 16p11.2 deletion mouse model of autism spectrum disorder (ASD) revealed brain endothelial abnormalities postnatally, but the endothelial alterations eliciting these changes remained unknown. Using 14-day-old and adult 16p11.2-deficient and wild-type male mice, we now show that the 16p11.2 deletion causes a bioenergetic failure selectively in endothelial cells (ECs) with reduced intracellular ATP levels. Intra- or extracellular ATP supplementation rescued the function of 16p11.2-deficient ECs in vitro via P2 purinergic receptor activation, specifically P2Y2 receptors. Activating P2Y2 receptors restored cerebrovascular reactivity in 16p11.2-deficient arterioles ex vivo, activity-dependent cerebral blood flow in vivo, and rescued 16p11.2 deletion-associated mouse behaviors. Taken together, this study demonstrates that metabolic reprogramming of brain ECs via purinergic receptor engagement represents a promising therapeutic avenue for ASD.

Long non-coding RNAs (lncRNAs) are increasingly implicated in autism spectrum disorder (ASD), yet their dynamic regulatory roles remain poorly characterized. Conventional static causal inference methods are limited in capturing the dynamic interplay of lncRNAs across diverse brain biological contexts. Here, we develop DCRNet (Dynamic Causal Regulation Network), a hidden causality-based framework to infer lncRNA regulatory relationships in ASD. Our analysis reveals that dark causality, representing a hybrid form of positive and negative interdependencies, is prevalent in lncRNA-mediated regulation. Moreover, these dynamic regulatory patterns emerge across multiple biological contexts, and eight ASD susceptibility lncRNAs may be linked to disrupted gene regulatory programs. We also indicate that lncRNA expression signatures distinguish sex and cell type with high accuracy, offering candidate diagnostic biomarkers. Notably, 20 immune-related lncRNAs are predicted to be involved in neuroimmune regulation. Additionally, cell-cell interaction networks show notable differences between ASD and control cohorts. Furthermore, DCRNet performs comparably to the other two causal inference methods and outperforms a correlation-based approach in capturing dynamic lncRNA regulation. This study provides a framework for delineating dynamic lncRNA regulation and suggests their potential relevance to ASD pathophysiology.