Background:Autism spectrum disorder (ASD) is a neurodevelopmental condition marked by pronounced heterogeneity in brain structure, which limits the development of targeted interventions. Morphological brain networks (MBNs) enable the mapping of coordinated structural features across brain regions at the individual level. However, the specific organisation of such networks in ASD and their potential relationships with underlying neurotransmitter systems remain largely unexplored. Aims:To characterise alterations in cortical thickness-based MBNs among adolescent males with ASD and to test whether these network changes spatially correspond to normative positron emission tomography-derived neurotransmitter receptor/transporter maps. Methods:In this cross-sectional study, T1-weighted magnetic resonance imaging (MRI) data from 424 adolescent males (207 with ASD, 217 typically developing) in the Autism Brain Imaging Data Exchange were analysed. MBNs were constructed using interregional cortical thickness similarity quantified by Jensen-Shannon divergence. Graph theoretical metrics were computed, and group differences were assessed with permutation tests controlling for age and intelligence quotient (IQ). Spatial correlations between left lateral orbitofrontal morphological similarity and atlas-based neurotransmitter maps were investigated using the JuSpace toolbox. Results:The ASD group exhibited a significantly increased normalised clustering coefficient (t = 2.40, p = 0.020) and decreased nodal centrality in the left lateral orbitofrontal cortex (OFC). This region showed reduced morphological similarity with 65 other brain regions. Furthermore, the OFC-based similarity patterns were significantly associated with the spatial distributions of gamma-aminobutyric acid type A (GABAa), 5-hydroxytryptamine receptor 1A (5-HT1a) and μ-opioid receptor systems (r = 0.22, p = 0.017, spin-corrected). These alterations were robust to stringent cross-family correction. Conclusions:These findings highlight the left lateral OFC as a structural key hub in adolescent males with ASD. The robustness of these OFC-centred network alterations under stringent cross-family correction, together with their associations with neurotransmitter systems, provides a potential neurobiological basis for targeted interventions in this population.

Self-processing abnormalities emerge in autism spectrum disorder (ASD) as early as 18–24 months. However, the underlying neural mechanisms remain untouched. Self-processing is conceptualized as integrating three nested levels: the interoceptive-self that represents bodily internal state, the exteroceptive-self that links the internal and external environment and marks a key step in self-awareness development, the mental-self that represents the mental world built on the abstract relationship between the internal and external environment. Therefore, we hypothesized that ASD involves early exteroceptive-self network dysfunction that subsequently cascades into broader interoceptive-self and mental-self networks during development. Resting-state functional magnetic resonance imaging data were collected from toddlers with ASD and typically developing controls, stratified into 18–24 month and 25–48 month cohorts. Functional connectivity and graph theoretical metrics were calculated for the three-level self-networks and compared against control models, including the prefrontal "four-stream" system and sensorimotor networks, using support vector machine (SVM) classifiers. In the 18–24-month ASD group, abnormalities were localized to the exteroceptive-self network, characterized by functional hyperconnectivity. By 25–48 months, this pattern escalated to complex hyper- and hypo-connectivity across three self-networks, with graph theory revealing expanded topological impairments in the thalamus and premotor cortex. Crucially, SVM models using these self-network features accurately distinguished ASD from TDC in both 18–24-month (82.9% accuracy) and 25–48-month (77.5% accuracy) groups, significantly outperforming models based on other network models. These findings elucidate a progressive neurobiological mechanism for self-processing deficits in ASD, and provide a robust neuroimaging marker for its early detection.

As a fundamental cognitive system with limited capacity, working memory (WM) strategically binds various features together to enhance its efficiency. However, the neural mechanisms governing feature binding in WM remain unsettled. Here, we employed functional magnetic resonance imaging combined with graph-based network analysis during a WM task in which participants maintained both color and location information throughout the delay period and subsequently detected and reported changes in color-location bindings versus individual features. Our results revealed a collaborative network that operates through a central workspace encompassing the somatomotor area, insula, and prefrontal cortex, underpinning the effective processing of bindings. Within these regions, we observed increased local efficiency and stronger connections during feature binding. Notably, connections within this workspace significantly correlated with behavioral performance. Among these regions, the somatomotor area, characterized by a shorter intrinsic timescale, responded more rapidly to visual input, carrying rich temporal information with more connections, and potentially served as the starting point during binding processes. These results highlight a dedicated workspace with sufficient and valid internal connections, facilitating successful binding through collaborative regional interactions.

As a complex system, the brain always operates as a functional whole, with multiple brain networks collectively facilitating the interaction between the intrinsic system and the external environment. On one hand, in the resting state without specific tasks, the brain maintains organized intrinsic neural activity and synchronizes the functions of different regions within the networks. Previous neuroimaging studies have identified a set of brain regions that exhibit relatively high energy consumption in the resting state and consistently show non-task-dependent negative activation in response to various task stimuli. The neural activities in these brain regions are highly synchronized, forming a functional network known as the default network (DN). On the other hand, when the brain is engaged in high-load, goal-oriented external tasks, it exhibits cognitive characteristics such as high arousal levels, focused attention, and the generation of action plans. The latest neuroimaging research has revealed that in this task-action state, there is also a set of brain regions that show non-task-dependent positive activation in response to various task stimuli, forming a functional network termed the action network (AN). From the perspective of working mechanism, the DN and the AN are complementary and constitute the "yin" and "yang" poles of the brain's complex dynamical system. The discovery of the AN not only deepens and completes our understanding of the brain's working principles but also provides a new perspective for the study of brain disease mechanisms and the development of brain-inspired intelligence. This paper first reviews the naming process of the DN and explores the naming of the AN. It then summarizes the discovery process and neuroimaging evidence of the AN, analyzes its anatomical composition and functional characteristics, and proposes a three-dimensional hierarchical view of the AN's anatomical structure from a clinical perspective (cortex-basal ganglia/thalamus-cerebellum). It is suggested that future research should integrate considerations from the three dimensions of cortex, basal ganglia/thalamus, and cerebellum to provide a systematic framework for clinical and basic research in neuroscience. Thirdly, the paper elaborates on the theoretical insight into the functional opposition and unity of the AN and the DN, emphasizing the need for future in-depth studies on the network neuroscience laws of the AN's anatomy and function, its development and evolution across the entire life span, and the innovation of multi-level, interdisciplinary scientific paradigms to reveal the cross-scale neurophysiological mechanisms underlying its neuroimaging findings. Finally, the paper looks forward to the important scientific significance and application value of the brain's AN for brain science and brain-inspired research.

AIM:Non-suicidal self-injury (NSSI) is a prevalent behavior among adolescents with major depressive disorder (MDD), yet the precise neural mechanisms remain unclear. This study aimed to investigate the temporal dynamics of brain connectivity associated with NSSI in adolescents with MDD using dynamic functional connectivity (dFC) analysis. METHODS:Resting-state fMRI data from 204 adolescents (154 with NSSI, 50 without) were analyzed. dFC variability within the fronto-limbic network was assessed using a seed-based dynamic conditional correlation approach. Group differences in dFC variability were examined, and a machine-learning model was used to predict NSSI based on dFC features. Mediation analysis explored the dFC's role in the relationship between depressive symptoms and NSSI. RESULTS:Adolescents with NSSI exhibited reduced dFC variability, which mediated the relationship between depressive severity and NSSI behavior (a*b = 0.144; p = 0.001). Key connections-insula, anterior cingulate cortex, orbitofrontal cortex, and hippocampus-were critical in distinguishing NSSI from non-NSSI groups. Machine learning models based on these connections achieved robust and stable performance with mean AUC of 0.84 and PR-AUC of 0.94 in predicting NSSI. CONCLUSIONS:Altered dFC within the fronto-limbic network may underlie NSSI in adolescents with MDD, identifying preliminary neural features for targeted interventions and highlighting neurobiological heterogeneity associated with NSSI in adolescents with MDD.

Non-suicidal self-injury (NSSI) is common among adolescents with depression, posing a significant public health concern. Although NSSI has been linked to heightened alexithymia—characterized by difficulties in identifying and describing emotions—the neurobiological mechanisms underlying this association remain insufficiently understood in adolescents. This study included 233 adolescents diagnosed with major depressive disorder (MDD), for whom resting-state functional magnetic resonance images were acquired. All participants completed the Toronto Alexithymia Scale (TAS) and the Functional Assessment of Self-Mutilation (FASM). We estimated intra-network functional connectivity (FC) within 17 large-scale brain sub-networks and examined associations between intra-network FC, TAS scores, and NSSI. Mediation models were then fitted to examine indirect pathways among alexithymia, intra-network FC, and NSSI. Adolescents with MDD who engaged in NSSI showed higher alexithymia than those without NSSI, and NSSI frequency was positively correlated with the difficulty of identifying feelings in the NSSI group. Lower intra-network FC within the frontoparietal control network was associated with the presence of NSSI, and higher TAS total scores were linked to reduced intra-network FC in this network. Mediation analyses further indicated that intra-network FC in the frontoparietal control network partially mediated the association between TAS total scores and NSSI. These findings suggest that alexithymia and aberrant connectivity within the frontoparietal control networks are jointly associated with NSSI in adolescent MDD. The identified neural markers and pathways offer insights into potential therapeutic targets, emphasizing the importance of addressing emotion regulation deficits and alexithymia in the treatment of NSSI.

Semantic control refers to the ability to flexibly retrieve and manipulate stored knowledge to support context-appropriate behavior. A left-lateralized network comprising the left inferior frontal gyrus (IFG), posterior middle temporal gyrus (pMTG), and dorsal medial prefrontal cortex (dmPFC) has been consistently implicated in this process. While previous studies have established the necessity of the IFG and pMTG in semantic control, the causal role of the left dmPFC remains unclear. Additionally, it is unknown whether each of these three regions exhibits internal functional differentiation and how they interact to support semantic control. To address these questions, we combined task-based functional magnetic resonance imaging (fMRI) with fMRI-guided transcranial magnetic stimulation (TMS). We found that dmPFC, like IFG and pMTG, is causally involved in semantic control. All three regions exhibited a consistent anterior–posterior functional gradient: anterior subregions were selectively engaged during high-demand semantic processing, whereas posterior subregions responded to both easy and hard tasks. Furthermore, combined activation patterns of these regions better predicted the behavioral differences between hard and easy semantic tasks compared to the activation patterns of any single region. Semantic control modulated both the autoinhibition within individual regions and the functional connectivity among them, suggesting these regions operate in a coordinated network rather than in isolation. These findings advance our understanding of the neural architecture supporting flexible semantic behavior. Significance Statement Understanding how the brain supports flexible semantic behavior is critical for both cognitive neuroscience and clinical neuropsychology. While prior studies have consistently implicated the left IFG, pMTG, and dmPFC in semantic control, the causal contribution of the dmPFC and the functional dynamics among these regions have remained unclear. This study provides the first causal evidence for the dmPFC’s role in semantic control, reveals functional differentiation within the IFG and pMTG, and shows that these regions interact as an integrated network. These findings challenge the notion of functionally homogeneous control nodes and highlight a topographically organized, interactive system underlying controlled semantic retrieval. This work refines our mechanistic understanding of semantic control and may inform clinical models of language and conceptual deficits. ### Competing Interest Statement The authors have declared no competing interest. the National Social Science Foundation of Chinathe National Social Science Foundation of China, , 20&ZD296 the Key-Area Research and Development Program of Guangdong Provincethe Key-Area Research and Development Program of Guangdong Province, , 2019B030335001 the National Natural Science Foundation of Chinathe National Natural Science Foundation of China, , 32100889, 32300881 Research Center for Brain Cognition and Human Development, Guangdong, ChinaResearch Center for Brain Cognition and Human Development, Guangdong, China, , 2024B0303390003

Background: Early screening for autism spectrum disorder (ASD) is crucial, yet current assessment tools in Chinese primary child care are limited in efficacy. Objective: This study aims to employ machine learning algorithms to identify key indicators from the 20-item Modified Checklist for Autism in Toddlers, revised (M-CHAT-R) combining with ASD-related sociodemographic and environmental factors, to distinguish ASD from typically developing children. Methods: Data from our prior validation study of the Chinese M-CHAT-R (August 2016-March 2017, n = 6,049 toddlers) were reviewed. We extracted the 20-item M-CHAT-R data and integrated 17 sociodemographic and environmental risk factors associated with ASD development to strengthen M-CHAT-R's machine learning screening. Five feature selection methods were used to extract subsets from the original set. Six machine learning algorithms were applied to identify the optimal subset distinguishing clinically diagnosed ASD toddlers from typically developing toddlers. Findings: Nine features were grouped into three subsets: subset 1 contained unanimously recommended items (A1 [Follows point], A3 [Pretend play], A9 [Brings objects to show], A10 [Response to name] and A16 [Gazing following]). Subset 2 added two items (A17 [Gaining parent's attention] and A18 [Understands what is said]), and subset 3 included two more items (A8 [Interest in other children] and child's age). The top-performing algorithm resulted in a seven-item classifier of subset 2 with 92.5 % sensitivity, 90.1 % specificity, and 10.0 % positive predictive value. Conclusions: Machine learning classifiers effectively differentiate ASD toddlers from typically developing toddlers using a reduced M-CHAT-R item set. Clinical implications: This highlights the clinical significance of machine learning-optimized models for ASD screening in primary health care centers and broader applications.

Autism spectrum disorder(ASD)is a neurodevelopmental condition.In addition to core symptoms including social impairments and restricted repetitive behaviors,about half of individuals with ASD also experience gastrointestinal symptoms and inflammatory bowel disease(IBD).IBD is a kind of chronic disease associated with immune dysregulation,gut microbiome alterations,micronutrient malabsorption and anaemia,which may be perinatal factors associated with ASD.It's likely that comorbidities such as IBD are diagnosed in children with ASD.Although there has been some initial success in treating IBD to reduce or prevent ASD in children,further clinical trials should be carried out in the future to verify the effectiveness and safety of IBD treatment.Additional evidence to support aetiological research,early diagnosis,and clinical management of ASD in children might result from examining the association between IBD and ASD,as well as the relationship between parental IBD and childhood ASD.

Automatic processing allows humans to perform tasks with minimal effort following learning. Although theories of automaticity propose that learning should result in faster processing, studies have universally found that learning reduces the amplitude of neural activity, not that it speeds neural activity. Here, we show that with intracranial activity recorded from the hippocampus of twenty-two humans, we could decode the target the participant was about to report faster across learning. Theta oscillations in the hippocampus afforded faster decoding of the to-be-reported target as learning progressed, unlike in prefrontal and temporal regions of the cortex. Furthermore, hippocampal ripples (70 to 180 Hz bursts) appear to support memory retrieval after learning established automaticity. Our findings demonstrate that the hippocampus plays a key role in speeding memory retrieval of previous learning episodes as humans gain expertise, supporting a critical but untested prediction of learning theories.

Background:Empathizing and systemizing abilities are respectively associated with key developmental outcomes like intelligence, executive function, and autistic traits, particularly in typically developing (TD) children. However, how specific cognitive styles-defined by the balance between empathizing and systemizing-relate to these outcomes remains unclear. Methods:We conducted a latent profile analysis on 502 TD children aged 6‒12 years to identify cognitive styles based on multiple dimensions of empathizing and systemizing, measured by the Children's Empathy Quotient and Systemizing Quotient. Intelligence, executive function, and autistic traits were assessed using the Wechsler Intelligence Scale for Children (Fourth Edition), the Behavior Rating Inventory of Executive Function, and the Social Responsiveness Scale, respectively. Results:Four cognitive styles emerged: High B (high empathizing and systemizing), E-dominance (empathizing-dominant), S-dominance (systemizing-dominant), and Low B (low empathizing and systemizing). The High B and E-dominance groups showed higher full-scale intelligence and verbal comprehension scores compared to the Low B group. In executive function, the Low B and S-dominance groups displayed more impairments, particularly in inhibitory control, emotional regulation, and overall executive function. For autistic traits, the S-dominance group showed higher levels of both social-communication difficulties and autistic mannerisms, while the Low B group primarily displayed increased social-communication challenges. Conclusion:Cognitive styles marked by high empathizing and systemizing ability correlate with stronger intelligence and social-communication skills, while a systemizing-dominant profile may lead to executive function difficulties and elevated autistic traits. These findings emphasize the role of cognitive styles in developmental outcomes, with implications for tailored educational and clinical interventions.

Individuals with autism spectrum disorder (ASD) have long been reported to exhibit atypical pain experiences. Chronic physical pain is a significant comorbidity in ASD, leading to substantial burdens on daily functioning and quality of life. This study aims to examine the potential associations between ASD and chronic physical pain, including its specific types. The study used data on chronic physical pain and headaches from the 2016 – 2021 National Survey of Children’s Health. Participants were children aged 3 to 17 years old. Generalized linear models were used to estimate the associations between ASD and pain-related indicators (e.g., chronic physical pain, headaches, and other back or body pain). The study included 177,539 children, of whom 5311 had a current ASD diagnosis. Among children with current ASD, 14.41

Semantic control enables flexible retrieval and manipulation of stored knowledge. A left-lateralized network including the inferior frontal gyrus, posterior middle temporal gyrus, and dorsal medial prefrontal cortex has been implicated in this process. However, the functional differentiation within each region and their interactions remain unclear. Combining functional MRI and transcranial magnetic stimulation, we demonstrate that all three regions are causally involved in semantic control. Anterior subregions are engaged under hard semantic tasks, whereas posterior subregions respond more generally. Machine learning prediction analyses indicate that combined activity across these regions predicts semantic performance better than any region alone. Dynamic causal modeling further reveals that semantic control demands modulate both self-inhibition and interregional connectivity. Bayesian multiple regression shows that stimulation effects in frontal cortex are best explained by an interaction between local activation and electric field strength, while effects in temporal cortex are better predicted by task-dependent network connectivity. These findings highlight the distributed and interactive mechanisms underlying flexible knowledge retrieval. fMRI and TMS reveal that inferior frontal, medial frontal, and posterior temporal cortices are causally involved in semantic control, forming a distributed network through functional interactions and showing heterogeneity within each region.

Children with Autism Spectrum Disorder(ASD)exhibit atypical facial emotional expressions,such as a prevalence of neutral expressions,reduced positive expressions,lower frequency of social smiles,and limited spontaneous facial mimicry.These characteristics remain stable from infancy to childhood,making them important markers for ASD risk assessment.However,traditional assessment methods,such as manual observation and facial electromyography,have limitations in analyzing facial emotional expressions in ASD children due to high subjectivity,time consumption,and difficulties in large-scale application.In recent years,advancements in artificial intelligence have facilitated the application of automated facial expression recognition technology based on computer vision and deep learning,significantly enhancing efficiency and reducing subjective bias,thereby providing strong support for large-scale ASD early screening based on atypical facial expressions.Future research could further optimize recognition models by designing more naturalistic induction paradigms to explore the diverse facial emotional expressions of ASD children,thus improving the accuracy and sensitivity of automated models and advancing ASD early screening and intervention efforts.

Predicting the postoperative outcome of stereoelectroencephalography-guided radiofrequency thermocoagulation (SEEG-guided RF-TC) remains challenging despite its increasing use in epilepsy treatment. Although SEEG-guided RF-TC has attracted extensive clinical interest, reliable biomarkers for treatment efficacy are still lacking. This study aims to address this gap by analyzing the altered brain network to predict postoperative outcome. Thirty-one focal cortical dysplasia epileptic patients who underwent RF-TC based on SEEG were enrolled in this study. They were included in the favorable outcome and poor outcome groups according to the follow-up. Partial Directed Coherence and Directed Transfer Function were applied to construct SEEG brain networks, and then brain network features were extracted. Subsequently, the differences in the presurgical and postsurgical brain network features were compared using the Wilcoxon test in the favorable and poor outcome groups, respectively. Finally, four machine learning models were applied to predict the outcome of RF-TC. After RF-TC surgery, the Characteristic Path Length (L) and average Betweenness Centrality (BC) increased while the average Clustering Coefficient (C) and Assortativity Coefficient (R1, R2) decreased in the favorable outcomes group. In contrast, there were no significant changes in the patient group with poor outcomes. The Support Vector Machine (SVM) model achieved the highest performance, with accuracy, sensitivity, specificity, and ROC values of 0.887, 0.821, 0.920, and 0.879, respectively. This study sheds light on the mechanisms of epilepsy from the perspective of brain networks and introduces a novel therapeutic strategy by altering network features. These feature alterations can also support machine learning models in effectively distinguishing favorable from poor outcomes.

Chronic insomnia (CI) is a complex disease involving multiple factors including genetics, gut microbiota, and brain structure and function. However, there lacks a unified framework to elucidate how these factors interact in CI. By combining data of clinical assessment, sleep behavior recording, cognitive test, multimodal MRI (structural, functional, and perfusion), gene, and gut microbiota, this study demonstrated that enhanced cerebral blood flow (CBF) similarities of the somatomotor network (SMN) acted as a key mediator to link multiple factors in CI. Specifically, we first demonstrated that only CBF but not morphological or functional networks exhibited alterations in patients with CI, characterized by increases within the SMN and between the SMN and higher-order associative networks. Moreover, these findings were highly reproducible and the CBF similarity method was test-retest reliable. Further, we showed that transcriptional profiles explained 60.4 % variance of the pattern of the increased CBF similarities with the most correlated genes enriched in regulation of cellular and protein localization and material transport, and gut microbiota explained 69.7 % inter-individual variance in the increased CBF similarities with the most contributions from Negativicutes and Lactobacillales. Finally, we found that the increased CBF similarities were correlated with clinical variables, accounted for sleep behaviors and cognitive deficits, and contributed the most to the patient-control classification (accuracy = 84.4 %). Altogether, our findings have important implications for understanding the neuropathology of CI and may inform ways of developing new therapeutic strategies for the disease.

Background Autistic traits are distributed along a continuum ranging from clinical presentations to the general population, being associated with high-risk of mental health problems. However, the underlying mechanism remains unclear. Given that alexithymia, which contributes to emotional difficulties related to autistic traits, along with the utilization of social camouflaging as a potential mechanism for coping, may exacerbate mental health issues. This study aimed to examine the mediating effects of alexithymia and social camouflaging in the associations between autistic traits and symptoms of anxiety/depression. Methods A total of 1085 Chinese university students (age: 21.8 ± 2.7 years, 51.9% males) were recruited through an online survey which measured autistic traits, mental health problems (anxiety and depression), alexithymia, and social camouflaging. The mediator effects of alexithymia and social camouflaging on the associations of autistic traits with anxiety and depression were conducted, and two serial mediation models were verified by regression analysis. Results The findings revealed a significant positive association of autistic traits with symptoms of anxiety and depression. The manifestation of anxiety symptoms was found to be influenced by autistic traits, mediated sequentially by alexithymia and social camouflaging (β = 0.022, 95% confidence interval [CI]: 0.011–0.035). Differently, the depression symptoms were linked to autistic traits solely through alexithymia (β = 0.499, 95%CI: 0.423–0.578). According to the subgroup analysis, the results within the female group were consistent with the observations made in the overall sample. However, in the male group, the statistical significance of the mediating effect of social camouflaging between autistic traits and anxiety was no longer evident. Limitations This study can only identify associations between autistic traits and social camouflaging, not causal relationships. Neuroimaging research is necessary to unravel the neural mechanisms that underlie these associations. Conclusions Our findings found that alexithymia and social camouflaging play a serial mediating role in the relationship between autistic traits and mental health problems in Chinese university students, especially anxiety. The associations between autistic traits and mental health issues may vary between males and females. This study highlights a significant pathway that has the potential to improve mental well-being in individuals exhibiting high autistic traits.

BackgroundHearing impairment is a common condition in the elderly. However, a comprehensive understanding of its neural correlates is still lacking.MethodsWe recruited 284 elderly adults who underwent structural MRI, magnetic resonance spectroscopy, audiometry, and cognitive assessments. Individual hearing abilities indexed by pure tone average (PTA) were correlated with multiple structural MRI-derived cortical morphological indices. For regions showing significant correlations, mediation analyses were performed to examine their role in the relationship between hearing ability and cognitive function. Finally, the correlation maps between hearing ability and cortical morphology were linked with publicly available connectomic gradient, transcriptomic, and neurotransmitter maps.FindingsPoorer hearing was related to cortical thickness (CT) reductions in widespread regions and gyrification index (GI) reductions in the right Area 52 and Insular Granular Complex. The GI in the right Area 52 mediated the relationship between hearing ability and executive function. This mediating effect was further modulated by glutamate and N-acetylaspartate levels in the right auditory region. The PTA-CT correlation map followed microstructural connectomic hierarchy, were related to genes involved in certain biological processes (e.g., glutamate metabolic process), cell types (e.g., excitatory neurons and astrocytes), and developmental stages (i.e., childhood to young adulthood), and covaried with dopamine receptor 1, dopamine transporter, and fluorodopa. The PTA-GI correlation map was related to 5-hydroxytryptamine receptor 2a.InterpretationPoorer hearing is associated with cortical thinning and folding reductions, which may be engaged in the relationship between hearing impairment and cognitive decline in the elderly and have different neurobiological substrates.FundingSee the Acknowledgements section.

Autism spectrum disorder (ASD) is a range of neurodevelopmental diseases characterized by social dysfunction and stereotypic behaviors. The etiology of ASD remains largely unexplored, resulting in a diverse array of described clinical manifestations and varying degrees of severity. Currently, there are no drugs approved by a supervisory organization that can effectively treat the core symptoms of ASD. Childhood and adolescence are crucial stages for making significant achievements in ASD treatment, necessitating the development of drugs specifically for these periods. Based on the drug targets and mechanisms of action, it can be found that atypical psychotropic medications, anti-inflammatory and antioxidant medications, hormonal medications, ion channel medications, and gastrointestinal medications have shown significant improvement in treating the core symptoms of ASD in both children and adolescents. In addition, comparisons of drugs within the same category regarding efficacy and safety have been made to identify better alternatives and promote drug development. While further evaluation of the effectiveness and safety of these medications is needed, they hold great potential for widespread application in the clinical treatment of the principal symptoms of ASD.