The prevalence of autism spectrum disorder (ASD) among children in the United States has risen dramatically over the past two decades. In 2022, an estimated 32.2 out of every 1,000 8-year-old children were identified with ASD, marking a nearly fivefold increase from the rate of 6.7 per 1,000 children in 2000. This significant upward trend underscores the growing importance of understanding and addressing ASD in American society. Gender disparities in autism diagnosis The increase in ASD prevalence is not uniform across genders. From 2016 to 2019, male children were nearly four times more likely to be diagnosed with ASD than their female counterparts. Approximately 4.8 percent of boys aged 3 to 17 years had received an ASD diagnosis at some point in their lives, compared to only 1.3 percent of girls in the same age group. This substantial gender gap highlights the need for further research into potential biological and social factors influencing ASD diagnosis rates. Racial and ethnic variations in autism prevalence Autism prevalence also varies across racial and ethnic groups. Data from 2016 to 2019 show that non-Hispanic white children aged 3 to 17 years had an ASD prevalence of 2.9 percent, while around 3.5 percent of Hispanic children had ASD. While this statistic provides insight, it is essential to consider potential disparities in diagnosis and access to services among different racial and ethnic communities. Further research and targeted interventions may be necessary to ensure equitable identification and support for children with ASD across all populations.

This data table provides a collection of information from peer-reviewed autism prevalence studies. Information reported from each study includes the autism prevalence estimate and additional study characteristics (e.g., case ascertainment and criteria). A PubMed search was conducted to identify studies published at any time through September 2020 using the search terms: autism (title/abstract) OR autistic (title/abstract) AND prevalence (title/abstract). Data were abstracted and included if the study fulfilled the following criteria: • The study was published in English; • The study produced at least one autism prevalence estimate; and • The study was population-based (any age range) within a defined geographic area.

Background/aimsNeuroimaging studies suggest altered functional brain organization in children with autism spectrum disorder (ASD), particularly in response to visual stimulation. However, how transitions between different visual states modulate brain network in ASD remains unclear. This study aimed to investigate how transitioning from minimal visual input (fixation in a dark room, DR) to a silent video (eyes open, EO) alters functional brain networks in children with ASD compared with their typically developing (TD) peers.MethodsWe analyzed magnetoencephalography (MEG) data from children with ASD (n=23) and TD children (n=31), aged 3–10 years. MEG signals were mapped to 68 cortical regions using the Desikan–Killiany atlas, and functional connectivity was assessed using the phase lag index across five frequency bands (delta, theta, alpha, beta, and gamma). Graph theoretical analyses quantified the clustering coefficient (C), characteristic path length (L), and small-worldness (SW) to evaluate network organization.ResultsBoth groups exhibited increased alpha-band clustering coefficients under EO. Notably, baseline (DR) graph metrics predicted EO-induced changes, with higher initial values associated with smaller subsequent increases. Diagnosis-by-condition interactions emerged in the delta and beta bands: children with ASD exhibited more pronounced increases in SW from DR to EO, whereas TD peers showed more modest or opposite shifts. Within the ASD group, larger beta-band SW increases correlated with greater autistic trait severity (Social Responsiveness Scale), whereas in TD children, delta-band increases associated with milder autistic-like traits.ConclusionThese findings reveal age- and diagnosis-specific differences in how visual stimulation reshapes functional brain network organization. They also highlight the potential of network metrics as biomarkers for ASD, though validation in larger, more diverse cohorts is needed to establish clinical relevance.

Understanding the brain differences present at the earliest possible diagnostic age for autism spectrum disorder (ASD) is crucial for delineating the underlying neuropathology of the disorder. However, knowledge of brain structural network changes in the early important developmental period between 2 and 7 years of age is limited in children with ASD. In this study, we aimed to fill the knowledge gap by characterizing age-related brain structural network changes in ASD from 2 to 7 years of age, and identify sensitive network-based imaging biomarkers that are significantly correlated with the symptom severity. Diffusion MRI was acquired in 30 children with ASD and 21 typically developmental (TD) children. With diffusion MRI and quantified clinical assessment, we conducted network-based analysis and correlation between graph-theory-based measurements and symptom severity. Significant age-by-group interaction was found in global network measures and nodal efficiencies during the developmental period of 2–7 years old. Compared with significant age-related growth of the structural network in TD, relatively flattened maturational trends were observed in ASD. Hyper-connectivity in the structural network with higher global efficiency, global network strength, and nodal efficiency were observed in children with ASD. Network edge strength in ASD also demonstrated hyper-connectivity in widespread anatomical connections, including those in default-mode, frontoparietal, and sensorimotor networks. Importantly, identified higher nodal efficiencies and higher network edge strengths were significantly correlated with symptom severity in ASD. Collectively, structural networks in ASD during this early developmental period of 2–7 years of age are characterized by hyper-connectivity and slower maturation, with aberrant hyper-connectivity significantly correlated with symptom severity. These aberrant network measures may serve as imaging biomarkers for ASD from 2 to 7 years of age.

With the release of the multi-site Autism Brain Imaging Data Exchange, many researchers have applied machine learning methods to distinguish between healthy subjects and autistic individuals by using features extracted from resting state functional MRI data. An important part of applying machine learning to this problem is extracting these features. Specifically, whether to include negative correlations between brain region activities as relevant features and how best to define these features. For the second question, the graph theoretical properties of the brain network may provide a reasonable answer. In this study, we investigated the first issue by comparing three different approaches. These included using the positive correlation matrix (comprising only the positive values of the original correlation matrix), the absolute value of the correlation matrix, or the anticorrelation matrix (comprising only the negative correlation values) as the starting point for extracting relevant features using graph theory. We then trained a multi-layer perceptron in a leave-one-site out manner in which the data from a single site was left out as testing data and the model was trained on the data from the other sites. Our results show that on average, using graph features extracted from the anti-correlation matrix led to the highest accuracy and AUC scores. This suggests that anti-correlations should not simply be discarded as they may include useful information that would aid the classification task. We also show that adding the PCA transformation of the original correlation matrix to the feature space leads to an increase in accuracy.

First-Then Charts for Kids Market Outlook

According to our latest research, the global First-Then Charts for Kids market size reached USD 512.4 million in 2024, reflecting the increasing demand for visual aids in early childhood education and special needs management. The market is projected to grow at a robust CAGR of 8.2% from 2025 to 2033, reaching an estimated USD 1,031.5 million by the end of the forecast period. This growth is primarily driven by the rising awareness about the benefits of structured visual schedules for children, especially those with developmental or behavioral challenges. As per the latest research, the market is witnessing significant traction due to the integration of technology, customization features, and the expanding role of these tools in both home and institutional settings.

One of the key growth factors propelling the First-Then Charts for Kids market is the increasing prevalence of developmental disorders, such as autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD). Parents, educators, and therapists are increasingly adopting first-then charts as effective tools to facilitate communication, improve task sequencing, and enhance behavioral outcomes among children. The growing emphasis on inclusive education and the need for differentiated instructional strategies have further fueled the adoption of these charts in schools and therapy centers. Additionally, the market is benefiting from the rising awareness campaigns by non-profit organizations and government bodies, which are educating caregivers and professionals about the efficacy of visual supports for children with special needs.

Another significant driver for the First-Then Charts for Kids market is the technological innovation in product offerings. The advent of digital first-then charts and mobile applications has revolutionized the way caregivers and educators plan and implement daily routines for children. Digital solutions offer enhanced customization, interactive features, and data tracking capabilities, making them highly appealing for modern users. Furthermore, manufacturers are focusing on developing eco-friendly and durable products, such as dry-erase and magnetic charts, to cater to the growing demand for sustainable educational tools. The convergence of technology and traditional teaching aids is expected to create new growth avenues for the market in the coming years.

The expansion of the First-Then Charts for Kids market is also supported by the increasing integration of visual schedules in mainstream educational curriculums and therapy programs. Schools and therapy centers are incorporating first-then charts as part of their intervention strategies to promote independence, reduce anxiety, and improve task completion rates among children. The trend is particularly prominent in developed regions, where educational institutions are investing in specialized resources to support diverse learning needs. Moreover, the growing popularity of home-based learning and teletherapy, especially in the aftermath of the COVID-19 pandemic, has heightened the demand for user-friendly and accessible visual aids.

Regionally, North America leads the First-Then Charts for Kids market, accounting for the largest share in 2024, owing to the high prevalence of developmental disorders, advanced healthcare infrastructure, and proactive government initiatives. Europe follows closely, driven by increasing investments in special education and early intervention programs. The Asia Pacific region is expected to witness the fastest growth rate, attributed to rising awareness, expanding educational budgets, and the increasing adoption of digital learning tools. Latin America and the Middle East & Africa are also showing promising growth potential, supported by improving access to educational resources and growing advocacy for children with special needs.

Product Type Analysis

The Product Type segment in the First-Then Charts for Kids market is diverse, encompassing magnetic first-then charts, dry-erase first-then charts, digital first-then charts, printable first-then charts, and other innovative formats. Magnetic first-then charts are particularly popular in both home and classroom settings due to their reusability, ease of customization, and tactile engagement, which appeals to young children. These charts often come with interchangeable picture cards, allowing parents and educators

SH3 and Multiple Ankyrin Repeat Domains 3 (SHANK3)-caused autism spectrum disorder (ASD) may present a unique opportunity to clarify the heterogeneous neuropathological mechanisms of ASD. However, the specificity and commonality of disrupted large-scale brain organization in SHANK3-deficient children remain largely unknown. The present study combined genetic tests, neurobehavioral evaluations, and magnetic resonance imaging, aiming to explore the disruptions of both local and networked cortical structural organization in ASD children with and without SHANK3 deficiency. Multiple surface morphological parameters such as cortical thickness (CT) and sulcus depth were estimated, and the graph theory was adopted to characterize the topological properties of structural covariance networks (SCNs). Finally, a correlation analysis between the alterations in brain morphological features and the neurobehavioral evaluations was performed. Compared with typically developed children, increased CT and reduced nodal degree were found in both ASD children with and without SHANK3 defects mainly in the lateral temporal cortex, prefrontal cortex (PFC), temporo-parietal junction (TPJ), superior temporal gyrus (STG), and limbic/paralimbic regions. Besides commonality, our findings showed some distinct abnormalities in ASD children with SHANK3 defects compared to those without. Locally, more changes in the STG and orbitofrontal cortex were exhibited in ASD children with SHANK3 defects, while more changes in the TPJ and inferior parietal lobe (IPL) in those without SHANK3 defects were observed. For the SCNs, a trend toward regular network topology was observed in ASD children with SHANK3 defects, but not in those without. In addition, ASD children with SHANK3 defects showed more alterations of nodal degrees in the anterior and posterior cingulate cortices and right insular, while there were more disruptions in the sensorimotor areas and the left insular and dorsomedial PFC in ASD without SHANK3 defects. Our findings indicate dissociable disruptions of local and networked brain morphological features in ASD children with and without SHANK3 deficiency. Moreover, this monogenic study may provide a valuable path for parsing the heterogeneity of brain disturbances in ASD.

Network of 32 papers and 47 citation links related to "QiFei: Assisting to Improve Cognitive Abilities for Autism Children Using a Mobile APP".

First-Then Charts for Kids Market Outlook

According to our latest research, the First-Then Charts for Kids market size reached USD 342.7 million in 2024, demonstrating a robust expansion driven by increased awareness and adoption across educational and therapeutic settings. The market is set to grow at a CAGR of 7.8% through the forecast period, with the overall market projected to attain USD 674.1 million by 2033. This impressive growth trajectory is primarily attributed to rising demand for visual learning aids, especially for children with special needs, and the growing integration of innovative, digital-first solutions in both home and institutional environments.

One of the principal growth factors for the First-Then Charts for Kids market is the increasing emphasis on inclusive education and early intervention strategies. Educational institutions and therapy centers are investing in visual aids to support children with autism spectrum disorder (ASD), ADHD, and other developmental challenges. First-Then Charts have proven effective in helping children understand routines, transitions, and expectations, reducing anxiety and enhancing learning outcomes. This has led to a surge in adoption across both mainstream and special education classrooms, as well as in home settings where parents seek structured behavioral support for their children. Furthermore, as awareness of neurodiversity grows globally, the demand for such visual tools is expected to continue its upward trajectory.

Another significant driver is the technological advancement and diversification of product offerings within the market. Traditional paper-based and magnetic First-Then Charts are now being complemented by digital and interactive formats, catering to the preferences of tech-savvy educators and parents. The proliferation of user-friendly mobile apps and customizable digital charts is making it easier to personalize routines and track progress in real-time. This digital transformation is further supported by the increasing penetration of smart devices and the internet, enabling broader access and adoption in both developed and emerging markets. The versatility and adaptability of these products are expanding their applications beyond educational settings into therapy centers and even mainstream households.

A third crucial factor fueling market growth is the supportive policy landscape and funding initiatives targeting special education. Governments and non-profit organizations are increasingly recognizing the importance of early intervention and are allocating resources to provide schools and therapy centers with the necessary tools. This has resulted in bulk procurement of First-Then Charts, particularly in North America and Europe, where special education funding is more robust. Additionally, ongoing research and advocacy efforts are highlighting the effectiveness of visual schedules, further driving institutional adoption and encouraging new entrants to innovate and expand their product portfolios.

From a regional perspective, North America continues to dominate the First-Then Charts for Kids market, accounting for the largest share in 2024 due to its advanced educational infrastructure, high awareness levels, and strong presence of key market players. Europe follows closely, benefiting from progressive educational policies and increasing integration of special needs support tools. Meanwhile, the Asia Pacific region is witnessing the fastest growth, propelled by rising investments in education technology, increasing rates of childhood developmental disorders, and a growing middle-class population seeking enhanced learning solutions for their children. Latin America and the Middle East & Africa are also showing promising potential, albeit from a smaller base, as awareness and access gradually improve.

Product Type Analysis

The First-Then Charts for Kids market is segmented by product type into Magnetic First-Then Charts, Dry-Erase First-Then Charts, Digital First-Then Charts, Printable First-Then Char

The nosology and epidemiology of Autism has undergone transformation following consolidation of once disparate disorders under the umbrella diagnostic, autism spectrum disorders. Despite this re-conceptualization, research initiatives, including the NIMH’s Research Domain Criteria and Precision Medicine, highlight the need to bridge psychiatric and psychological classification methodologies with biomedical techniques. Combining traditional bibliometric co-word techniques, with tenets of graph theory and network analysis, this article provides an objective thematic review of research between 1994 and 2015 to consider evolution and focus. Results illustrate growth in Autism research since 2006, with nascent focus on physiology. However, modularity and citation analytics demonstrate dominance of subjective psychological or psychiatric constructs, which may impede progress in the identification and stratification of biomarkers as endorsed by new research initiatives.

BackgroundThe ability to recognize, understand and interpret other’s actions and emotions has been linked to the mirror system or action-observation-network (AON). Although variations in these abilities are prevalent in the neuro-typical population, persons diagnosed with autism spectrum disorders (ASD) have deficits in the social domain and exhibit alterations in this neural network.MethodHere, we examined functional network properties of the AON using graph theory measures and region-to-region functional connectivity analyses of resting-state fMRI-data from adolescents and young adults with ASD and typical controls (TC).ResultsOverall, our graph theory analyses provided convergent evidence that the network integrity of the AON is altered in ASD, and that reductions in network efficiency relate to reductions in overall network density (i.e., decreased overall connection strength). Compared to TC, individuals with ASD showed significant reductions in network efficiency and increased shortest path lengths and centrality. Importantly, when adjusting for overall differences in network density between ASD and TC groups, participants with ASD continued to display reductions in network integrity, suggesting that also network-level organizational properties of the AON are altered in ASD.ConclusionWhile differences in empirical connectivity contributed to reductions in network integrity, graph theoretical analyses provided indications that also changes in the high-level network organization reduced integrity of the AON.

Automatic algorithms for disease diagnosis are being thoroughly researched for use in clinical settings. They usually rely on pre-identified biomarkers to highlight the existence of certain problems. However, finding such biomarkers for neurodevelopmental disorders such as Autism Spectrum Disorder (ASD) has challenged researchers for many years. With enough data and computational power, machine learning (ML) algorithms can be used to interpret the data and extract the best biomarkers from thousands of candidates. In this study, we used the fMRI data of 816 individuals enrolled in the Autism Brain Imaging Data Exchange (ABIDE) to introduce a new biomarker extraction pipeline for ASD that relies on the use of graph theoretical metrics of fMRI-based functional connectivity to inform a support vector machine (SVM). Furthermore, we split the dataset into 5 age groups to account for the effect of aging on functional connectivity. Our methodology achieved better results than most state-of-the-art investigations on this dataset with the best model for the >30 years age group achieving an accuracy, sensitivity, and specificity of 95, 97, and 95%, respectively. Our results suggest that measures of centrality provide the highest contribution to the classification power of the models.

BackgroundThe growing prevalence of autism spectrum disorder (ASD) underscores the urgent need for therapies that target underlying biological mechanisms, with cell-based interventions offering a potentially transformative approach by targeting core physiological disruptions rather than providing temporary symptom management. The purpose of this study was to report on our experience with an autologous cell-based intervention in children with ASD.MethodsThis retrospective data analysis included pre- and postinterventional data from 128 children with ASD who received intrathecal injections of autologous bone marrow concentrate. Patient and procedure related characteristics, complications, and the Autism Treatment Evaluation Checklist (ATEC) scores were extracted from patient's medical records.ResultsData were analyzed from 128 children (27 females and 101 males), aged between two and 16 years at their first intervention. A total of 32.8% underwent more than two single-step procedures. Significant improvements from the first to the second intervention were detected in the total and all subgroup ATEC scores, as well as in the severity groups (p < 0.001). Following the intervention, 4.6% of children transitioned from the “mild” or “moderate” to the “no symptoms” category, and 25.4% of the initially categorized “severe” group shifted to a milder symptom category. The average total ATEC score improved from the first to the second intervention by 19.0 ± 17.1 points, and one 60-point improvement was detected. The recorded ATEC score improvements in 85.9% of patients were similar between genders, as well as between age groups. A subgroup analysis of 39 patients who received three interventions also showed statistically significant differences in all ATEC scores between the three time points (p < 0.001). The highest improvements occurred after the first intervention, continued to improve over time, and remained reduced even three to four years after the intervention. There was not a single serious adverse event in the 307 treatments. All complications (e.g., nausea/vomiting) were resolved within a week or less after the procedure.ConclusionBoth a significant improvement in ATEC scores, and significant severity shifts to milder forms–even into the “no symptoms” category–suggest a measurable improvement in autism-related symptoms after autologous, bone marrow derived, intrathecally applied single procedures in children with ASD.

BackgroundThe growing prevalence of autism spectrum disorder (ASD) underscores the urgent need for therapies that target underlying biological mechanisms, with cell-based interventions offering a potentially transformative approach by targeting core physiological disruptions rather than providing temporary symptom management. The purpose of this study was to report on our experience with an autologous cell-based intervention in children with ASD.MethodsThis retrospective data analysis included pre- and postinterventional data from 128 children with ASD who received intrathecal injections of autologous bone marrow concentrate. Patient and procedure related characteristics, complications, and the Autism Treatment Evaluation Checklist (ATEC) scores were extracted from patient's medical records.ResultsData were analyzed from 128 children (27 females and 101 males), aged between two and 16 years at their first intervention. A total of 32.8% underwent more than two single-step procedures. Significant improvements from the first to the second intervention were detected in the total and all subgroup ATEC scores, as well as in the severity groups (p < 0.001). Following the intervention, 4.6% of children transitioned from the “mild” or “moderate” to the “no symptoms” category, and 25.4% of the initially categorized “severe” group shifted to a milder symptom category. The average total ATEC score improved from the first to the second intervention by 19.0 ± 17.1 points, and one 60-point improvement was detected. The recorded ATEC score improvements in 85.9% of patients were similar between genders, as well as between age groups. A subgroup analysis of 39 patients who received three interventions also showed statistically significant differences in all ATEC scores between the three time points (p < 0.001). The highest improvements occurred after the first intervention, continued to improve over time, and remained reduced even three to four years after the intervention. There was not a single serious adverse event in the 307 treatments. All complications (e.g., nausea/vomiting) were resolved within a week or less after the procedure.ConclusionBoth a significant improvement in ATEC scores, and significant severity shifts to milder forms–even into the “no symptoms” category–suggest a measurable improvement in autism-related symptoms after autologous, bone marrow derived, intrathecally applied single procedures in children with ASD.