Executive Functioning in Adults with Down Syndrome: Machine-Learning-Based Prediction of Inhibitory Capacity

Use of Serious Games in Interventions of Executive Functions in Neurodiverse Children: Systematic Review

BACKGROUND

Serious games (SG) have emerged as promising tools for cognitive training and therapeutic interventions, especially for enhancing executive functions. These games have demonstrated the potential to support individuals with diverse health conditions, including neurodevelopmental and cognitive disorders, through engaging and interactive experiences. However, a comprehensive understanding of the effectiveness of SG in enhancing executive functions is needed.

OBJECTIVE

This systematic review aims to assess the impact of serious games on executive functions (EF), focusing on attention, working memory, cognitive flexibility, and inhibitory control. In addition, it explores the integration of SG into educational and therapeutic settings for individuals with cognitive and neurodevelopmental conditions. Only open access articles published from 2019 to the search date were included to capture the most recent advancements in the field.

METHODS

A comprehensive search was conducted on June 20, 2024, across Scopus, Web of Science, and PubMed databases. Due to limited direct results linking SG and neurodiversity, separate searches were performed to analyze the relationship between SG and EF, as well as SG and neurodiverse populations. Two independent reviewers assessed the quality and risk of bias of the included studies using the Risk of Bias 2 tool for randomized studies and the Risk of Bias in Non-Randomized Studies of Interventions tool for nonrandomized studies.

RESULTS

The review identified 16 studies that met the inclusion criteria. Of these, 15 addressed the use of SG for improving EF in neurodiverse populations, such as children with attention-deficit/hyperactivity disorder, autism spectrum disorder, and down syndrome. These studies demonstrated significant improvements in various EF domains, including attention, working memory, and cognitive flexibility. However, there was notable heterogeneity in sample sizes, participant ages, and game types. Three studies specifically focused on individuals with down syndrome, showing promising results in improving cognitive functions.

CONCLUSIONS

SG hold considerable potential as therapeutic tools for enhancing EF across neurodiverse populations. They have shown positive effects in improving cognitive skills and promoting inclusion in both educational and therapeutic settings. However, further research is required to optimize game design, assess long-term outcomes, and address the variability in study quality. The exclusive inclusion of open access studies may have limited the scope of the review, and future research should incorporate a broader range of studies to provide a more comprehensive understanding of SG's impact on neurodiversity.

TRIAL REGISTRATION

PROSPERO CRD42024563231; https://tinyurl.com/ycxdymyb.

Artificial intelligence for prenatal chromosome analysis

This review article delves into the rapidly advancing domain of prenatal diagnostics, with a primary focus on the detection and management of chromosomal abnormalities such as trisomy 13 ("Patau syndrome)", "trisomy 18 (Edwards syndrome)", and "trisomy 21 (Down syndrome)". The objective of the study is to examine the utilization and effectiveness of novel computational methodologies, such as "machine learning (ML)", "deep learning (DL)", and data analysis, in enhancing the detection rates and accuracy of these prenatal conditions. The contribution of the article lies in its comprehensive examination of advancements in "Non-Invasive Prenatal Testing (NIPT)", prenatal screening, genomics, and medical imaging. It highlights the potential of these techniques for prenatal diagnosis and the contributions of ML and DL to these advancements. It highlights the application of ensemble models and transfer learning to improving model performance, especially with limited datasets. This also delves into optimal feature selection and fusion of high-dimensional features, underscoring the need for future research in these areas. The review finds that ML and DL have substantially improved the detection and management of prenatal conditions, despite limitations such as small sample sizes and issues related to model generalizability. It recognizes the promising results achieved through the use of ensemble models and transfer learning in prenatal diagnostics. The review also notes the increased importance of feature selection and high-dimensional feature fusion in the development and training of predictive models. The findings underline the crucial role of AI and machine learning techniques in early detection and improved therapeutic strategies in prenatal diagnostics, highlighting a pressing need for further research in this area.

Association between participation at home and functional skills in children and adolescents with Down syndrome: A cross-sectional study

BACKGROUND

According to a biopsychosocial approach to health and disability, participation at home and functional skills are important components of the functioning. Therefore, knowledge about interactions between these components allows for targeting specific interventions.

OBJECTIVE

This study investigated whether participation opportunities (frequency and involvement) for children/adolescents with Down syndrome (DS) in a realistic environment at their own home are associated with the functional skills related to the domains of Daily Activities, Mobility, Social/Cognitive and Responsibility.

METHODS

This was an observational study. Forty-eight children/adolescents with DS participated (mean age: 10.73 ± 3.43; n = 27 females). Participants were evaluated using the Participation and Environment Measure for Children and Youth (PEM-CY) home environment setting (raw frequency and engagement scores) and Pediatric Evaluation of Disability Inventory speedy version (PEDI-CAT-SV) (continuous score).

RESULTS

Significant and positive correlations were found between the frequency of participation at home with Daily Activities (ro = 0.320), Social/Cognitive (ro = 0.423) and Responsibility (ro = 0.455). For involvement, significant and positive correlations were found with Daily Activities (ro = 0.297), Social/Cognitive (ro = 0.380) and Responsibility (ro = 0.380). For the PEDI-CAT-SV Mobility, no significant correlation was found.

CONCLUSIONS

Higher frequency and involvement of participation at home are associated with greater functional skills assessed, except for Mobility. This study provided pioneering insights about the relationships between the level of home participation and functional skills in DS, generating evidence that could guide approaches to participation-focused intervention.

Unraveling Down Syndrome: From Genetic Anomaly to Artificial Intelligence-Enhanced Diagnosis

Down syndrome arises from chromosomal non-disjunction during gametogenesis, resulting in an additional chromosome. This anomaly presents with intellectual impairment, growth limitations, and distinct facial features. Positive correlation exists between maternal age, particularly in advanced cases, and the global annual incidence is over 200,000 cases. Early interventions, including first and second-trimester screenings, have improved DS diagnosis and care. The manifestations of Down syndrome result from complex interactions between genetic factors linked to various health concerns. To explore recent advancements in Down syndrome research, we focus on the integration of artificial intelligence (AI) and machine learning (ML) technologies for improved diagnosis and management. Recent developments leverage AI and ML algorithms to detect subtle Down syndrome indicators across various data sources, including biological markers, facial traits, and medical images. These technologies offer potential enhancements in accuracy, particularly in cases complicated by cognitive impairments. Integration of AI and ML in Down syndrome diagnosis signifies a significant advancement in medical science. These tools hold promise for early detection, personalized treatment, and a deeper comprehension of the complex interplay between genetics and environmental factors. This review provides a comprehensive overview of neurodevelopmental and cognitive profiles, comorbidities, diagnosis, and management within the Down syndrome context. The utilization of AI and ML represents a transformative step toward enhancing early identification and tailored interventions for individuals with Down syndrome, ultimately improving their quality of life.

Correlating MRI-based brain volumetry and cognitive assessment in people with Down syndrome

INTRODUCTION

Down syndrome (DS) is the most common genetic cause of intellectual disability. Children and adults with DS show deficits in language performance and explicit memory. Here, we used magnetic resonance imaging (MRI) on children and adults with DS to characterize changes in the volume of specific brain structures involved in memory and language and their relationship to features of cognitive-behavioral phenotypes.

METHODS

Thirteen children and adults with the DS phenotype and 12 age- and gender-matched healthy controls (age range 4-25) underwent an assessment by MRI and a psychological evaluation for language and cognitive abilities.

RESULTS

The cognitive profile of people with DS showed deficits in different cognition and language domains correlating with reduced volumes of specific regional and subregional brain structures, confirming previous related studies. Interestingly, in our study, people with DS also showed more significant parahippocampal gyrus volumes, in agreement with the results found in earlier reports.

CONCLUSIONS

The memory functions and language skills affected in studied individuals with DS correlate significantly with the reduced volume of specific brain regions, allowing us to understand DS's cognitive-behavioral phenotype. Our results provide an essential basis for early intervention and the design of rehabilitation management protocols.

Machine learning based analysis for intellectual disability in Down syndrome

Down syndrome (DS) or trisomy 21 is the most common genetic cause of intellectual disability (ID), but a pathogenic mechanism has not been identified yet. Studying a complex and not monogenic condition such as DS, a clear correlation between cause and effect might be difficult to find through classical analysis methods, thus different approaches need to be used. The increased availability of big data has made the use of artificial intelligence (AI) and in particular machine learning (ML) in the medical field possible. The purpose of this work is the application of ML techniques to provide an analysis of clinical records obtained from subjects with DS and study their association with ID. We have applied two tree-based ML models (random forest and gradient boosting machine) to the research question: how to identify key features likely associated with ID in DS. We analyzed 109 features (or variables) in 106 DS subjects. The outcome of the analysis was the age equivalent (AE) score as indicator of intellectual functioning, impaired in ID. We applied several methods to configure the models: feature selection through Boruta framework to minimize random correlation; data augmentation to overcome the issue of a small dataset; age effect mitigation to take into account the chronological age of the subjects. The results show that ML algorithms can be applied with good accuracy to identify variables likely involved in cognitive impairment in DS. In particular, we show how random forest and gradient boosting machine produce results with low error (MSE <0.12) and an acceptable R2 (0.70 and 0.93). Interestingly, the ranking of the variables point to several features of interest related to hearing, gastrointestinal alterations, thyroid state, immune system and vitamin B12 that can be considered with particular attention for improving care pathways for people with DS. In conclusion, ML-based model may assist researchers in identifying key features likely correlated with ID in DS, and ultimately, may improve research efforts focused on the identification of possible therapeutic targets and new care pathways. We believe this study can be the basis for further testing/validating of our algorithms with multiple and larger datasets.

Quality of life in adults with Down syndrome: A mixed methods systematic review

BACKGROUND

As the life expectancy of adults (aged ≥ 18 years) with Down syndrome increases for a plethora of reasons including recognition of rights, access, and technological and medical advances, there is a need to collate evidence about their quality of life.

OBJECTIVE

Using Schalock and Verdugo's multidimensional quality of life assessment model, this systematic review aimed to identify, synthesise and integrate the quantitative and qualitative evidence on quality of life in adults with Down syndrome via self-and proxy-reporting.

METHODS

Five databases were systematically searched: MEDLINE, CINAHL, PsycINFO, Scopus, and Web of Science to identify relevant articles published between 1980 and 2022 along with grey literature and reference lists from relevant studies. A mixed methods systematic review was performed according to the Joanna Briggs Institute methodology using the convergent integrated approach. The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.

RESULTS

Thirty-nine studies were included: 20 quantitative, 17 qualitative, and 2 mixed methods studies. The synthesised findings were grouped into the 8 core domains of quality of life: personal development, self-determination, interpersonal relations, social inclusion, rights, emotional, physical and material well-being. Of the 39 studies, 30 (76.92%) reported on emotional well-being and 10 (25.64%) on rights. Only 7 (17.94%) studies reported that adults with Down syndrome have a good quality of life centred around self-determination and interpersonal relations. Most adults with Down syndrome wanted to become more independent, have relationships, participate in the community, and exercise their human rights. Self-reported quality of life from adults with Down syndrome was rated higher than proxy reported quality of life. Discrepancies in quality of life instruments were discovered.

CONCLUSION

This review highlighted the need for a better systematic approach to improving the quality of life in adults with Down syndrome in targeted areas. Future research is required to evaluate self-and proxy-reporting methods and culture-specific quality of life instruments that are more appropriate for adults with Down syndrome. In addition, further studies should consider including digital assistive technologies to obtain self-reported quality of life data in adults with Down syndrome.

INTERNATIONAL PROSPECTIVE REGISTER OF SYSTEMATIC REVIEWS REGISTRATION NUMBER

CRD42019140056.

Serious Games for Executive Functions Training for Adults with Intellectual Disability: Overview

(1) Background: Throughout the history of medical and psychology practice, specialists have worked to improve the quality of treatment and rehabilitation, which has led to the emergence of concepts such as serious games. These tools focus on different areas of intervention procedures, one of which is to help people with intellectual disability (ID). Individuals with ID have problems with executive functions (EFs), which are related to adaptive functioning. Recent studies showed that serious games positively impact cognitive, social, and communication skills in people with ID. The purpose of this study is to analyze the solutions that have been found in EF training for adults with ID in recent years, evaluating them with a number of key parameters and identifying the features and possible problems in the further development of our system. (2) Methods: A review was conducted starting with 573 articles in English related to serious games and selected from studies that had been published since 2015. Finally, 10 were examined in detail as they focused on EFs in adults with ID. They were searched in seven major databases ("Association for Computing Machinery" (ACM), IEEE Xplore database, DBLP computer science bibliography, Google Scholar, PubMed, SCOPUS, and PsycInfo). (3) Results: It was determined that the most frequent EFs referred to in the studies analyzed were planning and decision-making, followed by working memory and social cognition, behavioral regulation, flexibility, and inhibition capacity. The basic approach to the creation of support systems was also analyzed in terms of technical and program execution. The trend results' analysis evidenced improvements in EFs, even though they were not significant. This comprehensive technique enabled the identification of the main features and aspects to be taken into account for further development of our system.

Application of Machine Learning Techniques to Help in the Feature Selection Related to Hospital Readmissions of Suicidal Behavior

Suicide was the main source of death from external causes in Spain in 2020, with 3,941 cases. The importance of identifying those mental disorders that influenced hospital readmissions will allow us to manage the health care of suicidal behavior. The feature selection of each hospital in this region was carried out by applying Machine learning (ML) and traditional statistical methods. The results of the characteristics that best explain the readmissions of each hospital after assessment by the psychiatry specialist are presented. Adjustment disorder, alcohol abuse, depressive syndrome, personality disorder, and dysthymic disorder were selected for this region. The most influential methods or characteristics associated with suicide were benzodiazepine poisoning, suicidal ideation, medication poisoning, antipsychotic poisoning, and suicide and/or self-harm by jumping. Suicidal behavior is a concern in our society, so the results are relevant for hospital management and decision-making for its prevention.