Comparison of Visual Fixation Trajectories in Toddlers with Autism Spectrum Disorder and Typical Development: A Markov Chain Model

Investigating causality and shared genetic architecture between body mass index and cognitive function: a genome-wide cross-trait analysis and bi-directional Mendelian randomization study

Background and objectives

Observational studies have established a connection between body mass index (BMI) and an increased risk of cognitive decline. However, a comprehensive investigation into the causal relationships between BMI and cognitive function across diverse age groups, as well as the genetic underpinnings of this relationship, has been notably lacking. This study aims to investigate causality and the shared genetic underpinnings of between BMI and cognitive function by conducting a thorough genome-wide analysis, thereby provide valuable insights for developing personalized intervention strategies to promote cognitive health.

Methods

Genetic associations between BMI and cognitive function were thoroughly investigated through covariate genetic analysis and chained imbalance score regression, utilizing data from genome-wide association studies (GWAS). Bi-directional Mendelian Randomization (MR) was employed to uncover associations and potential functional genes were further scrutinized through Cross-trait meta-analysis and Summary-data-based MR (SMR). Subsequently, a detailed examination of the expression profiles of the identified risk SNPs in tissues and cells was conducted.

Results

The study found a significant negative correlation between BMI and cognitive function (β = -0.16, P = 1.76E-05), suggesting a causal linkage where higher BMI values were predictive of cognitive impairment. We identified 5 genetic loci (rs6809216, rs7187776, rs11713193, rs13096480, and rs13107325) between BMI and cognitive function by cross-trait meta-analysis and 5 gene-tissue pairs were identified by SMR analysis. Moreover, two novel risk genes TUFM and MST1R were shared by both cross-trait analysis and SMR analysis, which had not been observed in previous studies. Furthermore, significant enrichment of single nucleotide polymorphisms (SNPs) at tissue- and cell-specific levels was identified for both BMI and cognitive function, predominantly within the brain.

Conclusion

This study uncovers a causal relationship between BMI and cognitive function, with the discovery of TUFM and MST1R as shared genetic factors associated with both conditions. This novel finding offers new insights into the development of preventative strategies for cognitive decline in obese individuals, and further enhances our understanding of the underlying pathophysiology of these conditions. Furthermore, these findings could serve as a guide for the development of innovative therapeutic approaches to address cognitive decline in obese individuals.

Outcome measures in Angelman syndrome

BACKGROUND

Angelman syndrome (AS) is a rare neurodevelopmental disorder characterized by severe intellectual disability, little to no expressive speech, visual and motor problems, emotional/behavioral challenges, and a tendency towards hyperphagia and weight gain. The characteristics of AS make it difficult to measure these children's functioning with standard clinical tests. Feasible outcome measures are needed to measure current functioning and change over time, in clinical practice and clinical trials.

AIM

Our first aim is to assess the feasibility of several functional tests. We target domains of neurocognitive functioning and physical growth using the following measurement methods: eye-tracking, functional Near-Infrared Spectroscopy (fNIRS), indirect calorimetry, bio-impedance analysis (BIA), and BOD POD (air-displacement plethysmography). Our second aim is to explore the results of the above measures, in order to better understand the AS phenotype.

METHODS

The study sample consisted of 28 children with AS aged 2-18 years. We defined an outcome measure as feasible when (1) at least 70% of participants successfully finished the measurement and (2) at least 60% of those participants had acceptable data quality. Adaptations to the test procedure and reasons for early termination were noted. Parents rated acceptability and importance and were invited to make recommendations to increase feasibility. The results of the measures were explored.

RESULTS

Outcome measures obtained with eye-tracking and BOD POD met the definition of feasibility, while fNIRS, indirect calorimetry, and BIA did not. The most important reasons for early termination of measurements were showing signs of protest, inability to sit still and poor/no calibration (eye-tracking specific). Post-calibration was often applied to obtain valid eye-tracking results. Parents rated the BOD POD als most acceptable and fNIRS as least acceptable for their child. All outcome measures were rated to be important. Exploratory results indicated longer reaction times to high salient visual stimuli (eye-tracking) as well as high body fat percentage (BOD POD).

CONCLUSIONS

Eye-tracking and BOD POD are feasible measurement methods for children with AS. Eye-tracking was successfully used to assess visual orienting functions in the current study and (with some practical adaptations) can potentially be used to assess other outcomes as well. BOD POD was successfully used to examine body composition.

TRIAL REGISTRATION

Registered d.d. 23-04-2020 under number 'NL8550' in the Dutch Trial Register: https://onderzoekmetmensen.nl/en/trial/23075.

Machine learning-based early diagnosis of autism according to eye movements of real and artificial faces scanning

Background

Studies on eye movements found that children with autism spectrum disorder (ASD) had abnormal gaze behavior to social stimuli. The current study aimed to investigate whether their eye movement patterns in relation to cartoon characters or real people could be useful in identifying ASD children.

Methods

Eye-tracking tests based on videos of cartoon characters and real people were performed for ASD and typically developing (TD) children aged between 12 and 60 months. A three-level hierarchical structure including participants, events, and areas of interest was used to arrange the data obtained from eye-tracking tests. Random forest was adopted as the feature selection tool and classifier, and the flattened vectors and diagnostic information were used as features and labels. A logistic regression was used to evaluate the impact of the most important features.

Results

A total of 161 children (117 ASD and 44 TD) with a mean age of 39.70 ± 12.27 months were recruited. The overall accuracy, precision, and recall of the model were 0.73, 0.73, and 0.75, respectively. Attention to human-related elements was positively related to the diagnosis of ASD, while fixation time for cartoons was negatively related to the diagnosis.

Conclusion

Using eye-tracking techniques with machine learning algorithms might be promising for identifying ASD. The value of artificial faces, such as cartoon characters, in the field of ASD diagnosis and intervention is worth further exploring.

Autism and intention attribution test: a non-verbal evaluation with comic strips

BACKGROUND

Despite autism spectrum disorder (ASD) and mentalization being two words often associated in the literature, the assessment of this ability in individuals with ASD in the clinical setting is still limited. Indeed, there are no standardized Theory of Mind (ToM) tests that are adaptable to different cognitive profiles, such as individuals with language poverty, and intellectual or memory impairments. This study proposes a non-verbal test (Intentions Attribution-Comic Strip Test; IA-CST) to evaluate the ability to infer the intentions of others, a basic component of ToM, in the clinical setting.

METHOD

In Study 1, the test was administered to 261 healthy individuals and we performed structural validation using Exploratory Graph Analysis. In Study 2, the final version of the test was administered to 32 individuals with ASD to assess the known group validity of the measure by comparing their scores with a sample of IQ-matched controls. Moreover, we performed logistic regression and ROC curve to preliminarily assess the diagnostic performance of the IA-CST.

RESULTS

The IA-CST resulted in a 3-dimension measure with good structural stability. Group comparison indicated that the ASD group shows significantly lower performance in intention attribution but not in inferring causal consequences. The test demonstrated known group validity and that, preliminarily, it is suitable for implementation within the clinical practice.

CONCLUSIONS

The results support the IA-CST as a valid non-verbal task for evaluating intentions attribution in the clinical setting. Difficulties in ToM are early and relevant in ASD, so assessing these aspects is valuable for structuring individualized and evidence-based interventions.

Measuring social orienting in preschoolers with autism spectrum disorder using cartoons stimuli

Altered social orienting (SO) was proposed as the primary source of socio-communicative difficulties in autism spectrum disorder (ASD). Eye-tracking studies generally confirm a decreased SO in ASD population. However, SO has been scarcely investigated using minimally social stimuli such as cartoons. The extent to which SO might be decreased when watching cartoons is therefore unknown. Yet, it could allow for malleable and child-friendly paradigms that could be sensitive to early atypical visual preference. In this study, 90 preschoolers with ASD (age = 3.19 ± 0 .88) and 20 TD (age = 2.95 ± 1.26) watched two eye-tracking preference tasks. One Realistic task, displaying children dancing versus geometric shapes moving repetitively and a Cartoon task, displaying social and non-social cartoon stimuli with similar movements. We measured SO percentage along with refined visual exploration parameters and compared those of ASD children to TDs. In addition, we investigated their relations with behavioral measures such as symptom severity, developmental and adaptive levels. We evidenced a decreased SO percentage in ASD compared to TD children when watching the Realistic task but not the Cartoon task. We did not identify any other between groups differences. However, we identified several correlations between eye-tracking measures and developmental as well as adaptive measures within the Cartoon task. Together, our results support a preferential orientation of children with autism towards repetitively moving shapes but no decreased SO when measured with minimally social stimuli. Nonetheless, when investigating finer visual exploration parameters, even socially simple stimuli elicited atypical gaze patterns related to early developmental delay.