time series and autism

INTRODUCTION: Childhood obesity is a global concern and has both nutritional and genetic causative factors. One of the most common monogenic causes of obesity is heterozygous mutations in the Melanocortin 4 receptor (MC4R), which are found in 5.7% to 8.6% of individuals with early-onset obesity. We report, the effect of Semaglutide, a long-acting Glucagon like peptide (GLP1) analogue, in the treatment of severe obesity in an adolescent boy with a heterozygous mutation in MC4R. CASE PRESENTATION: A 13-year-old boy with a history of excessive weight gain since infancy was referred to the specialised weight management team...

PURPOSE: Down Syndrome Regressive Disorder (DSRD) is a neuropsychiatric condition associated with severe symptomology and a negative impact on quality of life. DSRD frequently presents with catatonic symptoms. However, few studies have reported the specific catatonic symptoms that occur in DSRD. METHODS: We conducted a retrospective analysis of medical records in a large health system in the southern United States to identify patients with diagnoses of DS with catatonic symptoms who presented for clinical care between 1/1/2018 and 12/1/2023...

Introduction: The prevalence of Autism Spectrum Disorder (ASD) is four times higher in males than females; however, females are significantly more likely to go undiagnosed due to the existence of a "female autistic phenotype", a manifestation unique to females that conflicts with conventional, masculine conceptualizations of ASD. Furthermore, subthreshold autistic traits, which exert a significantly negative impact on quality of life and represent a vulnerability factor for the development of other psychopathological conditions, may remain even more under-recognized...

Functional brain networks represent dynamic and complex interactions among anatomical regions of interest (ROIs), providing crucial clinical insights for neural pattern discovery and disorder diagnosis. In recent years, graph neural networks (GNNs) have proven immense success and effectiveness in analyzing structured network data. However, due to the high complexity of data acquisition, resulting in limited training resources of neuroimaging data, GNNs, like all deep learning models, suffer from overfitting...

IMPORTANCE: Aggressive behavior is a prevalent and challenging issue in individuals with autism. OBJECTIVE: To investigate whether changes in peripheral physiology recorded by a wearable biosensor and machine learning can be used to predict imminent aggressive behavior before it occurs in inpatient youths with autism. DESIGN, SETTING, AND PARTICIPANTS: This noninterventional prognostic study used data collected from March 2019 to March 2020 from 4 primary care psychiatric inpatient hospitals...

Identifying causality from observational time-series data is a key problem in dealing with complex dynamic systems. Inferring the direction of connection between brain regions (i.e., causality) has become the central topic in the domain of fMRI. The purpose of this study is to obtain causal graphs that characterize the causal relationship between brain regions based on time series data. To address this issue, we designed a novel model named deep causal variational autoencoder (CVAE) to estimate the causal relationship between brain regions...

Introduction Applied behavior analysis (ABA) is commonly used to treat children with autism spectrum disorder (ASD). The objective of this study is to evaluate barriers to ABA treatment in ASD. Methods A voluntary 51-question survey, including demographics, socioeconomic status, parental assertiveness/self-advocacy, and parent perceptions, was provided to caregivers of children aged one to eight years old diagnosed with ASD. The survey consisted of a series of yes/no, five-point Likert scale, multiple-choice, and text field questions...

In response to a stimulus, distinct areas of the human brain are activated. Also, it is known that the regions interact with one another. This functional connectivity is helpful to diagnose any neurological abnormality, such as autism spectrum disorder (ASD). This work proposes an approach to construct a functional connectivity network from fMRI image data. For obtaining a functional connectivity network, the time series component of fMRI data is used and from it correlation matrix is calculated showing the degree of interaction among the brain regions...

INTRODUCTION: Autism spectrum disorder is a lifelong neurodevelopmental disorder. The profile of functioning in autistic people is very heterogeneous, and it is necessary to take into account individual characteristics to better support integration in the workplace. However, unemployment rates are higher for autistic people than for other types of disabilities. We present a prospective case series to explore the feasibility and efficacy of an individual-supported program to enhance placement in a sheltered work environment delivered by an Italian community day care center...

Dynamic functional networks (DFN) have considerably advanced modelling of the brain communication processes. The prevailing implementation capitalizes on the system and network-level correlations between time series. However, this approach does not account for the continuous impact of non-dynamic dependencies within the statistical correlation, resulting in relatively stable connectivity patterns of DFN over time with limited sensitivity for communication dynamic between brain regions. Here, we propose an activation network framework based on the activity of functional connectivity (AFC) to extract new types of connectivity patterns during brain communication process...

Loss of previously acquired developmental skills in children with Down syndrome (DS) is not a well characterized phenomenon. We identified 20 confirmed cases of childhood-onset skill loss for descriptive analysis. Eligible participants were recruited from a specialty clinic for persons with DS at a large medical center. Age and gender-matched participants also with DS but without skill loss were used as a comparison group. Case and control participants were between 3 and 14 years (mean 7.6 yr) at the time of evaluation...

Background: Restricted and repetitive behaviors (RRBs) are highly prevalent and reduce function in individuals with fragile X syndrome (FXS). As transdiagnostic features of intellectual disability, elevated rates of RRBs in FXS could represent various underlying known co-occurring conditions in FXS such as anxiety or autism spectrum disorder (ASD), yet this distinction has not been investigated. Further, delineating whether RRBs are more indicative of anxiety or ASD in FXS may clarify phenotypic profiles within FXS and improve differential assessment...

Functional connectivity MRI (fcMRI) is a technique used to study the functional connectedness of distinct regions of the brain by measuring the temporal correlation between their blood oxygen level-dependent (BOLD) signals. fcMRI is typically measured with the Pearson correlation (PC), which assumes that there is no lag between time series. Dynamic time warping (DTW) is an alternative measure of similarity between time series that is robust to such time lags. We used PC fcMRI data and DTW fcMRI data as predictors in machine learning models for classifying autism spectrum disorder (ASD)...

Autism Spectrum Disorder (ASD) is a highly heterogeneous condition, due to high variance in its etiology, comorbidity, pathogenesis, severity, genetics, and brain functional connectivity (FC). This makes it devoid of any robust universal biomarker. This study aims to analyze the role of age and multivariate patterns in brain FC and their accountability in diagnosing ASD by deep learning algorithms. We utilized functional magnetic resonance imaging data of three age groups (6 to 11, 11 to 18, and 6 to 18 years), available with public databases ABIDE-I and ABIDE-II, to discriminate between ASD and typically developing...

In the past two decades, network-based analysis has garnered considerable attention for analyzing time series data across various fields. Time series data can be transformed into graphs or networks using different methods, with the visibility graph (VG) being a widely utilized approach. The VG holds extensive applications in comprehending, identifying, and predicting specific characteristics of time series data. Its practicality extends to domains such as medicine, economics, meteorology, tourism, and others...

For neurological disorders and diseases, functional and anatomical connectomes of the human brain can be used to better inform targeted interventions and treatment strategies. Functional magnetic resonance imaging (fMRI) is a non-invasive neuroimaging technique that captures spatio-temporal brain function through change in blood-oxygen-level-dependent (BOLD) signals over time. FMRI can be used to study the functional connectome through the functional connectivity matrix; that is, Pearson's correlation matrix between time series from the regions of interest of an fMRI image...

Recent research extends our understanding of brain processes beyond just action potentials and chemical transmissions within neural circuits, emphasizing the mechanical forces generated by excitatory synapses on dendritic spines to modulate presynaptic function. From in vivo and in vitro studies, we outline five central principles of synaptic mechanics in brain function: P1: Stability - Underpinning the integral relationship between the structure and function of the spine synapses. P2: Extrinsic dynamics - Highlighting synapse-selective structural plasticity which plays a crucial role in Hebbian associative learning, distinct from pathway-selective long-term potentiation (LTP) and depression (LTD)...

The COVID-19 pandemic has strained the resources of the world's healthcare systems. Most individuals with neurodevelopmental disabilities (NDDs) experience significant mental health issues and face substantial barriers in accessing appropriate supports which have been exacerbated during the pandemic. It is unknown the extent to which COVID-19 impacted the demand for and effectiveness of inpatient care for those with NDDs. The impact of COVID-19 on the number of admissions of youth with NDDs to pediatric inpatient psychiatry units, as well as their functioning and length of stay during the first two years of the pandemic was analyzed using Bayesian structural time series models...

BACKGROUND: Despite the prevalence of Autism Spectrum Disorder (ASD) globally, there's a knowledge gap pertaining to autism in Arabic nations. Recognizing the need for validated biomarkers for ASD, our study leverages eye-tracking technology to understand gaze patterns associated with ASD, focusing on joint attention (JA) and atypical gaze patterns during face perception. While previous studies typically evaluate a single eye-tracking metric, our research combines multiple metrics to capture the multidimensional nature of autism, focusing on dwell times on eyes, left facial side, and joint attention...

OBJECTIVES: The electroretinogram is a clinical test commonly used in the diagnosis of retinal disorders with the peak time and amplitude of the a- and b-waves used as the main indicators of retinal function. However, subtle changes that affect the shape of the electroretinogram waveform may occur in the early stages of disease or in conditions that have a neurodevelopmental or neurodegenerative origin. In such cases, we introduce a statistical approach to mathematically model the shape of the electroretinogram waveform that may aid clinicians and researchers using the electroretinogram or other biological signal recordings to identify morphological features in the waveforms that may not be captured by the time or time-frequency domains of the waveforms...