time series and autism

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by deficits in social interaction and communication, and repetitive or stereotyped behaviors. Previous studies have reported altered brain connectivity in ASD children compared to typically developing children. In this study, we investigated the diversity of connectivity patterns between children with ASD and typically developing children using phase lag entropy (PLE), a measure of the variability of phase differences between two time series...

BACKGROUND: Acute mastoiditis (AM) is the most common complication of acute otitis media and primarily affects children under the age of two; current data on its prevalence in paediatric patients with cochlear implant (CI) are still scant. Proper management of AM in CI children is crucial in order to avoid the implications (financial and emotional) of an explant. Aim of this paper is to describe the cases of AM occurred among young patients with CI in follow up at our department, also in order to evaluate its prevalence, potential predisposing factors, clinical course and therapeutic strategies...

INTRODUCTION: High-order functional connectivity networks (FCNs) that reflect the connection relationships among multiple brain regions have become important tools for exploring the deep workings of the brain and revealing the mechanisms of brain diseases. The traditional high-order FCN constructed based on the "correlation of correlations" strategy, is a representative method for conducting whole-brain connectivity analysis and revealing global network characteristics. However, whole-brain connectivity analysis may be affected by noise carried by less important brain regions, resulting in redundant information and affecting the accuracy and reliability of the analysis...

INTRODUCTION: SLC6A1 pathogenic variants have been associated with epilepsy and neurodevelopmental disorders. The clinical phenotype includes different seizure types, intellectual disability, and psychiatric symptoms affecting mood and behavior. Few data regarding neuropsychological features have been described, and details on cognitive profiles are often missing due to the lack of standardized tests. METHODS: We retrospectively reviewed the neuropsychological assessments of five subjects carrying heterozygous missense genetic variants in SLC6A1 ...

Accurate diagnosis of neurodevelopmental disorders is a challenging task due to the time-consuming cognitive tests and potential human bias in clinics. To address this challenge, we propose a novel adversarial self-supervised graph neural network (GNN) based on graph contrastive learning, named A-GCL, for diagnosing neurodevelopmental disorders using functional magnetic resonance imaging (fMRI) data. Taking advantage of the success of GNNs in psychiatric disease diagnosis using fMRI, our proposed A-GCL model is expected to improve the performance of diagnosis and provide more robust results...

It has been previously proposed that interventions aimed at integrating and co-activating music processing and motor control systems could have therapeutic potential for priming social skill development in children with autism spectrum disorder (ASD). In this study, we assessed this hypothesis through a wearable sensor platform called Ambulosono ("Ambulo"-walk; "sono"-sound) in which pleasurable children's musical stimuli are contingently linked to effortful motor action (locomotor step size), thus creating a motivational state proposed to be conducive to joint attention (JA) operation...

Resting-state functional magnetic resonance imaging (rs-fMRI) has been widely used in the detection of brain disorders such as autism spectrum disorder based on various machine/deep learning techniques. Learning-based methods typically rely on functional connectivity networks (FCNs) derived from blood-oxygen-level-dependent time series of rs-fMRI data to capture interactions between brain regions-of-interest (ROIs). Graph neural networks have been recently used to extract fMRI features from graph-structured FCNs, but cannot effectively characterize spatiotemporal dynamics of FCNs, e...

Children and youth with autism use screens in their daily lives and in their rehabilitation programs. Although parents and clinicians experience specific challenges when supporting positive screen time use of children and youth with autism, no detailed information for this group exists. Therefore, this study aimed to develop clear guidelines that are agreed by expert clinicians and parents of children and youth with autism. Using a method called Delphi, 30 experts-20 clinicians and 10 caregivers, who have experience working with or caring for children and youth with autism were invited to complete a series of three surveys...

Since children with autism spectrum disorder (ASD) might exhibit a variety of aberrant response to joint attention (RJA) behaviors, there is growing interest in identifying robust, reliable and valid eye-tracking metrics for determining differences in RJA behaviors between typically developing (TD) children and those with ASD. Previous eye-tracking studies have not been deeply investigated nonlinear features of gaze time-series during RJA. As a main motivation, this study aimed to extract three nonlinear features (i...

Accurate diagnosis of autism spectrum disorder (ASD) is crucial for effective treatment and prognosis. Functional brain networks (FBNs) constructed from functional magnetic resonance imaging (fMRI) have become a popular tool for ASD diagnosis. However, existing model-driven approaches used to construct FBNs lack the ability to capture potential non-linear relationships between data and labels. Moreover, most existing studies treat the FBNs construction and disease classification as separate steps, leading to large inter-subject variability in the estimated FBNs and reducing the statistical power of subsequent group comparison...

BACKGROUND: Speech articulation difficulties have not traditionally been considered to be a feature of Autism Spectrum Disorder (ASD). In contrast, speech prosodic differences have been widely reported in ASD, and may even be expressed in subtle form among clinically unaffected first-degree relatives, representing the expression of underlying genetic liability. Some evidence has challenged this traditional dichotomy, suggesting that differences in speech articulatory mechanisms may be evident in ASD, and potentially related to perceived prosodic differences...

Autism spectrum disorder (ASD) is a neurodevelopmental condition that arises from a combination of both genetic and environmental risk factors. There is a lack of research investigating whether early acquired brain injury (ABI) may be a risk factor for ASD. The current study comprehensively reviewed all hospital records at The Brain Injury Service, Kids Rehab at the Children's Hospital at Westmead (Australia) from January 2000 to January 2020. Of the approximately 528 cases, 14 children with paediatric ABI were subsequently given an ASD diagnosis (2...

Our study used functional magnetic resonance imaging and fractal functional connectivity (FC) methods to analyze the brain networks of Autism Spectrum Disorder (ASD) and typically developing participants using data available on ABIDE databases. Blood-Oxygen-Level-Dependent time series were extracted from 236 regions of interest of cortical, subcortical, and cerebellar regions using Gordon's, Harvard Oxford, and Diedrichsen atlases respectively. We computed the fractal FC matrices which resulted in 27,730 features, ranked using XGBoost feature ranking...

Brain function connectivity, derived from functional magnetic resonance imaging (fMRI), has enjoyed high popularity in the studies of Autism Spectrum Disorder (ASD) diagnosis. Albeit rapid progress has been made, most studies still suffer from several knotty issues: (1) the hardship of modeling the sophisticated brain neuronal connectivity; (2) the mismatch of identically graph node setup to the variations of different brain regions; (3) the dimensionality explosion resulted from excessive voxels in each fMRI sample; (4) the poor interpretability giving rise to unpersuasive diagnosis...

BACKGROUND: Short-term spontaneous hemodynamic fluctuations were collected by the functional near-infrared spectroscopy (fNIRS) system to classify children with autism spectrum disorder (ASD) and typical development (TD), and to explore abnormalities in the left inferior frontal gyrus in ASD. METHODS: Using the fNIRS data of 25 children with ASD and 22 children with TD, a graph neural network combined with the temporal convolution module and the graph convolution module was used, to extract the spatio-temporal features of the data and achieve accurate classification of ASD...

This study examined the ability of children with autism spectrum disorder (ASD) to generate joint intention-based action prediction in a joint action task. Children were presented with a series of videos in which two actors either played with blocks based on joint intention (social condition) or played with blocks independently (nonsocial condition). In the familiarization stage, two actors demonstrated how they played with blocks three times. In the test stage, one actor left the scene, and another actor grasped a block and asked where she should place it...

INTRODUCTION: Autism spectrum disorder (ASD) has characteristics that have been observed to develop over time, such as the difficulty of affective, sensory, and emotional processing, which trigger some problems during childhood, limiting children's development. Applied behavior analysis (ABA) is among the therapeutic approaches for ASD, in which treatment can be tailored according to the patient's objectives. OBJECTIVE: Based on ABA, we aimed to analyze the therapeutic strategy for independence in different skill performance tasks of patients diagnosed with ASD...

Neuroimaging techniques have been widely adopted to detect the neurological brain structures and functions of the nervous system. As an effective noninvasive neuroimaging technique, functional magnetic resonance imaging (fMRI) has been extensively used in computer-aided diagnosis (CAD) of mental disorders, e.g., autism spectrum disorder (ASD) and attention deficit/hyperactivity disorder (ADHD). In this study, we propose a spatial-temporal co-attention learning (STCAL) model for diagnosing ASD and ADHD from fMRI data...

Program evaluation is an essential practice for providers of behavior analytic services, as it helps providers understand the extent to which they are achieving their intended mission to the community they serve. A proposed method for conducting such evaluations, is through the use of a consecutive case series design, for which cases are sequentially gathered following the onset of a specific occurrence. Given the sequential nature in which data are collected within a consecutive case series, analytic techniques that adopt a time-series framework may be particularly advantageous...

Autism spectrum disorders (ASD) is a neurodevelopmental disorder that causes repetitive stereotyped behavior and social difficulties, early diagnosis and intervention are beneficial to improve treatment effect. Although multi-site data expand sample size, they suffer from inter-site heterogeneitys, which degrades the performance of identitying ASD from normal controls (NC). To solve the problem, in this paper a multi-view ensemble learning network based on deep learning is proposed to improve the classification performance with multi-site functional MRI (fMRI)...