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Projects
Motion Tracking
In this study we collect body movements of children as they interact with a clinician during the ADOS test that is used to assess autism. We have developed a unique system of synchronized Kinect cameras that can track both the child's body movements and the clinicians body movements as they interact during a 45 minute period. We can then quantify different measures such as the distance between the therapist and child during this visit, the amount of time that the child faced the clinician, the presence of repetitive/stereotypical behaviors, and the existence of balance and postural problems. These measures give us a useful way of automatically scoring the severity of a child's social and motor problems. This is particularly useful for assessing change over time such as in response to therapy.
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Whole Exome Sequencing
This study involves the collection of saliva samples from family members (affected children and parents) for whole exome sequencing. The genetic information gathered from the participants will be used to identify potential cross-ethnic differences in genetic susceptibility to autism and to stratify research groups according to genetic sub-grouping.
Read the study>>
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Metabolic Biomarker Study
Several studies have shown a correlation between Autism Spectrum Disorders (ASD) and distinctive biomarkers.
The objective of the ANCAN Biomarker study is to pinpoint metabolic biomarkers through the collection and analysis of biological samples from children diagnosed with ASD.
This study aims to shed new light on the diverse biological mechanisms underlying different ASD subtypes and their distinct phenotypic outcomes. In addition, metabolites that distinguish between ASD subgroups could be useful as early biomarkers for both diagnosis and monitoring response to therapies.
Biological samples, primarily blood samples (5 mL), have been collected from children with ASD; additional samples such as urine, feces and hair will be collected as the study progresses. These samples are processed and stored at the ANCAN bio-bank at Ben-Gurion University of the Negev (BGU) where they will undergo further analysis for unique biomarkers.
By defining these biomarkers, we aspire to better understand the biological mechanisms that may attribute to ASD symptoms and behavioral phenotypes. Such characterization could aid in the diagnosis and treatment of children with ASD.
Eye Tracking
In this study toddlers suspected of autism are asked to view several short movies in a 15-20 minute experiment. These movies contain different types of stimuli that measure social preference, reliability and stability of eye movements, and spatial attention. This gives the researchers information about what interests the child and about the motor development of the child. A major goal of such research is to develop tools for early identification of autism using eye tracking and for examining how social preference and motor behaviors change in response to different treatments.
Prenatal Ultrasound & MRI Scans
A considerable percentage of children with autism in our database were born after high-risk pregnancies. The Soroka patient record system contains prenatal Ultrasound exams and MRI scans from some of these children. We are now conducting a retrospective assessment of these data to determine if we can identify early brain abnormalities that may be specific to high risk pregnancies where the child eventually developed autism versus high risk pregnancies where the child did not develop autism.
Sleep EEG
In this study we record overnight EEG from naturally sleeping children at the Soroka Sleep Lab. This study reveals important information about each child's brain activity during sleep, potential sleep problems, and potential existence of epileptic-like brain activity that is more evident during sleep. We believe that an EEG exam may be very important for identifying certain sub-types of autism who may be better candidates for specific clinical trials.
Voice analysis
Verbal children with autism often exhibit abnormal speech, which is expressed in high pitch and sometimes in echolalia (repetitive utterances and sentences). In this study, we are developing automated signal processing algorithms that assess speech recordings from the children's clinical exams to quantify speech patterns and estimate autism severity. For this study, we are developing noise removal algorithms to gain better signal quality. We are also studying the toy sounds children engage with during ADOS diagnosis, as we hypothesize that these sounds carry valuable information about autism symptoms. Furthermore, we are working on the identification and quantification of crying and screaming events, with the aim of integrating them into the autism severity estimation system. Lastly, we are utilizing machine learning algorithms to identify echolalia patterns in children's speech to gain a deeper understanding of this characteristic. These audio analysis algorithms may be useful for assessing the children's speech and autism severity in a longitudinal manner and in response to interventions..
Early Educational Settings
In this longitudinal study we are examining how 3-4 year old children with autism improve over time in special education versus inclusion in regular education. This comparison will examine a large number of children with autism who are assigned to each educational setting in Beer Sheva.
Effectiveness and safety of pharmacotherapy in children with ASD
Identifying characteristics associated with the effectiveness and safety of pharmacotherapy in children with Autism Spectrum Disorder(ASD).
Pharmacotherapy has an important role in the holistic treatment of children with ASD. These medications are prescribed to these children to alleviate various symptoms such as tantrums, self-harm, attention deficit and hyperactivity disorders, sleep problems, anxiety, and epileptic seizures, thus enabling a better life quality for the children and their caregivers.
Given the large variety of available medications and the variation in children's reactions to these medications, tailoring the best medication, in terms of effectiveness and or safety, for each child is a significant challenge for physicians. When the prescribed medication does not reduce the symptoms or elicit adverse reactions, it can cause suffering for the child and frustration for the parents, which could lead to future avoidance of other treatments.
A new study by the Azrieli National Center will try to identify characteristics of children with ASD that are associated with the children's reactions to their prescribed medications. For this purpose, we will follow children who are prescribed new pharmacotherapy and record changes in their behavior and ASD symptoms during six months from treatment initiation.
Identifying characteristics associated with the outcomes of various pharmacotherapy in children with ASD will help physicians to prescribe the most efficacious medication for each child and, therefore, lead to improved clinical outcomes.
Late Autism Diagnosis in Children and Adolescents:
A Big Data Analysis of Risk Factors, Psychiatric Comorbidities, and OutcomesLate diagnosis of autism spectrum disorder (ASD) in childhood is associated with distinct demographic, clinical, and developmental challenges compared to early diagnosis. Existing research indicates that demographic factors such as female gender, lower socioeconomic status, and parental psychiatric history are linked to an increased risk of delayed diagnosis.
Clinically, children diagnosed later in life tend to present with higher rates of psychiatric comorbidities—including anxiety, depression, and behavioral difficulties—as well as medical conditions such as sleep disturbances and gastrointestinal issues. These factors may obscure core autistic symptoms and contribute to diagnostic delays. Additionally, late diagnosis is associated with greater use of psychiatric medications and poorer long-term outcomes in academic, social, and adaptive functioning.
The current study aims to identify the demographic and clinical
characteristics associated with ASD diagnosis at different childhood stages—early childhood (0–6 years), latency (6–12 years), and adolescence (12–18 years)—using large-scale data analysis (Big Data).
Specifically, it will examine risk factors for late diagnosis, compare clinical profiles before and after diagnosis, and assess psychiatric comorbidities, medication use, and utilization of mental health services, including hospitalizations and follow-up care. The research will leverage data from Clalit Health Services, Israel’s largest healthcare provider, analyzing
anonymized data from over 18,000 children diagnosed with ASD between 2015 and 2024.
The findings are expected to improve understanding of the characteristics and trajectories of late-diagnosed individuals and contribute to the development of more effective diagnostic and intervention strategies tailored to this population.
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