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.

Whole Genome Sequencing

This study involves collection of saliva samples from all family members (affected child, siblings, and parents) for whole genome sequencing. The genetic information gathered from the participants will be used to identify potential cross-ethnic differences in genetic susceptibility to autism and will be used to stratify research groups according to genetic sub-grouping. In addition, the genetic sequences will be incorporated into large international efforts that collect thousands of samples world-wide to understand the genomics of autism.

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 pitch and intonation when they speak and some also exhibit 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 identify and score the severity of speech and voice abnormalities. We hope that this tool will prove useful for assessing the children's speech and intonation development 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. 

Facial features

In this study we use computerized algorithms to examine photos of children with autism in order to determine whether some of the children may have characteristic facial features that may distinguish them from toddlers who do not have autism. This may yield an easy and cost effective way of identifying toddlers who are at higher risk for developing autism. 

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