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Autism is a remarkably heterogeneous disorder where different individuals exhibit a wide variety of symptoms, challenges, and abilities. The disorder is so heterogeneous that in 10-15% of cases clinicians do not agree on its diagnosis. Heterogeneity is also apparent in the genetics of autism, in the clinical history and comorbidities of different individuals, and in their physiology and brain structure and function. This creates a huge challenge for research, because it means that autism is caused by a variety of genetic and environmental factors that give rise to multiple different types of autism. There is, therefore, no reason to expect that all individuals with autism will be identified using a single diagnostic tool or measure (e.g., genetic test or MRI scan). Similarly, there is no reason to expect that all individuals with autism will benefit from one specific treatment (e.g., medical cannabis). 

With this in mind, research in our lab attempts to characterize autism heterogeneity using techniques from several disciplines. We are developing multiple diagnostic and outcome measures for different autistic symptoms and potential physiological markers. We are also studying the heterogeneity of developmental trajectories to understand the impact of different interventions and treatments on longitudinal changes in the severity of specific symptoms. We expect this research to yield insights regarding the biological mechanisms underlying autism, and, more importantly, to identify the most effective treatments for each child (i.e., what works for whom?).

Specific research programs in the lab include:

Movement tracking using OpenPose

Movement analysis

Many children with autism move differently. This research focuses on three types of movement analyses that utilize computer vision techniques to analyze video recordings from ADOS assessments:

  1. Social exploration - in this project we study how the child moves in the assessment room using measures such as the distance between child and clinician, the amount of approaches towards the clinician, the amount of time facing the clinician (see relevant paper).

  2. Stereotypical movements - in this project we are training deep learning algorithms to identify video segments where children exhibit stereotypical movements. This will enable us to quantify the severity of this symptom in specific children.

  3. Facial expressions - movements of facial muscle groups are analyzed to identify difficulties in facial expressions.

Movement tracking using Kinect

Diagnosis and developmental trajectories

Children with autism are diagnosed at different ages and develop differently over time. Some demonstrate remarkable improvements while others deteriorate. This research attempts to identify children with better outcome in specific symptom domains and determine why:

  1. Diagnosis - how do differences in the age of diagnosis and subsequent placement in intervention programs impact 1-2 year outcomes of children with specific symptoms (relevant paper).

  2. Educational settings - here we are comparing 1-2 year outcomes of children with autism who are placed in special education versus mainstream education settings (relevant paper).

Autism education
Clinical assessment

Characterizing symptom severity and heterogeneity
How are different symptoms that are common in children with autism associated with each other? How are symptoms related to early etiology and/or family characteristics? Here we examine, for example, how s
leep disturbances are associated with sensory sensitivities (relevant paper), or how language regression is associated with early motor development (relevant paper).

EEG sleep lab

Sleep studies

Approximately 50% of children with autism have insomnia (i.e., severe sleep disturbances). We are performing several types of studies to characterize sleep physiology and better understand these disturbances, their potential causes, and relevant interventions:

  1. Polysomnography - we are performing classical sleep lab studies and have demonstrated that some children with autism have reduced sleep pressure, which may explain their difficulty to fall asleep (relevant paper).

  2. Sensors - we are using actigraphy and consumer devices such as Fitbit to study sleep at home over extended periods of time.

  3. Behavioral intervention - we are testing the effectiveness of parent training in sleep hygiene and techniques to reduce the dependency of child sleep onset on parent presence. 

Eye tracking

Some children with autism observe the world differently. This is apparent, for example, in the tendency of some children to avoid eye contact with others. Over the last several years we have acquired >400 eye tracking sessions from children with autism as they observe different movies and are attempting to understand:

  1. What are the best stimuli to use when trying to identify children with autism based on their eye movements (relevant paper).

  2. Whether differences in eye movements are related to basic oculomotor control (relevant paper) or regulation of pupil diameter. 

  3. Whether eye tracking measures are useful indices for assessing changes in social symptom severity. 

Eye tracking lab
White matter DTI

Structural and functional MRI
We have a extensive track record in studying brain structure and function in autism using a variety of imaging techniques. Studies include:

  1. Assessment ​of differences in brain volume, cortical thickness, and cortical surface area across individuals with and without autism (relevant paper) as well as comparisons of early head circumference development (relevant paper).

  2. Assessment of mirror system fMRI responses in adults with autism as they observe and perform movements including an analysis of movement-selectivity (relevant paper).

  3. Demonstration that some adults with autism have unreliable brain responses to sensory stimuli across multiple modalities (relevant paper).

  4. Demonstration that inter-hemispheric functional connectivity (i.e., neural synchronization) is weaker in some children with autism (relevant paper). The same children also exhibit DTI differences in corpus callosum white matter fibers indicative of poor inter-hemispheric structural connectivity (relevant paper).

  5. In newer projects we are examining whether some children with autism have excessive extra-axial CSF also known as benign external hydrocephalus during early periods of development.



Anatomical MRI
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