Graduate Student Seminar Series – Zeeshan Siddiqui

Graduate Student Seminar Series
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Location: 2nd Floor Auditorium (TRI/KITE) – 550 University Ave
Presentation Title: Examining Dimensionality Reduction and Clustering Techniques to Identify Neuroanatomical Subgroups Across Neurodevelopmental Conditions
Abstract: Neurodevelopmental conditions (NDCs) such as autism, ADHD, and OCD, are widely prevalent in children and youth, and are marked by developmental differences that can lead to distress and disability across multiple domains of life. Current clinical research has faced challenges in identifying effective treatments and interventions for these individuals. One reason for this lies in the emerging findings that reveal high within-diagnosis diversity and high between-diagnosis similarity in NDCs across brain structure/function, behavioural presentation, and cause. Current literature has attempted to characterize this neurodiversity by using clustering, an artificial intelligence (AI) technique for organizing data into groups with similar patterns. My work aims to build on existing works by leveraging novel, deep learning AI techniques to more intelligently learn patterns in complex neuroimaging data and optimize clustering to find NDC subgroups with improved distinguishability and characterizability. This work can ultimately help inform personalized intervention to improve outcomes in neurodiverse children.
Supervisor Name: Dr. Azadeh Kushki
Year of Study: 2
Program of Study: MASc
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