Abstract
The neurobiological basis of ADHD has been broadly characterized by corticostriatal interactions with the frontal lobe. To hasten clinical translation of neuroimaging-based insight in ADHD, it is essential to improve repeatability and reliability of efforts to map these circuits at the group and individual levels. In this work, we aim to accelerate biomarker discovery by improving reliability of corticostriatal circuit mapping using advanced machine learning techniques.
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