portrait of yangming ou
Yangming Ou, PhD
Associate Professor of Radiology, Harvard Medical School
Big-data Brain MRI Analysis and Machine Learning for Computational Neuroscience

The Ou lab studies normal and abnormal brain development across the lifespan.

On the technical side, the lab develops and releases advanced medical image analysis and machine/deep learning algorithms and software tool, primarily for brain MRI. We aim to develop algorithms and tools that can be used in big-data (>50,000), across-age (0-100 years), multi-site, and multi-scanner brain MRIs collected in both research and clinical settings. Such tools allows multi-modal, population, cross-sectional, and longitudinal studies of brain MRIs.

On the basic neuroscience side, we quantify normal brain development in structure and function across the lifespan and especially in infancy and early childhood, a period when the brain experiences most rapid changes. We study brain aging from birth to 100 years and how the brain ages differ by sex, environment, and various other conditions.

For (pre-)clinical applications, we study diseased populations. Mild abnormalities we study include malnutrition, moderate injuries we study include neonatal hypoxic ischemic encephalopathy (HIE), sturge-weber syndrome (SWS, a rare neurological disease), and more severe conditions we study include pediatric brain tumor survivors. We use brain MRI analysis and machine/deep learning aiming to automatically assist the clinical radiological workflow, including: (a) detecting abnormality in clinical images; (b) quantifying longitudinal disease progression; (c) predicting neurocognitive outcomes after treatment or survival; (d) establishing imaging and clinical biomarkers for early screening; (e) identify neural substrate underlying neurocognitive outcomes; and (f) estimating delayed development or accelerated brain aging associated with brain disorders or environmental changes.