Pradeep Reddy Raamana, PhD

  • Assistant Professor, Department of Radiology

Dr. Raamana is interested in developing multimodal biomarkers for brain disorders, and the necessary machine learning and data science frameworks for personalized medicine. He founded the special interest group on neuroimaging quality control (niQC) at the International Neuroinformatics Coordinating Facility. He is passionate about bridging the gap between the clinic and computer science, towards which he is developing standards and tools to remove the barriers for and improving the state-of-the-art for predictive modelling and quality control for neuroimagers. He is a passionate advocate for quality, reproducible and open science, and was invited to join the Editorial Board of the Aperture journal of the Organization for Human Brain Mapping (OHBM).


His academic foundations are built on a bachelor’s degree majoring in Mathematics, Physics and Computer Science, followed by a Master’s degree in Physics from the premier Indian Institute of Technology, Madras (IITM). He then worked for about 2 years at Aalborg University in Denmark conducting research in computer vision and artificial intelligence. Leveraging this unique and cutting-edge training and experience, he then pursued his long-term interest in healthcare applications, by enrolling into a PhD program in Biomedical Engineering at Simon Fraser University in Vancouver. During his PhD, he honed a broad array of skills in machine learning, statistics, biomedical image computing, data science, informatics and high-performance computing. In close collaboration with neurologists, he developed a keen appreciation for clinical challenges and focused his research on high-impact applications, such as early detection of Alzheimer’s disease (AD), and differential diagnosis of brain disorders. To broaden his expertise in multimodal analysis and to further diversify his portfolio, he then pursued a postdoctoral fellowship at the Rotman Research Institute in Toronto. During his postdoc, he contributed to two large multisite integrated discovery programs from the Ontario Brain Institute (OBI), developing prognostic techniques for both neurological and psychiatric disorders, and a range of data science tools to advance the related science and informatics.

Representative Publications

  • Raamana PR, Weiner MW, Wang L, Beg MF. Thickness network features for prognostic applications in dementia. Neurobiology of Aging. 2015 Jan 1;36:S91–102.
  • Raamana PR, Rosen H, Miller B, Weiner MW, Wang L, Beg MF. Three-Class Differential Diagnosis among Alzheimer Disease, Frontotemporal Dementia, and Controls. Front Neur. 2014 Jan 1;5:71.
  • Varoquaux G, Raamana PR, Engemann DA, Hoyos-Idrobo A, Schwartz Y, Thirion B. Assessing and tuning brain decoders: cross-validation, caveats, and guidelines. NeuroImage. 2016
  • Raamana PR, et al., “Does size matter? Relationship between predictive power of single subject morphometric networks to spatial scale and edge weight”, Brain Structure and Function, 2020, 225(8), 2475-2493.
  • Raamana PR. neuropredict: Easy Machine Learning And Standardized Predictive Analysis Of Biomarkers Zenodo; 2017,

Research Interests

  • Biomarkers development and evaluation
  • Machine learning (models, tools and interpretation)
  • Quality control and harmonization
  • Data science, informatics and reproducibility

Research Grants

  • Canadian Open Neuroscience program (2019): $50,000
  • Alzheimer Society Canada (2011): $60,000