Mahsa Dadar, PhD




6875 Boulevard LaSalle Montréal, QC H4H 1R3


 Twitter: Mahsa Dadar

Researcher, Douglas Research Centre
Assistant Professor, Department of Psychiatry, McGill University

Lab name: Aging, Cerebrovascular, and Neurodegenerative Disorders

Theme-Based Group: Aging, Cognition, and Alzheimer’s Disease
Division: Human Neuroscience


My team aims to investigate the role of cerebrovascular pathology in aging and neurodegenerative disease populations. My research program has three main components:

  1. Developing neuroimaging and machine learning tools to accurately detect and track signs of cerebrovascular and neurodegenerative pathologies
  2. Investigating the relationship between cerebrovascular and neurodegenerative pathologies, the impact of lifestyle and environmental factors on these diseases, and the impact of cerebrovascular pathology on clinical outcomes in neurodegenerative disease populations
  3. Ex-vivo assessment of cerebrovascular disease using post-mortem MRI and histology

Mahsa Dadar, PhD, received her Bachelor’s and Master’s Degrees in Electrical Engineering from the University of Tehran and Concordia University, and her PhD in Biomedical Engineering from McGill University. She did a postdoctoral fellowship with the International Progressive MS Alliance (IPMSA) team at McGill, followed by a joint postdoctoral fellowship between the CERVO Brain Research Centre in Quebec and the University of Alberta in Edmonton, Canada.

  • 2020-2021 Alzheimer Society Research Program (ASRP) Postdoctoral Fellowship
  • 2020-2021 Fonds de recherche du Québec-Santé (FRQS) Postdoctoral Fellowship
  • 2019 Quebec Bio-Imaging Network (QBIN) Postdoctoral Scholarship

Liste complète des publications


  1. M. Dadar, S. Mahmoud, S. Narayanan, D. L. Collins, D. L. Arnold, J. Maranzano (2022). Diffusely abnormal white matter converts to T2 lesion volume in the absence of MRI-detectable acute inflammation, Brain.
  2. M. Dadar, A.L. Manera, S. Ducharme, and D. L. Collins (2022). White matter hyperintensities are associated with grey matter atrophy and cognitive decline in Alzheimer’s disease and frontotemporal dementia. Neurobiology of Aging, 111, 54-63.
  3. M. Dadar, R. Camicioli, S. Duchesne, D. L. Collins (2020). The Temporal Relationships between White Matter Hyperintensities, Neurodegeneration, Amyloid β, and Cognition. Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring. 12 (1), e12091.
  4. M. Dadar, J. Miyasaki, S. Duchesne*, R. Camicioli* (2021). White matter hyperintensities mediate the impact of amyloid ß on future freezing of gait in Parkinson’s disease. Parkinsonism & Related Disorders. 85 (2021): 95-101.
  5. F. Morys, M. Dadar, A. Dagher (2021). Obesity impairs cognitive function via metabolic syndrome and cerebrovascular disease: an SEM analysis in 15,000 adults from the UK Biobank. JCEM. dgab135.
  6. M. Dadar, S. Duchesne (2020). Reliability Assessment of Tissue Classification Algorithms for Multi-Center and Multi-Scanner Data. NeuroImage. 217, 116928.
  7. M. Dadar, D. L. Collins (2020). BISON: Brain tISue segmentatiON pipeline using T1-weighted magnetic resonance images and a random forests classifier. Magnetic Resonance in Medicine. 85 (4).
  8. M. Dadar*, A. Manera*, V. Fonov, S. Ducharme, D.L. Collins. MNI-FTD Templates: Unbiased Average Templates of Frontotemporal Dementia Variants. Scientific Data. 8.1 (2021): 1-10.
  9. J. Maranzano, M. Dadar, A. Bertrand-Grenier, E. M. Frigon, J. Pellerin, S. Plante, S. Duchesne, C. L. Tardif, D. Boire, G. Bronchti (2020). A novel ex vivo, in situ method to study the human brain through MRI and histology. Journal of Neuroscience Methods. 345, 108903.
  10. A. Manera*, M. Dadar*, V. Fonov, D. L. Collins. (2020). CerebrA: Accurate registration and manual label correction of Mindboggle-101 atlas for MNI-ICBM152 template. Scientific Data. 7 (1), 1-9.
  11. M. Dadar, Y. Zeighami, Y. Yau, S. Fereshtehnejad, J. Maranzano, R. Postuma, A. Dagher, D. L. Collins (2018). White Matter Hyperintensities are linked to cognitive decline in de Novo Parkinson’s disease patients. NeuroImage: Clinical. 20: 892-900.
  12. M. Dadar, Vladimir S. Fonov, D. Louis Collins (2018). A Comparison of Publicly Available Linear MRI Stereotaxic Registration Techniques. NeuroImage. 174: 191-200.
  13. J. M. Mateos-Pérez*, M. Dadar*, M. Lacalle-Aurioles, Y. Iturria-Medina, Y. Zeighami, A. C. Evans (2018). Structural Neuroimaging as Clinical Predictor: A Review of Machine Learning Applications. NeuroImage: Clinical. 20: 506-522.
  14. M. Dadar, J. Maranzano, K. Misquitta, C. J. Anor, V. S. Fonov, M. C. Tartaglia, O. T. Carmichael, C. Decarli, D. L. Collins, Alzheimer’s Disease Neuroimaging Initiative (2017). Performance comparison of 10 different classification techniques in segmenting white matter hyperintensities in aging. NeuroImage. 157: 233-249.
  15. M. Dadar, T. A. Pascoal, S. Manitsirikul, K. Misquitta, M. C. Tartaglia, J. Brietner, P. Rosa-Neto, O. Carmichael, C. DeCarli, D. L. Collins (2017). Validation of a Regression Technique for Segmentation of White Matter Hyperintensities in Alzheimer’s Disease. IEEE Transactions on Medical Imaging. 36 (8): 1758-1768.