Yasser Iturria Medina, PhD
Yasser Iturria-Medina is an Assistant Professor in the Department of Neurology and Neurosurgery. He is also an associate member of the Ludmer Centre for Neuroinformatics and Mental Health and the McConnell Brain Imaging Centre.
Iturria-Medina’s Lab – Neuroinformatics for Personalized Medicine – pursues primarily the goal of making precision medicine in Neurology a reality. It focuses on defining and implementing multiscale and multifactorial brain models for understanding neurological disorders and identifying effective personalized interventions. The lab combines molecular, imaging and cognitive data using integrative mathematical modelling to create both individual and population-based mechanistic brain models.
Iturria-Medina’s research has spanned neurodegeneration modelling, brain multimodal connectivity estimation, and statistical analysis for characterizing/predicting abnormal brain states. He currently is focusing on the creation and validation of integrative molecular, neuroimaging, and computational tools for understanding complex causal interactions among ageing, neurodegeneration and different therapeutic conditions.
Iturria-Medina received his undergraduate degree in Nuclear Engineering from the Higher Institute for Nuclear Sciences and Technology, Cuba, in 2004 and his Master’s degree in Neurophysics and Neuroengineering from the Cuban Neuroscience Center in 2006. He then completed his PhD in Neuroimaging and Neuroinformatics at the National Center for Scientific Research and Havana’s University of Medical Science in 2013. He came to the MNI as a postdoctoral student in 2013, before being appointed as an Assistant Professor in 2018.
1. Quadri Adewale, Ahmed Faraz Khan, Sue-Jin Lin, Tobias R. Baumeister, Yashar Zeighami, Felix Carbonell, Daniel Ferreira, Yasser Iturria-Medina, 2025. Patient-centered brain transcriptomic and multimodal imaging determinants of clinical progression, physical activity, and treatment needs in Parkinson’s disease. npj Parkinson's Disease, 11, 29 (2025). https://doi.org/10.1038/s41531-025-00878-4
2. Ahmed F. Khan, Yasser Iturria-Medina, 2024. Beyond the usual suspects: multi-factorial computational models in the search for neurodegenerative disease mechanisms. Translational Psychiatry 14, 386 (2024). https://doi.org/10.1038/s41398-024-03073-w. PMID: 39313512
3. Quadri Adewale, Ahmed F Khan, David A. Bennett, Yasser Iturria-Medina, 2024. Single-nucleus RNA velocity reveals synaptic and cell-cycle dysregulations in neuropathologically confirmed Alzheimer’s disease. Nature's Scientific Reports. 14, 7269. https://doi.org/10.1038/s41598-024-57918-x. PMID: 38538816
4. Ahmed Faraz Khan, (...), Yasser Iturria-Medina, 2024. Patient-specific models link neurotransmitter receptor mechanisms with motor and visuospatial axes of Parkinson's disease. Nature Comms. 14, https://doi.org/10.1038/s41467-023-41677-w. PMID: 37752107
5. Lazaro Sanchez-Rodriguez, (...), Yasser Iturria-Medina, 2024. Personalized whole-brain neural mass models reveal combined Aβ and tau hyperexcitable influences in Alzheimer’s disease. Nature's Communications Biology, 7, 528. https://doi.org/10.1038/s42003-024-06217-2. PMID: 38704445
6. Ahmed Faraz Khan, (…), Yasser Iturria Medina, 2022. Personalized brain models identify neurotransmitter receptor changes in Alzheimer's disease. Brain, awab375. https://doi.org/10.1093/brain/awab375. PMID: 34605898
7. Yasser Iturria-Medina, (…), David A. Bennet, 2022. Unified Epigenomic, Transcriptomic, Proteomic, and Metabolomic Taxonomy of Alzheimer’s Disease Progression and Heterogeneity. Science Advances. DOI https://www.science.org/doi/10.1126/sciadv.abo6764. PMID: 36399579
8. Yasser Iturria-Medina et al., 2021. Integrating molecular, histopathological, neuroimaging and clinical neuroscience data with NeuroPM-box. Nature's Communications Biology, 4, 614. https://doi.org/10.1038/s42003-021-02133-x. PMID: 34021244
9. Quadri Adewale, (…), Yasser Iturria-Medina, 2021. Integrated Transcriptomic and Neuroimaging Brain Model Decodes Biological Mechanisms in Aging and Alzheimer’s Disease. eLife; 10:e62589. DOI: https://doi.org/10.7554/eLife.625. PMID: 34002691
10. Yasser Iturria-Medina et al., 2020. Blood and Brain Gene Expression Trajectories Mirror Neuropathology and Cognitive Impairment in Neurodegeneration. Brain 143, 1–13, https://doi.org/10.1093/brain/awz400. PMID: 31989163
11. Yasser Iturria-Medina et al., 2021. Integrating molecular, histopathological, neuroimaging and clinical neuroscience data with NeuroPM-box. Nature's Communications Biology, 4, 614. https://doi.org/10.1038/s42003-021-02133-x
12. Yasser Iturria-Medina et al., 2018. Multimodal Imaging-based Therapeutic Fingerprints for Optimizing Personalized Interventions: Application to Neurodegeneration. Neuroimage, Vol. 179, pages 40-50.
13. Yasser Iturria-Medina et al., 2016. Early Role of Vascular Dysregulation on Late-Onset Alzheimer´s Disease Progression: evidence from a multi-factorial data-driven analysis. Nature Communications, 7, # 11934, doi:10.1038/ncomms11934.
14. Yasser Iturria-Medina and Alan C. Evans, 2015. On the central role of brain connectivity in neurodegenerative disease progression. Frontiers in Aging Neuroscience, 7, article 90. PMID: 26052284
15. Yasser Iturria-Medina et al., 2014. Epidemic Spreading Model to Characterize Misfolded Proteins Propagation in Aging and Associated Neurodegenerative Disorders. PLOS Computational Biology, Vol. 10 (11), e1003956. 10.1371/journal.pcbi.1003956. PMID: 25412207
16. Yasser Iturria-Medina, 2013. Brain Anatomical Networks on the Prediction of Abnormal Brain States. Invited Review. Brain Connectivity, 3(1): 1-21. PMID: 23249224
17. Yasser Iturria-Medina et al., 2011. Brain Hemispheric Structural Efficiency and Interconnectivity Rightward Asymmetry in Human and Non-Human Primates. Cerebral Cortex, 21:56-67. PMID: 20382642
18. Yasser Iturria-Medina et al., 2008. Studying the Human Brain Anatomical Network via Diffusion-Weighted MRI and Graph Theory. Neuroimage, 40, 1064-1076. PMID: 18272400
19. Yasser Iturria-Medina et al. 2007. Characterizing Brain Anatomical Connections using Diffusion Weighted MRI and Graph Theory. Neuroimage, 36, 645-660. PMID: 17466539
