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Brain resilience in aging and dementia

While some individuals appear to be suspectable to poor and accelerated cognitive aging, others are more immune from these adverse outcomes. Our recent research aims at delineating the topological properties of resilient brains, that offer protection against aging and dementia. A major topological property we have focused on is redundancy, the existence of duplication within systems, that ensures functionality in case of failure.


Identifying major pathological processes in Alzheimer’s disease through explainable deep learning  

Machine and deep learning models have been invaluable tools in the study of dementia, but they typically fall short in generating interpretable findings. In our research, we are using explainable machine and deep learning models to study major pathological processes occurring in dementia.



Developing new diagnostic and prognostic methods for neurodegenerative diseases

Leveraging neuroimaging, clinical, fluid biomarker and genetic data we develop diagnostic, prognostic and stratification methods for neurodegenerative diseases. This line of work aims to constrain the heterogeneity observed in neurodegenerative diseases, and better predict long-term outcomes. Achieving these goals may ultimately assist in devising and optimizing personalized treatment strategies and monitoring their success.





Our research is supported by: