Building a Personalized Virtual Brain with Alzheimer’s to Guide Clinical Decisions

Randy McIntosh, PhD Baycrest Centre for Geriatric Care

Co-Principal Investigators

Kelly Shen, PhD Rotman Research Institute


Michael Breakspear, PhD QIMR Berghofer Medical Research Institute
Viktor Jirsa, PhD Aix-Marseille Université
Petra Ritter, PhD Charité Universitätsmedizin Berlin
Ana Solodkin, PhD University of California, Irvine


The brain is a complicated system whose different parts interact to support a variety of cognitive functions. This complexity makes it difficult to treat diseases such as Alzheimer’s and Parkinson’s, where many different brain areas can be affected, but lead to very similar deficits, such as memory dysfunction. Our research provides a framework of tools to “reconstruct” the brain and build models of different dementias to characterize the unique features of each disease and the final common paths to cognitive impairment. As our work progresses, it will be used to evaluate the potential of therapeutic interventions to help identify treatment targets, or areas of the brain that, if treated, are most likely to result in the best outcome for the individual.

Project Details

This research project is leading us towards a personalized medicine approach to understanding, preventing and treating brain disorders, specifically Alzheimer’s disease (AD) and Parkinson’s disease (PD), using a network dynamics approach via TheVirtualBrain.

With this grant from BrightFocus Foundation, we are characterizing commonalities and differences in brain network dynamics across AD & PD and mapping the underlying biophysical substrates to individual clinical profiles using TheVirtualBrain (TVB). TVB is a robust brain modeling platform that allows us to simulate network dynamics safely in a virtual environment, in contrast to traditional clinical trials or direct patient testing. The first aim directly tests the hypothesis that AD and PD can be differentiated by specific spatiotemporal patterns of local and distributed processes in the brain. The second aim tests the hypothesis that disease-specific alterations in brain network communication are detectable in prodromal forms of AD and PD and can be used to prevent disease progression and predict clinical outcome. The third aim identifies the underlying common and disease-specific biophysical substrates that lead to alterations in large-scale network dynamics. Importantly, here we identify the biophysical substrates that best predict an individual’s disease trajectory and clinical outcome. TVB is used to evaluate the clinical potential of therapeutic interventions early in development, thus helping to ensure that such efforts converge on targets that are most likely to have the best outcome.

Intensive efforts are underway to build large empirical neuroimaging datasets specific to AD and PD, yet we lack the framework to link these data with the brain function of individual patients. TheVirtualBrain addresses that need by providing a computational and theoretical framework for simulating whole-brain networks. We will use structural and functional patient data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), the Sydney Memory and Ageing Study (Sydney MAS), and the Parkinson’s Progression Markers Initiative (PPMI) to set the initial constraints for estimation of the model parameters.

The impact of our work has both direct and indirect effects on alleviation of human suffering, particularly for those afflicted with dementia. In this research, TVB acts as a “computational microscope” that allows for the inference of internal states and processes that are otherwise invisible to brain imaging devices. TVB is used to evaluate which biophysical model parameters best express the network alterations in AD and PD.  Our project provides the basis for more deliberate integration of computational neuroscience and clinical approaches for diagnosis and treatment of brain disorders. The implications for developing targeted therapeutics are clear, where computational models based on a patient’s own data help to guide diagnoses and inform the choices of individualized interventions for the best chance of success. Using TVB as a means to characterize the biophysical parameters that differentiate dementia sub-types imparts great promise for improved early diagnosis and prognosis, as well as treatment success.