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Viktor Jirsa, Director of the Inserm Institut de Neurosciences des Systèmes at Aix-Marseille-Université and Director of Research at the Centre National de la Recherche Scientifique (CNRS) in Marseille, France, will present “Translational Medicine: From Bifurcations to Epilepsy Surgery.”
Abstract: Over the past decade we have demonstrated that constraining computational brain network models by structural information obtained from human brain imaging (anatomical MRI, diffusion tensor imaging (DTI)) allows patient specific predictions, beyond the explanatory power of neuroimaging alone. This fusion of an individual’s brain structure with mathematical modelling allows creating one model per patient, systematically assessing the modeled parameters that relate to individual functional differences. The functions of the brain model are governed by realistic neuroelectric and neurovascular processes and allow executing dynamic neuroelectric simulation; further modeling features include refined geometry in 3D physical space; detailed personalized brain connectivity (Connectome); large repertoire of mathematical representations of brain region models, and a complete set of physical forward solutions mimicking commonly used in non-invasive brain mapping including functional Magnetic Resonance Imaging (fMRI), Magnetoencephalography (MEG) Electro-encephalography (EEG) and StereoElectroEncephalography (SEEG). So far our large-scale brain modeling approach has been successfully applied to the modeling of the resting state dynamics of individual human brains, as well as aging and clinical questions in stroke and epilepsy. In this talk I will focus on the example of epilepsy and systematically demonstrate the individual steps towards the creation of a personalized epileptic patient brain model.
Those unable to attend on the Homewood campus may view a simulcast in Traylor 709 on the Johns Hopkins School of Medicine campus. The lecture will also be streamed through Panopto. Click here to view the webcast.
Archana Venkataraman, assistant professor of electrical and computer engineering at Johns Hopkins University, will present “An Adaptable Framework to Extract Abnormal Brain Networks” as part of the Institute for Computational Medicine‘s Distinguished Seminar Series. The seminar begins at 11 a.m. in 110 Clark Hall on March 7.
There is increasing evidence that complex neurological disorders reflect distributed impairments across multiple brain systems. These findings underscore the importance of network-based approaches for functional data. However, network analyses in clinical neuroimaging is largely limited to aggregate measures, which do not pinpoint a concrete etiological mechanism. In contrast, I will present a novel Bayesian framework that captures the underlying topology of the altered functional pathways.
In the first part of this talk, I will introduce our core framework to extract abnormal network foci from functional MRI data. This model relies on a latent structure, which captures hidden interactions within the brain; the latent variables are complemented by an intuitive likelihood model for the observed neuroimaging measures. The resulting variational EM algorithm produces clinically meaningful results by simultaneously localizing the centers of abnormal activity and the network of altered connectivity. Next, I will address three technical challenges: flexible network topology, multimodal integration and patient-specific analysis. I will demonstrate that our core framework can elegantly be adapted to each of these scenarios and yields novel insights into autism, schizophrenia and epilepsy, respectively. Finally, I will highlight some exciting future directions for our methodology that revolve around clinical understanding and interventions.