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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.
Alexander, R.A. Anderson, the co-director of Integrated Mathematical Oncology and senior member of the Moffitt Cancer Center, will present on April 4, 2017, as part of the Institute for Computational Medicine’s Distinguished Seminar Series. The title of his presentation is “Steering Cancer Evolution: Harnessing Phenotypic Heterogeneity to Design Better Therapies.”
The seminar begins at 11 a.m. in Clark Hall 110 on the Homewood campus, and it will be video-teleconferenced to Traylor 709 on the Johns Hopkins School of Medicine campus. Click here to view webcast. Lunch will provided to those in attendance on the Homewood campus.
Abstract: Heterogeneity in cancer is an observed fact, both genetically and phenotypically. Cell-to-cell variation is seen in all aspects of cancer, from early development to invasion and subsequent metastasis. This heterogeneity is also at the heart of why many cancer treatments fail, as it facilitates the emergence of drug resistance. The complex spatial and temporal process by which tumors initiate, grow and evolve is a major focus of the oncology community and one that requires the integration of multiple disciplines. Tumor heterogeneity at the tissue scale is largely due to ecological variations in terms of the tumor habitat driven by spatially heterogeneous vascularity, which is readily observed on cross sectional imaging. Molecular techniques have historically averaged genomic signals from large numbers of cells obtained in a single biopsy site, thus smoothing and potentially hiding underlying spatial variations. The complex dialogue between tumor cells and environment that produces intra- and inter-tumoral heterogeneity is fundamentally governed by Darwinian dynamics. That is, local micro- environmental conditions select phenotypic clones that are best adapted to survive and proliferate and, conversely, the phenotypic properties of the cells affect the environmental properties. While these complex interactions have enormous clinical implications because they promote resistance to therapy, the dynamics are impossible to fully capture via experimentation alone.
Here we present an integrated theoretical/experimental approach to develop dynamical models of the complex multiscale interactions that manifest as temporal and spatial heterogeneity in cancers and ultimately govern tumor response and resistance to therapy. Specifically, we examine the impact of micro-environmental modulation on cancer evolution both in silico, using a hybrid multiscale mathematical model, and in vivo, using three different spontaneous murine cancers. These models allow the tumor to be steered into a less invasive pathway through the application of small but selective biological force. Our long term goal is explicitly translational as we focus our integrated approach on an emerging cancer treatment paradigm that actively harnesses evolutionary dynamics to improve patient outcomes.