To view more alumni events, click here.
Organized by the mHealth Regulatory Coalition and hosted by Johns Hopkins Technology Transfer and the Johns Hopkins University Global mHealth Initiative, this half day conference discuss the recently published FDA guidance on mobile medical applications and will feature prominent speakers including regulatory attorneys, regulatory affairs specialists, quality system experts, European law attorneys, experienced mHealth executives and the FDA. Topics discussed include how to develop mobile apps that come close to the FDA line, but don’t cross over, regulatory differences between the US and EU systems, premarket clearance strategies, and the FDA application classification system.
The MMA Roadshow is organized by the mHealth Regulatory Coalition and hosted by Johns Hopkins Technology Transfer and the Johns Hopkins University Global mHealth Initiative.
University/Government Code (free): MMA#0comp (ID required to confirm)
Sponsor Code (50% discount): MMA#50discount
For help registering, contact Lisa Blackburn at email@example.com. For questions about the event, call 410516-5665 or email firstname.lastname@example.org
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.