Research

How do fruit flies choose the right escape direction?

To survive, animals must convert sensory information into appropriate behaviors. Vision is a common sense for locating ethologically relevant stimuli and guiding motor responses. How circuitry converts object location in retinal coordinates to movement direction in body coordinates remains largely unknown. In this study, in collaboration with the Zipursky's and Card's labs,  we show through behaviour, physiology, anatomy and connectomics in Drosophila that visuomotor transformation occurs by conversion of topographic maps formed by the dendrites of feature-detecting visual projection neurons (VPNs) into synaptic weight gradients of VPN outputs onto central brain neurons.

More information available in the following:

• Synaptic gradients transform object location to action

• How do fruit flies see and what does computer science have to do with it?

• A neural strategy for directional behavior (D. Tosmic & J. Theobald)

Mapping interstiatial flow within brain tumors

Glioblastoma (GBM), a highly aggressive form of brain tumor, is a disease marked by extensive invasion into the surrounding brain. Interstitial fluid flow (IFF), or the movement of fluid within the spaces between cells, has been linked to increased invasion of GBM cells. Better characterization of IFF could elucidate underlying mechanisms driving this invasion in vivo. Here we develop a technique to non-invasively measure interstitial flow velocities in the glioma microenvironment of mice using dynamic contrast-enhanced MRI, a common clinical technique. In In collaboration with the Munson's lab at Virginia Tech, we show how MRI images can be used to measure average fluid velocities and accurately reconstruct their directions within biological systems. 

More information available in the following publication:

• MRI analysis to map interstitial flow in the brain tumor microenvironment

Modeling glymphatic transport mechanism

How does the brain get rid of waste? More specifically, if we shut down the lymphatic system of the brain, is there a difference in the disposal of waste? What effects does this have on the onset of Alzheimer? These questions are at the basis of a project conducted in collaboration with the Kipnis' Lab at the University of Virginia Neuroscience department. By using an optimization approach to match an advective-diffusive model to a temporal series of 3D-MRI stacks recording the transport of contrast agent (gadolinium) within murine brains under different level of lymphatic drainage, it is possible to evaluate the optimal parameters globally describing the glymphatic transport mechanism. These optimized parameters can be visualized as a series of overlay maps that illustrate the dominant transport process for each location within the brain. Together with results from pharmacological, surgical, and genetic models, these maps demonstrate a connection between meningeal lymphatics, brain ageing, and Alzheimer disease pathogenesis, thus suggesting novel therapeutic strategies for neurodegenerative diseases exacerbated by aging.  

More information available in the following publication:

• Functional aspects of meningeal lymphatics in ageing and Alzheimer’s disease

Web-based Network Level Decision Support System Application

The objective of this project is two-fold: develop and make available a set of novel analysis tools, fully integrated in ArcGIS, that leverages the rich information provided by satellite-based remote sensing data to detect and characterize geohazards of interest to the transportation community, and provide a modern web-based decision support system (DSS) where these novel analysis products can be seamlessly integrated with existing datasets. Specifically, interferometric synthetic aperture radar (InSAR) and its derivatives are employed in this work. 

Check the live DSS demo available online. 

More information available in the following publication:

• Integrating Remote Sensing Data in Decision Support Systems for Transportation Asset Management 

Automated analysis of InSAR images

The goal of this projects is to develop image analysis techniques to detect bridge settlements, landslide occurrences and sinkhole formation using space-borne interferometric synthetic aperture radar image stacks to allow early detection of potential transportation hazards to improve safety and reduce maintenance costs.

More information available in the following publications:

• LaWeCo: Active region detection in non-uniformly sampled data using Laplacian-weighted covariance

• Integration and delivery of interferometric Synthetic Aperture Radar [InSAR] data into stormwater planning within karst terrains

• Historical Analysis of Tunnel Approach Displacements with Satellite Remote Sensing

• Detection of geophysical features in InSAR point cloud data sets using spatiotemporal models

• Spatiotemporal Gaussian feature detection in sparsely sampled data with application to InSAR

• Validation of Interferometric Synthetic Aperture Radar as a tool for identification of geohazards and at-risk transportation infrastructure

• Graph Cut Segmentation of Sparsely Sampled Images with Application to InSAR-measured Changes in Elevation