PhD Proposal by Amrit Kashyap

Event Details
  • Date/Time:
    • Monday November 18, 2019 - Tuesday November 19, 2019
      2:00 pm - 3:59 pm
  • Location: HSRB E160
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Summary Sentence: Clinical application of PA/US (photoacoustic/ultrasound) dual-mode imaging based on the handheld ultrasound system

Full Summary: No summary paragraph submitted.

Amrit Kashyap

BME PhD Proposal Presentation


Date: 11/18/2019

Time: 2 PM

Location: HSRB E160


Committee Members:

Shella Keilholz, PhD (advisor)

Madeleine Hackney, PhD (co-advisor)

Lena Ting, PhD

Christopher Rozell, PhD

Bruce Crosson, PhD

Chethan Pandarinath, PhD


Title: Understanding the Neural Basis of Whole Brain Activity through Biological Circuitry and Resource Constraints



Large scale cortical architecture has not only evolved in order to fulfil organisms’ behavioral functions but also to maximize circuit performance under physical resource constraints. Neural circuitry is expensive with most of ATP consumption going directly into maintaining the action potential. The locations of the neurons in the smallest known nervous system, C. Elegans, have been shown to roughly minimize total wiring costs and in humans most axons are short and run to adjacent gyri. While most of these resource constraints problems have developed to explain static circuitry and the spatial localization of neural processing, they can also be used to understand how the brain functionally operates in real time. Large scale patterns of whole brain activity seen in fMRI are thought to arise from neural populations interacting with each other through the structural fiber network and mediated by task input. The structural network, therefore is a constraint that aids understand which regions coordinate together, and in turn the dynamics are reproduced by a network that utilizes the fewest possible fiber connections. I propose using one modality as a constraint in order to infer information about the other modality, both inferring the structural network from measured functional activation and using structural network as a constraint in a Machine Learning model to fit a dynamical system to the observed functional data. Since the central nervous system is optimized for the utilization of resources, these constraints improve our understanding of the relationship between large structural circuitry and how they are used by the organism for behavior. I then aim to translate this technique to address clinically relevant questions, where our constraints can aid as a prior to help understand gaps in knowledge on how changes in structural connectivity translate into observed deviations in functional activity.

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In Campus Calendar

Graduate Studies

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Faculty/Staff, Public, Undergraduate students
Phd proposal
  • Created By: Tatianna Richardson
  • Workflow Status: Published
  • Created On: Nov 14, 2019 - 3:11pm
  • Last Updated: Nov 18, 2019 - 2:16pm