MS proposal by Jon Starnes

Event Details
  • Date/Time:
    • Friday October 25, 2019 - Saturday October 26, 2019
      9:00 am - 10:59 am
  • Location: J.S. Coon Building, room 150
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Summary Sentence: : Towards Understanding Navigation Behavior Differences Due To Strategy Preferences, and Hippocampal, Entorhinal Cortex and Caudate Gray Matter Volumes

Full Summary: No summary paragraph submitted.

Name: Jon Starnes

Master’s Thesis Proposal Meeting

Date: Friday, October 25, 2019

Time: 09:00am

Location: J.S. Coon Building, room 150



Thackery Brown, Ph.D. (Georgia Tech)


Thesis Committee Members:

Thackery Brown, Ph.D. (Georgia Tech)

Scott Moffat, Ph.D. (Georgia Tech)

Lewis Wheaton, Ph.D. (Georgia Tech)


Title:  Towards Understanding Navigation Behavior Differences Due To Strategy Preferences, and Hippocampal, Entorhinal Cortex and Caudate Gray Matter Volumes


Abstract: Successful navigation of an environment is linked to hippocampal function and volume, but some environments and contexts may not depend exclusively on hippocampal volume. The neural basis for the cognitive map has been attributed to the hippocampus (HPC), and more recently to the integration between the HPC and the medial entorhinal cortex (MEC). The HPC and MEC both support navigation, and the HPC, in particular, is often implicated in allocentric spatial memory and in disambiguating route memories. Allocentric representations have a counterpart in egocentric spatial processing which has been localized to the caudate nucleus. We will refer to these respectively as place-based and response-based strategies. Humans show individual variation in navigation behavior and whether participants prefer place-based, response-based strategies or a mixture of flexibly switching strategies to navigate. This study seeks to examine individual differences in learning to navigate converging and diverging routes, where a mix of strategies could prove to be useful for successful navigation. This study will use hierarchical clustering on behavioral data from overlapping routes (that converge and later diverge, thus sharing a common stretch of road), to identify groups of people who differ in their ability to navigate different turns through the same location. I will a classifier based on a logistic regression algorithm to distinguish these learning ability groups.  I predict that using this machine learning approach to classify an independent set of people with voxel-based morphometry will reveal important individual differences in how regional brain volumes in the hippocampus, entorhinal cortex and caudate can explain their overlapping route learning abilities, and more broadly contribute to navigation performance and navigational strategy preference.


Additional Information

In Campus Calendar

Graduate Studies

Invited Audience
Public, Graduate students, Undergraduate students
MS Proposal
  • Created By: Tatianna Richardson
  • Workflow Status: Published
  • Created On: Oct 21, 2019 - 1:54pm
  • Last Updated: Oct 21, 2019 - 1:54pm