Data Analysis and Computational Modeling in Network Neuroscience

A brain network where nodes represent brain regions and edges are the density of white matter tracts connecting regions.

Learn how to map neuro-imaging data to brain networks to relate brain structure to brain (dys)function. 

Project is Not Currently Available

This project is not being offered for the current term. Please check back next semester for updates.

Project description

A network is a mathematical model used to represent relationships through connections, and this framework can be used to represent many real-world systems, such as the brain. There are multiple ways to build brain networks depending on the choice of nodes and edges. For example, at the large scale, network nodes could be brain regions and edges could be white matter tracts connecting those regions. At a smaller scale, a brain network could be composed of neurons connected by synapses.

In our lab, we preprocess neuroimaging data across many scales and from many modalities, map those data onto network representations, and perform computational analyses on the resulting networks. We also work on the development of novel computational modeling techniques in order to identify network properties of interest. Our ongoing projects work to relate these network features to epilepsy, concussion, dyslexia, cognitive training, and network evolution.

In this project, students will have the opportunity to choose from multiple ongoing projects studying brain network structure, based on their interest/experience and the lab’s current work. All projects will involve a combination of data analysis, computational modeling, and written/verbal presentations of the work. 

Project outcome

  1. Students will learn the foundations of network neuroscience analysis and how to map data to networks.

  2. Students will learn how to process neuro-imaging data.

  3. Students will gain familiarity with popular neuro-imaging software (e.g., FSL, DSI Studio, ANTs) and computational languages (e.g., Python, MATLAB).

  4. Students will develop skills in research communication and present their work at local conferences.

  5. Students will learn interdisciplinary skills and knowledge across multiple domains. Students with a computational/mathematical background will gain a knowledge of neuroscience and vice versa.

Project details

Timing, eligibility and other details
Length of commitment Spring 2025-Summer 2025
Start time Spring (January/February 2025)
In-person, remote, or hybrid? Hybrid Project (Can be remote and/or in-person; to be determined by mentor and student)
Level of collaboration You will work with graduate students in the lab to learn skills which can then be applied to a semi-independent project. 
Benefits Stipend
Who is eligible All undergraduate students; Basic programming experience and/or knowledge of the brain are preferred, but not required. We strive to maintain a diverse group of student researchers and therefore welcome applicants of all backgrounds. 

Project mentor

Sarah Muldoon

Associate Professor

Mathematics and Institute for Artificial Intelligence and Data Science

Phone: (716) 645-8774

Email: smuldoon@buffalo.edu

Start the project

  1. Email the project mentor using the contact information above to express your interest and get approval to work on the project. (Here are helpful tips on how to contact a project mentor.)
  2. After you receive approval from the mentor to start this project, click the button to start the digital badge. (Learn more about ELN's digital badge options.) 

Preparation activities

Once you begin the digital badge series, you will have access to all the necessary activities and instructions. Your mentor has indicated they would like you to also complete the specific preparation activities below. Please reference this when you get to Step 2 of the Preparation Phase. 

  1. Read Network Neuroscience paper (you are not expected to understand everything!). 
  2. Meet with graduate students in lab to learn more about projects. 

Keywords

neuroscience, networks, data analysis, neuro-imaging, computation, Mathematics and Institute for Artificial Intelligence and Data Science