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Integrative statistical models of neuroscience networks

Primary Investigators:
Mikail Rubinov
Brief Description of Project:
Discovery in network neuroscience proceeds through specification of empirically supported models of accurately reconstructed brain wiring diagrams, known as connectomes.

Within this framework, principled statistical confirmation of connectome features is critical, especially when such features have otherwise uncertain empirical function or developmental origin.

This project seeks to establish principled statistical confirmation for basic connectome features that may underpin functional segregation and integration associated with specific behaviors.

Desired Qualifications:
We are looking for students who have a strong quantitative background, good programming skills (preferably in Matlab and/or Python), and an interest in neuroscience and understanding the brain.
Nature of Supervision:
Day-to-day supervision will be shared between the PI and a postdoc.
A Brief Research Plan (period is for 10 weeks):
Week 1-4: Familiarization with network-neuroscience models.
Week 2-6: Adaptation of existing network-neuroscience models to answer specific biological questions.
Week 4-10: Analysis of scale- and species-specific connectomes with adapted models.
Week 8-10: Write up of results in a technical report. 

Number of Open Slots: 2
Contact Information:
Name: Mikail Rubinov
Department: Biomedical Engineering