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Improve reproducibility and transparency in the field of neuroimaging by applying nonparametricstatistical methods and writing R packages.

Stanford Neurosciences Institute, Christof Seiler

Brain data analyses involves many steps and every step is prone to errors and uncertainties. Ignoring uncertainties can potentially leading to overconfident conclusions. To improve reproducibility it is important to propagate errors throughout the anlaysis. One crucial step in functional imaging studies is image registration to align subject-specific brain anatomy to a common brain atlas. This step ensures that we compare anatomically similar brain regions. During my exchange at EPFL hosted by Professor Van De Ville, we will develop software that integrates registration uncertainty into a functional brain analysis workflow.

 

Participants

Lead Researcher(s): 

Advisor: Susan Holmes (statistics)

EPFL exchange host: Dimitri Van De Ville, Associate Professor of Bioengineering, Institute of Bioengineering, School of Engineering

Funding Type: 
EPFL-Stanford Exchange
Round: 
1
Award Year: 
2017