Title	Metrics for evaluating functional neuroimaging processing pipelines
Creator	Strother, Stephen
Publisher	Banff International Research Station for Mathematical Innovation and Discovery
Date Issued	2016-02-01T16:21
Description	I will survey the range of quantitative metrics used in the literature for evaluating the performance of functional neuroimaging processing pipelines and attempt to compare their strengths and weaknesses. Metrics to be discussed will include: ROC and Pseudo-ROC Curves, Cluster Overlap Metrics, Intra-class Correlation Coefficients and extensions, Spatial Pattern Reproducibility, Similarity Metric Ranking Approaches, Cross-validated Prediction, and Combinations of Metrics. In particular I will focus on interactions between data preprocessing/cleaning and data analysis, and whether it makes sense to treat these as separate independent choices when evaluating pipelines.
Extent	39 minutes
Subject	Mathematics; Statistics; Game theory, economics, social and behavioral sciences
Type	Moving Image
File Format	video/mp4
Language	eng
Notes	Author affiliation: Baycrest/University of Toronto
Series	BIRS Workshop Lecture Videos (Banff, Alta)
Date Available	2016-08-03
Provider	Vancouver : University of British Columbia Library
Rights	Attribution-NonCommercial-NoDerivatives 4.0 International
DOI	10.14288/1.0307306
URI	http://hdl.handle.net/2429/58658
Affiliation	Non UBC
Peer Review Status	Unreviewed
Scholarly Level	Faculty
Rights URI	http://creativecommons.org/licenses/by-nc-nd/4.0/
Aggregated Source Repository	DSpace

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Metrics for evaluating functional neuroimaging processing pipelines Strother, Stephen

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