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Opening the analysis black box: Improving robustness and interpretation

Banff International Research Station for Mathematical Innovation and Discovery

Neuroimaging data are very high dimensional and complicated. A human scientist cannot apprehend the raw data directly. One primary purpose of neuroimaging data analysis is to abstract away most of the dimensionality and complexity in the data by extracting just a small number of significant patterns from it. This analysis involves a long chain of steps that interact with the data at various points. Ideally, each step would "just work", yielding reliable outputs robust to noise and complexity in the data. In practice, the analysis can fail at various steps due to a host of reasons such as the influence of noise, bad convergence in some optimization algorithm, and so on. However, the final output of the analysis often provides no indication that such failures have occurred. By design, the analysis abstracts away the complexity of both the data and how the data interacts with the analysis itself. The analysis ends up hiding such failures, so it is necessary to look for them deliberately. Another important consideration is that the analysis often abstracts away too much of the structure in the neuroimaging data. Meaningful patterns go undetected. I will discuss several approaches for delving into what the data analysis is doing to allow for improved robustness through quality assurance checking as well as improved interpretation through consideration of important patterns in the data that often go unnoticed.

Mathematics; Statistics; Game theory, economics, social and behavioral sciences

video/mp4

Author affiliation: University of Alberta

2016-08-06

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/58700

Unreviewed

http://creativecommons.org/licenses/by-nc-nd/4.0/