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  Brainglance: visualizing group level MRI data at one glance

Stelzer, J., Lacosse, E., Bause, J., Scheffler, K., & Lohmann, G. (2019). Brainglance: visualizing group level MRI data at one glance. Frontiers in Neuroscience, 13: 972, pp. 1-13. doi:10.3389/fnins.2019.00972.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0004-994B-C Version Permalink: http://hdl.handle.net/21.11116/0000-0004-DF7D-6
Genre: Journal Article

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Stelzer, J1, 2, Author              
Lacosse, E1, 2, Author              
Bause, J1, 2, Author              
Scheffler, K1, 2, Author              
Lohmann, G1, 2, Author              
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1Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497796              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              

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 Abstract: The vast majority of studies using functional magnetic resonance imaging (fMRI) are analysed on the group level. Standard group-level analyses, however, come with severe drawbacks: First, they assume functional homogeneity within the group, building on the idea that we use our brains in similar ways. Second, group-level analyses require spatial warping and substantial smoothing to accommodate for anatomical variability across subjects. Such procedures massively distort the underlying fMRI data, which hampers the spatial specificity. Taken together, group statistics capture the effective overlap, rendering the modelling of individual deviations impossible-- a major source of false positivity and negativity. The alternative analysis approach is to leave the data in the native subject space, but this makes comparison across individuals difficult. Here, we propose a new framework for visualizing group-level information, better preserving the information of individual subjects. Our proposal is to limit the use of invasive data procedures such as spatial smoothing and warping and rather extract regional information from the individuals. This information is then visualized for all subjects and brain areas at one glance – hence we term the method brainglance. Additionally, our method incorporates a means for clustering individuals to further identify common traits. We showcase our method on two publicly available data sets and discuss our findings.

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 Dates: 2019-092019-10
 Publication Status: Published online
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 Identifiers: DOI: 10.3389/fnins.2019.00972
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Title: Frontiers in Neuroscience
  Other : Front Neurosci
Source Genre: Journal
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Publ. Info: Lausanne, Switzerland : Frontiers Research Foundation
Pages: - Volume / Issue: 13 Sequence Number: 972 Start / End Page: 1 - 13 Identifier: ISSN: 1662-4548
ISSN: 1662-453X
CoNE: https://pure.mpg.de/cone/journals/resource/1662-4548