NeAT: a Nonlinear Analysis Toolbox for Neuroimaging
NeAT: a Nonlinear Analysis Toolbox for Neuroimaging
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Publisher
New York: Springer US
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Language
English
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Publisher
New York: Springer US
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Scope and Contents
Contents
NeAT is a modular, flexible and user-friendly neuroimaging analysis toolbox for modeling linear and nonlinear effects overcoming the limitations of the standard neuroimaging methods which are solely based on linear models. NeAT provides a wide range of statistical and machine learning non-linear methods for model estimation, several metrics based on curve fitting and complexity for model inference and a graphical user interface (GUI) for visualization of results. We illustrate its usefulness on two study cases where non-linear effects have been previously established. Firstly, we study the nonlinear effects of Alzheimer’s disease on brain morphology (volume and cortical thickness). Secondly, we analyze the effect of the apolipoprotein APOE-ε4 genotype on brain aging and its interaction with age. NeAT is fully documented and publicly distributed at
https://imatge-upc.github.io/neat-tool/
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Alternative Titles
Full title
NeAT: a Nonlinear Analysis Toolbox for Neuroimaging
Authors, Artists and Contributors
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Primary Identifiers
Record Identifier
TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_7498484
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_7498484
Other Identifiers
ISSN
1539-2791
E-ISSN
1559-0089
DOI
10.1007/s12021-020-09456-w
