dc.contributor.author | Lerma-Usabiaga, Garikoitz | |
dc.contributor.author | Mukherjee, Pratik | |
dc.contributor.author | Ren, Zhimei | |
dc.contributor.author | Perry, Michael L. | |
dc.contributor.author | Wandell, Brian A. | |
dc.date.accessioned | 2020-02-10T14:47:39Z | |
dc.date.available | 2020-02-10T14:47:39Z | |
dc.date.issued | 2019 | |
dc.identifier.citation | Garikoitz Lerma-Usabiaga, Pratik Mukherjee, Zhimei Ren, Michael L. Perry, Brian A. Wandell, Replication and generalization in applied neuroimaging, NeuroImage, Volume 202, 2019, 116048, ISSN 1053-8119, https://doi.org/10.1016/j.neuroimage.2019.116048. | es_ES |
dc.identifier.issn | 1053-8119 | |
dc.identifier.uri | http://hdl.handle.net/10810/40535 | |
dc.description | Available online 26 July 2019. | es_ES |
dc.description.abstract | There is much interest in translating neuroimaging findings into meaningful clinical diagnostics. The goal of
scientific discoveries differs from clinical diagnostics. Scientific discoveries must replicate under a specific set of
conditions; to translate to the clinic we must show that findings using purpose-built scientific instruments will be
observable in clinical populations and instruments. Here we describe and evaluate data and computational
methods designed to translate a scientific observation to a clinical setting. Using diffusion weighted imaging
(DWI), Wahl et al. (2010) observed that across subjects the mean fractional anisotropy (FA) of homologous pairs
of tracts is highly correlated. We hypothesize that this is a fundamental biological trait that should be present in
most healthy participants, and deviations from this assessment may be a useful diagnostic metric. Using this
metric as an illustration of our methods, we analyzed six pairs of homologous white matter tracts in nine different
DWI datasets with 44 subjects each. Considering the original FA measurement as a baseline, we show that the new
metric is between 2 and 4 times more precise when used in a clinical context. Our framework to translate research
findings into clinical practice can be applied, in principle, to other neuroimaging results. | es_ES |
dc.description.sponsorship | This work was supported by a Marie Sklodowska-Curie (H2020-MSCA-IF-2017-795807-ReCiModel) grant to G.L.-U. We acknowledge research grant support from the James S. McDonnell Foundation, the Charles A. Dana Foundation, the American Society of Neuroradiology, the U.S. National Institutes of Health (R01NS060776), and the Academic Senate of the University of California, San Francisco for the Wahl 2010 et al. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | NeuroImage | es_ES |
dc.relation | info:eu-repo/grantAgreement/EC/H2020- MSCA-IF-2017-795807 | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.subject | Replication | es_ES |
dc.subject | Generalization | es_ES |
dc.subject | Generalizability | es_ES |
dc.subject | Computational reproducibility | es_ES |
dc.subject | Structural MRI | es_ES |
dc.subject | DWI | es_ES |
dc.subject | White matter tracts | es_ES |
dc.subject | Biomarker | es_ES |
dc.title | Replication and generalization in applied neuroimaging | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.holder | © 2019 Elsevier Inc. All rights reserved. | es_ES |
dc.relation.publisherversion | https://www.sciencedirect.com/journal/neuroimage | es_ES |
dc.identifier.doi | 10.1016/j.neuroimage.2019.116048 | |