Multisite reliability of cognitive BOLD data

Autor: Brown, Gregory G., Mathalon, Daniel H., Stern, Hal, Ford, Judith, Mueller, Bryon, Greve, Douglas N., McCarthy, Gregory, Voyvodic, James, Glover, Gary, Diaz, Michele, Yetter, Elizabeth, Ozyurt, I. Burak, Jorgensen, Kasper W., Wible, Cynthia G., Turner, Jessica A., Thompson, Wesley K., Potkin, Steven G.

Publikováno v: In NeuroImage 1 February 2011 54(3):2163-2175

The Cerebral Blood Flow Biomedical Informatics Research Network (CBFBIRN) data repository

Autor: Shin, David D, Ozyurt, I Burak, Brown, Gregory G, Fennema-Notestine, Christine, Liu, Thomas T

Publikováno v: NeuroImage, vol 124, iss Pt B

Arterial spin labeling (ASL) MRI provides an accurate and reliable measure of cerebral blood flow (CBF). A rapidly growing number of CBF measures are being collected both in clinical and research settings around the world, resulting in a large volume

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od_______325::b330fd954fbe499068dfa3a4c055a689
https://escholarship.org/uc/item/9d63m8k1

Quantitative evaluation of automated skull-stripping methods applied to contemporary and legacy images: Effects of diagnosis, bias correction, and slice location

Autor: Fennema-Notestine, Christine, Ozyurt, I Burak, Clark, Camellia P, Morris, Shaunna, Bischoff-Grethe, Amanda, Bondi, Mark W, Jernigan, Terry L, Fischl, Bruce, Segonne, Florent, Shattuck, David W, Leahy, Richard M, Rex, David E, Toga, Arthur W, Zou, Kelly H, Brown, Gregory G

Publikováno v: Fennema-Notestine, Christine; Ozyurt, I Burak; Clark, Camellia P; Morris, Shaunna; Bischoff-Grethe, Amanda; Bondi, Mark W; et al.(2006). Quantitative evaluation of automated skull-stripping methods applied to contemporary and legacy images: Effects of diagnosis, bias correction, and slice location. Human Brain Mapping, 27(2), 99-113. UC San Diego: Retrieved from: http://www.escholarship.org/uc/item/7jf1227x

Performance of automated methods to isolate brain from nonbrain tissues in magnetic resonance (MR) structural images may be influenced by MR signal inhomogeneities, type of MR image set, regional anatomy, and age and diagnosis of subjects studied. Th

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od_______325::fd09c84ba3421566408ba3896231dad8
http://www.escholarship.org/uc/item/7jf1227x

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Knowledge discovery via machine learning for neurodegenerative disease researchers.

Autor: Ozyurt IB; Department of Psychiatry, University of California - San Diego, La Jolla, CA, USA., Brown GG

Publikováno v: Methods in molecular biology (Clifton, N.J.) [Methods Mol Biol] 2009; Vol. 569, pp. 173-96.