Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/2044
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dc.contributor.authorSERROYEN, Jan-
dc.contributor.authorMOLENBERGHS, Geert-
dc.contributor.authorVerhoye, Marleen-
dc.contributor.authorVan Meir, Vincent-
dc.contributor.authorVan der Linden, Annemie-
dc.date.accessioned2007-11-11T09:21:11Z-
dc.date.available2007-11-11T09:21:11Z-
dc.date.issued2005-
dc.identifier.citationJOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS, 10(2). p. 170-183-
dc.identifier.issn1085-7117-
dc.identifier.urihttp://hdl.handle.net/1942/2044-
dc.description.abstractWe analyze data on the impact of testosterone on the dynamics of Mn2+ accumulation measured by magnetic resonance imaging in three songbird brain areas: the nucleus robustus arcopallii (RA), area X, and the high vocal center (HVC). Birds with and without testosterone were included in the experiment, and repeated measurements were available in both a pre- and post-drug administration period. We formulate a nonlinear modeling strategy, allowing for the incorporation of (1) within-bird correlation, (2) the nonlinearity of the profiles, and (3) the effect of treatment. For two of the outcomes (RA and area X), biological theory suggests a parametric form, but for HVC this is not the case. Because the HVC outcome bears some resemblance with the two-compartment model known from pharmacokinetics, this model was considered a sensible choice. We use a different model, based on fractional polynomials, as a sensitivity analysis for the latter. All methods used provide good fits to the data, confirm results from previous, simple analyses undertaken in the literature, but were able to detect additional effects of treatment that had so far gone undetected. The fractional polynomial and two-compartment models provide similar substantive conclusions; the two together can be seen as a form of sensitivity analysis.-
dc.description.sponsorshipWe gratefully acknowledge support from Belgian IUAP/PAI network “Statistical Techniques and Modeling for Complex Substantive Questions with Complex Data.”-
dc.languageEnglish-
dc.language.isoen-
dc.publisherAMER STATISTICAL ASSOC & INTERNATIONAL BIOMETRIC SOC-
dc.rights© 2005 American Statistical Association and the International Biometric Society-
dc.subject.otherfractional polynomial; pharmacokinetics; random effect; testosterone; two compartment model-
dc.subject.otherfractional polynomial; pharmacokinetics; random effect; testosterone; two-compartment model-
dc.titleDynamic manganese-enhanced MRI signal intensity processing based on nonlinear mixed modeling to study changes in neuronal activity-
dc.typeJournal Contribution-
dc.identifier.epage183-
dc.identifier.issue2-
dc.identifier.spage170-
dc.identifier.volume10-
local.format.pages14-
local.bibliographicCitation.jcatA1-
dc.description.notesLimburgs Univ Ctr, Ctr Stat, Diepenbeek, Belgium. Univ Antwerp, Bioimaging Lab, B-2020 Antwerp, Belgium.Serroyen, J, Limburgs Univ Ctr, Ctr Stat, Univ Campus, Diepenbeek, Belgium.jan.serroyen@luc.ac.be-
local.type.refereedRefereed-
local.type.specifiedArticle-
dc.bibliographicCitation.oldjcatA1-
dc.identifier.doi10.1198/108571105X46426-
dc.identifier.isi000229595500004-
dc.identifier.urlhttps://www.researchgate.net/profile/Geert_Molenberghs/publication/227192876_Dynamic_manganese-enhanced_MRI_signal_intensity_processing_based_on_nonlinear_mixed_modeling_to_study_changes_in_neuronal_activity/links/543bc7080cf24a6ddb97a093/Dynamic-manganese-enhanced-MRI-signal-intensity-processing-based-on-nonlinear-mixed-modeling-to-study-changes-in-neuronal-activity.pdf-
item.contributorSERROYEN, Jan-
item.contributorMOLENBERGHS, Geert-
item.contributorVerhoye, Marleen-
item.contributorVan Meir, Vincent-
item.contributorVan der Linden, Annemie-
item.validationecoom 2006-
item.fullcitationSERROYEN, Jan; MOLENBERGHS, Geert; Verhoye, Marleen; Van Meir, Vincent & Van der Linden, Annemie (2005) Dynamic manganese-enhanced MRI signal intensity processing based on nonlinear mixed modeling to study changes in neuronal activity. In: JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS, 10(2). p. 170-183.-
item.accessRightsRestricted Access-
item.fulltextWith Fulltext-
crisitem.journal.issn1085-7117-
crisitem.journal.eissn1537-2693-
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