000862440 001__ 862440
000862440 005__ 20210130001348.0
000862440 0247_ $$2doi$$a10.1259/bjr.20180886
000862440 0247_ $$2ISSN$$a0007-1285
000862440 0247_ $$2ISSN$$a1748-880X
000862440 0247_ $$2Handle$$a2128/23057
000862440 0247_ $$2altmetric$$aaltmetric:59186739
000862440 0247_ $$2pmid$$apmid:30994036
000862440 0247_ $$2WOS$$aWOS:000482099400005
000862440 037__ $$aFZJ-2019-02752
000862440 082__ $$a610
000862440 1001_ $$00000-0002-9461-1173$$aRubbert, Christian$$b0$$eCorresponding author
000862440 245__ $$aMachine-learning identifies parkinson's disease patients based on resting-state between-network functional connectivity
000862440 260__ $$aLondon$$bInst.$$c2019
000862440 3367_ $$2DRIVER$$aarticle
000862440 3367_ $$2DataCite$$aOutput Types/Journal article
000862440 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1569996203_15601
000862440 3367_ $$2BibTeX$$aARTICLE
000862440 3367_ $$2ORCID$$aJOURNAL_ARTICLE
000862440 3367_ $$00$$2EndNote$$aJournal Article
000862440 500__ $$aData were provided in part by the Human Connectome Project, WU-Minn Consortium(Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; andby the McDonnell Center for Systems Neuroscience at Washington University.
000862440 520__ $$aOBJECTIVES:Evaluation of a data-driven, model-based classification approach to discriminate idiopathic Parkinson's disease (PD) patients from healthy controls (HC) based on between-network connectivity in whole-brain resting-state functional MRI (rs-fMRI).METHODS:Whole-brain rs-fMRI (EPI/TR = 2.2  s/TE = 30  ms/flip angle = 90°/resolution = 3.1 × 3.1 × 3.1  mm/acquisition time≈11  min) was assessed in 42 PD patients (medical OFF) and 47 HC matched for age and gender. Between-network connectivity based on full and L2-regularized partial correlation measures were computed for each subject based on canonical functional network architectures of two cohorts at different levels of granularity (Human Connectome Project: 15/25/50/100/200 networks; 1000BRAINS: 15/25/50/70 networks). A Boosted Logistic Regression model was trained on the correlation matrices using a nested cross-validation (CV) with 10 outer and 10 inner folds for an unbiased performance estimate, treating the canonical functional network architecture and the type of correlation as hyperparameters. The number of boosting iterations was fixed at 100. The model with the highest mean accuracy over the inner folds was trained using an non-nested 10- fold 20-repeats CV over the whole dataset to determine feature importance.RESULTS:Over the outer folds the mean accuracy was found to be 76.2 % (median 77.8%, SD 18.2, IQR 69.4 - 87.1 %). Mean sensitivity was 81 % (median 80%, SD 21.1, IQR 75 - 100 %) and mean specificity was 72.7 % (median 75%, SD 20.4, IQR 66.7 - 80 %). The 1000BRAINS 50-network-parcellation, using full correlations, performed best over the inner folds. The top features predominantly included sensorimotor as well as sensory networks.CONCLUSIONS:A rs-fMRI whole-brain-connectivity, data-driven, model-based approach to discriminate PD patients from healthy controls shows a very good accuracy and a high sensitivity. Given the high sensitivity of the approach, it may be of use in a screening setting.ADVANCES IN KNOWLEDGE:Resting-state functional MRI could prove to be a valuable, non-invasive neuroimaging biomarker for neurodegenerative diseases. The current model-based, data-driven approach on whole-brain between-network connectivity to discriminate Parkinson's disease patients from healthy controls shows promising results with a very good accuracy and a very high sensitivity.
000862440 536__ $$0G:(DE-HGF)POF3-571$$a571 - Connectivity and Activity (POF3-571)$$cPOF3-571$$fPOF III$$x0
000862440 588__ $$aDataset connected to CrossRef
000862440 7001_ $$0P:(DE-HGF)0$$aMathys, Christian$$b1
000862440 7001_ $$0P:(DE-Juel1)145386$$aJockwitz, Christiane$$b2$$ufzj
000862440 7001_ $$0P:(DE-HGF)0$$aHartmann, Christian J$$b3
000862440 7001_ $$0P:(DE-Juel1)131678$$aEickhoff, Simon$$b4$$ufzj
000862440 7001_ $$0P:(DE-Juel1)131684$$aHoffstaedter, Felix$$b5$$ufzj
000862440 7001_ $$0P:(DE-Juel1)131675$$aCaspers, Svenja$$b6$$ufzj
000862440 7001_ $$0P:(DE-Juel1)174483$$aEickhoff, Claudia$$b7$$ufzj
000862440 7001_ $$0P:(DE-Juel1)171897$$aSigl, Benjamin$$b8
000862440 7001_ $$0P:(DE-HGF)0$$aTeichert, Nikolas A$$b9
000862440 7001_ $$0P:(DE-HGF)0$$aSüdmeyer, Martin$$b10
000862440 7001_ $$0P:(DE-HGF)0$$aTurowski, Bernd$$b11
000862440 7001_ $$0P:(DE-HGF)0$$aSchnitzler, Alfons$$b12
000862440 7001_ $$0P:(DE-Juel1)144344$$aCaspers, Julian$$b13
000862440 773__ $$0PERI:(DE-600)1468548-6$$a10.1259/bjr.20180886$$gp. 20180886 -$$n1101$$p20180886$$tThe British journal of radiology$$v92$$x1748-880X$$y2019
000862440 8564_ $$uhttps://juser.fz-juelich.de/record/862440/files/bjr.20180886.pdf$$yPublished on 2019-05-14. Available in OpenAccess from 2020-05-14.
000862440 8564_ $$uhttps://juser.fz-juelich.de/record/862440/files/bjr.20180886.pdf?subformat=pdfa$$xpdfa$$yPublished on 2019-05-14. Available in OpenAccess from 2020-05-14.
000862440 909CO $$ooai:juser.fz-juelich.de:862440$$pdnbdelivery$$pdriver$$pVDB$$popen_access$$popenaire
000862440 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)145386$$aForschungszentrum Jülich$$b2$$kFZJ
000862440 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131678$$aForschungszentrum Jülich$$b4$$kFZJ
000862440 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131684$$aForschungszentrum Jülich$$b5$$kFZJ
000862440 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131675$$aForschungszentrum Jülich$$b6$$kFZJ
000862440 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)174483$$aForschungszentrum Jülich$$b7$$kFZJ
000862440 9131_ $$0G:(DE-HGF)POF3-571$$1G:(DE-HGF)POF3-570$$2G:(DE-HGF)POF3-500$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bKey Technologies$$lDecoding the Human Brain$$vConnectivity and Activity$$x0
000862440 9141_ $$y2019
000862440 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS
000862440 915__ $$0StatID:(DE-HGF)1030$$2StatID$$aDBCoverage$$bCurrent Contents - Life Sciences
000862440 915__ $$0StatID:(DE-HGF)0600$$2StatID$$aDBCoverage$$bEbsco Academic Search
000862440 915__ $$0StatID:(DE-HGF)0530$$2StatID$$aEmbargoed OpenAccess
000862440 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bBRIT J RADIOL : 2017
000862440 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection
000862440 915__ $$0StatID:(DE-HGF)0110$$2StatID$$aWoS$$bScience Citation Index
000862440 915__ $$0StatID:(DE-HGF)0111$$2StatID$$aWoS$$bScience Citation Index Expanded
000862440 915__ $$0StatID:(DE-HGF)9900$$2StatID$$aIF < 5
000862440 915__ $$0StatID:(DE-HGF)1110$$2StatID$$aDBCoverage$$bCurrent Contents - Clinical Medicine
000862440 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bASC
000862440 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline
000862440 915__ $$0StatID:(DE-HGF)0320$$2StatID$$aDBCoverage$$bPubMed Central
000862440 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List
000862440 920__ $$lyes
000862440 9201_ $$0I:(DE-Juel1)INM-7-20090406$$kINM-7$$lGehirn & Verhalten$$x0
000862440 9201_ $$0I:(DE-Juel1)INM-1-20090406$$kINM-1$$lStrukturelle und funktionelle Organisation des Gehirns$$x1
000862440 980__ $$ajournal
000862440 980__ $$aVDB
000862440 980__ $$aUNRESTRICTED
000862440 980__ $$aI:(DE-Juel1)INM-7-20090406
000862440 980__ $$aI:(DE-Juel1)INM-1-20090406
000862440 9801_ $$aFullTexts