000188523 001__ 188523
000188523 005__ 20210129215216.0
000188523 0247_ $$2doi$$a10.1111/jon.12215
000188523 0247_ $$2ISSN$$a1051-2284
000188523 0247_ $$2ISSN$$a1552-6569
000188523 0247_ $$2WOS$$aWOS:000363334100005
000188523 0247_ $$2altmetric$$aaltmetric:3688006
000188523 0247_ $$2pmid$$apmid:25682721
000188523 037__ $$aFZJ-2015-01878
000188523 082__ $$a610
000188523 1001_ $$0P:(DE-HGF)0$$aMaximov, Ivan I.$$b0$$eCorresponding Author
000188523 245__ $$aStatistical Instability of TBSS Analysis Based on DTI Fitting Algorithm
000188523 260__ $$aBerlin [u.a.]$$bWiley-Blackwell$$c2015
000188523 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1445586145_1447
000188523 3367_ $$2DataCite$$aOutput Types/Journal article
000188523 3367_ $$00$$2EndNote$$aJournal Article
000188523 3367_ $$2BibTeX$$aARTICLE
000188523 3367_ $$2ORCID$$aJOURNAL_ARTICLE
000188523 3367_ $$2DRIVER$$aarticle
000188523 520__ $$aVoxel-based DTI analysis is an important approach in the comparison of subject groups by detecting and localizing gray and white matter changes in the brain. One of the principal problems for intersubject comparison is the absence of a “gold standard” processing pipeline. As a result, contradictory results may be obtained from identical data using different data processing pipelines, for example, in the data normalization or smoothing procedures. Tract-based spatial statistics (TBSS) shows potential to overcome this problem by automatic detection of white matter changes and decreasing variation in the performed analysis. However, skeleton projection approaches, such as TBSS, critically depend on the accuracy of the diffusion scalar metric estimations. In this work, we demonstrate that the agreement and reliability of TBSS results depend on the applied DTI data processing algorithm. Statistical tests have been performed using two in vivo measured datasets and compared with different implementations of the least squares algorithm. As a result, we recommend repeating TBSS analysis using different fitting algorithms, in particular, using on iteratively-assessed robust estimators, as accurate and more reliable approach in voxel-based analysis, particularly, for TBSS. Repeating TBSS analysis allows one to detect and localize suspicious regions in white matter which were estimated as the regions with significant difference. Finally, we did not find a favorite fitting algorithm (or class of them) which can be marked as more reliable for group comparison.
000188523 536__ $$0G:(DE-HGF)POF3-572$$a572 - (Dys-)function and Plasticity (POF3-572)$$cPOF3-572$$fPOF III$$x0
000188523 588__ $$aDataset connected to CrossRef, juser.fz-juelich.de
000188523 7001_ $$0P:(DE-Juel1)140573$$aThönneßen, Heike$$b1
000188523 7001_ $$0P:(DE-HGF)0$$aKonrad, Kerstin$$b2
000188523 7001_ $$0P:(DE-HGF)0$$aAmort, Laura$$b3
000188523 7001_ $$0P:(DE-Juel1)131781$$aNeuner, Irene$$b4$$ufzj
000188523 7001_ $$0P:(DE-Juel1)131794$$aShah, N. J.$$b5$$ufzj
000188523 773__ $$0PERI:(DE-600)2035400-9$$a10.1111/jon.12215$$gp. n/a - n/a$$n6$$p883 - 891$$tJournal of neuroimaging$$v25$$x1051-2284$$y2015
000188523 909CO $$ooai:juser.fz-juelich.de:188523$$pVDB
000188523 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-HGF)0$$aForschungszentrum Jülich GmbH$$b2$$kFZJ
000188523 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131781$$aForschungszentrum Jülich GmbH$$b4$$kFZJ
000188523 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131794$$aForschungszentrum Jülich GmbH$$b5$$kFZJ
000188523 9130_ $$0G:(DE-HGF)POF2-333$$1G:(DE-HGF)POF2-330$$2G:(DE-HGF)POF2-300$$aDE-HGF$$bGesundheit$$lFunktion und Dysfunktion des Nervensystems$$vPathophysiological Mechanisms of Neurological and Psychiatric Diseases$$x0
000188523 9131_ $$0G:(DE-HGF)POF3-572$$1G:(DE-HGF)POF3-570$$2G:(DE-HGF)POF3-500$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bKey Technologies$$lDecoding the Human Brain$$v(Dys-)function and Plasticity$$x0
000188523 9141_ $$y2015
000188523 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline
000188523 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR
000188523 915__ $$0StatID:(DE-HGF)9900$$2StatID$$aIF < 5
000188523 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS
000188523 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bThomson Reuters Master Journal List
000188523 915__ $$0StatID:(DE-HGF)0111$$2StatID$$aWoS$$bScience Citation Index Expanded
000188523 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection
000188523 915__ $$0StatID:(DE-HGF)1110$$2StatID$$aDBCoverage$$bCurrent Contents - Clinical Medicine
000188523 9201_ $$0I:(DE-Juel1)INM-3-20090406$$kINM-3$$lKognitive Neurowissenschaften$$x0
000188523 9201_ $$0I:(DE-Juel1)INM-4-20090406$$kINM-4$$lPhysik der Medizinischen Bildgebung$$x1
000188523 980__ $$ajournal
000188523 980__ $$aVDB
000188523 980__ $$aI:(DE-Juel1)INM-3-20090406
000188523 980__ $$aI:(DE-Juel1)INM-4-20090406
000188523 980__ $$aUNRESTRICTED
000188523 981__ $$aI:(DE-Juel1)INM-4-20090406