Statistical Instability of TBSS Analysis Based on DTI Fitting Algorithm. Issue 6 (14th February 2015)

• Record Type: Journal Article
• Authors: Maximov, Ivan I.; Thönneßen, Heike; Konrad, Kerstin; Amort, Laura; Neuner, Irene; Shah, N. Jon
• Is Part Of: Journal of neuroimaging. Volume 25:Issue 6(2015)
• Page Start: 883
• Page End: 891
• Publication Date: 2015-02-14
• Subjects: Diagnostic imaging -- Periodicals; Nervous system -- Diseases -- Diagnosis -- Periodicals; Imagerie pour le diagnostic -- Périodiques; Système nerveux -- Maladies -- Diagnostic -- Périodiques; Imagerie médicale; Neuroimagerie; Neurologie; Système nerveux; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 616.804754
• DOI: 10.1111/jon.12215
• Languages: English
• ISSNs: 1051-2284

Abstract

<abstract abstract-type="main"> <title>ABSTRACT</title> <p>Voxel‐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.</p> </abstract>