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Abstract #3650

TBSS Result Variations: Is the Analysis Dependent on the Fitting Algorithm?

Ivan I. Maximov1, Heike Thoennessen1, 2, Kerstin Konrad2, 3, Laura Amort1, 4, Irene Neuner1, 4, Nadim Jon Shah1, 5

1Institute of Neuroscience and Medicine 4, Forschungszentrum Juelich GmbH, Juelich, Germany; 2Department of Child and Adolescent Psychiatry and Psychotherapy, RWTH Aachen University, Aachen, Germany; 3Institute of Neuroscience and Medicine 3, Forschungszentrum Juelich GmbH, Juelich, Germany; 4Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Aachen, Germany; 5Department of Neurology, RWTH Aachen University, Aachen, Germany

Voxelwise analysis is a powerful and useful technique that allows one to detect white/grey matter changes and to perform inter-subject group comparisons. However, conventional voxelwise approaches suffer from multiple artefacts originating from the absence of a gold standard in the data processing pipeline. TBSS is a promising framework that reduces the voxelwise comparison in skeleton space. However, this approach is still under debate: does a transition to the FA skeleton space decrease the result variability? We demonstrate that application of the developed robust post-processing framework allows one to reduce the variability of TBSS results. We also found that the TBSS analysis with the developed robust framework exhibits higher reproducibility compared to other DTI algorithms.