Reliability and validity of DTI-based indirect disconnection measures
Description
Data accompanying the paper: Reliability and validity of DTI-based indirect disconnection measures. Authors: A.R. Smits, M.J.E. van Zandvoort, N.F. Ramsey, E.H.F. de Haan, M. Raemaekers Abstract White matter connections enable the interaction within and between brain networks. Brain lesions can cause structural disconnections that disrupt networks and thereby cognitive functions supported by them. In recent years, novel methods have been developed to quantify the extent of structural disconnection after focal lesions, using tractography data from healthy controls. These methods, however, are indirect and their reliability and validity have yet to be fully established. In this study, we present our implementation of this approach, in a toolkit supplemented by uncertainty metrics for the predictions overall and at voxel-level. These metrics give an indication of the reliability and are used to compare predictions with direct measures from patients’ diffusion tensor imaging (DTI) data in a sample of 95 first-ever stroke patients. Results show that, except for small lesions, our toolkit can predict fiber loss with high reliability and compares well to direct patient DTI estimates. Clinical utility of the method was demonstrated using lesion data from a subset of patients suffering from hemianopia. Both tract-based measures outperformed lesion localization in mapping visual field defects and showed a network consistent with the known anatomy of the visual system. This study offers an important contribution to the validation of structural disconnection mapping. We show that indirect measures of structural disconnection can be a reliable and valid substitute for direct estimations of fiber loss after focal lesions. Moreover, based on these results, we argue that indirect structural disconnection measures may even be preferable to lower-quality single subject diffusion MRI when based on high-quality healthy control datasets. Files/Folders: (1) Lesion overlap map (2) SnPM lesion-symptom maps: contains the thresholded p-value maps for the SnPM analysis for visual field defects, based on predictions of the 3 databases, the lesion maps, and the visitation-map based on the patient’s DTI as input. (3) Toolkit code