Reliability and validity of DTI-based indirect disconnection measures

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本数据集配套论文：《基于扩散张量成像（DTI，Diffusion Tensor Imaging）的间接脑结构断开测量方法的信度与效度》 作者：A.R.斯米茨、M.J.E.范赞德沃特、N.F.拉姆齐、E.H.F.德哈恩、M.拉马埃克斯 摘要 白质连接支持脑网络内部及网络间的信息交互。脑部局灶性病灶可引发结构断开，破坏脑网络功能，进而影响其支撑的认知能力。近年来，研究者基于健康对照者的纤维束追踪数据，开发出多种量化局灶性病灶后结构断开程度的新方法。但此类方法均为间接测量手段，其信度与效度仍有待全面验证。本研究介绍了该类方法的实现工具包，并补充了整体及体素层面的预测不确定性指标。这些指标可用于评估测量可靠性，同时我们在95例首发卒中患者的样本中，将该工具包的预测结果与患者自身扩散张量成像的直接测量结果进行了对比。结果显示，除小型病灶外，本工具包可高精度预测纤维丢失情况，且与患者DTI的直接估算结果吻合度较高。我们还通过部分视野缺损患者的病灶数据验证了该方法的临床实用性：基于该方法的纤维束测量指标在映射视野缺损时，均优于单纯病灶定位方法，且所得网络与已知视觉系统解剖结构一致。本研究为结构断开映射的验证工作提供了重要支撑。我们证实，间接结构断开测量方法可作为局灶性病灶后纤维丢失直接估算的可靠且有效的替代方案。此外，基于本研究结果，我们认为若依托高质量健康对照数据集，此类间接结构断开测量方法甚至可优于基于低质量单被试扩散磁共振成像的直接测量结果。 文件/文件夹： (1) 病灶重叠图 (2) SnPM病灶-症状映射图：包含针对视野缺损的SnPM分析的阈值化p值图，该分析分别基于3个数据库的预测结果、病灶图，以及以患者DTI数据为输入得到的造访映射图。 (3) 工具包代码