Chantal M. W. Tax

Methodological considerations on tract-based spatial statistics (TBSS)

Having gained a tremendous amount of popularity since its introduction in 2006, tract-based spatial statistics (TBSS) can now be considered as the standard approach for voxel-based analysis (VBA) of diffusion tensor imaging (DTI) data. Aiming to improve the sensitivity, objectivity, and interpretability of multi-subject DTI studies, TBSS includes a skeletonization step that alleviates residual image misalignment and obviates the need for data smoothing. Although TBSS represents an elegant and user-friendly framework that tackles numerous concerns existing in conventional VBA methods, it has limitations of its own, some of which have already been detailed in recent literature. In this work, we present general methodological considerations on TBSS and report on pitfalls that have not been described previously. In particular, we have identified specific assumptions of TBSS that may not be satisfied under typical conditions. Moreover, we demonstrate that the existence of such violations can severely affect the reliability of TBSS results. With TBSS being used increasingly, it is of paramount importance to acquaint TBSS users with these concerns, such that a well-informed decision can be made as to whether and how to pursue a TBSS analysis. Finally, in addition to raising awareness by providing our new insights, we provide constructive suggestions that could improve the validity and increase the impact of TBSS drastically.

The challenge of mapping the human connectome based on diffusion tractography

Tractography based on non-invasive diffusion imaging is central to the study of human brain connectivity. To date, the approach has not been systematically validated in ground truth studies. Based on a simulated human brain data set with ground truth tracts, we organized an open international tractography challenge, which resulted in 96 distinct submissions from 20 research groups. Here, we report the encouraging finding that most state-of-the-art algorithms produce tractograms containing 90% of the ground truth bundles (to at least some extent). However, the same tractograms contain many more invalid than valid bundles, and half of these invalid bundles occur systematically across research groups. Taken together, our results demonstrate and confirm fundamental ambiguities inherent in tract reconstruction based on orientation information alone, which need to be considered when interpreting tractography and connectivity results. Our approach provides a novel framework for estimating reliability of tractography and encourages innovation to address its current limitations.Though tractography is widely used, it has not been systematically validated. Here, authors report results from 20 groups showing that many tractography algorithms produce both valid and invalid bundles.

REKINDLE: Robust extraction of kurtosis INDices with linear estimation

Recent literature shows that diffusion tensor properties can be estimated more accurately with diffusion kurtosis imaging (DKI) than with diffusion tensor imaging (DTI). Furthermore, the additional non‐Gaussian diffusion features from DKI can be sensitive markers for tissue characterization. Despite these benefits, DKI is more susceptible to data artifacts than DTI due to its increased model complexity, higher acquisition demands, and longer scanning times. To increase the reliability of diffusion tensor and kurtosis estimates, we propose a robust estimation procedure for DKI.

Tractography-based connectomes are dominated by false-positive connections

Fiber tractography based on non-invasive diffusion imaging is at the heart of connectivity studies of the human brain. To date, the approach has not been systematically validated in ground truth studies. Based on a simulated human brain dataset with ground truth white matter tracts, we organized an open international tractography challenge, which resulted in 96 distinct submissions from 20 research groups. While most state-of-the-art algorithms reconstructed 90% of ground truth bundles to at least some extent, on average they produced four times more invalid than valid bundles. About half of the invalid bundles occurred systematically in the majority of submissions. Our results demonstrate fundamental ambiguities inherent to tract reconstruction methods based on diffusion orientation information, with critical consequences for the approach of diffusion tractography in particular and human connectivity studies in general.

The importance of correcting for signal drift in diffusion MRI

To investigate previously unreported effects of signal drift as a result of temporal scanner instability on diffusion MRI data analysis and to propose a method to correct this signal drift.

Microstructural brain abnormalities in Huntington's disease : A two-year follow-up

To investigate both cross‐sectional and time‐related changes of striatal and whole‐brain microstructural properties in different stages of Huntingtons disease (HD) using diffusion tensor imaging.

Evaluating Contextual Processing in Diffusion MRI: Application to Optic Radiation Reconstruction for Epilepsy Surgery

Diffusion MRI and tractography allow for investigation of the architectural configuration of white matter in vivo, offering new avenues for applications like presurgical planning. Despite the promising outlook, there are many pitfalls that complicate its use for (clinical) application. Amongst these are inaccuracies in the geometry of the diffusion profiles on which tractography is based, and poor alignment with neighboring profiles. Recently developed contextual processing techniques, including enhancement and well-posed geometric sharpening, have shown to result in sharper and better aligned diffusion profiles. However, the research that has been conducted up to now is mainly of theoretical nature, and so far these techniques have only been evaluated by visual inspection of the diffusion profiles. In this work, the method is evaluated in a clinically relevant application: the reconstruction of the optic radiation for epilepsy surgery. For this evaluation we have developed a framework in which we incorporate a novel scoring procedure for individual pathways. We demonstrate that, using enhancement and sharpening, the extraction of an anatomically plausible reconstruction of the optic radiation from a large amount of probabilistic pathways is greatly improved in three healthy controls, where currently used methods fail to do so. Furthermore, challenging reconstructions of the optic radiation in three epilepsy surgery candidates with extensive brain lesions demonstrate that it is beneficial to integrate these methods in surgical planning.

“MASSIVE” Brain Dataset: Multiple Acquisitions for Standardization of Structural Imaging Validation and Evaluation

In this work, we present the MASSIVE (Multiple Acquisitions for Standardization of Structural Imaging Validation and Evaluation) brain dataset of a single healthy subject, which is intended to facilitate diffusion MRI (dMRI) modeling and methodology development.

Sharpening fibers in diffusion weighted MRI via erosion

In this chapter erosion is generalized to the space of diffusion weighted MRI data. This is done effectively by solving a Hamilton-Jacobi-Bellman (HJB) system (erosion) on the coupled space of three dimensional positions and orientations, embedded as a quotient in the group of three dimensional rigid body motions. The solution to the HJB equations is given by a well-posed morphological convolution. We present two numerical approaches to solve the HJB equations: analytical kernels, and finite differences. Proof of concept is given by showing improved visibility of major fiber bundles in both artificial and human data. Furthermore, the method is shown to significantly improve the output of a probabilistic tractography algorithm used to extract the optic radiation.

Seeing More by Showing Less: Orientation-Dependent Transparency Rendering for Fiber Tractography Visualization

Fiber tractography plays an important role in exploring the architectural organization of fiber trajectories, both in fundamental neuroscience and in clinical applications. With the advent of diffusion MRI (dMRI) approaches that can also model “crossing fibers”, the complexity of the fiber network as reconstructed with tractography has increased tremendously. Many pathways interdigitate and overlap, which hampers an unequivocal 3D visualization of the network and impedes an efficient study of its organization. We propose a novel fiber tractography visualization approach that interactively and selectively adapts the transparency rendering of fiber trajectories as a function of their orientation to enhance the visibility of the spatial context. More specifically, pathways that are oriented (locally or globally) along a user-specified opacity axis can be made more transparent or opaque. This substantially improves the 3D visualization of the fiber network and the exploration of tissue configurations that would otherwise be largely covered by other pathways. We present examples of fiber bundle extraction and neurosurgical planning cases where the added benefit of our new visualization scheme is demonstrated over conventional fiber visualization approaches.