Tractography dissection variability: What happens when 42 groups dissect 14 white matter bundles on the same dataset?

White matter bundle segmentation using diffusion MRI fiber tractography has become the method of choice to identify white matter fiber pathways in vivo in human brains. However, like other analyses of complex data, there is considerable variability in segmentation protocols and techniques. This can result in different reconstructions of the same intended white matter pathways, which directly affects tractography results, quantification, and interpretation. In this study, we aim to evaluate and quantify the variability that arises from different protocols for bundle segmentation. Through an open call to users of fiber tractography, including anatomists, clinicians, and algorithm developers, 42 independent teams were given processed sets of human whole-brain streamlines and asked to segment 14 white matter fascicles on six subjects. In total, we received 57 different bundle segmentation protocols, which enabled detailed volume-based and streamline-based analyses of agreement and disagreement among protocols for each fiber pathway. Results show that even when given the exact same sets of underlying streamlines, the variability across protocols for bundle segmentation is greater than all other sources of variability in the virtual dissection process, including variability within protocols and variability across subjects. In order to foster the use of tractography bundle dissection in routine clinical settings, and as a fundamental analytical tool, future endeavors must aim to resolve and reduce this heterogeneity. Although external validation is needed to verify the anatomical accuracy of bundle dissections, reducing heterogeneity is a step towards reproducible research and may be achieved through the use of standard nomenclature and definitions of white matter bundles and well-chosen constraints and decisions in the dissection process

Subcortical volumes across the lifespan: Data from 18,605 healthy individuals aged 3-90 years

Age has a major effect on brain volume. However, the normative studies available are constrained by small sample sizes, restricted age coverage and significant methodological variability. These limitations introduce inconsistencies and may obscure or distort the lifespan trajectories of brain morphometry. In response, we capitalized on the resources of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to examine age-related trajectories inferred from cross-sectional measures of the ventricles, the basal ganglia (caudate, putamen, pallidum, and nucleus accumbens), the thalamus, hippocampus and amygdala using magnetic resonance imaging data obtained from 18,605 individuals aged 3-90 years. All subcortical structure volumes were at their maximum value early in life. The volume of the basal ganglia showed a monotonic negative association with age thereafter; there was no significant association between age and the volumes of the thalamus, amygdala and the hippocampus (with some degree of decline in thalamus) until the sixth decade of life after which they also showed a steep negative association with age. The lateral ventricles showed continuous enlargement throughout the lifespan. Age was positively associated with inter-individual variability in the hippocampus and amygdala and the lateral ventricles. These results were robust to potential confounders and could be used to examine the functional significance of deviations from typical age-related morphometric patterns

Porting Matlab Applications to High-Performance C++ Codes: CPU/GPU-Accelerated Spherical Deconvolution of Diffusion MRI Data

In many scientific research fields, Matlab has been established as de facto tool for application design. This approach offers multiple advantages such as rapid deployment prototyping and the use of high performance linear algebra, among others. However, the applications developed are highly dependent of the Matlab runtime, limiting the deployment in heterogeneous platforms. In this paper we present the migration of a Matlab-implemented application to the C++ programming language, allowing the parallelization in GPUs. In particular, we have chosen RUMBA-SD, a spherical deconvolution algorithm, which estimates the intravoxel white-matter fiber orientations from diffusion MRI data. We describe the methodology used along with the tools and libraries leveraged during the translation task of such application. To demonstrate the benefits of the migration process, we perform a series of experiments using different high performance computing heterogeneous platforms and linear algebra libraries. This work aims to be a guide for future developments that are implemented out of Matlab. The results show that the C++ version attains, on average, a speedup of 8 7 over the Matlab one

Spherical deconvolution of multichannel diffusion MRI data with non-Gaussian noise models and total variation spatial regularization

Due to a higher capability in resolving white matter fiber crossings, Spherical Deconvolution (SD) methods have become very popular in brain fiber-tracking applications. However, while some of these estimation algorithms assume a central Gaussian distribution for the MRI noise, its real distribution is known to be non-Gaussian and to depend on many factors such as the number of coils and the methodology used to combine multichannel signals. Indeed, the two prevailing methods for multichannel signal combination lead to noise patterns better described by Rician and noncentral Chi distributions. Here we develop a Robust and Unbiased Model-BAsed Spherical Deconvolution (RUMBA-SD) technique intended to deal with realistic MRI noise. The algorithm relies on a maximum a posteriori formulation based on Rician and noncentral Chi likelihood models and includes a total variation (TV) spatial regularization term. By means of a synthetic phantom contaminated with noise mimicking patterns generated by data processing in multichannel scanners, the performance of RUMBA-SD is compared to that of other well-established SD methods (i.e., CSD and dRL-SD). The inclusion of proper likelihood models and TV regularization in RUMBA-SD leads to an increased ability to resolve fiber crossings with smaller inter-fiber angles and an increased robustness to noise. Finally, the proposed method is also validated in human brain data, producing the most stable fiber reconstructions in front of differing noise types and diffusion schemes based on a small number of gradient directions

Spherical deconvolution of multichannel diffusion MRI data with non-Gaussian noise models and total variation spatial regularization

Due to a higher capability in resolving white matter fiber crossings, Spherical Deconvolution (SD) methods have become very popular in brain fiber-tracking applications. However, while some of these estimation algorithms assume a central Gaussian distribution for the MRI noise, its real distribution is known to be non-Gaussian and to depend on many factors such as the number of coils and the methodology used to combine multichannel signals. Indeed, the two prevailing methods for multichannel signal combination lead to noise patterns better described by Rician and noncentral Chi distributions. Here we develop a Robust and Unbiased Model-BAsed Spherical Deconvolution (RUMBA-SD) technique intended to deal with realistic MRI noise. The algorithm relies on a maximum a posteriori formulation based on Rician and noncentral Chi likelihood models and includes a total variation (TV) spatial regularization term. By means of a synthetic phantom contaminated with noise mimicking patterns generated by data processing in multichannel scanners, the performance of RUMBA-SD is compared to that of other well-established SD methods (i.e., CSD and dRL-SD). The inclusion of proper likelihood models and TV regularization in RUMBA-SD leads to an increased ability to resolve fiber crossings with smaller inter-fiber angles and an increased robustness to noise. Finally, the proposed method is also validated in human brain data, producing the most stable fiber reconstructions in front of differing noise types and diffusion schemes based on a small number of gradient directions