Beyond crossing fibers: bootstrap probabilistic tractography using complex subvoxel fiber geometries

A unified filtering method for estimating asymmetric orientation distribution functions

Numerous filtering methods have been proposed for estimating asymmetric orientation distribution functions (ODFs) for diffusion magnetic resonance imaging (dMRI). It can be hard to make sense of all these different methods, which share similar features and result in similar outputs. In this work, we disentangle these many filtering methods proposed in the past and combine them into a novel, unified filtering equation. We also propose a self-supervised data-driven approach for calibrating the filtering parameter values. Our equation is implemented in an open-source GPU-accelerated python software to facilitate its integration into any existing dMRI processing pipeline. Our method is applied on multi-shell multi-tissue fiber ODFs from the Human Connectome Project dataset (1.25 mm3 native resolution) and on single-shell single-tissue fiber ODFs from the Bilingualism and the Brain dataset (2.0 mm3 isotropic resolution) to evaluate the occurrence of asymmetric patterns on different spatial resolutions, representing cutting-edge and "clinical" research data. Asymmetry measures such as the asymmetric index (ASI) and our novel number of fiber directions (NuFiD) are then used to explain the behaviour of our method in these images. The contributions of this work are: (i) the disentanglement and unification of filtering methods for estimating asymmetric ODFs; (ii) a calibration method for automatically fixing the parameters governing the filtering; (iii) an open-source, efficient implementation of our unified filtering method for estimating asymmetric ODFs; (iv) a novel number of fiber directions (NuFiD) index for explaining asymmetric fiber configurations; and (v) a novel template of asymmetries, revealing that our filtering method estimates asymmetric configurations in at least 50% of the brain voxels (∼31% of the white matter and ∼63% of the gray matter).

The human raphe-hippocampal tract and affective sensitivity: a probabilistic tractography study

Serotonergic system plays critical roles in modulating affective control. The raphe nucleus has been known to be the origin of forebrain 5-HT afferents. Specifically, the Raphe-Hippocampal projection has been shown to modulate affective sensitivity in rodents. However, the human Raphe-Hippocampal tract is not well studied. We aimed to segment the Raphe-Hippocampal tract using probabilistic tractography on diffusion tensor imaging data from 502 subjects. The Raphe-Hippocampal tracts were successfully isolated in 464 individuals. There was a significant association between integrity of the Raphe-Hippocampal tract and affective sensitivity. To our best knowledge, this is the first study that isolated the human Raphe-Hippocampal tract. The integrity of the tract showed consistent characteristics with rodent findings, where affective sensitivity is modulated by the Raphe-Hippocampal projection. This study provides a technique to segment the human Raphe-Hippocampal tract and a translational knowledge that the tract in a human possesses consistent characteristics that have been found in rodent studies.

A quality control pipeline for probabilistic reconstruction of white-matter pathways

BACKGROUND

One of the most well-validated tools for DTI data analysis is TRACULA, part of the FreeSurfer software. TRACULA automatically segments 18 major white matter (WM) tracts. Occasionally, tracts may be only partially reconstructed, thus requiring intervention to avoid biasing analyses. A majority of studies have not reported any quality control procedures and those that have tend to discard partially reconstructed tracts from group analyses if they cannot be salvaged during TRACULA reinitialization.

NEW METHOD

We propose a semi-automated method to improve the detection and recovery of incomplete WM tracts. We detail several steps to maximize the quality of preprocessed DTI data. The steps include: (1) a visual inspection of eddy current corrected diffusion weighted images and (2) an automated evaluation of color- encoded FA images; (3) assessment of the volume of each tract saved in the TRACULA output file; (4) re-processing of tracts with a volume smaller than a specified threshold; (5) minimal manual editing of the control points for tracts that remained partially reconstructed; and (6) final re-initiation of TRACULA.

RESULTS

Our method can speed and improve quality control relative to tract-by-tract visual inspection and can recover data that otherwise would need to be excluded from analyses due to incomplete reconstruction.

COMPARISON WITH EXISTING METHODS

To our knowledge, there are no publications proposing alternative methods for quality control and recovering of partially reconstructed tracts in the TRACULA environment.

CONCLUSIONS

Our method helps TRACULA users automatically access the quality of reconstructed WM tracts and semi-automatically recover those in-complete WM tracts.

Tracking and validation techniques for topographically organized tractography

Topographic regularity of axonal connections is commonly understood as the preservation of spatial relationships between nearby neurons and is a fundamental structural property of the brain. In particular the retinotopic mapping of the visual pathway can even be quantitatively computed. Inspired from this previously untapped anatomical knowledge, we propose a novel tractography method that preserves both topographic and geometric regularity. We make use of parameterized curves with Frenet-Serret frame and introduce a highly flexible mechanism for controlling geometric regularity. At the same time, we incorporate a novel local data support term in order to account for topographic organization. Unifying geometry with topographic regularity, we develop a Bayesian framework for generating highly organized streamlines that accurately follow neuroanatomy. We additionally propose two novel validation techniques to quantify topographic regularity. In our experiments, we studied the results of our approach with respect to connectivity, reproducibility and topographic regularity aspects. We present both qualitative and quantitative comparisons of our technique against three algorithms from MRtrix3. We show that our method successfully generates highly organized fiber tracks while capturing bundle anatomy that are geometrically challenging for other approaches.

Improved tractography using asymmetric fibre orientation distributions

Diffusion MRI allows us to make inferences on the structural organisation of the brain by mapping water diffusion to white matter microstructure. However, such a mapping is generally ill-defined; for instance, diffusion measurements are antipodally symmetric (diffusion along x and -x are equal), whereas the distribution of fibre orientations within a voxel is generally not symmetric. Therefore, different sub-voxel patterns such as crossing, fanning, or sharp bending, cannot be distinguished by fitting a voxel-wise model to the signal. However, asymmetric fibre patterns can potentially be distinguished once spatial information from neighbouring voxels is taken into account. We propose a neighbourhood-constrained spherical deconvolution approach that is capable of inferring asymmetric fibre orientation distributions (A-fods). Importantly, we further design and implement a tractography algorithm that utilises the estimated A-fods, since the commonly used streamline tractography paradigm cannot directly take advantage of the new information. We assess performance using ultra-high resolution histology data where we can compare true orientation distributions against sub-voxel fibre patterns estimated from down-sampled data. Finally, we explore the benefits of A-fods-based tractography using in vivo data by evaluating agreement of tractography predictions with connectivity estimates made using different in-vivo modalities. The proposed approach can reliably estimate complex fibre patterns such as sharp bending and fanning, which voxel-wise approaches cannot estimate. Moreover, histology-based and in-vivo results show that the new framework allows more accurate tractography and reconstruction of maps quantifying (symmetric and asymmetric) fibre complexity.

Unraveling the multiscale structural organization and connectivity of the human brain: the role of diffusion MRI

The structural architecture and the anatomical connectivity of the human brain show different organizational principles at distinct spatial scales. Histological staining and light microscopy techniques have been widely used in classical neuroanatomical studies to unravel brain organization. Using such techniques is a laborious task performed on 2-dimensional histological sections by skilled anatomists possibly aided by semi-automated algorithms. With the recent advent of modern magnetic resonance imaging (MRI) contrast mechanisms, cortical layers and columns can now be reliably identified and their structural properties quantified post-mortem. These developments are allowing the investigation of neuroanatomical features of the brain at a spatial resolution that could be interfaced with that of histology. Diffusion MRI and tractography techniques, in particular, have been used to probe the architecture of both white and gray matter in three dimensions. Combined with mathematical network analysis, these techniques are increasingly influential in the investigation of the macro-, meso-, and microscopic organization of brain connectivity and anatomy, both in vivo and ex vivo. Diffusion MRI-based techniques in combination with histology approaches can therefore support the endeavor of creating multimodal atlases that take into account the different spatial scales or levels on which the brain is organized. The aim of this review is to illustrate and discuss the structural architecture and the anatomical connectivity of the human brain at different spatial scales and how recently developed diffusion MRI techniques can help investigate these.