Asymmetry in Cortical Thickness of the Heschl's Gyrus in Unilateral Ear Canal Atresia.

HYPOTHESIS: Unilateral congenital conductive hearing impairment in ear canal atresia leads to atrophy of the gray matter of the contralateral primary auditory cortex or changes in asymmetry pattern if left untreated in childhood. BACKGROUND: Unilateral ear canal atresia with associated severe conductive hearing loss results in deteriorated sound localization and difficulties in understanding of speech in a noisy environment. Cortical atrophy in the Heschl's gyrus has been reported in acquired sensorineural hearing loss but has not been studied in unilateral conductive hearing loss. METHODS: We obtained T1w and T2w FLAIR MRI data from 17 subjects with unilateral congenital ear canal atresia and 17 matched controls. Gray matter volume and thickness were measured in the Heschl's gyrus using Freesurfer. RESULTS: In unilateral congenital ear canal atresia, Heschl's gyrus exhibited cortical thickness asymmetry (right thicker than left, corrected p = 0.0012, mean difference 0.25 mm), while controls had symmetric findings. Gray matter volume and total thickness did not differ from controls with normal hearing. CONCLUSION: We observed cortical thickness asymmetry in congenital unilateral ear canal atresia but no evidence of contralateral cortex atrophy. Further research is needed to understand the implications of this asymmetry on central auditory processing deficits.

Randomized iterative spherical-deconvolution informed tractogram filtering.

Tractography has become an indispensable part of brain connectivity studies. However, it is currently facing problems with reliability. In particular, a substantial amount of nerve fiber reconstructions (streamlines) in tractograms produced by state-of-the-art tractography methods are anatomically implausible. To address this problem, tractogram filtering methods have been developed to remove faulty connections in a postprocessing step. This study takes a closer look at one such method, Spherical-deconvolution Informed Filtering of Tractograms (SIFT), which uses a global optimization approach to improve the agreement between the remaining streamlines after filtering and the underlying diffusion magnetic resonance imaging data. SIFT is not suitable for judging the compliance of individual streamlines with the acquired data since its results depend on the size and composition of the surrounding tractogram. To tackle this problem, we propose applying SIFT to randomly selected tractogram subsets in order to retrieve multiple assessments for each streamline. This approach makes it possible to identify streamlines with very consistent filtering results, which were used as pseudo-ground truths for training classifiers. The trained classifier is able to distinguish the obtained groups of complying and non-complying streamlines with the acquired data with an accuracy above 80%.

Fiber orientation downsampling compromises the computation of white matter tract-related deformation.

Incorporating neuroimaging-revealed structural details into finite element (FE) head models opens vast new opportunities to better understand brain injury mechanisms. Recently, growing efforts have been made to integrate fiber orientation from diffusion tensor imaging (DTI) into FE models to predict white matter (WM) tract-related deformation that is biomechanically characterized by tract-related strains. Commonly used approaches often downsample the spatially enriched fiber orientation to match the FE resolution with one orientation per element (i.e., element-wise orientation implementation). However, the validity of such downsampling operation and corresponding influences on the computed tract-related strains remain elusive. To address this, the current study proposed a new approach to integrate voxel-wise fiber orientation from one DTI atlas (isotropic resolution of 1 mm3) into FE models by embedding orientations from multiple voxels within one element (i.e., voxel-wise orientation implementation). By setting the responses revealed by the newly proposed voxel-wise orientation implementation as the reference, we evaluated the reliability of two previous downsampling approaches by examining the downsampled fiber orientation and the computationally predicted tract-related strains secondary to one concussive impact. Two FE models with varying element sizes (i.e., 6.4 ± 1.6 mm and 1.3 ± 0.6 mm, respectively) were incorporated. The results showed that, for the model with a large voxel-mesh resolution mismatch, the downsampled element-wise fiber orientation, with respect to its voxel-wise counterpart, exhibited an absolute deviation over 30° across the WM/gray matter interface and the pons regions. Accordingly, this orientation deviation compromised the computation of tract-related strains with normalized root-mean-square errors up to 30% and underestimated the peak tract-related strains up to 10%. For the other FE model with finer meshes, the downsampling-induced effects were lower, both on the fiber orientation and tract-related strains. Taken together, the voxel-wise orientation implementation is recommended in future studies as it leverages the DTI-delineated fiber orientation to a larger extent than the element-wise orientation implementation. Thus, this study yields novel insights on integrating neuroimaging-revealed fiber orientation into FE models and may better inform the computation of WM tract-related deformation.

Tractography and machine learning: Current state and open challenges.

Supervised machine learning (ML) algorithms have recently been proposed as an alternative to traditional tractography methods in order to address some of their weaknesses. They can be path-based and local-model-free, and easily incorporate anatomical priors to make contextual and non-local decisions that should help the tracking process. ML-based techniques have thus shown promising reconstructions of larger spatial extent of existing white matter bundles, promising reconstructions of less false positives, and promising robustness to known position and shape biases of current tractography techniques. But as of today, none of these ML-based methods have shown conclusive performances or have been adopted as a de facto solution to tractography. One reason for this might be the lack of well-defined and extensive frameworks to train, evaluate, and compare these methods. In this paper, we describe several datasets and evaluation tools that contain useful features for ML algorithms, along with the various methods proposed in the recent years. We then discuss the strategies that are used to evaluate and compare those methods, as well as their shortcomings. Finally, we describe the particular needs of ML tractography methods and discuss tangible solutions for future works.