White matter structural bases for phase accuracy during tapping synchronization.

We determined the intersubject association between the rhythmic entrainment abilities of human subjects during a synchronization-continuation tapping task (SCT) and the macro- and microstructural properties of their superficial (SWM) and deep (DWM) white matter. Diffusion-weighted images were obtained from 32 subjects who performed the SCT with auditory or visual metronomes and five tempos ranging from 550 to 950 ms. We developed a method to determine the density of short-range fibers that run underneath the cortical mantle, interconnecting nearby cortical regions (U-fibers). Notably, individual differences in the density of U-fibers in the right audiomotor system were correlated with the degree of phase accuracy between the stimuli and taps across subjects. These correlations were specific to the synchronization epoch with auditory metronomes and tempos around 1.5 Hz. In addition, a significant association was found between phase accuracy and the density and bundle diameter of the corpus callosum (CC), forming an interval-selective map where short and long intervals were behaviorally correlated with the anterior and posterior portions of the CC. These findings suggest that the structural properties of the SWM and DWM in the audiomotor system support the tapping synchronization abilities of subjects, as cortical U-fiber density is linked to the preferred tapping tempo and the bundle properties of the CC define an interval-selective topography.

Short fiber bundle filtering and test-retest reproducibility of the Superficial White Matter.

In recent years, there has been a growing interest in studying the Superficial White Matter (SWM). The SWM consists of short association fibers connecting near giry of the cortex, with a complex organization due to their close relationship with the cortical folding patterns. Therefore, their segmentation from dMRI tractography datasets requires dedicated methodologies to identify the main fiber bundle shape and deal with spurious fibers. This paper presents an enhanced short fiber bundle segmentation based on a SWM bundle atlas and the filtering of noisy fibers. The method was tuned and evaluated over HCP test-retest probabilistic tractography datasets (44 subjects). We propose four fiber bundle filters to remove spurious fibers. Furthermore, we include the identification of the main fiber fascicle to obtain well-defined fiber bundles. First, we identified four main bundle shapes in the SWM atlas, and performed a filter tuning in a subset of 28 subjects. The filter based on the Convex Hull provided the highest similarity between corresponding test-retest fiber bundles. Subsequently, we applied the best filter in the 16 remaining subjects for all atlas bundles, showing that filtered fiber bundles significantly improve test-retest reproducibility indices when removing between ten and twenty percent of the fibers. Additionally, we applied the bundle segmentation with and without filtering to the ABIDE-II database. The fiber bundle filtering allowed us to obtain a higher number of bundles with significant differences in fractional anisotropy, mean diffusivity, and radial diffusivity of Autism Spectrum Disorder patients relative to controls.

Superficial white matter bundle atlas based on hierarchical fiber clustering over probabilistic tractography data.

The study of short association fibers is still an incomplete task due to their higher inter-subject variability and the smaller size of this kind of fibers in comparison to known long association bundles. However, their description is essential to understand human brain dysfunction and better characterize the human brain connectome. In this work, we present a multi-subject atlas of short association fibers, which was computed using a superficial white matter bundle identification method based on fiber clustering. To create the atlas, we used probabilistic tractography from one hundred subjects from the HCP database, aligned with non-linear registration. The method starts with an intra-subject clustering of short fibers (30-85 mm). Based on a cortical atlas, the intra-subject cluster centroids from all subjects are segmented to identify the centroids connecting each region of interest (ROI) of the atlas. To reduce computational load, the centroids from each ROI group are randomly separated into ten subgroups. Then, an inter-subject hierarchical clustering is applied to each centroid subgroup, followed by a second level of clustering to select the most-reproducible clusters across subjects for each ROI group. Finally, the clusters are labeled according to the regions that they connect, and clustered to create the final bundle atlas. The resulting atlas is composed of 525 bundles of superficial short association fibers along the whole brain, with 384 bundles connecting pairs of different ROIs and 141 bundles connecting portions of the same ROI. The reproducibility of the bundles was verified using automatic segmentation on three different tractogram databases. Results for deterministic and probabilistic tractography data show high reproducibility, especially for probabilistic tractography in HCP data. In comparison to previous work, our atlas features a higher number of bundles and greater cortical surface coverage.

Superficial and deep white matter diffusion abnormalities in focal epilepsies.

OBJECTIVE: This study was undertaken to evaluate superficial-white matter (WM) and deep-WM magnetic resonance imaging diffusion tensor imaging (DTI) metrics and identify distinctive patterns of microstructural abnormalities in focal epilepsies of diverse etiology, localization, and response to antiseizure medication (ASM). METHODS: We examined DTI data for 113 healthy controls and 113 patients with focal epilepsies: 51 patients with temporal lobe epilepsy (TLE) and hippocampal sclerosis (HS) refractory to ASM, 27 with pharmacoresponsive TLE-HS, 15 with temporal lobe focal cortical dysplasia (FCD), and 20 with frontal lobe FCD. To assess WM microstructure, we used a multicontrast multiatlas parcellation of DTI. We evaluated fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD), and assessed within-group differences ipsilateral and contralateral to the epileptogenic lesion, as well as between-group differences, in regions of interest (ROIs). RESULTS: The TLE-HS groups presented more widespread superficial- and deep-WM diffusion abnormalities than both FCD groups. Concerning superficial WM, TLE-HS groups showed multilobar ipsilateral and contralateral abnormalities, with less extensive distribution in pharmacoresponsive patients. Both the refractory TLE-HS and pharmacoresponsive TLE-HS groups also presented pronounced changes in ipsilateral frontotemporal ROIs (decreased FA and increased MD, RD, and AD). Conversely, FCD patients showed diffusion changes almost exclusively adjacent to epileptogenic areas. SIGNIFICANCE: Our findings add further evidence of widespread abnormalities in WM diffusion metrics in patients with TLE-HS compared to other focal epilepsies. Notably, superficial-WM microstructural damage in patients with FCD is more restricted around the epileptogenic lesion, whereas TLE-HS groups showed diffuse WM damage with ipsilateral frontotemporal predominance. These findings suggest the potential of superficial-WM analysis for better understanding the biological mechanisms of focal epilepsies, and identifying dysfunctional networks and their relationship with the clinical-pathological phenotype. In addition, lobar superficial-WM abnormalities may aid in the diagnosis of subtle FCDs.

Automatic group-wise whole-brain short association fiber bundle labeling based on clustering and cortical surface information.

BACKGROUND: Diffusion MRI is the preferred non-invasive in vivo modality for the study of brain white matter connections. Tractography datasets contain 3D streamlines that can be analyzed to study the main brain white matter tracts. Fiber clustering methods have been used to automatically group similar fibers into clusters. However, due to inter-subject variability and artifacts, the resulting clusters are difficult to process for finding common connections across subjects, specially for superficial white matter. METHODS: We present an automatic method for labeling of short association bundles on a group of subjects. The method is based on an intra-subject fiber clustering that generates compact fiber clusters. Posteriorly, the clusters are labeled based on the cortical connectivity of the fibers, taking as reference the Desikan-Killiany atlas, and named according to their relative position along one axis. Finally, two different strategies were applied and compared for the labeling of inter-subject bundles: a matching with the Hungarian algorithm, and a well-known fiber clustering algorithm, called QuickBundles. RESULTS: Individual labeling was executed over four subjects, with an execution time of 3.6 min. An inspection of individual labeling based on a distance measure showed good correspondence among the four tested subjects. Two inter-subject labeling were successfully implemented and applied to 20 subjects and compared using a set of distance thresholds, ranging from a conservative value of 10 mm to a moderate value of 21 mm. Hungarian algorithm led to a high correspondence, but low reproducibility for all the thresholds, with 96 s of execution time. QuickBundles led to better correspondence, reproducibility and short execution time of 9 s. Hence, the whole processing for the inter-subject labeling over 20 subjects takes 1.17 h. CONCLUSION: We implemented a method for the automatic labeling of short bundles in individuals, based on an intra-subject clustering and the connectivity of the clusters with the cortex. The labels provide useful information for the visualization and analysis of individual connections, which is very difficult without any additional information. Furthermore, we provide two fast inter-subject bundle labeling methods. The obtained clusters could be used for performing manual or automatic connectivity analysis in individuals or across subjects.