Fiber Orientation Estimation Guided by a Deep Network

Diffusion magnetic resonance imaging (dMRI) is currently the only tool for noninvasively imaging the brain's white matter tracts. The fiber orientation (FO) is a key feature computed from dMRI for fiber tract reconstruction. Because the number of FOs in a voxel is usually small, dictionary-based sparse reconstruction has been used to estimate FOs with a relatively small number of diffusion gradients. However, accurate FO estimation in regions with complex FO configurations in the presence of noise can still be challenging. In this work we explore the use of a deep network for FO estimation in a dictionary-based framework and propose an algorithm named Fiber Orientation Reconstruction guided by a Deep Network (FORDN). FORDN consists of two steps. First, we use a smaller dictionary encoding coarse basis FOs to represent the diffusion signals. To estimate the mixture fractions of the dictionary atoms (and thus coarse FOs), a deep network is designed specifically for solving the sparse reconstruction problem. Here, the smaller dictionary is used to reduce the computational cost of training. Second, the coarse FOs inform the final FO estimation, where a larger dictionary encoding dense basis FOs is used and a weighted l1-norm regularized least squares problem is solved to encourage FOs that are consistent with the network output. FORDN was evaluated and compared with state-of-the-art algorithms that estimate FOs using sparse reconstruction on simulated and real dMRI data, and the results demonstrate the benefit of using a deep network for FO estimation.Comment: A shorter version is accepted by MICCAI 201

Brain Tumor Segmentation and Tractographic Feature Extraction from Structural MR Images for Overall Survival Prediction

This paper introduces a novel methodology to integrate human brain connectomics and parcellation for brain tumor segmentation and survival prediction. For segmentation, we utilize an existing brain parcellation atlas in the MNI152 1mm space and map this parcellation to each individual subject data. We use deep neural network architectures together with hard negative mining to achieve the final voxel level classification. For survival prediction, we present a new method for combining features from connectomics data, brain parcellation information, and the brain tumor mask. We leverage the average connectome information from the Human Connectome Project and map each subject brain volume onto this common connectome space. From this, we compute tractographic features that describe potential neural disruptions due to the brain tumor. These features are then used to predict the overall survival of the subjects. The main novelty in the proposed methods is the use of normalized brain parcellation data and tractography data from the human connectome project for analyzing MR images for segmentation and survival prediction. Experimental results are reported on the BraTS2018 dataset.Comment: 14 pages, 5 figures, 4 tables, accepted by BrainLes 2018 MICCAI worksho

Use of diffusion spectrum imaging in preliminary longitudinal evaluation of amyotrophic lateral sclerosis: Development of an imaging biomarker

Previous diffusion tensor imaging (DTI) studies have shown white matter pathology in amyotrophic lateral sclerosis (ALS), predominantly in the motor pathways. Further these studies have shown that DTI can be used longitudinally to track pathology over time, making white matter pathology a candidate as an outcome measure in future trials. DTI has demonstrated application in group studies, however its derived indices, for example fractional anisotropy, are susceptible to partial volume effects, making its role questionable in examining individual progression. We hypothesize that changes in the white matter are present in ALS beyond the motor tracts, and that the affected pathways and associated pattern of disease progression can be tracked longitudinally using automated diffusion connectometry analysis. Connectometry analysis is based on diffusion spectrum imaging and overcomes the limitations of a conventional tractography approach and DTI. The identified affected white matter tracts can then be assessed in a targeted fashion using High definition fiber tractography (a novel white matter MR imaging technique). Changes in quantitative and qualitative markers over time could then be correlated with clinical progression. We illustrate these principles toward developing an imaging biomarker for demonstrating individual progression, by presenting results for five ALS patients, including with longitudinal data in two. Preliminary analysis demonstrated a number of changes bilaterally and asymmetrically in motoric and extramotoric white matter pathways. Further the limbic system was also affected possibly explaining the cognitive symptoms in ALS. In the two longitudinal subjects, the white matter changes were less extensive at baseline, although there was evidence of disease progression in a frontal pattern with a relatively spared postcentral gyrus, consistent with the known pathology in ALS. © 2014 Abhinav, Yeh, El-Dokla, Ferrando, Chang, Lacomis, Friedlander and Fernandez-Miranda

Microsatellite analysis of populations of the endangered tree Gomortega keule suggests pre-Columbian differentiation

Temperate forests have been affected extensively by human activities, resulting in land cover changes and population fragmentation. However, these anthropogenic effects can be superimposed onto the natural history of species, making it difficult to determine which effect is more important for a particular species. Gomortega keule is an endangered tree that is found in one of the world’s biodiversity hotspots in central–south Chile. Human activities have significantly impacted on the original habitat in this region in recent years and are commonly considered to be the main cause of the scarcity of this species. However, aspects of the natural history of this evergreen tree may also help to explain its present-day genetic structure. In this study, we undertook microsatellite genotyping of the two southernmost populations of G. keule, which are 7.5 km apart and well isolated from other populations. We found that there was genetic differentiation between these populations, suggesting that they exhibited at least some differentiation before becoming isolated, most likely before human activities first impacted the region some two centuries ago. Molecular estimates of their divergence time supported a more ancient differentiation of the populations than would be explained by human activities alone. It is possible that their isolation may have followed the extinction of megafaunal seed dispersers around 12,000 years before present in this region, as indicated by fruit characteristics, the absence of recruitment by seedlings and the existence of clonal trees

Predicting Clinical Outcome of Stroke Patients with Tractographic Feature

The volume of stroke lesion is the gold standard for predicting the clinical outcome of stroke patients. However, the presence of stroke lesion may cause neural disruptions to other brain regions, and these potentially damaged regions may affect the clinical outcome of stroke patients. In this paper, we introduce the tractographic feature to capture these potentially damaged regions and predict the modified Rankin Scale (mRS), which is a widely used outcome measure in stroke clinical trials. The tractographic feature is built from the stroke lesion and average connectome information from a group of normal subjects. The tractographic feature takes into account different functional regions that may be affected by the stroke, thus complementing the commonly used stroke volume features. The proposed tractographic feature is tested on a public stroke benchmark Ischemic Stroke Lesion Segmentation 2017 and achieves higher accuracy than the stroke volume and the state-of-the-art feature on predicting the mRS grades of stroke patients. In addition, the tractographic feature also yields a lower average absolute error than the commonly used stroke volume feature.Comment: 12 pages, 4 figures, 3 tables. Accepted by MICCAI-BrainLesion 2019 as an oral presentatio

Fiber-Flux Diffusion Density for White Matter Tracts Analysis: Application to Mild Anomalies Localization in Contact Sports Players

We present the concept of fiber-flux density for locally quantifying white matter (WM) fiber bundles. By combining scalar diffusivity measures (e.g., fractional anisotropy) with fiber-flux measurements, we define new local descriptors called Fiber-Flux Diffusion Density (FFDD) vectors. Applying each descriptor throughout fiber bundles allows along-tract coupling of a specific diffusion measure with geometrical properties, such as fiber orientation and coherence. A key step in the proposed framework is the construction of an FFDD dissimilarity measure for sub-voxel alignment of fiber bundles, based on the fast marching method (FMM). The obtained aligned WM tract-profiles enable meaningful inter-subject comparisons and group-wise statistical analysis. We demonstrate our method using two different datasets of contact sports players. Along-tract pairwise comparison as well as group-wise analysis, with respect to non-player healthy controls, reveal significant and spatially-consistent FFDD anomalies. Comparing our method with along-tract FA analysis shows improved sensitivity to subtle structural anomalies in football players over standard FA measurements

Biochemical comparison of two Hypostomus populations (Siluriformes, Loricariidae) from the Atlântico Stream of the upper Paraná River basin, Brazil

Two syntopic morphotypes of the genus Hypostomus - H. nigromaculatus and H. cf. nigromaculatus (Atlântico Stream, Paraná State) - were compared through the allozyme electrophoresis technique. Twelve enzymatic systems (AAT, ADH, EST, GCDH, G3PDH, GPI, IDH, LDH, MDH, ME, PGM and SOD) were analyzed, attributing the score of 20 loci, with a total of 30 alleles. Six loci were diagnostic (Aat-2, Gcdh-1, Gpi-A, Idh-1, Ldh-A and Mdh-A), indicating the presence of interjacent reproductive isolation. The occurrence of few polymorphic loci acknowledge two morphotypes, with heterozygosity values He = 0.0291 for H. nigromaculatus and He = 0.0346 for H. cf. nigromaculatus. FIS statistics demonstrated fixation of the alleles in the two morphotypes. Genetic identity (I) and distance (D) of Nei (1978) values were I = 0.6515 and D = 0.4285. The data indicate that these two morphotypes from the Atlântico Stream belong to different species