scholarly journals Fiber Clustering Acceleration With a Modified Kmeans++ Algorithm Using Data Parallelism

2021 ◽  
Vol 15 ◽  
Author(s):  
Isaac Goicovich ◽  
Paulo Olivares ◽  
Claudio Román ◽  
Andrea Vázquez ◽  
Cyril Poupon ◽  
...  
Keyword(s):  
White Matter ◽  
Brain Research ◽  
Data Parallelism ◽  
Processing Unit ◽  
Brain White Matter ◽  
Fiber Clustering ◽  
New Variant ◽  
Clustering Quality ◽  
Kmeans Algorithm ◽  
The Impact

Fiber clustering methods are typically used in brain research to study the organization of white matter bundles from large diffusion MRI tractography datasets. These methods enable exploratory bundle inspection using visualization and other methods that require identifying brain white matter structures in individuals or a population. Some applications, such as real-time visualization and inter-subject clustering, need fast and high-quality intra-subject clustering algorithms. This work proposes a parallel algorithm using a General Purpose Graphics Processing Unit (GPGPU) for fiber clustering based on the FFClust algorithm. The proposed GPGPU implementation exploits data parallelism using both multicore and GPU fine-grained parallelism present in commodity architectures, including current laptops and desktop computers. Our approach implements all FFClust steps in parallel, improving execution times in all of them. In addition, our parallel approach includes a parallel Kmeans++ algorithm implementation and defines a new variant of Kmeans++ to reduce the impact of choosing outliers as initial centroids. The results show that our approach provides clustering quality results very similar to FFClust, and it requires an execution time of 3.5 s for processing about a million fibers, achieving a speedup of 11.5 times compared to FFClust.

scholarly journals The impact of image dynamic range on texture classification of brain white matter

2008 ◽  
Vol 8 (1) ◽  
Author(s):  
Doaa Mahmoud-Ghoneim ◽  
Mariam K Alkaabi ◽  
Jacques D de Certaines ◽  
Frank-M Goettsche
Keyword(s):  
White Matter ◽  
Dynamic Range ◽  
Texture Classification ◽  
Brain White Matter ◽  
The Impact

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

2020 ◽  
Author(s):  
Andrea Vázquez ◽  
Narciso López-López ◽  
Josselin Houenou ◽  
Cyril Poupon ◽  
Jean-François Mangin ◽  
...  
Keyword(s):  
White Matter ◽  
Execution Time ◽  
Fiber Bundle ◽  
Clustering Algorithm ◽  
Distance Measure ◽  
Clustering Methods ◽  
Good Correspondence ◽  
Hungarian Algorithm ◽  
Brain White Matter ◽  
Fiber Clustering

Abstract 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 regroup 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 minutes. 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 high correspondence, but low reproducibility for all the thresholds, with 96 seconds of execution time. QuickBundles led to better correspondence, reproducibility and short execution time of 9 seconds. Hence, the whole processing for the inter-subject labeling over 20 subjects takes 1.17 hours. 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, what 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. Keywords: fiber labeling; clustering; fiber bundle; tractography; superficial white matter

Increased body mass index makes an impact on brain white-matter integrity in adults with remitted first-episode mania

2013 ◽  
Vol 44 (3) ◽  
pp. 533-541 ◽  
Author(s):  
C. N. Kuswanto ◽  
M. Y. Sum ◽  
G. L. Yang ◽  
W. L. Nowinski ◽  
R. S. McIntyre ◽  
...  
Keyword(s):  
Body Mass Index ◽  
White Matter ◽  
Body Mass ◽  
Normal Weight ◽  
White Matter Integrity ◽  
First Episode ◽  
Obese Patients ◽  
Brain White Matter ◽  
The Right ◽  
The Impact

BackgroundObesity is increasingly prevalent in bipolar disorder (BD) but data about the impact of elevated body mass index (BMI) on brain white-matter integrity in BD are sparse. Based on extant literature largely from structural magnetic resonance imaging (MRI) studies, we hypothesize that increased BMI is associated with decreased fractional anisotropy (FA) in the frontal, temporal, parietal and occipital brain regions early in the course of BD.MethodA total of 26 euthymic adults (12 normal weight and 14 overweight/obese) with remitted first-episode mania (FEM) and 28 controls (13 normal weight and 15 overweight/obese) matched for age, handedness and years of education underwent structural MRI and diffusion tensor imaging scans.ResultsThere are significant effects of diagnosis by BMI interactions observed especially in the right parietal lobe (adjusted F1,48 = 5.02, p = 0.030), occipital lobe (adjusted F1,48 = 10.30, p = 0.002) and temporal lobe (adjusted F1,48 = 7.92, p = 0.007). Specifically, decreased FA is found in the right parietal (F1,23 = 5.864, p = 0.023) and occipital lobes (F1,23 = 4.397, p = 0.047) within overweight/obese patients compared with normal-weight patients with FEM. Compared with overweight/obese controls, decreased FA is observed in right parietal (F1,25 = 6.708, p = 0.015), temporal (F1,25 = 10.751, p = 0.003) and occipital (F1,25 = 9.531, p = 0.005) regions in overweight/obese patients with FEM.ConclusionsOur findings suggest that increased BMI affects temporo-parietal-occipital brain white-matter integrity in FEM. This highlights the need to further elucidate the relationship between obesity and other neural substrates (including subcortical changes) in BD which may clarify brain circuits subserving the association between obesity and clinical outcomes in BD.

scholarly journals White Matter Disruption in Pediatric Traumatic Brain Injury: Results from ENIGMA Pediatric msTBI

2020 ◽  
Author(s):  
Emily L Dennis ◽  
Karen Caeyenberghs ◽  
Kristen R Hoskinson ◽  
Tricia L Merkley ◽  
Stacy J Suskauer ◽  
...  
Keyword(s):  
White Matter ◽  
Behavioral Problems ◽  
Child Behavior Checklist ◽  
Injury Severity ◽  
Small Sample ◽  
Brain Maturation ◽  
Post Injury ◽  
Brain White Matter ◽  
Factors Influencing ◽  
The Impact

AbstractAnnually, approximately 3 million children around the world experience traumatic brain injuries (TBIs), of which up to 20% are characterized as moderate to severe (msTBI) and/or have abnormal imaging findings. Affected children are vulnerable to long-term cognitive and behavioral dysfunction, as injury can disrupt or alter ongoing brain maturation. Post-injury outcomes are highly variable, and there is only limited understanding of how inter-individual differences in outcomes arise. Small sample sizes have also complicated efforts to better understand factors influencing the impact of TBI on the developing brain. White matter (WM) disruption is a critical aspect of TBI neuropathology and diffusion MRI (dMRI) is particularly sensitive to microstructural abnormalities. Here we present the results of a coordinated analysis of dMRI data across ten cohorts from three countries. We had three primary aims: (1) to characterize the nature and extent of WM disruption across key post-injury intervals (acute/subacute - within 2 months, post-acute - 2-6 months, chronic - 6+ months); (2) evaluate the impact of age and sex on WM in the context of injury; and (3) to examine associations between WM and neurobehavioral outcomes. Based on data from 507 children and adolescents (244 with complicated mild to severe TBI and 263 control children), we report widespread WM disruption across all post-injury intervals. As expected, injury severity was a significant contributor to the pattern and extent of WM degradation, but explained less variance in dMRI measures with increasing time since injury, supporting other research indicating that other factors contribute increasingly to outcomes over time. The corpus callosum appears to be particularly vulnerable to injury, an effect that persists years post-TBI. We also report sex differences in the effect of TBI on the uncinate fasciculus (UNC), a structure with a key role in emotion regulation. Females with a TBI had significantly lower fractional anisotropy (FA) in the UNC than those with no TBI, and this phenomenon was further associated with more frequent parent-reported behavioral problems as measured by the Child Behavior Checklist (CBCL). These effects were not detected in males. With future harmonization of imaging and neurocognitive data, more complex modeling of factors influencing outcomes will be possible and help to identify clinically-meaningful patient subtypes.

Biomechanics Injury Analysis of Brain White Matter Using Cohesive Zone Method

2009 ◽  
Author(s):  
N. Abolfathi ◽  
A. R. Syed ◽  
G. Karami ◽  
M. Ziejewski
Keyword(s):  
White Matter ◽  
Brain Tissue ◽  
Cohesive Zone ◽  
Axonal Injury ◽  
Diffuse Axonal Injury ◽  
Severe Disabilities ◽  
Cohesive Zone Modeling ◽  
Zone Method ◽  
Brain White Matter ◽  
The Impact

Diffuse Axonal Injury (DAI) can happen due to sudden motion of the head and loading and is a major cause of fatality and severe disabilities. This injury can be biomechanically translated in terms of change in axon geometry and its separation and distortion from the surrounding cells and the extra cellular matrix (ECM). To study DAI, a microscale biomechanical modeling of tissue is forwarded. This modeling benefits from the studies on fibrous composite modeling procedure to examine the tissue and the fibrous axonal injury. Employing a developed micromechanics failure analysis for fibrous composites, the white mater of the brain is assumed as the composite with axon as the fiber and ECM as the matrix. The focus here is on the interface and adhesion of the axon and ECM on the material characteristics of the tissue. The cohesive zone modeling (CZM) is employed to model the interface. The impact due to interface is studied in detail on the characteristics of the white matter tissue. This modeling method enhances the previously proposed micromechanics modeling of brain tissue and enable one to predict the impact due to sliding, and separation of the axons and ECM on the load transfer, stress and strain distribution of axon, ECM and tissue for a microstructural examination of DAI and tissue failure. This can improve the understanding of injury from mechanical perspective and help in detail predicting of any injuries in cellular level in brain tissue.

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