scholarly journals ABrainVis: an android brain image visualization tool

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Ignacio Osorio ◽  
Miguel Guevara ◽  
Danilo Bonometti ◽  
Diego Carrasco ◽  
Maxime Descoteaux ◽  
...  
Keyword(s):  
White Matter ◽  
Brain Imaging ◽  
High Performance ◽  
Contextual Information ◽  
Imaging Data ◽  
Brain Images ◽  
Wide Range ◽  
Different Types ◽  
Brain Imaging Data ◽  
Brain Data

Abstract Background The visualization and analysis of brain data such as white matter diffusion tractography and magnetic resonance imaging (MRI) volumes is commonly used by neuro-specialist and researchers to help the understanding of brain structure, functionality and connectivity. As mobile devices are widely used among users and their technology shows a continuous improvement in performance, different types of applications have been designed to help users in different work areas. Results We present, ABrainVis, an Android mobile tool that allows users to visualize different types of brain images, such as white matter diffusion tractographies, represented as fibers in 3D, segmented fiber bundles, MRI 3D images as rendered volumes and slices, and meshes. The tool enables users to choose and combine different types of brain imaging data to provide visual anatomical context for specific visualization needs. ABrainVis provides high performance over a wide range of Android devices, including tablets and cell phones using medium and large tractography datasets. Interesting visualizations including brain tumors and arteries, along with fiber, are given as examples of case studies using ABrainVis. Conclusions The functionality, flexibility and performance of ABrainVis tool introduce an improvement in user experience enabling neurophysicians and neuroscientists fast visualization of large tractography datasets, as well as the ability to incorporate other brain imaging data such as MRI volumes and meshes, adding anatomical contextual information.

scholarly journals MaPPeRTrac: A Massively Parallel, Portable, and Reproducible Tractography Pipeline

2020 ◽  
Author(s):  
Joseph Moon ◽  
Peer-Timo Bremer ◽  
Pratik Mukherjee ◽  
Amy J Markowitz ◽  
Eva M Palacios ◽  
...  
Keyword(s):  
Diffusion Mri ◽  
High Performance ◽  
Large Scale ◽  
Computational Cost ◽  
Edge Density ◽  
Imaging Data ◽  
Brain Connectome ◽  
Wide Range ◽  
Fair Principles ◽  
Brain Imaging Data

Large scale diffusion MRI tractography remains a significant challenge. Users must orchestrate a complex sequence of instructions that require many software packages with complex dependencies and high computational cost. We developed MaPPeRTrac, a diffusion MRI tractography pipeline that simplifies and vastly accelerates this process on a wide range of high performance computing environments. It fully automates the entire tractography workflow, from management of raw MRI machine data to edge-density visualization of the connectome. Data and dependencies, handled by the Brain Imaging Data Structure (BIDS) and Containerization using Docker and Singularity, are de-coupled from code to enable rapid prototyping and modification. Data artifacts are designed to be findable, accessible, interoperable, and reusable in accordance with FAIR principles. The pipeline takes full advantage of HPC resources using the Parsl parallel programming framework, resulting in creation of connectome datasets of unprecedented size. MaPPeRTrac is publicly available and tested on commercial and scientific hardware, so that it may accelerate brain connectome research for a broader user community.

scholarly journals The Open Brain Consent : Informing research participants and obtaining consent to share brain imaging data

2021 ◽  
Author(s):  
Elise Bannier ◽  
Gareth Barker ◽  
Valentina Borghesani ◽  
Nils Broeckx ◽  
Patricia Clement ◽  
...  
Keyword(s):  
Brain Imaging ◽  
Imaging Data ◽  
Research Participants ◽  
Brain Imaging Data

Learning from Enhanced Contextual Similarity in Brain Imaging Data for Classification of Schizophrenia

2017 ◽  
pp. 265-275 ◽  
Author(s):  
Tewodros Mulugeta Dagnew ◽  
Letizia Squarcina ◽  
Massimo W. Rivolta ◽  
Paolo Brambilla ◽  
Roberto Sassi
Keyword(s):  
Brain Imaging ◽  
Imaging Data ◽  
Brain Imaging Data

scholarly journals Chronic subdural hematomas: challenges and solutions. Part I. Clinical variants and diagnosis

2021 ◽  
Vol 2 (2) ◽  
pp. 21-27
Author(s):  
Leonid B. Likhterman ◽  
◽  
Aleksandr D. Kravchuk ◽  
Vladimir A. Okhlopkov ◽  
◽  
...  
Keyword(s):  
Brain Imaging ◽  
Intracranial Hemorrhage ◽  
Subdural Hematoma ◽  
Clinical Course ◽  
Chronic Subdural Hematoma ◽  
Imaging Data ◽  
Clinical Variants ◽  
Brain Compression ◽  
Brain Imaging Data ◽  
Ct And Mri

Chronic subdural hematoma (cSDH) is a multifactorial extensive intracranial hemorrhage, causing the local and/or general brain compression. Hematoma has a delimiting capsule, which defines all pathophysiological features, clinical course and treatment tactics. The paper reports contemporary views on ethiology and clinical course of cSDH. Emphasis is placed on the diagnosis. Based on the analysis of 558 verified cSDH observations, the phasal course and brain imaging data are reported. CT and MRI signs of cSDH are defined.

scholarly journals Microstructural Integrity of White Matter Moderates an Association Between Childhood Adversity and Adult Trait Anger

2018 ◽  
Author(s):  
M. Justin Kim ◽  
Maxwell L. Elliott ◽  
Tracy C. d’Arbeloff ◽  
Annchen R. Knodt ◽  
Spenser R. Radtke ◽  
...  
Keyword(s):  
Prefrontal Cortex ◽  
Individual Differences ◽  
White Matter ◽  
Negative Emotion ◽  
Childhood Adversity ◽  
Trait Anger ◽  
Uncinate Fasciculus ◽  
Imaging Data ◽  
Wide Range ◽  
Microstructural Integrity

AbstractAmongst a number of negative life sequelae associated with childhood adversity is the later expression of a higher dispositional tendency to experience anger and frustration to a wide range of situations (i.e., trait anger). We recently reported that an association between childhood adversity and trait anger is moderated by individual differences in both threat-related amygdala activity and executive control-related dorsolateral prefrontal cortex (dlPFC) activity, wherein individuals with relatively low amygdala and high dlPFC activity do not express higher trait anger even when having experienced childhood adversity. Here, we examine possible structural correlates of this functional dynamic using diffusion magnetic resonance imaging data from 647 young adult men and women volunteers. Specifically, we tested whether the degree of white matter microstructural integrity as indexed by fractional anisotropy modulated the association between childhood adversity and trait anger. Our analyses revealed that higher microstructural integrity of multiple pathways was associated with an attenuated link between childhood adversity and adult trait anger. Amongst these pathways was the uncinate fasciculus, which not only provides a major anatomical link between the amygdala and prefrontal cortex but also is associated with individual differences in regulating negative emotion through top-down cognitive reappraisal. These findings suggest that higher microstructural integrity of distributed white matter pathways including but not limited to the uncinate fasciculus may represent an anatomical foundation serving to buffer against the expression of childhood adversity as later trait anger, which is itself associated with multiple negative health outcomes.

Supercomputing in the Study and Stimulation of the Brain

2021 ◽  
pp. 290-300
Author(s):  
Laura Dipietro ◽  
Seth Elkin-Frankston ◽  
Ciro Ramos-Estebanez ◽  
Timothy Wagner
Keyword(s):  
High Performance ◽  
Neurological Diseases ◽  
Data Sets ◽  
Imaging Data ◽  
Building Simulations ◽  
History Of ◽  
Brain Imaging Data ◽  
Evolution Of Science ◽  
Computational Systems ◽  
The Brain

The history of neuroscience has tracked with the evolution of science and technology. Today, neuroscience's trajectory is heavily dependent on computational systems and the availability of high-performance computing (HPC), which are becoming indispensable for building simulations of the brain, coping with high computational demands of analysis of brain imaging data sets, and developing treatments for neurological diseases. This chapter will briefly review the current and potential future use of supercomputers in neuroscience.

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