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Related Concept Videos

Brain Imaging01:14

Brain Imaging

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Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
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Introduction: MRI and CT scans are crucial advancements in medical imaging techniques, playing a vital role in diagnosing conditions related to the gastrointestinal (GI) system. Each scan serves distinct purposes, targets specific areas, and requires unique nursing duties.
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Positron emission tomography (PET) is a medical imaging technique involving radiopharmaceuticals — substances that emit short-lived radiation. Although the first PET scanner was introduced in 1961, it took 15 more years before radiopharmaceuticals were combined with the technique and revolutionized its potential.
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Anatomy of the Brain: Ventricles01:18

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There are hollow fluid-filled cavities known as ventricles deep inside the human brain. There are two lateral ventricles, one in each cerebral hemisphere, and each has three different projections — the anterior, inferior, and posterior horns visible from the lateral side. A thin membrane called the septum pellucidum separates the two lateral ventricles. The slender third ventricle in the diencephalon is connected to each lateral ventricle via a channel called the interventricular foramen.
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Magnetic Resonance Imaging01:24

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Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
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Updated: Jun 28, 2025

Neurovascular Network Explorer 2.0: A Simple Tool for Exploring and Sharing a Database of Optogenetically-evoked Vasomotion in Mouse Cortex In Vivo
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hyve, a compositional visualisation engine for brain imaging data.

Rastko Ciric1, Anna Xu2,3, Russell A Poldrack2,3

  • 1Department of Bioengineering, Stanford University, Stanford, CA, USA.

Biorxiv : the Preprint Server for Biology
|April 25, 2024
PubMed
Summary
This summary is machine-generated.

Introducing hyve, a Python library that simplifies brain geometry visualization. This tool enables custom, reusable visualization protocols for diverse neuroimaging data, enhancing interpretation and research efficiency.

Keywords:
connectivitygeometryneuroimagingsoftwarevisualisation

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Area of Science:

  • Neuroimaging
  • Scientific Visualization
  • Computational Neuroscience

Background:

  • Interpreting complex brain data requires effective visualization techniques.
  • Heterogeneous brain geometries (volumes, surfaces, networks) traditionally need separate software solutions.
  • A unified approach is needed to handle diverse neuroimaging data structures.

Approach:

  • Introduced hyve, a Python library with a compositional functional framework.
  • Developed reusable visualization protocols using geometric, input, and output primitives.
  • Enabled parametric implementation of custom visualizations for various brain geometries.

Key Points:

  • hyve allows users to compose visualization protocols from modular primitives.
  • The library supports common data formats and research objectives.
  • Automated documentation generation and serial visualization production are included.
  • An API for organizing editable multi-panel figures is provided.

Conclusions:

  • hyve facilitates the creation of custom visualizations for diverse neuroimaging research.
  • The compositional framework streamlines the visualization process for heterogeneous brain geometries.
  • This approach enhances the interpretation of structured neuroimaging data and results.