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Brain Imaging

Brain Imaging

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.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...

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Updated: Oct 12, 2025

3D Scanning Technology Bridging Microcircuits and Macroscale Brain Images in 3D Novel Embedding Overlapping Protocol

3D Scanning Technology Bridging Microcircuits and Macroscale Brain Images in 3D Novel Embedding Overlapping Protocol

Published on: May 12, 2019

大脑研究挑战超级计算机

Katrin Amunts¹, Thomas Lippert²

¹Institut fur Neurowissenschaften und Medizin, INM-1, Forschungszentrum Jülich, Jülich, Germany.

Science (New York, N.Y.)

|November 25, 2021

概括

此摘要是机器生成的。

解开人类的大脑

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Combined Invasive Subcortical and Non-invasive Surface Neurophysiological Recordings for the Assessment of Cognitive and Emotional Functions in Humans

Combined Invasive Subcortical and Non-invasive Surface Neurophysiological Recordings for the Assessment of Cognitive and Emotional Functions in Humans

Published on: May 19, 2016

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Last Updated: Oct 12, 2025

3D Scanning Technology Bridging Microcircuits and Macroscale Brain Images in 3D Novel Embedding Overlapping Protocol

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Published on: May 12, 2019

Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models

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Combined Invasive Subcortical and Non-invasive Surface Neurophysiological Recordings for the Assessment of Cognitive and Emotional Functions in Humans

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科学领域:

神经科学
计算生物学
数据科学

背景情况:

人类大脑是一个复杂的系统,
在神经成像和基因测序方面的进步产生了大量数据集.
分析这些大数据带来了重大的计算挑战.

研究的目的:

突出人类大脑日益增长的处理需求的大数据.
强调神经科学需要先进的计算方法.

主要方法:

对当前的神经成像和数据生成技术的审查.
对大脑数据处理的计算资源需求的分析.

主要成果:

人类大脑研究每年都会产生大量的数据.
现有的计算基础设施往往不足以进行实时分析.
需要大幅增加处理能力和存储能力.

结论:

人类大脑数据的规模需要新的计算方法.
未来的研究需要投资高性能计算和数据管理策略.
弥合数据生成与分析之间的差距对于推进大脑科学至关重要.