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相关概念视频

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.
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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Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

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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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相关实验视频

Updated: Jun 5, 2025

3D Scanning Technology Bridging Microcircuits and Macroscale Brain Images in 3D Novel Embedding Overlapping Protocol
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在脑MRI上使用人工智能推断神经认知.

Mohammad Arafat Hussain1, Patricia Ellen Grant1,2, Yangming Ou1,2,3

  • 1Department of Pediatrics, Boston Children's Hospital, Harvard Medical School, Boston, MA, United States.

Frontiers in neuroimaging
|December 12, 2024
PubMed
概括

磁共振成像 (MRI) 研究大脑结构和认知. 大数据和人工智能 (AI) 提供了了解个人智能差异的新方法,但需要仔细考虑方法.

关键词:
在P-FIT模式下,人工智能的人工智能是人工智能.大脑MRI脑部MRI脑部情报 情报 情报 的情报 情报 的情报.神经认知神经认知

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科学领域:

  • 神经成像是一种神经成像.
  • 认知神经科学 认知神经科学
  • 人工智能的人工智能

背景情况:

  • 大脑磁共振成像 (MRI) 提供了对人类神经认知的神经解剖基础的洞察.
  • 使用MRI了解神经认知和智力的个体差异仍然是一个重大挑战.
  • 样本大小和人口水平研究的局限性阻碍了个人层面的解释.

研究的目的:

  • 审查使用MRI研究神经认知方面的进展.
  • 在这个领域探索大数据和人工智能 (AI) 的潜力.
  • 讨论挑战,并促进对人工智能驱动的神经认知推理的未来研究.

主要方法:

  • 对MRI和神经认知现有文献的综述.
  • 讨论大数据和人工智能方法.
  • 对数据协调,研究设计和解释方面的考虑进行分析.

主要成果:

  • 在过去的40年里,在研究神经认知的神经解剖学基础方面取得了重大进展.
  • 大数据和人工智能为推进个人层面的理解提供了新的机会.
  • 仔细考虑数据源,协调,研究设计和解释对于成功实施人工智能至关重要.

结论:

  • 人工智能对从神经成像数据推断人类神经认知具有重大前景.
  • 解决方法挑战是释放人工智能在认知神经科学中的全部潜力的关键.
  • 需要进一步的跨学科研究来弥合人工智能和神经成像,以更深入地了解智能.