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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 Stimulation (TMS).

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Updated: Jul 14, 2026

Physiological, Morphological and Neurochemical Characterization of Neurons Modulated by Movement

Physiological, Morphological and Neurochemical Characterization of Neurons Modulated by Movement

Published on: April 21, 2011

尖端的方法用于标记和追踪大脑中活跃的神经元编码.

Kenichiro Nagahama¹, Veronica Hyeyoon Jung¹, Hyung-Bae Kwon²

¹Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA.

Current opinion in neurobiology

|March 8, 2025

概括

神经科学研究使用直接早期的基因和先进的标记技术来识别活跃的大脑细胞和电路. 这些方法有助于解码学习和行为的神经基质,进步我们对记忆的理解.

更多相关视频

Laser-guided Neuronal Tracing In Brain Explants

Laser-guided Neuronal Tracing In Brain Explants

Published on: November 25, 2015

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

相关实验视频

Last Updated: Jul 14, 2026

Physiological, Morphological and Neurochemical Characterization of Neurons Modulated by Movement

Physiological, Morphological and Neurochemical Characterization of Neurons Modulated by Movement

Published on: April 21, 2011

Laser-guided Neuronal Tracing In Brain Explants

Laser-guided Neuronal Tracing In Brain Explants

Published on: November 25, 2015

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

科学领域:

神经科学是一个神经科学.
分子生物学分子生物学
系统神经科学系统神经科学

背景情况:

了解学习和行为的神经基础是神经科学的一个关键挑战.
最近的分子技术为识别神经元件提供了高时空分辨率.
早期的基因是神经活动和可塑性的有效标记物.

研究的目的:

审查当前的分子系统以进行活动依赖的标签.
要突出在识别与行为相关的细胞组合和电路中的应用.
讨论这些方法的意义和未来方向.

主要方法:

利用直接早期的基因作为神经活动的标记.
使用依赖的标签系统.
整合光或生化近距离标签技术.

主要成果:

活动依赖标签可以识别基于记忆的行为中的活跃细胞.
这些方法描述了大脑区域内的活跃细胞组合和电路.
系统的整合扩大了跨行为范式的研究能力.

结论:

分子工具显著提高了识别学习和行为的神经基质的能力.
活动依赖标签对于理解细胞组合和神经回路至关重要.
分子方法的进一步创新将推动未来的神经科学发现.