中尺度的连接信息学:绘制大脑连接图的图像处理和分析的最新进展。

Q1 Computer Science Brain Informatics Pub Date : 2024-06-04 DOI:10.1186/s40708-024-00228-9
Yoon Kyoung Choi, Linqing Feng, Won-Ki Jeong, Jinhyun Kim
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引用次数: 0

摘要

绘制大脑内部的神经连接图一直是神经科学的一个基本目标,以便更好地了解大脑的功能以及衰老和疾病带来的变化。显微镜和标记工具等成像技术的发展,使研究人员能够通过高分辨率的全脑成像将这种连接可视化。因此,图像处理和分析变得更加重要。然而,尽管产生了大量的神经图像,但由于可用工具和方法的信息分散,使用集成的图像处理和分析管道来处理这些数据仍具有挑战性。要绘制神经连接图,必须根据地图集进行配准,并通过分割和信号检测进行特征提取。在这篇综述中,我们的目标是对这些图像处理方法的最新进展进行概述,并特别关注小鼠大脑的荧光图像。我们的目标是勾勒出一条为连接信息学量身定制的集成图像处理管道的途径。这些图像处理的集成工作流程将有助于研究人员绘制大脑连接图,从而更好地理解复杂的大脑网络及其潜在的大脑功能。本综述重点介绍了可用于小鼠大脑荧光成像的图像处理工具,有助于加深对连接信息学的理解,为更好地理解大脑连接性及其影响铺平道路。
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Connecto-informatics at the mesoscale: current advances in image processing and analysis for mapping the brain connectivity.

Mapping neural connections within the brain has been a fundamental goal in neuroscience to understand better its functions and changes that follow aging and diseases. Developments in imaging technology, such as microscopy and labeling tools, have allowed researchers to visualize this connectivity through high-resolution brain-wide imaging. With this, image processing and analysis have become more crucial. However, despite the wealth of neural images generated, access to an integrated image processing and analysis pipeline to process these data is challenging due to scattered information on available tools and methods. To map the neural connections, registration to atlases and feature extraction through segmentation and signal detection are necessary. In this review, our goal is to provide an updated overview of recent advances in these image-processing methods, with a particular focus on fluorescent images of the mouse brain. Our goal is to outline a pathway toward an integrated image-processing pipeline tailored for connecto-informatics. An integrated workflow of these image processing will facilitate researchers' approach to mapping brain connectivity to better understand complex brain networks and their underlying brain functions. By highlighting the image-processing tools available for fluroscent imaging of the mouse brain, this review will contribute to a deeper grasp of connecto-informatics, paving the way for better comprehension of brain connectivity and its implications.

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来源期刊
Brain Informatics
Brain Informatics Computer Science-Computer Science Applications
CiteScore
9.50
自引率
0.00%
发文量
27
审稿时长
13 weeks
期刊介绍: Brain Informatics is an international, peer-reviewed, interdisciplinary open-access journal published under the brand SpringerOpen, which provides a unique platform for researchers and practitioners to disseminate original research on computational and informatics technologies related to brain. This journal addresses the computational, cognitive, physiological, biological, physical, ecological and social perspectives of brain informatics. It also welcomes emerging information technologies and advanced neuro-imaging technologies, such as big data analytics and interactive knowledge discovery related to various large-scale brain studies and their applications. This journal will publish high-quality original research papers, brief reports and critical reviews in all theoretical, technological, clinical and interdisciplinary studies that make up the field of brain informatics and its applications in brain-machine intelligence, brain-inspired intelligent systems, mental health and brain disorders, etc. The scope of papers includes the following five tracks: Track 1: Cognitive and Computational Foundations of Brain Science Track 2: Human Information Processing Systems Track 3: Brain Big Data Analytics, Curation and Management Track 4: Informatics Paradigms for Brain and Mental Health Research Track 5: Brain-Machine Intelligence and Brain-Inspired Computing
期刊最新文献
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