通过最佳运输 (CAROT) 进行跨地图重新绘制:当原始数据无法获得时,为不同的地图库创建连接组
Javid Dadashkarimi1, Amin Karbasi2, Qinghao Liang3
1Computer Science Department, Yale University, New Haven, CT, USA.
Medical image analysis
|June 23, 2023
概括
这项研究介绍了Cross Atlas Remapping via Optimal Transport (CAROT),这是一种工具,可以在没有原始数据的情况下在不同大脑地图之间转换神经成像连接体. 这提高了开源数据的可重复使用性,用于更广泛的科学发现.
科学领域:
- 神经成像是一种神经成像.
- 计算神经科学是一种神经科学.
- 数据科学数据科学数据科学
背景情况:
- 开源神经成像数据集通过提供大样本大小来促进科学可重复性和发现.
- 当前的做法往往会将预处理的数据分为预定义的大脑地图,从而限制下游分析的灵活性.
- 来自不同地图集的Connectomes是不可比拟的,这阻碍了各种开源数据集的整合.
研究的目的:
- 开发一种方法来转换不同大脑图谱之间的神经成像数据.
- 为了能够直接比较和整合使用不同的地图集处理的连接体.
- 克服开源神经成像数据可重复使用的局限性.
主要方法:
- 通过最佳运输 (CAROT) 引入交叉地图重新绘制,以建立大脑地图之间的映射.
- 开发了一种计算方法,将预处理的连接体从一个亚特拉斯空间转换到另一个亚特拉斯空间.
- 通过比较重建的连接体和评估下游分析性能来验证该方法.
主要成果:
- 在不需要原始数据的情况下,CAROT成功地在广泛的地图上重建了连接组.
- 转变的连接体在下游分析中显示出实用性,包括预测建模.
- 基于Connectome的预测模型概括为在转换后使用不同的地图集处理的数据.
结论:
- 卡罗特促进了现有处理的神经成像连接器的无集成和重复使用.
- 该工具通过克服与地图集相关的可比性问题,使访问connectomic数据变得民主化.
- 通过提高开源大脑数据的实用性,CAROT促进了更广泛的科学合作和发现.
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