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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: Sep 11, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

基于表面的功能性脑成像中的虚假相关性.

Jayson Jeganathan^1,2, Nikitas C Koussis^2,3,4, Bryan Paton^1,2,3

¹School of Psychological Sciences, College of Engineering, Science and the Environment, University of Newcastle, Newcastle, NSW, Australia.

Imaging neuroscience (Cambridge, Mass.)

|August 13, 2025

概括

此摘要是机器生成的。

在表面网格上处理功能性MRI数据可以引入由于顶点间距不均的"旋转偏差". 这种偏见扭曲了常见的神经成像分析的结果,影响了功能连接性和可靠性研究.

关键词:

偏见偏见偏见偏见偏见功能磁力共振成像 (fMRI) 是一种土地分片的使用方式.表面的表面表面的表面表面的表面

更多相关视频

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Published on: November 8, 2012

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

Published on: March 21, 2019

相关实验视频

Last Updated: Sep 11, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Published on: November 8, 2012

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

Published on: March 21, 2019

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.
绘制大脑地图绘制大脑地图

背景情况:

功能性MRI (fMRI) 分析越来越多地使用基于表面的顶点映射.
常见的加工管道创建具有不均的顶点距离的网格,在sulci比gyri更密集.
这种不均的距离,称为"肌偏差",尚未完全理解,但影响着fMRI数据.

研究的目的:

解释fMRI表面映射中的旋转偏差的起源.
为了说明旋转偏差对fMRI数据的影响,使用in-silico模型.
确定受旋转偏差影响的常见分析,并提出解决方案.

主要方法:

开发了fMRI数据的in-silico模型,以模拟旋转偏差.
在模拟的fMRI时间序列上分析了不均的顶点间距的影响.
评估了旋转偏差对各种神经成像分析技术的影响.

主要成果:

陀螺偏差导致虚假的相关性和解剖折叠信息泄露到fMRI时间序列中.
常见的分析,如测试重复测试可靠性,指纹和功能分片受到重大影响.
onavg模板减少但不消除旋转偏差,显示剩余的顶点间距变化.

结论:

陀螺偏差是基于表面的fMRI分析中的关键文物,可以导致错误的结论.
了解和解决旋转偏差对于准确解释功能性大脑数据至关重要.
提供了建议,以减轻或纠正fMRI研究中的旋转偏差.