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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: Jun 24, 2025

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

Published on: June 9, 2018

多模式预测建模:可扩展成像信息化方法来预测未来的大脑健康

Meenu Ajith¹, Jeffrey S Spence², Sandra B Chapman²

¹Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, 55 Park Pl NE, Atlanta, 30303, GA, USA.

bioRxiv : the preprint server for biology

|June 10, 2024

概括

通过整合神经成像数据,预测未来的大脑健康得到了增强. 使用部分条件变异自编码器 (PCVAE) 的图像辅助方法显示,与仅评估或仅神经影像方法相比,对未来的大脑健康的预测优越.

关键词:

大脑健康大脑健康连接性的连接性这些因素是因素.有图像辅助的图像.多式联络是多式联络.预测建模的预测建模.rs-fMRI 是一个

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科学领域:

神经科学是一个神经科学.
计算精神病学是一种计算精神病学.
医学成像分析医学成像分析

背景情况:

预测未来的大脑健康是具有挑战性的,需要整合各种数据.
神经成像中的神经模式提供了行为和心理状态的早期指标.

研究的目的:

为未来的大脑健康引入和评估一种多式预测建模方法.
为了比较成像信息化的方法与传统的仅评估和仅神经成像的方法.

主要方法:

开发了一种使用部分条件变量自编码器 (PCVAE) 的图像辅助方法.
综合静态功能网络连接 (sFNC) 从静态功能磁共振成像 (rs-fMRI) 与行为评估.
根据支向量回归 (SVR) 和随机森林 (RF) 模型进行评估.

主要成果:

图像辅助方法在预测未来的大脑健康结构及其纵向变化方面表现出卓越的表现.
在PCVAE模型中,在训练期间有效地利用神经成像数据来增强仅从评估数据的预测.
在预测准确性方面表现优于仅评估和仅神经成像的方法.

结论:

基于神经成像的预测建模对理解大脑健康有很大的潜力.
拟议的多式联络方法有助于预测认知表现和神经连接关系.
这项研究强调了将神经成像与行为数据相结合的价值,以进行强大的大脑健康预测.