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Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...

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Updated: May 2, 2026

Basics of Multivariate Analysis in Neuroimaging Data

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

一个基于张数的变系数模型,用于多模式神经成像数据分析.

Pratim Guha Niyogi¹, Martin A Lindquist², Tapabrata Maiti³

¹Department of Biostatistics at Johns Hopkins Bloomberg School of Public Health.

IEEE transactions on signal processing : a publication of the IEEE Signal Processing Society

|October 31, 2024

概括

此摘要是机器生成的。

这项研究引入了一种新的张量回归模型,用于分析复杂的神经成像数据. 该方法有效地整合了多式联络数据,保持了结构完整性,以便进行强大的神经相关联发现.

关键词:

在B-spline上使用.在CP分解过程中,CP分解.功能性核磁共振成像 (MRI) 是一种功能性核磁共振成像.功能线性模型的功能线性模型.多模式分析多模式分析

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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

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Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

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Last Updated: May 2, 2026

Basics of Multivariate Analysis in Neuroimaging Data

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Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Published on: June 30, 2018

科学领域:

神经科学是一个神经科学.
统计统计统计统计
数据科学数据科学数据科学

背景情况:

神经成像研究越来越多地结合了多种模式来克服个人的局限性.
整合各种数据类型,如行为,遗传和神经成像数据是一个不断增长的趋势.
许多复杂的数据集,包括神经成像,可以用时间变量的张量来表示.

研究的目的:

为分析神经相关性提出一种新的时间变量张量回归模型.
为了处理随着时间的推移收集的张量值的大脑图像和张量值共变量.
扩展现有的复杂,大规模结构数据的回归模型,同时保持固有的数据结构.

主要方法:

开发了一个时间变化的张量回归模型,用于响应和共变量的结构组成.
使用B-spline技术来表达回归系数.
使用CP分解来通过最小化处罚损失函数来估计基础函数系数.
创建了一个可变系数模型,可以容纳张量值共变量和响应.

主要成果:

拟议的模型有效地分析复杂的多维神经成像数据.
通过模拟数据分析证明了该方法的有效性.
使用现实世界的数据验证了方法,包括功能磁共振成像 (fMRI) 和眼睛跟踪数据.

结论:

开发的张量回归模型为整合多式神经成像和非成像数据提供了一个强大的方法.
这种方法保留了复杂数据的固有结构,推进了神经相关的研究.
该方法为神经科学中分析大规模,时间变化,张量值数据集提供了显著的扩展.