To Xuan Vinh, Vegh Viktor, Nasrallah Fatima A
The Queensland Brain Institute, The University of Queensland, Australia.
The Centre for Advanced Imaging, The University of Queensland, Australia.
J Neurosci Methods. 2022 Jan 15;366:109411. doi: 10.1016/j.jneumeth.2021.109411. Epub 2021 Nov 15.
A trend in the development of resting-state fMRI (rsfMRI) data analysis is the drive towards more data-driven methods. Group Independent Component Analysis (GICA) is a well-proven data-driven method for performing fMRI group analysis, though not without issues, especially the back-reconstruction from group-level independent components to individual-level components. Group information-guided ICA (GIG-ICA) and Independent Vector Analysis (IVA) are recent extensions of GICA that were shown to outperform GICA in the identification of unique rsfMRI biomarkers in psychiatric conditions.
In this work, GICA, GIG-ICA, and IVA-GL analysis methods were applied to rsfMRI data acquired from 9 mice under different doses of medetomidine (0.1 - 0.3 mg/kg/h) in the before and after forepaw stimulation, and their performance was compared to determine whether GIG-ICA and IVA-GL outperform GICA in identifying robust and reliable resting-state networks in the rodent brain.
Our results showed IVA-GL method had certain desirable performance characteristics over the other two methods under minimal data pre-processing and data-driven assumptions in application to analysis of mouse resting-state functional MRI.
IVA-GL provides better stability towards detecting group differences at different model order assumptions and performed better at separating data well-defined and functionally reasonable components in mouse resting-state fMRI. At higher model order and more likely functional component assumptions, GIG-ICA and IVA-GL were found to have greater sensitivity at detecting functional connectivity changes due to physiological challenges compared to GICA.
This study indicates that IVA-GL yields better detection of resting-state networks in the rodent brain compared to other ICA methods and a promising data-driven analysis method for rodent rsfMRI.
静息态功能磁共振成像（rsfMRI）数据分析的一个发展趋势是朝着更多数据驱动方法的方向发展。组独立成分分析（GICA）是一种经过充分验证的数据驱动方法，用于进行功能磁共振成像组分析，尽管并非没有问题，尤其是从组水平独立成分到个体水平成分的反向重建。组信息引导独立成分分析（GIG - ICA）和独立向量分析（IVA）是GICA的最新扩展，已证明在识别精神疾病中独特的rsfMRI生物标志物方面优于GICA。
在这项工作中，将GICA、GIG - ICA和IVA - GL分析方法应用于9只小鼠在不同剂量美托咪定（0.1 - 0.3毫克/千克/小时）下前爪刺激前后采集的rsfMRI数据，并比较它们的性能，以确定GIG - ICA和IVA - GL在识别啮齿动物大脑中稳健且可靠的静息态网络方面是否优于GICA。
我们的结果表明，在对小鼠静息态功能磁共振成像分析应用最少数据预处理和数据驱动假设的情况下，IVA - GL方法比其他两种方法具有某些理想的性能特征。
IVA - GL在不同模型阶数假设下检测组间差异时具有更好的稳定性，并且在分离小鼠静息态fMRI中定义明确且功能合理的成分方面表现更好。在更高的模型阶数和更可能的功能成分假设下，发现GIG - ICA和IVA - GL在检测由于生理挑战引起的功能连接变化方面比GICA具有更高的灵敏度。
本研究表明，与其他独立成分分析方法相比，IVA - GL在检测啮齿动物大脑静息态网络方面具有更好的效果，是一种用于啮齿动物rsfMRI的有前景的数据驱动分析方法。
