Towards data-driven group inferences of resting-state fMRI data in rodents: Comparison of group ICA, GIG-ICA, and IVA-GL

Xuan Vinh To; Viktor Vegh; Fatima A Nasrallah

doi:10.1016/j.jneumeth.2021.109411

Towards data-driven group inferences of resting-state fMRI data in rodents: Comparison of group ICA, GIG-ICA, and IVA-GL

J Neurosci Methods. 2022 Jan 15:366:109411. doi: 10.1016/j.jneumeth.2021.109411. Epub 2021 Nov 15.

Authors

Xuan Vinh To¹, Viktor Vegh², Fatima A Nasrallah³

Affiliations

¹ The Queensland Brain Institute, The University of Queensland, Australia.
² The Centre for Advanced Imaging, The University of Queensland, Australia.
³ The Queensland Brain Institute, The University of Queensland, Australia; The Centre for Advanced Imaging, The University of Queensland, Australia. Electronic address: f.nasrallah@uq.edu.au.

PMID: 34793852
DOI: 10.1016/j.jneumeth.2021.109411

Abstract

Background: 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.

New method: 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.

Results: 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.

Comparison with existing methods: 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.

Conclusions: 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.

Keywords: Group independent component analysis-guided independent component analysis, IVA, GIG-ICA, electrical stimulation; Independent component analysis; Independent vector analysis; Medetomidine; Resting-state functional MRI.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Animals
Brain / diagnostic imaging
Brain / physiology
Brain Mapping / methods
Magnetic Resonance Imaging* / methods
Mice
Rodentia*