# A constrained ICA-EMD Model for Group Level fMRI Analysis

## TL;DR

This paper introduces a novel constrained ICA-EMD workflow for group-level fMRI analysis, using empirical mode decomposition to generate reference signals that improve the extraction of consistent resting state networks across subjects.

## Contribution

It proposes a new ICA-based method incorporating EMD-derived reference signals to enhance group fMRI analysis and improve the consistency of resting state network detection.

## Key findings

- EMD-derived intrinsic modes serve as effective references for cICA.
- The proposed method yields more consistent resting state networks across subjects.
- It offers a transparent way to balance similarity and individual features in group analysis.

## Abstract

Independent component analysis (ICA), as a data driven method, has shown to be a powerful tool for functional magnetic resonance imaging (fMRI) data analysis. One drawback of this multivariate approach is, that it is not compatible to the analysis of group data in general. Therefore various techniques have been proposed in order to overcome this limitation of ICA. In this paper a novel ICA-based work-flow for extracting resting state networks from fMRI group studies is proposed. An empirical mode decomposition (EMD) is used to generate reference signals in a data driven manner, which can be incorporated into a constrained version of ICA (cICA), what helps to eliminate the inherent ambiguities of ICA. The results of the proposed workflow are then compared to those obtained by a widely used group ICA approach for fMRI analysis. In this paper it is demonstrated that intrinsic modes, extracted by EMD, are suitable to serve as references for cICA to obtain typical resting state patterns, which are consistent over subjects. By introducing these reference signals into the ICA, our processing pipeline makes it transparent for the user, how comparable activity patterns across subjects emerge. This additionally allows adapting the trade-off between enforcing similarity across subjects and preserving individual subject features.