Alternative labeling tool: a minimal algorithm for denoising single-subject resting-state fMRI data with ICA-MELODIC

Subject-level independent component analysis (ICA) is a well-established and widely used approach in denoising of resting-state functional magnetic resonance imaging (fMRI) data. However, approaches such as ICA-FIX and ICA-AROMA require advanced setups and can be computationally intensive. Here, we aim to introduce a user-friendly, computationally lightweight toolbox for labeling independent signal and noise components, termed Alternative Labeling Tool (ALT). ALT uses two features that require manual tuning: proportion of an independent component's spatial map located inside gray matter and positive skew of the power spectrum. ALT is tightly integrated with the commonly used FMRIB's statistical library (FSL). Using the Open Access Series of Imaging Studies (OASIS-3) ageing dataset (n = 275), we found that ALT shows a high degree of inter-rater agreement with manual labeling (over 86% of true positives for both signal and noise components on average). In conclusion, ALT can be extended to small and large-scale datasets when the use of more complex tools such as ICA-FIX is not possible. ALT will thus allow for more widespread adoption of ICA-based denoising of resting-state fMRI data.

Automated summary

Subject-level independent component analysis (ICA) is widely used in denoising resting-state fMRI data. This study introduces a user-friendly and computationally lightweight tool, Alternative Labeling Tool (ALT), for labeling independent signal and noise components. The results show that ALT has a high degree of agreement with manual labeling, making it a valuable alternative in cases where more complex tools are not feasible.