# Strategies for motion- and respiration-robust estimation of fMRI intrinsic neural timescales

## TL;DR

This paper investigates how motion and respiration artifacts affect fMRI measurements of neural timescales and proposes strategies to reduce these effects.

## Contribution

The study introduces and evaluates denoising strategies to mitigate motion and respiration artifacts in rs-fMRI intrinsic neural timescale estimation.

## Key findings

- Non-clean fMRI runs showed increased intrinsic neural timescales compared to clean runs.
- Head motion correlated with the magnitude of error in intrinsic neural timescale estimation.
- Group-level correction reduced bias introduced by frame censoring.

## Abstract

Intrinsic neural timescales (INT) reflect the time window of neural integration within a brain region and can be measured via resting-state functional magnetic resonance imaging (rs-fMRI). Despite the potential relevance of INT to cognition, brain organization, and neuropsychiatric illness, the influences of physiological artifacts on rs-fMRI INT have not been systematically considered. Two artifacts, head motion and respiration, pose serious issues in rs-fMRI studies. Here, we described their impact on INT estimation and tested the ability of two denoising strategies for mitigating these artifacts, high-motion frame censoring and global signal regression (GSR). We used a subset of the Human Connectome Project Young Adult (HCP-YA) dataset with runs annotated for breathing patterns (Lynch et al., 2020) and at least one “clean” (reference) run that had minimal head motion and no respiration artifacts; other runs from the same participants (n= 46) were labeled as “non-clean.” We found that non-clean runs exhibited brain-wide increases in INT compared with their respective clean runs and that the magnitude of error in INT between non-clean and clean runs correlated with the amount of head motion. Importantly, effect sizes were comparable with INT effects reported in the clinical literature. GSR and high-motion frame censoring improved the similarity between INT maps from non-clean runs and their respective clean run. Using a pseudo-random frame-censoring approach, we uncovered a relationship between the number of censored frames and both the mean INT and mean error, suggesting that frame censoring itself biases INT estimation. A group-level correction procedure reduced this bias and improved similarity between non-clean runs and their respective clean run. Based on our findings, we offer recommendations for rs-fMRI INT studies, which include implementing GSR and high-motion frame censoring with Lomb–Scargle interpolation of censored frames, and performing group-level correction of the bias introduced by frame censoring.