A multi-dataset evaluation of frame censoring for task-based fMRI

Subject motion during fMRI can affect our ability to accurately measure signals of interest. In recent years, frame censoring—that is, statistically excluding motion-contaminated data within the general linear model using nuisance regressors—has appeared in several task-based fMRI studies as a mitigation strategy. However, there have been few systematic investigations quantifying its efficacy. In the present study, we compared the performance of frame censoring to several other common motion correction approaches for task-based fMRI using open data and reproducible workflows. We analyzed eight datasets available on OpenNeuro.org representing eleven distinct tasks in child, adolescent, and adult participants. Performance was quantified using maximum t-values in group analyses, and ROI-based mean activation and split-half reliability in single subjects. We compared frame censoring to the use of 6 and 24 canonical motion regressors, wavelet despiking, robust weighted least squares, and untrained ICA-based denoising. Thresholds used to identify censored frames were based on both motion estimates (FD) and image intensity changes (DVARS). Relative to standard motion regressors, we found consistent improvements for modest amounts of frame censoring (e.g., 1-2% data loss), although these gains were frequently comparable to what could be achieved using other techniques. Importantly, no single approach consistently outperformed the others across all datasets and tasks. These findings suggest that although frame censoring can improve results, the choice of a motion mitigation strategy depends on the dataset and the outcome metric of interest.

Naturalistic hyperscanning with wearable magnetoencephalography

The evolution of human cognitive function is reliant on complex social interactions which form the behavioural foundation of who we are. These social capacities are subject to dramatic change in disease and injury; yet their supporting neural substrates remain poorly understood. Hyperscanning employs functional neuroimaging to simultaneously assess brain activity in two individuals and offers the best means to understand the neural basis of social interaction. However, present technologies are limited, either by poor performance (low spatial/temporal precision) or unnatural scanning environment (claustrophobic scanners, with interactions via video). Here, we solve this problem by developing a new form of hyperscanning using wearable magnetoencephalography (MEG). This approach exploits quantum sensors for MEG signal detection, in combination with high-fidelity magnetic field control – afforded by a novel "matrix coil" system – to enable simultaneous scanning of two freely moving participants. We demonstrate our approach in a somatosensory task and an interactive ball game. Despite large and unpredictable subject motion, sensorimotor brain activity was delineated clearly in space and time, and correlation of the envelope of neuronal oscillations between people was demonstrated. In sum, unlike existing modalities, wearable-MEG combines high fidelity data acquisition and a naturalistic setting, which will facilitate a new generation of hyperscanning.

Getting the Nod: Characterizing pediatric head motion in movie- and resting-state fMRI.

Head motion continues to be a major problem in fMRI research, particularly in developmental studies where an inverse relationship exists between head motion and age. Despite multifaceted and costly efforts to mitigate motion and motion-related signal artifact, few studies have characterized in-scanner head motion itself. This study leverages a large public dataset (N=1388, age 5-21y, The Healthy Brain Network Biobank) to characterize pediatric head motion in space, frequency, and time. We focus on practical aspects of head motion that could impact future study design, including comparing motion across groups (low, medium, and high movers), across conditions (movie-watching and rest), and between males and females. Analyses showed that in all conditions, high movers exhibited a different pattern of motion than low and medium movers that was dominated by x-rotation, and z- and y-translation. High motion spikes (>0.3mm) from all participants also showed this pitch-z-y pattern. Problematic head motion is thus composed of a single type of biomechanical motion, which we infer to be a nodding movement, providing a focused target for motion reduction strategies. A second type of motion was evident via spectral analysis of raw displacement data. This was observed in low and medium movers and was consistent with respiration rates. We consider this to be a baseline of motion best targeted in data preprocessing. Further, we found that males moved more than, but not differently from, females. Significant cross-condition differences in head motion were found. Movies had lower mean motion, and especially in high movers, movie-watching reduced within-run linear increases in head motion (i.e., temporal drift). Finally, we used intersubject correlations of framewise displacement (FD-ISCs) to assess for stimulus-correlated motion trends. Subject motion was more correlated in movie than rest and stimulus-correlated stillness occurred more often than stimulus-correlated motion. Possible reasons and future implications of these findings are discussed.

Advancing motion denoising of multiband resting state functional connectivity fMRI data

Multiband-accelerated fMRI provides dramatically improved temporal and spatial resolution of resting state functional connectivity (RSFC) studies of the human brain, but poses unique challenges for denoising of subject motion induced data artifacts, a major confound in RSFC research. We comprehensively evaluated existing and novel approaches to volume censoring-based motion denoising in the Human Connectome Project dataset. We show that assumptions underlying common metrics for evaluating motion denoising pipelines, especially those based on quality control-functional connectivity (QC-FC) correlations and differences between high- and low-motion participants, are problematic, making these criteria inappropriate for quantifying pipeline performance. We further develop two new quantitative metrics that are free from these issues and demonstrate their use as benchmarks for comparing volume censoring methods. Finally, we develop rigorous, quantitative methods for determining optimal censoring thresholds and provide straightforward recommendations and code for all investigators to apply this optimized approach to their own RSFC datasets.

Prospective Motion correction improves the sensitivity of fMRI pattern decoding

ABSTRACTWe evaluated the effectiveness of prospective motion correction (PMC) on a simple visual task when no deliberate subject motion was present. The PMC system utilizes an in-bore optical camera to track an external marker attached to the participant via a custom-moulded mouthpiece. The study was conducted at two resolutions (1.5mm vs 3mm) and under three conditions (PMC On and Mouthpiece On vs PMC Off and Mouthpiece On vs PMC Off and Mouthpiece Off). Multiple data analysis methods were conducted, including univariate and multivariate approaches, and we demonstrated that the benefit of PMC is most apparent for multi-voxel pattern decoding at higher resolutions. Additional testing on two participants showed that our inexpensive, commercially available mouthpiece solution produced comparable results to a dentist-moulded mouthpiece. Our results showed that PMC is increasingly important at higher resolutions for analyses that require accurate voxel registration across time.