Dataset Information

Physical characteristics not psychological state or trait characteristics predict motion during resting state fMRI.

ABSTRACT: Head motion (HM) during fMRI acquisition can significantly affect measures of brain activity or connectivity even after correction with preprocessing methods. Moreover, any systematic relationship between HM and variables of interest can introduce systematic bias. There is a large and growing interest in identifying neural biomarkers for psychiatric disorders using resting state fMRI (rsfMRI). However, the relationship between HM and different psychiatric symptoms domains is not well understood. The aim of this investigation was to determine whether psychiatric symptoms and other characteristics of the individual predict HM during rsfMRI. A sample of n?=?464 participants (174 male) from the Tulsa1000, a naturalistic longitudinal study recruiting subjects with different levels of severity in mood/anxiety/substance use disorders based on the dimensional NIMH Research Domain Criteria framework was used for this study. Based on a machine learning (ML) pipeline with nested cross-validation to avoid overfitting, the stacked model with 15 anthropometric (like body mass index, BMI) and demographic (age and sex) variables identifies BMI and weight as the most important variables and explained 10.9 percent of the HM variance (95% CI: 9.9-11.8). In comparison ML models with 105 self-report measures for state and trait psychological characteristics identified nicotine and alcohol use variables as well as impulsivity inhibitory control variables but explain only 5 percent of HM variance (95% CI: 3.5-6.4). A combined ML model using all 120 variables did not perform significantly better than the model using only 15 physical variables (combined model 95% confidence interval: 10.2-12.4). Taken together, after considering physical variables, state or trait psychological characteristics do not provide additional power to predict motion during rsfMRI.

SUBMITTER: Ekhtiari H

PROVIDER: S-EPMC6344520 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

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Physical characteristics not psychological state or trait characteristics predict motion during resting state fMRI.

Ekhtiari Hamed H Kuplicki Rayus R Yeh Hung-Wen HW Paulus Martin P MP

Scientific reports 20190123 1

Head motion (HM) during fMRI acquisition can significantly affect measures of brain activity or connectivity even after correction with preprocessing methods. Moreover, any systematic relationship between HM and variables of interest can introduce systematic bias. There is a large and growing interest in identifying neural biomarkers for psychiatric disorders using resting state fMRI (rsfMRI). However, the relationship between HM and different psychiatric symptoms domains is not well understood. ...[more]

PMID: 30674933

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Project description: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 transdiagnostic 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 8 out of top 10 FD-ISC windows had FD below the mean. Possible reasons and future implications of these findings are discussed.

Project description:The quality of functional MRI (fMRI) data is affected by head motion. It has been shown that fMRI data quality can be improved by prospectively updating the gradients and radio-frequency pulses in response to head motion during image acquisition by using an MR-compatible optical tracking system (prospective motion correction, or PMC). Recent studies showed that PMC improves the temporal Signal to Noise Ratio (tSNR) of resting state fMRI data (rs-fMRI) acquired from subjects not moving intentionally. Besides that, the time courses of Independent Components (ICs), resulting from Independent Component Analysis (ICA), were found to present significant temporal correlation with the motion parameters recorded by the camera. However, the benefits of applying PMC for improving the quality of rs-fMRI acquired under large head movements and its effects on resting state networks (RSN) and connectivity matrices are still unknown. In this study, subjects were instructed to cross their legs at will while rs-fMRI data with and without PMC were acquired, which generated head motion velocities ranging from 4 to 30 mm/s. We also acquired fMRI data without intentional motion. Independent component analysis of rs-fMRI was performed to evaluate IC maps and time courses of RSNs. We also calculated the temporal correlation among different brain regions and generated connectivity matrices for the different motion and PMC conditions. In our results we verified that the crossing leg movements reduced the tSNR of sessions without and with PMC by 45 and 20%, respectively, when compared to sessions without intentional movements. We have verified an interaction between head motion speed and PMC status, showing stronger attenuation of tSNR for acquisitions without PMC than for those with PMC. Additionally, the spatial definition of major RSNs, such as default mode, visual, left and right central executive networks, was improved when PMC was enabled. Furthermore, motion altered IC-time courses by decreasing power at low frequencies and increasing power at higher frequencies (typically associated with artefacts). PMC partially reversed these alterations of the power spectra. Finally, we showed that PMC provides temporal correlation matrices for data acquired under motion conditions more comparable to those obtained by fMRI sessions where subjects were instructed not to move.

Dataset Information

Physical characteristics not psychological state or trait characteristics predict motion during resting state fMRI.

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Physical characteristics not psychological state or trait characteristics predict motion during resting state fMRI.

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