scholarly journals Effective Preprocessing Procedures Virtually Eliminate Distance-Dependent Motion Artifacts in Resting State FMRI

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Hang Joon Jo ◽  
Stephen J. Gotts ◽  
Richard C. Reynolds ◽  
Peter A. Bandettini ◽  
Alex Martin ◽  
...  
Keyword(s):  
Resting State ◽  
Correlation Coefficients ◽  
Resting State Fmri ◽  
Motion Artifacts ◽  
Head Motion ◽  
Time Points ◽  
Global Signal ◽  
Motion Time ◽  
Correlation Estimate ◽  
The Brain

Artifactual sources of resting-state (RS) FMRI can originate from head motion, physiology, and hardware. Of these sources, motion has received considerable attention and was found to induce corrupting effects by differentially biasing correlations between regions depending on their distance. Numerous corrective approaches have relied on the identification and censoring of high-motion time points and the use of the brain-wide average time series as a nuisance regressor to which the data are orthogonalized (Global Signal Regression, GSReg). We replicate the previously reported head-motion bias on correlation coefficients and then show that while motion can be the source of artifact in correlations, the distance-dependent bias is exacerbated by GSReg. Put differently, correlation estimates obtained after GSReg are more susceptible to the presence of motion and by extension to the levels of censoring. More generally, the effect of motion on correlation estimates depends on the preprocessing steps leading to the correlation estimate, with certain approaches performing markedly worse than others. For this purpose, we consider various models for RS FMRI preprocessing and show that the local white matter regressor (WMeLOCAL), a subset of ANATICOR, results in minimal sensitivity to motion and reduces by extension the dependence of correlation results on censoring.

scholarly journals Transient arousal modulations contribute to resting-state functional connectivity changes associated with head motion parameters

2018 ◽  
Author(s):  
Yameng Gu ◽  
Feng Han ◽  
Lucas E. Sainburg ◽  
Xiao Liu
Keyword(s):  
Resting State ◽  
Template Matching ◽  
Brain Connectivity ◽  
Head Motion ◽  
Resting State Functional Connectivity ◽  
Time Points ◽  
Motion Time ◽  
Health And Disease ◽  
Motion Parameters ◽  
Proper Interpretation

AbstractCorrelations of resting-state functional magnetic resonance imaging (rsfMRI) signals are being widely used for assessing functional brain connectivity in health and disease. However, an association was recently observed between rsfMRI connectivity modulations and the head motion parameters and regarded as a causal relationship, which has raised serious concerns about the validity of many rsfMRI findings. Here, we studied the origin of this rsfMRI-motion association and its relationship to arousal modulations. By using a template-matching method to locate arousal-related fMRI changes, we showed that the effects of high motion time points on rsfMRI connectivity are largely due to their significant overlap with arousal-affected time points. The finding suggests that the association between rsfMRI connectivity and the head motion parameters arises from their co-modulations at transient arousal modulations, and this information is critical not only for proper interpretation of motion-associated rsfMRI connectivity changes but also for controlling the potential confounding effects of arousal modulation on rsfMRI metrics.

scholarly journals Resting state connectivity during total sleep deprivation: A functional magnetic resonance study

2020 ◽  
Author(s):  
Vivianne Jakobsson
Keyword(s):  
Magnetic Resonance ◽  
Sleep Deprivation ◽  
Resting State ◽  
Resting State Fmri ◽  
Head Motion ◽  
Functional Magnetic Resonance ◽  
Global Signal ◽  
Significant Difference ◽  
Before And After ◽  
Eyes Open

Introduction: Sleep deprivation is a common problem that may have serious consequences. In this study, functional magnetic resonance imaging (fMRI), a technique frequently used to study networks in the brain, was used to investigate the resting state of the sleep deprived brain, in order to discover whether this state affects the intrinsic connectivity and the global signal variability (GSV). Aims: To investigate whether GSV increases with sleep deprivation. Material and Methods: In this cross over study 18 healthy participants, age 20 – 30, underwent in randomized order resting-state fMRI for 20min before and after 24h sleep deprivation. We extracted the global signal, calculated the standard deviation per participant, and analysed it with respect to sleep depraved yes/no, head motion, eyes open/closed and self-evaluation of sleepiness using Karolinska Sleepiness Score (KSS). Results: We found that GSV was higher during sleep deprivation (0.3362 ± 0.0241, p<0.0001) without KSS data. With KSS, sleep deprivation was not significant (0.0619 ± 0.1145, p=0.5889). High KSS rating had a significant effect on GSV (0.1497 ± 0.0409, p=0.0003), as had head motion (1.7974 ± 0.1539, p<0.0001). There was no significant difference between having eyes open or closed (0.0126 ± 0.0578, p=0.8278), and no significant increase for each time period of 20s in the scanner (0.0065 ± 0.0021, p=0.0029). Conclusions: We found that the global signal variation is increased by sleep deprivation and sleepiness. More specific conclusions cannot be made from our data so far.

scholarly journals Typicality of Functional Connectivity robustly captures motion artifacts in rs-fMRI across datasets, atlases and preprocessing pipelines

2020 ◽  
Author(s):  
Jakub Kopal ◽  
Anna Pidnebesna ◽  
David Tomeček ◽  
Jaroslav Tintěra ◽  
Jaroslav Hlinka
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Resting State Fmri ◽  
Motion Artifacts ◽  
Connectivity Pattern ◽  
Head Motion ◽  
Connectivity Matrix ◽  
Normal Brain ◽  
Functional Connections ◽  
Human Connectome Project

AbstractFunctional connectivity analysis of resting state fMRI data has recently become one of the most common approaches to characterizing individual brain function. It has been widely suggested that the functional connectivity matrix, calculated by correlating signals from regions of interest, is a useful approximate representation of the brain’s connectivity, potentially providing behaviorally or clinically relevant markers. However, functional connectivity estimates are known to be detrimentally affected by various artifacts, including those due to in-scanner head motion. Treatment of such artifacts poses a standing challenge because of their high variability. Moreover, as individual functional connections generally covary only very weakly with head motion estimates, motion influence is difficult to quantify robustly, and prone to be neglected in practice. Although the use of individual estimates of head motion, or group-level correlation of motion and functional connectivity has been suggested, a sufficiently sensitive measure of individual functional connectivity quality has not yet been established. We propose a new intuitive summary index, the Typicality of Functional Connectivity, to capture deviations from normal brain functional connectivity pattern. Based on results of resting state fMRI for 245 healthy subjects we show that this measure is significantly correlated with individual head motion metrics. The results were further robustly reproduced across atlas granularity and preprocessing options, as well as other datasets including 1081 subjects from the Human Connectome Project. The Typicality of Functional Connectivity provides individual proxy measure of motion effect on functional connectivity and is more sensitive to inter-individual variation of motion than individual functional connections. In principle it should be sensitive also to other types of artifacts, processing errors and possibly also brain pathology, allowing wide use in data quality screening and quantification in functional connectivity studies as well as methodological investigations.

scholarly journals Customized head molds reduce motion during resting state fMRI scans

2018 ◽  
Author(s):  
Jonathan D Power ◽  
Benjamin Silver ◽  
Melanie R. Silverman ◽  
Eliana L. Ajodan ◽  
Dienke J. Bos ◽  
...  
Keyword(s):  
Resting State ◽  
Standard Procedure ◽  
Resting State Fmri ◽  
Motion Artifacts ◽  
Head Motion ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Quality Indices ◽  
Age Range ◽  
Head Coil

Head motion causes artifacts in functional magnetic resonance imaging (fMRI) scans, a problem especially relevant for task-free resting state paradigms and for developmental, aging, and clinical populations. In a cohort spanning 7-28 years old (mean age 15) we produced customized head-anatomy-specific Styrofoam molds for each subject that inserted into an MRI head coil. We scanned these subjects under two conditions: using our standard procedure of packing the head coil with foam padding about the head to reduce head motion, and using the customized molds to reduce head motion. Here we report the effects found in our first 13 subjects. In 12 of 13 subjects, the molds reduced head motion throughout the scan, and reduced the fraction of a scan with substantial motion (i.e., volumes with motion notably above baseline levels of motion). Motion was reduced in all 6 head position estimates, especially in rotational, left-right, and superior-inferior directions. Motion was reduced throughout the full age range studied, including children, adolescents, and young adults. In terms of the fMRI data itself, quality indices improved with the head mold on, scrubbing analyses detected less distance-dependent artifact in scans with the head mold on, and distant-dependent artifact was less evident in the scans with the molds on, both for the entire scan and also during only low-motion volumes. Subjects found the molds comfortable. Head molds are thus effective tools for reducing head motion, and motion artifacts, during fMRI scans.

scholarly journals Transient Arousal Modulations Contribute to Resting-State Functional Connectivity Changes Associated with Head Motion Parameters

2020 ◽  
Vol 30 (10) ◽  
pp. 5242-5256 ◽  
Author(s):  
Yameng Gu ◽  
Feng Han ◽  
Lucas E Sainburg ◽  
Xiao Liu
Keyword(s):  
Resting State ◽  
Template Matching ◽  
Brain Connectivity ◽  
Head Motion ◽  
Resting State Functional Connectivity ◽  
Time Points ◽  
Motion Time ◽  
Health And Disease ◽  
Motion Parameters ◽  
Proper Interpretation

Abstract Correlations of resting-state functional magnetic resonance imaging (rsfMRI) signals are being widely used for assessing the functional brain connectivity in health and disease. However, an association was recently observed between rsfMRI connectivity modulations and the head motion parameters and regarded as a causal relationship, which has raised serious concerns about the validity of many rsfMRI findings. Here, we studied the origin of this rsfMRI-motion association and its relationship to arousal modulations. By using a template-matching method to locate arousal-related fMRI changes, we showed that the effects of high motion time points on rsfMRI connectivity are largely due to their significant overlap with arousal-affected time points. The finding suggests that the association between rsfMRI connectivity and the head motion parameters arises from their comodulations at transient arousal modulations, and this information is critical not only for proper interpretation of motion-associated rsfMRI connectivity changes, but also for controlling the potential confounding effects of arousal modulation on rsfMRI metrics.

A Critical, Event-Related Appraisal of Denoising in Resting-State fMRI Studies

2020 ◽  
Vol 30 (10) ◽  
pp. 5544-5559 ◽  
Author(s):  
Jonathan D Power ◽  
Charles J Lynch ◽  
Babatunde Adeyemo ◽  
Steven E Petersen
Keyword(s):  
Resting State ◽  
Resting State Fmri ◽  
Critical Event ◽  
Head Motion ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Decay Properties ◽  
Signal Decay ◽  
Biological Variables ◽  
Related Approach

Abstract This article advances two parallel lines of argument about resting-state functional magnetic resonance imaging (fMRI) signals, one empirical and one conceptual. The empirical line creates a four-part organization of the text: (1) head motion and respiration commonly cause distinct, major, unwanted influences (artifacts) in fMRI signals; (2) head motion and respiratory changes are, confoundingly, both related to psychological and clinical and biological variables of interest; (3) many fMRI denoising strategies fail to identify and remove one or the other kind of artifact; and (4) unremoved artifact, due to correlations of artifacts with variables of interest, renders studies susceptible to identifying variance of noninterest as variance of interest. Arising from these empirical observations is a conceptual argument: that an event-related approach to task-free scans, targeting common behaviors during scanning, enables fundamental distinctions among the kinds of signals present in the data, information which is vital to understanding the effects of denoising procedures. This event-related perspective permits statements like “Event X is associated with signals A, B, and C, each with particular spatial, temporal, and signal decay properties”. Denoising approaches can then be tailored, via performance in known events, to permit or suppress certain kinds of signals based on their desirability.

Aging effect on head motion: A Machine Learning study on resting state fMRI data

2021 ◽  
Vol 352 ◽  
pp. 109084
Author(s):  
Valeria Saccà ◽  
Alessia Sarica ◽  
Andrea Quattrone ◽  
Federico Rocca ◽  
Aldo Quattrone ◽  
...  
Keyword(s):  
Machine Learning ◽  
Resting State ◽  
Aging Effect ◽  
Resting State Fmri ◽  
Fmri Data ◽  
Head Motion ◽  
Learning Study

scholarly journals Wrist and finger motor representations embedded in the cerebral and cerebellar resting-state activation

2021 ◽  
Author(s):  
Toshiki Kusano ◽  
Hiroki Kurashige ◽  
Isao Nambu ◽  
Yoshiya Moriguchi ◽  
Takashi Hanakawa ◽  
...  
Keyword(s):  
Resting State ◽  
Brain Activity ◽  
Body Part ◽  
Resting State Fmri ◽  
Body Parts ◽  
Finger Movements ◽  
Multiple Components ◽  
Brain Activity Pattern ◽  
Motor Representations ◽  
The Brain

AbstractSeveral functional magnetic resonance imaging (fMRI) studies have demonstrated that resting-state brain activity consists of multiple components, each corresponding to the spatial pattern of brain activity induced by performing a task. Especially in a movement task, such components have been shown to correspond to the brain activity pattern of the relevant anatomical region, meaning that the voxels of pattern that are cooperatively activated while using a body part (e.g., foot, hand, and tongue) also behave cooperatively in the resting state. However, it is unclear whether the components involved in resting-state brain activity correspond to those induced by the movement of discrete body parts. To address this issue, in the present study, we focused on wrist and finger movements in the hand, and a cross-decoding technique trained to discriminate between the multi-voxel patterns induced by wrist and finger movement was applied to the resting-state fMRI. We found that the multi-voxel pattern in resting-state brain activity corresponds to either wrist or finger movements in the motor-related areas of each hemisphere of the cerebrum and cerebellum. These results suggest that resting-state brain activity in the motor-related areas consists of the components corresponding to the elementary movements of individual body parts. Therefore, the resting-state brain activity possibly has a finer structure than considered previously.

scholarly journals Graph Ricci Curvatures Reveal Disease-related Changes in Autism Spectrum Disorder

2021 ◽  
Author(s):  
Pavithra Elumalai ◽  
Yasharth Yadav ◽  
Nitin Williams ◽  
Emil Saucan ◽  
Jürgen Jost ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Resting State ◽  
Neurodevelopmental Disorders ◽  
Data Exchange ◽  
Meta Analysis ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
The Brain

Autism Spectrum Disorder (ASD) is a set of neurodevelopmental disorders that pose a significant global health burden. Measures from graph theory have been used to characterise ASD-related changes in resting-state fMRI functional connectivity networks (FCNs), but recently developed geometry-inspired measures have not been applied so far. In this study, we applied geometry-inspired graph Ricci curvatures to investigate ASD-related changes in resting-state fMRI FCNs. To do this, we applied Forman-Ricci and Ollivier-Ricci curvatures to compare networks of ASD and healthy controls (N = 1112) from the Autism Brain Imaging Data Exchange I (ABIDE-I) dataset. We performed these comparisons at the brain-wide level as well as at the level of individual brain regions, and further, determined the behavioral relevance of region-specific differences with Neurosynth meta-analysis decoding. We found brain-wide ASD-related differences for both Forman-Ricci and Ollivier-Ricci curvatures. For Forman-Ricci curvature, these differences were distributed across 83 of the 200 brain regions studied, and concentrated within the Default Mode, Somatomotor and Ventral Attention Network. Meta-analysis decoding identified the brain regions showing curvature differences as involved in social cognition, memory, language and movement. Notably, comparison with results from previous non-invasive stimulation (TMS/tDCS) experiments revealed that the set of brain regions showing curvature differences overlapped with the set of brain regions whose stimulation resulted in positive cognitive or behavioural outcomes in ASD patients. These results underscore the utility of geometry-inspired graph Ricci curvatures in characterising disease-related changes in ASD, and possibly, other neurodevelopmental disorders.

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