scholarly journals Removal of high frequency contamination from motion estimates in single-band fMRI saves data without biasing functional connectivity

2019 ◽  
Author(s):  
Caterina Gratton ◽  
Ally Dworetsky ◽  
Rebecca S. Coalson ◽  
Babatunde Adeyemo ◽  
Timothy O. Laumann ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
High Frequency ◽  
Large Data ◽  
Motion Artifacts ◽  
Single Band ◽  
Fmri Data ◽  
Head Motion ◽  
List Type ◽  
Motion Parameters ◽  
Filtering Approach

AbstractDenoising fMRI data requires assessment of frame-to-frame head motion and removal of the biases motion introduces. This is usually done through analysis of the parameters calculated during retrospective head motion correction (i.e., ‘motion’ parameters). However, it is increasingly recognized that respiration introduces factitious head motion via perturbations of the main (B0) field. This effect appears as higher-frequency fluctuations in the motion parameters (> 0.1 Hz, here referred to as ‘HF-motion’), primarily in the phase-encoding direction. This periodicity can sometimes be obscured in standard single-band fMRI (TR 2.0 – 2.5 s.) due to aliasing. Here we examined (1) how prevalent HF-motion effects are in seven single-band datasets with TR from 2.0 - 2.5 s and (2) how HF-motion affects functional connectivity. We demonstrate that HF-motion is relatively trait-like and more common in older adults, those with higher body mass index, and those with lower cardiorespiratory fitness. We propose a low-pass filtering approach to remove the contamination of high frequency effects from motion summary measures, such as framewise displacement (FD). We demonstrate that in most datasets this filtering approach saves a substantial amount of data from FD-based frame censoring, while at the same time reducing motion biases in functional connectivity measures. These findings suggest that filtering motion parameters is an effective way to improve the fidelity of head motion estimates, even in single band datasets. Particularly large data savings may accrue in datasets acquired in older and less fit participants.Highlights-Single-band fMRI motion traces show factitious high-frequency content (HF-motion)-The magnitude of HF-motion relates to age and other demographic factors-HF-motion elevates framewise displacement (FD) and causes data loss-Substantial fMRI data can be recovered from censoring by filtering motion traces-Filtering motion traces reduces motion artifacts in functional connectivity

scholarly journals Tonic resting-state hubness supports high-frequency activity defined verbal-memory encoding network in epilepsy

2019 ◽  
Author(s):  
Chaitanya Ganne ◽  
Walter Hinds ◽  
James Kragel ◽  
Xiaosong He ◽  
Noah Sideman ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Verbal Memory ◽  
Resting State ◽  
High Frequency ◽  
Cognitive Networks ◽  
Gamma Activity ◽  
List Type ◽  
Healthy Controls ◽  
Memory Encoding ◽  
Memory Network

AbstractHigh-frequency gamma activity of verbal-memory encoding using invasive-electroencephalogram coupled has laid the foundation for numerous studies testing the integrity of memory in diseased populations. Yet, the functional connectivity characteristics of networks subserving these HFA-memory linkages remains uncertain. By integrating this electrophysiological biomarker of memory encoding from IEEG with resting-state BOLD fluctuations, we estimated the segregation and hubness of HFA-memory regions in drug-resistant epilepsy patients and matched healthy controls. HFA-memory regions express distinctly different hubness compared to neighboring regions in health and in epilepsy, and this hubness was more relevant than segregation in predicting verbal memory encoding. The HFA-memory network comprised regions from both the cognitive control and primary processing networks, validating that effective verbal-memory encoding requires multiple functions, and is not dominated by a central cognitive core. Our results demonstrate a tonic intrinsic set of functional connectivity, which provides the necessary conditions for effective, phasic, task-dependent memory encoding.HighlightsHigh frequency memory activity in IEEG corresponds to specific BOLD changes in resting-state data.HFA-memory regions had lower hubness relative to control brain nodes in both epilepsy patients and healthy controls.HFA-memory network displayed hubness and participation (interaction) values distinct from other cognitive networks.HFA-memory network shared regional membership and interacted with other cognitive networks for successful memory encoding.HFA-memory network hubness predicted both concurrent task (phasic) and baseline (tonic) verbal-memory encoding success.

scholarly journals Physiological and head motion signatures in static and time-varying functional connectivity and their subject discriminability

2020 ◽  
Author(s):  
Alba Xifra-Porxas ◽  
Michalis Kassinopoulos ◽  
Georgios D. Mitsis
Keyword(s):  
Functional Connectivity ◽  
Brain Connectivity ◽  
Brain Activity ◽  
Large Data ◽  
Head Motion ◽  
Time Varying ◽  
Resting State Networks ◽  
Human Connectome Project ◽  
Noninvasive Biomarker ◽  
Recurrent Patterns

AbstractHuman brain connectivity yields significant potential as a noninvasive biomarker. Several studies have used fMRI-based connectivity fingerprinting to characterize individual patterns of brain activity. However, it is not clear whether these patterns mainly reflect neural activity or the effect of physiological and motion processes. To answer this question, we capitalize on a large data sample from the Human Connectome Project and rigorously investigate the contribution of the aforementioned processes on functional connectivity (FC) and time-varying FC, as well as their contribution to subject identifiability. We find that head motion, as well as heart rate and breathing fluctuations, induce artifactual connectivity within distinct resting-state networks and that they correlate with recurrent patterns in time-varying FC. Even though the spatiotemporal signatures of these processes yield above-chance levels in subject identifiability, removing their effects at the preprocessing stage improves identifiability, suggesting a neural component underpinning the inter-individual differences in connectivity.

scholarly journals Benchmarking common preprocessing strategies in early childhood functional connectivity MRI

2020 ◽  
Author(s):  
Kirk Graff ◽  
Ryann Tansey ◽  
Amanda Ip ◽  
Christiane Rohr ◽  
Dennis Dimond ◽  
...  
Keyword(s):  
Early Childhood ◽  
Functional Connectivity ◽  
Young Children ◽  
Noise Removal ◽  
Head Motion ◽  
List Type ◽  
Unique Challenge ◽  
Global Signal ◽  
Passive Viewing ◽  
The Impact

AbstractFunctional connectivity magnetic resonance imaging (FC-MRI) has been widely used to investigate neurodevelopment. However, FC-MRI is vulnerable to head motion, which is associated with age and distorts FC estimates. Numerous preprocessing strategies have been developed to mitigate confounds, each with advantages and drawbacks. Preprocessing strategies for FC-MRI have typically been validated and compared using resting state data from adults. However, FC-MRI in young children presents a unique challenge due to relatively high head motion and a growing use of passive viewing paradigms to mitigate motion. This highlights a need to compare processing choices in pediatric samples. To this end, we leveraged longitudinal, passive viewing fMRI data collected from 4 to 8-year-old children. We systematically investigated combinations of widely used and debated preprocessing strategies, namely global signal regression, volume censoring, ICA-AROMA, and bandpass filtering. We implemented commonly used metrics of noise removal (i.e. quality control-functional connectivity), metrics sensitive to individual differences (i.e. connectome fingerprinting), and, because data was collected during a passive viewing task, we also assessed the impact on stimulus-evoked responses (i.e. intersubject correlations; ISC). We found that the most efficacious pipeline included censoring, global signal regression, bandpass filtering, and head motion parameter regression. Despite the drawbacks of noise-mitigation steps, our findings show benefits for both noise removal and information retention in a high-motion early childhood sample.Highlights- We evaluated 27 preprocessing pipelines in passive viewing data from young children- Pipelines were evaluated on noise-removed and information retained- Pipelines that included censoring and GSR outperformed alternatives across benchmarks- For high-motion scans, preprocessing choices substantially alter connectomes

scholarly journals Head motion during fMRI tasks is reduced in children and adults if participants take breaks

2019 ◽  
Author(s):  
Tobias W. Meissner ◽  
Jon Walbrin ◽  
Marisa Nordt ◽  
Kami Koldewyn ◽  
Sarah Weigelt
Keyword(s):  
Data Acquisition ◽  
Time Lag ◽  
Task Engagement ◽  
Fmri Data ◽  
Head Motion ◽  
List Type ◽  
Positive Effects ◽  
Scan Time ◽  
Pediatric Neuroimaging ◽  
Child Friendly

AbstractHead motion remains a challenging confound in functional magnetic resonance imaging (fMRI) studies of both children and adults. Most pediatric neuroimaging labs have developed experience-based, child-friendly standards concerning e.g. the maximum length of a session or the time between mock scanner training and actual scanning. However, it is unclear which factors of child-friendly neuroimaging approaches are effective in reducing head motion. Here, we investigate three main factors including (i) time lag of mock scanner training to the actual scan, (ii) prior scan time, and (iii) task engagement in a dataset of 77 children (aged 6-13) and 64 adults (aged 18-35) using a multilevel modeling approach. In children, distributing fMRI data acquisition across multiple same-day sessions reduces head motion. In adults, motion is reduced after inside-scanner breaks. Despite these positive effects of splitting up data acquisition, motion increases over the course of a study as well as over the course of a run in both children and adults. Our results suggest that splitting up fMRI data acquisition is an effective tool to reduce head motion in general. At the same time, different ways of splitting up data acquisition benefit children and adults.HighlightsIn children, fMRI data acquisition split into multiple sessions reduces head motionIn adults, fMRI data acquisition split by inside-scanner breaks reduces head motionIn both children and adults, motion increases over the duration of a studyIn both children and adults, motion increases over the duration of a run

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 Task activations produce spurious but systematic inflation of task functional connectivity estimates

2018 ◽  
Author(s):  
Michael W. Cole ◽  
Takuya Ito ◽  
Douglas Schultz ◽  
Ravi Mill ◽  
Richard Chen ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Computational Model ◽  
Brain Activity ◽  
Pearson Correlation ◽  
Order Effect ◽  
Evoked Response ◽  
Fmri Data ◽  
List Type ◽  
Functional Relationships ◽  
Psychophysiological Interaction

AbstractMost neuroscientific studies have focused on task-evoked activations (activity amplitudes at specific brain locations), providing limited insight into the functional relationships between separate brain locations. Task-state functional connectivity (FC) - statistical association between brain activity time series during task performance moves beyond task-evoked activations by quantifying functional interactions during tasks. However, many task-state FC studies do not remove the first-order effect of taskevoked activations prior to estimating task-state FC. It has been argued that this results in the ambiguous inference “likely active or interacting during the task”, rather than the intended inference “likely interacting during the task”. Utilizing a neural mass computational model, we verified that task-evoked activations substantially and inappropriately inflate task-state FC estimates, especially in functional MRI (fMRI) data. Various methods attempting to address this problem have been developed, yet the efficacies of these approaches have not been systematically assessed. We found that most standard approaches for fitting and removing mean task-evoked activations were unable to correct these inflated correlations. In contrast, methods that flexibly fit mean task-evoked response shapes effectively corrected the inflated correlations without reducing effects of interest. Results with empirical fMRI data confirmed the model’s predictions, revealing activation-induced task-state FC inflation for both Pearson correlation and psychophysiological interaction (PPI) approaches. These results demonstrate that removal of mean task-evoked activations using an approach that flexibly models task-evoked response shape is an important preprocessing step for valid estimation of task-state FC.HighlightsComputational model shows task inflation of functional connectivity estimatesHemodynamic responses cause task activations to further inflate estimatesStandard approaches to remove task activations leave many false positivesMethods that flexibly fit hemodynamic response shape effectively correct inflationCorrection of functional connectivity inflation verified with empirical fMRI data

scholarly journals Assessing uncertainty in connective field estimations from resting state fMRI activity

2020 ◽  
Author(s):  
Azzurra Invernizzi ◽  
Nicolas Gravel ◽  
Koen V. Haak ◽  
Remco J. Renken ◽  
Frans W. Cornelissen
Keyword(s):  
Visual Field ◽  
Functional Connectivity ◽  
Resting State ◽  
Resting State Fmri ◽  
Fmri Data ◽  
List Type ◽  
Spatial Integration ◽  
Cortical Areas ◽  
Local Functional ◽  
Visual Cortical

AbstractConnective Field (CF) modeling estimates the local spatial integration between signals in distinct cortical visual field areas. As we have shown previously using 7T data, CF can reveal the visuotopic organization of visual cortical areas even when applied to BOLD activity recorded in the absence of external stimulation. This indicates that CF modeling can be used to evaluate cortical processing in participants in which the visual input may be compromised. Furthermore, by using Bayesian CF modelling it is possible to estimate the co-variability of the parameter estimates and therefore, apply CF modeling to single cases. However, no previous studies evaluated the (Bayesian) CF model using 3T resting-state fMRI data, although this is important since 3T scanners are much more abundant and more often used in clinical research than 7T ones. In this study, we investigate whether it is possible to obtain meaningful CF estimates from 3T resting state (RS) fMRI data. To do so, we applied the standard and Bayesian CF modeling approaches on two RS scans interleaved by the acquisition of visual stimulation in 12 healthy participants.Our results show that both approaches reveal good agreement between RS- and visual field (VF)-based maps. Moreover, the 3T observations were similar to those previously reported at 7T. In addition, to quantify the uncertainty associated with each estimate in both RS and VF data, we applied our Bayesian CF framework to provide the underlying marginal distribution of the CF parameters. Finally, we show how an additional CF parameter, beta, can be used as a data-driven threshold on the RS data to further improve CF estimates. We conclude that Bayesian CF modeling can characterize local functional connectivity between visual cortical areas from RS data at 3T. In particular, we expect the ability to assess parameter uncertainty in individual participants will be important for future clinical studies.HighlightsLocal functional connectivity between visual cortical areas can be estimated from RS-fMRI data at 3T using both standard CF and Bayesian CF modelling.Bayesian CF modelling quantifies the model uncertainty associated with each CF parameter on RS and VF data, important in particular for future studies on clinical populations.3T observations were qualitatively similar to those previously reported at 7T.

scholarly journals Removal of high frequency contamination from motion estimates in single-band fMRI saves data without biasing functional connectivity

NeuroImage ◽  
2020 ◽  
Vol 217 ◽  
pp. 116866 ◽  
Author(s):  
Caterina Gratton ◽  
Ally Dworetsky ◽  
Rebecca S. Coalson ◽  
Babatunde Adeyemo ◽  
Timothy O. Laumann ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
High Frequency ◽  
Single Band

scholarly journals An evaluation of the efficacy, reliability, and sensitivity of motion correction strategies for resting-state functional MRI

2017 ◽  
Author(s):  
Linden Parkes ◽  
Ben Fulcher ◽  
Murat Yücel ◽  
Alex Fornito
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Degrees Of Freedom ◽  
Best Practice ◽  
Low Cost ◽  
Head Motion ◽  
Healthy Controls ◽  
Obsessive Compulsive ◽  
Compulsive Disorder ◽  
Motion Parameters

AbstractEstimates of functional connectivity derived from resting-state functional magnetic resonance imaging (rs-fMRI) are sensitive to artefacts caused by in-scanner head motion. This susceptibility has motivated the development of numerous denoising methods designed to mitigate motion-related artefacts. Here, we compare popular retrospective rs-fMRI denoising methods, such as regression of head motion parameters and mean white matter (WM) and cerebrospinal fluid (CSF) (with and without expansion terms), aCompCor, volume censoring (e.g., scrubbing and spike regression), global signal regression and ICA-AROMA, combined into 19 different pipelines. These pipelines were evaluated across five different quality control benchmarks in four independent datasets associated with varying levels of motion. Pipelines were benchmarked by examining the residual relationship between in-scanner movement and functional connectivity after denoising; the effect of distance on this residual relationship; whole-brain differences in functional connectivity between high- and low-motion healthy controls (HC); the temporal degrees of freedom lost during denoising; and the test-retest reliability of functional connectivity estimates. We also compared the sensitivity of each pipeline to clinical differences in functional connectivity in independent samples of schizophrenia and obsessive-compulsive disorder. Our results indicate that (1) simple linear regression of regional fMRI time series against head motion parameters and WM/CSF signals (with or without expansion terms) is not sufficient to remove head motion artefacts; (2) aCompCor pipelines may only be viable in low-motion data; (3) volume censoring performs well at minimising motion-related artefact but a major benefit of this approach derives from the exclusion of high-motion individuals; (4) while not as effective as volume censoring, ICA-AROMA performed well across our benchmarks for relatively low cost in terms of data loss; and (5) group comparisons in functional connectivity between healthy controls and schizophrenia patients are highly dependent on preprocessing strategy. We offer some recommendations for best practice and outline some simple analyses to facilitate transparent reporting of the degree to which a given set of findings may be affected by motion-related artefact.

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