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Active head motion reduction in Magnetic Resonance Imaging using tactile feedback
Abstract
Head motion is a common problem in clinical as well as empirical (functional) Magnetic Resonance Imaging applications, as it can lead to severe artefacts that reduce image quality. The scanned individuals themselves, however, are often not aware of their head motion. The current study explored whether providing subjects with this information using tactile feedback would reduce their head motion and consequently improve image quality. In a single session that included six runs, 24 participants performed three different cognitive tasks: (1) passive viewing, (2) mental imagery, and (3) speeded responses. These tasks occurred in two different conditions: (a) with a strip of medical tape applied from one side of the MR head-coil, via the participant's forehead, to the other side, and (b) without the medical tape being applied. Results revealed that application of medical tape to the forehead of subjects to provide tactile feedback significantly reduced both translational as well as rotational head motion. While this effect did not differ between the three cognitive tasks, there was a negative quadratic relationship between head motion with and without feedback. That is, the more head motion a subject produced without feedback, the stronger the motion reduction given the feedback. In conclusion, the here tested method provides a simple and cost-efficient way to reduce subjects' head motion, and might be especially beneficial when extensive head motion is expected a priori.
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A major limitation to structural and functional MRI (fMRI) scans is their susceptibility to head motion artifacts. Even submillimeter movements can systematically distort functional connectivity, morphometric, and diffusion imaging results. In patient care, sedation is often used to minimize head motion, but it incurs increased costs and risks. In research settings, sedation is typically not an ethical option. Therefore, safe methods that reduce head motion are critical for improving MRI quality, especially in high movement individuals such as children and neuropsychiatric patients. We investigated the effects of (1) viewing movies and (2) receiving real-time visual feedback about head movement in 24 children (5-15 years old). Children completed fMRI scans during which they viewed a fixation cross (i.e., rest) or a cartoon movie clip, and during some of the scans they also received real-time visual feedback about head motion. Head motion was significantly reduced during movie watching compared to rest and when receiving feedback compared to receiving no feedback. However, these results depended on age, such that the effects were largely driven by the younger children. Children older than 10 years showed no significant benefit. We also found that viewing movies significantly altered the functional connectivity of fMRI data, suggesting that fMRI scans during movies cannot be equated to standard resting-state fMRI scans. The implications of these results are twofold: (1) given the reduction in head motion with behavioral interventions, these methods should be tried first for all clinical and structural MRIs in lieu of sedation; and (2) for fMRI research scans, these methods can reduce head motion in certain groups, but investigators must keep in mind the effects on functional MRI data.
Human neuroimaging, specifically magnetic resonance imaging (MRI), is being used with increasing popularity to study brain structure and function in development and disease. When applying these methods to developmental and clinical populations, careful consideration must be taken with regard to study design and implementation. In this article, we discuss two major considerations particularly pertinent to brain research in special populations. First, we discuss considerations for subject selection and characterization, including issues related to comorbid conditions, medication status, and clinical assessment. Second, we discuss methods and considerations for acquisition of adequate, useable MRI data. Given that children and patients may experience anxiety with the scanner environment, preventing participation, and that they have a higher risk of motion artifact, resulting in data loss, successful subject compliance and data acquisition are not trivial tasks. We conclude that, as researchers, we must consider a number of issues when using neuroimaging tools to study children and patients, and we should thoughtfully justify our choices of methods and study design.
High field MRI systems, such as 7 Tesla (T) scanners, can deliver higher signal to noise ratio (SNR) than lower field scanners and thus allow for the acquisition of data with higher spatial resolution, which is often demanded by users in the fields of clinical and neuroscientific imaging. However, high resolution scans may require long acquisition times, which in turn increase the discomfort for the subject and the risk of subject motion. Even with a cooperative and trained subject, involuntary motion due to heartbeat, swallowing, respiration and changes in muscle tone can cause image artifacts that reduce the effective resolution. In addition, scanning with higher resolution leads to increased sensitivity to even very small movements. Prospective motion correction (PMC) at 3T and 7T has proven to increase image quality in case of subject motion. Although the application of prospective motion correction is becoming more popular, previous articles focused on proof of concept studies and technical descriptions, whereas this paper briefly describes the technical aspects of the optical tracking system, marker fixation and cross calibration and focuses on the application of PMC to very high resolution imaging without intentional motion. In this study we acquired in vivo MR images at 7T using prospective motion correction during long acquisitions. As a result, we present images among the highest, if not the highest resolution of in vivo human brain MRI ever acquired.
We evaluated the performance of an optical camera based prospective motion correction (PMC) system in improving the quality of 3D echo-planar imaging functional MRI data. An optical camera and external marker were used to dynamically track the head movement of subjects during fMRI scanning. PMC was performed by using the motion information to dynamically update the sequence's RF excitation and gradient waveforms such that the field-of-view was realigned to match the subject's head movement. Task-free fMRI experiments on five healthy volunteers followed a 2×2×3 factorial design with the following factors: PMC on or off; 3.0mm or 1.5mm isotropic resolution; and no, slow, or fast head movements. Visual and motor fMRI experiments were additionally performed on one of the volunteers at 1.5mm resolution comparing PMC on vs PMC off for no and slow head movements. Metrics were developed to quantify the amount of motion as it occurred relative to k-space data acquisition. The motion quantification metric collapsed the very rich camera tracking data into one scalar value for each image volume that was strongly predictive of motion-induced artifacts. The PMC system did not introduce extraneous artifacts for the no motion conditions and improved the time series temporal signal-to-noise by 30% to 40% for all combinations of low/high resolution and slow/fast head movement relative to the standard acquisition with no prospective correction. The numbers of activated voxels (p<0.001, uncorrected) in both task-based experiments were comparable for the no motion cases and increased by 78% and 330%, respectively, for PMC on versus PMC off in the slow motion cases. The PMC system is a robust solution to decrease the motion sensitivity of multi-shot 3D EPI sequences and thereby overcoming one of the main roadblocks to their widespread use in fMRI studies.
Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.
Signal-to-noise ratio, the ratio between signal and noise, is a quantity that has been well established for MRI data but is still subject of ongoing debate and confusion when it comes to fMRI data. fMRI data are characterised by small activation fluctuations in a background of noise. Depending on how the signal of interest and the noise are identified, signal-to-noise ratio for fMRI data is reported by using many different definitions. Since each definition comes with a different scale, interpreting and comparing signal-to-noise ratio values for fMRI data can be a very challenging job. In this paper, we provide an overview of existing definitions. Further, the relationship with activation detection power is investigated. Reference tables and conversion formulae are provided to facilitate comparability between fMRI studies.
Expyriment is an open-source and platform-independent lightweight Python library for designing and conducting timing-critical behavioral and neuroimaging experiments. The major goal is to provide a well-structured Python library for script-based experiment development, with a high priority being the readability of the resulting program code. Expyriment has been tested extensively under Linux and Windows and is an all-in-one solution, as it handles stimulus presentation, the recording of input/output events, communication with other devices, and the collection and preprocessing of data. Furthermore, it offers a hierarchical design structure, which allows for an intuitive transition from the experimental design to a running program. It is therefore also suited for students, as well as for experimental psychologists and neuroscientists with little programming experience.
Head motion systematically alters correlations in resting state functional connectivity fMRI (RSFC). In this report we examine impact of motion on signal intensity and RSFC correlations. We find that motion-induced signal changes (1) are often complex and variable waveforms, (2) are often shared across nearly all brain voxels, and (3) often persist more than 10seconds after motion ceases. These signal changes, both during and after motion, increase observed RSFC correlations in a distance-dependent manner. Motion-related signal changes are not removed by a variety of motion-based regressors, but are effectively reduced by global signal regression. We link several measures of data quality to motion, changes in signal intensity, and changes in RSFC correlations. We demonstrate that improvements in data quality measures during processing may represent cosmetic improvements rather than true correction of the data. We demonstrate a within-subject, censoring-based artifact removal strategy based on volume censoring that reduces group differences due to motion to chance levels. We note conditions under which group-level regressions do and do not correct motion-related effects.
This article describes the design, construction, application, and effectiveness of a simple bite bar for use with an MRI whole-head
coil to reduce head-movement-related artifacts during functional brain imaging. The device is comfortable to use and allows
considerable flexibility in positioning the subject’s head. Eleven subjects were scanned while performing a motor-sequencing
experiment with and without the bite bar. The bite bar was generally effective in reducing maximum head movement to less than
0.5 mm (translation) and 0.5° (rotation).
Presenting confidence intervals around means is a common method of expressing uncertainty in data. Loftus and Masson (1994) describe confidence intervals for means in within-subjects designs. These confidence intervals are based on the ANOVA mean squared error. Cousineau (2005) presents an alternative to the Loftus and Masson method, but his method produces confidence intervals that are smaller than those of Loftus and Masson. I show why this is the case and offer a simple correction that makes the expected size of Cousineau confidence intervals the same as that of Loftus and Masson confidence intervals.
Moving dots can evoke a percept of the spatial structure of a three-dimensional object in the absence of other visual cues. This phenomenon, called structure from motion (SFM), suggests that the motion flowfield represented in the dorsal stream can form the basis of object recognition performed in the ventral stream. SFM processing is likely to contribute to object perception whenever there is relative motion between the observer and the object viewed. Here we investigate the motion flowfield component of object recognition with functional magnetic resonance imaging. Our SFM stimuli encoded face surfaces and random three-dimensional control shapes with matched curvature properties. We used two different types of an SFM stimulus with the dots either fixed to the surface of the object or moving on it. Despite the radically different encoding of surface structure in the two types of SFM, both elicited strong surface percepts and involved the same network of cortical regions. From early visual areas, this network extends dorsally into the human motion complex and parietal regions and ventrally into object-related cortex. The SFM stimuli elicited a face-selective response in the fusiform face area. The human motion complex appears to have a central role in SFM object recognition, not merely representing the motion flowfield but also the surface structure of the motion-defined object. The motion complex and a region in the intraparietal sulcus reflected the motion state of the SFM-implicit object, responding more strongly when the implicit object was in motion than when it was stationary.
Motion correction of echo-planar imaging (EPI) data used in functional MRI (fMRI) is an essential preprocessing step performed prior to statistical analysis. At ultra-high resolution fMRI, current requirements regarding translational and rotational motion may no longer be acceptable. This prompts the need for a systematic investigation of the effects of motion correction procedures with in vivo fMRI data. Here we systematically evaluated the effect of retrospective motion correction with freely available fMRI analysis software packages (FSL, AFNI, and SPM) on activation maps using fMRI data acquired with prospective motion detection, to identify and quantify confounding effects of retrospective motion correction, and to evaluate its dependence on spatial resolution and motion correction algorithms. Brain activation maps were obtained for two different resolutions, an ultrahigh, that is, 0.65(3) mm(3) , and a more widely used 2.0(3) mm(3) isotropic resolutions at 7 T. The EPI data were acquired using simultaneous non-image-based optical moiré phase tracking (MPT) of physical motion. The results showed that image-based motion detection, performed by SPM8 software package, may be erroneous in high-field fMRI data with partial brain coverage and can introduce spurious motion leading to false-positive and false-negative activation. Further analyses demonstrated that limited acquisition field of view has the dominant influence on the effect. Hum Brain Mapp, 2017. © 2017 Wiley Periodicals, Inc.
The current study is a first exploration of real-time self-regulation of functional magnetic resonance imaging (fMRI) activation based on several different visual neurofeedback presentations. Six healthy participants were engaged in self-regulation of regional fMRI activation in the posterior parietal cortex (PPC), by performing a mental calculation task. In different MR sessions, feedback was presented in the form of either a thermometer display (in vertical orientation), a circle display (increasing or decreasing in physical size), or a numbers display (Arabic digits). While self-regulation levels did differ between individuals, all six participants were able to significantly up-regulate their PPC activation with all three neurofeedback presentations. In addition to a successful general up-regulation, five out of six participants were furthermore capable of gradual self-regulation to multiple intensity levels. Taken together, the current study is a proof-of-concept demonstration of the feasibility of using multiple visual feedback presentations during gradual self-regulation of regional fMRI activation. Implications for future neurofeedback research and applications are discussed.
Head motion is a significant source of noise in the estimation of functional connectivity from resting-state functional MRI (rs-fMRI). Current strategies to reduce this noise include image realignment, censoring time points corrupted by motion, and including motion realignment parameters and their derivatives as additional nuisance regressors in the general linear model. However, this nuisance regression approach assumes that the motion-induced signal changes are linearly related to the estimated realignment parameters, which is not always the case. In this study we develop an improved model of motion-related signal changes, where nuisance regressors are formed by first rotating and translating a single brain volume according to the estimated motion, re-registering the data, and then performing a principal components analysis (PCA) on the resultant time series of both moved and re-registered data. We show that these "Motion Simulated (MotSim)" regressors account for significantly greater fraction of variance, result in higher temporal signal-to-noise, and lead to functional connectivity estimates that are less affected by motion compared to the most common current approach of using the realignment parameters and their derivatives as nuisance regressors. This improvement should lead to more accurate estimates of functional connectivity, particularly in populations where motion is prevalent, such as patients and young children.
Head motion during functional MRI (fMRI) scanning can induce spurious findings and/or harm detection of true effects. Solutions have been proposed, including deleting ('scrubbing') or regressing out ('spike regression') motion volumes from fMRI time-series. These strategies remove motion-induced signal variations at the cost of destroying the autocorrelation structure of the fMRI time-series and reducing temporal degrees of freedom. ICA-based fMRI denoising strategies overcome these drawbacks but typically require re-training of a classifier, needing manual labeling of derived components (e.g. ICA-FIX; [42]). Here, we propose an ICA-based strategy for Automatic Removal Of Motion Artifacts (ICA-AROMA) that uses a small (n=4), but robust set of theoretically motivated temporal and spatial features. Our strategy does not require classifier re-training, retains the data's autocorrelation structure and largely preserves temporal degrees of freedom. We describe ICA-AROMA, its implementation, and initial validation. ICA-AROMA identified motion components with high accuracy and robustness as illustrated by leave-N-out cross-validation. We additionally validated ICA-AROMA in resting-state (100 participants) and task-based fMRI data (118 participants). Our approach removed (motion-related) spurious noise from both rfMRI and task-based fMRI data to larger extent than regression using 24 motion parameters or spike regression. Furthermore, ICA-AROMA increased sensitivity to group-level activation. Our results show that ICA-AROMA effectively reduces motion-induced signal variations in fMRI data, is applicable across datasets without requiring classifier re-training, and preserves the temporal characteristics of the fMRI data.
Copyright © 2015. Published by Elsevier Inc.
Head motion in functional MRI and resting-state MRI is a major problem. Existing methods do not robustly reflect the true level of motion artifact for in vivo fMRI data. The primary issue is that current methods assume motion is synchronized to the volume acquisition and thus ignore intra-volume motion. This manuscript covers three sections in the use of gold-standard motion-corrupted data to pursue an intra-volume motion correction. First, we present a way to get motion corrupted data with accurately known motion at the slice acquisition level. This technique simulates important data acquisition-related motion artifacts while acquiring real BOLD MRI data. It is based on a novel motion-injection pulse sequence that introduces known motion independently for every slice: Simulated Prospective Acquisition CorrEction (SimPACE). Secondly, with data acquired using SimPACE, we evaluate several motion correction and characterization techniques, including several commonly used BOLD signal- and motion parameter-based metrics. Finally, we introduce and evaluate a novel, slice-based motion correction technique. Our novel method, SLice-Oriented MOtion COrrection (SLOMOCO) performs better than the volumetric methods and, moreover, accurately detects the motion of independent slices, in this case equivalent to the known injected motion. We demonstrate that SLOMOCO can model and correct for nearly all effects of motion in BOLD data. Also, none of the commonly used motion metrics was observed to robustly identify motion corrupted events, especially in the most realistic scenario of sudden head movement. For some popular metrics, performance was poor even when using the ideal known slice motion instead of volumetric parameters. This has negative implications for methods relying on these metrics, such as recently proposed motion correction methods such as data censoring and global signal regression.
This paper concerns the spatial and intensity transformations that map one image onto another. We present a general technique that facilitates nonlinear spatial (stereotactic) normalization and image realignment. This technique minimizes the sum of squares between two images following nonlinear spatial deformations and transformations of the voxel (intensity) values. The spatial and intensity transformations are obtained simultaneously, and explicitly, using a least squares solution and a series of linearising devices. The approach is completely noninteractive (automatic), nonlinear, and noniterative. It can be applied in any number of dimensions.
Various applications are considered, including the realignment of functional magnetic resonance imaging (MRI) time-series, the linear (affine) and nonlinear spatial normalization of positron emission tomography (PET) and structural MRI images, the coregistration of PET to structural MRI, and, implicitly, the conjoining of PET and MRI to obtain high resolution functional images.
This paper concerns the spatial and intensity transformations that are required to adjust for the confounding effects of subject movement during functional MRI (fMRI) activation studies. An approach is presented that models, and removes, movement-related artifacts from fMRI time-series. This approach is predicated on the observation that movement-related effects are extant even after perfect realignment. Movement-related effects can be divided into those that are a function of position of the object in the frame of reference of the scanner and those that are due to movement in previous scans. This second component depends on the history of excitation experienced by spins in a small volume and consequent differences in local saturation. The spin excitation history thus will itself be a function of previous positions, suggesting an autoregression-moving average model for the effects of previous displacements on the current signal. A model is described as well as the adjustments for movement-related components that ensue. The empirical analyses suggest that (in extreme situations) over 90% of fMRI signal can be attributed to movement, and that this artifactual component can be successfully removed.
Motion correction in magnetic resonance imaging by real-time adjustment of the imaging pulse sequence was first proposed more than 20 years ago. Recent advances have resulted from combining real-time correction with new navigator and external tracking mechanisms capable of quantifying rigid-body motion in all 6 degrees of freedom. The technique is now often referred to as "prospective motion correction." This article describes the fundamentals of prospective motion correction and reviews the latest developments in its application to brain imaging and spectroscopy. Although emphasis is placed on the brain as the organ of interest, the same principles apply whenever the imaged object can be approximated as a rigid body. Prospective motion correction can be used with most MR sequences, so it has potential to make a large impact in clinical routine. To maximize the benefits obtained from the technique, there are, however, several challenges still to be met. These include practical implementation issues, such as obtaining tracking data with minimal delay, and more fundamental problems, such as the magnetic field distortions caused by a moving object. This review discusses these challenges and summarizes the state of the art. We hope that this work will motivate further developments in prospective motion correction and help the technique to reach its full potential. Magn Reson Med, 2012. © 2012 Wiley Periodicals, Inc.
BrainVoyager started as a simple fMRI analysis tool in the mid 1990s; the software was primarily created to fulfill the needs of its author and his colleagues to analyze anatomical and functional MRI data in a way that would be most appropriate for their research questions in visual and auditory perception. More specifically, the software was designed with three major goals in mind. First, it should allow analyses that would exploit optimally the high-resolution information available in fMRI data. Second, it should integrate volume-based analysis and cortex-based analysis including the possibility to visualize topographic activation data on flattened cortex representations. Third, it should combine hypothesis testing with data-driven analysis including interactive visualization tools that would make it as easy as possible to look at and explore data. A fourth guiding principle was to develop a software package that fulfilled the author's preference for elegant user interfaces, beautiful visualizations and high-performance computing. These major guiding principles from the beginning of BrainVoyager development are still noticeable in the most recent incarnations of the software that has grown from a small fMRI analysis tool on the Windows platform to a comprehensive cross-platform multi-modal software package integrating (real-time) fMRI, DWI/DTI, (i)EEG, MEG, TMS and fNIRS analyses.
SciPy is a Python-based ecosystem of open-source software for mathematics, science, and engineering.
See http://www.scipy.org/ .
Here, we demonstrate that subject motion produces substantial changes in the timecourses of resting state functional connectivity MRI (rs-fcMRI) data despite compensatory spatial registration and regression of motion estimates from the data. These changes cause systematic but spurious correlation structures throughout the brain. Specifically, many long-distance correlations are decreased by subject motion, whereas many short-distance correlations are increased. These changes in rs-fcMRI correlations do not arise from, nor are they adequately countered by, some common functional connectivity processing steps. Two indices of data quality are proposed, and a simple method to reduce motion-related effects in rs-fcMRI analyses is demonstrated that should be flexibly implementable across a variety of software platforms. We demonstrate how application of this technique impacts our own data, modifying previous conclusions about brain development. These results suggest the need for greater care in dealing with subject motion, and the need to critically revisit previous rs-fcMRI work that may not have adequately controlled for effects of transient subject movements.
A new head holder for the fixation and repositioning of the patient head in PET, CT, and MR scanners has been designed and tested. With this device, a bidimensional correlation between functional and anatomical brain images can also be obtained. Head fixation and repositioning are achieved using the patient's dental morphology as an anatomical reference. With a dentistic material, a mold of the patient's teeth is obtained in a few minutes. The molding substance rests on a plastic support, fixed to the head holder. Each time the patient undergoes a new study, his/her personal mold is used, ensuring accurate head repositioning. External markers fixed on the head holder (made visible in lateral PET and CT projection images, midsaggital MR images, and also on the axial images) make it possible to record and recognize the angular orientation and the position of the brain in the three-dimensional space, to correlate images of the same patient obtained with different neuroimaging modalities, and to accurately reposition patients for follow-up studies. The head holder was tested on several subjects. Fixation and repositioning accuracy of within 2.5 mm were achieved in the three-dimensional space. Orientation accuracy was 1 degree.
A brain-computer interface (BCI) based on real-time functional magnetic resonance imaging (fMRI) is presented which allows human subjects to observe and control changes of their own blood oxygen level-dependent (BOLD) response. This BCI performs data preprocessing (including linear trend removal, 3D motion correction) and statistical analysis on-line. Local BOLD signals are continuously fed back to the subject in the magnetic resonance scanner with a delay of less than 2 s from image acquisition. The mean signal of a region of interest is plotted as a time-series superimposed on color-coded stripes which indicate the task, i.e., to increase or decrease the BOLD signal. We exemplify the presented BCI with one volunteer intending to control the signal of the rostral-ventral and dorsal part of the anterior cingulate cortex (ACC). The subject achieved significant changes of local BOLD responses as revealed by region of interest analysis and statistical parametric maps. The percent signal change increased across fMRI-feedback sessions suggesting a learning effect with training. This methodology of fMRI-feedback can assess voluntary control of circumscribed brain areas. As a further extension, behavioral effects of local self-regulation become accessible as a new field of research.
Quantification of fMRI artifact reduction by a novel plaster cast head holder
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All rights reserved. No reuse allowed without permission.
(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint
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http://dx.doi.org/10.1101/595777
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bioRxiv preprint first posted online Apr. 9, 2019;