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Jeong S, Kim SH, Park KN. Effects of core stability and feedback music on upper body mediolateral movements during cycling. BMC Sports Sci Med Rehabil 2024; 16:29. [PMID: 38509568 PMCID: PMC10956249 DOI: 10.1186/s13102-024-00822-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 01/22/2024] [Indexed: 03/22/2024]
Abstract
BACKGROUND Asymmetry in involuntary trunk motion during voluntary movements of the lower extremities is a risk factor for musculoskeletal injuries and may be related to core stability. Core stability plays a pivotal role in maintaining postural stability during distal segment movements. Because mediolateral head motion partially represents trunk motion during rhythmic movements, controlling it can help ensure symmetric trunk motion. This study aimed to investigate the relationship between core stability and asymmetric trunk motion during rhythmic movements, and to evaluate the effects of feedback music on mediolateral head motion. METHODS We developed a system that uses a wireless earbud and a high-resolution inertial measurement unit sensor to measure head angle and provide feedback music. When the head angle exceeds a predefined threshold, the music is muted in the earbud on the side of the head tilt. In our lab-based study, we measured head angles during cycling at 70% of maximum speed using this self-developed system, and compared them between individuals with good (Sahrmann core stability test: 2-5 level) and poor core stability (0-1 level). The amplitude of mediolateral head motion was represented by the difference between the left and right peak angles, and the symmetry in mediolateral head motion was represented by the average of left and right peak angles. RESULTS Individuals with poor core stability demonstrated significantly greater amplitude of, and less symmetry in, mediolateral head motion than those with good core stability. Additionally, feedback music significantly reduced the amplitude of mediolateral head motion in both the good- and poor-core-stability groups. CONCLUSION Our findings indicate that core stability is crucial for maintaining symmetric head motion during rhythmic movements like cycling. Feedback music could serve as an effective tool for promoting symmetry in head motion and thus preventing musculoskeletal injuries.
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Affiliation(s)
- Siwoo Jeong
- Department of Sports Rehabilitation Medicine, College of Smart Sports, Kyungil University, Gyeongsan, South Korea
| | - Si-Hyun Kim
- Department of Physical Therapy, Sangji University, Wonju, South Korea
| | - Kyue-Nam Park
- Department of Physical Education, Yonsei University, Seoul, South Korea.
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Zhou J, Hagberg GE, Aghaeifar A, Bause J, Zaitsev M, Scheffler K. Prediction of motion induced magnetic fields for human brain MRI at 3 T. MAGMA 2023; 36:797-813. [PMID: 36964797 PMCID: PMC10504152 DOI: 10.1007/s10334-023-01076-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 03/08/2023] [Accepted: 03/13/2023] [Indexed: 03/26/2023]
Abstract
OBJECTIVE Maps of B0 field inhomogeneities are often used to improve MRI image quality, even in a retrospective fashion. These field inhomogeneities depend on the exact head position within the static field but acquiring field maps (FM) at every position is time consuming. Here we propose a forward simulation strategy to obtain B0 predictions at different head-positions. METHODS FM were predicted by combining (1) a multi-class tissue model for estimation of tissue-induced fields, (2) a linear k-space model for capturing gradient imperfections, (3) a dipole estimation for quantifying lower-body perturbing fields (4) and a position-dependent tissue mask to model FM alterations caused by large motion effects. The performance of the combined simulation strategy was compared with an approach based on a rigid body transformation of the FM measured in the reference position to the new position. RESULTS The transformed FM provided inconsistent results for large head movements (> 5° rotation, approximately), while the simulation strategy had a superior prediction accuracy for all positions. The simulated FM was used to optimize B0 shims with up to 22.2% improvement with respect to the transformed FM approach. CONCLUSION The proposed simulation strategy is able to predict movement-induced B0 field inhomogeneities yielding more precise estimates of the ground truth field homogeneity than the transformed FM.
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Affiliation(s)
- Jiazheng Zhou
- Max Planck Institute for Biological Cybernetics, High-Field Magnetic Resonance Center, Max-Planck-Ring 11, 72076, Tübingen, Germany.
- IMPRS for Cognitive and Systems Neuroscience, University of Tübingen, Tübingen, Germany.
| | - Gisela E Hagberg
- Max Planck Institute for Biological Cybernetics, High-Field Magnetic Resonance Center, Max-Planck-Ring 11, 72076, Tübingen, Germany
- Department of Biomedical Magnetic Resonance, University Hospital Tübingen, Tübingen, Germany
| | - Ali Aghaeifar
- Max Planck Institute for Biological Cybernetics, High-Field Magnetic Resonance Center, Max-Planck-Ring 11, 72076, Tübingen, Germany
- Department of Biomedical Magnetic Resonance, University Hospital Tübingen, Tübingen, Germany
| | - Jonas Bause
- Max Planck Institute for Biological Cybernetics, High-Field Magnetic Resonance Center, Max-Planck-Ring 11, 72076, Tübingen, Germany
| | - Maxim Zaitsev
- Department of Radiology, Medical Physics, Faculty of Medicine, Medical Center University of Freiburg, University of Freiburg, Freiburg, Germany
| | - Klaus Scheffler
- Max Planck Institute for Biological Cybernetics, High-Field Magnetic Resonance Center, Max-Planck-Ring 11, 72076, Tübingen, Germany
- Department of Biomedical Magnetic Resonance, University Hospital Tübingen, Tübingen, Germany
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Vakli P, Weiss B, Szalma J, Barsi P, Gyuricza I, Kemenczky P, Somogyi E, Nárai Á, Gál V, Hermann P, Vidnyánszky Z. Automatic brain MRI motion artifact detection based on end-to-end deep learning is similarly effective as traditional machine learning trained on image quality metrics. Med Image Anal 2023; 88:102850. [PMID: 37263108 DOI: 10.1016/j.media.2023.102850] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 04/28/2023] [Accepted: 05/22/2023] [Indexed: 06/03/2023]
Abstract
Head motion artifacts in magnetic resonance imaging (MRI) are an important confounding factor concerning brain research as well as clinical practice. For this reason, several machine learning-based methods have been developed for the automatic quality control of structural MRI scans. Deep learning offers a promising solution to this problem, however, given its data-hungry nature and the scarcity of expert-annotated datasets, its advantage over traditional machine learning methods in identifying motion-corrupted brain scans is yet to be determined. In the present study, we investigated the relative advantage of the two methods in structural MRI quality control. To this end, we collected publicly available T1-weighted images and scanned subjects in our own lab under conventional and active head motion conditions. The quality of the images was rated by a team of radiologists from the point of view of clinical diagnostic use. We present a relatively simple, lightweight 3D convolutional neural network trained in an end-to-end manner that achieved a test set (N = 411) balanced accuracy of 94.41% in classifying brain scans into clinically usable or unusable categories. A support vector machine trained on image quality metrics achieved a balanced accuracy of 88.44% on the same test set. Statistical comparison of the two models yielded no significant difference in terms of confusion matrices, error rates, or receiver operating characteristic curves. Our results suggest that these machine learning methods are similarly effective in identifying severe motion artifacts in brain MRI scans, and underline the efficacy of end-to-end deep learning-based systems in brain MRI quality control, allowing the rapid evaluation of diagnostic utility without the need for elaborate image pre-processing.
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Affiliation(s)
- Pál Vakli
- Brain Imaging Centre, Research Centre for Natural Sciences, Budapest 1117, Hungary.
| | - Béla Weiss
- Brain Imaging Centre, Research Centre for Natural Sciences, Budapest 1117, Hungary.
| | - János Szalma
- Brain Imaging Centre, Research Centre for Natural Sciences, Budapest 1117, Hungary
| | - Péter Barsi
- Brain Imaging Centre, Research Centre for Natural Sciences, Budapest 1117, Hungary
| | - István Gyuricza
- Brain Imaging Centre, Research Centre for Natural Sciences, Budapest 1117, Hungary
| | - Péter Kemenczky
- Brain Imaging Centre, Research Centre for Natural Sciences, Budapest 1117, Hungary
| | - Eszter Somogyi
- Brain Imaging Centre, Research Centre for Natural Sciences, Budapest 1117, Hungary
| | - Ádám Nárai
- Brain Imaging Centre, Research Centre for Natural Sciences, Budapest 1117, Hungary
| | - Viktor Gál
- Brain Imaging Centre, Research Centre for Natural Sciences, Budapest 1117, Hungary
| | - Petra Hermann
- Brain Imaging Centre, Research Centre for Natural Sciences, Budapest 1117, Hungary
| | - Zoltán Vidnyánszky
- Brain Imaging Centre, Research Centre for Natural Sciences, Budapest 1117, Hungary.
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Sun C, Revilla EM, Zhang J, Fontaine K, Toyonaga T, Gallezot JD, Mulnix T, Onofrey JA, Carson RE, Lu Y. An objective evaluation method for head motion estimation in PET-Motion corrected centroid-of-distribution. Neuroimage 2022; 264:119678. [PMID: 36261057 DOI: 10.1016/j.neuroimage.2022.119678] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 09/16/2022] [Accepted: 10/07/2022] [Indexed: 11/06/2022] Open
Abstract
Head motion presents a continuing problem in brain PET studies. A wealth of motion correction (MC) algorithms had been proposed in the past, including both hardware-based methods and data-driven methods. However, in most real brain PET studies, in the absence of ground truth or gold standard of motion information, it is challenging to objectively evaluate MC quality. For MC evaluation, image-domain metrics, e.g., standardized uptake value (SUV) change before and after MC are commonly used, but this measure lacks objectivity because 1) other factors, e.g., attenuation correction, scatter correction and parameters used in the reconstruction, will confound MC effectiveness; 2) SUV only reflects final image quality, and it cannot precisely inform when an MC method performed well or poorly during the scan time period; 3) SUV is tracer-dependent and head motion may cause increases or decreases in SUV for different tracers, so evaluating MC effectiveness is complicated. Here, we present a new algorithm, i.e., motion corrected centroid-of-distribution (MCCOD) to perform objective quality control for measured or estimated rigid motion information. MCCOD is a three-dimensional surrogate trace of the center of tracer distribution after performing rigid MC using the existing motion information. MCCOD is used to inform whether the motion information is accurate, using the PET raw data only, i.e., without PET image reconstruction, where inaccurate motion information typically leads to abrupt changes in the MCCOD trace. MCCOD was validated using simulation studies and was tested on real studies acquired from both time-of-flight (TOF) and non-TOF scanners. A deep learning-based brain mask segmentation was implemented, which is shown to be necessary for non-TOF MCCOD generation. MCCOD is shown to be effective in detecting abrupt translation motion errors in slowly varying tracer distribution caused by the motion tracking hardware and can be used to compare different motion estimation methods as well as to improve existing motion information.
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Affiliation(s)
- Chen Sun
- Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada
| | - Enette Mae Revilla
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, United States
| | - Jiazhen Zhang
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, United States
| | - Kathryn Fontaine
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, United States
| | - Takuya Toyonaga
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, United States
| | - Jean-Dominique Gallezot
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, United States
| | - Tim Mulnix
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, United States
| | - John A Onofrey
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, United States; Department of Urology, Yale University, New Haven, CT, United States; Department of Biomedical Engineering, Yale University, New Haven, CT, United States
| | - Richard E Carson
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, United States; Department of Biomedical Engineering, Yale University, New Haven, CT, United States
| | - Yihuan Lu
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, United States.
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Rivera-Rivera LA, Kecskemeti S, Jen ML, Miller Z, Johnson SC, Eisenmenger L, Johnson KM. Motion-corrected 4D-Flow MRI for neurovascular applications. Neuroimage 2022; 264:119711. [PMID: 36307060 PMCID: PMC9801539 DOI: 10.1016/j.neuroimage.2022.119711] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Revised: 10/10/2022] [Accepted: 10/24/2022] [Indexed: 11/06/2022] Open
Abstract
Neurovascular 4D-Flow MRI has emerged as a powerful tool for comprehensive cerebrovascular hemodynamic characterization. Clinical studies in at risk populations such as aging adults indicate hemodynamic markers can be confounded by motion-induced bias. This study develops and characterizes a high fidelity 3D self-navigation approach for retrospective rigid motion correction of neurovascular 4D-Flow data. A 3D radial trajectory with pseudorandom ordering was combined with a multi-resolution low rank regularization approach to enable high spatiotemporal resolution self-navigators from extremely undersampled data. Phantom and volunteer experiments were performed at 3.0T to evaluate the ability to correct for different amounts of induced motions. In addition, the approach was applied to clinical-research exams from ongoing aging studies to characterize performance in the clinical setting. Simulations, phantom and volunteer experiments with motion correction produced images with increased vessel conspicuity, reduced image blurring, and decreased variability in quantitative measures. Clinical exams revealed significant changes in hemodynamic parameters including blood flow rates, flow pulsatility index, and lumen areas after motion correction in probed cerebral arteries (Flow: P<0.001 Lt ICA, P=0.002 Rt ICA, P=0.004 Lt MCA, P=0.004 Rt MCA; Area: P<0.001 Lt ICA, P<0.001 Rt ICA, P=0.004 Lt MCA, P=0.004 Rt MCA; flow pulsatility index: P=0.042 Rt ICA, P=0.002 Lt MCA). Motion induced bias can lead to significant overestimation of hemodynamic markers in cerebral arteries. The proposed method reduces measurement bias from rigid motion in neurovascular 4D-Flow MRI in challenging populations such as aging adults.
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Affiliation(s)
- Leonardo A Rivera-Rivera
- Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, 1111 Highland Ave, Rm 1005, Madison, WI, 53705-2275, United States; Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, WI, 53792, United States
| | - Steve Kecskemeti
- Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, 1111 Highland Ave, Rm 1005, Madison, WI, 53705-2275, United States
| | - Mu-Lan Jen
- Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, 1111 Highland Ave, Rm 1005, Madison, WI, 53705-2275, United States
| | - Zachary Miller
- Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, 1111 Highland Ave, Rm 1005, Madison, WI, 53705-2275, United States
| | - Sterling C Johnson
- Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, WI, 53792, United States
| | - Laura Eisenmenger
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI, 53792, United States
| | - Kevin M Johnson
- Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, 1111 Highland Ave, Rm 1005, Madison, WI, 53705-2275, United States; Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI, 53792, United States.
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6
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Hsu YC, Tseng WI. DACO: Distortion/artefact correction for diffusion MRI data. Neuroimage 2022; 262:119571. [PMID: 35985619 DOI: 10.1016/j.neuroimage.2022.119571] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Revised: 08/12/2022] [Accepted: 08/15/2022] [Indexed: 01/14/2023] Open
Abstract
In this paper, we propose a registration-based algorithm to correct various distortions or artefacts (DACO) commonly observed in diffusion-weighted (DW) magnetic resonance images (MRI). The registration in DACO is accomplished by means of a pseudo b0 image, which is synthesized from the anatomical images such as T1-weighted image or T2-weighted image, and a pseudo diffusion MRI (dMRI) data, which is derived from the Gaussian model of diffusion tensor imaging (DTI) or the Hermite model of mean apparent propagator (MAP)-MRI. DACO corrects (1) the susceptibility-induced distortions and (2) the misalignment between the dMRI data and anatomical images by registering the real b0 image to the pseudo b0 image, and corrects (3) the eddy current-induced distortions and (4) the head motions by registering each image in the real dMRI data to the corresponding image in the pseudo dMRI data. DACO estimates the models of artefacts simultaneously in an iterative and interleaved manner. The mathematical formulation of the models and the estimation procedures are detailed in this paper. Using the human connectome project (HCP) data the evaluation shows that DACO could estimate the model parameters accurately. Furthermore, the evaluation conducted on the real human data acquired from clinical MRI scanners reveals that the method could reduce the artefacts effectively. The DACO method leverages the anatomical image, which is routinely acquired in clinical practice, to correct the artefacts, omitting the additional acquisitions needed to conduct the algorithm. Therefore, our method should be beneficial to most dMRI data, particularly to those acquired without field maps or reverse phase-encoding images.
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7
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Hausman HK, Hardcastle C, Kraft JN, Evangelista ND, Boutzoukas EM, O’Shea A, Albizu A, Langer K, Van Etten EJ, Bharadwaj PK, Song H, Smith SG, Porges E, Hishaw GA, Wu S, DeKosky S, Alexander GE, Marsiske M, Cohen R, Woods AJ. The association between head motion during functional magnetic resonance imaging and executive functioning in older adults. Neuroimage Rep 2022; 2:100085. [PMID: 37377763 PMCID: PMC10299743 DOI: 10.1016/j.ynirp.2022.100085] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/29/2023]
Abstract
Minimizing head motion during functional magnetic resonance imaging (fMRI) is important for maintaining the integrity of neuroimaging data. While there are a variety of techniques to control for head motion, oftentimes, individuals with excessive in-scanner motion are removed from analyses. Movement in the scanner tends to increase with age; however, the cognitive profile of these "high-movers" in older adults has yet to be explored. This study aimed to assess the association between in-scanner head motion (i.e., number of "invalid scans" flagged as motion outliers) and cognitive functioning (e.g., executive functioning, processing speed, and verbal memory performance) in a sample of 282 healthy older adults. Spearman's Rank-Order correlations showed that a higher number of invalid scans was significantly associated with poorer performance on tasks of inhibition and cognitive flexibility and with older age. Since performance in these domains tend to decline as a part of the non-pathological aging process, these findings raise concerns regarding the potential systematic exclusion due to motion of older adults with lower executive functioning in neuroimaging samples. Future research should continue to explore prospective motion correction techniques to better ensure the collection of quality neuroimaging data without excluding informative participants from the sample.
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Affiliation(s)
- Hanna K. Hausman
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, FL, USA
- Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA
| | - Cheshire Hardcastle
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, FL, USA
- Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA
| | - Jessica N. Kraft
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, FL, USA
- Department of Neuroscience, College of Medicine, University of Florida, Gainesville, FL, USA
| | - Nicole D. Evangelista
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, FL, USA
- Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA
| | - Emanuel M. Boutzoukas
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, FL, USA
- Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA
| | - Andrew O’Shea
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, FL, USA
- Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA
| | - Alejandro Albizu
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, FL, USA
- Department of Neuroscience, College of Medicine, University of Florida, Gainesville, FL, USA
| | - Kailey Langer
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, FL, USA
- Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA
| | - Emily J. Van Etten
- Brain Imaging, Behavior and Aging Laboratory, Department of Psychology and Evelyn F. McKnight Brain Institute, University of Arizona, Tucson, AZ, USA
| | - Pradyumna K. Bharadwaj
- Brain Imaging, Behavior and Aging Laboratory, Department of Psychology and Evelyn F. McKnight Brain Institute, University of Arizona, Tucson, AZ, USA
| | - Hyun Song
- Brain Imaging, Behavior and Aging Laboratory, Department of Psychology and Evelyn F. McKnight Brain Institute, University of Arizona, Tucson, AZ, USA
| | - Samantha G. Smith
- Brain Imaging, Behavior and Aging Laboratory, Department of Psychology and Evelyn F. McKnight Brain Institute, University of Arizona, Tucson, AZ, USA
| | - Eric Porges
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, FL, USA
- Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA
| | - Georg A. Hishaw
- Department of Psychiatry, Neuroscience and Physiological Sciences Graduate Interdisciplinary Programs and BIO5 Institute, University of Arizona and Arizona Alzheimer’s Disease Consortium, Tucson, AZ, USA
| | - Samuel Wu
- Department of Biostatistics, College of Public Health and Health Professions, College of Medicine, University of Florida, Gainesville, FL, USA
| | - Steven DeKosky
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, FL, USA
- Department of Neurology, College of Medicine, University of Florida, Gainesville, FL, USA
| | - Gene E. Alexander
- Brain Imaging, Behavior and Aging Laboratory, Department of Psychology and Evelyn F. McKnight Brain Institute, University of Arizona, Tucson, AZ, USA
- Department of Psychiatry, Neuroscience and Physiological Sciences Graduate Interdisciplinary Programs and BIO5 Institute, University of Arizona and Arizona Alzheimer’s Disease Consortium, Tucson, AZ, USA
| | - Michael Marsiske
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, FL, USA
- Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA
| | - Ronald Cohen
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, FL, USA
- Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA
| | - Adam J. Woods
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, FL, USA
- Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA
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Revilla EM, Gallezot JD, Naganawa M, Toyonaga T, Fontaine K, Mulnix T, Onofrey JA, Carson RE, Lu Y. Adaptive data-driven motion detection and optimized correction for brain PET. Neuroimage 2022; 252:119031. [PMID: 35257856 PMCID: PMC9206767 DOI: 10.1016/j.neuroimage.2022.119031] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 02/16/2022] [Accepted: 02/21/2022] [Indexed: 12/03/2022] Open
Abstract
Head motion during PET scans causes image quality degradation, decreased concentration in regions with high uptake and incorrect outcome measures from kinetic analysis of dynamic datasets. Previously, we proposed a data-driven method, center of tracer distribution (COD), to detect head motion without an external motion tracking device. There, motion was detected using one dimension of the COD trace with a semiautomatic detection algorithm, requiring multiple user defined parameters and manual intervention. In this study, we developed a new data-driven motion detection algorithm, which is automatic, self-adaptive to noise level, does not require user-defined parameters and uses all three dimensions of the COD trace (3DCOD). 3DCOD was first validated and tested using 30 simulation studies (18F-FDG, N = 15; 11C-raclopride (RAC), N = 15) with large motion. The proposed motion correction method was tested on 22 real human datasets, with 20 acquired from a high resolution research tomograph (HRRT) scanner (18F-FDG, N = 10; 11C-RAC, N = 10) and 2 acquired from the Siemens Biograph mCT scanner. Real-time hardware-based motion tracking information (Vicra) was available for all real studies and was used as the gold standard. 3DCOD was compared to Vicra, no motion correction (NMC), one-direction COD (our previous method called 1DCOD) and two conventional frame-based image registration (FIR) algorithms, i.e., FIR1 (based on predefined frames reconstructed with attenuation correction) and FIR2 (without attenuation correction) for both simulation and real studies. For the simulation studies, 3DCOD yielded -2.3 ± 1.4% (mean ± standard deviation across all subjects and 11 brain regions) error in region of interest (ROI) uptake for 18F-FDG (-3.4 ± 1.7% for 11C-RAC across all subjects and 2 regions) as compared to Vicra (perfect correction) while NMC, FIR1, FIR2 and 1DCOD yielded -25.4 ± 11.1% (-34.5 ± 16.1% for 11C- RAC), -13.4 ± 3.5% (-16.1 ± 4.6%), -5.7 ± 3.6% (-8.0 ± 4.5%) and -2.6 ± 1.5% (-5.1 ± 2.7%), respectively. For real HRRT studies, 3DCOD yielded -0.3 ± 2.8% difference for 18F-FDG (-0.4 ± 3.2% for 11C-RAC) as compared to Vicra while NMC, FIR1, FIR2 and 1DCOD yielded -14.9 ± 9.0% (-24.5 ± 14.6%), -3.6 ± 4.9% (-13.4 ± 14.3%), -0.6 ± 3.4% (-6.7 ± 5.3%) and -1.5 ± 4.2% (-2.2 ± 4.1%), respectively. In summary, the proposed motion correction method yielded comparable performance to the hardware-based motion tracking method for multiple tracers, including very challenging cases with large frequent head motion, in studies performed on a non-TOF scanner.
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Affiliation(s)
- Enette Mae Revilla
- Department of Radiology and Biomedical Imaging, Yale University, PO Box 208048, New Haven, CT 06520-8048, USA
| | - Jean-Dominique Gallezot
- Department of Radiology and Biomedical Imaging, Yale University, PO Box 208048, New Haven, CT 06520-8048, USA
| | - Mika Naganawa
- Department of Radiology and Biomedical Imaging, Yale University, PO Box 208048, New Haven, CT 06520-8048, USA
| | - Takuya Toyonaga
- Department of Radiology and Biomedical Imaging, Yale University, PO Box 208048, New Haven, CT 06520-8048, USA
| | - Kathryn Fontaine
- Department of Radiology and Biomedical Imaging, Yale University, PO Box 208048, New Haven, CT 06520-8048, USA
| | - Tim Mulnix
- Department of Radiology and Biomedical Imaging, Yale University, PO Box 208048, New Haven, CT 06520-8048, USA
| | - John A Onofrey
- Department of Radiology and Biomedical Imaging, Yale University, PO Box 208048, New Haven, CT 06520-8048, USA; Department of Urology, Yale University, New Haven, CT, USA; Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Richard E Carson
- Department of Radiology and Biomedical Imaging, Yale University, PO Box 208048, New Haven, CT 06520-8048, USA; Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Yihuan Lu
- Department of Radiology and Biomedical Imaging, Yale University, PO Box 208048, New Haven, CT 06520-8048, USA.
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Cosgrove KT, Mcdermott TJ, White EJ, Mosconi MW, Thompson WK, Paulus MP, Cardenas-iniguez C, Aupperle RL. Limits to the generalizability of resting-state functional magnetic resonance imaging studies of youth: An examination of ABCD Study® baseline data. Brain Imaging Behav. [PMID: 35552993 DOI: 10.1007/s11682-022-00665-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/18/2022] [Indexed: 11/02/2022]
Abstract
This study examined how resting-state functional magnetic resonance imaging (rs-fMRI) data quality and availability relate to clinical and sociodemographic variables within the Adolescent Brain Cognitive Development Study. A sample of participants with an adequate sample of quality baseline rs-fMRI data containing low average motion (framewise displacement ≤ 0.15; low-noise; n = 4,356) was compared to a sample of participants without an adequate sample of quality data and/or containing high average motion (higher-noise; n = 7,437) using Chi-squared analyses and t-tests. A linear mixed model examined relationships between clinical and sociodemographic characteristics and average head motion in the sample with low-noise data. Relative to the sample with higher-noise data, the low-noise sample included more females, youth identified by parents as non-Hispanic white, and youth with married parents, higher parent education, and greater household incomes (ORs = 1.32-1.42). Youth in the low-noise sample were also older and had higher neurocognitive skills, lower BMIs, and fewer externalizing and neurodevelopmental problems (ds = 0.12-0.30). Within the low-noise sample, several clinical and demographic characteristics related to motion. Thus, participants with low-noise rs-fMRI data may be less representative of the general population and motion may remain a confound in this sample. Future rs-fMRI studies of youth should consider these limitations in the design and analysis stages in order to optimize the representativeness and clinical relevance of analyses and results.
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González-Cely AX, Callejas-Cuervo M, Bastos-Filho T. Wheelchair prototype controlled by position, speed and orientation using head movement. HardwareX 2022; 11:e00306. [PMID: 35509895 PMCID: PMC9058847 DOI: 10.1016/j.ohx.2022.e00306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 03/28/2022] [Accepted: 04/09/2022] [Indexed: 06/14/2023]
Abstract
A prototype that simulates a wheelchair was built using electronic commercial devices and software implementation with the aim to operate the prototype using head movement and analyzing the system response. The controllers were simulated using MATLAB® toolbox and Python™ libraries. The mean time response of the system with manual control was 37,8 s. The mean orientation control response with constant speed was 36,5 s and the mean orientation control response with variable speed was 44,2 s in a specific route. The variable speed response is slower than constant speed due to head motion error. The system was rated such as" very good" by 10 participants using a System Usability Scale (SUS).
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Affiliation(s)
- Aura Ximena González-Cely
- Postgraduate Program in Electrical Engineering, Federal University of Esṕırito Santo, Av. Fernando Ferrari, 514, Vitoria 29075-910, Brazil
| | - Mauro Callejas-Cuervo
- Software Research Group, Universidad Pedaǵogica y Tecnoĺogica de Colombia, Av. Central del Norte 39- 115, Tunja 150001, Colombia
| | - Teodiano Bastos-Filho
- Postgraduate Program in Electrical Engineering, Federal University of Esṕırito Santo, Av. Fernando Ferrari, 514, Vitoria 29075-910, Brazil
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Rubio Barañano A, Faisal M, Barrett BT, Buckley JG. Using a smartphone on the move: do visual constraints explain why we slow walking speed? Exp Brain Res 2021; 240:467-480. [PMID: 34792640 PMCID: PMC8858309 DOI: 10.1007/s00221-021-06267-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Accepted: 10/29/2021] [Indexed: 11/24/2022]
Abstract
Viewing one’s smartphone whilst walking commonly leads to a slowing of walking. Slowing walking speed may occur because of the visual constraints related to reading the hand-held phone whilst in motion. We determine how walking-induced phone motion affects the ability to read on-screen information. Phone-reading performance (PRP) was assessed whilst participants walked on a treadmill at various speeds (Slow, Customary, Fast). The fastest speed was repeated, wearing an elbow brace (Braced) or with the phone mounted stationary (Fixed). An audible cue (‘text-alert’) indicated participants had 2 s to lift/view the phone and read aloud a series of digits. PRP was the number of digits read correctly. Each condition was repeated 5 times. 3D-motion analyses determined phone motion relative to the head, from which the variability in acceleration in viewing distance, and in the point of gaze in space in the up-down and right-left directions were assessed. A main effect of condition indicated PRP decreased with walking speed; particularly so for the Braced and Fixed conditions (p = 0.022). Walking condition also affected the phone’s relative motion (p < 0.001); post-hoc analysis indicated that acceleration variability for the Fast, Fixed and Braced conditions were increased compared to that for Slow and Customary speed walking (p ≤ 0.05). There was an inverse association between phone acceleration variability and PRP (p = 0.02). These findings may explain why walking speed slows when viewing a hand-held phone: at slower speeds, head motion is smoother/more regular, enabling the motion of the phone to be coupled with head motion, thus making fewer demands on the oculomotor system. Good coupling ensures that the retinal image is stable enough to allow legibility of the information presented on the screen.
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Affiliation(s)
| | - Muhammad Faisal
- Faculty of Health Studies, University of Bradford, Bradford, UK
| | - Brendan T Barrett
- School of Optometry and Vision Science, University of Bradford, Bradford, UK
| | - John G Buckley
- Department of Biomedical and Electronics Engineering, University of Bradford, Bradford, UK.
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Abstract
Rhythms of the brain are generated by neural oscillations across multiple frequencies. These oscillations can be decomposed into distinct frequency intervals associated with specific physiological processes. In practice, the number and ranges of decodable frequency intervals are determined by sampling parameters, often ignored by researchers. To improve the situation, we report on an open toolbox with a graphical user interface for decoding rhythms of the brain system (DREAM). We provide worked examples of DREAM to investigate frequency-specific performance of both neural (spontaneous brain activity) and neurobehavioral (in-scanner head motion) oscillations. DREAM decoded the head motion oscillations and uncovered that younger children moved their heads more than older children across all five frequency intervals whereas boys moved more than girls in the age of 7 to 9 years. It is interesting that the higher frequency bands contain more head movements, and showed stronger age-motion associations but weaker sex-motion interactions. Using data from the Human Connectome Project, DREAM mapped the amplitude of these neural oscillations into multiple frequency bands and evaluated their test-retest reliability. The resting-state brain ranks its spontaneous oscillation's amplitudes spatially from high in ventral-temporal areas to low in ventral-occipital areas when the frequency band increased from low to high, while those in part of parietal and ventral frontal regions are reversed. The higher frequency bands exhibited more reliable amplitude measurements, implying more inter-individual variability of the amplitudes for the higher frequency bands. In summary, DREAM adds a reliable and valid tool to mapping human brain function from a multiple-frequency window into brain waves.
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Affiliation(s)
- Zhu-Qing Gong
- Key Laboratory of Behavioral Sciences, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- National Basic Public Science Data Center, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Peng Gao
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Chao Jiang
- Key Laboratory of Behavioral Sciences, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- National Basic Public Science Data Center, Beijing, China
| | - Xiu-Xia Xing
- Department of Applied Mathematics, College of Mathematics, Faculty of Science, Beijing University of Technology, Beijing, China
| | - Hao-Ming Dong
- National Basic Public Science Data Center, Beijing, China
| | - Tonya White
- Department of Child and Adolescent Psychiatry, Erasmus University, Rotterdam, Netherlands
| | - F Xavier Castellanos
- Langone Medical Center, Child Study Center, New York University, New York, NY, USA
- Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Hai-Fang Li
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China.
| | - Xi-Nian Zuo
- Key Laboratory of Behavioral Sciences, Institute of Psychology, Chinese Academy of Sciences, Beijing, China.
- National Basic Public Science Data Center, Beijing, China.
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China.
- Key Laboratory for Brain and Education Science, Nanning Normal University, Nanning, China.
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.
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Pardoe HR, Martin SP, Zhao Y, George A, Yuan H, Zhou J, Liu W, Devinsky O. Estimation of in-scanner head pose changes during structural MRI using a convolutional neural network trained on eye tracker video. Magn Reson Imaging 2021; 81:101-108. [PMID: 34147591 DOI: 10.1016/j.mri.2021.06.010] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Revised: 05/06/2021] [Accepted: 06/15/2021] [Indexed: 10/21/2022]
Abstract
INTRODUCTION In-scanner head motion is a common cause of reduced image quality in neuroimaging, and causes systematic brain-wide changes in cortical thickness and volumetric estimates derived from structural MRI scans. There are few widely available methods for measuring head motion during structural MRI. Here, we train a deep learning predictive model to estimate changes in head pose using video obtained from an in-scanner eye tracker during an EPI-BOLD acquisition with participants undertaking deliberate in-scanner head movements. The predictive model was used to estimate head pose changes during structural MRI scans, and correlated with cortical thickness and subcortical volume estimates. METHODS 21 healthy controls (age 32 ± 13 years, 11 female) were studied. Participants carried out a series of stereotyped prompted in-scanner head motions during acquisition of an EPI-BOLD sequence with simultaneous recording of eye tracker video. Motion-affected and motion-free whole brain T1-weighted MRI were also obtained. Image coregistration was used to estimate changes in head pose over the duration of the EPI-BOLD scan, and used to train a predictive model to estimate head pose changes from the video data. Model performance was quantified by assessing the coefficient of determination (R2). We evaluated the utility of our technique by assessing the relationship between video-based head pose changes during structural MRI and (i) vertex-wise cortical thickness and (ii) subcortical volume estimates. RESULTS Video-based head pose estimates were significantly correlated with ground truth head pose changes estimated from EPI-BOLD imaging in a hold-out dataset. We observed a general brain-wide overall reduction in cortical thickness with increased head motion, with some isolated regions showing increased cortical thickness estimates with increased motion. Subcortical volumes were generally reduced in motion affected scans. CONCLUSIONS We trained a predictive model to estimate changes in head pose during structural MRI scans using in-scanner eye tracker video. The method is independent of individual image acquisition parameters and does not require markers to be to be fixed to the patient, suggesting it may be well suited to clinical imaging and research environments. Head pose changes estimated using our approach can be used as covariates for morphometric image analyses to improve the neurobiological validity of structural imaging studies of brain development and disease.
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Affiliation(s)
- Heath R Pardoe
- Comprehensive Epilepsy Center, Department of Neurology, NYU Grossman School of Medicine, New York, USA.
| | - Samantha P Martin
- Comprehensive Epilepsy Center, Department of Neurology, NYU Grossman School of Medicine, New York, USA
| | | | - Allan George
- Comprehensive Epilepsy Center, Department of Neurology, NYU Grossman School of Medicine, New York, USA
| | - Hui Yuan
- Fordham University, New York, USA
| | | | - Wei Liu
- Fordham University, New York, USA
| | - Orrin Devinsky
- Comprehensive Epilepsy Center, Department of Neurology, NYU Grossman School of Medicine, New York, USA
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Berges AJ, Razavi C, Shahbazi M, Taylor R, Carey JP, Creighton FX. Characterization of patient head motion in otologic surgery: Implications for TEES. Am J Otolaryngol 2021; 42:102875. [PMID: 33418180 DOI: 10.1016/j.amjoto.2020.102875] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Accepted: 12/20/2020] [Indexed: 10/22/2022]
Abstract
PURPOSE Middle ear disease is increasingly being managed via transcanal endoscopic ear surgery (TEES). A limitation of TEES is that it restricts the surgeon to single-handed dissection. One solution to this would be an endoscope holder to facilitate two-handed dissection. Current endoscope holders are stationary, and can cause potential damage from endoscope contact with the ossicles or ear canal if unintended head motion occurs from inadequate anesthetic. A dynamic device that could detect and react to patient motion would mitigate these concerns, but currently there is little formal characterization of the frequency, velocity and acceleration of unintended patient head motion during otologic procedures performed under general anesthesia. The present study aims to characterize intraoperative patient head motion kinematics during cases utilizing TEES. MATERIALS AND METHODS This is a prospective study of adults undergoing otologic procedures performed with general anesthesia and without paralysis. Head motion was characterized using a nine-axis inertial measurement unit (IMU), (LPMS-B2, Life Performance Research) mounted to each patient's forehead for the procedure duration. RESULTS Data was collected across 10 cases; 50% of patients were female and mean age was 50 ± 14 years. There was observed patient head motion in 40% of cases with maximum linear acceleration of 0.75 m/s2 and angular velocity of 12.50 degrees/s. CONCLUSIONS Patient movement during otologic procedures was commonly observed, demonstrating the need for a dynamic holder to allow two-handed TEES. Results from this study are the first objective characterization of patient head motion kinematics during otologic procedures performed under general anesthesia.
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Berges AJ, Razavi C, Shahbazi M, Taylor R, Carey JP, Creighton FX. Characterization of patient head motion in otologic surgery: Implications for TEES. Am J Otolaryngol 2021; 42:102827. [PMID: 33181483 DOI: 10.1016/j.amjoto.2020.102827] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Accepted: 10/30/2020] [Indexed: 11/23/2022]
Abstract
PURPOSE Middle ear disease is increasingly being managed via transcanal endoscopic ear surgery (TEES). A limitation of TEES is that it restricts the surgeon to single-handed dissection. One solution to this would be an endoscope holder to facilitate two-handed dissection. Current endoscope holders are stationary, and can cause potential damage from endoscope contact with the ossicles or ear canal if unintended head motion occurs from inadequate anesthetic. A dynamic device that could detect and react to patient motion would mitigate these concerns, but currently there is little formal characterization of the frequency, velocity and acceleration of unintended patient head motion during otologic procedures performed under general anesthesia. The present study aims to characterize intraoperative patient head motion kinematics during cases utilizing TEES. MATERIALS AND METHODS This is a prospective study of adults undergoing otologic procedures performed with general anesthesia and without paralysis. Head motion was characterized using a nine-axis inertial measurement unit (IMU), (LPMS-B2, Life Performance Research) mounted to each patient's forehead for the procedure duration. RESULTS Data was collected across 10 cases; 50% of patients were female and mean age was 50 ± 14 years. There was observed patient head motion in 40% of cases with maximum linear acceleration of 0.75 m/s2 and angular velocity of 12.50 degrees/s. CONCLUSIONS Patient movement during otologic procedures was commonly observed, demonstrating the need for a dynamic holder to allow two-handed TEES. Results from this study are the first objective characterization of patient head motion kinematics during otologic procedures performed under general anesthesia.
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Natrup J, de Lussanet MHE, Boström KJ, Lappe M, Wagner H. Gaze, head and eye movements during somersaults with full twists. Hum Mov Sci 2020; 75:102740. [PMID: 33307374 DOI: 10.1016/j.humov.2020.102740] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Revised: 11/27/2020] [Accepted: 11/27/2020] [Indexed: 11/28/2022]
Abstract
Somersaults with or without twists are the most important elements in sports such as gymnastics or trampolining. Moreover, to perform elements with the highest possible difficulty gymnasts should show good form and execution during the flight phase. In order to ensure perfect body control and a safe landing, gaze behavior has been proven to be crucial for athletes to orientate in the air. As eye movement and head movement are closely coordinated, both must be examined while investigating gaze behavior. The aim of the current study is to analyze athletes' head motion and gaze behavior during somersaults with full twists. 15 skilled trampoline gymnasts performed back straight somersaults with a full twist (back full) on the trampoline. Eye movement and head movement were recorded using a portable eye-tracking device and a motion capture suit. The results indicate that gymnasts use the trampoline bed as a fixation point for orientation and control the back full, whereas the fixation onsets for athletes of a better performance class occur significantly later. A strong coordination between gymnasts' eye movement and head movement could be determined: stabilizing the gaze during the fixation period, the eyes move in combination with the head against the twisted somersault direction to counteract the whole body rotation. Although no significant differences could be found between the performance classes with regard to the maximum axial head rotations and maximum head extensions, there seems to be a trend that less skilled gymnasts need orientation as early as possible resulting in greater head rotation angles but a poorer execution.
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Affiliation(s)
- Jens Natrup
- Department of Movement Science, University of Muenster, Muenster, Germany.
| | - Marc H E de Lussanet
- Department of Movement Science, University of Muenster, Muenster, Germany; Otto Creutzfeld Center, University of Muenster, Muenster, Germany
| | - Kim Joris Boström
- Department of Movement Science, University of Muenster, Muenster, Germany
| | - Markus Lappe
- Otto Creutzfeld Center, University of Muenster, Muenster, Germany; Institute for Psychology, University of Muenster, Muenster, Germany
| | - Heiko Wagner
- Department of Movement Science, University of Muenster, Muenster, Germany; Otto Creutzfeld Center, University of Muenster, Muenster, Germany
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Paholpak P, Vega A, Formanek B, Tamai K, Wang JC, Buser Z. Impact of cervical sagittal balance and cervical spine alignment on craniocervical junction motion: an analysis using upright multi-positional MRI. Eur Spine J 2020; 30:444-453. [PMID: 32770266 DOI: 10.1007/s00586-020-06559-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Revised: 07/13/2020] [Accepted: 07/27/2020] [Indexed: 11/29/2022]
Abstract
PURPOSE To evaluate the effect of cervical sagittal alignment on craniocervical junction kinematic. METHODS We retrospectively reviewed 359 patients (119 cervical lordosis, 38 cervical sagittal imbalances, 111 cervical straight, and 91 cervical kyphosis) who underwent cervical spine multi-positional magnetic resonance imaging (mMRI). The C2-7 angle, disc degeneration grading and cSVA were analyzed in neutral position. The C3-5 OCI, O-C2 angle, and OCD were analyzed in neutral, flexion, and extension position. The Kruskal-Wallis test was used to detect difference among four groups. The post hoc analysis was performed by Mann-Whitney U test. RESULTS The cervical sagittal imbalance, cervical straight, and cervical kyphosis groups had significantly more lordosis angle in C3 and C4 OCI and O-C2 angle than the cervical lordosis group (p < 0.0125). Head motion in relation to C2, C3, and C4 (O-C2 angle, C3-4 OCI) in the kyphosis group was significantly greater than in the cervical lordosis group (p < 0.0125). The cervical sagittal imbalance group showed significantly increased O-C2 angle than the cervical lordosis group (p = 0.008). Regression analysis showed that an increase in O-C2 angle by one unit had a relative risk of 4.3% and 3.5% for a patient to be in the cervical sagittal imbalance and cervical kyphosis groups, respectively. CONCLUSIONS Cervical sagittal alignment affected craniocervical junction motion with the head exhibiting greater extension and motion in the cervical sagittal imbalance and cervical kyphosis groups. Motion of the head in relation to C2 can be used to predict the cervical sagittal alignment.
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Affiliation(s)
- Permsak Paholpak
- Department of Orthopaedic Surgery, Keck School of Medicine, University of Southern California, 1450 Biggy Street, NRT-4513, Los Angeles, CA, 90033, USA.,Department of Orthopaedics, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand
| | - Andrew Vega
- Department of Orthopaedic Surgery, Keck School of Medicine, University of Southern California, 1450 Biggy Street, NRT-4513, Los Angeles, CA, 90033, USA
| | - Blake Formanek
- Department of Orthopaedic Surgery, Keck School of Medicine, University of Southern California, 1450 Biggy Street, NRT-4513, Los Angeles, CA, 90033, USA
| | - Koji Tamai
- Department of Orthopaedic Surgery, Keck School of Medicine, University of Southern California, 1450 Biggy Street, NRT-4513, Los Angeles, CA, 90033, USA
| | - Jeffrey C Wang
- Department of Orthopaedic Surgery, Keck School of Medicine, University of Southern California, 1450 Biggy Street, NRT-4513, Los Angeles, CA, 90033, USA
| | - Zorica Buser
- Department of Orthopaedic Surgery, Keck School of Medicine, University of Southern California, 1450 Biggy Street, NRT-4513, Los Angeles, CA, 90033, USA.
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Albalwi AA, Johnson EG, Alharbi AA, Daher NS, Cordett TK, Ambode OI, Alshehri FH. Effects of head motion on postural stability in healthy young adults with chronic motion sensitivity. Arch Physiother 2020; 10:6. [PMID: 32257386 PMCID: PMC7106606 DOI: 10.1186/s40945-020-00077-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2018] [Accepted: 03/20/2020] [Indexed: 11/17/2022] Open
Abstract
Background Motion sensitivity, or motion sickness, is common in modern vehicular and visually stimulating environments. Several studies have shown a relationship between motion sensitivity and decreased postural stability. We aimed to evaluate the effects of head motion (horizontal and vertical) on postural stability in healthy adults with and without chronic motion sensitivity (CMS). Methods Sixty healthy adult men and women (age, 20–40 years) with CMS (CMS group, n = 30) and without CMS (non-CMS group, n = 30) participated in the study. Postural stability was assessed during three conditions (static, horizontal head motion, and vertical head motion) using computerized dynamic posturography. Group and condition-related differences in equilibrium scores were evaluated. Results There was no significant group x condition interaction (F2,114 = 0.9, partial ƞ2 = 0.04, p = 0.35). However, significant condition-related differences in equilibrium scores were observed (F2,114 = 26.4, partial ƞ2 = 0.31, p < 0.001). Equilibrium scores were significantly worse in the horizontal and vertical head motion conditions compared to those in the static condition (p < 0.001), but were comparable in vertical and horizontal head motion conditions (p = 0.27). Conclusions Postural stability was lower in the horizontal and vertical conditions compared to the static condition. However, horizontal and vertical head motions had comparable effects on postural stability in both CMS and non-CMS groups, contrary to our expectations.
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Affiliation(s)
- Abdulaziz A Albalwi
- 1Department of Physical Therapy, Faculty of Applied Medical Sciences, Tabuk University, Duba Road, Tabuk, 71491 Saudi Arabia
| | - Eric G Johnson
- 2Department of Physical Therapy, School of Allied Health Professions, Loma Linda University, Loma Linda, CA USA
| | - Ahmad A Alharbi
- 1Department of Physical Therapy, Faculty of Applied Medical Sciences, Tabuk University, Duba Road, Tabuk, 71491 Saudi Arabia
| | - Noha S Daher
- 3Department of Allied Health Studies, School of Allied Health Professions, Loma Linda University, Loma Linda, CA USA
| | - Tim K Cordett
- 2Department of Physical Therapy, School of Allied Health Professions, Loma Linda University, Loma Linda, CA USA
| | - Oluwaseun I Ambode
- 2Department of Physical Therapy, School of Allied Health Professions, Loma Linda University, Loma Linda, CA USA
| | - Fahad H Alshehri
- 2Department of Physical Therapy, School of Allied Health Professions, Loma Linda University, Loma Linda, CA USA
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Aquino KM, Fulcher BD, Parkes L, Sabaroedin K, Fornito A. Identifying and removing widespread signal deflections from fMRI data: Rethinking the global signal regression problem. Neuroimage 2020; 212:116614. [PMID: 32084564 DOI: 10.1016/j.neuroimage.2020.116614] [Citation(s) in RCA: 58] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2019] [Revised: 02/05/2020] [Accepted: 02/05/2020] [Indexed: 12/13/2022] Open
Abstract
One of the most controversial procedures in the analysis of resting-state functional magnetic resonance imaging (rsfMRI) data is global signal regression (GSR): the removal, via linear regression, of the mean signal averaged over the entire brain. On one hand, the global mean signal contains variance associated with respiratory, scanner-, and motion-related artifacts, and its removal via GSR improves various quality-control metrics, enhances the anatomical specificity of functional-connectivity patterns, and can increase the behavioral variance explained by such patterns. On the other hand, GSR alters the distribution of regional signal correlations in the brain, can induce artifactual anticorrelations, may remove real neural signal, and can distort case-control comparisons of functional-connectivity measures. Global signal fluctuations can be identified visually from a matrix of colour-coded signal intensities, called a carpet plot, in which rows represent voxels and columns represent time. Prior to GSR, large, periodic bands of coherent signal changes that affect most of the brain are often apparent; after GSR, these apparently global changes are greatly diminished. Here, using three independent datasets, we show that reordering carpet plots to emphasize cluster structure in the data reveals a greater diversity of spatially widespread signal deflections (WSDs) than previously thought. Their precise form varies across time and participants, and GSR is only effective in removing specific kinds of WSDs. We present an alternative, iterative correction method called Diffuse Cluster Estimation and Regression (DiCER), that identifies representative signals associated with large clusters of coherent voxels. DiCER is more effective than GSR at removing diverse WSDs as visualized in carpet plots, reduces correlations between functional connectivity and head-motion estimates, reduces inter-individual variability in global correlation structure, and results in comparable or improved identification of canonical functional-connectivity networks. Using task fMRI data across 47 contrasts from 7 tasks in the Human Connectome Project, we also present evidence that DiCER is more successful than GSR in preserving the spatial structure of expected task-related activation patterns. Our findings indicate that care must be exercised when examining WSDs (and their possible removal) in rsfMRI data, and that DiCER is a viable alternative to GSR for removing anatomically widespread and temporally coherent signals. All code for implementing DiCER and replicating our results is available at https://github.com/BMHLab/DiCER.
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Nørgaard M, Ganz M, Svarer C, Frokjaer VG, Greve DN, Strother SC, Knudsen GM. Optimization of preprocessing strategies in Positron Emission Tomography (PET) neuroimaging: A [ 11C]DASB PET study. Neuroimage 2019; 199:466-479. [PMID: 31158479 DOI: 10.1016/j.neuroimage.2019.05.055] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2018] [Revised: 03/21/2019] [Accepted: 05/21/2019] [Indexed: 11/26/2022] Open
Abstract
Positron Emission Tomography (PET) is an important neuroimaging tool to quantify the distribution of specific molecules in the brain. The quantification is based on a series of individually designed data preprocessing steps (pipeline) and an optimal preprocessing strategy is per definition associated with less noise and improved statistical power, potentially allowing for more valid neurobiological interpretations. In spite of this, it is currently unclear how to design the best preprocessing pipeline and to what extent the choice of each preprocessing step in the pipeline minimizes subject-specific errors. To evaluate the impact of various preprocessing strategies, we systematically examined 384 different pipeline strategies in data from 30 healthy participants scanned twice with the serotonin transporter (5-HTT) radioligand [11C]DASB. Five commonly used preprocessing steps with two to four options were investigated: (1) motion correction (MC) (2) co-registration (3) delineation of volumes of interest (VOI's) (4) partial volume correction (PVC), and (5) kinetic modeling. To quantitatively compare and evaluate the impact of various preprocessing strategies, we used the performance metrics: test-retest bias, within- and between-subject variability, the intraclass-correlation coefficient, and global signal-to-noise ratio. We also performed a power analysis to estimate the required sample size to detect either a 5% or 10% difference in 5-HTT binding as a function of preprocessing pipeline. The results showed a complex downstream dependency between the various preprocessing steps on the performance metrics. The choice of MC had the most profound effect on 5-HTT binding, prior to the effects caused by PVC and kinetic modeling, and the effects differed across VOI's. Notably, we observed a negative bias in 5-HTT binding across test and retest in 98% of pipelines, ranging from 0 to 6% depending on the pipeline. Optimization of the performance metrics revealed a trade-off in within- and between-subject variability at the group-level with opposite effects (i.e. minimization of within-subject variability increased between-subject variability and vice versa). The sample size required to detect a given effect size was also compromised by the preprocessing strategy, resulting in up to 80% increases in sample size needed to detect a 5% difference in 5-HTT binding. This is the first study to systematically investigate and demonstrate the effect of choosing different preprocessing strategies on the outcome of dynamic PET studies. We provide a framework to show how optimal and maximally powered neuroimaging results can be obtained by choosing appropriate preprocessing strategies and we provide recommendations depending on the study design. In addition, the results contribute to a better understanding of methodological uncertainty and variability in preprocessing decisions for future group- and/or longitudinal PET studies.
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Affiliation(s)
- Martin Nørgaard
- Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark; Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Melanie Ganz
- Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark; Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - Claus Svarer
- Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Vibe G Frokjaer
- Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Douglas N Greve
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Stephen C Strother
- Rotman Research Institute, Baycrest, Department of Medical Biophysics, University of Toronto, Toronto, Canada
| | - Gitte M Knudsen
- Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark; Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
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21
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Achterberg M, van der Meulen M. Genetic and environmental influences on MRI scan quantity and quality. Dev Cogn Neurosci 2019; 38:100667. [PMID: 31170550 PMCID: PMC6969338 DOI: 10.1016/j.dcn.2019.100667] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2018] [Revised: 05/14/2019] [Accepted: 05/20/2019] [Indexed: 01/24/2023] Open
Abstract
The current study provides an overview of quantity and quality of MRI data in a large developmental twin sample (N = 512, aged 7–9), and investigated to what extent scan quantity and quality were influenced by genetic and environmental factors. This was examined in a fixed scan protocol consisting of two functional MRI tasks, high resolution structural anatomy (3DT1) and connectivity (DTI) scans, and a resting state scan. Overall, scan quantity was high (88% of participants completed all runs), while scan quality decreased with increasing session length. Scanner related distress was negatively associated with scan quantity (i.e., completed runs), but not with scan quality (i.e., included runs). In line with previous studies, behavioral genetic analyses showed that genetics explained part of the variation in head motion, with heritability estimates of 29% for framewise displacement and 65% for absolute displacement. Additionally, our results revealed that subtle head motion (after exclusion of excessive head motion) showed lower heritability estimates (0–14%), indicating that findings of motion-corrected and quality-controlled MRI data may be less confounded by genetic factors. These findings provide insights in factors contributing to scan quality in children, an issue that is highly relevant for the field of developmental neuroscience.
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Affiliation(s)
- Michelle Achterberg
- Leiden Consortium on Individual Development, Leiden University, the Netherlands; Institute of Psychology, Leiden University, the Netherlands; Leiden Institute for Brain and Cognition, Leiden University, the Netherlands.
| | - Mara van der Meulen
- Leiden Consortium on Individual Development, Leiden University, the Netherlands; Institute of Psychology, Leiden University, the Netherlands; Leiden Institute for Brain and Cognition, Leiden University, the Netherlands
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22
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Vanderwal T, Eilbott J, Castellanos FX. Movies in the magnet: Naturalistic paradigms in developmental functional neuroimaging. Dev Cogn Neurosci 2019; 36:100600. [PMID: 30551970 PMCID: PMC6969259 DOI: 10.1016/j.dcn.2018.10.004] [Citation(s) in RCA: 96] [Impact Index Per Article: 19.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2018] [Revised: 09/13/2018] [Accepted: 10/08/2018] [Indexed: 11/28/2022] Open
Abstract
The use of movie-watching as an acquisition state for functional connectivity (FC) MRI has recently enabled multiple groups to obtain rich data sets in younger children with both substantial sample sizes and scan durations. Using naturalistic paradigms such as movies has also provided analytic flexibility for these developmental studies that extends beyond conventional resting state approaches. This review highlights the advantages and challenges of using movies for developmental neuroimaging and explores some of the methodological issues involved in designing pediatric studies with movies. Emerging themes from movie-watching studies are discussed, including an emphasis on intersubject correlations, developmental changes in network interactions under complex naturalistic conditions, and dynamic age-related changes in both sensory and higher-order network FC even in narrow age ranges. Converging evidence suggests an enhanced ability to identify brain-behavior correlations in children when using movie-watching data relative to both resting state and conventional tasks. Future directions and cautionary notes highlight the potential and the limitations of using movies to study FC in pediatric populations.
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Affiliation(s)
- Tamara Vanderwal
- University of British Columbia, 2255 Wesbrook Mall, Vancouver, BC, V6T 2A1, Canada; Yale Child Study Center, 230 South Frontage Road, New Haven CT, 06519, United States.
| | - Jeffrey Eilbott
- Yale Child Study Center, 230 South Frontage Road, New Haven CT, 06519, United States
| | - F Xavier Castellanos
- The Child Study Center at New York University Langone Medical Center, 1 Park Avenue, New York, NY, 10016, United States; Nathan Kline Institute for Psychiatric Research, 140 Old Orangeburg Road, Orangeburg, NY, 10962, United States
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23
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Raut RV, Mitra A, Snyder AZ, Raichle ME. On time delay estimation and sampling error in resting-state fMRI. Neuroimage 2019; 194:211-227. [PMID: 30902641 DOI: 10.1016/j.neuroimage.2019.03.020] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2018] [Revised: 01/26/2019] [Accepted: 03/08/2019] [Indexed: 01/21/2023] Open
Abstract
Accumulating evidence indicates that resting-state functional magnetic resonance imaging (rsfMRI) signals correspond to propagating electrophysiological infra-slow activity (<0.1 Hz). Thus, pairwise correlations (zero-lag functional connectivity (FC)) and temporal delays among regional rsfMRI signals provide useful, complementary descriptions of spatiotemporal structure in infra-slow activity. However, the slow nature of fMRI signals implies that practical scan durations cannot provide sufficient independent temporal samples to stabilize either of these measures. Here, we examine factors affecting sampling variability in both time delay estimation (TDE) and FC. Although both TDE and FC accuracy are highly sensitive to data quantity, we use surrogate fMRI time series to study how the former is additionally related to the magnitude of a given pairwise correlation and, to a lesser extent, the temporal sampling rate. These contingencies are further explored in real data comprising 30-min rsfMRI scans, where sampling error (i.e., limited accuracy owing to insufficient data quantity) emerges as a significant but underappreciated challenge to FC and, even more so, to TDE. Exclusion of high-motion epochs exacerbates sampling error; thus, both sides of the bias-variance (or data quality-quantity) tradeoff associated with data exclusion should be considered when analyzing rsfMRI data. Finally, we present strategies for TDE in motion-corrupted data, for characterizing sampling error in TDE and FC, and for mitigating the influence of sampling error on lag-based analyses.
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Affiliation(s)
- Ryan V Raut
- Departments of Radiology, Washington University, St. Louis, MO, 63110, USA.
| | - Anish Mitra
- Departments of Radiology, Washington University, St. Louis, MO, 63110, USA
| | - Abraham Z Snyder
- Departments of Radiology, Washington University, St. Louis, MO, 63110, USA; Departments of Neurology, Washington University, St. Louis, MO, 63110, USA
| | - Marcus E Raichle
- Departments of Radiology, Washington University, St. Louis, MO, 63110, USA; Departments of Neurology, Washington University, St. Louis, MO, 63110, USA
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Treleaven J, Takasaki H, Grip H. Altered trunk head co-ordination in those with persistent neck pain. Musculoskelet Sci Pract 2019; 39:45-50. [PMID: 30476827 DOI: 10.1016/j.msksp.2018.11.010] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/30/2018] [Revised: 11/06/2018] [Accepted: 11/17/2018] [Indexed: 11/24/2022]
Abstract
BACKGROUND Decreased neck motion and sensorimotor deficits have been identified in those with neck pain. It is thought that these might be related to altered reflex mechanisms between the neck, eyes and the vestibular system. Trunk, head co-ordination might also be altered in neck pain. OBJECTIVES This study investigated trunk head co-ordination ability in subjects with neck pain compared to asymptomatic controls. METHOD Twenty-four subjects with persistent neck pain and twenty-six age and gender matched healthy controls performed 3 trials of 3 trunk movements whilst trying to keep the head still - (1) alternate trunk movement to the left and right (2) trunk movement to the left (3) trunk movement to the right. Wireless motion sensors positioned over the sternum and the forehead measured trunk and head range and velocity of motion. ANALYSIS ANOVA was used to compare trunk and head range and velocity of motion during the 3 tasks. RESULTS Neck pain subjects had significantly less trunk movement (p < 0.05) and velocity (p=<0.02) as well as significantly increased head movement (p=<0.03) during most tasks compared to control subjects. DISCUSSION The results of the study suggest that neck pain subjects have difficulty moving their trunk independently of their head. They are less able to keep the head still while moving the trunk and perform the tasks more slowly. These findings might be related to altered reflex activity of the cervico-collic reflex and sensorimotor control. Further research is required.
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25
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Ballenghein U, Baccino T. Referential processing during reading: concurrent recordings of eye movements and head motion. Cogn Process 2019; 20:371-84. [PMID: 30535580 DOI: 10.1007/s10339-018-0894-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2018] [Accepted: 11/28/2018] [Indexed: 10/27/2022]
Abstract
The present study utilized a new experimental set-up synchronizing eye movements and head motion for investigating referential change occurring in a reading task. We examined the effects of a change in narrative perspective during reading on eye movements and head motion. Forty-four participants read texts on a digital tablet, and both participants' eye movements and head movements were recorded using eye tracker and motion capture. The results showed longer eye fixation duration, longer reading time and decreasing head motions when perspective changed. Recent studies have supported the dynamic engagement hypothesis suggesting that there is a fluctuation in cognitive engagement reflected by postural movements. Our findings on head movements seem to validate this hypothesis of a bodily engagement in reading. The results provided by our study showed that the novel methodology of eye and head movement recordings we used was proven to be informative in studying the reader's embodiment during reading.
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26
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Fishburn FA, Ludlum RS, Vaidya CJ, Medvedev AV. Temporal Derivative Distribution Repair (TDDR): A motion correction method for fNIRS. Neuroimage 2018; 184:171-179. [PMID: 30217544 DOI: 10.1016/j.neuroimage.2018.09.025] [Citation(s) in RCA: 137] [Impact Index Per Article: 22.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2018] [Revised: 09/04/2018] [Accepted: 09/09/2018] [Indexed: 11/24/2022] Open
Abstract
Functional near-infrared spectroscopy (fNIRS) is an optical neuroimaging technique of growing interest as a tool for investigation of cortical activity. Due to the on-head placement of optodes, artifacts arising from head motion are relatively less severe than for functional magnetic resonance imaging (fMRI). However, it is still necessary to remove motion artifacts. We present a novel motion correction procedure based on robust regression, which effectively removes baseline shift and spike artifacts without the need for any user-supplied parameters. Our simulations show that this method yields better activation detection performance than 5 other current motion correction methods. In our empirical validation on a working memory task in a sample of children 7-15 years, our method produced stronger and more extensive activation than any of the other methods tested. The new motion correction method enhances the viability of fNIRS as a functional neuroimaging modality for use in populations not amenable to fMRI.
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Affiliation(s)
- Frank A Fishburn
- Interdisciplinary Program in Neuroscience, Georgetown University, Washington, DC, USA.
| | - Ruth S Ludlum
- Department of Psychology, Georgetown University, Washington, DC, USA
| | - Chandan J Vaidya
- Department of Psychology, Georgetown University, Washington, DC, USA; Children's Research Institute, Children's National Health System, Washington, DC, USA
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27
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Kotasidis FA, Angelis GI, Anton-Rodriguez JM, Zaidi H. Robustness of post-reconstruction and direct kinetic parameter estimates under rigid head motion in dynamic brain PET imaging. Phys Med 2018; 53:40-55. [PMID: 30241754 DOI: 10.1016/j.ejmp.2018.08.006] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/02/2017] [Revised: 08/06/2018] [Accepted: 08/07/2018] [Indexed: 11/25/2022] Open
Abstract
OBJECTIVE Dynamic PET imaging is extensively used in brain imaging to estimate parametric maps. Inter-frame motion can substantially disrupt the voxel-wise time-activity curves (TACs), leading to erroneous maps during kinetic modelling. Therefore, it is important to characterize the robustness of kinetic parameters under various motion and kinetic model related factors. METHODS Fully 4D brain simulations ([15O]H2O and [18F]FDG dynamic datasets) were performed using a variety of clinically observed motion patterns. Increasing levels of head motion were investigated as well as varying temporal frames of motion initiation. Kinetic parameter estimation was performed using both post-reconstruction kinetic analysis and direct 4D image reconstruction to assess bias from inter-frame emission blurring and emission/attenuation mismatch. RESULTS Kinetic parameter bias heavily depends on the time point of motion initiation. Motion initiated towards the end of the scan results in the most biased parameters. For the [18F]FDG data, k4 is the more sensitive parameter to positional changes, while K1 and blood volume were proven to be relatively robust to motion. Direct 4D image reconstruction appeared more sensitive to changes in TACs due to motion, with parameter bias spatially propagating and depending on the level of motion. CONCLUSION Kinetic parameter bias highly depends upon the time frame at which motion occurred, with late frame motion-induced TAC discontinuities resulting in the least accurate parameters. This is of importance during prolonged data acquisition as is often the case in neuro-receptor imaging studies. In the absence of a motion correction, use of TOF information within 4D image reconstruction could limit the error propagation.
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Affiliation(s)
- F A Kotasidis
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva, Switzerland; Wolfson Molecular Imaging Centre, MAHSC, University of Manchester, M20 3LJ Manchester, UK
| | - G I Angelis
- Faculty of Health Sciences, Brain and Mind Centre, The University of Sydney, NSW 2050 Sydney, Australia
| | - J M Anton-Rodriguez
- Wolfson Molecular Imaging Centre, MAHSC, University of Manchester, M20 3LJ Manchester, UK
| | - H Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva, Switzerland; Geneva Neuroscience Centre, Geneva University, CH-1205 Geneva, Switzerland; Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Centre Groningen, 9700 RB Groningen, The Netherlands; Department of Nuclear Medicine, University of Southern Denmark, DK-500 Odense, Denmark.
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28
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Abstract
Cortical morphology is known to differ with age, as measured by cortical thickness, fractal dimensionality, and gyrification. However, head motion during MRI scanning has been shown to influence estimates of cortical thickness as well as increase with age. Studies have also found task-related differences in head motion and relationships between body–mass index (BMI) and head motion. Here I replicated these prior findings, as well as several others, within a large, open-access dataset (Centre for Ageing and Neuroscience, CamCAN). This is a larger dataset than these results have been demonstrated previously, within a sample size of more than 600 adults across the adult lifespan. While replicating prior findings is important, demonstrating these key findings concurrently also provides an opportunity for additional related analyses: critically, I test for the influence of head motion on cortical fractal dimensionality and gyrification; effects were statistically significant in some cases, but small in magnitude.
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29
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Eklund A, Lindquist MA, Villani M. A Bayesian heteroscedastic GLM with application to fMRI data with motion spikes. Neuroimage 2017; 155:354-69. [PMID: 28473287 DOI: 10.1016/j.neuroimage.2017.04.069] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2016] [Revised: 04/26/2017] [Accepted: 04/28/2017] [Indexed: 11/24/2022] Open
Abstract
We propose a voxel-wise general linear model with autoregressive noise and heteroscedastic noise innovations (GLMH) for analyzing functional magnetic resonance imaging (fMRI) data. The model is analyzed from a Bayesian perspective and has the benefit of automatically down-weighting time points close to motion spikes in a data-driven manner. We develop a highly efficient Markov Chain Monte Carlo (MCMC) algorithm that allows for Bayesian variable selection among the regressors to model both the mean (i.e., the design matrix) and variance. This makes it possible to include a broad range of explanatory variables in both the mean and variance (e.g., time trends, activation stimuli, head motion parameters and their temporal derivatives), and to compute the posterior probability of inclusion from the MCMC output. Variable selection is also applied to the lags in the autoregressive noise process, making it possible to infer the lag order from the data simultaneously with all other model parameters. We use both simulated data and real fMRI data from OpenfMRI to illustrate the importance of proper modeling of heteroscedasticity in fMRI data analysis. Our results show that the GLMH tends to detect more brain activity, compared to its homoscedastic counterpart, by allowing the variance to change over time depending on the degree of head motion.
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30
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Weng L, Xie Q, Zhao L, Zhang R, Ma Q, Wang J, Jiang W, He Y, Chen Y, Li C, Ni X, Xu Q, Yu R, Huang R. Abnormal structural connectivity between the basal ganglia, thalamus, and frontal cortex in patients with disorders of consciousness. Cortex 2017; 90:71-87. [PMID: 28365490 DOI: 10.1016/j.cortex.2017.02.011] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2016] [Revised: 09/28/2016] [Accepted: 02/20/2017] [Indexed: 12/17/2022]
Abstract
Consciousness loss in patients with severe brain injuries is associated with reduced functional connectivity of the default mode network (DMN), fronto-parietal network, and thalamo-cortical network. However, it is still unclear if the brain white matter connectivity between the above mentioned networks is changed in patients with disorders of consciousness (DOC). In this study, we collected diffusion tensor imaging (DTI) data from 13 patients and 17 healthy controls, constructed whole-brain white matter (WM) structural networks with probabilistic tractography. Afterward, we estimated and compared topological properties, and revealed an altered structural organization in the patients. We found a disturbance in the normal balance between segregation and integration in brain structural networks and detected significantly decreased nodal centralities primarily in the basal ganglia and thalamus in the patients. A network-based statistical analysis detected a subnetwork with uniformly significantly decreased structural connections between the basal ganglia, thalamus, and frontal cortex in the patients. Further analysis indicated that along the WM fiber tracts linking the basal ganglia, thalamus, and frontal cortex, the fractional anisotropy was decreased and the radial diffusivity was increased in the patients compared to the controls. Finally, using the receiver operating characteristic method, we found that the structural connections within the NBS-derived component that showed differences between the groups demonstrated high sensitivity and specificity (>90%). Our results suggested that major consciousness deficits in DOC patients may be related to the altered WM connections between the basal ganglia, thalamus, and frontal cortex.
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Affiliation(s)
- Ling Weng
- Center for the Study of Applied Psychology, Guangdong Key Laboratory of Mental Health and Cognitive Science, School of Psychology, Institute of Brain Science and Rehabilitation, South China Normal University, Guangzhou 510631, PR China
| | - Qiuyou Xie
- Centre for Hyperbaric Oxygen and Neurorehabilitation, Guangzhou General Hospital of Guangzhou Military Command, Guangzhou 510010, PR China
| | - Ling Zhao
- Center for the Study of Applied Psychology, Guangdong Key Laboratory of Mental Health and Cognitive Science, School of Psychology, Institute of Brain Science and Rehabilitation, South China Normal University, Guangzhou 510631, PR China
| | - Ruibin Zhang
- Department of Psychology, The University of Hong Kong, Hong Kong, PR China
| | - Qing Ma
- Centre for Hyperbaric Oxygen and Neurorehabilitation, Guangzhou General Hospital of Guangzhou Military Command, Guangzhou 510010, PR China
| | - Junjing Wang
- Center for the Study of Applied Psychology, Guangdong Key Laboratory of Mental Health and Cognitive Science, School of Psychology, Institute of Brain Science and Rehabilitation, South China Normal University, Guangzhou 510631, PR China
| | - Wenjie Jiang
- Center for the Study of Applied Psychology, Guangdong Key Laboratory of Mental Health and Cognitive Science, School of Psychology, Institute of Brain Science and Rehabilitation, South China Normal University, Guangzhou 510631, PR China
| | - Yanbin He
- Centre for Hyperbaric Oxygen and Neurorehabilitation, Guangzhou General Hospital of Guangzhou Military Command, Guangzhou 510010, PR China
| | - Yan Chen
- Centre for Hyperbaric Oxygen and Neurorehabilitation, Guangzhou General Hospital of Guangzhou Military Command, Guangzhou 510010, PR China
| | - Changhong Li
- Center for the Study of Applied Psychology, Guangdong Key Laboratory of Mental Health and Cognitive Science, School of Psychology, Institute of Brain Science and Rehabilitation, South China Normal University, Guangzhou 510631, PR China
| | - Xiaoxiao Ni
- Centre for Hyperbaric Oxygen and Neurorehabilitation, Guangzhou General Hospital of Guangzhou Military Command, Guangzhou 510010, PR China
| | - Qin Xu
- Center for the Study of Applied Psychology, Guangdong Key Laboratory of Mental Health and Cognitive Science, School of Psychology, Institute of Brain Science and Rehabilitation, South China Normal University, Guangzhou 510631, PR China
| | - Ronghao Yu
- Centre for Hyperbaric Oxygen and Neurorehabilitation, Guangzhou General Hospital of Guangzhou Military Command, Guangzhou 510010, PR China.
| | - Ruiwang Huang
- Center for the Study of Applied Psychology, Guangdong Key Laboratory of Mental Health and Cognitive Science, School of Psychology, Institute of Brain Science and Rehabilitation, South China Normal University, Guangzhou 510631, PR China.
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31
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Engelhardt LE, Roe MA, Juranek J, DeMaster D, Harden KP, Tucker-Drob EM, Church JA. Children's head motion during fMRI tasks is heritable and stable over time. Dev Cogn Neurosci 2017; 25:58-68. [PMID: 28223034 PMCID: PMC5478437 DOI: 10.1016/j.dcn.2017.01.011] [Citation(s) in RCA: 54] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2016] [Revised: 11/21/2016] [Accepted: 01/30/2017] [Indexed: 11/30/2022] Open
Abstract
Head motion during fMRI scans negatively impacts data quality, and as post-acquisition techniques for addressing motion become increasingly stringent, data retention decreases. Studies conducted with adult participants suggest that movement acts as a relatively stable, heritable phenotype that serves as a marker for other genetically influenced phenotypes. Whether these patterns extend downward to childhood has critical implications for the interpretation and generalizability of fMRI data acquired from children. We examined factors affecting scanner motion in two samples: a population-based twin sample of 73 participants (ages 7–12 years) and a case-control sample of 32 non-struggling and 78 struggling readers (ages 8–11 years), 30 of whom were scanned multiple times. Age, but not ADHD symptoms, was significantly related to scanner movement. Movement also varied as a function of task type, run length, and session length. Twin pair concordance for head motion was high for monozygotic twins and moderate for dizygotic twins. Cross-session test-retest reliability was high. Together, these findings suggest that children’s head motion is a genetically influenced trait that has the potential to systematically affect individual differences in BOLD changes within and across groups. We discuss recommendations for future work and best practices for pediatric neuroimaging.
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Affiliation(s)
- Laura E Engelhardt
- Department of Psychology, The University of Texas at Austin, United States.
| | - Mary Abbe Roe
- Department of Psychology, The University of Texas at Austin, United States
| | - Jenifer Juranek
- The Children's Learning Institute, The University of Texas Health Science Center at Houston, United States
| | - Dana DeMaster
- The Children's Learning Institute, The University of Texas Health Science Center at Houston, United States
| | - K Paige Harden
- Department of Psychology, The University of Texas at Austin, United States; Population Research Center, The University of Texas at Austin, United States
| | - Elliot M Tucker-Drob
- Department of Psychology, The University of Texas at Austin, United States; Population Research Center, The University of Texas at Austin, United States
| | - Jessica A Church
- Department of Psychology, The University of Texas at Austin, United States; Imaging Research Center, The University of Texas at Austin, United States
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32
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Fassbender C, Mukherjee P, Schweitzer JB. Minimizing noise in pediatric task-based functional MRI; Adolescents with developmental disabilities and typical development. Neuroimage 2017; 149:338-347. [PMID: 28130195 DOI: 10.1016/j.neuroimage.2017.01.021] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2016] [Revised: 01/09/2017] [Accepted: 01/10/2017] [Indexed: 12/21/2022] Open
Abstract
Functional Magnetic Resonance Imaging (fMRI) represents a powerful tool with which to examine brain functioning and development in typically developing pediatric groups as well as children and adolescents with clinical disorders. However, fMRI data can be highly susceptible to misinterpretation due to the effects of excessive levels of noise, often related to head motion. Imaging children, especially with developmental disorders, requires extra considerations related to hyperactivity, anxiety and the ability to perform and maintain attention to the fMRI paradigm. We discuss a number of methods that can be employed to minimize noise, in particular movement-related noise. To this end we focus on strategies prior to, during and following the data acquisition phase employed primarily within our own laboratory. We discuss the impact of factors such as experimental design, screening of potential participants and pre-scan training on head motion in our adolescents with developmental disorders and typical development. We make some suggestions that may minimize noise during data acquisition itself and finally we briefly discuss some current processing techniques that may help to identify and remove noise in the data. Many advances have been made in the field of pediatric imaging, particularly with regard to research involving children with developmental disorders. Mindfulness of issues such as those discussed here will ensure continued progress and greater consistency across studies.
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Affiliation(s)
- Catherine Fassbender
- Department of Psychiatry and Behavioral Sciences, United States; UC Davis MIND Institute, United States; UC Davis Imaging Research Center, United States.
| | - Prerona Mukherjee
- Department of Psychiatry and Behavioral Sciences, United States; UC Davis MIND Institute, United States
| | - Julie B Schweitzer
- Department of Psychiatry and Behavioral Sciences, United States; UC Davis MIND Institute, United States
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Faraji-Dana Z, Tam F, Chen JJ, Graham SJ. Interactions between head motion and coil sensitivity in accelerated fMRI. J Neurosci Methods 2016; 270:46-60. [PMID: 27288867 DOI: 10.1016/j.jneumeth.2016.06.005] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2016] [Revised: 05/03/2016] [Accepted: 06/07/2016] [Indexed: 10/21/2022]
Abstract
BACKGROUND Parallel imaging is widely adopted to accelerate functional MRI (fMRI) data acquisition, through various strategies that involve multi-channel receiver coils. However, the non-uniform spatial sensitivity of multi-channel receiver coils may introduce unwanted artifacts when head motion occurs during the few-minute long fMRI scans. Although prospective correction provides a promising solution for alleviating the head motion artifacts in fMRI, the relative position of the fixed multi-channel receiver coils moves in the moving reference frame, potentially resulting in artifactual signal. NEW METHOD We used numerical simulations to investigate this effect on fMRI using two parallel imaging schemes: sensitivity encoding (SENSE) and generalized autocalibrating partially parallel acquisitions (GRAPPA) with acceleration factors 2 and 4, towards characterizing the regime over which parallel-imaging fMRI with prospective motion correction will benefit from updating coil sensitivities to reflect relative positional change between the head and the receiver coil. Moreover, six subjects were scanned with acceleration factors 2 and 4 while performing a simple finger-tapping task with and without overt head motion. RESULTS Updating coil sensitivities showed significant positive impact on standard deviation and activation maps in presence of overt head motion compared to that obtained with no overt head motion. COMPARISON WITH EXISTING METHODS The parallel imaging fMRI with updated coil sensitivity maps were compared to that with the coil sensitivity maps acquired at the reference position. CONCLUSIONS Head motion in relation to a fixed multi-channel coil can adversely affect the quality of parallel imaging fMRI data; and updating coil sensitivity map can mitigate this effect.
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Affiliation(s)
- Z Faraji-Dana
- Department of Medical Biophysics, University of Toronto, Toronto, Canada; Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, Canada.
| | - F Tam
- Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, Canada
| | - J J Chen
- Department of Medical Biophysics, University of Toronto, Toronto, Canada; Rotman Research Institute of Baycrest, Toronto, Canada
| | - S J Graham
- Department of Medical Biophysics, University of Toronto, Toronto, Canada; Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, Canada
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Tisdall MD, Reuter M, Qureshi A, Buckner RL, Fischl B, van der Kouwe AJW. Prospective motion correction with volumetric navigators (vNavs) reduces the bias and variance in brain morphometry induced by subject motion. Neuroimage 2015; 127:11-22. [PMID: 26654788 DOI: 10.1016/j.neuroimage.2015.11.054] [Citation(s) in RCA: 82] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2015] [Revised: 11/20/2015] [Accepted: 11/21/2015] [Indexed: 10/22/2022] Open
Abstract
Recent work has demonstrated that subject motion produces systematic biases in the metrics computed by widely used morphometry software packages, even when the motion is too small to produce noticeable image artifacts. In the common situation where the control population exhibits different behaviors in the scanner when compared to the experimental population, these systematic measurement biases may produce significant confounds for between-group analyses, leading to erroneous conclusions about group differences. While previous work has shown that prospective motion correction can improve perceived image quality, here we demonstrate that, in healthy subjects performing a variety of directed motions, the use of the volumetric navigator (vNav) prospective motion correction system significantly reduces the motion-induced bias and variance in morphometry.
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Affiliation(s)
- M Dylan Tisdall
- Athinoula A. Martinos Center for Biomedical Research, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, USA; Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA.
| | - Martin Reuter
- Athinoula A. Martinos Center for Biomedical Research, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, USA; Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Abid Qureshi
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - Randy L Buckner
- Athinoula A. Martinos Center for Biomedical Research, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, USA; Center for Brain Science, Harvard University, Cambridge, MA 02138, USA; Department of Psychology, Harvard University, Cambridge, MA 02138, USA; Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - Bruce Fischl
- Athinoula A. Martinos Center for Biomedical Research, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, USA; Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - André J W van der Kouwe
- Athinoula A. Martinos Center for Biomedical Research, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, USA; Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
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Vanderwal T, Kelly C, Eilbott J, Mayes LC, Castellanos FX. Inscapes: A movie paradigm to improve compliance in functional magnetic resonance imaging. Neuroimage 2015; 122:222-32. [PMID: 26241683 DOI: 10.1016/j.neuroimage.2015.07.069] [Citation(s) in RCA: 197] [Impact Index Per Article: 21.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2015] [Revised: 07/20/2015] [Accepted: 07/24/2015] [Indexed: 01/15/2023] Open
Abstract
The examination of functional connectivity in fMRI data collected during task-free "rest" has provided a powerful tool for studying functional brain organization. Limitations of this approach include susceptibility to head motion artifacts and participant drowsiness or sleep. These issues are especially relevant when studying young children or clinical populations. Here we introduce a movie paradigm, Inscapes, that features abstract shapes without a narrative or scene-cuts. The movie was designed to provide enough stimulation to improve compliance related to motion and wakefulness while minimizing cognitive load during the collection of functional imaging data. We compare Inscapes to eyes-open rest and to age-appropriate movie clips in healthy adults (Ocean's Eleven, n=22) and a pilot sample of typically developing children ages 3-7 (Fantasia, n=13). Head motion was significantly lower during both movies relative to rest for both groups. In adults, movies decreased the number of participants who self-reported sleep. Intersubject correlations, used to quantify synchronized, task-evoked activity across movie and rest conditions in adults, involved less cortex during Inscapes than Ocean's Eleven. To evaluate the effect of movie-watching on intrinsic functional connectivity networks, we examined mean functional connectivity using both whole-brain functional parcellation and network-based approaches. Both inter- and intra-network metrics were more similar between Inscapes and Rest than between Ocean's Eleven and Rest, particularly in comparisons involving the default network. When comparing movies to Rest, the mean functional connectivity of somatomotor, visual and ventral attention networks differed significantly across various analyses. We conclude that low-demand movies like Inscapes may represent a useful intermediate condition between task-free rest and typical narrative movies while still improving participant compliance. Inscapes is publicly available for download at headspacestudios.org/inscapes.
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Abstract
This paper presents a computational study of head motion in human interaction, notably of its role in conveying interlocutors' behavioral characteristics. Head motion is physically complex and carries rich information; current modeling approaches based on visual signals, however, are still limited in their ability to adequately capture these important properties. Guided by the methodology of kinesics, we propose a data driven approach to identify typical head motion patterns. The approach follows the steps of first segmenting motion events, then parametrically representing the motion by linear predictive features, and finally generalizing the motion types using Gaussian mixture models. The proposed approach is experimentally validated using video recordings of communication sessions from real couples involved in a couples therapy study. In particular we use the head motion model to classify binarized expert judgments of the interactants' specific behavioral characteristics where entrainment in head motion is hypothesized to play a role: Acceptance, Blame, Positive, and Negative behavior. We achieve accuracies in the range of 60% to 70% for the various experimental settings and conditions. In addition, we describe a measure of motion similarity between the interaction partners based on the proposed model. We show that the relative change of head motion similarity during the interaction significantly correlates with the expert judgments of the interactants' behavioral characteristics. These findings demonstrate the effectiveness of the proposed head motion model, and underscore the promise of analyzing human behavioral characteristics through signal processing methods.
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Affiliation(s)
- Bo Xiao
- Signal and Image Processing Institute, Department of Electrical Engineering, University of Southern California, Los Angeles, CA, 90089 USA
| | - Panayiotis Georgiou
- Signal and Image Processing Institute, Department of Electrical Engineering, University of Southern California, Los Angeles, CA, 90089 USA
| | - Brian Baucom
- Department of Psychology, University of Utah, Salt Lack City, UT, 84112 USA
| | - Shrikanth S Narayanan
- Signal and Image Processing Institute, Department of Electrical Engineering, University of Southern California, Los Angeles, CA, 90089 USA
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Jorge J, Grouiller F, Gruetter R, van der Zwaag W, Figueiredo P. Towards high-quality simultaneous EEG-fMRI at 7 T: Detection and reduction of EEG artifacts due to head motion. Neuroimage 2015; 120:143-53. [PMID: 26169325 DOI: 10.1016/j.neuroimage.2015.07.020] [Citation(s) in RCA: 41] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2015] [Revised: 07/03/2015] [Accepted: 07/07/2015] [Indexed: 11/16/2022] Open
Abstract
The enhanced functional sensitivity offered by ultra-high field imaging may significantly benefit simultaneous EEG-fMRI studies, but the concurrent increases in artifact contamination can strongly compromise EEG data quality. In the present study, we focus on EEG artifacts created by head motion in the static B0 field. A novel approach for motion artifact detection is proposed, based on a simple modification of a commercial EEG cap, in which four electrodes are non-permanently adapted to record only magnetic induction effects. Simultaneous EEG-fMRI data were acquired with this setup, at 7 T, from healthy volunteers undergoing a reversing-checkerboard visual stimulation paradigm. Data analysis assisted by the motion sensors revealed that, after gradient artifact correction, EEG signal variance was largely dominated by pulse artifacts (81-93%), but contributions from spontaneous motion (4-13%) were still comparable to or even larger than those of actual neuronal activity (3-9%). Multiple approaches were tested to determine the most effective procedure for denoising EEG data incorporating motion sensor information. Optimal results were obtained by applying an initial pulse artifact correction step (AAS-based), followed by motion artifact correction (based on the motion sensors) and ICA denoising. On average, motion artifact correction (after AAS) yielded a 61% reduction in signal power and a 62% increase in VEP trial-by-trial consistency. Combined with ICA, these improvements rose to a 74% power reduction and an 86% increase in trial consistency. Overall, the improvements achieved were well appreciable at single-subject and single-trial levels, and set an encouraging quality mark for simultaneous EEG-fMRI at ultra-high field.
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Affiliation(s)
- João Jorge
- Laboratory for Functional and Metabolic Imaging, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Institute for Systems and Robotics/Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal.
| | | | - Rolf Gruetter
- Laboratory for Functional and Metabolic Imaging, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Department of Radiology, University of Geneva, Geneva, Switzerland; Department of Radiology, University of Lausanne, Lausanne, Switzerland
| | - Wietske van der Zwaag
- Biomedical Imaging Research Center, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Spinoza Centre for Neuroimaging, Amsterdam, The Netherlands
| | - Patrícia Figueiredo
- Institute for Systems and Robotics/Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
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Reuter M, Tisdall MD, Qureshi A, Buckner RL, van der Kouwe AJW, Fischl B. Head motion during MRI acquisition reduces gray matter volume and thickness estimates. Neuroimage 2014; 107:107-115. [PMID: 25498430 DOI: 10.1016/j.neuroimage.2014.12.006] [Citation(s) in RCA: 297] [Impact Index Per Article: 29.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2014] [Revised: 12/01/2014] [Accepted: 12/02/2014] [Indexed: 12/19/2022] Open
Abstract
Imaging biomarkers derived from magnetic resonance imaging (MRI) data are used to quantify normal development, disease, and the effects of disease-modifying therapies. However, motion during image acquisition introduces image artifacts that, in turn, affect derived markers. A systematic effect can be problematic since factors of interest like age, disease, and treatment are often correlated with both a structural change and the amount of head motion in the scanner, confounding the ability to distinguish biology from artifact. Here we evaluate the effect of head motion during image acquisition on morphometric estimates of structures in the human brain using several popular image analysis software packages (FreeSurfer 5.3, VBM8 SPM, and FSL Siena 5.0.7). Within-session repeated T1-weighted MRIs were collected on 12 healthy volunteers while performing different motion tasks, including two still scans. We show that volume and thickness estimates of the cortical gray matter are biased by head motion with an average apparent volume loss of roughly 0.7%/mm/min of subject motion. Effects vary across regions and remain significant after excluding scans that fail a rigorous quality check. In view of these results, the interpretation of reported morphometric effects of movement disorders or other conditions with increased motion tendency may need to be revisited: effects may be overestimated when not controlling for head motion. Furthermore, drug studies with hypnotic, sedative, tranquilizing, or neuromuscular-blocking substances may contain spurious "effects" of reduced atrophy or brain growth simply because they affect motion distinct from true effects of the disease or therapeutic process.
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Affiliation(s)
- Martin Reuter
- Massachusetts General Hospital, Department of Neurology, 55 Fruit Street, Boston, MA 02114, USA; Massachusetts General Hospital, Department of Radiology, A.A. Martinos Center for Biomedical Imaging, 149 Thirteenth Street, Suite 2301, Charlestown, MA 02129, USA; Massachusetts Institute of Technology, Computer Science and Artificial Intelligence Laboratory, Cambridge, MA 02139, USA; Harvard Medical School, 25 Shattuck St., Boston, MA 02115, USA.
| | - M Dylan Tisdall
- Massachusetts General Hospital, Department of Radiology, A.A. Martinos Center for Biomedical Imaging, 149 Thirteenth Street, Suite 2301, Charlestown, MA 02129, USA; Harvard Medical School, 25 Shattuck St., Boston, MA 02115, USA
| | - Abid Qureshi
- Massachusetts General Hospital, Department of Neurology, 55 Fruit Street, Boston, MA 02114, USA; Harvard Medical School, 25 Shattuck St., Boston, MA 02115, USA
| | - Randy L Buckner
- Massachusetts General Hospital, Department of Radiology, A.A. Martinos Center for Biomedical Imaging, 149 Thirteenth Street, Suite 2301, Charlestown, MA 02129, USA; Harvard Medical School, 25 Shattuck St., Boston, MA 02115, USA
| | - André J W van der Kouwe
- Massachusetts General Hospital, Department of Radiology, A.A. Martinos Center for Biomedical Imaging, 149 Thirteenth Street, Suite 2301, Charlestown, MA 02129, USA; Harvard Medical School, 25 Shattuck St., Boston, MA 02115, USA
| | - Bruce Fischl
- Massachusetts General Hospital, Department of Radiology, A.A. Martinos Center for Biomedical Imaging, 149 Thirteenth Street, Suite 2301, Charlestown, MA 02129, USA; Massachusetts Institute of Technology, Computer Science and Artificial Intelligence Laboratory, Cambridge, MA 02139, USA; Harvard Medical School, 25 Shattuck St., Boston, MA 02115, USA
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Couvy-Duchesne B, Blokland GA, Hickie IB, Thompson PM, Martin NG, de Zubicaray GI, McMahon KL, Wright MJ. Heritability of head motion during resting state functional MRI in 462 healthy twins. Neuroimage 2014; 102 Pt 2:424-34. [PMID: 25132021 DOI: 10.1016/j.neuroimage.2014.08.010] [Citation(s) in RCA: 50] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2013] [Revised: 08/01/2014] [Accepted: 08/05/2014] [Indexed: 01/28/2023] Open
Abstract
Head motion (HM) is a critical confounding factor in functional MRI. Here we investigate whether HM during resting state functional MRI (RS-fMRI) is influenced by genetic factors in a sample of 462 twins (65% female; 101 MZ (monozygotic) and 130 DZ (dizygotic) twin pairs; mean age: 21 (SD = 3.16), range 16-29). Heritability estimates for three HM components-mean translation (MT), maximum translation (MAXT) and mean rotation (MR)-ranged from 37 to 51%. We detected a significant common genetic influence on HM variability, with about two-thirds (genetic correlations range 0.76-1.00) of the variance shared between MR, MT and MAXT. A composite metric (HM-PC1), which aggregated these three, was also moderately heritable (h(2) = 42%). Using a sub-sample (N = 35) of the twins we confirmed that mean and maximum translational and rotational motions were consistent "traits" over repeated scans (r = 0.53-0.59); reliability was even higher for the composite metric (r = 0.66). In addition, phenotypic and cross-trait cross-twin correlations between HM and resting state functional connectivities (RS-FCs) with Brodmann areas (BA) 44 and 45, in which RS-FCs were found to be moderately heritable (BA44: h(2) = 0.23 (sd = 0.041), BA45: h(2) = 0.26 (sd = 0.061)), indicated that HM might not represent a major bias in genetic studies using FCs. Even so, the HM effect on FC was not completely eliminated after regression. HM may be a valuable endophenotype whose relationship with brain disorders remains to be elucidated.
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40
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Muschelli J, Nebel MB, Caffo BS, Barber AD, Pekar JJ, Mostofsky SH. Reduction of motion-related artifacts in resting state fMRI using aCompCor. Neuroimage 2014; 96:22-35. [PMID: 24657780 PMCID: PMC4043948 DOI: 10.1016/j.neuroimage.2014.03.028] [Citation(s) in RCA: 267] [Impact Index Per Article: 26.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2013] [Revised: 02/28/2014] [Accepted: 03/10/2014] [Indexed: 11/17/2022] Open
Abstract
Recent studies have illustrated that motion-related artifacts remain in resting-state fMRI (rs-fMRI) data even after common corrective processing procedures have been applied, but the extent to which head motion distorts the data may be modulated by the corrective approach taken. We compare two different methods for estimating nuisance signals from tissues not expected to exhibit BOLD fMRI signals of neuronal origin: 1) the more commonly used mean signal method and 2) the principal components analysis approach (aCompCor: Behzadi et al., 2007). Further, we investigate the added benefit of "scrubbing" (Power et al., 2012) following both methods. We demonstrate that the use of aCompCor removes motion artifacts more effectively than tissue-mean signal regression. In addition, inclusion of more components from anatomically defined regions of no interest better mitigates motion-related artifacts and improves the specificity of functional connectivity estimates. While scrubbing further attenuates motion-related artifacts when mean signals are used, scrubbing provides no additional benefit in terms of motion artifact reduction or connectivity specificity when using aCompCor.
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Affiliation(s)
- John Muschelli
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, 615 N. Wolfe Street Baltimore, MD 21205, USA
| | - Mary Beth Nebel
- Laboratory for Neurocognitive and Imaging Research, Kennedy Krieger Institute, 716 North Broadway Baltimore, MD 21205, USA; Department of Neurology, Johns Hopkins University School of Medicine, 1800 Orleans Street Baltimore, MD 21287, USA.
| | - Brian S Caffo
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, 615 N. Wolfe Street Baltimore, MD 21205, USA
| | - Anita D Barber
- Laboratory for Neurocognitive and Imaging Research, Kennedy Krieger Institute, 716 North Broadway Baltimore, MD 21205, USA; Department of Neurology, Johns Hopkins University School of Medicine, 1800 Orleans Street Baltimore, MD 21287, USA
| | - James J Pekar
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 601 North Caroline Street Baltimore, MD 21287, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, 707 North Broadway Baltimore, MD 21205, USA
| | - Stewart H Mostofsky
- Laboratory for Neurocognitive and Imaging Research, Kennedy Krieger Institute, 716 North Broadway Baltimore, MD 21205, USA; Department of Neurology, Johns Hopkins University School of Medicine, 1800 Orleans Street Baltimore, MD 21287, USA; Department of Psychiatry, Johns Hopkins University School of Medicine, 1800 Orleans Street Baltimore, MD 21287, USA
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Pujol J, Macià D, Blanco-Hinojo L, Martínez-Vilavella G, Sunyer J, de la Torre R, Caixàs A, Martín-Santos R, Deus J, Harrison BJ. Does motion-related brain functional connectivity reflect both artifacts and genuine neural activity? Neuroimage 2014; 101:87-95. [PMID: 24999036 DOI: 10.1016/j.neuroimage.2014.06.065] [Citation(s) in RCA: 51] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2014] [Revised: 06/18/2014] [Accepted: 06/27/2014] [Indexed: 11/15/2022] Open
Abstract
Imaging research on functional connectivity is uniquely contributing to characterize the functional organization of the human brain. Functional connectivity measurements, however, may be significantly influenced by head motion that occurs during image acquisition. The identification of how motion influences such measurements is therefore highly relevant to the interpretation of a study's results. We have mapped the effect of head motion on functional connectivity in six different populations representing a wide range of potential influences of motion on functional connectivity. Group-level voxel-wise maps of the correlation between a summary head motion measurement and functional connectivity degree were estimated in 80 young adults, 71 children, 53 older adults, 20 patients with Down syndrome, 24 with Prader-Willi syndrome and 20 with Williams syndrome. In highly compliant young adults, motion correlated with functional connectivity measurements showing a system-specific anatomy involving the sensorimotor cortex, visual areas and default mode network. Further characterization was strongly indicative of these changes expressing genuine neural activity related to motion, as opposed to pure motion artifact. In the populations with larger head motion, results were more indicative of widespread artifacts, but showing notably distinct spatial distribution patterns. Group-level regression of motion effects was efficient in removing both generalized changes and changes putatively related to neural activity. Overall, this study endorses a relatively simple approach for mapping distinct effects of head motion on functional connectivity. Importantly, our findings support the intriguing hypothesis that a component of motion-related changes may reflect system-specific neural activity.
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Affiliation(s)
- Jesus Pujol
- MRI Research Unit, CRC Mar, Hospital del Mar, Barcelona, Spain; Centro Investigación Biomédica en Red de Salud Mental, CIBERSAM G21, Barcelona, Spain.
| | - Dídac Macià
- MRI Research Unit, CRC Mar, Hospital del Mar, Barcelona, Spain
| | - Laura Blanco-Hinojo
- MRI Research Unit, CRC Mar, Hospital del Mar, Barcelona, Spain; Human Pharmacology and Clinical Neurosciences, Hospital del Mar Medical Research Institute, Barcelona, Spain
| | | | - Jordi Sunyer
- Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain; Universitat Pompeu Fabra (UFP), Barcelona, Spain
| | - Rafael de la Torre
- Human Pharmacology and Clinical Neurosciences, Hospital del Mar Medical Research Institute, Barcelona, Spain; CIBER-Fisiopatología de la Obesidad y Nutrición (CIBEROBN), S. de Compostela, Spain
| | - Assumpta Caixàs
- Diabetes Endocrinology and Nutrition Department, Hospital de Sabadell, Institut Universitari Parc Taulí, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Rocío Martín-Santos
- Department of Psychiatry and Clinical Psychobiology, Clinical Institute of Neuroscience, Hospital Clínic, CIBERSAM, University of Barcelona, Barcelona, Spain
| | - Joan Deus
- MRI Research Unit, CRC Mar, Hospital del Mar, Barcelona, Spain; Department of Clinical and Health Psychology, Autonomous University of Barcelona, Barcelona, Spain
| | - Ben J Harrison
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Melbourne, Australia
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Kong XZ. Association between in-scanner head motion with cerebral white matter microstructure: a multiband diffusion-weighted MRI study. PeerJ 2014; 2:e366. [PMID: 24795856 PMCID: PMC4006224 DOI: 10.7717/peerj.366] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2014] [Accepted: 04/09/2014] [Indexed: 12/16/2022] Open
Abstract
Diffusion-weighted Magnetic Resonance Imaging (DW-MRI) has emerged as the most popular neuroimaging technique used to depict the biological microstructural properties of human brain white matter. However, like other MRI techniques, traditional DW-MRI data remains subject to head motion artifacts during scanning. For example, previous studies have indicated that, with traditional DW-MRI data, head motion artifacts significantly affect the evaluation of diffusion metrics. Actually, DW-MRI data scanned with higher sampling rate are important for accurately evaluating diffusion metrics because it allows for full-brain coverage through the acquisition of multiple slices simultaneously and more gradient directions. Here, we employed a publicly available multiband DW-MRI dataset to investigate the association between motion and diffusion metrics with the standard pipeline, tract-based spatial statistics (TBSS). The diffusion metrics used in this study included not only the commonly used metrics (i.e., FA and MD) in DW-MRI studies, but also newly proposed inter-voxel metric, local diffusion homogeneity (LDH). We found that the motion effects in FA and MD seems to be mitigated to some extent, but the effect on MD still exists. Furthermore, the effect in LDH is much more pronounced. These results indicate that researchers shall be cautious when conducting data analysis and interpretation. Finally, the motion-diffusion association is discussed.
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Affiliation(s)
- Xiang-zhen Kong
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
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