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Honari H, Choe AS, Lindquist MA. Evaluating phase synchronization methods in fMRI: A comparison study and new approaches. Neuroimage 2021; 228:117704. [PMID: 33385554 PMCID: PMC8011682 DOI: 10.1016/j.neuroimage.2020.117704] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [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: 08/16/2020] [Revised: 11/14/2020] [Accepted: 12/21/2020] [Indexed: 12/25/2022] Open
Abstract
In recent years there has been growing interest in measuring time-varying functional connectivity between different brain regions using resting-state functional magnetic resonance imaging (rs-fMRI) data. One way to assess the relationship between signals from different brain regions is to measure their phase synchronization (PS) across time. There are several ways to perform such analyses, and we compare methods that utilize a PS metric together with a sliding window, referred to here as windowed phase synchronization (WPS), with those that directly measure the instantaneous phase synchronization (IPS). In particular, IPS has recently gained popularity as it offers single time-point resolution of time-resolved fMRI connectivity. In this paper, we discuss the underlying assumptions required for performing PS analyses and emphasize the importance of band-pass filtering the data to obtain valid results. Further, we contrast this approach with the use of Empirical Mode Decomposition (EMD) to achieve similar goals. We review various methods for evaluating PS and introduce a new approach within the IPS framework denoted the cosine of the relative phase (CRP). We contrast methods through a series of simulations and application to rs-fMRI data. Our results indicate that CRP outperforms other tested methods and overcomes issues related to undetected temporal transitions from positive to negative associations common in IPS analysis. Further, in contrast to phase coherence, CRP unfolds the distribution of PS measures, which benefits subsequent clustering of PS matrices into recurring brain states.
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Affiliation(s)
- Hamed Honari
- Department of Electrical and Computer Engineering, Johns Hopkins University, USA
| | - Ann S Choe
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, USA; International Center for Spinal Cord Injury, Kennedy Krieger Institute, USA; Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins School of Medicine, USA
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Honari H, Choe AS, Pekar JJ, Lindquist MA. Investigating the impact of autocorrelation on time-varying connectivity. Neuroimage 2019; 197:37-48. [PMID: 31022568 PMCID: PMC6684286 DOI: 10.1016/j.neuroimage.2019.04.042] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [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: 02/27/2019] [Revised: 04/10/2019] [Accepted: 04/15/2019] [Indexed: 11/27/2022] Open
Abstract
In recent years, a number of studies have reported on the existence of time-varying functional connectivity (TVC) in resting-state functional magnetic resonance imaging (rs-fMRI) data. The sliding-window technique is currently one of the most commonly used methods to estimate TVC. Although previous studies have shown that autocorrelation can negatively impact estimates of static functional connectivity, its impact on TVC estimates is not well known at this time. In this paper, we show both theoretically and empirically that the existence of autocorrelation within a time series can inflate the sampling variability of TVC estimated using the sliding-window technique. This can in turn increase the risk of misinterpreting noise as true TVC and negatively impact subsequent estimation of whole-brain time-varying FC profiles, or "brain states". The latter holds as more variable input measures lead to more variable output measures in the state estimation procedure. Finally, we demonstrate that prewhitening the data prior to analysis can lower the variance of the estimated TVC and improve brain state estimation. These results suggest that careful consideration is required when making inferences on TVC.
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Affiliation(s)
- Hamed Honari
- Department of Electrical and Computer Engineering, Johns Hopkins University, USA
| | - Ann S Choe
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, USA
| | - James J Pekar
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, USA
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Mejia AF, Nebel MB, Barber AD, Choe AS, Pekar JJ, Caffo BS, Lindquist MA. Improved estimation of subject-level functional connectivity using full and partial correlation with empirical Bayes shrinkage. Neuroimage 2018; 172:478-491. [PMID: 29391241 PMCID: PMC5957759 DOI: 10.1016/j.neuroimage.2018.01.029] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [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: 10/09/2017] [Revised: 01/07/2018] [Accepted: 01/12/2018] [Indexed: 02/04/2023] Open
Abstract
Reliability of subject-level resting-state functional connectivity (FC) is determined in part by the statistical techniques employed in its estimation. Methods that pool information across subjects to inform estimation of subject-level effects (e.g., Bayesian approaches) have been shown to enhance reliability of subject-level FC. However, fully Bayesian approaches are computationally demanding, while empirical Bayesian approaches typically rely on using repeated measures to estimate the variance components in the model. Here, we avoid the need for repeated measures by proposing a novel measurement error model for FC describing the different sources of variance and error, which we use to perform empirical Bayes shrinkage of subject-level FC towards the group average. In addition, since the traditional intra-class correlation coefficient (ICC) is inappropriate for biased estimates, we propose a new reliability measure denoted the mean squared error intra-class correlation coefficient (ICCMSE) to properly assess the reliability of the resulting (biased) estimates. We apply the proposed techniques to test-retest resting-state fMRI data on 461 subjects from the Human Connectome Project to estimate connectivity between 100 regions identified through independent components analysis (ICA). We consider both correlation and partial correlation as the measure of FC and assess the benefit of shrinkage for each measure, as well as the effects of scan duration. We find that shrinkage estimates of subject-level FC exhibit substantially greater reliability than traditional estimates across various scan durations, even for the most reliable connections and regardless of connectivity measure. Additionally, we find partial correlation reliability to be highly sensitive to the choice of penalty term, and to be generally worse than that of full correlations except for certain connections and a narrow range of penalty values. This suggests that the penalty needs to be chosen carefully when using partial correlations.
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Affiliation(s)
| | - Mary Beth Nebel
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, USA; Department of Neurology, Johns Hopkins University, USA
| | - Anita D Barber
- Center for Psychiatric Neuroscience, Feinstein Institute for Medical Research, USA
| | - Ann S Choe
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, USA
| | - James J Pekar
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, USA
| | - Brian S Caffo
- Department of Biostatistics, Johns Hopkins University, USA
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Gao Y, Schilling KG, Stepniewska I, Plassard AJ, Choe AS, Li X, Landman BA, Anderson AW. Tests of cortical parcellation based on white matter connectivity using diffusion tensor imaging. Neuroimage 2018; 170:321-331. [PMID: 28235566 PMCID: PMC5568504 DOI: 10.1016/j.neuroimage.2017.02.048] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [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: 11/01/2016] [Revised: 01/23/2017] [Accepted: 02/18/2017] [Indexed: 10/20/2022] Open
Abstract
The cerebral cortex is conventionally divided into a number of domains based on cytoarchitectural features. Diffusion tensor imaging (DTI) enables noninvasive parcellation of the cortex based on white matter connectivity patterns. However, the correspondence between DTI-connectivity-based and cytoarchitectural parcellation has not been systematically established. In this study, we compared histological parcellation of New World monkey neocortex to DTI- connectivity-based classification and clustering in the same brains. First, we used supervised classification to parcellate parieto-frontal cortex based on DTI tractograms and the cytoarchitectural prior (obtained using Nissl staining). We performed both within and across sample classification, showing reasonable classification performance in both conditions. Second, we used unsupervised clustering to parcellate the cortex and compared the clusters to the cytoarchitectonic standard. We then explored the similarities and differences with several post-hoc analyses, highlighting underlying principles that drive the DTI-connectivity-based parcellation. The differences in parcellation between DTI-connectivity and Nissl histology probably represent both DTI's bias toward easily-tracked bundles and true differences between cytoarchitectural and connectivity defined domains. DTI tractograms appear to cluster more according to functional networks, rather than mapping directly onto cytoarchitectonic domains. Our results show that caution should be used when DTI-tractography classification, based on data from another brain, is used as a surrogate for cytoarchitectural parcellation.
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Affiliation(s)
- Yurui Gao
- Institute of Imaging Science, Vanderbilt University, United States; Department of Biomedical Engineering, Vanderbilt University, United States
| | - Kurt G Schilling
- Institute of Imaging Science, Vanderbilt University, United States; Department of Biomedical Engineering, Vanderbilt University, United States
| | | | - Andrew J Plassard
- Department of Computer Science, Vanderbilt University, United States
| | - Ann S Choe
- Institute of Imaging Science, Vanderbilt University, United States; Department of Biomedical Engineering, Vanderbilt University, United States
| | - Xia Li
- Institute of Imaging Science, Vanderbilt University, United States; Department of Radiology and Radiological Science, Vanderbilt University, United States
| | - Bennett A Landman
- Institute of Imaging Science, Vanderbilt University, United States; Department of Biomedical Engineering, Vanderbilt University, United States; Department of Computer Science, Vanderbilt University, United States; Department of Electrical Engineering, Vanderbilt University, United States
| | - Adam W Anderson
- Institute of Imaging Science, Vanderbilt University, United States; Department of Biomedical Engineering, Vanderbilt University, United States; Department of Radiology and Radiological Science, Vanderbilt University, United States
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Syed MF, Lindquist MA, Pillai JJ, Agarwal S, Gujar SK, Choe AS, Caffo B, Sair HI. Dynamic Functional Connectivity States Between the Dorsal and Ventral Sensorimotor Networks Revealed by Dynamic Conditional Correlation Analysis of Resting-State Functional Magnetic Resonance Imaging. Brain Connect 2017; 7:635-642. [DOI: 10.1089/brain.2017.0533] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Affiliation(s)
| | - Martin A. Lindquist
- Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Heath, Baltimore, Maryland
| | - Jay J. Pillai
- Division of Neuroradiology, Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Shruti Agarwal
- Division of Neuroradiology, Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Sachin K. Gujar
- Division of Neuroradiology, Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Ann S. Choe
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland
| | - Brian Caffo
- Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Heath, Baltimore, Maryland
| | - Haris I. Sair
- Division of Neuroradiology, Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland
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Abstract
Purpose of Review This review provides an overview of the current spinal functional magnetic resonance imaging (fMRI) studies that investigate the healthy and injured spinal cords. Recent Findings Spinal fMRI-derived outcome measures have previously been suggested to be sensitive to changes in neurological function in the spinal cord. A body of recent task-activated fMRI studies seems to confirm that detecting neural activity in the spinal cord using spinal fMRI may be feasible as well as reliable. Furthermore, a growing number of studies has shown that resting state fMRI in the spinal cord is also feasible, demonstrating that the investigation of changes in neural activity can also be performed in the absence of explicit tasks. Summary Current task-activated and resting state fMRI studies suggest that spinal fMRI has a strong potential to provide novel imaging biomarkers that can be used to investigate plastic changes in the injured spinal cord.
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Affiliation(s)
- Ann S Choe
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD 21205 USA.,F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD 21205 USA
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Choe AS, Nebel MB, Barber AD, Cohen JR, Xu Y, Pekar JJ, Caffo B, Lindquist MA. Comparing test-retest reliability of dynamic functional connectivity methods. Neuroimage 2017; 158:155-175. [PMID: 28687517 DOI: 10.1016/j.neuroimage.2017.07.005] [Citation(s) in RCA: 120] [Impact Index Per Article: 17.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2017] [Revised: 06/09/2017] [Accepted: 07/03/2017] [Indexed: 12/21/2022] Open
Abstract
Due to the dynamic, condition-dependent nature of brain activity, interest in estimating rapid functional connectivity (FC) changes that occur during resting-state functional magnetic resonance imaging (rs-fMRI) has recently soared. However, studying dynamic FC is methodologically challenging, due to the low signal-to-noise ratio of the blood oxygen level dependent (BOLD) signal in fMRI and the massive number of data points generated during the analysis. Thus, it is important to establish methods and summary measures that maximize reliability and the utility of dynamic FC to provide insight into brain function. In this study, we investigated the reliability of dynamic FC summary measures derived using three commonly used estimation methods - sliding window (SW), tapered sliding window (TSW), and dynamic conditional correlations (DCC) methods. We applied each of these techniques to two publicly available rs-fMRI test-retest data sets - the Multi-Modal MRI Reproducibility Resource (Kirby Data) and the Human Connectome Project (HCP Data). The reliability of two categories of dynamic FC summary measures were assessed, specifically basic summary statistics of the dynamic correlations and summary measures derived from recurring whole-brain patterns of FC ("brain states"). The results provide evidence that dynamic correlations are reliably detected in both test-retest data sets, and the DCC method outperforms SW methods in terms of the reliability of summary statistics. However, across all estimation methods, reliability of the brain state-derived measures was low. Notably, the results also show that the DCC-derived dynamic correlation variances are significantly more reliable than those derived using the non-parametric estimation methods. This is important, as the fluctuations of dynamic FC (i.e., its variance) has a strong potential to provide summary measures that can be used to find meaningful individual differences in dynamic FC. We therefore conclude that utilizing the variance of the dynamic connectivity is an important component in any dynamic FC-derived summary measure.
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Affiliation(s)
- Ann S Choe
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, USA
| | - Mary Beth Nebel
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, USA; Department of Neurology, Johns Hopkins University, USA
| | - Anita D Barber
- Center for Psychiatric Neuroscience, Feinstein Institute for Medical Research, USA
| | - Jessica R Cohen
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, USA
| | - Yuting Xu
- Department of Biostatistics, Johns Hopkins University, USA
| | - James J Pekar
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, USA
| | - Brian Caffo
- Department of Biostatistics, Johns Hopkins University, USA
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Choe AS, Sadowsky CL, Smith SA, van Zijl PCM, Pekar JJ, Belegu V. Subject-specific regional measures of water diffusion are associated with impairment in chronic spinal cord injury. Neuroradiology 2017; 59:747-758. [PMID: 28597208 DOI: 10.1007/s00234-017-1860-9] [Citation(s) in RCA: 5] [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] [Subscribe] [Scholar Register] [Received: 03/20/2017] [Accepted: 05/28/2017] [Indexed: 12/14/2022]
Abstract
PURPOSE We aimed to identify non-invasive imaging parameters that can serve as biomarkers for the integrity of the spinal cord, which is paramount to neurological function. Diffusion tensor imaging (DTI) indices are sensitive to axonal and myelin damage, and have strong potential to serve as such biomarkers. However, averaging DTI indices over large regions of interest (ROIs), a common approach to analyzing the images of injured spinal cord, leads to loss of subject-specific information. We investigated if DTI-tractography-driven, subject-specific demarcation approach can yield measures that are more specific to impairment. METHODS In 18 individuals with chronic spinal cord injury (SCI), subject-specific demarcation of the injury region was performed using DTI tractography, which yielded three regions relative to injury (RRI; regions superior to, at, and below injury epicenter). DTI indices averaged over each RRI were correlated with measures of residual motor and sensory function, obtained using the International Standard of Neurological Classification for Spinal Cord Injury (ISNCSCI). RESULTS Total ISNCSCI score (ISNCSCI-tot; sum of ISNCSCI motor and sensory scores) was significantly (p < 0.05) correlated with fractional anisotropy and axial and radial diffusivities. ISNCSCI-tot showed strongest correlation with indices measured from the region inferior to the injury epicenter (IRRI), the degree of which exceeded that of those measured from the entire cervical cord-suggesting contribution from Wallerian degeneration. CONCLUSION DTI tractography-driven, subject-specific injury demarcation approach provided measures that were more specific to impairment. Notably, DTI indices obtained from the IRRI region showed the highest specificity to impairment, demonstrating their strong potential as biomarkers for the SCI severity.
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Affiliation(s)
- Ann S Choe
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA. .,F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, 707 North Broadway, Baltimore, MD, 21205, USA.
| | - Cristina L Sadowsky
- International Center for Spinal Cord Injury, Kennedy Krieger Institute, Baltimore, MD, 21205, USA.,Physical Medicine and Rehabilitation, Kennedy Krieger Institute, Baltimore, MD, 21205, USA
| | - Seth A Smith
- Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN, 37235, USA.,Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, 37235, USA
| | - Peter C M van Zijl
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA.,F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, 707 North Broadway, Baltimore, MD, 21205, USA
| | - James J Pekar
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA.,F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, 707 North Broadway, Baltimore, MD, 21205, USA
| | - Visar Belegu
- International Center for Spinal Cord Injury, Kennedy Krieger Institute, Baltimore, MD, 21205, USA.,Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA
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Schilling K, Gao Y, Stepniewska I, Choe AS, Landman BA, Anderson AW. Reproducibility and variation of diffusion measures in the squirrel monkey brain, in vivo and ex vivo. Magn Reson Imaging 2016; 35:29-38. [PMID: 27587226 DOI: 10.1016/j.mri.2016.08.015] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [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: 05/25/2016] [Revised: 08/11/2016] [Accepted: 08/20/2016] [Indexed: 01/07/2023]
Abstract
PURPOSE Animal models are needed to better understand the relationship between diffusion MRI (dMRI) and the underlying tissue microstructure. One promising model for validation studies is the common squirrel monkey, Saimiri sciureus. This study aims to determine (1) the reproducibility of in vivo diffusion measures both within and between subjects; (2) the agreement between in vivo and ex vivo data acquired from the same specimen and (3) normal diffusion values and their variation across brain regions. METHODS Data were acquired from three healthy squirrel monkeys, each imaged twice in vivo and once ex vivo. Reproducibility of fractional anisotropy (FA), mean diffusivity (MD), and principal eigenvector (PEV) was assessed, and normal values were determined both in vivo and ex vivo. RESULTS The calculated coefficients of variation (CVs) for both intra-subject and inter-subject MD were below 10% (low variability) while FA had a wider range of CVs, 2-14% intra-subject (moderate variability), and 3-31% inter-subject (high variability). MD in ex vivo tissue was lower than in vivo (30%-50% decrease), while FA values increased in all regions (30-39% increase). The mode of angular differences between in vivo and ex vivo PEVs was 12 degrees. CONCLUSION This study characterizes the diffusion properties of the squirrel monkey brain and serves as the groundwork for using the squirrel monkey, both in vivo and ex vivo, as a model for diffusion MRI studies.
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Affiliation(s)
- Kurt Schilling
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA.
| | - Yurui Gao
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | | | - Ann S Choe
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Bennett A Landman
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA; Department of Electrical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Adam W Anderson
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
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Nebel MB, Eloyan A, Nettles CA, Sweeney KL, Ament K, Ward RE, Choe AS, Barber AD, Pekar JJ, Mostofsky SH. Intrinsic Visual-Motor Synchrony Correlates With Social Deficits in Autism. Biol Psychiatry 2016; 79:633-41. [PMID: 26543004 PMCID: PMC4777671 DOI: 10.1016/j.biopsych.2015.08.029] [Citation(s) in RCA: 110] [Impact Index Per Article: 13.8] [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: 11/11/2014] [Revised: 07/21/2015] [Accepted: 08/13/2015] [Indexed: 12/27/2022]
Abstract
BACKGROUND Imitation, which is impaired in children with autism spectrum disorder (ASD) and critically depends on the integration of visual input with motor output, likely impacts both motor and social skill acquisition in children with ASD; however, it is unclear what brain mechanisms contribute to this impairment. Children with ASD also exhibit what appears to be an ASD-specific bias against using visual feedback during motor learning. Does the temporal congruity of intrinsic activity, or functional connectivity, between motor and visual brain regions contribute to ASD-associated deficits in imitation, motor, and social skills? METHODS We acquired resting-state functional magnetic resonance imaging scans from 100 8- to 12-year-old children (50 ASD). Group independent component analysis was used to estimate functional connectivity between visual and motor systems. Brain-behavior relationships were assessed by regressing functional connectivity measures with social deficit severity, imitation, and gesture performance scores. RESULTS We observed increased intrinsic asynchrony between visual and motor systems in children with ASD and replicated this finding in an independent sample from the Autism Brain Imaging Data Exchange. Moreover, children with more out-of-sync intrinsic visual-motor activity displayed more severe autistic traits, while children with greater intrinsic visual-motor synchrony were better imitators. CONCLUSIONS Our twice replicated findings confirm that visual-motor functional connectivity is disrupted in ASD. Furthermore, the observed temporal incongruity between visual and motor systems, which may reflect diminished integration of visual consequences with motor output, was predictive of the severity of social deficits and may contribute to impaired social-communicative skill development in children with ASD.
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Affiliation(s)
- Mary Beth Nebel
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, Maryland; Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland.
| | - Ani Eloyan
- Department of Biostatistics, School of Public Health, Brown University, Providence, RI
| | - Carrie A. Nettles
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, MD
| | - Kristie L. Sweeney
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, MD
| | - Katarina Ament
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, MD
| | - Rebecca E. Ward
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, MD
| | - Ann S. Choe
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD,F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD
| | - Anita D. Barber
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, MD,Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD
| | - James J. Pekar
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD,F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD
| | - Stewart H. Mostofsky
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, MD,Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD,Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD
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Gao Y, Parvathaneni P, Schilling KG, Wang F, Stepniewska I, Xu Z, Choe AS, Ding Z, Gore JC, Chen LM, Landman BA, Anderson AW. A 3D high resolution ex vivo white matter atlas of the common squirrel monkey ( Saimiri sciureus) based on diffusion tensor imaging. Proc SPIE Int Soc Opt Eng 2016; 9784:97843K. [PMID: 27064328 PMCID: PMC4825691 DOI: 10.1117/12.2217325] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Modern magnetic resonance imaging (MRI) brain atlases are high quality 3-D volumes with specific structures labeled in the volume. Atlases are essential in providing a common space for interpretation of results across studies, for anatomical education, and providing quantitative image-based navigation. Extensive work has been devoted to atlas construction for humans, macaque, and several non-primate species (e.g., rat). One notable gap in the literature is the common squirrel monkey - for which the primary published atlases date from the 1960's. The common squirrel monkey has been used extensively as surrogate for humans in biomedical studies, given its anatomical neuro-system similarities and practical considerations. This work describes the continued development of a multi-modal MRI atlas for the common squirrel monkey, for which a structural imaging space and gray matter parcels have been previously constructed. This study adds white matter tracts to the atlas. The new atlas includes 49 white matter (WM) tracts, defined using diffusion tensor imaging (DTI) in three animals and combines these data to define the anatomical locations of these tracks in a standardized coordinate system compatible with previous development. An anatomist reviewed the resulting tracts and the inter-animal reproducibility (i.e., the Dice index of each WM parcel across animals in common space) was assessed. The Dice indices range from 0.05 to 0.80 due to differences of local registration quality and the variation of WM tract position across individuals. However, the combined WM labels from the 3 animals represent the general locations of WM parcels, adding basic connectivity information to the atlas.
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Affiliation(s)
- Yurui Gao
- Biomedical Engineering, Vanderbilt University, Nashville, TN USA
- Institute of Imaging Science, Vanderbilt University, Nashville, TN USA
| | - Prasanna Parvathaneni
- Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN USA
| | - Kurt G. Schilling
- Biomedical Engineering, Vanderbilt University, Nashville, TN USA
- Institute of Imaging Science, Vanderbilt University, Nashville, TN USA
| | - Feng Wang
- Institute of Imaging Science, Vanderbilt University, Nashville, TN USA
- Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN USA
| | | | - Zhoubing Xu
- Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN USA
| | - Ann S. Choe
- Biomedical Engineering, Vanderbilt University, Nashville, TN USA
- Institute of Imaging Science, Vanderbilt University, Nashville, TN USA
| | - Zhaohua Ding
- Biomedical Engineering, Vanderbilt University, Nashville, TN USA
- Institute of Imaging Science, Vanderbilt University, Nashville, TN USA
- Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN USA
- Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN USA
| | - John C. Gore
- Biomedical Engineering, Vanderbilt University, Nashville, TN USA
- Institute of Imaging Science, Vanderbilt University, Nashville, TN USA
- Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN USA
- Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN USA
| | - Li Min Chen
- Institute of Imaging Science, Vanderbilt University, Nashville, TN USA
- Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN USA
| | - Bennett A. Landman
- Biomedical Engineering, Vanderbilt University, Nashville, TN USA
- Institute of Imaging Science, Vanderbilt University, Nashville, TN USA
- Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN USA
- Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN USA
| | - Adam W. Anderson
- Biomedical Engineering, Vanderbilt University, Nashville, TN USA
- Institute of Imaging Science, Vanderbilt University, Nashville, TN USA
- Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN USA
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12
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Sair HI, Yahyavi-Firouz-Abadi N, Calhoun VD, Airan RD, Agarwal S, Intrapiromkul J, Choe AS, Gujar SK, Caffo B, Lindquist MA, Pillai JJ. Presurgical brain mapping of the language network in patients with brain tumors using resting-state fMRI: Comparison with task fMRI. Hum Brain Mapp 2015; 37:913-23. [PMID: 26663615 DOI: 10.1002/hbm.23075] [Citation(s) in RCA: 76] [Impact Index Per Article: 8.4] [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: 08/19/2015] [Revised: 11/16/2015] [Accepted: 11/23/2015] [Indexed: 01/23/2023] Open
Abstract
PURPOSE To compare language networks derived from resting-state fMRI (rs-fMRI) with task-fMRI in patients with brain tumors and investigate variables that affect rs-fMRI vs task-fMRI concordance. MATERIALS AND METHODS Independent component analysis (ICA) of rs-fMRI was performed with 20, 30, 40, and 50 target components (ICA20 to ICA50) and language networks identified for patients presenting for presurgical fMRI mapping between 1/1/2009 and 7/1/2015. 49 patients were analyzed fulfilling criteria for presence of brain tumors, no prior brain surgery, and adequate task-fMRI performance. Rs-vs-task-fMRI concordance was measured using Dice coefficients across varying fMRI thresholds before and after noise removal. Multi-thresholded Dice coefficient volume under the surface (DiceVUS) and maximum Dice coefficient (MaxDice) were calculated. One-way Analysis of Variance (ANOVA) was performed to determine significance of DiceVUS and MaxDice between the four ICA order groups. Age, Sex, Handedness, Tumor Side, Tumor Size, WHO Grade, number of scrubbed volumes, image intensity root mean square (iRMS), and mean framewise displacement (FD) were used as predictors for VUS in a linear regression. RESULTS Artificial elevation of rs-fMRI vs task-fMRI concordance is seen at low thresholds due to noise. Noise-removed group-mean DiceVUS and MaxDice improved as ICA order increased, however ANOVA demonstrated no statistically significant difference between the four groups. Linear regression demonstrated an association between iRMS and DiceVUS for ICA30-50, and iRMS and MaxDice for ICA50. CONCLUSION Overall there is moderate group level rs-vs-task fMRI language network concordance, however substantial subject-level variability exists; iRMS may be used to determine reliability of rs-fMRI derived language networks.
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Affiliation(s)
- Haris I Sair
- Division of Neuroradiology, the Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Noushin Yahyavi-Firouz-Abadi
- Division of Neuroradiology, the Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Vince D Calhoun
- The Mind Research Network, Departments of Electrical and Computer Engineering, University of New Mexico, Albuquerque, New Mexico
| | - Raag D Airan
- Division of Neuroradiology, the Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Shruti Agarwal
- Division of Neuroradiology, the Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Jarunee Intrapiromkul
- Division of Neuroradiology, the Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Ann S Choe
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland
| | - Sachin K Gujar
- Division of Neuroradiology, the Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Brian Caffo
- Department of Biostatistics, Johns Hopkins University, Baltimore, Maryland
| | - Martin A Lindquist
- Department of Biostatistics, Johns Hopkins University, Baltimore, Maryland
| | - Jay J Pillai
- Division of Neuroradiology, the Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland
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13
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Choe AS, Jones CK, Joel SE, Muschelli J, Belegu V, Caffo BS, Lindquist MA, van Zijl PCM, Pekar JJ. Reproducibility and Temporal Structure in Weekly Resting-State fMRI over a Period of 3.5 Years. PLoS One 2015; 10:e0140134. [PMID: 26517540 PMCID: PMC4627782 DOI: 10.1371/journal.pone.0140134] [Citation(s) in RCA: 83] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2015] [Accepted: 09/22/2015] [Indexed: 11/18/2022] Open
Abstract
Resting-state functional MRI (rs-fMRI) permits study of the brain’s functional networks without requiring participants to perform tasks. Robust changes in such resting state networks (RSNs) have been observed in neurologic disorders, and rs-fMRI outcome measures are candidate biomarkers for monitoring clinical trials, including trials of extended therapeutic interventions for rehabilitation of patients with chronic conditions. In this study, we aim to present a unique longitudinal dataset reporting on a healthy adult subject scanned weekly over 3.5 years and identify rs-fMRI outcome measures appropriate for clinical trials. Accordingly, we assessed the reproducibility, and characterized the temporal structure of, rs-fMRI outcome measures derived using independent component analysis (ICA). Data was compared to a 21-person dataset acquired on the same scanner in order to confirm that the values of the single-subject RSN measures were within the expected range as assessed from the multi-participant dataset. Fourteen RSNs were identified, and the inter-session reproducibility of outcome measures—network spatial map, temporal signal fluctuation magnitude, and between-network connectivity (BNC)–was high, with executive RSNs showing the highest reproducibility. Analysis of the weekly outcome measures also showed that many rs-fMRI outcome measures had a significant linear trend, annual periodicity, and persistence. Such temporal structure was most prominent in spatial map similarity, and least prominent in BNC. High reproducibility supports the candidacy of rs-fMRI outcome measures as biomarkers, but the presence of significant temporal structure needs to be taken into account when such outcome measures are considered as biomarkers for rehabilitation-style therapeutic interventions in chronic conditions.
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Affiliation(s)
- Ann S. Choe
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States of America
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States of America
- International Center for Spinal Cord Injury, Kennedy Krieger Institute, Baltimore, MD, United States of America
- * E-mail:
| | - Craig K. Jones
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States of America
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States of America
| | - Suresh E. Joel
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States of America
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States of America
| | - John Muschelli
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, United States of America
| | - Visar Belegu
- International Center for Spinal Cord Injury, Kennedy Krieger Institute, Baltimore, MD, United States of America
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, United States of America
| | - Brian S. Caffo
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, United States of America
| | - Martin A. Lindquist
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, United States of America
| | - Peter C. M. van Zijl
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States of America
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States of America
| | - James J. Pekar
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States of America
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States of America
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14
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Gao Y, Khare SP, Panda S, Choe AS, Stepniewska I, Li X, Ding Z, Anderson A, Landman BA. A brain MRI atlas of the common squirrel monkey, Saimiri sciureus.. Proc SPIE Int Soc Opt Eng 2014; 9038:90380C. [PMID: 24817811 DOI: 10.1117/12.2043589] [Citation(s) in RCA: 10] [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] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
The common squirrel monkey, Saimiri sciureus, is a New World monkey with functional and microstructural organization of central nervous system similar to that of humans. It is one of the most commonly used South American primates in biomedical research. Unlike its Old World macaque cousins, no digital atlases have described the organization of the squirrel monkey brain. Here, we present a multi-modal magnetic resonance imaging (MRI) atlas constructed from the brain of an adult female squirrel monkey. In vivo MRI acquisitions include T2 structural imaging and diffusion tensor imaging. Ex vivo MRI acquisitions include T2 structural imaging and diffusion tensor imaging. Cortical regions were manually annotated on the co-registered volumes based on published histological sections.
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Affiliation(s)
- Yurui Gao
- Biomedical Engineering, Vanderbilt University, Nashville, TN USA ; Institute of Image Science, Vanderbilt University, Nashville, TN USA
| | - Shweta P Khare
- Computer Science, Vanderbilt University, Nashville, TN USA
| | - Swetasudha Panda
- Electrical Engineering, Vanderbilt University, Nashville, TN USA
| | - Ann S Choe
- Biomedical Engineering, Vanderbilt University, Nashville, TN USA ; Institute of Image Science, Vanderbilt University, Nashville, TN USA
| | | | - Xia Li
- Institute of Image Science, Vanderbilt University, Nashville, TN USA
| | - Zhoahua Ding
- Institute of Image Science, Vanderbilt University, Nashville, TN USA ; Electrical Engineering, Vanderbilt University, Nashville, TN USA
| | - Adam Anderson
- Biomedical Engineering, Vanderbilt University, Nashville, TN USA ; Institute of Image Science, Vanderbilt University, Nashville, TN USA
| | - Bennett A Landman
- Biomedical Engineering, Vanderbilt University, Nashville, TN USA ; Institute of Image Science, Vanderbilt University, Nashville, TN USA ; Computer Science, Vanderbilt University, Nashville, TN USA ; Electrical Engineering, Vanderbilt University, Nashville, TN USA
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15
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Gao Y, Choe AS, Stepniewska I, Li X, Avison MJ, Anderson AW. Validation of DTI tractography-based measures of primary motor area connectivity in the squirrel monkey brain. PLoS One 2013; 8:e75065. [PMID: 24098365 PMCID: PMC3788067 DOI: 10.1371/journal.pone.0075065] [Citation(s) in RCA: 45] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2013] [Accepted: 08/09/2013] [Indexed: 11/18/2022] Open
Abstract
Diffusion tensor imaging (DTI) tractography provides noninvasive measures of structural cortico-cortical connectivity of the brain. However, the agreement between DTI-tractography-based measures and histological 'ground truth' has not been quantified. In this study, we reconstructed the 3D density distribution maps (DDM) of fibers labeled with an anatomical tracer, biotinylated dextran amine (BDA), as well as DTI tractography-derived streamlines connecting the primary motor (M1) cortex to other cortical regions in the squirrel monkey brain. We evaluated the agreement in M1-cortical connectivity between the fibers labeled in the brain tissue and DTI streamlines on a regional and voxel-by-voxel basis. We found that DTI tractography is capable of providing inter-regional connectivity comparable to the neuroanatomical connectivity, but is less reliable measuring voxel-to-voxel variations within regions.
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Affiliation(s)
- Yurui Gao
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, United States of America
- Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee, United States of America
| | - Ann S. Choe
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, United States of America
- Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee, United States of America
| | - Iwona Stepniewska
- Department of Psychology, Vanderbilt University, Nashville, Tennessee, United States of America
| | - Xia Li
- Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee, United States of America
| | - Malcolm J. Avison
- Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee, United States of America
- Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville, Tennessee, United States of America
- Department of Pharmacology, Vanderbilt University, Nashville, Tennessee, United States of America
- Department of Neurology, Vanderbilt University, Nashville, Tennessee, United States of America
| | - Adam W. Anderson
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, United States of America
- Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee, United States of America
- Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville, Tennessee, United States of America
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16
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Choe AS, Belegu V, Yoshida S, Joel S, Sadowsky CL, Smith SA, van Zijl PCM, Pekar JJ, McDonald JW. Extensive neurological recovery from a complete spinal cord injury: a case report and hypothesis on the role of cortical plasticity. Front Hum Neurosci 2013; 7:290. [PMID: 23805087 PMCID: PMC3691521 DOI: 10.3389/fnhum.2013.00290] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.5] [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: 02/25/2013] [Accepted: 06/03/2013] [Indexed: 12/14/2022] Open
Abstract
Neurological recovery in patients with severe spinal cord injury (SCI) is extremely rare. We have identified a patient with chronic cervical traumatic SCI, who suffered a complete loss of motor and sensory function below the injury for 6 weeks after the injury, but experienced a progressive neurological recovery that continued for 17 years. The extent of the patient's recovery from the severe trauma-induced paralysis is rare and remarkable. A detailed study of this patient using diffusion tensor imaging (DTI), magnetization transfer imaging (MTI), and resting state fMRI (rs-fMRI) revealed structural and functional changes in the central nervous system that may be associated with the neurological recovery. Sixty-two percent cervical cord white matter atrophy was observed. DTI-derived quantities, more sensitive to axons, demonstrated focal changes, while MTI-derived quantity, more sensitive to myelin, showed a diffuse change. No significant cortical structural changes were observed, while rs-fMRI revealed increased brain functional connectivity between sensorimotor and visual networks. The study provides comprehensive description of the structural and functional changes in the patient using advanced MR imaging technique. This multimodal MR imaging study also shows the potential of rs-fMRI to measure the extent of cortical plasticity.
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Affiliation(s)
- Ann S Choe
- Department of Neurology, Johns Hopkins University School of Medicine Baltimore, MD, USA ; International Center for Spinal Cord Injury, Hugo W. Moser Research Institute at Kennedy Krieger, Inc. Baltimore, MD, USA ; F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute Baltimore, MD, USA
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17
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Choe AS, Stepniewska I, Colvin DC, Ding Z, Anderson AW. Validation of diffusion tensor MRI in the central nervous system using light microscopy: quantitative comparison of fiber properties. NMR Biomed 2012; 25:900-908. [PMID: 22246940 DOI: 10.1002/nbm.v25.7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/24/2010] [Revised: 09/05/2011] [Accepted: 10/18/2011] [Indexed: 05/22/2023]
Abstract
Diffusion tensor imaging (DTI) provides an indirect measure of tissue structure on a microscopic scale. To date, DTI is the only imaging method that provides such information in vivo, and has proven to be a valuable tool in both research and clinical settings. In this study, we investigated the relationship between white matter structure and diffusion parameters measured by DTI. We used micrographs from light microscopy of fixed, myelin-stained brain sections as a gold standard for direct comparison with data from DTI. Relationships between microscopic tissue properties observed with light microscopy (fiber orientation, density and coherence) and fiber properties observed by DTI (tensor orientation, diffusivities and fractional anisotropy) were investigated. Agreement between the major eigenvector of the tensor and myelinated fibers was excellent in voxels with high fiber coherence. In addition, increased fiber spread was strongly associated with increased radial diffusivity (p = 6 × 10(-6)) and decreased fractional anisotropy (p = 5 × 10(-8)), and was weakly associated with decreased axial diffusivity (p = 0.07). Increased fiber density was associated with increased fractional anisotropy (p = 0.03), and weakly associated with decreased radial diffusivity (p < 0.06), but not with axial diffusivity (p = 0.97). The mean diffusivity was largely independent of fiber spread (p = 0.24) and fiber density (p = 0.34).
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Affiliation(s)
- A S Choe
- Vanderbilt University Institute of Imaging Science, Nashville, TN, USA.
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18
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Choe AS, Stepniewska I, Colvin DC, Ding Z, Anderson AW. Validation of diffusion tensor MRI in the central nervous system using light microscopy: quantitative comparison of fiber properties. NMR Biomed 2012; 25:900-8. [PMID: 22246940 PMCID: PMC4818098 DOI: 10.1002/nbm.1810] [Citation(s) in RCA: 53] [Impact Index Per Article: 4.4] [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: 11/24/2010] [Revised: 09/05/2011] [Accepted: 10/18/2011] [Indexed: 05/11/2023]
Abstract
Diffusion tensor imaging (DTI) provides an indirect measure of tissue structure on a microscopic scale. To date, DTI is the only imaging method that provides such information in vivo, and has proven to be a valuable tool in both research and clinical settings. In this study, we investigated the relationship between white matter structure and diffusion parameters measured by DTI. We used micrographs from light microscopy of fixed, myelin-stained brain sections as a gold standard for direct comparison with data from DTI. Relationships between microscopic tissue properties observed with light microscopy (fiber orientation, density and coherence) and fiber properties observed by DTI (tensor orientation, diffusivities and fractional anisotropy) were investigated. Agreement between the major eigenvector of the tensor and myelinated fibers was excellent in voxels with high fiber coherence. In addition, increased fiber spread was strongly associated with increased radial diffusivity (p = 6 × 10(-6)) and decreased fractional anisotropy (p = 5 × 10(-8)), and was weakly associated with decreased axial diffusivity (p = 0.07). Increased fiber density was associated with increased fractional anisotropy (p = 0.03), and weakly associated with decreased radial diffusivity (p < 0.06), but not with axial diffusivity (p = 0.97). The mean diffusivity was largely independent of fiber spread (p = 0.24) and fiber density (p = 0.34).
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Affiliation(s)
- A S Choe
- Vanderbilt University Institute of Imaging Science, Nashville, TN, USA.
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19
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Choe AS, Gao Y, Li X, Compton KB, Stepniewska I, Anderson AW. Accuracy of image registration between MRI and light microscopy in the ex vivo brain. Magn Reson Imaging 2011; 29:683-92. [PMID: 21546191 DOI: 10.1016/j.mri.2011.02.022] [Citation(s) in RCA: 65] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2010] [Accepted: 02/24/2011] [Indexed: 11/29/2022]
Abstract
A multistep procedure was developed to register magnetic resonance imaging (MRI) and histological data from the same sample in the light microscopy image space, with the ultimate goal of allowing quantitative comparisons of the two datasets. The fixed brain of an owl monkey was used to develop and test the procedure. In addition to the MRI and histological data, photographic images of the brain tissue block acquired during sectioning were assembled into a blockface volume to provide an intermediate step for the overall registration process. The MR volume was first registered to the blockface volume using a combination of linear and nonlinear registration, and two dimensional (2D) blockface sections were registered to corresponding myelin-stained sections using a combination of linear and nonlinear registration. Before this 2D registration, two major types of tissue distortions were corrected: tissue tearing and independent movement of different parts of the brain, both introduced during histological processing of the sections. The correction procedure utilized a 2D method to close tissue tears and a multiple iterative closest point (ICP) algorithm to reposition separate pieces of tissue in the image. The accuracy of the overall MR to micrograph registration procedure was assessed by measuring the distance between registered landmarks chosen in the MR image space and the corresponding landmarks chosen in the micrograph space. The average error distance of the MR data registered to micrograph data was 0.324±0.277 mm, only 8% larger than the width of the MRI voxel (0.3 mm).
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Affiliation(s)
- Ann S Choe
- Vanderbilt University Institute of Imaging Science, Nashville, TN, USA.
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20
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Anderson AW, Choe AS, Stepniewska I, Colvin DC. Comparison of brain white matter fiber orientation measurements based on diffusion tensor imaging and light microscopy. Conf Proc IEEE Eng Med Biol Soc 2008; 2006:2249-51. [PMID: 17946945 DOI: 10.1109/iembs.2006.259554] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Diffusion tensor magnetic resonance imaging (MRI) was used to estimate white matter fiber orientations in fixed brain specimens. The specimens were subsequently sectioned, stained for myelinated fibers, and imaged with light microscopy. The MRI data were registered with the micrographs, allowing direct comparison of fiber orientation estimates between the two methods. Fiber orientation was measured in regions of interest in the frontal lateral corpus callosum. The results of diffusion MRI and light microscopy agreed within 2 degrees-the difference was not statistically significant.
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Affiliation(s)
- Adam W Anderson
- Biomedical Engineering Department and Institute of Imaging Science, Vanderbilt University, Nashville, TN 37232, USA.
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21
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Mishra A, Lu Y, Choe AS, Aldroubi A, Gore JC, Anderson AW, Ding Z. An image-processing toolset for diffusion tensor tractography. Magn Reson Imaging 2006; 25:365-76. [PMID: 17371726 PMCID: PMC2719760 DOI: 10.1016/j.mri.2006.10.006] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2006] [Revised: 09/25/2006] [Indexed: 11/19/2022]
Abstract
Diffusion tensor imaging (DTI)-based fiber tractography holds great promise in delineating neuronal fiber tracts and, hence, providing connectivity maps of the neural networks in the human brain. An array of image-processing techniques has to be developed to turn DTI tractography into a practically useful tool. To this end, we have developed a suite of image-processing tools for fiber tractography with improved reliability. This article summarizes the main technical developments we have made to date, which include anisotropic smoothing, anisotropic interpolation, Bayesian fiber tracking and automatic fiber bundling. A primary focus of these techniques is the robustness to noise and partial volume averaging, the two major hurdles to reliable fiber tractography. Performance of these techniques has been comprehensively examined with simulated and in vivo DTI data, demonstrating improvements in the robustness and reliability of DTI tractography.
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Affiliation(s)
- Arabinda Mishra
- Institute of Imaging Science, Vanderbilt University, Nashville, TN 37232-2657, USA.
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Choe AS, Rhee Y, Lee J, Han PS, Borisov SK, Kuzmina MA, Mishin VA. Effective excitation method of a three-level medium in a selective photoionization. Phys Rev A 1995; 52:382-386. [PMID: 9912258 DOI: 10.1103/physreva.52.382] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2023]
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