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Challenges in diffusion MRI tractography - Lessons learned from international benchmark competitions.
Schilling KG, Daducci A, Maier-Hein K, Poupon C, Houde JC, Nath V, Anderson AW, Landman BA, Descoteaux M
(2019) Magn Reson Imaging 57: 194-209
MeSH Terms: Algorithms, Benchmarking, Brain, Diffusion Tensor Imaging, Humans, Internationality, Neuroimaging, Reproducibility of Results
Show Abstract · Added March 26, 2019
Diffusion MRI (dMRI) fiber tractography has become a pillar of the neuroimaging community due to its ability to noninvasively map the structural connectivity of the brain. Despite widespread use in clinical and research domains, these methods suffer from several potential drawbacks or limitations. Thus, validating the accuracy and reproducibility of techniques is critical for sound scientific conclusions and effective clinical outcomes. Towards this end, a number of international benchmark competitions, or "challenges", has been organized by the diffusion MRI community in order to investigate the reliability of the tractography process by providing a platform to compare algorithms and results in a fair manner, and evaluate common and emerging algorithms in an effort to advance the state of the field. In this paper, we summarize the lessons from a decade of challenges in tractography, and give perspective on the past, present, and future "challenges" that the field of diffusion tractography faces.
Copyright © 2018 Elsevier Inc. All rights reserved.
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8 MeSH Terms
Limits to anatomical accuracy of diffusion tractography using modern approaches.
Schilling KG, Nath V, Hansen C, Parvathaneni P, Blaber J, Gao Y, Neher P, Aydogan DB, Shi Y, Ocampo-Pineda M, Schiavi S, Daducci A, Girard G, Barakovic M, Rafael-Patino J, Romascano D, Rensonnet G, Pizzolato M, Bates A, Fischi E, Thiran JP, Canales-Rodríguez EJ, Huang C, Zhu H, Zhong L, Cabeen R, Toga AW, Rheault F, Theaud G, Houde JC, Sidhu J, Chamberland M, Westin CF, Dyrby TB, Verma R, Rathi Y, Irfanoglu MO, Thomas C, Pierpaoli C, Descoteaux M, Anderson AW, Landman BA
(2019) Neuroimage 185: 1-11
MeSH Terms: Brain, Brain Mapping, Diffusion Tensor Imaging, Humans, Image Processing, Computer-Assisted, Neural Pathways
Show Abstract · Added March 26, 2019
Diffusion MRI fiber tractography is widely used to probe the structural connectivity of the brain, with a range of applications in both clinical and basic neuroscience. Despite widespread use, tractography has well-known pitfalls that limits the anatomical accuracy of this technique. Numerous modern methods have been developed to address these shortcomings through advances in acquisition, modeling, and computation. To test whether these advances improve tractography accuracy, we organized the 3-D Validation of Tractography with Experimental MRI (3D-VoTEM) challenge at the ISBI 2018 conference. We made available three unique independent tractography validation datasets - a physical phantom and two ex vivo brain specimens - resulting in 176 distinct submissions from 9 research groups. By comparing results over a wide range of fiber complexities and algorithmic strategies, this challenge provides a more comprehensive assessment of tractography's inherent limitations than has been reported previously. The central results were consistent across all sub-challenges in that, despite advances in tractography methods, the anatomical accuracy of tractography has not dramatically improved in recent years. Taken together, our results independently confirm findings from decades of tractography validation studies, demonstrate inherent limitations in reconstructing white matter pathways using diffusion MRI data alone, and highlight the need for alternative or combinatorial strategies to accurately map the fiber pathways of the brain.
Copyright © 2018 Elsevier Inc. All rights reserved.
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6 MeSH Terms
Viewing the Future of IR through Molecular Histology: An Overview of Imaging Mass Spectrometry.
Cressman ENK, Spraggins JM
(2018) J Vasc Interv Radiol 29: 1543-1546.e1
MeSH Terms: Diffusion of Innovation, Forecasting, Humans, Molecular Imaging, Predictive Value of Tests, Radiology, Interventional, Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization
Added March 26, 2019
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7 MeSH Terms
Anatomical accuracy of standard-practice tractography algorithms in the motor system - A histological validation in the squirrel monkey brain.
Schilling KG, Gao Y, Stepniewska I, Janve V, Landman BA, Anderson AW
(2019) Magn Reson Imaging 55: 7-25
MeSH Terms: Algorithms, Animals, Brain, Brain Mapping, Diffusion Tensor Imaging, Image Processing, Computer-Assisted, Models, Anatomic, Motor Cortex, Probability, Reproducibility of Results, Saimiri, Sensitivity and Specificity, Software, White Matter
Show Abstract · Added March 26, 2019
For two decades diffusion fiber tractography has been used to probe both the spatial extent of white matter pathways and the region to region connectivity of the brain. In both cases, anatomical accuracy of tractography is critical for sound scientific conclusions. Here we assess and validate the algorithms and tractography implementations that have been most widely used - often because of ease of use, algorithm simplicity, or availability offered in open source software. Comparing forty tractography results to a ground truth defined by histological tracers in the primary motor cortex on the same squirrel monkey brains, we assess tract fidelity on the scale of voxels as well as over larger spatial domains or regional connectivity. No algorithms are successful in all metrics, and, in fact, some implementations fail to reconstruct large portions of pathways or identify major points of connectivity. The accuracy is most dependent on reconstruction method and tracking algorithm, as well as the seed region and how this region is utilized. We also note a tremendous variability in the results, even though the same MR images act as inputs to all algorithms. In addition, anatomical accuracy is significantly decreased at increased distances from the seed. An analysis of the spatial errors in tractography reveals that many techniques have trouble properly leaving the gray matter, and many only reveal connectivity to adjacent regions of interest. These results show that the most commonly implemented algorithms have several shortcomings and limitations, and choices in implementations lead to very different results. This study should provide guidance for algorithm choices based on study requirements for sensitivity, specificity, or the need to identify particular connections, and should serve as a heuristic for future developments in tractography.
Copyright © 2018 Elsevier Inc. All rights reserved.
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14 MeSH Terms
Resting-state white matter-cortical connectivity in non-human primate brain.
Wu TL, Wang F, Li M, Schilling KG, Gao Y, Anderson AW, Chen LM, Ding Z, Gore JC
(2019) Neuroimage 184: 45-55
MeSH Terms: Animals, Brain, Brain Mapping, Diffusion Tensor Imaging, Gray Matter, Magnetic Resonance Imaging, Neural Pathways, Saimiri, White Matter
Show Abstract · Added September 21, 2018
Numerous studies have used functional magnetic resonance imaging (fMRI) to characterize functional connectivity between cortical regions by analyzing correlations in blood oxygenation level dependent (BOLD) signals in a resting state. However, to date, there have been only a handful of studies reporting resting state BOLD signals in white matter. Nonetheless, a growing number of reports has emerged in recent years suggesting white matter BOLD signals can be reliably detected, though their biophysical origins remain unclear. Moreover, recent studies have identified robust correlations in a resting state between signals from cortex and specific white matter tracts. In order to further validate and interpret these findings, we studied a non-human primate model to investigate resting-state connectivity patterns between parcellated cortical volumes and specific white matter bundles. Our results show that resting-state connectivity patterns between white and gray matter structures are not randomly distributed but share notable similarities with diffusion- and histology-derived anatomic connectivities. This suggests that resting-state BOLD correlations between white matter fiber tracts and the gray matter regions to which they connect are directly related to the anatomic arrangement and density of WM fibers. We also measured how different levels of baseline neural activity, induced by varying levels of anesthesia, modulate these patterns. As anesthesia levels were raised, we observed weakened correlation coefficients between specific white matter tracts and gray matter regions while key features of the connectivity pattern remained similar. Overall, results from this study provide further evidence that neural activity is detectable by BOLD fMRI in both gray and white matter throughout the resting brain. The combined use of gray and white matter functional connectivity could also offer refined full-scale functional parcellation of the entire brain to characterize its functional architecture.
Published by Elsevier Inc.
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2 Members
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9 MeSH Terms
Voxel-wise detection of functional networks in white matter.
Huang Y, Bailey SK, Wang P, Cutting LE, Gore JC, Ding Z
(2018) Neuroimage 183: 544-552
MeSH Terms: Adult, Diffusion Tensor Imaging, Functional Neuroimaging, Gray Matter, Humans, Nerve Net, Neurovascular Coupling, Visual Perception, White Matter, Young Adult
Show Abstract · Added March 26, 2019
Functional magnetic resonance imaging (fMRI) depicts neural activity in the brain indirectly by measuring blood oxygenation level dependent (BOLD) signals. The majority of fMRI studies have focused on detecting cortical activity in gray matter (GM), but whether functional BOLD signal changes also arise in white matter (WM), and whether neural activities trigger hemodynamic changes in WM similarly to GM, remain controversial, particularly in light of the much lower vascular density in WM. However, BOLD effects in WM are readily detected under hypercapnic challenges, and the number of reports supporting reliable detections of stimulus-induced activations in WM continues to grow. Rather than assume a particular hemodynamic response function, we used a voxel-by-voxel analysis of frequency spectra in WM to detect WM activations under visual stimulation, whose locations were validated with fiber tractography using diffusion tensor imaging (DTI). We demonstrate that specific WM regions are robustly activated in response to visual stimulation, and that regional distributions of WM activation are consistent with fiber pathways reconstructed using DTI. We further examined the variation in the concordance between WM activation and fiber density in groups of different sample sizes, and compared the signal profiles of BOLD time series between resting state and visual stimulation conditions in activated GM as well as activated and non-activated WM regions. Our findings confirm that BOLD signal variations in WM are modulated by neural activity and are detectable with conventional fMRI using appropriate methods, thus offering the potential of expanding functional connectivity measurements throughout the brain.
Copyright © 2018 Elsevier Inc. All rights reserved.
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MeSH Terms
Neurofilament relates to white matter microstructure in older adults.
Moore EE, Hohman TJ, Badami FS, Pechman KR, Osborn KE, Acosta LMY, Bell SP, Babicz MA, Gifford KA, Anderson AW, Goldstein LE, Blennow K, Zetterberg H, Jefferson AL
(2018) Neurobiol Aging 70: 233-241
MeSH Terms: Aged, Aging, Amyloid beta-Peptides, Biomarkers, Brain, Cognitive Dysfunction, Cohort Studies, Diffusion Tensor Imaging, Female, Humans, Magnetic Resonance Imaging, Male, Neurofilament Proteins, Peptide Fragments, White Matter
Show Abstract · Added March 26, 2019
Cerebrospinal fluid (CSF) neurofilament light (NFL) is a protein biomarker of axonal injury. To study whether NFL is associated with diffusion tensor imaging (DTI) measurements of white matter (WM) microstructure, Vanderbilt Memory & Aging Project participants with normal cognition (n = 77), early mild cognitive impairment (n = 15), and MCI (n = 55) underwent lumbar puncture to obtain CSF and 3T brain MRI. Voxel-wise analyses cross-sectionally related NFL to DTI metrics, adjusting for demographic and vascular risk factors. Increased NFL correlated with multiple DTI metrics (p-values < 0.05). An NFL × diagnosis interaction (excluding early mild cognitive impairment) on WM microstructure (p-values < 0.05) was detected, with associations strongest among MCI. Multiple NFL × CSF biomarker interactions were detected. Associations between NFL and worse WM metrics were strongest among amyloid-β-negative, tau-positive, and suspected nonamyloid pathology participants. Findings suggest increased NFL, a biomarker of axonal injury, is correlated with compromised WM microstructure. Results highlight the role of elevated NFL in predicting WM damage in cognitively impaired older adults who are amyloid-negative, tau-positive, or meet suspected nonamyloid pathology criteria.
Copyright © 2018 Elsevier Inc. All rights reserved.
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1 Members
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15 MeSH Terms
A Web-Based Atlas Combining MRI and Histology of the Squirrel Monkey Brain.
Schilling KG, Gao Y, Christian M, Janve V, Stepniewska I, Landman BA, Anderson AW
(2019) Neuroinformatics 17: 131-145
MeSH Terms: Animals, Atlases as Topic, Brain, Diffusion Tensor Imaging, Internet, Male, Saimiri
Show Abstract · Added March 26, 2019
The squirrel monkey (Saimiri sciureus) is a commonly-used surrogate for humans in biomedical research. In the neuroimaging community, MRI and histological atlases serve as valuable resources for anatomical, physiological, and functional studies of the brain; however, no digital MRI/histology atlas is currently available for the squirrel monkey. This paper describes the construction of a web-based multi-modal atlas of the squirrel monkey brain. The MRI-derived information includes anatomical MRI contrast (i.e., T2-weighted and proton-density-weighted) and diffusion MRI metrics (i.e., fractional anisotropy and mean diffusivity) from data acquired both in vivo and ex vivo on a 9.4 Tesla scanner. The histological images include Nissl and myelin stains, co-registered to the corresponding MRI, allowing identification of cyto- and myelo-architecture. In addition, a bidirectional neuronal tracer, biotinylated dextran amine (BDA) was injected into the primary motor cortex, enabling highly specific identification of regions connected to the injection location. The atlas integrates the results of common image analysis methods including diffusion tensor imaging glyphs, labels of 57 white-matter tracts identified using DTI-tractography, and 18 cortical regions of interest identified from Nissl-revealed cyto-architecture. All data are presented in a common space, and all image types are accessible through a web-based atlas viewer, which allows visualization and interaction of user-selectable contrasts and varying resolutions. By providing an easy to use reference system of anatomical information, our web-accessible multi-contrast atlas forms a rich and convenient resource for comparisons of brain findings across subjects or modalities. The atlas is called the Combined Histology-MRI Integrated Atlas of the Squirrel Monkey (CHIASM). All images are accessible through our web-based viewer ( https://chiasm.vuse.vanderbilt.edu /), and data are available for download at ( https://www.nitrc.org/projects/smatlas/ ).
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7 MeSH Terms
Biophysical Modeling of In Vivo Glioma Response After Whole-Brain Radiation Therapy in a Murine Model of Brain Cancer.
Hormuth DA, Weis JA, Barnes SL, Miga MI, Quaranta V, Yankeelov TE
(2018) Int J Radiat Oncol Biol Phys 100: 1270-1279
MeSH Terms: Animals, Brain Neoplasms, Cell Death, Cell Proliferation, Contrast Media, Cranial Irradiation, Diffusion Magnetic Resonance Imaging, Disease Models, Animal, Female, Glioma, Magnetic Resonance Imaging, Models, Biological, Radiation Dosage, Rats, Rats, Wistar, Treatment Outcome, Tumor Burden
Show Abstract · Added July 23, 2018
PURPOSE - To develop and investigate a set of biophysical models based on a mechanically coupled reaction-diffusion model of the spatiotemporal evolution of tumor growth after radiation therapy.
METHODS AND MATERIALS - Post-radiation therapy response is modeled using a cell death model (M), a reduced proliferation rate model (M), and cell death and reduced proliferation model (M). To evaluate each model, rats (n = 12) with C6 gliomas were imaged with diffusion-weighted magnetic resonance imaging (MRI) and contrast-enhanced MRI at 7 time points over 2 weeks. Rats received either 20 or 40 Gy between the third and fourth imaging time point. Diffusion-weighted MRI was used to estimate tumor cell number within enhancing regions in contrast-enhanced MRI data. Each model was fit to the spatiotemporal evolution of tumor cell number from time point 1 to time point 5 to estimate model parameters. The estimated model parameters were then used to predict tumor growth at the final 2 imaging time points. The model prediction was evaluated by calculating the error in tumor volume estimates, average surface distance, and voxel-based cell number.
RESULTS - For both the rats treated with either 20 or 40 Gy, significantly lower error in tumor volume, average surface distance, and voxel-based cell number was observed for the M and M models compared with the M model. The M model fit, however, had significantly lower sum squared error compared with the M and M models.
CONCLUSIONS - The results of this study indicate that for both doses, the M and M models result in accurate predictions of tumor growth, whereas the M model poorly describes response to radiation therapy.
Copyright © 2017 Elsevier Inc. All rights reserved.
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17 MeSH Terms
Multi-compartmental diffusion characterization of the human cervical spinal cord in vivo using the spherical mean technique.
By S, Xu J, Box BA, Bagnato FR, Smith SA
(2018) NMR Biomed 31: e3894
MeSH Terms: Adult, Cervical Cord, Cohort Studies, Diffusion Magnetic Resonance Imaging, Humans, Multiple Sclerosis, Reproducibility of Results
Show Abstract · Added March 14, 2018
The purpose of this work was to evaluate the feasibility and reproducibility of the spherical mean technique (SMT), a multi-compartmental diffusion model, in the spinal cord of healthy controls, and to assess its ability to improve spinal cord characterization in multiple sclerosis (MS) patients at 3 T. SMT was applied in the cervical spinal cord of eight controls and six relapsing-remitting MS patients. SMT provides an elegant framework to model the apparent axonal volume fraction v , intrinsic diffusivity D , and extra-axonal transverse diffusivity D (which is estimated as a function of v and D ) without confounds related to complex fiber orientation distribution that reside in diffusion MRI modeling. SMT's reproducibility was assessed with two different scans within a month, and SMT-derived indices in healthy and MS cohorts were compared. The influence of acquisition scheme on SMT was also evaluated. SMT's v , D , and D measurements all showed high reproducibility. A decrease in v was observed at the site of lesions and normal appearing white matter (p < 0.05), and trends towards a decreased D and increased D were seen. Importantly, a twofold reduction in acquisition yielded similarly high accuracy with SMT. SMT provides a fast, reproducible, and accurate method to improve characterization of the cervical spinal cord, and may have clinical potential for MS patients.
Copyright © 2018 John Wiley & Sons, Ltd.
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7 MeSH Terms