1
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Hanycz SA, Noorani A, Hung PSP, Walker MR, Zhang AB, Latypov TH, Hodaie M. Hippocampus diffusivity abnormalities in classical trigeminal neuralgia. Pain Rep 2024; 9:e1159. [PMID: 38655236 PMCID: PMC11037743 DOI: 10.1097/pr9.0000000000001159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 02/16/2024] [Accepted: 02/24/2024] [Indexed: 04/26/2024] Open
Abstract
Introduction Patients with chronic pain frequently report cognitive symptoms that affect memory and attention, which are functions attributed to the hippocampus. Trigeminal neuralgia (TN) is a chronic neuropathic pain disorder characterized by paroxysmal attacks of unilateral orofacial pain. Given the stereotypical nature of TN pain and lack of negative symptoms including sensory loss, TN provides a unique model to investigate the hippocampal implications of chronic pain. Recent evidence demonstrated that TN is associated with macrostructural hippocampal abnormalities indicated by reduced subfield volumes; however, there is a paucity in our understanding of hippocampal microstructural abnormalities associated with TN. Objectives To explore diffusivity metrics within the hippocampus, along with its functional and structural subfields, in patients with TN. Methods To examine hippocampal microstructure, we utilized diffusion tensor imaging in 31 patients with TN and 21 controls. T1-weighted magnetic resonance images were segmented into hippocampal subfields and registered into diffusion-weighted imaging space. Fractional anisotropy (FA) and mean diffusivity were extracted for hippocampal subfields and longitudinal axis segmentations. Results Patients with TN demonstrated reduced FA in bilateral whole hippocampi and hippocampal body and contralateral subregions CA2/3 and CA4, indicating microstructural hippocampal abnormalities. Notably, patients with TN showed significant correlation between age and hippocampal FA, while controls did not exhibit this correlation. These effects were driven chiefly by female patients with TN. Conclusion This study demonstrates that TN is associated with microstructural hippocampal abnormalities, which may precede and potentially be temporally linked to volumetric hippocampal alterations demonstrated previously. These findings provide further evidence for the role of the hippocampus in chronic pain and suggest the potential for targeted interventions to mitigate cognitive symptoms in patients with chronic pain.
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Affiliation(s)
- Shaun Andrew Hanycz
- Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada
- Division of Brain, Imaging, and Behaviour—Systems Neuroscience, Krembil Research Institute, Toronto Western Hospital, University Health Network, Toronto, ON, Canada
- Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Alborz Noorani
- Division of Brain, Imaging, and Behaviour—Systems Neuroscience, Krembil Research Institute, Toronto Western Hospital, University Health Network, Toronto, ON, Canada
- Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Peter Shih-Ping Hung
- Division of Brain, Imaging, and Behaviour—Systems Neuroscience, Krembil Research Institute, Toronto Western Hospital, University Health Network, Toronto, ON, Canada
- Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Matthew R. Walker
- Division of Brain, Imaging, and Behaviour—Systems Neuroscience, Krembil Research Institute, Toronto Western Hospital, University Health Network, Toronto, ON, Canada
| | - Ashley B. Zhang
- MD Program, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA, USA
| | - Timur H. Latypov
- Division of Brain, Imaging, and Behaviour—Systems Neuroscience, Krembil Research Institute, Toronto Western Hospital, University Health Network, Toronto, ON, Canada
- Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Mojgan Hodaie
- Division of Brain, Imaging, and Behaviour—Systems Neuroscience, Krembil Research Institute, Toronto Western Hospital, University Health Network, Toronto, ON, Canada
- Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Division of Neurosurgery, Krembil Brain Institute, Toronto Western Hospital, University Health Network, Toronto, ON, Canada
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2
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Harms RL, Fritz FJ, Schoenmakers S, Roebroeck A. Fast and robust quantification of uncertainty in non-linear diffusion MRI models. Neuroimage 2024; 285:120496. [PMID: 38101495 DOI: 10.1016/j.neuroimage.2023.120496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Revised: 12/08/2023] [Accepted: 12/10/2023] [Indexed: 12/17/2023] Open
Abstract
Diffusion MRI (dMRI) allows for non-invasive investigation of brain tissue microstructure. By fitting a model to the dMRI signal, various quantitative measures can be derived from the data, such as fractional anisotropy, neurite density and axonal radii maps. We investigate the Fisher Information Matrix (FIM) and uncertainty propagation as a generally applicable method for quantifying the parameter uncertainties in linear and non-linear diffusion MRI models. In direct comparison with Markov Chain Monte Carlo (MCMC) sampling, the FIM produces similar uncertainty estimates at much lower computational cost. Using acquired and simulated data, we then list several characteristics that influence the parameter variances, including data complexity and signal-to-noise ratio. For practical purposes we investigate a possible use of uncertainty estimates in decreasing intra-group variance in group statistics by uncertainty-weighted group estimates. This has potential use cases for detection and suppression of imaging artifacts.
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Affiliation(s)
- R L Harms
- Department of Cognitive Neuroscience, Faculty of Psychology & Neuroscience, Maastricht University, The Netherlands.
| | - F J Fritz
- Department of Cognitive Neuroscience, Faculty of Psychology & Neuroscience, Maastricht University, The Netherlands
| | - S Schoenmakers
- Department of Cognitive Neuroscience, Faculty of Psychology & Neuroscience, Maastricht University, The Netherlands
| | - A Roebroeck
- Department of Cognitive Neuroscience, Faculty of Psychology & Neuroscience, Maastricht University, The Netherlands.
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3
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Johnson JT, Irfanoglu MO, Manninen E, Ross TJ, Yang Y, Laun FB, Martin J, Topgaard D, Benjamini D. In vivo disentanglement of diffusion frequency-dependence, tensor shape, and relaxation using multidimensional MRI. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.10.561702. [PMID: 37987005 PMCID: PMC10659440 DOI: 10.1101/2023.10.10.561702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
Diffusion MRI with free gradient waveforms, combined with simultaneous relaxation encoding, referred to as multidimensional MRI (MD-MRI), offers microstructural specificity in complex biological tissue. This approach delivers intravoxel information about the microstructure, local chemical composition, and importantly, how these properties are coupled within heterogeneous tissue containing multiple microenvironments. Recent theoretical advances incorporated diffusion time dependency and integrated MD-MRI with concepts from oscillating gradients. This framework probes the diffusion frequency, ω , in addition to the diffusion tensor, D , and relaxation, R 1 , R 2 , correlations. A D ( ω ) - R 1 - R 2 clinical imaging protocol was then introduced, with limited brain coverage and 3 mm3 voxel size, which hinder brain segmentation and future cohort studies. In this study, we introduce an efficient, sparse in vivo MD-MRI acquisition protocol providing whole brain coverage at 2 mm3 voxel size. We demonstrate its feasibility and robustness using a well-defined phantom and repeated scans of five healthy individuals. Additionally, we test different denoising strategies to address the sparse nature of this protocol, and show that efficient MD-MRI encoding design demands a nuanced denoising approach. The MD-MRI framework provides rich information that allows resolving the diffusion frequency dependence into intravoxel components based on their D ( ω ) - R 1 - R 2 distribution, enabling the creation of microstructure-specific maps in the human brain. Our results encourage the broader adoption and use of this new imaging approach for characterizing healthy and pathological tissues.
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Affiliation(s)
- Jessica T.E. Johnson
- Multiscale Imaging and Integrative Biophysics Unit, National Institute on Aging, NIH, Baltimore, MD, USA
| | - M. Okan Irfanoglu
- Quantitative Medical Imaging Section, National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, MD, USA
| | - Eppu Manninen
- Multiscale Imaging and Integrative Biophysics Unit, National Institute on Aging, NIH, Baltimore, MD, USA
| | - Thomas J. Ross
- Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, MD, USA
| | - Yihong Yang
- Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, MD, USA
| | - Frederik B. Laun
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Jan Martin
- Department of Chemistry, Lund University, Lund, Sweden
| | | | - Dan Benjamini
- Multiscale Imaging and Integrative Biophysics Unit, National Institute on Aging, NIH, Baltimore, MD, USA
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4
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Valcourt Caron A, Shmuel A, Hao Z, Descoteaux M. versaFlow: a versatile pipeline for resolution adapted diffusion MRI processing and its application to studying the variability of the PRIME-DE database. Front Neuroinform 2023; 17:1191200. [PMID: 37637471 PMCID: PMC10449583 DOI: 10.3389/fninf.2023.1191200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 06/27/2023] [Indexed: 08/29/2023] Open
Abstract
The lack of "gold standards" in Diffusion Weighted Imaging (DWI) makes validation cumbersome. To tackle this task, studies use translational analysis where results in humans are benchmarked against findings in other species. Non-Human Primates (NHP) are particularly interesting for this, as their cytoarchitecture is closely related to humans. However, tools used for processing and analysis must be adapted and finely tuned to work well on NHP images. Here, we propose versaFlow, a modular pipeline implemented in Nextflow, designed for robustness and scalability. The pipeline is tailored to in vivo NHP DWI at any spatial resolution; it allows for maintainability and customization. Processes and workflows are implemented using cutting-edge and state-of-the-art Magnetic Resonance Imaging (MRI) processing technologies and diffusion modeling algorithms, namely Diffusion Tensor Imaging (DTI), Constrained Spherical Deconvolution (CSD), and DIstribution of Anisotropic MicrOstructural eNvironments in Diffusion-compartment imaging (DIAMOND). Using versaFlow, we provide an in-depth study of the variability of diffusion metrics computed on 32 subjects from 3 sites of the Primate Data Exchange (PRIME-DE), which contains anatomical T1-weighted (T1w) and T2-weighted (T2w) images, functional MRI (fMRI), and DWI of NHP brains. This dataset includes images acquired over a range of resolutions, using single and multi-shell gradient samplings, on multiple scanner vendors. We perform a reproducibility study of the processing of versaFlow using the Aix-Marseilles site's data, to ensure that our implementation has minimal impact on the variability observed in subsequent analyses. We report very high reproducibility for the majority of metrics; only gamma distribution parameters of DIAMOND display less reproducible behaviors, due to the absence of a mechanism to enforce a random number seed in the software we used. This should be taken into consideration when future applications are performed. We show that the PRIME-DE diffusion data exhibits a great level of variability, similar or greater than results obtained in human studies. Its usage should be done carefully to prevent instilling uncertainty in statistical analyses. This hints at a need for sufficient harmonization in acquisition protocols and for the development of robust algorithms capable of managing the variability induced in imaging due to differences in scanner models and/or vendors.
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Affiliation(s)
- Alex Valcourt Caron
- Sherbrooke Connectivity Imaging Laboratory, Computer Science Department, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Amir Shmuel
- Brain Imaging Signals Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Ziqi Hao
- Brain Imaging Signals Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Maxime Descoteaux
- Sherbrooke Connectivity Imaging Laboratory, Computer Science Department, Université de Sherbrooke, Sherbrooke, QC, Canada
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5
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Olesen JL, Ianus A, Østergaard L, Shemesh N, Jespersen SN. Tensor denoising of multidimensional MRI data. Magn Reson Med 2023; 89:1160-1172. [PMID: 36219475 PMCID: PMC10092037 DOI: 10.1002/mrm.29478] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 08/17/2022] [Accepted: 09/15/2022] [Indexed: 02/02/2023]
Abstract
PURPOSE To develop a denoising strategy leveraging redundancy in high-dimensional data. THEORY AND METHODS The SNR fundamentally limits the information accessible by MRI. This limitation has been addressed by a host of denoising techniques, recently including the so-called MPPCA: principal component analysis of the signal followed by automated rank estimation, exploiting the Marchenko-Pastur distribution of noise singular values. Operating on matrices comprised of data patches, this popular approach objectively identifies noise components and, ideally, allows noise to be removed without introducing artifacts such as image blurring, or nonlocal averaging. The MPPCA rank estimation, however, relies on a large number of noise singular values relative to the number of signal components to avoid such ill effects. This condition is unlikely to be met when data patches and therefore matrices are small, for example due to spatially varying noise. Here, we introduce tensor MPPCA (tMPPCA) for the purpose of denoising multidimensional data, such as from multicontrast acquisitions. Rather than combining dimensions in matrices, tMPPCA uses each dimension of the multidimensional data's inherent tensor-structure to better characterize noise, and to recursively estimate signal components. RESULTS Relative to matrix-based MPPCA, tMPPCA requires no additional assumptions, and comparing the two in a numerical phantom and a multi-TE diffusion MRI data set, tMPPCA dramatically improves denoising performance. This is particularly true for small data patches, suggesting that tMPPCA can be especially beneficial in such cases. CONCLUSIONS The MPPCA denoising technique can be extended to high-dimensional data with improved performance for smaller patch sizes.
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Affiliation(s)
- Jonas L Olesen
- Center of Functionally Integrative Neuroscience, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark.,Department of Physics and Astronomy, Aarhus University, Aarhus, Denmark
| | - Andrada Ianus
- Champalimaud Research, Champalimaud Foundation, Lisbon, Portugal
| | - Leif Østergaard
- Center of Functionally Integrative Neuroscience, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Noam Shemesh
- Champalimaud Research, Champalimaud Foundation, Lisbon, Portugal
| | - Sune N Jespersen
- Center of Functionally Integrative Neuroscience, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark.,Department of Physics and Astronomy, Aarhus University, Aarhus, Denmark
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6
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Machine learning with textural analysis of longitudinal multiparametric MRI and molecular subtypes accurately predicts pathologic complete response in patients with invasive breast cancer. PLoS One 2023; 18:e0280320. [PMID: 36649274 PMCID: PMC9844845 DOI: 10.1371/journal.pone.0280320] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 12/27/2022] [Indexed: 01/18/2023] Open
Abstract
PURPOSE To predict pathological complete response (pCR) after neoadjuvant chemotherapy using extreme gradient boosting (XGBoost) with MRI and non-imaging data at multiple treatment timepoints. MATERIAL AND METHODS This retrospective study included breast cancer patients (n = 117) who underwent neoadjuvant chemotherapy. Data types used included tumor ADC values, diffusion-weighted and dynamic-contrast-enhanced MRI at three treatment timepoints, and patient demographics and tumor data. GLCM textural analysis was performed on MRI data. An extreme gradient boosting machine learning algorithm was used to predict pCR. Prediction performance was evaluated using the area under the curve (AUC) of the receiver operating curve along with precision and recall. RESULTS Prediction using texture features of DWI and DCE images at multiple treatment time points (AUC = 0.871; 95% CI: (0.768, 0.974; p<0.001) and (AUC = 0.903 95% CI: 0.854, 0.952; p<0.001) respectively), outperformed that using mean tumor ADC (AUC = 0.850 (95% CI: 0.764, 0.936; p<0.001)). The AUC using all MRI data was 0.933 (95% CI: 0.836, 1.03; p<0.001). The AUC using non-MRI data was 0.919 (95% CI: 0.848, 0.99; p<0.001). The highest AUC of 0.951 (95% CI: 0.909, 0.993; p<0.001) was achieved with all MRI and all non-MRI data at all time points as inputs. CONCLUSION Using XGBoost on extracted GLCM features and non-imaging data accurately predicts pCR. This early prediction of response can minimize exposure to toxic chemotherapy, allowing regimen modification mid-treatment and ultimately achieving better outcomes.
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7
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Öztekin I, Garic D, Bayat M, Hernandez ML, Finlayson MA, Graziano PA, Dick AS. Structural and diffusion-weighted brain imaging predictors of attention-deficit/hyperactivity disorder and its symptomology in very young (4- to 7-year-old) children. Eur J Neurosci 2022; 56:6239-6257. [PMID: 36215144 PMCID: PMC10165616 DOI: 10.1111/ejn.15842] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Revised: 09/15/2022] [Accepted: 09/21/2022] [Indexed: 12/29/2022]
Abstract
The current study aimed to identify the key neurobiology of attention-deficit/hyperactivity disorder (ADHD), as it relates to ADHD diagnostic category and symptoms of hyperactive/impulsive behaviour and inattention. To do so, we adapted a predictive modelling approach to identify the key structural and diffusion-weighted brain imaging measures and their relative standing with respect to teacher ratings of executive function (EF) (measured by the Metacognition Index of the Behavior Rating Inventory of Executive Function [BRIEF]) and negativity and emotion regulation (ER) (measured by the Emotion Regulation Checklist [ERC]), in a critical young age range (ages 4 to 7, mean age 5.52 years, 82.2% Hispanic/Latino), where initial contact with educators and clinicians typically take place. Teacher ratings of EF and ER were predictive of both ADHD diagnostic category and symptoms of hyperactive/impulsive behaviour and inattention. Among the neural measures evaluated, the current study identified the critical importance of the largely understudied diffusion-weighted imaging measures for the underlying neurobiology of ADHD and its associated symptomology. Specifically, our analyses implicated the inferior frontal gyrus as a critical predictor of ADHD diagnostic category and its associated symptomology, above and beyond teacher ratings of EF and ER. Collectively, the current set of findings have implications for theories of ADHD, the relative utility of neurobiological measures with respect to teacher ratings of EF and ER, and the developmental trajectory of its underlying neurobiology.
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Affiliation(s)
- Ilke Öztekin
- Center for Children and Families and Department of Psychology, Florida International University, Miami, Florida, USA.,Exponent, Inc., Philadelphia, Pennsylvania, USA
| | - Dea Garic
- Carolina Institute for Developmental Disabilities, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Mohammadreza Bayat
- Center for Children and Families and Department of Psychology, Florida International University, Miami, Florida, USA
| | - Melissa L Hernandez
- Center for Children and Families and Department of Psychology, Florida International University, Miami, Florida, USA
| | - Mark A Finlayson
- School of Computing and Information Sciences, Florida International University, Miami, Florida, USA
| | - Paulo A Graziano
- Center for Children and Families and Department of Psychology, Florida International University, Miami, Florida, USA
| | - Anthony Steven Dick
- Center for Children and Families and Department of Psychology, Florida International University, Miami, Florida, USA
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8
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Vlegels N, Ossenkoppele R, van der Flier WM, Koek HL, Reijmer YD, Wisse LEM, Biessels GJ. Does Loss of Integrity of the Cingulum Bundle Link Amyloid-β Accumulation and Neurodegeneration in Alzheimer’s Disease? J Alzheimers Dis 2022; 89:39-49. [DOI: 10.3233/jad-220024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Background: Alzheimer’s disease is characterized by the accumulation of amyloid-β (Aβ) into plaques, aggregation of tau into neurofibrillary tangles, and neurodegenerative processes including atrophy. However, there is a poorly understood spatial discordance between initial Aβ deposition and local neurodegeneration. Objective: Here, we test the hypothesis that the cingulum bundle links Aβ deposition in the cingulate cortex to medial temporal lobe (MTL) atrophy. Methods: 21 participants with mild cognitive impairment (MCI) from the UMC Utrecht memory clinic (UMCU, discovery sample) and 37 participants with MCI from Alzheimer’s Disease Neuroimaging Initiative (ADNI, replication sample) with available Aβ-PET scan, T1-weighted and diffusion-weighted MRI were included. Aβ load of the cingulate cortex was measured by the standardized uptake value ratio (SUVR), white matter integrity of the cingulum bundle was assessed by mean diffusivity and atrophy of the MTL by normalized MTL volume. Relationships were tested with linear mixed models, to accommodate multiple measures for each participant. Results: We found at most a weak association between cingulate Aβ and MTL volume (added R2 <0.06), primarily for the posterior hippocampus. In neither sample, white matter integrity of the cingulum bundle was associated with cingulate Aβ or MTL volume (added R2 <0.01). Various sensitivity analyses (Aβ-positive individuals only, posterior cingulate SUVR, MTL sub region volume) provided similar results. Conclusion: These findings, consistent in two independent cohorts, do not support our hypothesis that loss of white matter integrity of the cingulum is a connecting factor between cingulate gyrus Aβ deposition and MTL atrophy.
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Affiliation(s)
- Naomi Vlegels
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Rik Ossenkoppele
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Clinical Memory Research Unit, Lund University, Lund, Sweden
| | - Wiesje M. van der Flier
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Department of Epidemiology and Data Science, VU University Medical Center, Amsterdam, The Netherlands
| | - Huiberdina L. Koek
- Department of Geriatrics, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Yael D. Reijmer
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Laura EM Wisse
- Department of Diagnostic Radiology, Lund University, Lund, Sweden
| | - Geert Jan Biessels
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
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9
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Muncy NM, Kimbler A, Hedges-Muncy AM, McMakin DL, Mattfeld AT. General additive models address statistical issues in diffusion MRI: An example with clinically anxious adolescents. Neuroimage Clin 2022; 33:102937. [PMID: 35033812 PMCID: PMC8762458 DOI: 10.1016/j.nicl.2022.102937] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Revised: 12/10/2021] [Accepted: 01/03/2022] [Indexed: 11/29/2022]
Abstract
Statistical models employed to test for group differences in quantized diffusion-weighted MRI white matter tracts often fail to account for the large number of data points per tract in addition to the distribution, type, and interdependence of the data. To address these issues, we propose the use of Generalized Additive Models (GAMs) and supply code and examples to aid in their implementation. Specifically, using diffusion data from 73 periadolescent clinically anxious and no-psychiatric-diagnosis control participants, we tested for group tract differences and show that a GAM allows for the identification of differences within a tract while accounting for the nature of the data as well as covariates and group factors. Further, we then used these tract differences to investigate their association with performance on a memory test. When comparing our high versus low anxiety groups, we observed a positive association between the left uncinate fasciculus and memory overgeneralization for negatively valenced stimuli. This same association was not evident in the right uncinate or anterior forceps. These findings illustrate that GAMs are well-suited for modeling diffusion data while accounting for various aspects of the data, and suggest that the adoption of GAMs will be a powerful investigatory tool for diffusion-weighted analyses.
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Affiliation(s)
- Nathan M Muncy
- Center for Children and Families, Florida International University, Miami, Florida, USA.
| | - Adam Kimbler
- Center for Children and Families, Florida International University, Miami, Florida, USA
| | | | - Dana L McMakin
- Center for Children and Families, Florida International University, Miami, Florida, USA
| | - Aaron T Mattfeld
- Center for Children and Families, Florida International University, Miami, Florida, USA
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10
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Schilling KG, Tax CM, Rheault F, Hansen C, Yang Q, Yeh FC, Cai L, Anderson AW, Landman BA. Fiber tractography bundle segmentation depends on scanner effects, vendor effects, acquisition resolution, diffusion sampling scheme, diffusion sensitization, and bundle segmentation workflow. Neuroimage 2021; 242:118451. [PMID: 34358660 PMCID: PMC9933001 DOI: 10.1016/j.neuroimage.2021.118451] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 07/08/2021] [Accepted: 08/03/2021] [Indexed: 01/08/2023] Open
Abstract
When investigating connectivity and microstructure of white matter pathways of the brain using diffusion tractography bundle segmentation, it is important to understand potential confounds and sources of variation in the process. While cross-scanner and cross-protocol effects on diffusion microstructure measures are well described (in particular fractional anisotropy and mean diffusivity), it is unknown how potential sources of variation effect bundle segmentation results, which features of the bundle are most affected, where variability occurs, nor how these sources of variation depend upon the method used to reconstruct and segment bundles. In this study, we investigate six potential sources of variation, or confounds, for bundle segmentation: variation (1) across scan repeats, (2) across scanners, (3) across vendors (4) across acquisition resolution, (5) across diffusion schemes, and (6) across diffusion sensitization. We employ four different bundle segmentation workflows on two benchmark multi-subject cross-scanner and cross-protocol databases, and investigate reproducibility and biases in volume overlap, shape geometry features of fiber pathways, and microstructure features within the pathways. We find that the effects of acquisition protocol, in particular acquisition resolution, result in the lowest reproducibility of tractography and largest variation of features, followed by vendor-effects, scanner-effects, and finally diffusion scheme and b-value effects which had similar reproducibility as scan-rescan variation. However, confounds varied both across pathways and across segmentation workflows, with some bundle segmentation workflows more (or less) robust to sources of variation. Despite variability, bundle dissection is consistently able to recover the same location of pathways in the deep white matter, with variation at the gray matter/ white matter interface. Next, we show that differences due to the choice of bundle segmentation workflows are larger than any other studied confound, with low-to-moderate overlap of the same intended pathway when segmented using different methods. Finally, quantifying microstructure features within a pathway, we show that tractography adds variability over-and-above that which exists due to noise, scanner effects, and acquisition effects. Overall, these confounds need to be considered when harmonizing diffusion datasets, interpreting or combining data across sites, and when attempting to understand the successes and limitations of different methodologies in the design and development of new tractography or bundle segmentation methods.
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Affiliation(s)
- Kurt G. Schilling
- Department of Radiology & Radiological Science, Vanderbilt University Medical Center, Nashville, TN, United States,Vanderbilt Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Chantal M.W. Tax
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, United Kingdom
| | - Francois Rheault
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, United States
| | - Colin Hansen
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, United States
| | - Qi Yang
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, United States
| | - Fang-Cheng Yeh
- Department of Neurological Surgery, University of Pittsburgh, United States
| | - Leon Cai
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, United States
| | - Adam W. Anderson
- Department of Radiology & Radiological Science, Vanderbilt University Medical Center, Nashville, TN, United States,Vanderbilt Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States,Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, United States
| | - Bennett A. Landman
- Department of Radiology & Radiological Science, Vanderbilt University Medical Center, Nashville, TN, United States,Vanderbilt Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States,Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, United States,Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, United States
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11
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Hakulinen U, Brander A, Ilvesmäki T, Helminen M, Öhman J, Luoto TM, Eskola H. Reliability of the freehand region-of-interest method in quantitative cerebral diffusion tensor imaging. BMC Med Imaging 2021; 21:144. [PMID: 34607554 PMCID: PMC8491381 DOI: 10.1186/s12880-021-00663-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Accepted: 09/01/2021] [Indexed: 11/10/2022] Open
Abstract
Background Diffusion tensor imaging (DTI) is a magnetic resonance imaging (MRI) technique used for evaluating changes in the white matter in brain parenchyma. The reliability of quantitative DTI analysis is influenced by several factors, such as the imaging protocol, pre-processing and post-processing methods, and selected diffusion parameters. The region-of-interest (ROI) method is most widely used of the post-processing methods because it is found in commercial software. The focus of our research was to study the reliability of the freehand ROI method using various intra- and inter-observer analyses. Methods This study included 40 neurologically healthy participants who underwent diffusion MRI of the brain with a 3 T scanner. The measurements were performed at nine different anatomical locations using a freehand ROI method. The data extracted from the ROIs included the regional mean values, intra- and inter-observer variability and reliability. The used DTI parameters were fractional anisotropy (FA), the apparent diffusion coefficient (ADC), and axial (AD) and radial (RD) diffusivity. Results The average intra-class correlation coefficient (ICC) of the intra-observer was found to be 0.9 (excellent). The single ICC results were excellent (> 0.8) or adequate (> 0.69) in eight out of the nine regions in terms of FA and ADC. The most reliable results were found in the frontobasal regions. Significant differences between age groups were also found in the frontobasal regions. Specifically, the FA and AD values were significantly higher and the RD values lower in the youngest age group (18–30 years) compared to the other age groups. Conclusions The quantitative freehand ROI method can be considered highly reliable for the average ICC and mostly adequate for the single ICC. The freehand method is suitable for research work with a well-experienced observer. Measurements should be performed at least twice in the same region to ensure that the results are sufficiently reliable. In our study, reliability was slightly undermined by artifacts in some regions such as the cerebral peduncle and centrum semiovale. From a clinical point of view, the results are most reliable in adults under the age of 30, when age-related changes in brain white matter have not yet occurred.
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Affiliation(s)
- Ullamari Hakulinen
- Department of Medical Physics, Medical Imaging Center of Pirkanmaa Hospital District, Tampere, Finland. .,Department of Radiology, Medical Imaging Center of Pirkanmaa Hospital District, Tampere, Finland. .,Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.
| | - Antti Brander
- Department of Radiology, Medical Imaging Center of Pirkanmaa Hospital District, Tampere, Finland
| | - Tero Ilvesmäki
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Mika Helminen
- Faculty of Social Sciences, Health Sciences, Tampere University, Tampere, Finland.,Tays Research Services, Tampere University Hospital, Tampere, Finland
| | - Juha Öhman
- Department of Neurosurgery, Tampere University Hospital and Tampere University, Tampere, Finland
| | - Teemu M Luoto
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.,Department of Neurosurgery, Tampere University Hospital and Tampere University, Tampere, Finland
| | - Hannu Eskola
- Department of Radiology, Medical Imaging Center of Pirkanmaa Hospital District, Tampere, Finland.,Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
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12
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Oglesby RT, Lam WW, Stanisz GJ. A strategy to prevent a temperature-induced MRI artifact in warm liquid phantoms due to convection currents. NMR IN BIOMEDICINE 2021; 34:e4494. [PMID: 33586271 DOI: 10.1002/nbm.4494] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Revised: 01/16/2021] [Accepted: 02/02/2021] [Indexed: 06/12/2023]
Abstract
MRI phantom studies often fail to mimic the temperature of the human body, which can negatively impact accuracy. An artifact induced by increasing temperature in liquid phantoms was observed, presenting a significant challenge to temperature-controlled experiments. In this study we characterize and provide a solution to eliminate this temperature-induced MRI artifact. Low concentration (0.5-2.5 mM) agar phantoms were prepared. Utilizing a temperature-controlled phantom holder, T1 - and T2 -weighted structural images were acquired at 7 T along with quantitative B0 , B1 , T1 , T2 and ADC maps at both 25 and 37°C. Additionally, computer simulations were conducted to demonstrate the fluid flow and thermal flux patterns in water to provide an insight into the origins of the artifact. Evidence from computer simulation and quantitative MRI strongly suggest the artifact was caused by heat transfer in the form of natural convection leading to structured patterns of signal loss in MR images. The artifact was present up to agar concentrations of 1.5 mM (T1 = 3068 ± 16 ms, T2 = 1052 ± 20 ms, ADC = 2.29 ± 0.36 × 10-3 mm2 /s at 25°C; T1 = 3928 ± 44 ms, T2 = 1122 ± 24 ms, ADC = 2.64 ± 0.49 × 10-3 mm2 /s at 37°C), above which point increased sample viscosity no longer allows for convection currents, thereby eliminating the artifact. The methodology described in this work simplifies quantitative MR acquisition of liquid phantoms at physiological temperature by suppressing convection currents with relatively small changes to intrinsic MR parameters (T1 increased by 1.4% and T2 decreased by 17% for 1.5 mM agar at 25°C).
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Affiliation(s)
- Ryan T Oglesby
- Physical Sciences, Sunnybrook Research Institute, Toronto, ON, Canada
- Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Wilfred W Lam
- Physical Sciences, Sunnybrook Research Institute, Toronto, ON, Canada
| | - Greg J Stanisz
- Physical Sciences, Sunnybrook Research Institute, Toronto, ON, Canada
- Medical Biophysics, University of Toronto, Toronto, ON, Canada
- Neurosurgery and Paediatric Neurosurgery, Medical University of Lublin, Lublin, Poland
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13
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Mokry T, Pantke J, Mlynarska-Bujny A, Hasse FC, Kuder TA, Schlemmer HP, Kauczor HU, Rom J, Bickelhaupt S. Diffusivity mapping of the ovaries: Variability of apparent diffusion and kurtosis variables over the menstrual cycle and influence of oral contraceptives. Magn Reson Imaging 2021; 80:50-57. [PMID: 33905830 DOI: 10.1016/j.mri.2021.04.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Revised: 04/14/2021] [Accepted: 04/21/2021] [Indexed: 11/26/2022]
Abstract
PURPOSE We aimed to investigate whether quantitative diffusivity variables of healthy ovaries vary during the menstrual cycle and to evaluate alterations in women using oral contraceptives (OC). METHODS This prospective study (S-339/2016) included 30 healthy female volunteers, with (n = 15) and without (n = 15) intake of OC between 07/2017 and 09/2019. Participants underwent 3T diffusion-weighted MRI (b-values 0-2000 s/mm2) three times during a menstrual cycle (T1 = day 1-5; T2 = day 7-12; T3 = day 19-24). Both ovaries were manually three-dimensionally segmented on b = 1500 s/mm2; apparent diffusion coefficient (ADC) calculation and kurtosis fitting (Dapp, Kapp) were performed. Differences in ADC, Dapp and Kapp between time points and groups were compared using repeated measures ANOVA and t-test after Shapiro-Wilk and Brown-Forsythe test for normality and equal variance. RESULTS In women with a natural menstrual cycle, ADC and kurtosis variables showed significant changes in ovaries with the dominant follicle between T1 vs T2 and T1 vs T3, whilst no differences were observed between T2 vs T3: ADC ± SD for T1 1.524 ± 0.160, T2 1.737 ± 0.160, and T3 1.747 ± 0.241 μm2/ms (p = 0.01 T2 vs T1; p = 1.0 T2 vs T3, p = 0.003 T3 vs T1); Dapp ± SD for T1 2.018 ± 0.140, T2 2.272 ± 0.189, and T3 2.230 ± 0.256 μm2/ms (p = 0.003 T2 vs T1, p = 1.0 T2 vs T3, p = 0.02 T3 vs T1); Kapp ± SD for T1 0.614 ± 0.0339, T2 0.546 ± 0.0637, and T3 0.529 ± 0.0567 (p < 0.001 T2 vs T1, p = 0.86 T2 vs T3, p < 0.001 T3 vs T1). No significant differences were found in the contralateral ovaries or in females taking OC. CONCLUSION Physiological cycle-dependent changes in quantitative diffusivity variables of ovaries should be considered especially when interpreting radiomics analyses in reproductive women.
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Affiliation(s)
- Theresa Mokry
- Department of Diagnostic and Interventional Radiology, Heidelberg University Hospital, Im Neuenheimer Feld 110, 69120 Heidelberg, Germany.
| | - Judith Pantke
- Department of Diagnostic and Interventional Radiology, Heidelberg University Hospital, Im Neuenheimer Feld 110, 69120 Heidelberg, Germany
| | - Anna Mlynarska-Bujny
- Department of Radiology, German Cancer Research Center, Heidelberg, Germany; Department of Medical Physics in Radiology, German Cancer Research Center, Heidelberg, Germany; Medical Faculty Heidelberg, Heidelberg University, Germany
| | - Felix Christian Hasse
- Department of Diagnostic and Interventional Radiology, Heidelberg University Hospital, Im Neuenheimer Feld 110, 69120 Heidelberg, Germany
| | - Tristan Anselm Kuder
- Department of Medical Physics in Radiology, German Cancer Research Center, Heidelberg, Germany
| | | | - Hans-Ulrich Kauczor
- Department of Diagnostic and Interventional Radiology, Heidelberg University Hospital, Im Neuenheimer Feld 110, 69120 Heidelberg, Germany
| | - Joachim Rom
- Hospital for General Obstetrics and Gynecology, Hospital Frankfurt Hoechst, Frankfurt, Germany
| | - Sebastian Bickelhaupt
- Department of Radiology, German Cancer Research Center, Heidelberg, Germany; Junior Group Medical Imaging and Radiology - Cancer Prevention, German Cancer Research Center, Heidelberg, Germany; Institute of Radiology, Erlangen University Hospital, Erlangen, Germany
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14
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Turner S, Lazarus R, Marion D, Main KL. Molecular and Diffusion Tensor Imaging Biomarkers of Traumatic Brain Injury: Principles for Investigation and Integration. J Neurotrauma 2021; 38:1762-1782. [PMID: 33446015 DOI: 10.1089/neu.2020.7259] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
The last 20 years have seen the advent of new technologies that enhance the diagnosis and prognosis of traumatic brain injury (TBI). There is recognition that TBI affects the brain beyond initial injury, in some cases inciting a progressive neuropathology that leads to chronic impairments. Medical researchers are now searching for biomarkers to detect and monitor this condition. Perhaps the most promising developments are in the biomolecular and neuroimaging domains. Molecular assays can identify proteins indicative of neuronal injury and/or degeneration. Diffusion imaging now allows sensitive evaluations of the brain's cellular microstructure. As the pace of discovery accelerates, it is important to survey the research landscape and identify promising avenues of investigation. In this review, we discuss the potential of molecular and diffusion tensor imaging (DTI) biomarkers in TBI research. Integration of these technologies could advance models of disease prognosis, ultimately improving care. To date, however, few studies have explored relationships between molecular and DTI variables in patients with TBI. Here, we provide a short primer on each technology, review the latest research, and discuss how these biomarkers may be incorporated in future studies.
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Affiliation(s)
- Stephanie Turner
- Defense and Veterans Brain Injury Center, Silver Spring, Maryland, USA.,General Dynamics Information Technology, Falls Church, Virginia, USA
| | - Rachel Lazarus
- Defense and Veterans Brain Injury Center, Silver Spring, Maryland, USA.,General Dynamics Information Technology, Falls Church, Virginia, USA
| | - Donald Marion
- Defense and Veterans Brain Injury Center, Silver Spring, Maryland, USA.,General Dynamics Information Technology, Falls Church, Virginia, USA
| | - Keith L Main
- Defense and Veterans Brain Injury Center, Silver Spring, Maryland, USA.,General Dynamics Information Technology, Falls Church, Virginia, USA
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15
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Kemėšienė J, Rühle A, Gomolka R, Wurnig MC, Rossi C, Boss A. Advanced diffusion imaging of abdominal organs in different hydration states of the human body: stability of biomarkers. Heliyon 2021; 7:e06072. [PMID: 33553749 PMCID: PMC7848648 DOI: 10.1016/j.heliyon.2021.e06072] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2020] [Revised: 07/24/2020] [Accepted: 01/20/2021] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND MR diffusion weighted imaging (DWI) may provide important information regarding the pathophysiology of parenchymal abdominal organs. The purpose of our study was to investigate the stability of imaging biomarkers of diffusion weighted imaging (DWI), intravoxel incoherent motion (IVIM) and diffusion kurtosis imaging (DKI) in abdominal parenchymal organs regarding two body hydration states. METHODS Ten healthy volunteers twice underwent DWI of abdominal organs using a double-refocused spin-echo echo-planar imaging sequences with 11 different b-values (ranging from 0 to 1,500 s/mm2): after 4 h of fluid deprivation; 45 min following 1000 ml of water intake. Four different diffusion models were evaluated and compared: standard DWI, DKI with mono-exponential fitting, multistep algorithm with variable b-value threshold for IVIM, combined IVIM-Kurtosis; in four abdominal organs: kidneys, liver, spleen and psoas muscle. RESULTS Diffusion parameters from all four models remained similar for the renal parenchyma before and after the water challenge. Significant differences were found for the liver, spleen, and psoas muscle. The largest effects were seen for: the liver parenchyma after the water challenge by means of IVIM model's true diffusion (p < 0.02); the spleen, for IVIM's perfusion fraction (p < 0.03), the psoas muscle for the ADC value (p < 0.02). CONCLUSIONS Herein, we showed that diffusion parameters of the kidney remain remarkably stable regarding the hydration status. This may be attributed to the kidney-specific compensatory mechanisms. For the liver, spleen and psoas muscle the diffusion parameters were sensitive to changes of the hydration. This phenomenon needs to be considered when evaluating diffusion data of these organs.
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Affiliation(s)
- Jūratė Kemėšienė
- Department of Radiology, Hospital of Lithuanian University of Health Sciences, Kaunas Clinics, Lithuania
| | - Alexander Rühle
- Department of Molecular Radiation Oncology, German Cancer Research Center (dkfz), Heidelberg, Germany
| | - Ryszard Gomolka
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Switzerland
| | - Moritz C. Wurnig
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Switzerland
| | - Cristina Rossi
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Switzerland
| | - Andreas Boss
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Switzerland
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16
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Pinto MS, Paolella R, Billiet T, Van Dyck P, Guns PJ, Jeurissen B, Ribbens A, den Dekker AJ, Sijbers J. Harmonization of Brain Diffusion MRI: Concepts and Methods. Front Neurosci 2020; 14:396. [PMID: 32435181 PMCID: PMC7218137 DOI: 10.3389/fnins.2020.00396] [Citation(s) in RCA: 64] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2019] [Accepted: 03/30/2020] [Indexed: 11/13/2022] Open
Abstract
MRI diffusion data suffers from significant inter- and intra-site variability, which hinders multi-site and/or longitudinal diffusion studies. This variability may arise from a range of factors, such as hardware, reconstruction algorithms and acquisition settings. To allow a reliable comparison and joint analysis of diffusion data across sites and over time, there is a clear need for robust data harmonization methods. This review article provides a comprehensive overview of diffusion data harmonization concepts and methods, and their limitations. Overall, the methods for the harmonization of multi-site diffusion images can be categorized in two main groups: diffusion parametric map harmonization (DPMH) and diffusion weighted image harmonization (DWIH). Whereas DPMH harmonizes the diffusion parametric maps (e.g., FA, MD, and MK), DWIH harmonizes the diffusion-weighted images. Defining a gold standard harmonization technique for dMRI data is still an ongoing challenge. Nevertheless, in this paper we provide two classification tools, namely a feature table and a flowchart, which aim to guide the readers in selecting an appropriate harmonization method for their study.
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Affiliation(s)
- Maíra Siqueira Pinto
- Department of Radiology, Antwerp University Hospital, University of Antwerp, Antwerp, Belgium.,imec-Vision Lab, University of Antwerp, Antwerp, Belgium
| | - Roberto Paolella
- imec-Vision Lab, University of Antwerp, Antwerp, Belgium.,Icometrix, Leuven, Belgium
| | | | - Pieter Van Dyck
- Department of Radiology, Antwerp University Hospital, University of Antwerp, Antwerp, Belgium
| | | | - Ben Jeurissen
- imec-Vision Lab, University of Antwerp, Antwerp, Belgium
| | | | | | - Jan Sijbers
- imec-Vision Lab, University of Antwerp, Antwerp, Belgium
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17
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Kassubek J, Müller HP. Advanced neuroimaging approaches in amyotrophic lateral sclerosis: refining the clinical diagnosis. Expert Rev Neurother 2020; 20:237-249. [PMID: 31937156 DOI: 10.1080/14737175.2020.1715798] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Introduction: In the last decade, multiparametric magnetic resonance imaging (MRI) has achieved tremendous advances in applications to amyotrophic lateral sclerosis (ALS) to increase the understanding of the associated pathophysiology. The aim of this review is to summarize recent progress in the development of MRI-based techniques aiming to support the clinical diagnosis in ALS.Areas covered: The review of structural and functional MRI applications to ALS and its variants (restricted phenotypes) is focused on the potential of MRI techniques which contribute to the diagnostic work-up of patients with the clinical presentation of a motor neuron disease. The potential of specific MRI methods for patient diagnosis and monitoring is discussed, and the future design of clinical MRI applications to ALS is conceptualized.Expert opinion: Current multiparametric MRI allows for the use as a clinical biological marker and a technical instrument in the clinical diagnosis of patients with ALS and also of patients with ALS variants. Composite neuroimaging indices of specific anatomical areas derived from different MRI techniques might guide in the diagnostic applications to ALS. Such a development of ALS-specific MRI-based composite scores with sufficient discriminative power versus ALS mimics at an individual level requires standardized advanced protocols and comprehensive analysis approaches.
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Affiliation(s)
- Jan Kassubek
- Department of Neurology, University of Ulm, Ulm, Germany
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18
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Coad BM, Craig E, Louch R, Aggleton JP, Vann SD, Metzler-Baddeley C. Precommissural and postcommissural fornix microstructure in healthy aging and cognition. Brain Neurosci Adv 2020; 4:2398212819899316. [PMID: 32219177 PMCID: PMC7085915 DOI: 10.1177/2398212819899316] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2019] [Accepted: 12/11/2019] [Indexed: 11/23/2022] Open
Abstract
The fornix is a key tract of the hippocampal formation, whose status is presumed to contribute to age-related cognitive decline. The precommissural and postcommissural fornix subdivisions form respective basal forebrain/frontal and diencephalic networks that may differentially affect aging and cognition. We employed multi-parametric magnetic resonance imaging (MRI) including neurite orientation density and dispersion imaging, quantitative magnetization transfer (qMT), and T1-relaxometry MRI to investigate the microstructural properties of these fornix subdivisions and their relationship with aging and cognition in 149 asymptomatic participants (38-71 years). Aging was associated with increased free water signal and reductions in myelin-sensitive R1 and qMT indices but no apparent axon density differences in both precommissural and postcommissural fibers. Precommissural relative to postcommissural fibers showed a distinct microstructural pattern characterised by larger free water signal and axon orientation dispersion, with lower apparent myelin and axon density. Furthermore, differences in postcommissural microstructure were related to performance differences in object-location paired-associate learning. These results provide novel in vivo neuroimaging evidence for distinct microstructural properties of precommissural and postcommissural fibers that are consistent with their anatomy as found in axonal tracer studies, as well as for a contribution of postcommissural fibers to the learning of spatial configurations.
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Affiliation(s)
- Bethany M. Coad
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff, UK
- School of Psychology, Cardiff University, Cardiff, UK
| | - Emma Craig
- School of Psychology, Cardiff University, Cardiff, UK
| | - Rebecca Louch
- School of Psychology, Cardiff University, Cardiff, UK
| | | | | | - Claudia Metzler-Baddeley
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff, UK
- School of Psychology, Cardiff University, Cardiff, UK
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19
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Drakesmith M, Harms R, Rudrapatna SU, Parker GD, Evans CJ, Jones DK. Estimating axon conduction velocity in vivo from microstructural MRI. Neuroimage 2019; 203:116186. [PMID: 31542512 PMCID: PMC6854468 DOI: 10.1016/j.neuroimage.2019.116186] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2019] [Revised: 09/09/2019] [Accepted: 09/10/2019] [Indexed: 11/19/2022] Open
Abstract
The conduction velocity (CV) of action potentials along axons is a key neurophysiological property central to neural communication. The ability to estimate CV in humans in vivo from non-invasive MRI methods would therefore represent a significant advance in neuroscience. However, there are two major challenges that this paper aims to address: (1) Much of the complexity of the neurophysiology of action potentials cannot be captured with currently available MRI techniques. Therefore, we seek to establish the variability in CV that can be captured when predicting CV purely from parameters that have been reported to be estimatable from MRI: inner axon diameter (AD) and g-ratio. (2) errors inherent in existing MRI-based biophysical models of tissue will propagate through to estimates of CV, the extent to which is currently unknown. Issue (1) is investigated by performing a sensitivity analysis on a comprehensive model of axon electrophysiology and determining the relative sensitivity to various morphological and electrical parameters. The investigations suggest that 85% of the variance in CV is accounted for by variation in AD and g-ratio. The observed dependency of CV on AD and g-ratio is well characterised by the previously reported model by Rushton. Issue (2) is investigated through simulation of diffusion and relaxometry MRI data for a range of axon morphologies, applying models of restricted diffusion and relaxation processes to derive estimates of axon volume fraction (AVF), AD and g-ratio and estimating CV from the derived parameters. The results show that errors in the AVF have the biggest detrimental impact on estimates of CV, particularly for sparse fibre populations (AVF<0.3). For our equipment set-up and acquisition protocol, CV estimates are most accurate (below 5% error) where AVF is above 0.3, g-ratio is between 0.6 and 0.85 and AD is high (above 4μm). CV estimates are robust to errors in g-ratio estimation but are highly sensitive to errors in AD estimation, particularly where ADs are small. We additionally show CV estimates in human corpus callosum in a small number of subjects. In conclusion, we demonstrate accurate CV estimates are possible in regions of the brain where AD is sufficiently large. Problems with estimating ADs for smaller axons presents a problem for estimating CV across the whole CNS and should be the focus of further study.
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Affiliation(s)
- Mark Drakesmith
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom; Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, United Kingdom.
| | - Robbert Harms
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, the Netherlands
| | - Suryanarayana Umesh Rudrapatna
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom; Phillips Inovation Campus, Bangalore, India
| | - Greg D Parker
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom; Experimental MRI Centre (EMRIC), School of Biosciences, Cardiff University, Cardiff, United Kingdom
| | - C John Evans
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom; Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, United Kingdom
| | - Derek K Jones
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom; Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, United Kingdom; Mary McKillop Institute for Health Research, Faculty of Health Sciences, Australian Catholic University, Melbourne, Victoria, 3065, Australia
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20
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Tax CM, Grussu F, Kaden E, Ning L, Rudrapatna U, John Evans C, St-Jean S, Leemans A, Koppers S, Merhof D, Ghosh A, Tanno R, Alexander DC, Zappalà S, Charron C, Kusmia S, Linden DE, Jones DK, Veraart J. Cross-scanner and cross-protocol diffusion MRI data harmonisation: A benchmark database and evaluation of algorithms. Neuroimage 2019; 195:285-299. [PMID: 30716459 PMCID: PMC6556555 DOI: 10.1016/j.neuroimage.2019.01.077] [Citation(s) in RCA: 62] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2018] [Revised: 01/16/2019] [Accepted: 01/30/2019] [Indexed: 01/01/2023] Open
Abstract
Diffusion MRI is being used increasingly in studies of the brain and other parts of the body for its ability to provide quantitative measures that are sensitive to changes in tissue microstructure. However, inter-scanner and inter-protocol differences are known to induce significant measurement variability, which in turn jeopardises the ability to obtain 'truly quantitative measures' and challenges the reliable combination of different datasets. Combining datasets from different scanners and/or acquired at different time points could dramatically increase the statistical power of clinical studies, and facilitate multi-centre research. Even though careful harmonisation of acquisition parameters can reduce variability, inter-protocol differences become almost inevitable with improvements in hardware and sequence design over time, even within a site. In this work, we present a benchmark diffusion MRI database of the same subjects acquired on three distinct scanners with different maximum gradient strength (40, 80, and 300 mT/m), and with 'standard' and 'state-of-the-art' protocols, where the latter have higher spatial and angular resolution. The dataset serves as a useful testbed for method development in cross-scanner/cross-protocol diffusion MRI harmonisation and quality enhancement. Using the database, we compare the performance of five different methods for estimating mappings between the scanners and protocols. The results show that cross-scanner harmonisation of single-shell diffusion data sets can reduce the variability between scanners, and highlight the promises and shortcomings of today's data harmonisation techniques.
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Affiliation(s)
- Chantal Mw Tax
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom.
| | - Francesco Grussu
- Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom; Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
| | - Enrico Kaden
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
| | - Lipeng Ning
- Harvard Medical School, Boston, MA, United States
| | - Umesh Rudrapatna
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - C John Evans
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - Samuel St-Jean
- Image Sciences Institute, Department of Radiology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Alexander Leemans
- Image Sciences Institute, Department of Radiology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Simon Koppers
- Department of Radiology, University of Pennsylvania and the Children's Hospital of Philadelphia, Philadelphia, PA, United States; Institute of Imaging & Computer Vision, RWTH Aachen University, Aachen, Germany
| | - Dorit Merhof
- Institute of Imaging & Computer Vision, RWTH Aachen University, Aachen, Germany
| | - Aurobrata Ghosh
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
| | - Ryutaro Tanno
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom; Machine Intelligence and Perception Group, Microsoft Research Cambridge, Cambridge, United Kingdom
| | - Daniel C Alexander
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
| | - Stefano Zappalà
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - Cyril Charron
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - Slawomir Kusmia
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - David Ej Linden
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - Derek K Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom; Mary McKillop Institute for Health Research, Australian Catholic University, Melbourne, Australia
| | - Jelle Veraart
- New York University, New York, NY, United States; imec-Vision Lab, Department of Physics, University of Antwerp, Antwerp, Belgium
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Samsir S, Zakaria R, Razak SA, Ismail MS, Rahim MZA, Lin CS, Osman NMFN, Asri MA, Ahmad AH. Characterisation of the Corticospinal Tract Using Diffusion Magnetic Resonance Imaging in Unilateral and Bilateral Cerebral Palsy Patients. Malays J Med Sci 2019; 25:68-78. [PMID: 30914864 PMCID: PMC6419886 DOI: 10.21315/mjms2018.25.5.7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2018] [Accepted: 08/05/2018] [Indexed: 11/06/2022] Open
Abstract
Background Neuroimaging is increasingly used to locate the lesion that causes cerebral palsy (CP) and its extent in the brains of CP patients. Conventional structural magnetic resonance imaging (MRI) does not indicate the connectional pattern of white matter; however, with the help of diffusion MRI, fibre tracking of white matter can be done. Methods We used diffusion MRI and probabilistic tractography to identify the putative white matter connectivity in the brains of 10 CP patients. We tracked the corticospinal tract (CST) of the patients’ upper and lower limbs and calculated the white matter connectivity, as indexed by streamlines representing the probability of connection of the CST. Results Our results show that diffusion MRI with probabilistic tractography, while having some relation with the clinical diagnosis of CP, reveals a high degree of individual variation in the streamlines representing the CST for upper and lower limbs. Conclusion Diffusion MRI with probabilistic tractography provides the state of connectivity from lesioned areas to other parts of the brain and is potentially beneficial to be used as an adjunct to the clinical management of CP, providing a means to monitor intervention outcomes.
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Affiliation(s)
- Safwan Samsir
- Faculty of Psychology & Education, Universiti Malaysia Sabah, Sabah, Malaysia.,Department of Physiology, School of Medical Sciences, Health Campus, Universiti Sains Malaysia, 16150 Kubang Kerian, Kelantan, Malaysia
| | - Rahimah Zakaria
- Department of Physiology, School of Medical Sciences, Health Campus, Universiti Sains Malaysia, 16150 Kubang Kerian, Kelantan, Malaysia
| | - Salmi Abdul Razak
- Department of Paediatrics, School of Medical Sciences, Health Campus, Universiti Sains Malaysia, 16150 Kubang Kerian, Kelantan, Malaysia
| | - Mohamed Saat Ismail
- School of Health Sciences, Health Campus, Universiti Sains Malaysia, 16150 Kubang Kerian, Kelantan, Malaysia
| | - Mohd Zulkifli Abdul Rahim
- School of Health Sciences, Health Campus, Universiti Sains Malaysia, 16150 Kubang Kerian, Kelantan, Malaysia
| | - Chia-Shu Lin
- Department of Dentistry, School of Dentistry, National Yang-Ming University, Taipei, Taiwan
| | - Nik Mohammad Faez Nik Osman
- Department of Physiology, School of Medical Sciences, Health Campus, Universiti Sains Malaysia, 16150 Kubang Kerian, Kelantan, Malaysia
| | - Mohammad Afiq Asri
- Department of Physiology, School of Medical Sciences, Health Campus, Universiti Sains Malaysia, 16150 Kubang Kerian, Kelantan, Malaysia
| | - Asma Hayati Ahmad
- Department of Physiology, School of Medical Sciences, Health Campus, Universiti Sains Malaysia, 16150 Kubang Kerian, Kelantan, Malaysia
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22
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Goddings AL, Beltz A, Peper JS, Crone EA, Braams BR. Understanding the Role of Puberty in Structural and Functional Development of the Adolescent Brain. JOURNAL OF RESEARCH ON ADOLESCENCE : THE OFFICIAL JOURNAL OF THE SOCIETY FOR RESEARCH ON ADOLESCENCE 2019; 29:32-53. [PMID: 30869842 DOI: 10.1111/jora.12408] [Citation(s) in RCA: 92] [Impact Index Per Article: 18.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2023]
Abstract
Over the past two decades, there has been a tremendous increase in our understanding of structural and functional brain development in adolescence. However, understanding the role of puberty in this process has received much less attention. This review examines this relationship by summarizing recent research studies where the role of puberty was investigated in relation to brain structure, connectivity, and task-related functional magnetic resonance imaging (fMRI). The studies together suggest that puberty may contribute to adolescent neural reorganization and maturational advancement, and sex differences also emerge in puberty. The current body of work shows some mixed results regarding impact and exact direction of pubertal influence. We discuss several limitations of current studies and propose future directions on how to move the field forward.
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Affiliation(s)
| | | | - Jiska S Peper
- Leiden University
- Leiden Institute for Brain and Cognition
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23
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Sani I, McPherson BC, Stemmann H, Pestilli F, Freiwald WA. Functionally defined white matter of the macaque monkey brain reveals a dorso-ventral attention network. eLife 2019; 8:e40520. [PMID: 30601116 PMCID: PMC6345568 DOI: 10.7554/elife.40520] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2018] [Accepted: 12/20/2018] [Indexed: 12/18/2022] Open
Abstract
Classical studies of attention have identified areas of parietal and frontal cortex as sources of attentional control. Recently, a ventral region in the macaque temporal cortex, the posterior infero-temporal dorsal area PITd, has been suggested as a third attentional control area. This raises the question of whether and how spatially distant areas coordinate a joint focus of attention. Here we tested the hypothesis that parieto-frontal attention areas and PITd are directly interconnected. By combining functional MRI with ex-vivo high-resolution diffusion MRI, we found that PITd and dorsal attention areas are all directly connected through three specific fascicles. These results ascribe a new function, the communication of attention signals, to two known fiber-bundles, highlight the importance of vertical interactions across the two visual streams, and imply that the control of endogenous attention, hitherto thought to reside in macaque dorsal cortical areas, is exerted by a dorso-ventral network.
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Affiliation(s)
- Ilaria Sani
- Laboratory of Neural SystemsThe Rockefeller UniversityNew YorkUnited States
| | - Brent C McPherson
- Department of Psychological and Brain SciencesIndiana UniversityBloomingtonUnited States
| | - Heiko Stemmann
- Institute for Brain Research and Center for Advanced ImagingUniversity of BremenBremenGermany
| | - Franco Pestilli
- Department of Psychological and Brain SciencesIndiana UniversityBloomingtonUnited States
| | - Winrich A Freiwald
- Laboratory of Neural SystemsThe Rockefeller UniversityNew YorkUnited States
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24
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Swerkersson S, Grundberg O, Kölbeck K, Carlberg A, Nyrén S, Skorpil M. Optimizing diffusion-weighted magnetic resonance imaging for evaluation of lung tumors: A comparison of respiratory triggered and free breathing techniques. Eur J Radiol Open 2018; 5:189-193. [PMID: 30450371 PMCID: PMC6222289 DOI: 10.1016/j.ejro.2018.10.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2018] [Revised: 10/09/2018] [Accepted: 10/21/2018] [Indexed: 12/17/2022] Open
Abstract
Purpose The aim of this study was to compare respiratory-triggered (RT) and free breathing (FB) diffusion weighted imaging (DWI) techniques regarding apparent diffusion coefficient (ADC) measurements and repeatability in non-squamous non-small cell lung cancer (NSCLC) measuring the total tumor volume. Material and Methods A total of 57 magnetic resonance imaging (MRI) examinations were analyzed. DWI was obtained by a single-shot spin-echo echo-planar imaging sequence, and for each MRI examination 2 consecutive RT and 2 consecutive FB DWI sequences were performed. Two radiologists independently read the images and made measurements. For each tumor the mean ADC value of the whole tumor volume was calculated. The difference in mean ADCs between FB and RT DWI was evaluated using the paired-sample t-test. The repeatability of ADC measurements related to imaging method was evaluated by intra class correlations (ICC) for each of the FB and RT DWI pairs. Results There were no significant differences in mean ADCs between FB and RT (Reader 1 p = 0.346, Reader 2 p = 0.583). The overall repeatability of ADC measurement was good for both acquisition methods, with ICCs > 0.9. Subgroup analysis showed somewhat poorer repeatability in small tumors (50 ml or less) and tumors in the lower lung zones for the RT acquisition, with ICC as low as 0.72. Conclusions No difference in ADC measurement or repeatability between FB and RT DWI in whole lesion ADC measurements of adenocarcinomas in the lung was demonstrated. The results imply that in this setting the FB acquisition method is accurate and possibly more robust than the RT acquisition technique.
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Key Words
- ADC, apparent diffusion coefficient
- BH, breath-hold
- DWI, diffusion weighted imaging
- Diffusion weighted imaging
- FB, free breathing
- ICC, intra class correlations
- MRI, magnetic resonance imaging
- Magnetic resonance imaging
- NSCLC, non-squamous non-small cell lung cancer
- ROI, region of interest
- RT, respiratory-triggered
- SNR, signal-to-noise ratio
- non-Small cell lung cancer
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Affiliation(s)
- Signe Swerkersson
- Department of Radiology, St Göran’s Hospital, St Göransplan 1, 11281 Stockholm, Sweden
- Corresponding author.
| | - Oscar Grundberg
- Department of Respiratory Medicine & Allergology, Karolinska University Hospital, 17176 Solna, Sweden
| | - Karl Kölbeck
- Department of Respiratory Medicine & Allergology, Karolinska University Hospital, 17176 Solna, Sweden
| | - Andreas Carlberg
- Siemens Healthcare AB, Johanneslundsvägen 12, 194 61 Upplands Väsby, Sweden
| | - Sven Nyrén
- Department of Thoracic radiology, Karolinska University Hospital, 17176 Solna, Sweden
| | - Mikael Skorpil
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Solnavägen 1, 171 77 Solna, Sweden
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25
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Müller HP, Kassubek J. MRI-Based Mapping of Cerebral Propagation in Amyotrophic Lateral Sclerosis. Front Neurosci 2018; 12:655. [PMID: 30319338 PMCID: PMC6168650 DOI: 10.3389/fnins.2018.00655] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2018] [Accepted: 08/31/2018] [Indexed: 11/13/2022] Open
Abstract
Neuropathological studies revealed the propagation of amyotrophic lateral sclerosis (ALS) in a sequence of four separate disease-related regional patterns. Diffusion tensor imaging (DTI)-based analysis was established for the individual mapping of sequential disease spreading in ALS as the in vivo transfer to neuroimaging. The aim of this review is to summarize cross-sectional and longitudinal results of these technical approaches in ALS as an in vivo tool to image ALS propagation stages. This concept was also applied to restricted phenotypes of ALS, e.g., lower motor neuron disease (LMND) or primary lateral sclerosis (PLS). In summary, the regional disease patterns in the course of ALS have been successfully mapped by DTI in vivo both cross-sectionally and longitudinally so that this technique might have the potential as a read-out in clinical trials.
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Affiliation(s)
| | - Jan Kassubek
- Department of Neurology, University of Ulm, Ulm, Germany
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26
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Kassubek J, Müller HP, Del Tredici K, Lulé D, Gorges M, Braak H, Ludolph AC. Imaging the pathoanatomy of amyotrophic lateral sclerosis in vivo: targeting a propagation-based biological marker. J Neurol Neurosurg Psychiatry 2018; 89:374-381. [PMID: 29101254 PMCID: PMC5869447 DOI: 10.1136/jnnp-2017-316365] [Citation(s) in RCA: 69] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/03/2017] [Revised: 10/06/2017] [Accepted: 10/06/2017] [Indexed: 11/04/2022]
Abstract
OBJECTIVE Neuropathological studies in amyotrophic lateral sclerosis (ALS) have shown a dissemination in a regional sequence in four anatomically defined patterns. The aim of this retrospective study was to see whether longitudinal diffusion tensor imaging (DTI) data support the pathological findings. METHODS The application of DTI analysis to fibre structures that are prone to be involved at each neuropathological pattern of ALS was performed in a monocentre sample of 67 patients with ALS and 31 controls that obtained at least one follow-up scan after a median of 6 months. RESULTS At the group level, longitudinal ALS data showed significant differences for the stage-related tract systems. At the individual level, 27% of the longitudinally scanned patients with ALS showed an increase in ALS stage, while the remaining were stable or were at the highest ALS stage. Longitudinal fractional anisotropy changes in the respective tract systems correlated significantly with the slope of the revised ALS functional rating scale. INTERPRETATION The DTI-based protocol was able to image the disease patterns of ALS in vivo cross-sectionally and longitudinally, in support of DTI as a technical marker to image ALS stages.
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Affiliation(s)
- Jan Kassubek
- Department of Neurology, University of Ulm, Ulm, Germany
| | | | - Kelly Del Tredici
- Clinical Neuroanatomy, Department of Neurology, University of Ulm, Ulm, Germany
| | - Dorothée Lulé
- Department of Neurology, University of Ulm, Ulm, Germany
| | - Martin Gorges
- Department of Neurology, University of Ulm, Ulm, Germany
| | - Heiko Braak
- Clinical Neuroanatomy, Department of Neurology, University of Ulm, Ulm, Germany
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27
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Aliotta E, Moulin K, Magrath P, Ennis DB. Quantifying precision in cardiac diffusion tensor imaging with second-order motion-compensated convex optimized diffusion encoding. Magn Reson Med 2018; 80:1074-1087. [PMID: 29427349 DOI: 10.1002/mrm.27107] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2017] [Revised: 01/04/2018] [Accepted: 01/05/2018] [Indexed: 12/13/2022]
Affiliation(s)
- Eric Aliotta
- Department of Radiological Sciences, University of California, Los Angeles, California.,Biomedical Physics Interdepartmental Program, University of California, Los Angeles, California
| | - Kévin Moulin
- Department of Radiological Sciences, University of California, Los Angeles, California
| | - Patrick Magrath
- Department of Bioengineering, University of California, Los Angeles, California
| | - Daniel B Ennis
- Department of Radiological Sciences, University of California, Los Angeles, California.,Biomedical Physics Interdepartmental Program, University of California, Los Angeles, California.,Department of Bioengineering, University of California, Los Angeles, California
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28
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Rodrigues NB, Mithani K, Meng Y, Lipsman N, Hamani C. The Emerging Role of Tractography in Deep Brain Stimulation: Basic Principles and Current Applications. Brain Sci 2018; 8:brainsci8020023. [PMID: 29382119 PMCID: PMC5836042 DOI: 10.3390/brainsci8020023] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2017] [Revised: 01/24/2018] [Accepted: 01/26/2018] [Indexed: 12/30/2022] Open
Abstract
Diffusion tensor imaging (DTI) is an MRI-based technique that delineates white matter tracts in the brain by tracking the diffusion of water in neural tissue. This methodology, known as “tractography”, has been extensively applied in clinical neuroscience to explore nervous system architecture and diseases. More recently, tractography has been used to assist with neurosurgical targeting in functional neurosurgery. This review provides an overview of DTI principles, and discusses current applications of tractography for improving and helping develop novel deep brain stimulation (DBS) targets.
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Affiliation(s)
- Nelson B Rodrigues
- Sunnybrook Research Institute, Toronto, ON M4N 3M5, Canada.
- Division of Neurosurgery, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON M4N 3M5, Canada.
- Institute of Medical Sciences, University of Toronto, Toronto, ON M5S 1A8, Canada.
| | - Karim Mithani
- Sunnybrook Research Institute, Toronto, ON M4N 3M5, Canada.
- Division of Neurosurgery, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON M4N 3M5, Canada.
| | - Ying Meng
- Sunnybrook Research Institute, Toronto, ON M4N 3M5, Canada.
- Division of Neurosurgery, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON M4N 3M5, Canada.
- Institute of Medical Sciences, University of Toronto, Toronto, ON M5S 1A8, Canada.
| | - Nir Lipsman
- Sunnybrook Research Institute, Toronto, ON M4N 3M5, Canada.
- Division of Neurosurgery, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON M4N 3M5, Canada.
- Harquail Centre for Neuromodulation, Sunnybrook Health Sciences Centre, Toronto, ON M4N 3M5, Canada.
| | - Clement Hamani
- Sunnybrook Research Institute, Toronto, ON M4N 3M5, Canada.
- Division of Neurosurgery, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON M4N 3M5, Canada.
- Harquail Centre for Neuromodulation, Sunnybrook Health Sciences Centre, Toronto, ON M4N 3M5, Canada.
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29
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Jelescu IO, Budde MD. Design and validation of diffusion MRI models of white matter. FRONTIERS IN PHYSICS 2017; 28:61. [PMID: 29755979 PMCID: PMC5947881 DOI: 10.3389/fphy.2017.00061] [Citation(s) in RCA: 132] [Impact Index Per Article: 18.9] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/23/2023]
Abstract
Diffusion MRI is arguably the method of choice for characterizing white matter microstructure in vivo. Over the typical duration of diffusion encoding, the displacement of water molecules is conveniently on a length scale similar to that of the underlying cellular structures. Moreover, water molecules in white matter are largely compartmentalized which enables biologically-inspired compartmental diffusion models to characterize and quantify the true biological microstructure. A plethora of white matter models have been proposed. However, overparameterization and mathematical fitting complications encourage the introduction of simplifying assumptions that vary between different approaches. These choices impact the quantitative estimation of model parameters with potential detriments to their biological accuracy and promised specificity. First, we review biophysical white matter models in use and recapitulate their underlying assumptions and realms of applicability. Second, we present up-to-date efforts to validate parameters estimated from biophysical models. Simulations and dedicated phantoms are useful in assessing the performance of models when the ground truth is known. However, the biggest challenge remains the validation of the "biological accuracy" of estimated parameters. Complementary techniques such as microscopy of fixed tissue specimens have facilitated direct comparisons of estimates of white matter fiber orientation and densities. However, validation of compartmental diffusivities remains challenging, and complementary MRI-based techniques such as alternative diffusion encodings, compartment-specific contrast agents and metabolites have been used to validate diffusion models. Finally, white matter injury and disease pose additional challenges to modeling, which are also discussed. This review aims to provide an overview of the current state of models and their validation and to stimulate further research in the field to solve the remaining open questions and converge towards consensus.
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Affiliation(s)
- Ileana O Jelescu
- Centre d'Imagerie Biomédicale, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Matthew D Budde
- Zablocki VA Medical Center, Dept. of Neurosurgery, Medical College Wisconsin, Milwaukee, WI, USA
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30
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Schilling KG, Janve V, Gao Y, Stepniewska I, Landman BA, Anderson AW. Histological validation of diffusion MRI fiber orientation distributions and dispersion. Neuroimage 2017; 165:200-221. [PMID: 29074279 DOI: 10.1016/j.neuroimage.2017.10.046] [Citation(s) in RCA: 125] [Impact Index Per Article: 17.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2017] [Revised: 10/04/2017] [Accepted: 10/21/2017] [Indexed: 12/13/2022] Open
Abstract
Diffusion magnetic resonance imaging (dMRI) is widely used to probe tissue microstructure, and is currently the only non-invasive way to measure the brain's fiber architecture. While a large number of approaches to recover the intra-voxel fiber structure have been utilized in the scientific community, a direct, 3D, quantitative validation of these methods against relevant histological fiber geometries is lacking. In this study, we investigate how well different high angular resolution diffusion imaging (HARDI) models and reconstruction methods predict the ground-truth histologically defined fiber orientation distribution (FOD), as well as investigate their behavior over a range of physical and experimental conditions. The dMRI methods tested include constrained spherical deconvolution (CSD), Q-ball imaging (QBI), diffusion orientation transform (DOT), persistent angular structure (PAS), and neurite orientation dispersion and density imaging (NODDI) methods. Evaluation criteria focus on overall agreement in FOD shape, correct assessment of the number of fiber populations, and angular accuracy in orientation. In addition, we make comparisons of the histological orientation dispersion with the fiber spread determined from the dMRI methods. As a general result, no HARDI method outperformed others in all quality criteria, with many showing tradeoffs in reconstruction accuracy. All reconstruction techniques describe the overall continuous angular structure of the histological FOD quite well, with good to moderate correlation (median angular correlation coefficient > 0.70) in both single- and multiple-fiber voxels. However, no method is consistently successful at extracting discrete measures of the number and orientations of FOD peaks. The major inaccuracies of all techniques tend to be in extracting local maxima of the FOD, resulting in either false positive or false negative peaks. Median angular errors are ∼10° for the primary fiber direction and ∼20° for the secondary fiber, if present. For most methods, these results did not vary strongly over a wide range of acquisition parameters (number of diffusion weighting directions and b value). Regardless of acquisition parameters, all methods show improved successes at resolving multiple fiber compartments in a voxel when fiber populations cross at near-orthogonal angles, with no method adequately capturing low to moderate angle (<60°) crossing fibers. Finally, most methods are limited in their ability to capture orientation dispersion, resulting in low to moderate, yet statistically significant, correlation with histologically-derived dispersion with both HARDI and NODDI methodologies. Together, these results provide quantitative measures of the reliability and limitations of dMRI reconstruction methods and can be used to identify relative advantages of competing approaches as well as potential strategies for improving accuracy.
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Affiliation(s)
- Kurt G Schilling
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA.
| | - Vaibhav Janve
- 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
| | | | - Bennett A Landman
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA; Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN, United States; 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; Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN, United States
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31
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Ballester-Plané J, Schmidt R, Laporta-Hoyos O, Junqué C, Vázquez É, Delgado I, Zubiaurre-Elorza L, Macaya A, Póo P, Toro E, de Reus MA, van den Heuvel MP, Pueyo R. Whole-brain structural connectivity in dyskinetic cerebral palsy and its association with motor and cognitive function. Hum Brain Mapp 2017; 38:4594-4612. [PMID: 28608616 DOI: 10.1002/hbm.23686] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2017] [Accepted: 06/01/2017] [Indexed: 12/31/2022] Open
Abstract
Dyskinetic cerebral palsy (CP) has long been associated with basal ganglia and thalamus lesions. Recent evidence further points at white matter (WM) damage. This study aims to identify altered WM pathways in dyskinetic CP from a standardized, connectome-based approach, and to assess structure-function relationship in WM pathways for clinical outcomes. Individual connectome maps of 25 subjects with dyskinetic CP and 24 healthy controls were obtained combining a structural parcellation scheme with whole-brain deterministic tractography. Graph theoretical metrics and the network-based statistic were applied to compare groups and to correlate WM state with motor and cognitive performance. Results showed a widespread reduction of WM volume in CP subjects compared to controls and a more localized decrease in degree (number of links per node) and fractional anisotropy (FA), comprising parieto-occipital regions and the hippocampus. However, supramarginal gyrus showed a significantly higher degree. At the network level, CP subjects showed a bilateral pathway with reduced FA, comprising sensorimotor, intraparietal and fronto-parietal connections. Gross and fine motor functions correlated with FA in a pathway comprising the sensorimotor system, but gross motor also correlated with prefrontal, temporal and occipital connections. Intelligence correlated with FA in a network with fronto-striatal and parieto-frontal connections, and visuoperception was related to right occipital connections. These findings demonstrate a disruption in structural brain connectivity in dyskinetic CP, revealing general involvement of posterior brain regions with relative preservation of prefrontal areas. We identified pathways in which WM integrity is related to clinical features, including but not limited to the sensorimotor system. Hum Brain Mapp 38:4594-4612, 2017. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Júlia Ballester-Plané
- Departament de Psicologia Clínica i Psicobiologia, Universitat de Barcelona, Barcelona, Spain.,Institut de Neurociències, Universitat de Barcelona, Barcelona, Spain.,Institut de Recerca Pediàtrica Hospital Sant Joan de Déu, Barcelona, Spain
| | - Ruben Schmidt
- Department of Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Olga Laporta-Hoyos
- Departament de Psicologia Clínica i Psicobiologia, Universitat de Barcelona, Barcelona, Spain.,Institut de Neurociències, Universitat de Barcelona, Barcelona, Spain.,Institut de Recerca Pediàtrica Hospital Sant Joan de Déu, Barcelona, Spain
| | - Carme Junqué
- Institut de Neurociències, Universitat de Barcelona, Barcelona, Spain.,Departament de Medicina, Universitat de Barcelona, Barcelona, Spain.,Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Élida Vázquez
- Servei de Radiologia Pediàtrica, Hospital Universitari Vall d'Hebron, Barcelona, Spain
| | - Ignacio Delgado
- Servei de Radiologia Pediàtrica, Hospital Universitari Vall d'Hebron, Barcelona, Spain
| | - Leire Zubiaurre-Elorza
- Departamento de Fundamentos y Métodos de la Psicología, Facultad de Psicología y Educación, Universidad de Deusto, Bilbo-Bizkaia, Spain
| | - Alfons Macaya
- Grup de Recerca en Neurologia Pediàtrica, Vall d'Hebron Institut de Recerca, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Pilar Póo
- Servei de Neurologia, Hospital Universitari Sant Joan de Déu, Barcelona, Spain
| | - Esther Toro
- Servei de Rehabilitació i Medicina Física, Hospital Universitari Vall d'Hebron, Barcelona, Spain
| | - Marcel A de Reus
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Martijn P van den Heuvel
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Roser Pueyo
- Departament de Psicologia Clínica i Psicobiologia, Universitat de Barcelona, Barcelona, Spain.,Institut de Neurociències, Universitat de Barcelona, Barcelona, Spain.,Institut de Recerca Pediàtrica Hospital Sant Joan de Déu, Barcelona, Spain
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32
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Tudela R, Muñoz-Moreno E, López-Gil X, Soria G. Effects of Orientation and Anisometry of Magnetic Resonance Imaging Acquisitions on Diffusion Tensor Imaging and Structural Connectomes. PLoS One 2017; 12:e0170703. [PMID: 28118397 PMCID: PMC5261617 DOI: 10.1371/journal.pone.0170703] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2016] [Accepted: 01/09/2017] [Indexed: 11/19/2022] Open
Abstract
Diffusion-weighted imaging (DWI) quantifies water molecule diffusion within tissues and is becoming an increasingly used technique. However, it is very challenging as correct quantification depends on many different factors, ranging from acquisition parameters to a long pipeline of image processing. In this work, we investigated the influence of voxel geometry on diffusion analysis, comparing different acquisition orientations as well as isometric and anisometric voxels. Diffusion-weighted images of one rat brain were acquired with four different voxel geometries (one isometric and three anisometric in different directions) and three different encoding orientations (coronal, axial and sagittal). Diffusion tensor scalar measurements, tractography and the brain structural connectome were analyzed for each of the 12 acquisitions. The acquisition direction with respect to the main magnetic field orientation affected the diffusion results. When the acquisition slice-encoding direction was not aligned with the main magnetic field, there were more artifacts and a lower signal-to-noise ratio that led to less anisotropic tensors (lower fractional anisotropic values), producing poorer quality results. The use of anisometric voxels generated statistically significant differences in the values of diffusion metrics in specific regions. It also elicited differences in tract reconstruction and in different graph metric values describing the brain networks. Our results highlight the importance of taking into account the geometric aspects of acquisitions, especially when comparing diffusion data acquired using different geometries.
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Affiliation(s)
- Raúl Tudela
- CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Barcelona, Spain
| | | | | | - Guadalupe Soria
- CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Barcelona, Spain
- Experimental MRI 7T Unit, IDIBAPS, Barcelona, Spain
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Yiannakas MC, Grussu F, Louka P, Prados F, Samson RS, Battiston M, Altmann DR, Ourselin S, Miller DH, Gandini Wheeler-Kingshott CAM. Reduced Field-of-View Diffusion-Weighted Imaging of the Lumbosacral Enlargement: A Pilot In Vivo Study of the Healthy Spinal Cord at 3T. PLoS One 2016; 11:e0164890. [PMID: 27741303 PMCID: PMC5065166 DOI: 10.1371/journal.pone.0164890] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2016] [Accepted: 10/03/2016] [Indexed: 11/21/2022] Open
Abstract
Diffusion tensor imaging (DTI) has recently started to be adopted into clinical investigations of spinal cord (SC) diseases. However, DTI applications to the lower SC are limited due to a number of technical challenges, related mainly to the even smaller size of the SC structure at this level, its position relative to the receiver coil elements and the effects of motion during data acquisition. Developing methods to overcome these problems would offer new means to gain further insights into microstructural changes of neurological conditions involving the lower SC, and in turn could help explain symptoms such as bladder and sexual dysfunction. In this work, the feasibility of obtaining grey and white matter (GM/WM) DTI indices such as axial/radial/mean diffusivity (AD/RD/MD) and fractional anisotropy (FA) within the lumbosacral enlargement (LSE) was investigated using a reduced field-of-view (rFOV) single-shot echo-planar imaging (ss-EPI) acquisition in 14 healthy participants using a clinical 3T MR system. The scan-rescan reproducibility of the measurements was assessed by calculating the percentage coefficient of variation (%COV). Mean FA was higher in WM compared to GM (0.58 and 0.4 in WM and GM respectively), AD and MD were higher in WM compared to GM (1.66 μm2ms-1 and 0.94 μm2ms-1 in WM and 1.2 μm2ms-1 and 0.82 μm2ms-1 in GM for AD and MD respectively) and RD was lower in WM compared to GM (0.58 μm2ms-1 and 0.63 μm2ms-1 respectively). The scan-rescan %COV was lower than 10% in all cases with the highest values observed for FA and the lowest for MD. This pilot study demonstrates that it is possible to obtain reliable tissue-specific estimation of DTI indices within the LSE using a rFOV ss-EPI acquisition. The DTI acquisition and analysis protocol presented here is clinically feasible and may be used in future investigations of neurological conditions implicating the lower SC.
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Affiliation(s)
- Marios C. Yiannakas
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, University College London, London, United Kingdom
| | - Francesco Grussu
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, University College London, London, United Kingdom
| | - Polymnia Louka
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, University College London, London, United Kingdom
| | - Ferran Prados
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, University College London, London, United Kingdom
- Translational Imaging Group, Centre for Medical Image Computing, Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
- University College London / University College London Hospitals National Institute for Health Research (NIHR) Biomedical Research Centre, London, United Kingdom
| | - Rebecca S. Samson
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, University College London, London, United Kingdom
| | - Marco Battiston
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, University College London, London, United Kingdom
| | - Daniel R. Altmann
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, University College London, London, United Kingdom
- Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Sebastien Ourselin
- Translational Imaging Group, Centre for Medical Image Computing, Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
- University College London / University College London Hospitals National Institute for Health Research (NIHR) Biomedical Research Centre, London, United Kingdom
| | - David H. Miller
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, University College London, London, United Kingdom
- University College London / University College London Hospitals National Institute for Health Research (NIHR) Biomedical Research Centre, London, United Kingdom
| | - Claudia A. M. Gandini Wheeler-Kingshott
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, University College London, London, United Kingdom
- Brain MRI 3T Mondino Research Center, C. Mondino National Neurological Institute, Pavia, Italy
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, PV, Italy
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Haakma W, Jongbloed BA, Froeling M, Goedee HS, Bos C, Leemans A, van den Berg LH, Hendrikse J, van der Pol WL. MRI shows thickening and altered diffusion in the median and ulnar nerves in multifocal motor neuropathy. Eur Radiol 2016; 27:2216-2224. [PMID: 27655303 PMCID: PMC5374174 DOI: 10.1007/s00330-016-4575-0] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2016] [Revised: 08/09/2016] [Accepted: 08/22/2016] [Indexed: 12/13/2022]
Abstract
Objectives To study disease mechanisms in multifocal motor neuropathy (MMN) with magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) of the median and ulnar nerves. Methods We enrolled ten MMN patients, ten patients with amyotrophic lateral sclerosis (ALS) and ten healthy controls (HCs). Patients underwent MRI (in a prone position) and nerve conduction studies. DTI and fat-suppressed T2-weighted scans of the forearms were performed on a 3.0T MRI scanner. Fibre tractography of the median and ulnar nerves was performed to extract diffusion parameters: fractional anisotropy (FA), mean (MD), axial (AD) and radial (RD) diffusivity. Cross-sectional areas (CSA) were measured on T2-weighted scans. Results Forty-five out of 60 arms were included in the analysis. AD was significantly lower in MMN patients (2.20 ± 0.12 × 10-3 mm2/s) compared to ALS patients (2.31 ± 0.17 × 10-3 mm2/s; p < 0.05) and HCs (2.31± 0.17 × 10-3 mm2/s; p < 0.05). Segmental analysis showed significant restriction of AD, RD and MD (p < 0.005) in the proximal third of the nerves. CSA was significantly larger in MMN patients compared to ALS patients and HCs (p < 0.01). Conclusions Thickening of nerves is compatible with changes in the myelin sheath structure, whereas lowered AD values suggest axonal dysfunction. These findings suggest that myelin and axons are diffusely involved in MMN pathogenesis. Key Points • Diffusion magnetic resonance imaging provides quantitative information about multifocal motor neuropathy (MMN). • Diffusion tensor imaging allows non-invasive evaluation of the forearm nerves in MMN. • Nerve thickening and lowered diffusion parameters suggests myelin and axonal changes. • This study can help to provide insight into pathological mechanisms of MMN. Electronic supplementary material The online version of this article (doi:10.1007/s00330-016-4575-0) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Wieke Haakma
- Department of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands. .,Department of Forensic Medicine and Comparative Medicine Lab, Aarhus University, Aarhus, Denmark.
| | - Bas A Jongbloed
- Brain Centre Rudolf Magnus, Department of Neurology and Neurosurgery, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Martijn Froeling
- Department of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - H Stephan Goedee
- Brain Centre Rudolf Magnus, Department of Neurology and Neurosurgery, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Clemens Bos
- Department of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Alexander Leemans
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Leonard H van den Berg
- Brain Centre Rudolf Magnus, Department of Neurology and Neurosurgery, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Jeroen Hendrikse
- Department of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - W Ludo van der Pol
- Brain Centre Rudolf Magnus, Department of Neurology and Neurosurgery, University Medical Center Utrecht, Utrecht, The Netherlands
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35
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Veraart J, Novikov DS, Christiaens D, Ades-Aron B, Sijbers J, Fieremans E. Denoising of diffusion MRI using random matrix theory. Neuroimage 2016; 142:394-406. [PMID: 27523449 DOI: 10.1016/j.neuroimage.2016.08.016] [Citation(s) in RCA: 987] [Impact Index Per Article: 123.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2016] [Revised: 08/04/2016] [Accepted: 08/09/2016] [Indexed: 11/30/2022] Open
Abstract
We introduce and evaluate a post-processing technique for fast denoising of diffusion-weighted MR images. By exploiting the intrinsic redundancy in diffusion MRI using universal properties of the eigenspectrum of random covariance matrices, we remove noise-only principal components, thereby enabling signal-to-noise ratio enhancements. This yields parameter maps of improved quality for visual, quantitative, and statistical interpretation. By studying statistics of residuals, we demonstrate that the technique suppresses local signal fluctuations that solely originate from thermal noise rather than from other sources such as anatomical detail. Furthermore, we achieve improved precision in the estimation of diffusion parameters and fiber orientations in the human brain without compromising the accuracy and spatial resolution.
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Affiliation(s)
- Jelle Veraart
- iMinds Vision Lab (Dept. of Physics), University of Antwerp, Antwerp, Belgium; Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, NY, USA.
| | - Dmitry S Novikov
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, NY, USA
| | - Daan Christiaens
- ESAT/PSI, Department of Electrical Engineering, KU Leuven, Leuven, Belgium
| | - Benjamin Ades-Aron
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, NY, USA
| | - Jan Sijbers
- iMinds Vision Lab (Dept. of Physics), University of Antwerp, Antwerp, Belgium
| | - Els Fieremans
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, NY, USA
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36
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Petersen MV, Lund TE, Sunde N, Frandsen J, Rosendal F, Juul N, Østergaard K. Probabilistic versus deterministic tractography for delineation of the cortico-subthalamic hyperdirect pathway in patients with Parkinson disease selected for deep brain stimulation. J Neurosurg 2016; 126:1657-1668. [PMID: 27392264 DOI: 10.3171/2016.4.jns1624] [Citation(s) in RCA: 65] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
OBJECTIVE Diffusion-weighted MRI (DWI) and tractography allows noninvasive mapping of the structural connections of the brain, and may provide important information for neurosurgical planning. The hyperdirect pathway, connecting the subthalamic nucleus (STN) with the motor cortex, is assumed to play a key role in mediating the effects of deep brain stimulation (DBS), which is an effective but poorly understood treatment for Parkinson disease. This study aimed to apply recent methodological advances in DWI acquisition and analysis to the delineation of the hyperdirect pathway in patients with Parkinson disease selected for surgery. METHODS High spatial and angular resolution DWI data were acquired preoperatively from 5 patients with Parkinson disease undergoing DBS. The authors compared the delineated hyperdirect pathways and associated STN target maps generated by 2 different tractography methods: a tensor-based deterministic method, typically available in clinical settings, and an advanced probabilistic method based on constrained spherical deconvolution. In addition, 10 high-resolution data sets with the same scanning parameters were acquired from a healthy control participant to assess the robustness of the tractography results. RESULTS Both tractography approaches identified connections between the ipsilateral motor cortex and the STN. However, the 2 methods provided substantially different target regions in the STN, with the target center of gravity differing by > 1.4 mm on average. The probabilistic method (based on constrained spherical deconvolution) plausibly reconstructed a continuous set of connections from the motor cortex, terminating in the dorsolateral region of the STN. In contrast, the tensor-based method reconstructed a comparatively sparser and more variable subset of connections. Furthermore, across the control scans, the probabilistic method identified considerably more consistent targeting regions within the STN compared with the deterministic tensor-based method, which demonstrated a 1.9-2.4 times higher variation. CONCLUSIONS These data provide a strong impetus for the use of a robust probabilistic tractography framework based on constrained spherical deconvolution, or similar advanced DWI models, in clinical settings. The inherent limitations and demonstrated inaccuracy of the tensor-based method leave it questionable for use in high-precision stereotactic DBS surgery. The authors have also described a straightforward method for importing tractography-derived information into any clinical neuronavigation system, based on the generation of track-density images.
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Affiliation(s)
- Mikkel V Petersen
- Center of Functionally Integrative Neuroscience and.,Departments of 2 Neurosurgery
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37
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Steventon JJ, Trueman RC, Rosser AE, Jones DK. Robust MR-based approaches to quantifying white matter structure and structure/function alterations in Huntington's disease. J Neurosci Methods 2016; 265:2-12. [PMID: 26335798 PMCID: PMC4863525 DOI: 10.1016/j.jneumeth.2015.08.027] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2015] [Revised: 08/17/2015] [Accepted: 08/20/2015] [Indexed: 12/02/2022]
Abstract
BACKGROUND Huge advances have been made in understanding and addressing confounds in diffusion MRI data to quantify white matter microstructure. However, there has been a lag in applying these advances in clinical research. Some confounds are more pronounced in HD which impedes data quality and interpretability of patient-control differences. This study presents an optimised analysis pipeline and addresses specific confounds in a HD patient cohort. METHOD 15 HD gene-positive and 13 matched control participants were scanned on a 3T MRI system with two diffusion MRI sequences. An optimised post processing pipeline included motion, eddy current and EPI correction, rotation of the B matrix, free water elimination (FWE) and tractography analysis using an algorithm capable of reconstructing crossing fibres. The corpus callosum was examined using both a region-of-interest and a deterministic tractography approach, using both conventional diffusion tensor imaging (DTI)-based and spherical deconvolution analyses. RESULTS Correcting for CSF contamination significantly altered microstructural metrics and the detection of group differences. Reconstructing the corpus callosum using spherical deconvolution produced a more complete reconstruction with greater sensitivity to group differences, compared to DTI-based tractography. Tissue volume fraction (TVF) was reduced in HD participants and was more sensitive to disease burden compared to DTI metrics. CONCLUSION Addressing confounds in diffusion MR data results in more valid, anatomically faithful white matter tract reconstructions with reduced within-group variance. TVF is recommended as a complementary metric, providing insight into the relationship with clinical symptoms in HD not fully captured by conventional DTI metrics.
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Affiliation(s)
- Jessica J Steventon
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Park Place, Cardiff CF10 3AT, UK; Brain Repair Group, Life Science Building, 3rd Floor, School of Biosciences, Cardiff University, Museum Avenue, Cardiff CF10 3AX, UK; Neuroscience and Mental Health Research Institute, Cardiff University, Hadyn Ellis Building, Cathays, Cardiff CF24 4HQ, UK.
| | - Rebecca C Trueman
- Brain Repair Group, Life Science Building, 3rd Floor, School of Biosciences, Cardiff University, Museum Avenue, Cardiff CF10 3AX, UK; School of Biomedical Sciences, Queen's Medical Centre, Nottingham University, Nottingham NG7 2UH, UK
| | - Anne E Rosser
- Brain Repair Group, Life Science Building, 3rd Floor, School of Biosciences, Cardiff University, Museum Avenue, Cardiff CF10 3AX, UK; Neuroscience and Mental Health Research Institute, Cardiff University, Hadyn Ellis Building, Cathays, Cardiff CF24 4HQ, UK; Institute of Psychological Medicine and Neurology, School of Medicine, Hadyn Ellis Building, Maindy Road, Cathays, Cardiff CF24 4HQ, UK
| | - Derek K Jones
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Park Place, Cardiff CF10 3AT, UK; Neuroscience and Mental Health Research Institute, Cardiff University, Hadyn Ellis Building, Cathays, Cardiff CF24 4HQ, UK
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38
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Berl MM, Walker L, Modi P, Irfanoglu MO, Sarlls JE, Nayak A, Pierpaoli C. Investigation of vibration-induced artifact in clinical diffusion-weighted imaging of pediatric subjects. Hum Brain Mapp 2015; 36:4745-57. [PMID: 26350492 DOI: 10.1002/hbm.22846] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2014] [Revised: 05/04/2015] [Accepted: 05/08/2015] [Indexed: 11/07/2022] Open
Abstract
It has been reported that mechanical vibrations of the magnetic resonance imaging scanner could produce spurious signal dropouts in diffusion-weighted images resulting in artifactual anisotropy in certain regions of the brain with red appearance in the Directionally Encoded Color maps. We performed a review of the frequency of this artifact across pediatric studies, noting differences by scanner manufacturer, acquisition protocol, as well as weight and position of the subject. We also evaluated the ability of automated and quantitative methods to detect this artifact. We found that the artifact may be present in over 50% of data in certain protocols and is not limited to one scanner manufacturer. While a specific scanner had the highest incidence, low body weight and positioning were also associated with appearance of the artifact for both scanner types evaluated, making children potentially more susceptible than adults. Visual inspection remains the best method for artifact identification. Software for automated detection showed very low sensitivity (10%). The artifact may present inconsistently in longitudinal studies. We discuss a published case report that has been widely cited and used as evidence to set policy about diagnostic criteria for determining vegetative state. That report attributed longitudinal changes in anisotropy to white matter plasticity without considering the possibility that the changes were caused by this artifact. Our study underscores the need to check for the presence of this artifact in clinical studies, analyzes circumstances for when it may be more likely to occur, and suggests simple strategies to identify and potentially avoid its effects.
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Affiliation(s)
- Madison M Berl
- Division of Pediatric Neuropsychology, Washington, District of Columbia, Children's Research Institute, Children's National Health System, Washington, DC
| | - Lindsay Walker
- Program on Pediatric Imaging and Tissue Sciences, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, MD
| | - Pooja Modi
- Program on Pediatric Imaging and Tissue Sciences, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, MD
| | - M Okan Irfanoglu
- Program on Pediatric Imaging and Tissue Sciences, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, MD.,Henry Jackson Foundation, Bethesda, Maryland
| | - Joelle E Sarlls
- NMRF, NINDS, National Institutes of Health, Bethesda, Maryland
| | - Amritha Nayak
- Program on Pediatric Imaging and Tissue Sciences, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, MD.,Henry Jackson Foundation, Bethesda, Maryland
| | - Carlo Pierpaoli
- Program on Pediatric Imaging and Tissue Sciences, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, MD.,Center for Neuroscience and Regenerative Medicine, Uniformed Services University of the Health Sciences, Bethesda, Maryland
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39
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Cognitive Flexibility through Metastable Neural Dynamics Is Disrupted by Damage to the Structural Connectome. J Neurosci 2015; 35:9050-63. [PMID: 26085630 DOI: 10.1523/jneurosci.4648-14.2015] [Citation(s) in RCA: 106] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Current theory proposes that healthy neural dynamics operate in a metastable regime, where brain regions interact to simultaneously maximize integration and segregation. Metastability may confer important behavioral properties, such as cognitive flexibility. It is increasingly recognized that neural dynamics are constrained by the underlying structural connections between brain regions. An important challenge is, therefore, to relate structural connectivity, neural dynamics, and behavior. Traumatic brain injury (TBI) is a pre-eminent structural disconnection disorder whereby traumatic axonal injury damages large-scale connectivity, producing characteristic cognitive impairments, including slowed information processing speed and reduced cognitive flexibility, that may be a result of disrupted metastable dynamics. Therefore, TBI provides an experimental and theoretical model to examine how metastable dynamics relate to structural connectivity and cognition. Here, we use complementary empirical and computational approaches to investigate how metastability arises from the healthy structural connectome and relates to cognitive performance. We found reduced metastability in large-scale neural dynamics after TBI, measured with resting-state functional MRI. This reduction in metastability was associated with damage to the connectome, measured using diffusion MRI. Furthermore, decreased metastability was associated with reduced cognitive flexibility and information processing. A computational model, defined by empirically derived connectivity data, demonstrates how behaviorally relevant changes in neural dynamics result from structural disconnection. Our findings suggest how metastable dynamics are important for normal brain function and contingent on the structure of the human connectome.
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40
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Sahama I, Sinclair K, Fiori S, Doecke J, Pannek K, Reid L, Lavin M, Rose S. Motor pathway degeneration in young ataxia telangiectasia patients: A diffusion tractography study. Neuroimage Clin 2015; 9:206-15. [PMID: 26413479 PMCID: PMC4561673 DOI: 10.1016/j.nicl.2015.08.007] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2015] [Revised: 07/17/2015] [Accepted: 08/13/2015] [Indexed: 12/13/2022]
Abstract
BACKGROUND Our understanding of the effect of ataxia-telangiectasia mutated gene mutations on brain structure and function is limited. In this study, white matter motor pathway integrity was investigated in ataxia telangiectasia patients using diffusion MRI and probabilistic tractography. METHODS Diffusion MRI were obtained from 12 patients (age range: 7-22 years, mean: 12 years) and 12 typically developing age matched participants (age range 8-23 years, mean: 13 years). White matter fiber tracking and whole tract statistical analyses were used to assess quantitative fractional anisotropy and mean diffusivity differences along the cortico-ponto-cerebellar, cerebellar-thalamo-cortical, somatosensory and lateral corticospinal tract length in patients using a linear mixed effects model. White matter tract streamline number and apparent fiber density in patient and control tracts were also assessed. RESULTS Reduced fractional anisotropy along all analyzed patient tracts were observed (p < 0.001). Mean diffusivity was significantly elevated in anterior tract locations but was reduced within cerebellar peduncle regions of all patient tracts (p < 0.001). Reduced tract streamline number and tract volume in the left and right corticospinal and somatosensory tracts were observed in patients (p < 0.006). In addition, reduced apparent fiber density in the left and right corticospinal and right somatosensory tracts (p < 0.006) occurred in patients. CONCLUSIONS Whole tract analysis of the corticomotor, corticospinal and somatosensory pathways in ataxia telangiectasia showed significant white matter degeneration along the entire length of motor circuits, highlighting that ataxia-telangiectasia gene mutation impacts the cerebellum and multiple other motor circuits in young patients.
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Affiliation(s)
- Ishani Sahama
- University of Queensland, School of Medicine, Brisbane, Australia
| | - Kate Sinclair
- Neurology, The Royal Children's Hospital, Brisbane, Australia
| | | | - James Doecke
- Digital Productivity Flagship/The Australian E-Health Research Centre, Commonwealth Scientific and Industrial Research Organization, Brisbane, Australia
| | | | - Lee Reid
- Digital Productivity Flagship/The Australian E-Health Research Centre, Commonwealth Scientific and Industrial Research Organization, Brisbane, Australia
| | - Martin Lavin
- University of Queensland Centre for Clinical Research, Brisbane, Australia
| | - Stephen Rose
- Digital Productivity Flagship/The Australian E-Health Research Centre, Commonwealth Scientific and Industrial Research Organization, Brisbane, Australia
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Conti E, Calderoni S, Gaglianese A, Pannek K, Mazzotti S, Rose S, Scelfo D, Tosetti M, Muratori F, Cioni G, Guzzetta A. Lateralization of Brain Networks and Clinical Severity in Toddlers with Autism Spectrum Disorder: A HARDI Diffusion MRI Study. Autism Res 2015; 9:382-92. [PMID: 26280255 DOI: 10.1002/aur.1533] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2014] [Accepted: 07/25/2015] [Indexed: 12/20/2022]
Abstract
Recent diffusion tensor imaging studies in adolescents and children with Autism Spectrum Disorder (ASD) have reported a loss or an inversion of the typical left-right lateralization in fronto-temporal regions crucial for sociocommunicative skills. No studies explored atypical lateralization in toddlers and its correlation with clinical severity of ASD. We recruited a cohort of 20 subjects aged 36 months or younger receiving a first clinical diagnosis of ASD (15 males; age range 20-36 months). Patients underwent diffusion MRI (High-Angular-Resolution Diffusion Imaging protocol). Data from cortical parcellation were combined with tractography to obtain a connection matrix and diffusion indexes (DI ) including mean fractional anisotropy (DFA ), number of tracts (DNUM ), and total tract length (DTTL ). A laterality index was generated for each measure, and then correlated with the Autism Diagnostic Observation Schedule-Generic (ADOS-G) total score. Laterality indexes of DFA were significantly correlated with ADOS-G total scores only in two intrafrontal connected areas (correlation was positive in one case and negative in the other). Laterality indexes of DTTL and DNUM showed significant negative correlations (P < 0.05) in six connected areas, mainly fronto-temporal. This study provides first evidence of a significant correlation between brain lateralization of diffusion indexes and clinical severity in toddlers with a first diagnosis of ASD. Significant correlations mainly involved regions within the fronto-temporal circuits, known to be crucial for sociocommunicative skills. It is of interest that all correlations but one were negative, suggesting an inversion of the typical left-right asymmetry in subjects with most severe clinical impairment.
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Affiliation(s)
- Eugenia Conti
- Department of Developmental Neuroscience, Stella Maris Scientific Institute, Pisa, Italy.,Department of Clinical and Experimental Medicine, University of Pisa, Italy
| | - Sara Calderoni
- Department of Developmental Neuroscience, Stella Maris Scientific Institute, Pisa, Italy
| | - Anna Gaglianese
- Department of Developmental Neuroscience, Stella Maris Scientific Institute, Pisa, Italy
| | - Kerstin Pannek
- The Australian eHealth Research Centre, CSIRO, Brisbane, Queensland, Australia
| | - Sara Mazzotti
- Department of Developmental Neuroscience, Stella Maris Scientific Institute, Pisa, Italy
| | - Stephen Rose
- The Australian eHealth Research Centre, CSIRO, Brisbane, Queensland, Australia
| | - Danilo Scelfo
- Department of Developmental Neuroscience, Stella Maris Scientific Institute, Pisa, Italy
| | - Michela Tosetti
- Department of Developmental Neuroscience, Stella Maris Scientific Institute, Pisa, Italy
| | - Filippo Muratori
- Department of Developmental Neuroscience, Stella Maris Scientific Institute, Pisa, Italy.,Department of Clinical and Experimental Medicine, University of Pisa, Italy
| | - Giovanni Cioni
- Department of Developmental Neuroscience, Stella Maris Scientific Institute, Pisa, Italy.,Department of Clinical and Experimental Medicine, University of Pisa, Italy
| | - Andrea Guzzetta
- Department of Developmental Neuroscience, Stella Maris Scientific Institute, Pisa, Italy.,Department of Clinical and Experimental Medicine, University of Pisa, Italy
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Seehaus A, Roebroeck A, Bastiani M, Fonseca L, Bratzke H, Lori N, Vilanova A, Goebel R, Galuske R. Histological validation of high-resolution DTI in human post mortem tissue. Front Neuroanat 2015; 9:98. [PMID: 26257612 PMCID: PMC4511840 DOI: 10.3389/fnana.2015.00098] [Citation(s) in RCA: 97] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2015] [Accepted: 07/10/2015] [Indexed: 11/13/2022] Open
Abstract
Diffusion tensor imaging (DTI) is amongst the simplest mathematical models available for diffusion magnetic resonance imaging, yet still by far the most used one. Despite the success of DTI as an imaging tool for white matter fibers, its anatomical underpinnings on a microstructural basis remain unclear. In this study, we used 65 myelin-stained sections of human premotor cortex to validate modeled fiber orientations and oft used microstructure-sensitive scalar measures of DTI on the level of individual voxels. We performed this validation on high spatial resolution diffusion MRI acquisitions investigating both white and gray matter. We found a very good agreement between DTI and myelin orientations with the majority of voxels showing angular differences less than 10°. The agreement was strongest in white matter, particularly in unidirectional fiber pathways. In gray matter, the agreement was good in the deeper layers highlighting radial fiber directions even at lower fractional anisotropy (FA) compared to white matter. This result has potentially important implications for tractography algorithms applied to high resolution diffusion MRI data if the aim is to move across the gray/white matter boundary. We found strong relationships between myelin microstructure and DTI-based microstructure-sensitive measures. High FA values were linked to high myelin density and a sharply tuned histological orientation profile. Conversely, high values of mean diffusivity (MD) were linked to bimodal or diffuse orientation distributions and low myelin density. At high spatial resolution, DTI-based measures can be highly sensitive to white and gray matter microstructure despite being relatively unspecific to concrete microarchitectural aspects.
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Affiliation(s)
- Arne Seehaus
- Faculty of Psychology and Neuroscience, Maastricht University Maastricht, Netherlands ; Systems Neurophysiology, Technische Universität Darmstadt Darmstadt, Germany
| | - Alard Roebroeck
- Faculty of Psychology and Neuroscience, Maastricht University Maastricht, Netherlands
| | - Matteo Bastiani
- Faculty of Psychology and Neuroscience, Maastricht University Maastricht, Netherlands ; Jülich Research Centre, Institute of Neuroscience and Medicine (INM-4) Jülich, Germany
| | - Lúcia Fonseca
- Department of Biomedical Engineering, Eindhoven University of Technology Eindhoven, Netherlands
| | | | - Nicolás Lori
- Visual Neuroscience Laboratory, Institute for Biomedical Imaging and Life Sciences (IBILI), Faculty of Medicine, University of Coimbra Coimbra, Portugal
| | - Anna Vilanova
- Department of Biomedical Engineering, Eindhoven University of Technology Eindhoven, Netherlands
| | - Rainer Goebel
- Faculty of Psychology and Neuroscience, Maastricht University Maastricht, Netherlands
| | - Ralf Galuske
- Systems Neurophysiology, Technische Universität Darmstadt Darmstadt, Germany
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Belli G, Busoni S, Ciccarone A, Coniglio A, Esposito M, Giannelli M, Mazzoni LN, Nocetti L, Sghedoni R, Tarducci R, Zatelli G, Anoja RA, Belmonte G, Bertolino N, Betti M, Biagini C, Ciarmatori A, Cretti F, Fabbri E, Fedeli L, Filice S, Fulcheri CPL, Gasperi C, Mangili PA, Mazzocchi S, Meliadò G, Morzenti S, Noferini L, Oberhofer N, Orsingher L, Paruccini N, Princigalli G, Quattrocchi M, Rinaldi A, Scelfo D, Freixas GV, Tenori L, Zucca I, Luchinat C, Gori C, Gobbi G. Quality assurance multicenter comparison of different MR scanners for quantitative diffusion-weighted imaging. J Magn Reson Imaging 2015; 43:213-9. [PMID: 26013043 DOI: 10.1002/jmri.24956] [Citation(s) in RCA: 66] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2015] [Accepted: 05/11/2015] [Indexed: 12/13/2022] Open
Abstract
PURPOSE To propose a magnetic resonance imaging (MRI) quality assurance procedure that can be used for multicenter comparison of different MR scanners for quantitative diffusion-weighted imaging (DWI). MATERIALS AND METHODS Twenty-six centers (35 MR scanners with field strengths: 1T, 1.5T, and 3T) were enrolled in the study. Two different DWI acquisition series (b-value ranges 0-1000 and 0-3000 s/mm(2) , respectively) were performed for each MR scanner. All DWI acquisitions were performed by using a cylindrical doped water phantom. Mean apparent diffusion coefficient (ADC) values as well as ADC values along each of the three main orthogonal directions of the diffusion gradients (x, y, and z) were calculated. Short-term repeatability of ADC measurement was evaluated for 26 MR scanners. RESULTS A good agreement was found between the nominal and measured mean ADC over all the centers. More than 80% of mean ADC measurements were within 5% from the nominal value, and the highest deviation and overall standard deviation were 9.3% and 3.5%, respectively. Short-term repeatability of ADC measurement was found <2.5% for all MR scanners. CONCLUSION A specific and widely accepted protocol for quality controls in DWI is still lacking. The DWI quality assurance protocol proposed in this study can be applied in order to assess the reliability of DWI-derived indices before tackling single- as well as multicenter studies.
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Affiliation(s)
| | | | | | - Angela Coniglio
- Medical Physics Department, Fondazione Fatebenefratelli per la Ricerca e la Formazione sanitaria e sociale, San Giovanni Calibita Hospital, Rome, Italy
| | | | | | | | - Luca Nocetti
- Medical Phisics Department, AOU Policlinico Modena, Italy
| | - Roberto Sghedoni
- Medical Physics Unit, Arcispedale Santa Maria Nuova - IRCCS, Reggio Emilia, Italy
| | | | | | | | | | - Nicola Bertolino
- UO Direzione Sanitaria, IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Margherita Betti
- Health Physics Unit, Centro Oncologico Fiorentino, Sesto Fiorentino, Italy
| | - Cristiano Biagini
- Radiodiagnostic Unit, Centro Oncologico Fiorentino, Sesto Fiorentino, Italy
| | | | | | - Emma Fabbri
- Health Physics Unit, Ospedale Sant'Orsola-Malpighi, Bologna, Italy
| | - Luca Fedeli
- Health Physics Unit, AOU Careggi, Florence, Italy
| | | | | | | | | | | | | | | | | | - Nadia Oberhofer
- Health Physics Unit, Azienda Sanitaria dell'Alto Adige-Ospedale Bolzano, Italy
| | - Laura Orsingher
- Medical Physics Unit, Arcispedale Santa Maria Nuova - IRCCS, Reggio Emilia, Italy
| | | | | | | | - Adele Rinaldi
- Medical Physics Department, Fondazione Fatebenefratelli per la Ricerca e la Formazione sanitaria e sociale, San Giovanni Calibita Hospital, Rome, Italy
| | - Danilo Scelfo
- Department of Developmental Neuroscience, Stella Maris Scientific Institute, Pisa, Italy
| | | | - Leonardo Tenori
- Magnetic Resonance Center (CERM), University of Florence, Sesto Fiorentino, Italy; FiorGen Foundation, Sesto Fiorentino, Italy
| | - Ileana Zucca
- UO Direzione Scientifica, IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Claudio Luchinat
- Magnetic Resonance Center (CERM), University of Florence, Sesto Fiorentino, Italy; FiorGen Foundation, Sesto Fiorentino, Italy
| | - Cesare Gori
- Health Physics Unit, AOU Careggi, Florence, Italy
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Goveas J, O'Dwyer L, Mascalchi M, Cosottini M, Diciotti S, De Santis S, Passamonti L, Tessa C, Toschi N, Giannelli M. Diffusion-MRI in neurodegenerative disorders. Magn Reson Imaging 2015; 33:853-76. [PMID: 25917917 DOI: 10.1016/j.mri.2015.04.006] [Citation(s) in RCA: 59] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2014] [Revised: 04/18/2015] [Accepted: 04/19/2015] [Indexed: 12/11/2022]
Abstract
The ability to image the whole brain through ever more subtle and specific methods/contrasts has come to play a key role in understanding the basis of brain abnormalities in several diseases. In magnetic resonance imaging (MRI), "diffusion" (i.e. the random, thermally-induced displacements of water molecules over time) represents an extraordinarily sensitive contrast mechanism, and the exquisite structural detail it affords has proven useful in a vast number of clinical as well as research applications. Since diffusion-MRI is a truly quantitative imaging technique, the indices it provides can serve as potential imaging biomarkers which could allow early detection of pathological alterations as well as tracking and possibly predicting subtle changes in follow-up examinations and clinical trials. Accordingly, diffusion-MRI has proven useful in obtaining information to better understand the microstructural changes and neurophysiological mechanisms underlying various neurodegenerative disorders. In this review article, we summarize and explore the main applications, findings, perspectives as well as challenges and future research of diffusion-MRI in various neurodegenerative disorders including Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis, Huntington's disease and degenerative ataxias.
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Affiliation(s)
- Joseph Goveas
- Department of Psychiatry and Behavioral Medicine, and Institute for Health and Society, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Laurence O'Dwyer
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, Goethe University, Frankfurt, Germany
| | - Mario Mascalchi
- Department of Experimental and Clinical Biomedical Sciences, University of Florence, Florence, Italy; Quantitative and Functional Neuroradiology Research Program at Meyer Children and Careggi Hospitals of Florence, Florence, Italy
| | - Mirco Cosottini
- Department of Translational Research and New Surgical and Medical Technologies, University of Pisa, Pisa, Italy; Unit of Neuroradiology, Pisa University Hospital "Azienda Ospedaliero-Universitaria Pisana", Pisa, Italy
| | - Stefano Diciotti
- Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi", University of Bologna, Cesena, Italy
| | - Silvia De Santis
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
| | - Luca Passamonti
- Institute of Bioimaging and Molecular Physiology, National Research Council, Catanzaro, Italy; Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Carlo Tessa
- Division of Radiology, "Versilia" Hospital, AUSL 12 Viareggio, Lido di Camaiore, Italy
| | - Nicola Toschi
- Department of Biomedicine and Prevention, Medical Physics Section, University of Rome "Tor Vergata", Rome, Italy; Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Marco Giannelli
- Unit of Medical Physics, Pisa University Hospital "Azienda Ospedaliero-Universitaria Pisana", Pisa, Italy.
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Evaluating the accuracy of diffusion MRI models in white matter. PLoS One 2015; 10:e0123272. [PMID: 25879933 PMCID: PMC4400066 DOI: 10.1371/journal.pone.0123272] [Citation(s) in RCA: 54] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2014] [Accepted: 02/18/2015] [Indexed: 11/24/2022] Open
Abstract
Models of diffusion MRI within a voxel are useful for making inferences about the properties of the tissue and inferring fiber orientation distribution used by tractography algorithms. A useful model must fit the data accurately. However, evaluations of model-accuracy of commonly used models have not been published before. Here, we evaluate model-accuracy of the two main classes of diffusion MRI models. The diffusion tensor model (DTM) summarizes diffusion as a 3-dimensional Gaussian distribution. Sparse fascicle models (SFM) summarize the signal as a sum of signals originating from a collection of fascicles oriented in different directions. We use cross-validation to assess model-accuracy at different gradient amplitudes (b-values) throughout the white matter. Specifically, we fit each model to all the white matter voxels in one data set and then use the model to predict a second, independent data set. This is the first evaluation of model-accuracy of these models. In most of the white matter the DTM predicts the data more accurately than test-retest reliability; SFM model-accuracy is higher than test-retest reliability and also higher than the DTM model-accuracy, particularly for measurements with (a) a b-value above 1000 in locations containing fiber crossings, and (b) in the regions of the brain surrounding the optic radiations. The SFM also has better parameter-validity: it more accurately estimates the fiber orientation distribution function (fODF) in each voxel, which is useful for fiber tracking.
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46
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Affected connectivity organization of the reward system structure in obesity. Neuroimage 2015; 111:100-6. [PMID: 25687594 DOI: 10.1016/j.neuroimage.2015.02.012] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2014] [Revised: 02/04/2015] [Accepted: 02/08/2015] [Indexed: 12/13/2022] Open
Abstract
With the prevalence of obesity rapidly increasing worldwide, understanding the processes leading to excessive eating behavior becomes increasingly important. Considering the widely recognized crucial role of reward processes in food intake, we examined the white matter wiring and integrity of the anatomical reward network in obesity. Anatomical wiring of the reward network was reconstructed derived from diffusion weighted imaging in 31 obese participants and 32 normal-weight participants. Network wiring was compared in terms of the white matter volume as well as in terms of white matter microstructure, revealing lower number of streamlines and lower fiber integrity within the reward network in obese subjects. Specifically, the orbitofrontal cortex and striatum nuclei including accumbens, caudate and putamen showed lower strength and network clustering in the obesity group as compared to healthy controls. Our results provide evidence for obesity-related disruptions of global and local anatomical connectivity of the reward circuitry in regions that are key in the reinforcing mechanisms of eating-behavior processes.
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Motor skill learning is associated with diffusion characteristics of white matter in individuals with chronic stroke. J Neurol Phys Ther 2015; 38:151-60. [PMID: 23934017 DOI: 10.1097/npt.0b013e3182a3d353] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
BACKGROUND AND PURPOSE Imaging advances allow investigation of white matter after stroke; a growing body of literature has shown links between diffusion-based measures of white matter microstructure and motor function. However, the relationship between these measures and motor skill learning has not been considered in individuals with stroke. The aim of this study was to investigate the relationships between posttraining white matter microstructural status, as indexed by diffusion tensor imaging within the ipsilesional posterior limb of the internal capsule (PLIC), and learning of a novel motor task in individuals with chronic stroke. METHODS A total of 13 participants with chronic stroke and 9 healthy controls practiced a visuomotor pursuit task across 5 sessions. Change in motor behavior associated with learning was indexed by comparing baseline performance with a delayed retention test. Fractional anisotropy (FA) indexed at the retention test was the primary diffusion tensor imaging-derived outcome measure. RESULTS In individuals with chronic stroke, we discovered an association between posttraining ipsilesional PLIC FA and the magnitude of change associated with motor learning; hierarchical multiple linear regression analyses revealed that the combination of age, time poststroke, and ipsilesional PLIC FA posttraining was associated with motor learning-related change (R = 0.649; P = 0.02). Baseline motor performance was not related to posttraining ipsilesional PLIC FA. DISCUSSION AND CONCLUSIONS Diffusion characteristics of posttraining ipsilesional PLIC were linked to the magnitude of change in skilled motor behavior. These results imply that the microstructural properties of regional white matter indexed by diffusion behavior may be an important factor to consider when determining potential response to rehabilitation in persons with stroke. VIDEO ABSTRACT AVAILABLE (see Video, Supplemental Digital Content 1, http://links.lww.com/JNPT/A59) for more insights from the authors.
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Viehweger A, Riffert T, Dhital B, Knösche TR, Anwander A, Stepan H, Sorge I, Hirsch W. The Gini coefficient: a methodological pilot study to assess fetal brain development employing postmortem diffusion MRI. Pediatr Radiol 2014; 44:1290-301. [PMID: 24816372 DOI: 10.1007/s00247-014-3002-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/27/2013] [Revised: 02/20/2014] [Accepted: 04/09/2014] [Indexed: 12/19/2022]
Abstract
BACKGROUND Diffusion-weighted imaging (DWI) is important in the assessment of fetal brain development. However, it is clinically challenging and time-consuming to prepare neuromorphological examinations to assess real brain age and to detect abnormalities. OBJECTIVE To demonstrate that the Gini coefficient can be a simple, intuitive parameter for modelling fetal brain development. MATERIALS AND METHODS Postmortem fetal specimens(n = 28) were evaluated by diffusion-weighted imaging (DWI) on a 3-T MRI scanner using 60 directions, 0.7-mm isotropic voxels and b-values of 0, 150, 1,600 s/mm(2). Constrained spherical deconvolution (CSD) was used as the local diffusion model. Fractional anisotropy (FA), apparent diffusion coefficient (ADC) and complexity (CX) maps were generated. CX was defined as a novel diffusion metric. On the basis of those three parameters, the Gini coefficient was calculated. RESULTS Study of fetal brain development in postmortem specimens was feasible using DWI. The Gini coefficient could be calculated for the combination of the three diffusion parameters. This multidimensional Gini coefficient correlated well with age (Adjusted R(2) = 0.59) between the ages of 17 and 26 gestational weeks. CONCLUSIONS We propose a new method that uses an economics concept, the Gini coefficient, to describe the whole brain with one simple and intuitive measure, which can be used to assess the brain's developmental state.
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Affiliation(s)
- Adrian Viehweger
- Department of Pediatric Radiology, University Hospital Leipzig, Liebigstraße 20a, 04103, Leipzig, Germany,
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Diffusion tensor MRI of chemotherapy-induced cognitive impairment in non-CNS cancer patients: a review. Brain Imaging Behav 2014; 7:409-35. [PMID: 23329357 DOI: 10.1007/s11682-012-9220-1] [Citation(s) in RCA: 72] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Patients with non-central nervous system cancers often experience subtle cognitive deficits after treatment with cytotoxic agents. Therapy-induced structural changes to the brain could be one of the possible causes underlying these reported cognitive deficits. In this review, we evaluate the use of diffusion tensor imaging (DTI) for assessing possible therapy-induced changes in the microstructure of the cerebral white matter (WM) and provide a critical overview of the published DTI research on therapy-induced cognitive impairment. Both cross-sectional and longitudinal DTI studies have demonstrated abnormal microstructural properties in WM regions involved in cognition. These findings correlated with cognitive performance, suggesting that there is a link between reduced "WM integrity" and chemotherapy-induced impaired cognition. In this paper, we will also introduce the basics of diffusion tensor imaging and how it can be applied to evaluate effects of therapy on structural changes in cerebral WM. The review concludes with considerations and discussion regarding DTI data interpretation and possible future directions for investigating therapy-induced WM changes in cancer patients. This review article is part of a Special Issue entitled: Neuroimaging Studies of Cancer and Cancer Treatment.
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50
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The control of global brain dynamics: opposing actions of frontoparietal control and default mode networks on attention. J Neurosci 2014; 34:451-61. [PMID: 24403145 DOI: 10.1523/jneurosci.1853-13.2014] [Citation(s) in RCA: 140] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Understanding how dynamic changes in brain activity control behavior is a major challenge of cognitive neuroscience. Here, we consider the brain as a complex dynamic system and define two measures of brain dynamics: the synchrony of brain activity, measured by the spatial coherence of the BOLD signal across regions of the brain; and metastability, which we define as the extent to which synchrony varies over time. We investigate the relationship among brain network activity, metastability, and cognitive state in humans, testing the hypothesis that global metastability is "tuned" by network interactions. We study the following two conditions: (1) an attentionally demanding choice reaction time task (CRT); and (2) an unconstrained "rest" state. Functional MRI demonstrated increased synchrony, and decreased metastability was associated with increased activity within the frontoparietal control/dorsal attention network (FPCN/DAN) activity and decreased default mode network (DMN) activity during the CRT compared with rest. Using a computational model of neural dynamics that is constrained by white matter structure to test whether simulated changes in FPCN/DAN and DMN activity produce similar effects, we demonstate that activation of the FPCN/DAN increases global synchrony and decreases metastability. DMN activation had the opposite effects. These results suggest that the balance of activity in the FPCN/DAN and DMN might control global metastability, providing a mechanistic explanation of how attentional state is shifted between an unfocused/exploratory mode characterized by high metastability, and a focused/constrained mode characterized by low metastability.
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