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MacIver CL, Jones D, Green K, Szewczyk-Krolikowski K, Doring A, Tax CMW, Peall KJ. White Matter Microstructural Changes Using Ultra-Strong Diffusion Gradient MRI in Adult-Onset Idiopathic Focal Cervical Dystonia. Neurology 2024; 103:e209695. [PMID: 39110927 PMCID: PMC11319067 DOI: 10.1212/wnl.0000000000209695] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Accepted: 05/28/2024] [Indexed: 08/15/2024] Open
Abstract
BACKGROUND AND OBJECTIVES Adult-onset idiopathic focal cervical dystonia (AOIFCD) involves abnormal posturing of the cervical musculature and, in some individuals, an associated head tremor. Existing neuroimaging studies have implicated key motor networks. However, measures used to date lack specificity toward underlying pathophysiologic differences. We aim to assess white matter motor pathways for localized, microstructural differences, which may aid in understanding underlying mechanisms. METHODS Individuals diagnosed with AOIFCD and an age- and sex-matched control group were prospectively recruited through the Welsh Movement Disorders Research Network. All participants underwent in-depth clinical phenotyping and MRI (structural and diffusion sequences) using ultra-strong diffusion gradients. Tractography (whole-tract median values) and tractometry (along tract profiling) were performed for key white matter motor pathways assessing diffusion kurtosis imaging (DKI), neurite orientation dispersion and density imaging (NODDI), and standard model parameters. Groups were compared using linear model analysis with Bonferroni multiple comparison correction. RESULTS Fifty participants with AOIFCD and 30 healthy control participants were recruited, with 46 with AOIFCD and 30 healthy controls included for analysis (33 without head tremor, 13 with head tremor). Significant differences were observed in the anterior thalamic radiations (lower mid-tract fractional anisotropy [estimate = -0.046, p = 3.07 × 10-3], radial kurtosis [estimate = -0.165, p = 1.42 × 10-4], f-intra-axonal signal fraction [estimate = -0.044, p = 2.78 × 10-3], p2 orientation coherence [estimate = -0.043, p = 1.64 × 10-3], higher Orientation Dispersion Index [ODI, estimate = 0.023, p = 2.22 × 10-3]) and thalamopremotor tracts (higher mid-tract mean kurtosis [estimate = 0.064, p = 7.56 × 10-4], lower Neurite Density Index [estimate = 0.062, p = 2.1 × 10-3], higher distal tract ODI [estimate = 0.062, p = 3.1 × 10-3], lower f [estimate = -0.1, p = 2.3 × 10-3], and striatopremotor tracts [proximal lower f: estimate = -0.075, p = 1.06 × 10-3]). These measures correlated with clinical measures: dystonia duration (right thalamopremotor distal ODI: r = -0.9, p = 1.29 × 10-14), psychiatric symptoms (obsessive compulsive symptoms: left anterior thalamic radiation p2 r = 0.92, p = 2.797 × 10-11), sleep quality (Sleep Disorders Questionnaire Score: left anterior thalamic radiation ODI: r = -0.84, p = 4.84 × 10-11), pain (left anterior thalamic radiation ODI: r = -0.89, p = 1.4 × 10-13), and cognitive functioning (paired associated learning task p2, r = 0.94, p = 6.68 × 10-20). DISCUSSION Overall, localized microstructural differences were identified within tracts linking the prefrontal and premotor cortices with thalamic and basal ganglia regions, suggesting pathophysiologic processes involve microstructural aberrances of motor system modulatory pathways, particularly involving intra-axonal and fiber orientation dispersion measures.
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Affiliation(s)
- Claire L MacIver
- From the Cardiff University Brain Research Imaging Centre (C.L.M., D.J., K.G., A.D., C.M.W.T.), Cardiff University; Neuroscience and Mental Health Research Institute (C.L.M., K.J.P.), Division of Psychological Medicine and Clinical Neurosciences, Cardiff University School of Medicine; North Bristol NHS Trust (K.S.-K.), United Kingdom; and Image Sciences Institute (C.M.W.T.), University Medical Center Utrecht, the Netherlands
| | - Derek Jones
- From the Cardiff University Brain Research Imaging Centre (C.L.M., D.J., K.G., A.D., C.M.W.T.), Cardiff University; Neuroscience and Mental Health Research Institute (C.L.M., K.J.P.), Division of Psychological Medicine and Clinical Neurosciences, Cardiff University School of Medicine; North Bristol NHS Trust (K.S.-K.), United Kingdom; and Image Sciences Institute (C.M.W.T.), University Medical Center Utrecht, the Netherlands
| | - Katy Green
- From the Cardiff University Brain Research Imaging Centre (C.L.M., D.J., K.G., A.D., C.M.W.T.), Cardiff University; Neuroscience and Mental Health Research Institute (C.L.M., K.J.P.), Division of Psychological Medicine and Clinical Neurosciences, Cardiff University School of Medicine; North Bristol NHS Trust (K.S.-K.), United Kingdom; and Image Sciences Institute (C.M.W.T.), University Medical Center Utrecht, the Netherlands
| | - Konrad Szewczyk-Krolikowski
- From the Cardiff University Brain Research Imaging Centre (C.L.M., D.J., K.G., A.D., C.M.W.T.), Cardiff University; Neuroscience and Mental Health Research Institute (C.L.M., K.J.P.), Division of Psychological Medicine and Clinical Neurosciences, Cardiff University School of Medicine; North Bristol NHS Trust (K.S.-K.), United Kingdom; and Image Sciences Institute (C.M.W.T.), University Medical Center Utrecht, the Netherlands
| | - Andre Doring
- From the Cardiff University Brain Research Imaging Centre (C.L.M., D.J., K.G., A.D., C.M.W.T.), Cardiff University; Neuroscience and Mental Health Research Institute (C.L.M., K.J.P.), Division of Psychological Medicine and Clinical Neurosciences, Cardiff University School of Medicine; North Bristol NHS Trust (K.S.-K.), United Kingdom; and Image Sciences Institute (C.M.W.T.), University Medical Center Utrecht, the Netherlands
| | - Chantal M W Tax
- From the Cardiff University Brain Research Imaging Centre (C.L.M., D.J., K.G., A.D., C.M.W.T.), Cardiff University; Neuroscience and Mental Health Research Institute (C.L.M., K.J.P.), Division of Psychological Medicine and Clinical Neurosciences, Cardiff University School of Medicine; North Bristol NHS Trust (K.S.-K.), United Kingdom; and Image Sciences Institute (C.M.W.T.), University Medical Center Utrecht, the Netherlands
| | - Kathryn J Peall
- From the Cardiff University Brain Research Imaging Centre (C.L.M., D.J., K.G., A.D., C.M.W.T.), Cardiff University; Neuroscience and Mental Health Research Institute (C.L.M., K.J.P.), Division of Psychological Medicine and Clinical Neurosciences, Cardiff University School of Medicine; North Bristol NHS Trust (K.S.-K.), United Kingdom; and Image Sciences Institute (C.M.W.T.), University Medical Center Utrecht, the Netherlands
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Molendowska M, Palombo M, Foley KG, Narahari K, Fasano F, Jones DK, Alexander DC, Panagiotaki E, Tax CMW. Diffusion MRI in prostate cancer with ultra-strong whole-body gradients. NMR IN BIOMEDICINE 2024:e5229. [PMID: 39191529 DOI: 10.1002/nbm.5229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 06/07/2024] [Accepted: 07/15/2024] [Indexed: 08/29/2024]
Abstract
Diffusion-weighted MRI (dMRI) is universally recommended for the detection and classification of prostate cancer (PCa), with PI-RADS recommendations to acquire b-values of ≥1.4 ms/μm2. However, clinical dMRI suffers from a low signal-to-noise ratio (SNR) as the consequence of prolonged echo times (TEs) attributable to the limited gradient power in the range of 40-80 mT/m. To overcome this, MRI systems with strong gradients have been designed but so far have mainly been applied in the brain. The aim of this work was to assess the feasibility, data quality, SNR and contrast-to-noise ratio (CNR) of measurements in PCa with a 300 mT/m whole-body system. A cohort of men without and with diagnosed PCa were imaged on a research-only 3T Connectom Siemens MRI system equipped with a gradient amplitude of 300 mT/m. dMRI at high b-values were acquired using high gradient amplitudes and compared with gradient capabilities mimicking clinical systems. Data artefacts typically amplified with stronger gradients were assessed and their correction evaluated. The SNR gains and lesion-to-healthy tissue CNR were statistically tested investigating the effect of protocol and b-value. The diagnostic quality of the images for different dMRI protocols was assessed by an experienced radiologist using a 5-point Likert scale and an adapted PI-QUAL scoring system. The strong gradients for prostate dMRI allowed a significant gain in SNR per unit time compared with clinical gradients. Furthermore, a 1.6-2.1-fold increase in CNR was observed. Despite the more pronounced artefacts typically associated with strong gradients, a satisfactory correction could be achieved. Smoother and less biased parameter maps were obtained with protocols at shorter TEs. The results of this study show that dMRI in PCa with a whole-body 300-mT/m scanner is feasible without a report of physiological effects, SNR and CNR can be improved compared with lower gradient strengths, and artefacts do not negate the benefits of strong gradients and can be ameliorated. This assessment provides the first essential step towards unveiling the full potential of cutting-edge scanners, now increasingly becoming available, to advance early detection and diagnostic precision.
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Affiliation(s)
- Malwina Molendowska
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, UK
| | - Marco Palombo
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, UK
- School of Computer Science and Informatics, Cardiff University, Cardiff, UK
| | - Kieran G Foley
- Division of Cancer and Genetics, School of Medicine, Cardiff University, Cardiff, UK
| | - Krishna Narahari
- Cardiff and Vale University Health Board, Heath Park Campus, Cardiff, UK
| | - Fabrizio Fasano
- Siemens Healthcare Ltd, Camberley, UK
- Siemens Healthcare GmbH, Erlangen, Germany
| | - Derek K Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, UK
| | - Daniel C Alexander
- Centre for Medical Image Computing, University College London, London, UK
| | | | - Chantal M W Tax
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, UK
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands
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Karat BG, Genc S, Raven EP, Palombo M, Khan AR, Jones DK. The developing hippocampus: Microstructural evolution through childhood and adolescence. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.19.608590. [PMID: 39229062 PMCID: PMC11370384 DOI: 10.1101/2024.08.19.608590] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2024]
Abstract
The hippocampus is a structure in the medial temporal lobe which serves multiple cognitive functions. While important, the development of the hippocampus in the formative period of childhood and adolescence has not been extensively investigated, with most contemporary research focusing on macrostructural measures of volume. Thus, there has been little research on the development of the micron-scale structures (i.e., microstructure) of the hippocampus, which engender its cognitive functions. The current study examined age-related changes of hippocampal microstructure using diffusion MRI data acquired with an ultra-strong gradient (300 mT/m) MRI scanner in a sample of children and adolescents (N=88; 8-19 years). Surface-based hippocampal modelling was combined with established microstructural approaches, such as Diffusion Tensor Imaging (DTI) and Neurite Orientation Dispersion Density Imaging (NODDI), and a more advanced gray matter diffusion model Soma And Neurite Density Imaging (SANDI). No significant changes in macrostructural measures (volume, gyrification, and thickness) were found between 8-19 years, while significant changes in microstructure measures related to neurites (from NODDI and SANDI), soma (from SANDI), and mean diffusivity (from DTI) were found. In particular, there was a significant increase across age in neurite MR signal fraction and a significant decrease in extracellular MR signal fraction and mean diffusivity across the hippocampal subfields and long-axis. A significant negative correlation between age and MR apparent soma radius was found in the subiculum and CA1 throughout the anterior and body of the hippocampus. Further surface-based analyses uncovered variability in age-related microstructural changes between the subfields and long-axis, which may reflect ostensible developmental differences along these two axes. Finally, correlation of hippocampal surfaces representing age-related changes of microstructure with maps derived from histology allowed for postulation of the potential underlying microstructure that diffusion changes across age may be capturing. Overall, distinct neurite and soma developmental profiles in the human hippocampus during late childhood and adolescence are reported for the first time.
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Affiliation(s)
- Bradley G Karat
- Robarts Research Institute, Western University, London, ON, Canada
- Centre for Functional and Metabolic Mapping, Western University, London, ON, Canada
| | - Sila Genc
- Department of Neurosurgery, The Royal Children's Hospital, Melbourne, Australia
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, United Kingdom
| | - Erika P Raven
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, United Kingdom
- Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, United States
| | - Marco Palombo
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, United Kingdom
- School of Computer Science and Informatics, Cardiff University, Cardiff, United Kingdom
| | - Ali R Khan
- Robarts Research Institute, Western University, London, ON, Canada
- Centre for Functional and Metabolic Mapping, Western University, London, ON, Canada
- Department of Medical Biophysics, Western University, London, ON, Canada
| | - Derek K Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, United Kingdom
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MacIver CL, Bailey G, Laguna PL, Wadon ME, Schalkamp AK, Sandor C, Jones DK, Tax CMW, Peall KJ. Macro- and micro-structural insights into primary dystonia: a UK Biobank study. J Neurol 2024; 271:1416-1427. [PMID: 37995010 PMCID: PMC10896800 DOI: 10.1007/s00415-023-12086-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 10/25/2023] [Accepted: 10/27/2023] [Indexed: 11/24/2023]
Abstract
BACKGROUND Dystonia is a hyperkinetic movement disorder with key motor network dysfunction implicated in pathophysiology. The UK Biobank encompasses > 500,000 participants, of whom 42,565 underwent brain MRI scanning. This study applied an optimized pre-processing pipeline, aimed at better accounting for artifact and improving data reliability, to assess for grey and white matter structural MRI changes between individuals diagnosed with primary dystonia and an unaffected control cohort. METHODS Individuals with dystonia (n = 76) were identified from the UK Biobank using published algorithms, alongside an age- and sex-matched unaffected control cohort (n = 311). Grey matter morphometric and diffusion measures were assessed, together with white matter diffusion tensor and diffusion kurtosis metrics using tractography and tractometry. Post-hoc Neurite Orientation and Density Distribution Imaging (NODDI) was also undertaken for tracts in which significant differences were observed. RESULTS Grey matter tremor-specific striatal differences were observed, with higher radial kurtosis. Tractography identified no white matter differences, however segmental tractometry identified localised differences, particularly in the superior cerebellar peduncles and anterior thalamic radiations, including higher fractional anisotropy and lower orientation distribution index in dystonia, compared to controls. Additional tremor-specific changes included lower neurite density index in the anterior thalamic radiations. CONCLUSIONS Analysis of imaging data from one of the largest dystonia cohorts to date demonstrates microstructural differences in cerebellar and thalamic white matter connections, with architectural differences such as less orientation dispersion potentially being a component of the morphological structural changes implicated in dystonia. Distinct tremor-related imaging features are also implicated in both grey and white matter.
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Affiliation(s)
- Claire L MacIver
- Division of Psychological Medicine and Clinical Neurosciences, Neuroscience and Mental Health Research Institute, Cardiff University School of Medicine, Cardiff, UK.
- Cardiff University Brain Imaging Centre (CUBRIC), Cardiff University, Cardiff, UK.
| | - Grace Bailey
- Division of Psychological Medicine and Clinical Neurosciences, Neuroscience and Mental Health Research Institute, Cardiff University School of Medicine, Cardiff, UK
| | - Pedro Luque Laguna
- Cardiff University Brain Imaging Centre (CUBRIC), Cardiff University, Cardiff, UK
| | - Megan E Wadon
- Cardiff University Brain Imaging Centre (CUBRIC), Cardiff University, Cardiff, UK
| | - Ann-Kathrin Schalkamp
- Division of Psychological Medicine and Clinical Neurosciences, UK Dementia Research Institute, Cardiff University, Cardiff, UK
| | - Cynthia Sandor
- Division of Psychological Medicine and Clinical Neurosciences, UK Dementia Research Institute, Cardiff University, Cardiff, UK
| | - Derek K Jones
- Cardiff University Brain Imaging Centre (CUBRIC), Cardiff University, Cardiff, UK
| | - Chantal M W Tax
- Cardiff University Brain Imaging Centre (CUBRIC), Cardiff University, Cardiff, UK
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Kathryn J Peall
- Division of Psychological Medicine and Clinical Neurosciences, Neuroscience and Mental Health Research Institute, Cardiff University School of Medicine, Cardiff, UK
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Sairanen V, Andersson J. Outliers in diffusion-weighted MRI: Exploring detection models and mitigation strategies. Neuroimage 2023; 283:120397. [PMID: 37820862 DOI: 10.1016/j.neuroimage.2023.120397] [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: 03/10/2023] [Revised: 09/17/2023] [Accepted: 09/27/2023] [Indexed: 10/13/2023] Open
Abstract
Diffusion-weighted MRI (dMRI) is a medical imaging method that can be used to investigate the brain microstructure and structural connections between different brain regions. The method, however, requires relatively complex data processing frameworks and analysis pipelines. Many of these approaches are vulnerable to signal dropout artefacts that can originate from subjects moving their head during the scan. To combat these artefacts and eliminate such outliers, researchers have proposed two approaches: to replace outliers or to downweight outliers during modelling and analysis. With the rising interest in dMRI for clinical research, these types of corrections are increasingly important. Therefore, we set out to investigate the differences between outlier replacement and weighting approaches to help the dMRI community to select the best tool for their data processing pipelines. We evaluated dMRI motion correction registration and single tensor model fit pipelines using Gaussian Process and Spherical Harmonic based replacement approaches and outlier downweighting using highly realistic whole-brain simulations. As a proof of concept, we applied these approaches to dMRI infant data sets that contained varying numbers of dropout artefacts. Based on our results, we concluded that the Gaussian Process based outlier replacement provided similar tensor fit results to Gaussian Process based outlier detection and downweighting. Therefore, if only the least-squares estimate of the single tensor model is of interest, our recommendation is to use outlier replacement. However, outlier downweighting can potentially provide a more accurate estimate of the model precision which could be relevant for applications such as probabilistic tractoraphy.
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Affiliation(s)
- Viljami Sairanen
- Baby Brain Activity Center, Children's Hospital, Helsinki University Hospital and University of Helsinki, Helsinki, Finland; Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, United Kingdom; Department of Radiology, Kanta-Häme Central Hospital, Hämeenlinna, Finland.
| | - Jesper Andersson
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, United Kingdom
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Raven EP, Veraart J, Kievit RA, Genc S, Ward IL, Hall J, Cunningham A, Doherty J, van den Bree MBM, Jones DK. In vivo evidence of microstructural hypo-connectivity of brain white matter in 22q11.2 deletion syndrome. Mol Psychiatry 2023; 28:4342-4352. [PMID: 37495890 PMCID: PMC7615578 DOI: 10.1038/s41380-023-02178-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Revised: 06/26/2023] [Accepted: 07/03/2023] [Indexed: 07/28/2023]
Abstract
22q11.2 deletion syndrome, or 22q11.2DS, is a genetic syndrome associated with high rates of schizophrenia and autism spectrum disorders, in addition to widespread structural and functional abnormalities throughout the brain. Experimental animal models have identified neuronal connectivity deficits, e.g., decreased axonal length and complexity of axonal branching, as a primary mechanism underlying atypical brain development in 22q11.2DS. However, it is still unclear whether deficits in axonal morphology can also be observed in people with 22q11.2DS. Here, we provide an unparalleled in vivo characterization of white matter microstructure in participants with 22q11.2DS (12-15 years) and those undergoing typical development (8-18 years) using a customized magnetic resonance imaging scanner which is sensitive to axonal morphology. A rich array of diffusion MRI metrics are extracted to present microstructural profiles of typical and atypical white matter development, and provide new evidence of connectivity differences in individuals with 22q11.2DS. A recent, large-scale consortium study of 22q11.2DS identified higher diffusion anisotropy and reduced overall diffusion mobility of water as hallmark microstructural alterations of white matter in individuals across a wide age range (6-52 years). We observed similar findings across the white matter tracts included in this study, in addition to identifying deficits in axonal morphology. This, in combination with reduced tract volume measurements, supports the hypothesis that abnormal microstructural connectivity in 22q11.2DS may be mediated by densely packed axons with disproportionately small diameters. Our findings provide insight into the in vivo white matter phenotype of 22q11.2DS, and promote the continued investigation of shared features in neurodevelopmental and psychiatric disorders.
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Affiliation(s)
- Erika P Raven
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK.
- Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA.
| | - Jelle Veraart
- Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
| | - Rogier A Kievit
- Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
- Cognitive Neuroscience Department, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Sila Genc
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
- Neuroscience Advanced Clinical Imaging Service (NACIS), Department of Neurosurgery, The Royal Children's Hospital, Parkville, VIC, Australia
| | - Isobel L Ward
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
| | - Jessica Hall
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
| | - Adam Cunningham
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
| | - Joanne Doherty
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
| | - Marianne B M van den Bree
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
- Neuroscience and Mental Health Innovation Institute, Cardiff University, Cardiff, UK
| | - Derek K Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
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Kleban E, Jones DK, Tax CM. The impact of head orientation with respect to B 0 on diffusion tensor MRI measures. IMAGING NEUROSCIENCE (CAMBRIDGE, MASS.) 2023; 1:1-17. [PMID: 38405373 PMCID: PMC10884544 DOI: 10.1162/imag_a_00012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Accepted: 07/27/2023] [Indexed: 02/27/2024]
Abstract
Diffusion tensor MRI (DT-MRI) remains the most commonly used approach to characterise white matter (WM) anisotropy. However, DT estimates may be affected by tissue orientation w.r.t. B → 0 due to local gradients and intrinsic T 2 orientation dependence induced by the microstructure. This work aimed to investigate whether and how diffusion tensor MRI-derived measures depend on the orientation of the head with respect to the static magnetic field, B → 0 . By simulating WM as two compartments, we demonstrated that compartmental T 2 anisotropy can induce the dependence of diffusion tensor measures on the angle between WM fibres and the magnetic field. In in vivo experiments, reduced radial diffusivity and increased axial diffusivity were observed in white matter fibres perpendicular to B → 0 compared to those parallel to B → 0 . Fractional anisotropy varied by up to 20 % as a function of the angle between WM fibres and the orientation of the main magnetic field. To conclude, fibre orientation w.r.t. B → 0 is responsible for up to 7 % variance in diffusion tensor measures across the whole brain white matter from all subjects and head orientations. Fibre orientation w.r.t. B → 0 may introduce additional variance in clinical research studies using diffusion tensor imaging, particularly when it is difficult to control for (e.g., fetal or neonatal imaging, or when the trajectories of fibres change due to, e.g., space occupying lesions).
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Affiliation(s)
- Elena Kleban
- CUBRIC, School of Psychology, Cardiff University, Cardiff, United Kingdom
- Inselspital, University of Bern, Bern, Switzerland
| | - Derek K. Jones
- CUBRIC, School of Psychology, Cardiff University, Cardiff, United Kingdom
- MMIHR, Faculty of Health Sciences, Australian Catholic University, Melbourne, Australia
| | - Chantal M.W. Tax
- CUBRIC, School of Physics and Astronomy, Cardiff University, Cardiff, United Kingdom
- UMC Utrecht, Utrecht University, Utrecht, The Netherlands
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Aho L, Sairanen V, Lönnberg P, Wolford E, Lano A, Metsäranta M. Visual alertness and brain diffusion tensor imaging at term age predict neurocognitive development at preschool age in extremely preterm-born children. Brain Behav 2023; 13:e3048. [PMID: 37165734 PMCID: PMC10338808 DOI: 10.1002/brb3.3048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 04/12/2023] [Accepted: 04/19/2023] [Indexed: 05/12/2023] Open
Abstract
INTRODUCTION Cognitive development is characterized by the structural and functional maturation of the brain. Diffusion-weighted magnetic resonance imaging (dMRI) provides methods of investigating the brain structure and connectivity and their correlations with the neurocognitive outcome. Our aim was to examine the relationship between early visual abilities, brain white matter structures, and the later neurocognitive outcome. METHODS This study included 20 infants who were born before 28 gestational weeks and followed until the age of 6.5 years. At term age, visual alertness was evaluated and dMRI was used to investigate the brain white matter structure using fractional anisotropy (FA) in tract-based spatial statistics analysis. The JHU DTI white matter atlas was used to locate the findings. The neuropsychological assessment was used to assess neurocognitive performance at 6.5 years. RESULTS Optimal visual alertness at term age was significantly associated with better visuospatial processing (p < .05), sensorimotor functioning (p < .05), and social perception (p < .05) at 6.5 years of age. Optimal visual alertness related to higher FA values, and further, the FA values positively correlated with the neurocognitive outcome. The tract-based spatial differences in FA values were detected between children with optimal and nonoptimal visual alertness according to performance at 6.5 years. CONCLUSION We provide neurobiological evidence for the global and tract-based spatial differences in the white matter maturation between extremely preterm children with optimal and nonoptimal visual alertness at term age and a link between white matter maturation, visual alertness and the neurocognitive outcome at 6.5 years proposing that early visual function is a building block for the later neurocognitive development.
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Affiliation(s)
- Leena Aho
- New Children's Hospital, Pediatric Research CenterUniversity of Helsinki and Helsinki University HospitalHelsinkiFinland
| | - Viljami Sairanen
- BABA Center, Pediatric Research Center, Department of Clinical NeurophysiologyChildren's HospitalHelsinki University Hospital and University of HelsinkiHelsinkiFinland
| | - Piia Lönnberg
- New Children's Hospital, Pediatric Research CenterUniversity of Helsinki and Helsinki University HospitalHelsinkiFinland
| | - Elina Wolford
- Department of Psychology and LogopedicsUniversity of HelsinkiHelsinkiFinland
| | - Aulikki Lano
- New Children's Hospital, Pediatric Research CenterUniversity of Helsinki and Helsinki University HospitalHelsinkiFinland
| | - Marjo Metsäranta
- New Children's Hospital, Pediatric Research CenterUniversity of Helsinki and Helsinki University HospitalHelsinkiFinland
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Tax CM, Genc S, MacIver CL, Nilsson M, Wardle M, Szczepankiewicz F, Jones DK, Peall KJ. Ultra-strong diffusion-weighted MRI reveals cerebellar grey matter abnormalities in movement disorders. Neuroimage Clin 2023; 38:103419. [PMID: 37192563 PMCID: PMC10199248 DOI: 10.1016/j.nicl.2023.103419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Revised: 02/28/2023] [Accepted: 04/23/2023] [Indexed: 05/18/2023]
Abstract
Structural brain MRI has proven invaluable in understanding movement disorder pathophysiology. However, most work has focused on grey/white matter volumetric (macrostructural) and white matter microstructural effects, limiting understanding of frequently implicated grey matter microstructural differences. Using ultra-strong spherical tensor encoding diffusion-weighted MRI, a persistent MRI signal was seen in healthy cerebellar grey matter even at high diffusion-weightings (b ≥ 10,000 s/mm2). Quantifying the proportion of this signal (denoted fs), previously ascertained to originate from inside small spherical spaces, provides a potential proxy for cell body density. In this work, this approach was applied for the first time to a clinical cohort, including patients with diagnosed movement disorders in which the cerebellum has been implicated in symptom pathophysiology. Five control participants (control group 1, median age 24.5 years (20-39 years), imaged at two timepoints, demonstrated consistency in measurement of all three measures - MD (Mean Diffusivity) fs, and Ds (dot diffusivity)- with intraclass correlation coefficients (ICC) of 0.98, 0.86 and 0.76, respectively. Comparison with an older control group (control group 2 (n = 5), median age 51 years (43-58 years)) found no significant differences, neither with morphometric nor microstructural (MD (p = 0.36), fs (p = 0.17) and Ds (p = 0.22)) measures. The movement disorder cohort (Parkinson's Disease, n = 5, dystonia, n = 5. Spinocerebellar Ataxia 6, n = 5) when compared to the age-matched control cohort (Control Group 2) identified significantly lower MD (p < 0.0001 and p < 0.0001) and higher fs values (p < 0.0001 and p < 0.0001) in SCA6 and dystonia cohorts respectively. Lobar division of the cerebellum found these same differences in the superior and inferior posterior lobes, while no differences were seen in either the anterior lobes or with Ds measurements. In contrast to more conventional measures from diffusion tensor imaging, this framework provides enhanced specificity to differences in restricted spherical spaces in grey matter (including small cells) by eliminating signals from cerebrospinal fluid and axons. In the context of human and animal histopathology studies, these findings potentially implicate the cerebellar Purkinje and granule cells as contributors to the observed signal differences, with both cell types having been implicated in several neurological disorders through both postmortem and animal model studies. This novel microstructural imaging approach shows promise for improving movement disorder diagnosis, prognosis, and treatment.
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Affiliation(s)
- Chantal M.W. Tax
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Physics and Astronomy, Cardiff University, Cardiff, UK
- University Medical Center Utrecht, Utrecht, The Netherlands
| | - Sila Genc
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
- Neuroscience Advanced Clinical Imaging Service (NACIS), Department of Neurosurgery, The Royal Children's Hospital, Parkville, Victoria, Australia
| | - Claire L MacIver
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
- Neuroscience and Mental Health Research Institute, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
| | - Markus Nilsson
- Diagnostic Radiology, Clinical Sciences Lund, Lund University, Lund, Sweden
| | - Mark Wardle
- Cardiff and Vale University Health Board, University Hospital of Wales Cardiff, Heath Park, Cardiff, UK
| | - Filip Szczepankiewicz
- Diagnostic Radiology, Clinical Sciences Lund, Lund University, Lund, Sweden
- Medical Radiation Physics, Clinical Sciences Lund, Lund University, Lund, Sweden
| | - Derek K. Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
| | - Kathryn J. Peall
- Neuroscience and Mental Health Research Institute, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
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10
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Ward IL, Raven EP, de la Rosa S, Jones DK, Teufel C, von dem Hagen E. White matter microstructure in face and body networks predicts facial expression and body posture perception across development. Hum Brain Mapp 2023; 44:2307-2322. [PMID: 36661194 PMCID: PMC10028674 DOI: 10.1002/hbm.26211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Revised: 12/05/2022] [Accepted: 01/07/2023] [Indexed: 01/21/2023] Open
Abstract
Facial expression and body posture recognition have protracted developmental trajectories. Interactions between face and body perception, such as the influence of body posture on facial expression perception, also change with development. While the brain regions underpinning face and body processing are well-defined, little is known about how white-matter tracts linking these regions relate to perceptual development. Here, we obtained complementary diffusion magnetic resonance imaging (MRI) measures (fractional anisotropy [FA], spherical mean Ṧμ ), and a quantitative MRI myelin-proxy measure (R1), within white-matter tracts of face- and body-selective networks in children and adolescents and related these to perceptual development. In tracts linking occipital and fusiform face areas, facial expression perception was predicted by age-related maturation, as measured by Ṧμ and R1, as well as age-independent individual differences in microstructure, captured by FA and R1. Tract microstructure measures linking posterior superior temporal sulcus body region with anterior temporal lobe (ATL) were related to the influence of body on facial expression perception, supporting ATL as a site of face and body network convergence. Overall, our results highlight age-dependent and age-independent constraints that white-matter microstructure poses on perceptual abilities during development and the importance of complementary microstructural measures in linking brain structure and behaviour.
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Affiliation(s)
- Isobel L. Ward
- Cardiff University Brain Research Imaging Centre, School of PsychologyCardiff UniversityCardiffUK
| | - Erika P. Raven
- Cardiff University Brain Research Imaging Centre, School of PsychologyCardiff UniversityCardiffUK
- Center for Biomedical Imaging, Department of RadiologyNew York University Grossman School of MedicineNew YorkNew YorkUSA
| | | | - Derek K. Jones
- Cardiff University Brain Research Imaging Centre, School of PsychologyCardiff UniversityCardiffUK
| | - Christoph Teufel
- Cardiff University Brain Research Imaging Centre, School of PsychologyCardiff UniversityCardiffUK
| | - Elisabeth von dem Hagen
- Cardiff University Brain Research Imaging Centre, School of PsychologyCardiff UniversityCardiffUK
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11
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White matter microstructure and receptive vocabulary in children with cerebral palsy: The role of interhemispheric connectivity. PLoS One 2023; 18:e0280055. [PMID: 36649231 PMCID: PMC9844879 DOI: 10.1371/journal.pone.0280055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 12/20/2022] [Indexed: 01/18/2023] Open
Abstract
BACKGROUND Communication and cognitive impairments are common impediments to participation and social functioning in children with cerebral palsy (CP). Bilateral language networks underlie the function of some high-level language-related cognitive functions. PURPOSE To explore the association between receptive vocabulary and white-matter microstructure in the temporal lobes and the central part of the temporo-temporal bundles in children with CP. MATERIALS AND METHODS 37 children with spastic motor type CP (mean age 9.6 years, 25 male) underwent a receptive vocabulary test (Peabody Picture Vocabulary Test, PPVT-IV) and 3T MRI. Mean fractional anisotropy (FA) and mean diffusivity (MD) were calculated for the temporal lobes and the interhemispheric bundles traversing the splenium of the corpus callosum and the anterior commissure. Associations between microstructure and receptive vocabulary function were explored using univariable linear regression. RESULTS PPVT-IV scores were significantly associated with mean white matter MD in the left temporal lobe, but not the right temporal lobe. There was no association between PPVT-IV and mean white matter FA in the temporal lobes. PPVT-IV scores were not significantly associated with the laterality of these diffusion tensor metrics. Within the corpus callosum, FA, but not MD of the temporo-temporal bundles was significantly associated with the PPVT-IV scores. Within the anterior commissure no equivalent relationship between diffusion metrics and PPVT-IV was found. CONCLUSION Our findings add further understanding to the pathophysiological basis underlying receptive vocabulary skills in children with CP that could extend to other patients with early brain damage. This study highlights the importance of interhemispheric connections for receptive vocabulary.
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12
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Shastin D, Genc S, Parker GD, Koller K, Tax CMW, Evans J, Hamandi K, Gray WP, Jones DK, Chamberland M. Surface-based tracking for short association fibre tractography. Neuroimage 2022; 260:119423. [PMID: 35809886 PMCID: PMC10009610 DOI: 10.1016/j.neuroimage.2022.119423] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 05/30/2022] [Accepted: 06/29/2022] [Indexed: 11/16/2022] Open
Abstract
It is estimated that in the human brain, short association fibres (SAF) represent more than half of the total white matter volume and their involvement has been implicated in a range of neurological and psychiatric conditions. This population of fibres, however, remains relatively understudied in the neuroimaging literature. Some of the challenges pertinent to the mapping of SAF include their variable anatomical course and proximity to the cortical mantle, leading to partial volume effects and potentially affecting streamline trajectory estimation. This work considers the impact of seeding and filtering strategies and choice of scanner, acquisition, data resampling to propose a whole-brain, surface-based short (≤30-40 mm) SAF tractography approach. The framework is shown to produce longer streamlines with a predilection for connecting gyri as well as high cortical coverage. We further demonstrate that certain areas of subcortical white matter become disproportionally underrepresented in diffusion-weighted MRI data with lower angular and spatial resolution and weaker diffusion weighting; however, collecting data with stronger gradients than are usually available clinically has minimal impact, making our framework translatable to data collected on commonly available hardware. Finally, the tractograms are examined using voxel- and surface-based measures of consistency, demonstrating moderate reliability, low repeatability and high between-subject variability, urging caution when streamline count-based analyses of SAF are performed.
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Affiliation(s)
- Dmitri Shastin
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Maindy Rd, Cardiff CF24 4HQ, United Kingdom; Department of Neurosurgery, University Hospital of Wales, Cardiff, United Kingdom; BRAIN Biomedical Research Unit, Health & Care Research Wales, Cardiff, United Kingdom.
| | - Sila Genc
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Maindy Rd, Cardiff CF24 4HQ, United Kingdom
| | - Greg D Parker
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Maindy Rd, Cardiff CF24 4HQ, United Kingdom
| | - Kristin Koller
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Maindy Rd, Cardiff CF24 4HQ, United Kingdom
| | - Chantal M W Tax
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Maindy Rd, Cardiff CF24 4HQ, United Kingdom; Image Sciences Institute, University Medical Center Utrecht, Utrecht, Netherlands
| | - John Evans
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Maindy Rd, Cardiff CF24 4HQ, United Kingdom
| | - Khalid Hamandi
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Maindy Rd, Cardiff CF24 4HQ, United Kingdom; BRAIN Biomedical Research Unit, Health & Care Research Wales, Cardiff, United Kingdom; Department of Neurology, University Hospital of Wales, Cardiff, United Kingdom
| | - William P Gray
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Maindy Rd, Cardiff CF24 4HQ, United Kingdom; Department of Neurosurgery, University Hospital of Wales, Cardiff, United Kingdom; BRAIN Biomedical Research Unit, Health & Care Research Wales, Cardiff, United Kingdom
| | - Derek K Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Maindy Rd, Cardiff CF24 4HQ, United Kingdom; BRAIN Biomedical Research Unit, Health & Care Research Wales, Cardiff, United Kingdom
| | - Maxime Chamberland
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Maindy Rd, Cardiff CF24 4HQ, United Kingdom; Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
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13
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Casella C, Chamberland M, Laguna PL, Parker GD, Rosser AE, Coulthard E, Rickards H, Berry SC, Jones DK, Metzler‐Baddeley C. Mutation-related magnetization-transfer, not axon density, drives white matter differences in premanifest Huntington disease: Evidence from in vivo ultra-strong gradient MRI. Hum Brain Mapp 2022; 43:3439-3460. [PMID: 35396899 PMCID: PMC9248323 DOI: 10.1002/hbm.25859] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Revised: 03/07/2022] [Accepted: 03/27/2022] [Indexed: 11/10/2022] Open
Abstract
White matter (WM) alterations have been observed in Huntington disease (HD) but their role in the disease-pathophysiology remains unknown. We assessed WM changes in premanifest HD by exploiting ultra-strong-gradient magnetic resonance imaging (MRI). This allowed to separately quantify magnetization transfer ratio (MTR) and hindered and restricted diffusion-weighted signal fractions, and assess how they drove WM microstructure differences between patients and controls. We used tractometry to investigate region-specific alterations across callosal segments with well-characterized early- and late-myelinating axon populations, while brain-wise differences were explored with tract-based cluster analysis (TBCA). Behavioral measures were included to explore disease-associated brain-function relationships. We detected lower MTR in patients' callosal rostrum (tractometry: p = .03; TBCA: p = .03), but higher MTR in their splenium (tractometry: p = .02). Importantly, patients' mutation-size and MTR were positively correlated (all p-values < .01), indicating that MTR alterations may directly result from the mutation. Further, MTR was higher in younger, but lower in older patients relative to controls (p = .003), suggesting that MTR increases are detrimental later in the disease. Finally, patients showed higher restricted diffusion signal fraction (FR) from the composite hindered and restricted model of diffusion (CHARMED) in the cortico-spinal tract (p = .03), which correlated positively with MTR in the posterior callosum (p = .033), potentially reflecting compensatory mechanisms. In summary, this first comprehensive, ultra-strong gradient MRI study in HD provides novel evidence of mutation-driven MTR alterations at the premanifest disease stage which may reflect neurodevelopmental changes in iron, myelin, or a combination of these.
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Affiliation(s)
- Chiara Casella
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of PsychologyCardiff UniversityCardiffUK
- Department of Perinatal Imaging and Health, School of Biomedical Engineering & Imaging SciencesKing's College London, St Thomas' HospitalLondonUK
| | - Maxime Chamberland
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of PsychologyCardiff UniversityCardiffUK
- Donders Institute for Brain, Cognition and BehaviorRadboud UniversityNijmegenThe Netherlands
| | - Pedro L. Laguna
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of PsychologyCardiff UniversityCardiffUK
| | - Greg D. Parker
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of PsychologyCardiff UniversityCardiffUK
| | - Anne E. Rosser
- Department of Neurology and Psychological MedicineHayden Ellis BuildingCardiffUK
- School of BiosciencesCardiff UniversityCardiffUK
| | | | - Hugh Rickards
- Birmingham and Solihull Mental Health NHS Foundation TrustBirminghamUK
- Institute of Clinical Sciences, College of Medical and Dental SciencesUniversity of BirminghamBirminghamUK
| | - Samuel C. Berry
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of PsychologyCardiff UniversityCardiffUK
| | - Derek K. Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of PsychologyCardiff UniversityCardiffUK
| | - Claudia Metzler‐Baddeley
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of PsychologyCardiff UniversityCardiffUK
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14
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Tax CMW, Bastiani M, Veraart J, Garyfallidis E, Okan Irfanoglu M. What's new and what's next in diffusion MRI preprocessing. Neuroimage 2022; 249:118830. [PMID: 34965454 PMCID: PMC9379864 DOI: 10.1016/j.neuroimage.2021.118830] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 10/26/2021] [Accepted: 12/15/2021] [Indexed: 02/07/2023] Open
Abstract
Diffusion MRI (dMRI) provides invaluable information for the study of tissue microstructure and brain connectivity, but suffers from a range of imaging artifacts that greatly challenge the analysis of results and their interpretability if not appropriately accounted for. This review will cover dMRI artifacts and preprocessing steps, some of which have not typically been considered in existing pipelines or reviews, or have only gained attention in recent years: brain/skull extraction, B-matrix incompatibilities w.r.t the imaging data, signal drift, Gibbs ringing, noise distribution bias, denoising, between- and within-volumes motion, eddy currents, outliers, susceptibility distortions, EPI Nyquist ghosts, gradient deviations, B1 bias fields, and spatial normalization. The focus will be on "what's new" since the notable advances prior to and brought by the Human Connectome Project (HCP), as presented in the predecessing issue on "Mapping the Connectome" in 2013. In addition to the development of novel strategies for dMRI preprocessing, exciting progress has been made in the availability of open source tools and reproducible pipelines, databases and simulation tools for the evaluation of preprocessing steps, and automated quality control frameworks, amongst others. Finally, this review will consider practical considerations and our view on "what's next" in dMRI preprocessing.
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Affiliation(s)
- Chantal M W Tax
- Image Sciences Institute, University Medical Center Utrecht, The Netherlands; Cardiff University Brain Research Imaging Centre, School of Physics and Astronomy, Cardiff University, UK.
| | - Matteo Bastiani
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, UK; Wellcome Centre for Integrative Neuroimaging (WIN), Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, UK
| | - Jelle Veraart
- Center for Biomedical Imaging, New York University Grossman School of Medicine, NY, USA
| | | | - M Okan Irfanoglu
- Quantitative Medical Imaging Section, National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, MD, USA
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15
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Sairanen V, Ocampo-Pineda M, Granziera C, Schiavi S, Daducci A. Incorporating outlier information into diffusion-weighted MRI modeling for robust microstructural imaging and structural brain connectivity analyses. Neuroimage 2021; 247:118802. [PMID: 34896584 DOI: 10.1016/j.neuroimage.2021.118802] [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: 06/10/2021] [Revised: 11/01/2021] [Accepted: 12/09/2021] [Indexed: 11/28/2022] Open
Abstract
The white matter structures of the human brain can be represented using diffusion-weighted MRI tractography. Unfortunately, tractography is prone to find false-positive streamlines causing a severe decline in its specificity and limiting its feasibility in accurate structural brain connectivity analyses. Filtering algorithms have been proposed to reduce the number of invalid streamlines but the currently available filtering algorithms are not suitable to process data that contains motion artefacts which are typical in clinical research. We augmented the Convex Optimization Modelling for Microstructure Informed Tractography (COMMIT) algorithm to adjust for these signals drop-out motion artefacts. We demonstrate with comprehensive Monte-Carlo whole brain simulations and in vivo infant data that our robust algorithm is capable of properly filtering tractography reconstructions despite these artefacts. We evaluated the results using parametric and non-parametric statistics and our results demonstrate that if not accounted for, motion artefacts can have severe adverse effects in human brain structural connectivity analyses as well as in microstructural property mappings. In conclusion, the usage of robust filtering methods to mitigate motion related errors in tractogram filtering is highly beneficial, especially in clinical studies with uncooperative patient groups such as infants. With our presented robust augmentation and open-source implementation, robust tractogram filtering is readily available.
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Affiliation(s)
- Viljami Sairanen
- Department of Computer Science, University of Verona, Verona, Italy; Translational Imaging in Neurology, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Neurologic Clinic and Policlinic, Basel, Switzerland; BABA Center, Pediatric Research Center, Department of Clinical Neurophysiology, Children's Hospital, Helsinki University Hospital and University of Helsinki, Helsinki, Finland.
| | | | - Cristina Granziera
- Translational Imaging in Neurology, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Neurologic Clinic and Policlinic, Basel, Switzerland
| | - Simona Schiavi
- Department of Computer Science, University of Verona, Verona, Italy
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16
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Xu SX, Deng WF, Qu YY, Lai WT, Huang TY, Rong H, Xie XH. The integrated understanding of structural and functional connectomes in depression: A multimodal meta-analysis of graph metrics. J Affect Disord 2021; 295:759-770. [PMID: 34517250 DOI: 10.1016/j.jad.2021.08.120] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Revised: 07/26/2021] [Accepted: 08/28/2021] [Indexed: 11/28/2022]
Abstract
BACKGROUND From the perspective of information processing, an integrated understanding of the structural and functional connectomes in depression patients is important, a multimodal meta-analysis is required to detect the robust alterations in graph metrics across studies. METHODS Following a systematic search, 952 depression patients and 1447 controls in nine diffusion magnetic resonance imaging (dMRI) and twelve rest state functional MRI (rs-fMRI) studies with high methodological quality met the inclusion criteria and were included in the meta-analysis. RESULTS Regarding the dMRI results, no significant differences of meta-analytic metrics were found; regarding the rs-fMRI results, the modularity and local efficiency were found to be significantly lower in the depression group than in the controls (Hedge's g = -0.330 and -0.349, respectively). CONCLUSION Our findings suggested a lower modularity and network efficiency in the rs-fMRI network in depression patients, indicating that the pathological imbalances in brain connectomes needs further exploration. LIMITATIONS Included number of trials was low and heterogeneity should be noted.
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Affiliation(s)
- Shu-Xian Xu
- Brain Function and Psychosomatic Medicine Institute, Second People's Hospital of Huizhou, Huizhou, Guangdong, China; Department of Psychiatry, Shenzhen Kangning Hospital and Shenzhen Mental Health Center, Shenzhen, Guangdong, China; Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, China
| | - Wen-Feng Deng
- Huizhou Center for Disease Control and Prevention, Huizhou, Guangdong, China
| | - Ying-Ying Qu
- Center of Acute Psychiatry Service, Second People's Hospital of Huizhou, Huizhou, Guangdong, China
| | - Wen-Tao Lai
- Department of Radiology, Shenzhen Kangning Hospital and Shenzhen Mental Health Center, Shenzhen, Guangdong, China
| | - Tan-Yu Huang
- Department of Radiology, Second People's Hospital of Huizhou, Huizhou, Guangdong, China
| | - Han Rong
- Department of Psychiatry, Shenzhen Kangning Hospital and Shenzhen Mental Health Center, Shenzhen, Guangdong, China; Affiliated Shenzhen Clinical College of Psychiatry, Jining Medical University, Jining, Shandong, China
| | - Xin-Hui Xie
- Brain Function and Psychosomatic Medicine Institute, Second People's Hospital of Huizhou, Huizhou, Guangdong, China; Department of Psychiatry, Shenzhen Kangning Hospital and Shenzhen Mental Health Center, Shenzhen, Guangdong, China; Center of Acute Psychiatry Service, Second People's Hospital of Huizhou, Huizhou, Guangdong, China.
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17
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Tax CMW, Kleban E, Chamberland M, Baraković M, Rudrapatna U, Jones DK. Measuring compartmental T 2-orientational dependence in human brain white matter using a tiltable RF coil and diffusion-T 2 correlation MRI. Neuroimage 2021; 236:117967. [PMID: 33845062 PMCID: PMC8270891 DOI: 10.1016/j.neuroimage.2021.117967] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 02/15/2021] [Accepted: 03/08/2021] [Indexed: 02/08/2023] Open
Abstract
The anisotropy of brain white matter microstructure manifests itself in orientational-dependence of various MRI contrasts, and can result in significant quantification biases if ignored. Understanding the origins of this orientation-dependence could enhance the interpretation of MRI signal changes in development, ageing and disease and ultimately improve clinical diagnosis. Using a novel experimental setup, this work studies the contributions of the intra- and extra-axonal water to the orientation-dependence of one of the most clinically-studied parameters, apparent transverse relaxation T2. Specifically, a tiltable receive coil is interfaced with an ultra-strong gradient MRI scanner to acquire multidimensional MRI data with an unprecedented range of acquisition parameters. Using this setup, compartmental T2 can be disentangled based on differences in diffusional-anisotropy, and its orientation-dependence further elucidated by re-orienting the head with respect to the main magnetic field B→0. A dependence of (compartmental) T2 on the fibre orientation w.r.t. B→0 was observed, and further quantified using characteristic representations for susceptibility- and magic angle effects. Across white matter, anisotropy effects were dominated by the extra-axonal water signal, while the intra-axonal water signal decay varied less with fibre-orientation. Moreover, the results suggest that the stronger extra-axonal T2 orientation-dependence is dominated by magnetic susceptibility effects (presumably from the myelin sheath) while the weaker intra-axonal T2 orientation-dependence may be driven by a combination of microstructural effects. Even though the current design of the tiltable coil only offers a modest range of angles, the results demonstrate an overall effect of tilt and serve as a proof-of-concept motivating further hardware development to facilitate experiments that explore orientational anisotropy. These observations have the potential to lead to white matter microstructural models with increased compartmental sensitivity to disease, and can have direct consequences for longitudinal and group-wise T2- and diffusion-MRI data analysis, where the effect of head-orientation in the scanner is commonly ignored.
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Affiliation(s)
- Chantal M W Tax
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Physics and Astronomy, Cardiff University, Cardiff, UK; University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands.
| | - Elena Kleban
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
| | - Maxime Chamberland
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
| | - Muhamed Baraković
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK; Signal Processing Laboratory 5, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland; Translational Imaging in Neurology Basel, Department of Biomedical Engineering, University Hospital Basel, Basel, Switzerland
| | - Umesh Rudrapatna
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
| | - Derek K Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK; Mary MacKillop Institute for Health Research, Faculty of Health Sciences, Australian Catholic University, Melbourne, Australia
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18
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Dennis EL, Caeyenberghs K, Asarnow RF, Babikian T, Bartnik-Olson B, Bigler ED, Figaji A, Giza CC, Goodrich-Hunsaker NJ, Hodges CB, Hoskinson KR, Königs M, Levin HS, Lindsey HM, Livny A, Max JE, Merkley TL, Newsome MR, Olsen A, Ryan NP, Spruiell MS, Suskauer SJ, Thomopoulos SI, Ware AL, Watson CG, Wheeler AL, Yeates KO, Zielinski BA, Thompson PM, Tate DF, Wilde EA. Challenges and opportunities for neuroimaging in young patients with traumatic brain injury: a coordinated effort towards advancing discovery from the ENIGMA pediatric moderate/severe TBI group. Brain Imaging Behav 2021; 15:555-575. [PMID: 32734437 PMCID: PMC7855317 DOI: 10.1007/s11682-020-00363-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Traumatic brain injury (TBI) is a major cause of death and disability in children in both developed and developing nations. Children and adolescents suffer from TBI at a higher rate than the general population, and specific developmental issues require a unique context since findings from adult research do not necessarily directly translate to children. Findings in pediatric cohorts tend to lag behind those in adult samples. This may be due, in part, both to the smaller number of investigators engaged in research with this population and may also be related to changes in safety laws and clinical practice that have altered length of hospital stays, treatment, and access to this population. The ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) Pediatric Moderate/Severe TBI (msTBI) group aims to advance research in this area through global collaborative meta-analysis of neuroimaging data. In this paper, we discuss important challenges in pediatric TBI research and opportunities that we believe the ENIGMA Pediatric msTBI group can provide to address them. With the paucity of research studies examining neuroimaging biomarkers in pediatric patients with TBI and the challenges of recruiting large numbers of participants, collaborating to improve statistical power and to address technical challenges like lesions will significantly advance the field. We conclude with recommendations for future research in this field of study.
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Affiliation(s)
- Emily L Dennis
- TBI and Concussion Center, Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, USA.
- Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of USC, Marina del Rey, Los Angeles, CA, USA.
- Psychiatry Neuroimaging Laboratory, Brigham & Women's Hospital, Boston, MA, USA.
| | - Karen Caeyenberghs
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, Australia
| | - Robert F Asarnow
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, UCLA, Los Angeles, CA, USA
- Brain Research Institute, UCLA, Los Angeles, CA, USA
- Department of Psychology, UCLA, Los Angeles, CA, USA
| | - Talin Babikian
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, UCLA, Los Angeles, CA, USA
- UCLA Steve Tisch BrainSPORT Program, Los Angeles, CA, USA
| | - Brenda Bartnik-Olson
- Department of Radiology, Loma Linda University Medical Center, Loma Linda, CA, USA
| | - Erin D Bigler
- TBI and Concussion Center, Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, USA
- Department of Psychology, Brigham Young University, Provo, UT, USA
- Neuroscience Center, Brigham Young University, Provo, UT, USA
| | - Anthony Figaji
- Division of Neurosurgery, University of Cape Town, Cape Town, South Africa
- Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - Christopher C Giza
- UCLA Steve Tisch BrainSPORT Program, Los Angeles, CA, USA
- Department of Neurosurgery, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Naomi J Goodrich-Hunsaker
- TBI and Concussion Center, Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, USA
- Department of Psychology, Brigham Young University, Provo, UT, USA
- George E. Wahlen Veterans Affairs Salt Lake City Healthcare System, Salt Lake City, UT, USA
| | - Cooper B Hodges
- TBI and Concussion Center, Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, USA
- Department of Psychology, Brigham Young University, Provo, UT, USA
- George E. Wahlen Veterans Affairs Salt Lake City Healthcare System, Salt Lake City, UT, USA
| | - Kristen R Hoskinson
- Center for Biobehavioral Health, The Abigail Wexner Research Institute at Nationwide Children's Hospital, Columbus, OH, USA
- Department of Pediatrics, The Ohio State University College of Medicine, Columbus, OH, USA
| | - Marsh Königs
- Emma Children's Hospital, Amsterdam UMC, University of Amsterdam, Emma Neuroscience Group, Amsterdam, The Netherlands
| | - Harvey S Levin
- H. Ben Taub Department of Physical Medicine and Rehabilitation, Baylor College of Medicine, Houston, TX, USA
- Michael E. DeBakey Veterans Affairs Medical Center, Houston, TX, USA
| | - Hannah M Lindsey
- TBI and Concussion Center, Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, USA
- Department of Psychology, Brigham Young University, Provo, UT, USA
- George E. Wahlen Veterans Affairs Salt Lake City Healthcare System, Salt Lake City, UT, USA
| | - Abigail Livny
- Department of Diagnostic Imaging, Sheba Medical Center, Ramat Gan, Tel-Hashomer, Israel
- Joseph Sagol Neuroscience Center, Sheba Medical Center, Ramat Gan, Tel-Hashomer, Israel
| | - Jeffrey E Max
- Department of Psychiatry, University of California, La Jolla, San Diego, CA, USA
- Department of Psychiatry, Rady Children's Hospital, San Diego, CA, USA
| | - Tricia L Merkley
- TBI and Concussion Center, Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, USA
- Department of Psychology, Brigham Young University, Provo, UT, USA
- Neuroscience Center, Brigham Young University, Provo, UT, USA
| | - Mary R Newsome
- H. Ben Taub Department of Physical Medicine and Rehabilitation, Baylor College of Medicine, Houston, TX, USA
- Michael E. DeBakey Veterans Affairs Medical Center, Houston, TX, USA
| | - Alexander Olsen
- Department of Psychology, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Physical Medicine and Rehabilitation, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Nicholas P Ryan
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, Australia
- Department of Paediatrics, The University of Melbourne, Melbourne, Australia
- Department of Clinical Sciences, Murdoch Children's Research Institute, Melbourne, Australia
| | - Matthew S Spruiell
- H. Ben Taub Department of Physical Medicine and Rehabilitation, Baylor College of Medicine, Houston, TX, USA
| | - Stacy J Suskauer
- Kennedy Krieger Institute, Baltimore, MD, USA
- Departments of Physical Medicine & Rehabilitation and Pediatrics, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Sophia I Thomopoulos
- Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of USC, Marina del Rey, Los Angeles, CA, USA
| | - Ashley L Ware
- Department of Psychology, University of Calgary, Calgary, Alberta, Canada
| | - Christopher G Watson
- Department of Pediatrics, Children's Learning Institute, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Anne L Wheeler
- Hospital for Sick Children, Neuroscience and Mental Health Program, Toronto, Canada
- Physiology Department, University of Toronto, Toronto, Canada
| | - Keith Owen Yeates
- Department of Psychology, University of Calgary, Calgary, Alberta, Canada
- Alberta Children's Hospital Research Institute and Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
- Departments of Pediatrics and Clinical Neurosciences, University of Calgary, Calgary, Alberta, Canada
| | - Brandon A Zielinski
- TBI and Concussion Center, Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, USA
- Department of Pediatrics, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Paul M Thompson
- Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of USC, Marina del Rey, Los Angeles, CA, USA
- Departments of Neurology, Pediatrics, Psychiatry, Radiology, Engineering, and Ophthalmology, USC, Los Angeles, CA, USA
| | - David F Tate
- TBI and Concussion Center, Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, USA
- Department of Psychology, Brigham Young University, Provo, UT, USA
- George E. Wahlen Veterans Affairs Salt Lake City Healthcare System, Salt Lake City, UT, USA
- Missouri Institute of Mental Health and University of Missouri, St Louis, MO, USA
| | - Elisabeth A Wilde
- TBI and Concussion Center, Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, USA
- George E. Wahlen Veterans Affairs Salt Lake City Healthcare System, Salt Lake City, UT, USA
- H. Ben Taub Department of Physical Medicine and Rehabilitation, Baylor College of Medicine, Houston, TX, USA
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19
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Aikio R, Laaksonen K, Sairanen V, Parkkonen E, Abou Elseoud A, Kujala J, Forss N. CMC is more than a measure of corticospinal tract integrity in acute stroke patients. NEUROIMAGE: CLINICAL 2021; 32:102818. [PMID: 34555801 PMCID: PMC8458977 DOI: 10.1016/j.nicl.2021.102818] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Revised: 06/06/2021] [Accepted: 08/30/2021] [Indexed: 11/17/2022] Open
Abstract
CMC is weaker and occurs at lower frequencies in acute stroke patients. Both afferent and efferent input signals contribute to CMC. CMC should not be used as a direct measure of corticospinal tract integrity.
In healthy subjects, motor cortex activity and electromyographic (EMG) signals from contracting contralateral muscle show coherence in the beta (15–30 Hz) range. Corticomuscular coherence (CMC) is considered a sign of functional coupling between muscle and brain. Based on prior studies, CMC is altered in stroke, but functional significance of this finding has remained unclear. Here, we examined CMC in acute stroke patients and correlated the results with clinical outcome measures and corticospinal tract (CST) integrity estimated with diffusion tensor imaging (DTI). During isometric contraction of the extensor carpi radialis muscle, EMG and magnetoencephalographic oscillatory signals were recorded from 29 patients with paresis of the upper extremity due to ischemic stroke and 22 control subjects. CMC amplitudes and peak frequencies at 13–30 Hz were compared between the two groups. In the patients, the peak frequency in both the affected and the unaffected hemisphere was significantly (p < 0.01) lower and the strength of CMC was significantly (p < 0.05) weaker in the affected hemisphere compared to the control subjects. The strength of CMC in the patients correlated with the level of tactile sensitivity and clinical test results of hand function. In contrast, no correlation between measures of CST integrity and CMC was found. The results confirm the earlier findings that CMC is altered in acute stroke and demonstrate that CMC is bidirectional and not solely a measure of integrity of the efferent corticospinal tract.
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20
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Pannek K, George JM, Boyd RN, Colditz PB, Rose SE, Fripp J. Brain microstructure and morphology of very preterm-born infants at term equivalent age: Associations with motor and cognitive outcomes at 1 and 2 years. Neuroimage 2020; 221:117163. [DOI: 10.1016/j.neuroimage.2020.117163] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2019] [Revised: 06/27/2020] [Accepted: 07/08/2020] [Indexed: 12/13/2022] Open
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21
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Gong T, Tong Q, Li Z, He H, Zhang H, Zhong J. Deep learning-based method for reducing residual motion effects in diffusion parameter estimation. Magn Reson Med 2020; 85:2278-2293. [PMID: 33058279 DOI: 10.1002/mrm.28544] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2020] [Revised: 09/14/2020] [Accepted: 09/15/2020] [Indexed: 11/08/2022]
Abstract
PURPOSE Conventional motion-correction techniques for diffusion MRI can introduce motion-level-dependent bias in derived metrics. To address this challenge, a deep learning-based technique was developed to minimize such residual motion effects. METHODS The data-rejection approach was adopted in which motion-corrupted data are discarded before model-fitting. A deep learning-based parameter estimation algorithm, using a hierarchical convolutional neural network (H-CNN), was combined with motion assessment and corrupted volume rejection. The method was designed to overcome the limitations of existing methods of this kind that produce parameter estimations whose quality depends strongly on a proportion of the data discarded. Evaluation experiments were conducted for the estimation of diffusion kurtosis and diffusion-tensor-derived measures at both the individual and group levels. The performance was compared with the robust approach of iteratively reweighted linear least squares (IRLLS) after motion correction with and without outlier replacement. RESULTS Compared with IRLLS, the H-CNN-based technique is minimally sensitive to motion effects. It was tested at severe motion levels when 70% to 90% of the data are rejected and when random motion is present. The technique had a stable performance independent of the numbers and schemes of data rejection. A further test on a data set from children with attention-deficit hyperactivity disorder shows the technique can potentially ameliorate spurious group-level difference caused by head motion. CONCLUSION This method shows great potential for reducing residual motion effects in motion-corrupted diffusion-weighted-imaging data, bringing benefits that include reduced bias in derived metrics in individual scans and reduced motion-level-dependent bias in population studies employing diffusion MRI.
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Affiliation(s)
- Ting Gong
- Center for Brain Imaging Science and Technology, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, China.,Department of Computer Science & Centre for Medical Image Computing, University College London, London, UK
| | - Qiqi Tong
- Center for Brain Imaging Science and Technology, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, China
| | - Zhiwei Li
- Department of Instrument Science & Technology, Zhejiang University, Hangzhou, China
| | - Hongjian He
- Center for Brain Imaging Science and Technology, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, China
| | - Hui Zhang
- Department of Computer Science & Centre for Medical Image Computing, University College London, London, UK
| | - Jianhui Zhong
- Center for Brain Imaging Science and Technology, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, China.,Department of Imaging Sciences, University of Rochester, Rochester, NY, USA
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22
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Torres ER, Tumey TA, Dean DC, Kassahun-Yimer W, Lopez-Lambert ED, Hitchcock ME. Non-pharmacological strategies to obtain usable magnetic resonance images in non-sedated infants: Systematic review and meta-analysis. Int J Nurs Stud 2020; 106:103551. [PMID: 32294563 DOI: 10.1016/j.ijnurstu.2020.103551] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2019] [Revised: 02/12/2020] [Accepted: 02/14/2020] [Indexed: 10/24/2022]
Abstract
BACKGROUND Although the use of sedation is commonly practiced to keep infants still while receiving magnetic resonance imaging, non-pharmacological strategies are a potential alternative. OBJECTIVES The purpose of this study was to determine the success rate of obtaining usable magnetic resonance images in infants with the sole use of non-pharmacological strategies. DESIGN Systematic literature review and meta-analysis SETTING: A search was conducted in PubMed, CINAHL and Cochrane Library. PARTICIPANTS Human infants from birth to 24 months of age who did not receive any sedation or anesthesia during magnetic resonance imaging METHOD: Articles that reported the success rate of obtaining usable images were included. RESULTS Of the 521 non-duplicate articles found, 58 articles were included in the systematic review with sample sizes ranging from 2-457, an average success rate of 87.8%, and an average scan time of 30 min. The most common non-pharmacological technique included feeding and swaddling infants before imaging to encourage infants to sleep during the scan. Meta-analysis performed on 53 articles comprising 3,410 infants found a success rate of 87%, but significant heterogeneity was found (I2 = 98.30%). It was more difficult to obtain usable images solely with non-pharmacological techniques if infants were critically ill or a structural magnetic resonance imaging of the brain was required. CONCLUSION Non-pharmacological techniques are effective for obtaining usable magnetic resonance imaging scans in most but not all infants. Tweetable abstract: Non-pharmacological techniques are effective for obtaining usable magnetic resonance imaging scans in most infants.
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Affiliation(s)
- Elisa R Torres
- School of Nursing, University of Mississippi Medical Center, 2500 North State Street, Jackson 39216, MS, United States.
| | - Tyler A Tumey
- Burrell College of Osteopathic Medicine, 3501 Arrowhead Dr Las Cruces, NM 88001, United States.
| | - Douglas C Dean
- Waisman Center, University of Wisconsin-Madison, 1500 Highland Ave, Madison WI 53705, United States.
| | - Wondwosen Kassahun-Yimer
- Department of Data Science, University of Mississippi Medical Center, School of Population Health,2500 North State Street, Jackson, MS 39216, United States.
| | - Eloise D Lopez-Lambert
- School of Nursing, University of Mississippi Medical Center, 2500 North State Street, Jackson 39216, MS, United States
| | - Mary E Hitchcock
- Ebling Library, University of Wisconsin-Madison, 750 Highland Ave, Madison WI 53705, United States.
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23
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Kleban E, Tax CMW, Rudrapatna US, Jones DK, Bowtell R. Strong diffusion gradients allow the separation of intra- and extra-axonal gradient-echo signals in the human brain. Neuroimage 2020; 217:116793. [PMID: 32335263 PMCID: PMC7613126 DOI: 10.1016/j.neuroimage.2020.116793] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2019] [Revised: 02/28/2020] [Accepted: 03/19/2020] [Indexed: 12/12/2022] Open
Abstract
The quantification of brain white matter properties is a key area of application of Magnetic Resonance Imaging (MRI), with much effort focused on using MR techniques to quantify tissue microstructure. While diffusion MRI probes white matter (WM) microstructure by characterising the sensitivity of Brownian motion of water molecules to anisotropic structures, susceptibility-based techniques probe the tissue microstructure by observing the effect of interaction between the tissue and the magnetic field. Here, we unify these two complementary approaches by combining ultra-strong (300 mT/m) gradients with a novel Diffusion-Filtered Asymmetric Spin Echo (D-FASE) technique. Using D-FASE we can separately assess the evolution of the intra- and extra-axonal signals under the action of susceptibility effects, revealing differences in the behaviour in different fibre tracts. We observed that the effective relaxation rate of the ASE signal in the corpus callosum decreases with increasing b-value in all subjects (from 17.1 ± 0.7 s−1 at b = 0 s/mm2 to 14.6 ± 0.7 s−1 at b = 4800 s/mm2), while this dependence on b in the corticospinal tract is less pronounced (from 12.0± 1.1 s−1 at b = 0s/mm2 to 10.7 ± 0.5 s−1 at b = 4800 s/mm2). Voxelwise analysis of the signal evolution with respect to b-factor and acquisition delay using a microscopic model demonstrated differences in gradient echo signal evolution between the intra- and extra-axonal pools.
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Affiliation(s)
- Elena Kleban
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, UK.
| | - Chantal M W Tax
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, UK
| | - Umesh S Rudrapatna
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, UK
| | - Derek K Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, UK; Mary MacKillop Institute for Health Research, Faculty of Health Sciences, Australian Catholic University, Melbourne, Victoria, 3065, Australia
| | - Richard Bowtell
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, NG7 2RD, UK
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24
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Genc S, Tax CMW, Raven EP, Chamberland M, Parker GD, Jones DK. Impact of b-value on estimates of apparent fibre density. Hum Brain Mapp 2020; 41:2583-2595. [PMID: 32216121 PMCID: PMC7294071 DOI: 10.1002/hbm.24964] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2020] [Accepted: 02/13/2020] [Indexed: 12/13/2022] Open
Abstract
Recent advances in diffusion magnetic resonance imaging (dMRI) analysis techniques have improved our understanding of fibre‐specific variations in white matter microstructure. Increasingly, studies are adopting multi‐shell dMRI acquisitions to improve the robustness of dMRI‐based inferences. However, the impact of b‐value choice on the estimation of dMRI measures such as apparent fibre density (AFD) derived from spherical deconvolution is not known. Here, we investigate the impact of b‐value sampling scheme on estimates of AFD. First, we performed simulations to assess the correspondence between AFD and simulated intra‐axonal signal fraction across multiple b‐value sampling schemes. We then studied the impact of sampling scheme on the relationship between AFD and age in a developmental population (n = 78) aged 8–18 (mean = 12.4, SD = 2.9 years) using hierarchical clustering and whole brain fixel‐based analyses. Multi‐shell dMRI data were collected at 3.0T using ultra‐strong gradients (300 mT/m), using 6 diffusion‐weighted shells ranging from b = 0 to 6,000 s/mm2. Simulations revealed that the correspondence between estimated AFD and simulated intra‐axonal signal fraction was improved with high b‐value shells due to increased suppression of the extra‐axonal signal. These results were supported by in vivo data, as sensitivity to developmental age‐relationships was improved with increasing b‐value (b = 6,000 s/mm2, median R2 = .34; b = 4,000 s/mm2, median R2 = .29; b = 2,400 s/mm2, median R2 = .21; b = 1,200 s/mm2, median R2 = .17) in a tract‐specific fashion. Overall, estimates of AFD and age‐related microstructural development were better characterised at high diffusion‐weightings due to improved correspondence with intra‐axonal properties.
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Affiliation(s)
- Sila Genc
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Wales, UK
| | - Chantal M W Tax
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Wales, UK
| | - Erika P Raven
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Wales, UK
| | - Maxime Chamberland
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Wales, UK
| | - Greg D Parker
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Wales, UK.,Experimental MRI Centre (EMRIC), School of Biosciences, Cardiff University, Wales, UK
| | - Derek K Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Wales, UK.,Mary MacKillop Institute for Health Research, Australian Catholic University, Melbourne, Australia
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25
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Tax CMW, Szczepankiewicz F, Nilsson M, Jones DK. The dot-compartment revealed? Diffusion MRI with ultra-strong gradients and spherical tensor encoding in the living human brain. Neuroimage 2020; 210:116534. [PMID: 31931157 PMCID: PMC7429990 DOI: 10.1016/j.neuroimage.2020.116534] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Revised: 12/12/2019] [Accepted: 01/09/2020] [Indexed: 11/29/2022] Open
Abstract
The so-called “dot-compartment” is conjectured in diffusion MRI to represent small spherical spaces, such as cell bodies, in which the diffusion is restricted in all directions. Previous investigations inferred its existence from data acquired with directional diffusion encoding which does not permit a straightforward separation of signals from ‘sticks’ (axons) and signals from ‘dots’. Here we combine isotropic diffusion encoding with ultra-strong diffusion gradients (240 mT/m) to achieve high diffusion-weightings with high signal to noise ratio, while suppressing signal arising from anisotropic water compartments with significant mobility along at least one axis (e.g., axons). A dot-compartment, defined to have apparent diffusion coefficient equal to zero and no exchange, would result in a non-decaying signal at very high b-values (b≳7000s/mm2). With this unique experimental setup, a residual yet slowly decaying signal above the noise floor for b-values as high as 15000s/mm2 was seen clearly in the cerebellar grey matter (GM), and in several white matter (WM) regions to some extent. Upper limits of the dot-signal-fraction were estimated to be 1.8% in cerebellar GM and 0.5% in WM. By relaxing the assumption of zero diffusivity, the signal at high b-values in cerebellar GM could be represented more accurately by an isotropic water pool with a low apparent diffusivity of 0.12 μm2/ms and a substantial signal fraction of 9.7%. The T2 of this component was estimated to be around 61ms. This remaining signal at high b-values has potential to serve as a novel and simple marker for isotropically-restricted water compartments in cerebellar GM. The “dot-compartment” is conjectured in diffusion MRI to represent e.g. cell bodies. We combine isotropic encoding with ultra-strong gradients to study the dot-compartment. A slowly decaying signal for high b-values was seen in cerebellar GM. An apparent diffusivity of 0.12 and signal fraction of 9.7% were estimated. The signal could serve as a novel and simple marker for spherical compartments.
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Affiliation(s)
- Chantal M W Tax
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, UK.
| | - Filip Szczepankiewicz
- Radiology, Brigham and Women's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA; Medical Radiation Physics, Clinical Sciences Lund, Lund University, Lund, Sweden
| | - Markus Nilsson
- Radiology, Clinical Sciences Lund, Lund University, Lund, Sweden
| | - Derek K Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, UK; Mary MacKillop Institute for Health Research, Australian Catholic University, Melbourne, Australia
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26
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Maximov II, Alnæs D, Westlye LT. Towards an optimised processing pipeline for diffusion magnetic resonance imaging data: Effects of artefact corrections on diffusion metrics and their age associations in UK Biobank. Hum Brain Mapp 2019; 40:4146-4162. [PMID: 31173439 PMCID: PMC6865652 DOI: 10.1002/hbm.24691] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2019] [Revised: 05/14/2019] [Accepted: 05/27/2019] [Indexed: 12/30/2022] Open
Abstract
Increasing interest in the structural and functional organisation of the human brain encourages the acquisition of big data sets comprising multiple neuroimaging modalities, often accompanied by additional information obtained from health records, cognitive tests, biomarkers and genotypes. Diffusion weighted magnetic resonance imaging data enables a range of promising imaging phenotypes probing structural connections as well as macroanatomical and microstructural properties of the brain. The reliability and biological sensitivity and specificity of diffusion data depend on processing pipeline. A state-of-the-art framework for data processing facilitates cross-study harmonisation and reduces pipeline-related variability. Using diffusion magnetic resonance imaging (MRI) data from 218 individuals in the UK Biobank, we evaluate the effects of different processing steps that have been suggested to reduce imaging artefacts and improve reliability of diffusion metrics. In lack of a ground truth, we compared diffusion metric sensitivity to age between pipelines. By comparing distributions and age sensitivity of the resulting diffusion metrics based on different approaches (diffusion tensor imaging, diffusion kurtosis imaging and white matter tract integrity), we evaluate a general pipeline comprising seven postprocessing blocks: noise correction; Gibbs ringing correction; evaluation of field distortions; susceptibility, eddy-current and motion-induced distortion corrections; bias field correction; spatial smoothing and final diffusion metric estimations. Based on this evaluation, we suggest an optimised processing pipeline for diffusion weighted MRI data.
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Affiliation(s)
- Ivan I. Maximov
- Department of PsychologyUniversity of OsloOsloNorway
- Department of Mental Health and AddictionNorwegian Centre for Mental Disorders Research spiepr132 (NORMENT), Oslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
| | - Dag Alnæs
- Department of Mental Health and AddictionNorwegian Centre for Mental Disorders Research spiepr132 (NORMENT), Oslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
| | - Lars T. Westlye
- Department of PsychologyUniversity of OsloOsloNorway
- Department of Mental Health and AddictionNorwegian Centre for Mental Disorders Research spiepr132 (NORMENT), Oslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
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27
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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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28
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Koch A, Zhukov A, Stöcker T, Groeschel S, Schultz T. SHORE-based detection and imputation of dropout in diffusion MRI. Magn Reson Med 2019; 82:2286-2298. [PMID: 31273856 DOI: 10.1002/mrm.27893] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2019] [Revised: 05/24/2019] [Accepted: 06/14/2019] [Indexed: 11/08/2022]
Abstract
PURPOSE In diffusion MRI, dropout refers to a strong attenuation of the measured signal that is caused by bulk motion during the diffusion encoding. When left uncorrected, dropout will be erroneously interpreted as high diffusivity in the affected direction. We present a method to automatically detect dropout, and to replace the affected measurements with imputed values. METHODS Signal dropout is detected by deriving an outlier score from a simple harmonic oscillator-based reconstruction and estimation (SHORE) fit of all measurements. The outlier score is defined to detect measurements that are substantially lower than predicted by SHORE in a relative sense, while being less sensitive to measurement noise in cases of weak baseline signal. A second SHORE fit is based on detected inliers only, and its predictions are used to replace outliers. RESULTS Our method is shown to reliably detect and accurately impute dropout in simulated data, and to achieve plausible results in corrupted in vivo dMRI measurements. Computational effort is much lower than with previously proposed alternatives. CONCLUSIONS Deriving a suitable outlier score from SHORE results in a fast and accurate method for detection and imputation of dropout in diffusion MRI. It requires measurements with multiple b values (such as multi-shell or DSI), but is independent from the models used for analysis (such as DKI, NODDI, deconvolution, etc.).
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Affiliation(s)
- Alexandra Koch
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.,Department of Computer Science, University of Bonn, Bonn, Germany
| | - Andrei Zhukov
- Department of Computer Science, University of Bonn, Bonn, Germany
| | - Tony Stöcker
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.,Department of Physics and Astronomy, University of Bonn, Bonn, Germany
| | - Samuel Groeschel
- Department of Child Neurology, University Children's Hospital Tübingen, Tübingen, Germany
| | - Thomas Schultz
- Department of Computer Science, University of Bonn, Bonn, Germany.,Bonn-Aachen International Center for Information Technology, Bonn, Germany
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29
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Dimensionality reduction of diffusion MRI measures for improved tractometry of the human brain. Neuroimage 2019; 200:89-100. [PMID: 31228638 PMCID: PMC6711466 DOI: 10.1016/j.neuroimage.2019.06.020] [Citation(s) in RCA: 56] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2019] [Revised: 06/05/2019] [Accepted: 06/07/2019] [Indexed: 12/13/2022] Open
Abstract
Various diffusion MRI (dMRI) measures have been proposed for characterising tissue microstructure over the last 15 years. Despite the growing number of experiments using different dMRI measures in assessments of white matter, there has been limited work on: 1) examining their covariance along specific pathways; and on 2) combining these different measures to study tissue microstructure. Indeed, it quickly becomes intractable for existing analysis pipelines to process multiple measurements at each voxel and at each vertex forming a streamline, highlighting the need for new ways to visualise or analyse such high-dimensional data. In a sample of 36 typically developing children aged 8–18 years, we profiled various commonly used dMRI measures across 22 brain pathways. Using a data-reduction approach, we identified two biologically-interpretable components that capture 80% of the variance in these dMRI measures. The first derived component captures properties related to hindrance and restriction in tissue microstructure, while the second component reflects characteristics related to tissue complexity and orientational dispersion. We then demonstrate that the components generated by this approach preserve the biological relevance of the original measurements by showing age-related effects across developmentally sensitive pathways. In summary, our findings demonstrate that dMRI analyses can benefit from dimensionality reduction techniques, to help disentangling the neurobiological underpinnings of white matter organisation.
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