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Kaufmann BC, Pastore-Wapp M, Bartolomeo P, Geiser N, Nyffeler T, Cazzoli D. Severity-Dependent Interhemispheric White Matter Connectivity Predicts Poststroke Neglect Recovery. J Neurosci 2024; 44:e1311232024. [PMID: 38565290 PMCID: PMC11112644 DOI: 10.1523/jneurosci.1311-23.2024] [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: 07/13/2023] [Revised: 12/15/2023] [Accepted: 03/15/2024] [Indexed: 04/04/2024] Open
Abstract
Left-sided spatial neglect is a very common and challenging issue after right-hemispheric stroke, which strongly and negatively affects daily living behavior and recovery of stroke survivors. The mechanisms underlying recovery of spatial neglect remain controversial, particularly regarding the involvement of the intact, contralesional hemisphere, with potential contributions ranging from maladaptive to compensatory. In the present prospective, observational study, we assessed neglect severity in 54 right-hemispheric stroke patients (32 male; 22 female) at admission to and discharge from inpatient neurorehabilitation. We demonstrate that the interaction of initial neglect severity and spared white matter (dis)connectivity resulting from individual lesions (as assessed by diffusion tensor imaging, DTI) explains a significant portion of the variability of poststroke neglect recovery. In mildly impaired patients, spared structural connectivity within the lesioned hemisphere is sufficient to attain good recovery. Conversely, in patients with severe impairment, successful recovery critically depends on structural connectivity within the intact hemisphere and between hemispheres. These distinct patterns, mediated by their respective white matter connections, may help to reconcile the dichotomous perspectives regarding the role of the contralesional hemisphere as exclusively compensatory or not. Instead, they suggest a unified viewpoint wherein the contralesional hemisphere can - but must not necessarily - assume a compensatory role. This would depend on initial impairment severity and on the available, spared structural connectivity. In the future, our findings could serve as a prognostic biomarker for neglect recovery and guide patient-tailored therapeutic approaches.
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Affiliation(s)
- Brigitte C Kaufmann
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, Inserm, CNRS, Paris 75013, France
- Neurocenter, Luzerner Kantonsspital, Lucerne 6016, Switzerland
| | - Manuela Pastore-Wapp
- Neurocenter, Luzerner Kantonsspital, Lucerne 6016, Switzerland
- ARTORG Center for Biomedical Engineering Research, Gerontechnology and Rehabilitation Group, University of Bern, Bern 3010, Switzerland
| | - Paolo Bartolomeo
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, Inserm, CNRS, Paris 75013, France
| | - Nora Geiser
- Neurocenter, Luzerner Kantonsspital, Lucerne 6016, Switzerland
- ARTORG Center for Biomedical Engineering Research, Gerontechnology and Rehabilitation Group, University of Bern, Bern 3010, Switzerland
- Graduate School for Health Sciences, University of Bern, Bern 3012, Switzerland
| | - Thomas Nyffeler
- Neurocenter, Luzerner Kantonsspital, Lucerne 6016, Switzerland
- Graduate School for Health Sciences, University of Bern, Bern 3012, Switzerland
- Department of Neurology, Inselspital, Bern University Hospital, University of Bern 3010, Switzerland
| | - Dario Cazzoli
- Neurocenter, Luzerner Kantonsspital, Lucerne 6016, Switzerland
- ARTORG Center for Biomedical Engineering Research, Gerontechnology and Rehabilitation Group, University of Bern, Bern 3010, Switzerland
- Department of Psychology, University of Bern, Bern 3012, Switzerland
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Luo KL, Santos L, Tokaria R, Jambawalikar S, Duong PT, Raya JG, Mostoufi-Moab S, Jaramillo D. Generalizing Diffusion Tensor Imaging of the Physis and Metaphysis. J Magn Reson Imaging 2024. [PMID: 38757966 DOI: 10.1002/jmri.29455] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Revised: 05/02/2024] [Accepted: 05/05/2024] [Indexed: 05/18/2024] Open
Abstract
BACKGROUND Current methods to predict height potential are inaccurate. Predicting height by using MRI of the physeal cartilage has shown promise but the applicability of this technique in different imaging setups has not been well-evaluated. PURPOSE To assess variability in diffusion tensor imaging of the physis and metaphysis (DTI-P/M) of the distal femur between different scanners, imaging parameters, tractography software, and resolution. STUDY TYPE Prospective. POPULATION/SUBJECTS Eleven healthy subjects (five males and six females ages 10-16.94). FIELD STRENGTH/SEQUENCE 3 T; DTI single shot echo planar sequences. ASSESSMENT Physeal DTI tract measurements of the distal femur were compared between different scanners, imaging parameters, tractography settings, interpolation correction, and tractography software. STATISTICAL TESTS Bland-Altman, Spearman correlation, linear regression, and Shapiro-Wilk tests. Threshold for statistical significance was set at P = 0.05. RESULTS DTI tract values consistently showed low variability with different imaging and analysis settings. Vendor to vendor comparison exhibited strong correlation (ρ = 0.93) and small but significant bias (bias -5.76, limits of agreement [LOA] -24.31 to 12.78). Strong correlation and no significant difference were seen between technical replicates of the General Electric MRI scanner (ρ = 1, bias 0.17 [LOA -1.5 to 1.2], P = 0.42) and the Siemens MRI scanner (ρ = 0.89, bias = 0.56, P = 0.71). Different voxel sizes (1 × 1 × 2 mm3 vs. 2 × 2 × 3 mm3) did not significantly affect DTI values (bias = 1.4 [LOA -5.7 to 8.4], P = 0.35) but maintained a strong correlation (ρ = 0.82). Gap size (0 mm vs. 0.6 mm) significantly affects tract volume (bias = 1.8 [LOA -5.4 to 1.8]) but maintains a strong correlation (ρ = 0.93). Comparison of tractography algorithms generated significant differences in tract number, length, and volume while maintaining correlation (ρ = 0.86, 0.99, 0.93, respectively). Comparison of interobserver variability between different tractography software also revealed significant differences while maintaining high correlation (ρ = 0.85-0.98). DATA CONCLUSION DTI of the pediatric physis cartilage shows high reproducibility between different imaging and analytic parameters. EVIDENCE LEVEL 2 TECHNICAL EFFICACY: Stage 1.
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Affiliation(s)
- Katherine L Luo
- Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Laura Santos
- Department of Radiology, Columbia University Irving Medical Center, New York, New York, USA
| | - Rumana Tokaria
- Department of Radiology, Columbia University Irving Medical Center, New York, New York, USA
| | - Sachin Jambawalikar
- Department of Radiology, Columbia University Irving Medical Center, New York, New York, USA
| | - Phuong T Duong
- Department of Radiology, Columbia University Irving Medical Center, New York, New York, USA
| | - José G Raya
- New York University Langone Medical Center, New York, New York, USA
| | | | - Diego Jaramillo
- Department of Radiology, Columbia University Irving Medical Center, New York, New York, USA
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Sanvito F, Kaufmann TJ, Cloughesy TF, Wen PY, Ellingson BM. Standardized brain tumor imaging protocols for clinical trials: current recommendations and tips for integration. FRONTIERS IN RADIOLOGY 2023; 3:1267615. [PMID: 38152383 PMCID: PMC10751345 DOI: 10.3389/fradi.2023.1267615] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 11/24/2023] [Indexed: 12/29/2023]
Abstract
Standardized MRI acquisition protocols are crucial for reducing the measurement and interpretation variability associated with response assessment in brain tumor clinical trials. The main challenge is that standardized protocols should ensure high image quality while maximizing the number of institutions meeting the acquisition requirements. In recent years, extensive effort has been made by consensus groups to propose different "ideal" and "minimum requirements" brain tumor imaging protocols (BTIPs) for gliomas, brain metastases (BM), and primary central nervous system lymphomas (PCSNL). In clinical practice, BTIPs for clinical trials can be easily integrated with additional MRI sequences that may be desired for clinical patient management at individual sites. In this review, we summarize the general concepts behind the choice and timing of sequences included in the current recommended BTIPs, we provide a comparative overview, and discuss tips and caveats to integrate additional clinical or research sequences while preserving the recommended BTIPs. Finally, we also reflect on potential future directions for brain tumor imaging in clinical trials.
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Affiliation(s)
- Francesco Sanvito
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, United States
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | | | - Timothy F. Cloughesy
- UCLA Neuro-Oncology Program, University of California, Los Angeles, Los Angeles, CA, United States
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Patrick Y. Wen
- Center for Neuro-Oncology, Dana-Farber/Brigham and Women’s Cancer Center, Harvard Medical School, Boston, MA, United States
| | - Benjamin M. Ellingson
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, United States
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
- Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, University of California, Los Angeles, Los Angeles, CA, United States
- Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
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4
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Nicholson S, Russo AW, Brewer K, Bien H, Tobyne SM, Eloyan A, Klawiter EC. The effect of ibudilast on thalamic volume in progressive multiple sclerosis. Mult Scler 2023; 29:1819-1830. [PMID: 37947294 PMCID: PMC10841081 DOI: 10.1177/13524585231204710] [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] [Indexed: 11/12/2023]
Abstract
BACKGROUND Thalamic volume loss is known to be associated with clinical and cognitive disability in progressive multiple sclerosis (PMS). OBJECTIVE To investigate the treatment effect of ibudilast on thalamic atrophy more than 96 weeks in the phase 2 trial in progressive(MS Secondary and Primary Progressive Ibudilast NeuroNEXT Trial in Multiple Sclerosis [SPRINT-MS]). METHODS A total of 231 participants were randomized to either ibudilast (n = 114) or placebo (n = 117). Thalamic volume change was computed using Bayesian Sequence Adaptive Multimodal Segmentation tool (SAMseg) incorporating T1, fluid-attenuated inversion recovery (FLAIR), and fractional anisotropy maps and analyzed with a mixed-effects repeated-measures model. RESULTS There was no significant difference in thalamic volumes between treatment groups. On exploratory analysis, participants with primary progressive multiple sclerosis (PPMS) on placebo had a 0.004% greater rate of thalamic atrophy than PPMS participants on ibudilast (p = 0.058, 95% confidence interval (CI) = -0.008 to <0.001). Greater reductions in thalamic volumes at more than 96 weeks were associated with worsening multiple sclerosis functional composite (MSFC-4) scores (p = 0.002) and worsening performance on the symbol digit modality test (SDMT) (p < 0.001). CONCLUSION In a phase 2 trial evaluating ibudilast in PMS, no treatment effect was demonstrated in preventing thalamic atrophy. Participants with PPMS exhibited a treatment effect that trended toward significance. Longitudinal changes in thalamic volume were related to worsening of physical and cognitive disability, highlighting this outcome's clinical importance.
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Affiliation(s)
- Showly Nicholson
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Andrew W Russo
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Kristina Brewer
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Heidi Bien
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Sean M Tobyne
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Ani Eloyan
- Department of Biostatistics, Brown University, Providence, RI, USA
| | - Eric C Klawiter
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
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5
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Zanon Zotin MC, Yilmaz P, Sveikata L, Schoemaker D, van Veluw SJ, Etherton MR, Charidimou A, Greenberg SM, Duering M, Viswanathan A. Peak Width of Skeletonized Mean Diffusivity: A Neuroimaging Marker for White Matter Injury. Radiology 2023; 306:e212780. [PMID: 36692402 PMCID: PMC9968775 DOI: 10.1148/radiol.212780] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Revised: 10/01/2022] [Accepted: 10/14/2022] [Indexed: 01/25/2023]
Abstract
A leading cause of white matter (WM) injury in older individuals is cerebral small vessel disease (SVD). Cerebral SVD is the most prevalent vascular contributor to cognitive impairment and dementia. Therapeutic progress for cerebral SVD and other WM disorders depends on the development and validation of neuroimaging markers suitable as outcome measures in future interventional trials. Diffusion-tensor imaging (DTI) is one of the best-suited MRI techniques for assessing the extent of WM damage in the brain. But the optimal method to analyze individual DTI data remains hindered by labor-intensive and time-consuming processes. Peak width of skeletonized mean diffusivity (PSMD), a recently developed fast, fully automated DTI marker, was designed to quantify the WM damage secondary to cerebral SVD and reflect related cognitive impairment. Despite its promising results, knowledge about PSMD is still limited in the radiologic community. This focused review provides an overview of the technical details of PSMD while synthesizing the available data on its clinical and neuroimaging associations. From a critical expert viewpoint, the authors discuss the limitations of PSMD and its current validation status as a neuroimaging marker for vascular cognitive impairment. Finally, they point out the gaps to be addressed to further advance the field.
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Affiliation(s)
| | | | - Lukas Sveikata
- From the J. Philip Kistler Stroke Research Center, Department of
Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass
(M.C.Z.Z., P.Y., L.S., D.S., S.J.v.V., M.R.E., A.C., S.M.G., A.V.); Center for
Imaging Sciences and Medical Physics, Department of Medical Imaging, Hematology
and Clinical Oncology, Ribeirão Preto Medical School, University of
São Paulo, 3900 Ten. Catão Roxo Street, Monte Alegre, Campus
Universitário, Ribeirão Preto, SP 14015-010, Brazil (M.C.Z.Z.);
Departments of Epidemiology and Radiology and Nuclear Medicine, Erasmus Medical
Center, Rotterdam, the Netherlands (P.Y.); Division of Neurology, Department of
Clinical Neurosciences, Geneva University Hospital, Faculty of Medicine,
University of Geneva, Geneva, Switzerland (L.S.); Institute of Cardiology,
Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
(L.S.); and Medical Image Analysis Center (MIAC AG) and Quantitative Biomedical
Imaging Group (qbig), Department of Biomedical Engineering, University of Basel,
Basel, Switzerland (M.D.)
| | - Dorothee Schoemaker
- From the J. Philip Kistler Stroke Research Center, Department of
Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass
(M.C.Z.Z., P.Y., L.S., D.S., S.J.v.V., M.R.E., A.C., S.M.G., A.V.); Center for
Imaging Sciences and Medical Physics, Department of Medical Imaging, Hematology
and Clinical Oncology, Ribeirão Preto Medical School, University of
São Paulo, 3900 Ten. Catão Roxo Street, Monte Alegre, Campus
Universitário, Ribeirão Preto, SP 14015-010, Brazil (M.C.Z.Z.);
Departments of Epidemiology and Radiology and Nuclear Medicine, Erasmus Medical
Center, Rotterdam, the Netherlands (P.Y.); Division of Neurology, Department of
Clinical Neurosciences, Geneva University Hospital, Faculty of Medicine,
University of Geneva, Geneva, Switzerland (L.S.); Institute of Cardiology,
Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
(L.S.); and Medical Image Analysis Center (MIAC AG) and Quantitative Biomedical
Imaging Group (qbig), Department of Biomedical Engineering, University of Basel,
Basel, Switzerland (M.D.)
| | - Susanne J. van Veluw
- From the J. Philip Kistler Stroke Research Center, Department of
Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass
(M.C.Z.Z., P.Y., L.S., D.S., S.J.v.V., M.R.E., A.C., S.M.G., A.V.); Center for
Imaging Sciences and Medical Physics, Department of Medical Imaging, Hematology
and Clinical Oncology, Ribeirão Preto Medical School, University of
São Paulo, 3900 Ten. Catão Roxo Street, Monte Alegre, Campus
Universitário, Ribeirão Preto, SP 14015-010, Brazil (M.C.Z.Z.);
Departments of Epidemiology and Radiology and Nuclear Medicine, Erasmus Medical
Center, Rotterdam, the Netherlands (P.Y.); Division of Neurology, Department of
Clinical Neurosciences, Geneva University Hospital, Faculty of Medicine,
University of Geneva, Geneva, Switzerland (L.S.); Institute of Cardiology,
Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
(L.S.); and Medical Image Analysis Center (MIAC AG) and Quantitative Biomedical
Imaging Group (qbig), Department of Biomedical Engineering, University of Basel,
Basel, Switzerland (M.D.)
| | - Mark R. Etherton
- From the J. Philip Kistler Stroke Research Center, Department of
Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass
(M.C.Z.Z., P.Y., L.S., D.S., S.J.v.V., M.R.E., A.C., S.M.G., A.V.); Center for
Imaging Sciences and Medical Physics, Department of Medical Imaging, Hematology
and Clinical Oncology, Ribeirão Preto Medical School, University of
São Paulo, 3900 Ten. Catão Roxo Street, Monte Alegre, Campus
Universitário, Ribeirão Preto, SP 14015-010, Brazil (M.C.Z.Z.);
Departments of Epidemiology and Radiology and Nuclear Medicine, Erasmus Medical
Center, Rotterdam, the Netherlands (P.Y.); Division of Neurology, Department of
Clinical Neurosciences, Geneva University Hospital, Faculty of Medicine,
University of Geneva, Geneva, Switzerland (L.S.); Institute of Cardiology,
Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
(L.S.); and Medical Image Analysis Center (MIAC AG) and Quantitative Biomedical
Imaging Group (qbig), Department of Biomedical Engineering, University of Basel,
Basel, Switzerland (M.D.)
| | - Andreas Charidimou
- From the J. Philip Kistler Stroke Research Center, Department of
Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass
(M.C.Z.Z., P.Y., L.S., D.S., S.J.v.V., M.R.E., A.C., S.M.G., A.V.); Center for
Imaging Sciences and Medical Physics, Department of Medical Imaging, Hematology
and Clinical Oncology, Ribeirão Preto Medical School, University of
São Paulo, 3900 Ten. Catão Roxo Street, Monte Alegre, Campus
Universitário, Ribeirão Preto, SP 14015-010, Brazil (M.C.Z.Z.);
Departments of Epidemiology and Radiology and Nuclear Medicine, Erasmus Medical
Center, Rotterdam, the Netherlands (P.Y.); Division of Neurology, Department of
Clinical Neurosciences, Geneva University Hospital, Faculty of Medicine,
University of Geneva, Geneva, Switzerland (L.S.); Institute of Cardiology,
Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
(L.S.); and Medical Image Analysis Center (MIAC AG) and Quantitative Biomedical
Imaging Group (qbig), Department of Biomedical Engineering, University of Basel,
Basel, Switzerland (M.D.)
| | - Steven M. Greenberg
- From the J. Philip Kistler Stroke Research Center, Department of
Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass
(M.C.Z.Z., P.Y., L.S., D.S., S.J.v.V., M.R.E., A.C., S.M.G., A.V.); Center for
Imaging Sciences and Medical Physics, Department of Medical Imaging, Hematology
and Clinical Oncology, Ribeirão Preto Medical School, University of
São Paulo, 3900 Ten. Catão Roxo Street, Monte Alegre, Campus
Universitário, Ribeirão Preto, SP 14015-010, Brazil (M.C.Z.Z.);
Departments of Epidemiology and Radiology and Nuclear Medicine, Erasmus Medical
Center, Rotterdam, the Netherlands (P.Y.); Division of Neurology, Department of
Clinical Neurosciences, Geneva University Hospital, Faculty of Medicine,
University of Geneva, Geneva, Switzerland (L.S.); Institute of Cardiology,
Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
(L.S.); and Medical Image Analysis Center (MIAC AG) and Quantitative Biomedical
Imaging Group (qbig), Department of Biomedical Engineering, University of Basel,
Basel, Switzerland (M.D.)
| | - Marco Duering
- From the J. Philip Kistler Stroke Research Center, Department of
Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass
(M.C.Z.Z., P.Y., L.S., D.S., S.J.v.V., M.R.E., A.C., S.M.G., A.V.); Center for
Imaging Sciences and Medical Physics, Department of Medical Imaging, Hematology
and Clinical Oncology, Ribeirão Preto Medical School, University of
São Paulo, 3900 Ten. Catão Roxo Street, Monte Alegre, Campus
Universitário, Ribeirão Preto, SP 14015-010, Brazil (M.C.Z.Z.);
Departments of Epidemiology and Radiology and Nuclear Medicine, Erasmus Medical
Center, Rotterdam, the Netherlands (P.Y.); Division of Neurology, Department of
Clinical Neurosciences, Geneva University Hospital, Faculty of Medicine,
University of Geneva, Geneva, Switzerland (L.S.); Institute of Cardiology,
Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
(L.S.); and Medical Image Analysis Center (MIAC AG) and Quantitative Biomedical
Imaging Group (qbig), Department of Biomedical Engineering, University of Basel,
Basel, Switzerland (M.D.)
| | - Anand Viswanathan
- From the J. Philip Kistler Stroke Research Center, Department of
Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass
(M.C.Z.Z., P.Y., L.S., D.S., S.J.v.V., M.R.E., A.C., S.M.G., A.V.); Center for
Imaging Sciences and Medical Physics, Department of Medical Imaging, Hematology
and Clinical Oncology, Ribeirão Preto Medical School, University of
São Paulo, 3900 Ten. Catão Roxo Street, Monte Alegre, Campus
Universitário, Ribeirão Preto, SP 14015-010, Brazil (M.C.Z.Z.);
Departments of Epidemiology and Radiology and Nuclear Medicine, Erasmus Medical
Center, Rotterdam, the Netherlands (P.Y.); Division of Neurology, Department of
Clinical Neurosciences, Geneva University Hospital, Faculty of Medicine,
University of Geneva, Geneva, Switzerland (L.S.); Institute of Cardiology,
Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
(L.S.); and Medical Image Analysis Center (MIAC AG) and Quantitative Biomedical
Imaging Group (qbig), Department of Biomedical Engineering, University of Basel,
Basel, Switzerland (M.D.)
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Stenberg J, Skandsen T, Gøran Moen K, Vik A, Eikenes L, Håberg AK. Diffusion Tensor and Kurtosis Imaging Findings the First Year following Mild Traumatic Brain Injury. J Neurotrauma 2023; 40:457-471. [PMID: 36305387 PMCID: PMC9986024 DOI: 10.1089/neu.2022.0206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Despite enormous research interest in diffusion tensor imaging and diffusion kurtosis imaging (DTI; DKI) following mild traumatic brain injury (MTBI), it remains unknown how diffusion in white matter evolves post-injury and relates to acute MTBI characteristics. This prospective cohort study aimed to characterize diffusion changes in white matter the first year after MTBI. Patients with MTBI (n = 193) and matched controls (n = 83) underwent 3T magnetic resonance imaging (MRI) within 72 h and 3- and 12-months post-injury. Diffusion data were analyzed in three steps: 1) voxel-wise comparisons between the MTBI and control group were performed with tract-based spatial statistics at each time-point; 2) clusters of significant voxels identified in step 1 above were evaluated longitudinally with mixed-effect models; 3) the MTBI group was divided into: (A) complicated (with macrostructural findings on MRI) and uncomplicated MTBI; (B) long (1-24 h) and short (< 1 h) post-traumatic amnesia (PTA); and (C) other and no other concurrent injuries to investigate if findings in step 1 were driven mainly by aberrant diffusion in patients with a more severe injury. At 72 h, voxel-wise comparisons revealed significantly lower fractional anisotropy (FA) in one tract and significantly lower mean kurtosis (Kmean) in 11 tracts in the MTBI compared with control group. At 3 months, the MTBI group had significantly higher mean diffusivity in eight tracts compared with controls. At 12 months, FA was significantly lower in four tracts and Kmean in 10 tracts in patients with MTBI compared with controls. There was considerable overlap in affected tracts across time, including the corpus callosum, corona radiata, internal and external capsule, and cerebellar peduncles. Longitudinal analyses revealed that the diffusion metrics remained relatively stable throughout the first year after MTBI. The significant group*time interactions identified were driven by changes in the control rather than the MTBI group. Further, differences identified in step 1 did not result from greater diffusion abnormalities in patients with complicated MTBI, long PTA, or other concurrent injuries, as standardized mean differences in diffusion metrics between the groups were small (0.07 ± 0.11) and non-significant. However, follow-up voxel-wise analyses revealed that other concurrent injuries had effects on diffusion metrics, but predominantly in other metrics and at other time-points than the effects observed in the MTBI versus control group analysis. In conclusion, patients with MTBI differed from controls in white matter integrity already 72 h after injury. Diffusion metrics remained relatively stable throughout the first year after MTBI and were not driven by deviating diffusion in patients with a more severe MTBI.
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Affiliation(s)
- Jonas Stenberg
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Toril Skandsen
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway.,Department of Physical Medicine and Rehabilitation, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Kent Gøran Moen
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway.,Department of Radiology, Vestre Viken Hospital Trust, Drammen Hospital, Drammen, Norway.,Department of Radiology, Nord-Trøndelag Hospital Trust, Levanger Hospital, Levanger, Norway
| | - Anne Vik
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway.,Department of Neurosurgery, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Live Eikenes
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Asta K Håberg
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway.,Department of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
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7
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Gonzalez-Gomez R, Ibañez A, Moguilner S. Multiclass characterization of frontotemporal dementia variants via multimodal brain network computational inference. Netw Neurosci 2023; 7:322-350. [PMID: 37333999 PMCID: PMC10270711 DOI: 10.1162/netn_a_00285] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Accepted: 10/03/2022] [Indexed: 04/03/2024] Open
Abstract
Characterizing a particular neurodegenerative condition against others possible diseases remains a challenge along clinical, biomarker, and neuroscientific levels. This is the particular case of frontotemporal dementia (FTD) variants, where their specific characterization requires high levels of expertise and multidisciplinary teams to subtly distinguish among similar physiopathological processes. Here, we used a computational approach of multimodal brain networks to address simultaneous multiclass classification of 298 subjects (one group against all others), including five FTD variants: behavioral variant FTD, corticobasal syndrome, nonfluent variant primary progressive aphasia, progressive supranuclear palsy, and semantic variant primary progressive aphasia, with healthy controls. Fourteen machine learning classifiers were trained with functional and structural connectivity metrics calculated through different methods. Due to the large number of variables, dimensionality was reduced, employing statistical comparisons and progressive elimination to assess feature stability under nested cross-validation. The machine learning performance was measured through the area under the receiver operating characteristic curves, reaching 0.81 on average, with a standard deviation of 0.09. Furthermore, the contributions of demographic and cognitive data were also assessed via multifeatured classifiers. An accurate simultaneous multiclass classification of each FTD variant against other variants and controls was obtained based on the selection of an optimum set of features. The classifiers incorporating the brain's network and cognitive assessment increased performance metrics. Multimodal classifiers evidenced specific variants' compromise, across modalities and methods through feature importance analysis. If replicated and validated, this approach may help to support clinical decision tools aimed to detect specific affectations in the context of overlapping diseases.
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Affiliation(s)
- Raul Gonzalez-Gomez
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibañez, Santiago de Chile, Chile
- Center for Social and Cognitive Neuroscience, School of Psychology, Universidad Adolfo Ibañez, Santiago de Chile, Chile
| | - Agustín Ibañez
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibañez, Santiago de Chile, Chile
- Cognitive Neuroscience Center, Universidad de San Andres, Buenos Aires, Argentina
- Global Brain Health Institute, University of California San Francisco, San Francisco, CA, USA
- Trinity College Dublin, Dublin, Ireland
| | - Sebastian Moguilner
- Center for Social and Cognitive Neuroscience, School of Psychology, Universidad Adolfo Ibañez, Santiago de Chile, Chile
- Cognitive Neuroscience Center, Universidad de San Andres, Buenos Aires, Argentina
- Global Brain Health Institute, University of California San Francisco, San Francisco, CA, USA
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
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8
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Differences in white matter microstructure in first-episode schizophrenia spectrum disorders vs healthy volunteers and their association with cognition. Schizophr Res 2022; 250:196-202. [PMID: 36436499 DOI: 10.1016/j.schres.2022.11.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Revised: 05/06/2022] [Accepted: 11/15/2022] [Indexed: 11/27/2022]
Abstract
OBJECTIVE Both cognitive impairment and alterations in white matter tissue microstructure are well recognised in schizophrenia. We investigated whether differences in white matter microstructure underpin cognitive impairments in patients with first-episode schizophrenia spectrum disorders when controlling for multiple confounding factors. METHODS We employed a cross-sectional study design and compared fractional anisotropy (FA) between individuals diagnosed with first- episode schizophrenia spectrum disorders (FES) (n = 68) and matched healthy controls (n = 120). We conducted multiple analyses of covariance (ANCOVAs) to compare the mean FA values for patients and controls across 27 white matter tracts. We conducted exploratory correlation analyses to determine if white matter tract differences were associated with global cognitive impairment as well as deficits across seven cognitive domains. RESULTS We found widespread reductions in FA in patients compared to controls, after controlling for confounding variables, such as age, biological sex, education, substances, and childhood adversities. We found a significant positive correlation between the attention/vigilance domain and the splenium of the corpus collosum and external capsule after correction for multiple comparisons. In the control group we found no significant correlations between FA and cognition. CONCLUSION Our findings provide a neurobiological basis for attentional cognitive deficits in schizophrenia, highlighting a potential role for the splenium of the corpus collosum and external capsule.
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Richter S, Winzeck S, Correia MM, Kornaropoulos EN, Manktelow A, Outtrim J, Chatfield D, Posti JP, Tenovuo O, Williams GB, Menon DK, Newcombe VF. Validation of cross-sectional and longitudinal ComBat harmonization methods for magnetic resonance imaging data on a travelling subject cohort. NEUROIMAGE. REPORTS 2022; 2:None. [PMID: 36507071 PMCID: PMC9726680 DOI: 10.1016/j.ynirp.2022.100136] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Revised: 09/07/2022] [Accepted: 09/14/2022] [Indexed: 11/05/2022]
Abstract
Background The growth in multi-center neuroimaging studies generated a need for methods that mitigate the differences in hardware and acquisition protocols across sites i.e., scanner effects. ComBat harmonization methods have shown promise but have not yet been tested on all the data types commonly studied with magnetic resonance imaging (MRI). This study aimed to validate neuroCombat, longCombat and gamCombat on both structural and diffusion metrics in both cross-sectional and longitudinal data. Methods We used a travelling subject design whereby 73 healthy volunteers contributed 161 scans across two sites and four machines using one T1 and five diffusion MRI protocols. Scanner was defined as a composite of site, machine and protocol. A common pipeline extracted two structural metrics (volumes and cortical thickness) and two diffusion tensor imaging metrics (mean diffusivity and fractional anisotropy) for seven regions of interest including gray and (except for cortical thickness) white matter regions. Results Structural data exhibited no significant scanner effect and therefore did not benefit from harmonization in our particular cohort. Indeed, attempting harmonization obscured the true biological effect for some regions of interest. Diffusion data contained marked scanner effects and was successfully harmonized by all methods, resulting in smaller scanner effects and better detection of true biological effects. LongCombat less effectively reduced the scanner effect for cross-sectional white matter data but had a slightly lower probability of incorrectly finding group differences in simulations, compared to neuroCombat and gamCombat. False positive rates for all methods and all metrics did not significantly exceed 5%. Conclusions Statistical harmonization of structural data is not always necessary and harmonization in the absence of a scanner effect may be harmful. Harmonization of diffusion MRI data is highly recommended with neuroCombat, longCombat and gamCombat performing well in cross-sectional and longitudinal settings.
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Affiliation(s)
- Sophie Richter
- Division of Anaesthesia, Department of Medicine, University of Cambridge, Cambridge, UK
- Corresponding author.
| | - Stefan Winzeck
- Division of Anaesthesia, Department of Medicine, University of Cambridge, Cambridge, UK
- BioMedIA Group, Department of Computing, Imperial College London, London, UK
| | - Marta M. Correia
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | | | - Anne Manktelow
- Division of Anaesthesia, Department of Medicine, University of Cambridge, Cambridge, UK
| | - Joanne Outtrim
- Division of Anaesthesia, Department of Medicine, University of Cambridge, Cambridge, UK
| | - Doris Chatfield
- Division of Anaesthesia, Department of Medicine, University of Cambridge, Cambridge, UK
| | - Jussi P. Posti
- Turku Brain Injury Center, Turku University Hospital & University of Turku, Turku, Finland
- Department of Neurosurgery, Turku University Hospital, Turku, Finland
| | - Olli Tenovuo
- Turku Brain Injury Center, Turku University Hospital & University of Turku, Turku, Finland
| | - Guy B. Williams
- Wolfson Brain Imaging Centre, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - David K. Menon
- Division of Anaesthesia, Department of Medicine, University of Cambridge, Cambridge, UK
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10
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Onda K, Catenaccio E, Chotiyanonta J, Chavez-Valdez R, Meoded A, Soares BP, Tekes A, Spahic H, Miller SC, Parker SJ, Parkinson C, Vaidya DM, Graham EM, Stafstrom CE, Everett AD, Northington FJ, Oishi K. Development of a composite diffusion tensor imaging score correlating with short-term neurological status in neonatal hypoxic-ischemic encephalopathy. Front Neurosci 2022; 16:931360. [PMID: 35983227 PMCID: PMC9379310 DOI: 10.3389/fnins.2022.931360] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 07/08/2022] [Indexed: 11/13/2022] Open
Abstract
Hypoxic-ischemic encephalopathy (HIE) is the most common cause of neonatal acquired brain injury. Although conventional MRI may predict neurodevelopmental outcomes, accurate prognostication remains difficult. As diffusion tensor imaging (DTI) may provide an additional diagnostic and prognostic value over conventional MRI, we aimed to develop a composite DTI (cDTI) score to relate to short-term neurological function. Sixty prospective neonates treated with therapeutic hypothermia (TH) for HIE were evaluated with DTI, with a voxel size of 1 × 1 × 2 mm. Fractional anisotropy (FA) and mean diffusivity (MD) from 100 neuroanatomical regions (FA/MD *100 = 200 DTI parameters in total) were quantified using an atlas-based image parcellation technique. A least absolute shrinkage and selection operator (LASSO) regression was applied to the DTI parameters to generate the cDTI score. Time to full oral nutrition [short-term oral feeding (STO) score] was used as a measure of short-term neurological function and was correlated with extracted DTI features. Seventeen DTI parameters were selected with LASSO and built into the final unbiased regression model. The selected factors included FA or MD values of the limbic structures, the corticospinal tract, and the frontotemporal cortices. While the cDTI score strongly correlated with the STO score (rho = 0.83, p = 2.8 × 10-16), it only weakly correlated with the Sarnat score (rho = 0.27, p = 0.035) and moderately with the NICHD-NRN neuroimaging score (rho = 0.43, p = 6.6 × 10-04). In contrast to the cDTI score, the NICHD-NRN score only moderately correlated with the STO score (rho = 0.37, p = 0.0037). Using a mixed-model analysis, interleukin-10 at admission to the NICU (p = 1.5 × 10-13) and tau protein at the end of TH/rewarming (p = 0.036) and after rewarming (p = 0.0015) were significantly associated with higher cDTI scores, suggesting that high cDTI scores were related to the intensity of the early inflammatory response and the severity of neuronal impairment after TH. In conclusion, a data-driven unbiased approach was applied to identify anatomical structures associated with some aspects of neurological function of HIE neonates after cooling and to build a cDTI score, which was correlated with the severity of short-term neurological functions.
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Affiliation(s)
- Kengo Onda
- The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Eva Catenaccio
- Division of Pediatric Neurology, Department of Pediatrics, Children's Hospital of Philadelphia, Philadelphia, PA, United States
| | - Jill Chotiyanonta
- The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Raul Chavez-Valdez
- Neuroscience Intensive Care Nursery Program, Division of Neonatology, The Johns Hopkins University School of Medicine, Baltimore, MD, United States
- Division of Neonatology, Department of Pediatrics, The Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Avner Meoded
- Edward B. Singleton Department of Radiology, Texas Children's Hospital, Baylor College of Medicine, Houston, TX, United States
| | - Bruno P. Soares
- Division of Neuroradiology, Department of Radiology, Larner College of Medicine at the University of Vermont, Burlington, VT, United States
| | - Aylin Tekes
- Neuroscience Intensive Care Nursery Program, Division of Neonatology, The Johns Hopkins University School of Medicine, Baltimore, MD, United States
- Division of Pediatric Radiology and Pediatric Neuroradiology, Department of Radiology, The Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Harisa Spahic
- Division of Neonatology, Department of Pediatrics, The Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Sarah C. Miller
- Division of Neonatology, Department of Pediatrics, The Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | | | - Charlamaine Parkinson
- Neuroscience Intensive Care Nursery Program, Division of Neonatology, The Johns Hopkins University School of Medicine, Baltimore, MD, United States
- Division of Neonatology, Department of Pediatrics, The Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Dhananjay M. Vaidya
- Department of General Internal Medicine, The Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Ernest M. Graham
- Division of Maternal-Fetal Medicine, Department of Gynecology and Obstetrics, The Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Carl E. Stafstrom
- Neuroscience Intensive Care Nursery Program, Division of Neonatology, The Johns Hopkins University School of Medicine, Baltimore, MD, United States
- Division of Pediatric Neurology, Department of Neurology, The Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Allen D. Everett
- Division of Pediatric Cardiology, Department of Pediatrics, The Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Frances J. Northington
- Neuroscience Intensive Care Nursery Program, Division of Neonatology, The Johns Hopkins University School of Medicine, Baltimore, MD, United States
- Division of Neonatology, Department of Pediatrics, The Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Kenichi Oishi
- The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, United States
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Langheinrich T, Chen C, Thomas O. Update on the Cognitive Presentations of iNPH for Clinicians. Front Neurol 2022; 13:894617. [PMID: 35937049 PMCID: PMC9350547 DOI: 10.3389/fneur.2022.894617] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Accepted: 05/19/2022] [Indexed: 11/16/2022] Open
Abstract
This mini-review focuses on cognitive impairment in iNPH. This symptom is one of the characteristic triad of symptoms in a condition long considered to be the only treatable dementia. We present an update on recent developments in clinical, neuropsychological, neuroimaging and biomarker aspects. Significant advances in our understanding have been made, notably regarding biomarkers, but iNPH remains a difficult diagnosis. Stronger evidence for permanent surgical treatment is emerging but selection for treatment remains challenging, particularly with regards to cognitive presentations. Encouragingly, there has been increasing interest in iNPH, but more research is required to better define the underlying pathology and delineate it from overlapping conditions, in order to inform best practise for the clinician managing the cognitively impaired patient. In the meantime, we strongly encourage a multidisciplinary approach and a structured service pathway to maximise patient benefit.
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Affiliation(s)
- Tobias Langheinrich
- Department of Neurology, Manchester Centre for Clinical Neurosciences, Salford Royal NHS Foundation Trust, Salford, United Kingdom
- Division of Neuroscience and Experimental Psychology, School of Biological Sciences, University of Manchester, Manchester, United Kingdom
- *Correspondence: Tobias Langheinrich
| | - Cliff Chen
- Department of Neuropsychology, Manchester Centre for Clinical Neurosciences, Salford Royal NHS Foundation Trust, Salford, United Kingdom
| | - Owen Thomas
- Division of Neuroscience and Experimental Psychology, School of Biological Sciences, University of Manchester, Manchester, United Kingdom
- Department of Neuroradiology, Manchester Centre for Clinical Neurosciences, Salford Royal NHS Foundation Trust, Salford, United Kingdom
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12
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Schneider R, Matusche B, Ladopoulos T, Ayzenberg I, Biesalski AS, Gold R, Bellenberg B, Lukas C. Quantification of individual remyelination during short-term disease course by synthetic magnetic resonance imaging. Brain Commun 2022; 4:fcac172. [PMID: 35938071 PMCID: PMC9351729 DOI: 10.1093/braincomms/fcac172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 03/21/2022] [Accepted: 06/25/2022] [Indexed: 11/29/2022] Open
Abstract
MRI is an important diagnostic tool for evaluation of myelin content in multiple sclerosis and other CNS diseases, being especially relevant for studies investigating remyelinating pharmacotherapies. In this study, we evaluated a new synthetic MRI–based myelin estimation in methylenetetrahydrofolate reductase deficiency as a treatable primary demyelinating disorder and compared this method with established diffusion tensor imaging in both methylenetetrahydrofolate reductase deficiency patients and healthy controls. This is the first synthetic MRI–based in vivo evaluation of treatment-associated remyelination. 1.5 T synthetic MRI and 3 T diffusion MRI were obtained from three methylenetetrahydrofolate reductase deficiency patients at baseline and 6 months after therapy initiation, as well as from age-matched healthy controls (diffusion tensor imaging: n = 14, synthetic MRI: n = 9). Global and regional synthetic MRI parameters (myelin volume fraction, proton density, and relaxation rates) were compared with diffusion metrics (fractional anisotropy, mean/radial/axial diffusivity) and related to healthy controls by calculating z-scores and z-deviation maps. Whole-brain myelin (% of intracranial volume) of the index patient was reduced to 6 versus 10% in healthy controls, which recovered to a nonetheless subnormal level of 6.6% following initiation of high-dosage betaine. Radial diffusivity was higher at baseline compared with healthy controls (1.34 versus 0.79 × 10−3 mm2/s), recovering at follow-up (1.19 × 10−3 mm2/s). The index patient’s lesion volume diminished by 58% under treatment. Regional analysis within lesion area and atlas-based regions revealed lower mean myelin volume fraction (12.7Baseline/14.71Follow-up%) and relaxation rates, higher proton density, as well as lower fractional anisotropy and higher radial diffusivity (1.08 × 10−3Baseline/0.94 × 10−3Follow-up) compared with healthy controls. The highest z-scores were observed for myelin volume fraction in the posterior thalamic radiation, with greater deviation from controls at baseline and reduced deviation at follow-up. Z-deviations of diffusion metrics were less pronounced for radial and mean diffusivity than for myelin volume fraction. Z-maps for myelin volume fraction of the index patient demonstrated high deviation within and beyond lesion areas, among others in the precentral and postcentral gyrus, as well as in the cerebellum, and partial remission of these alterations at follow-up, while radial diffusivity demonstrated more widespread deviations in supra- and infratentorial regions. Concordant changes of myelin volume fraction and radial diffusivity after treatment initiation, accompanied by dramatic clinical and paraclinical improvement, indicate the consistency of the methods, while myelin volume fraction seems to characterize remyelinated regions more specifically. Synthetic MRI–based myelin volume fraction provides myelin estimation consistent with changes of diffusion metrics to monitor short-term myelin changes on individual patient level.
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Affiliation(s)
- Ruth Schneider
- Department of Neurology, St Josef Hospital, Ruhr-University Bochum , 44791 Bochum , Germany
| | - Britta Matusche
- Institute of Neuroradiology, St Josef Hospital, Ruhr-University Bochum , 44791 Bochum , Germany
| | - Theodoros Ladopoulos
- Department of Neurology, St Josef Hospital, Ruhr-University Bochum , 44791 Bochum , Germany
| | - Ilya Ayzenberg
- Department of Neurology, St Josef Hospital, Ruhr-University Bochum , 44791 Bochum , Germany
| | - Anne Sophie Biesalski
- Department of Neurology, St Josef Hospital, Ruhr-University Bochum , 44791 Bochum , Germany
| | - Ralf Gold
- Department of Neurology, St Josef Hospital, Ruhr-University Bochum , 44791 Bochum , Germany
| | - Barbara Bellenberg
- Institute of Neuroradiology, St Josef Hospital, Ruhr-University Bochum , 44791 Bochum , Germany
| | - Carsten Lukas
- Institute of Neuroradiology, St Josef Hospital, Ruhr-University Bochum , 44791 Bochum , Germany
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, St Josef Hospital, Ruhr-University Bochum , 44791 Bochum , Germany
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13
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Granziera C, Wuerfel J, Barkhof F, Calabrese M, De Stefano N, Enzinger C, Evangelou N, Filippi M, Geurts JJG, Reich DS, Rocca MA, Ropele S, Rovira À, Sati P, Toosy AT, Vrenken H, Gandini Wheeler-Kingshott CAM, Kappos L. Quantitative magnetic resonance imaging towards clinical application in multiple sclerosis. Brain 2021; 144:1296-1311. [PMID: 33970206 PMCID: PMC8219362 DOI: 10.1093/brain/awab029] [Citation(s) in RCA: 64] [Impact Index Per Article: 21.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Revised: 10/25/2020] [Accepted: 11/16/2020] [Indexed: 12/11/2022] Open
Abstract
Quantitative MRI provides biophysical measures of the microstructural integrity of the CNS, which can be compared across CNS regions, patients, and centres. In patients with multiple sclerosis, quantitative MRI techniques such as relaxometry, myelin imaging, magnetization transfer, diffusion MRI, quantitative susceptibility mapping, and perfusion MRI, complement conventional MRI techniques by providing insight into disease mechanisms. These include: (i) presence and extent of diffuse damage in CNS tissue outside lesions (normal-appearing tissue); (ii) heterogeneity of damage and repair in focal lesions; and (iii) specific damage to CNS tissue components. This review summarizes recent technical advances in quantitative MRI, existing pathological validation of quantitative MRI techniques, and emerging applications of quantitative MRI to patients with multiple sclerosis in both research and clinical settings. The current level of clinical maturity of each quantitative MRI technique, especially regarding its integration into clinical routine, is discussed. We aim to provide a better understanding of how quantitative MRI may help clinical practice by improving stratification of patients with multiple sclerosis, and assessment of disease progression, and evaluation of treatment response.
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Affiliation(s)
- Cristina Granziera
- Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Jens Wuerfel
- Medical Image Analysis Center, Basel, Switzerland
- Quantitative Biomedical Imaging Group (qbig), Department of Biomedical Engineering, University of Basel, Basel, Switzerland
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, multiple sclerosis Center Amsterdam, Amsterdam University Medical Center, Amsterdam, The Netherlands
- UCL Institutes of Healthcare Engineering and Neurology, London, UK
| | - Massimiliano Calabrese
- Neurology B, Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
| | - Nicola De Stefano
- Neurology, Department of Medicine, Surgery and Neuroscience, University of Siena, Italy
| | - Christian Enzinger
- Department of Neurology and Division of Neuroradiology, Medical University of Graz, Graz, Austria
| | - Nikos Evangelou
- Division of Clinical Neuroscience, University of Nottingham, Nottingham, UK
| | - Massimo Filippi
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, and Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
| | - Jeroen J G Geurts
- Department of Anatomy and Neurosciences, multiple sclerosis Center Amsterdam, Neuroscience Amsterdam, Amsterdam University Medical Centers, location VUmc, Amsterdam, The Netherlands
| | - Daniel S Reich
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health (NIH), Bethesda, MD, USA
| | - Maria A Rocca
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, and Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Stefan Ropele
- Neuroimaging Research Unit, Department of Neurology, Medical University of Graz, Graz, Austria
| | - Àlex Rovira
- Section of Neuroradiology (Department of Radiology), Vall d'Hebron University Hospital and Research Institute, Barcelona, Spain
| | - Pascal Sati
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health (NIH), Bethesda, MD, USA
- Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Ahmed T Toosy
- Queen Square multiple sclerosis Centre, Department of Neuroinflammation, Queen Square Institute of Neurology, University College London, London, UK
| | - Hugo Vrenken
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, multiple sclerosis Center Amsterdam, Amsterdam University Medical Center, Amsterdam, The Netherlands
| | - Claudia A M Gandini Wheeler-Kingshott
- Queen Square multiple sclerosis Centre, Department of Neuroinflammation, Queen Square Institute of Neurology, University College London, London, UK
- Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
- Brain MRI 3T Research Centre, IRCCS Mondino Foundation, Pavia, Italy
| | - Ludwig Kappos
- Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
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14
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Sakaie K, Fedler JK, Yankey JW, Nakamura K, Debbins J, Lowe MJ, Raska P, Fox RJ. Influence of equipment changes on MRI measures of brain atrophy and brain microstructure in a placebo-controlled trial of ibudilast in progressive multiple sclerosis. Mult Scler J Exp Transl Clin 2021; 7:20552173211010843. [PMID: 34046185 PMCID: PMC8138298 DOI: 10.1177/20552173211010843] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Accepted: 03/28/2021] [Indexed: 01/10/2023] Open
Abstract
Background Hardware changes can be an unavoidable confound in imaging trials. Understanding the impact of such changes may play an important role in the analysis of imaging data. Objective To characterize the effect of equipment changes in a longitudinal, multi-site multiple sclerosis trial. Methods Using data from a clinical trial in progressive multiple sclerosis, we explored how major changes in imaging hardware affected data. We analyzed the extent to which these changes affected imaging biomarkers and the estimated treatment effects by including such changes as a time-dependent covariate. Results Significant differences whole brain atrophy (brain parenchymal fraction, BPF) and microstructure (transverse diffusivity, TD) between scans with and without changes were found and depended on the type of hardware change. A switch from GE HDxt to Siemens Skyra led to significant shifts in BPF (p < 0.04) and TD (p < 0.0001). However, we could not detect the influence of hardware changes on overall trial outcomes- differences between placebo and treatment arms in change over time of BPF and TD (p > 0.5). Conclusions The results suggest that differences among hardware types should be considered when planning and analyzing brain atrophy and diffusivity in a longitudinal clinical trial.
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Affiliation(s)
- Ken Sakaie
- Imaging Institute, The Cleveland Clinic, Cleveland, OH, USA
| | - Janel K Fedler
- Data Coordinating Center, NeuroNEXT, University of Iowa, Iowa City, IA, USA
| | - Jon W Yankey
- Data Coordinating Center, NeuroNEXT, University of Iowa, Iowa City, IA, USA
| | - Kunio Nakamura
- Biomedical Engineering, Lerner Research Institute, The Cleveland Clinic, Cleveland, OH, USA
| | - Josef Debbins
- Keller Center for Imaging Innovation, Barrow Neurological Institute, Phoenix, AZ, USA
| | - Mark J Lowe
- Imaging Institute, The Cleveland Clinic, Cleveland, OH, USA
| | - Paola Raska
- Quantitative Health Sciences, Lerner Research Institute, Cleveland, OH, USA
| | - Robert J Fox
- Mellen Center for Multiple Sclerosis, Neurological Institute, The Cleveland Clinic, Cleveland, OH, USA
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15
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Badji A, Westman E. Cerebrovascular pathology in Alzheimer's disease: Hopes and gaps. Psychiatry Res Neuroimaging 2020; 306:111184. [PMID: 32950333 DOI: 10.1016/j.pscychresns.2020.111184] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/01/2020] [Revised: 06/27/2020] [Accepted: 09/03/2020] [Indexed: 01/10/2023]
Abstract
Alzheimer's disease (AD) is recognized as multifactorial and heterogeneous disease with multiple contributors to its pathophysiology, including vascular dysfunction. Given that a revision of the AT(N) classification is expected in the near future, the present work supports the importance to add an additional vascular (V) category to the framework. In particular, we attempt to shed light on the vascular markers and risk factors that are currently ready-to-be-added to the framework: i) lacunes, ii) white matter hyperintensities and iii) microbleeds seen in Flair, T2* weighted imaging and susceptibility images (SWI). Next, we discuss the added value of other types of imaging, such as diffusion-based metrics and advanced perfusion sequences to encompass more subtle vascular dysfunction. Finally, we highlight the importance to add information about the following cardiovascular risk factors to the framework: history of hypertension, obesity, and diabetes. We believe that adding a V category to the AT(N) framework will improve AD classification and foster efforts to apply the right drug(s) at the right time in the right AD subgroups. Brief communication The present work supports the importance to add an additional vascular (V) category to the AT(N) framework and shed light on the vascular MRI markers and risk factors that are currently ready-to-be-added to the framework.
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Affiliation(s)
- Atef Badji
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montréal, QC, Canada; Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden.
| | - Eric Westman
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
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Spinal cord atrophy in a primary progressive multiple sclerosis trial: Improved sample size using GBSI. NEUROIMAGE-CLINICAL 2020; 28:102418. [PMID: 32961403 PMCID: PMC7509079 DOI: 10.1016/j.nicl.2020.102418] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/22/2020] [Revised: 08/29/2020] [Accepted: 09/03/2020] [Indexed: 01/18/2023]
Abstract
The GBSI provided clinically meaningful measurements of spinal cord atrophy, with low sample size. Deriving spinal cord atrophy from brain MRI using the GBSI is easier than spinal cord MRI. Spinal cord atrophy on GBSI could be used as a secondary outcome measure.
Background We aimed to evaluate the implications for clinical trial design of the generalised boundary-shift integral (GBSI) for spinal cord atrophy measurement. Methods We included 220 primary-progressive multiple sclerosis patients from a phase 2 clinical trial, with baseline and week-48 3DT1-weighted MRI of the brain and spinal cord (1 × 1 × 1 mm3), acquired separately. We obtained segmentation-based cross-sectional spinal cord area (CSA) at C1-2 (from both brain and spinal cord MRI) and C2-5 levels (from spinal cord MRI) using DeepSeg, and, then, we computed corresponding GBSI. Results Depending on the spinal cord segment, we included 67.4–98.1% patients for CSA measurements, and 66.9–84.2% for GBSI. Spinal cord atrophy measurements obtained with GBSI had lower measurement variability, than corresponding CSA. Looking at the image noise floor, the lowest median standard deviation of the MRI signal within the cerebrospinal fluid surrounding the spinal cord was found on brain MRI at the C1-2 level. Spinal cord atrophy derived from brain MRI was related to the corresponding measures from dedicated spinal cord MRI, more strongly for GBSI than CSA. Spinal cord atrophy measurements using GBSI, but not CSA, were associated with upper and lower limb motor progression. Discussion Notwithstanding the reduced measurement variability, the clinical correlates, and the possibility of using brain acquisitions, spinal cord atrophy using GBSI should remain a secondary outcome measure in MS studies, until further advancements increase the quality of acquisition and reliability of processing.
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Generalization of diffusion magnetic resonance imaging–based brain age prediction model through transfer learning. Neuroimage 2020; 217:116831. [DOI: 10.1016/j.neuroimage.2020.116831] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2019] [Revised: 03/18/2020] [Accepted: 03/19/2020] [Indexed: 11/23/2022] Open
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Melzer TR, Keenan RJ, Leeper GJ, Kingston-Smith S, Felton SA, Green SK, Henderson KJ, Palmer NJ, Shoorangiz R, Almuqbel MM, Myall DJ. Test-retest reliability and sample size estimates after MRI scanner relocation. Neuroimage 2020; 211:116608. [PMID: 32032737 DOI: 10.1016/j.neuroimage.2020.116608] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2019] [Revised: 01/30/2020] [Accepted: 02/03/2020] [Indexed: 12/21/2022] Open
Abstract
OBJECTIVE Many factors can contribute to the reliability and robustness of MRI-derived metrics. In this study, we assessed the reliability and reproducibility of three MRI modalities after an MRI scanner was relocated to a new hospital facility. METHODS Twenty healthy volunteers (12 females, mean age (standard deviation) = 41 (11) years, age range [25-66]) completed three MRI sessions. The first session (S1) was one week prior to the 3T GE HDxt scanner relocation. The second (S2) occurred nine weeks after S1 and at the new location; a third session (S3) was acquired 4 weeks after S2. At each session, we acquired structural T1-weighted, pseudo-continuous arterial spin labelled, and diffusion tensor imaging sequences. We used longitudinal processing streams to create 12 summary MRI metrics, including total gray matter (GM), cortical GM, subcortical GM, white matter (WM), and lateral ventricle volume; mean cortical thickness; total surface area; average gray matter perfusion, and average diffusion tensor metrics along principal white matter pathways. We compared mean MRI values and variance at the old scanner location to multiple sessions at the new location using Bayesian multi-level regression models. K-fold cross validation allowed identification of important predictors. Whole-brain analyses were used to investigate any regional differences. Furthermore, we calculated within-subject coefficient of variation (wsCV), intraclass correlation coefficient (ICC), and dice similarity index (SI) of cortical segmentations across scanner relocation and within-site. Additionally, we estimated sample sizes required to robustly detect a 4% difference between two groups across MRI metrics. RESULTS All global MRI metrics exhibited little mean difference and small variability (bar cortical gray matter perfusion) both across scanner relocation and within-site repeat. T1- and DTI-derived tissue metrics showed < |0.3|% mean difference and <1.2% variance across scanner location and <|0.4|% mean difference and <0.8% variance within the new location, with between-site intraclass correlation coefficient (ICC) > 0.80 and within-subject coefficient of variation (wsCV) < 1.4%. Mean cortical gray matter perfusion had the highest between-session variability (6.7% [0.3, 16.7], estimate [95% uncertainty interval]), and hence the smallest ICC (0.71 [0.44,0.92]) and largest wsCV (13.4% [5.4, 18.1]). No global metric exhibited evidence of a meaningful mean difference between scanner locations. However, surface area showed evidence of a mean difference within-site repeat (between S2 and S3). Whole-brain analyses revealed no significant areas of difference between scanner relocation or within-site. For all metrics, we found no support for a systematic difference in variance across relocation sites compared to within-site test-retest reliability. Necessary sample sizes to detect a 4% difference between two independent groups varied from a maximum of n = 362 per group (cortical gray matter perfusion), to total gray matter volume (n = 114), average fractional anisotropy (n = 23), total gray matter volume normalized by intracranial volume (n = 19), and axial diffusivity (n = 3 per group). CONCLUSION Cortical gray matter perfusion was the most variable metric investigated (necessitating large sample sizes to identify group differences), with other metrics showing substantially less variability. Scanner relocation appeared to have a negligible effect on variability of the global MRI metrics tested. This manuscript reports within-site test-retest variability to act as a tool for calculating sample size in future investigations. Our results suggest that when all other parameters are held constant (e.g., sequence parameters and MRI processing), the effect of scanner relocation is indistinguishable from within-site variability, but may need to be considered depending on the question being investigated.
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Affiliation(s)
- Tracy R Melzer
- Department of Medicine, University of Otago, Christchurch, New Zealand; New Zealand Brain Research Institute, Christchurch, New Zealand; Brain Research New Zealand - Rangahau Roro Aotearoa Centre of Research Excellence, New Zealand.
| | - Ross J Keenan
- New Zealand Brain Research Institute, Christchurch, New Zealand; Department of Radiology, Christchurch Hospital, Christchurch, New Zealand; Pacific Radiology Group, Christchurch, New Zealand.
| | | | | | | | | | | | | | - Reza Shoorangiz
- New Zealand Brain Research Institute, Christchurch, New Zealand; Department of Electrical and Computer Engineering, University of Canterbury, Christchurch, New Zealand.
| | - Mustafa M Almuqbel
- Department of Medicine, University of Otago, Christchurch, New Zealand; New Zealand Brain Research Institute, Christchurch, New Zealand; Pacific Radiology Group, Christchurch, New Zealand.
| | - Daniel J Myall
- New Zealand Brain Research Institute, Christchurch, New Zealand.
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Luque Laguna PA, Combes AJE, Streffer J, Einstein S, Timmers M, Williams SCR, Dell'Acqua F. Reproducibility, reliability and variability of FA and MD in the older healthy population: A test-retest multiparametric analysis. NEUROIMAGE-CLINICAL 2020; 26:102168. [PMID: 32035272 PMCID: PMC7011084 DOI: 10.1016/j.nicl.2020.102168] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/07/2019] [Revised: 01/10/2020] [Accepted: 01/10/2020] [Indexed: 12/13/2022]
Abstract
In older healthy subjects, FA and MD show overall good test-retest reliability & reproducibility. MD is sistematically more reproducible than FA across the entire brain anatomy. FA is more reliable than MD in subcortical white matter regions. In high reliability & low reproducibility regions, variability between subjects is high and statistical power is low. In low reliability & high reproducibility regions, variability between subjects is low and statistical power is high.
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Affiliation(s)
- Pedro A Luque Laguna
- Department 5 of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK; Natbrainlab, Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College, London, UK; Sackler Institute for Translational Neurodevelopment, Institute of Psychiatry, Psychology and Neuroscience, King's College, London, UK.
| | - Anna J E Combes
- Department 5 of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
| | - Johannes Streffer
- UCB Biopharma SPRL, Chemin du Foriest B-1420 Braine-l'Alleud, Belgium; Reference Center for Biological Markers of Dementia (BIODEM), Institute Born-Bunge, University of Antwerp, Antwerp, Belgium
| | - Steven Einstein
- Janssen Research and Development LLC, Titusville, NJ, US; UCB Biopharma SPRL, Chemin du Foriest B-1420 Braine-l'Alleud, Belgium
| | - Maarten Timmers
- Janssen Research and Development, a division of Janssen Pharmaceutica NV, Beerse, Belgium; Reference Center for Biological Markers of Dementia (BIODEM), Institute Born-Bunge, University of Antwerp, Antwerp, Belgium
| | - Steve C R Williams
- Department 5 of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK.
| | - Flavio Dell'Acqua
- Natbrainlab, Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College, London, UK; Sackler Institute for Translational Neurodevelopment, Institute of Psychiatry, Psychology and Neuroscience, King's College, London, UK.
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20
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Freund P, Seif M, Weiskopf N, Friston K, Fehlings MG, Thompson AJ, Curt A. MRI in traumatic spinal cord injury: from clinical assessment to neuroimaging biomarkers. Lancet Neurol 2019; 18:1123-1135. [DOI: 10.1016/s1474-4422(19)30138-3] [Citation(s) in RCA: 72] [Impact Index Per Article: 14.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2018] [Revised: 03/22/2019] [Accepted: 03/28/2019] [Indexed: 01/18/2023]
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21
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Smith EE, Biessels GJ, De Guio F, de Leeuw FE, Duchesne S, Düring M, Frayne R, Ikram MA, Jouvent E, MacIntosh BJ, Thrippleton MJ, Vernooij MW, Adams H, Backes WH, Ballerini L, Black SE, Chen C, Corriveau R, DeCarli C, Greenberg SM, Gurol ME, Ingrisch M, Job D, Lam BY, Launer LJ, Linn J, McCreary CR, Mok VC, Pantoni L, Pike GB, Ramirez J, Reijmer YD, Romero JR, Ropele S, Rost NS, Sachdev PS, Scott CJ, Seshadri S, Sharma M, Sourbron S, Steketee RM, Swartz RH, van Oostenbrugge R, van Osch M, van Rooden S, Viswanathan A, Werring D, Dichgans M, Wardlaw JM. Harmonizing brain magnetic resonance imaging methods for vascular contributions to neurodegeneration. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2019; 11:191-204. [PMID: 30859119 PMCID: PMC6396326 DOI: 10.1016/j.dadm.2019.01.002] [Citation(s) in RCA: 53] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
INTRODUCTION Many consequences of cerebrovascular disease are identifiable by magnetic resonance imaging (MRI), but variation in methods limits multicenter studies and pooling of data. The European Union Joint Program on Neurodegenerative Diseases (EU JPND) funded the HARmoNizing Brain Imaging MEthodS for VaScular Contributions to Neurodegeneration (HARNESS) initiative, with a focus on cerebral small vessel disease. METHODS Surveys, teleconferences, and an in-person workshop were used to identify gaps in knowledge and to develop tools for harmonizing imaging and analysis. RESULTS A framework for neuroimaging biomarker development was developed based on validating repeatability and reproducibility, biological principles, and feasibility of implementation. The status of current MRI biomarkers was reviewed. A website was created at www.harness-neuroimaging.org with acquisition protocols, a software database, rating scales and case report forms, and a deidentified MRI repository. CONCLUSIONS The HARNESS initiative provides resources to reduce variability in measurement in MRI studies of cerebral small vessel disease.
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Affiliation(s)
- Eric E. Smith
- Department of Clinical Neurosciences, University of Calgary, Calgary, Alberta, Canada
- Department of Radiology, University of Calgary, Calgary, Alberta, Canada
- Hotchkiss Brain Institute, University of Calgary, Alberta, Canada
| | - Geert Jan Biessels
- Department of Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, Netherlands
| | - François De Guio
- Department of Neurology, Lariboisière Hospital, University Paris Diderot, Paris, France
| | - Frank Erik de Leeuw
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Donders Center for Medical Neuroscience, Radboud University Medical Center, Nijmegen, Netherlands
| | - Simon Duchesne
- CERVO Research Center, Quebec Mental Health Institute, Québec, Canada
- Radiology Department, Université Laval, Québec, Canada
| | - Marco Düring
- Institute for Stroke and Dementia Research (ISD), University Hospital, Ludwig-Maximilians-Universität LMU, Munich, Germany
- German Center for Neurodegenerative Diseases (DZNE, Munich), Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Richard Frayne
- Department of Clinical Neurosciences, University of Calgary, Calgary, Alberta, Canada
- Department of Radiology, University of Calgary, Calgary, Alberta, Canada
- Hotchkiss Brain Institute, University of Calgary, Alberta, Canada
- Seaman Family MR Centre, Foothills Medical Centre, Calgary, Alberta, Canada
| | - M. Arfan Ikram
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, Netherlands
| | - Eric Jouvent
- Department of Neurology, Lariboisière Hospital, University Paris Diderot, Paris, France
| | - Bradley J. MacIntosh
- Heart and Stroke Foundation Canadian Partnership for Stroke Recovery, Department of Medical Biophysics, Sunnybrook Research Institute, University of Toronto, Ontario, Canada
| | - Michael J. Thrippleton
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
- Edinburgh Imaging, University of Edinburgh, Edinburgh, UK
- UK Dementia Research Institute, University of Edinburgh, Edinburgh, UK
| | - Meike W. Vernooij
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, Netherlands
- Department of Radiology and Nuclear Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Hieab Adams
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, Netherlands
- Department of Radiology and Nuclear Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Walter H. Backes
- Department of Radiology & Nuclear Medicine, School for Mental Health & Neuroscience, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Lucia Ballerini
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
- Edinburgh Imaging, University of Edinburgh, Edinburgh, UK
- UK Dementia Research Institute, University of Edinburgh, Edinburgh, UK
| | - Sandra E. Black
- Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Toronto, Ontario, Canada
- Heart and Stroke Foundation Canadian Partnership for Stroke Recovery, Toronto, Ontario, Canada
- Department of Medicine (Neurology), Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada
| | - Christopher Chen
- Memory Aging and Cognition Centre, Department of Pharmacology, National University of Singapore, Singapore
| | - Rod Corriveau
- National Institute of Neurological Disorders and Stroke, Bethesda, MD, USA
| | - Charles DeCarli
- Department of Neurology and Center for Neuroscience, University of California at Davis, Davis, CA, USA
| | - Steven M. Greenberg
- J. Philip Kistler Stroke Research Center, Stroke Service and Memory Disorders Unit, Massachusetts General Hospital, Boston, MA, USA
| | - M. Edip Gurol
- J. Philip Kistler Stroke Research Center, Stroke Service and Memory Disorders Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Michael Ingrisch
- Department of Radiology, Ludwig-Maximilians-University Hospital Munich, Munich, Germany
| | - Dominic Job
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
- Edinburgh Imaging, University of Edinburgh, Edinburgh, UK
- UK Dementia Research Institute, University of Edinburgh, Edinburgh, UK
| | - Bonnie Y.K. Lam
- Therese Pei Fong Chow Research Centre for Prevention of Dementia, Gerald Choa Neuroscience Centre, Lui Che Woo Institute of Innovative Medicine, Department of Medicine and Therapeutics, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong
| | - Lenore J. Launer
- National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
| | - Jennifer Linn
- Institute of Neuroradiology, University Hospital Carl Gustav Carus, Dresden, Germany
| | - Cheryl R. McCreary
- Department of Clinical Neurosciences, University of Calgary, Calgary, Alberta, Canada
- Department of Radiology, University of Calgary, Calgary, Alberta, Canada
- Seaman Family MR Centre, Foothills Medical Centre, Calgary, Alberta, Canada
| | - Vincent C.T. Mok
- Therese Pei Fong Chow Research Centre for Prevention of Dementia, Gerald Choa Neuroscience Centre, Lui Che Woo Institute of Innovative Medicine, Department of Medicine and Therapeutics, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong
| | - Leonardo Pantoni
- Luigi Sacco Department of Biomedical and Clinical Sciences, University of Milan, Milan, Italy
| | - G. Bruce Pike
- Department of Clinical Neurosciences, University of Calgary, Calgary, Alberta, Canada
- Department of Radiology, University of Calgary, Calgary, Alberta, Canada
- Hotchkiss Brain Institute, University of Calgary, Alberta, Canada
| | - Joel Ramirez
- Heart and Stroke Foundation Canadian Partnership for Stroke Recovery, Department of Medical Biophysics, Sunnybrook Research Institute, University of Toronto, Ontario, Canada
- Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Toronto, Ontario, Canada
| | - Yael D. Reijmer
- Department of Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, Netherlands
| | - Jose Rafael Romero
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA
- Framingham Heart Study, Framingham, MA, USA
| | - Stefan Ropele
- Department of Neurology, Medical University of Graz, Graz, Austria
| | - Natalia S. Rost
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Perminder S. Sachdev
- Centre for Healthy Brain Ageing, University of New South Wales, Sydney, Australia
| | - Christopher J.M. Scott
- Heart and Stroke Foundation Canadian Partnership for Stroke Recovery, Department of Medical Biophysics, Sunnybrook Research Institute, University of Toronto, Ontario, Canada
- Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Toronto, Ontario, Canada
| | - Sudha Seshadri
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio, TX, USA
| | - Mukul Sharma
- Population Health Research Institute, Hamilton, Ontario, Canada
- Department of Medicine (Neurology) McMaster University, Hamilton, Ontario, Canada
| | - Steven Sourbron
- Imaging Biomarkers Group, Department of Biomedical Imaging Sciences, University of Leeds, Leeds, UK
| | - Rebecca M.E. Steketee
- Department of Radiology and Nuclear Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Richard H. Swartz
- Department of Medicine (Neurology), University of Toronto, Toronto, Canada
- Hurvitz Brain Sciences Program, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada
| | - Robert van Oostenbrugge
- Department of Neurology, School for Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, the Netherlands
| | - Matthias van Osch
- C.J. Gorter Center for high field MRI, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Sanneke van Rooden
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Anand Viswanathan
- J. Philip Kistler Stroke Research Center, Stroke Service and Memory Disorders Unit, Massachusetts General Hospital, Boston, MA, USA
| | - David Werring
- University College London Queen Square institute of Neurology, London, UK
| | - Martin Dichgans
- Institute for Stroke and Dementia Research (ISD), University Hospital, Ludwig-Maximilians-Universität LMU, Munich, Germany
- German Center for Neurodegenerative Diseases (DZNE, Munich), Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Joanna M. Wardlaw
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
- Edinburgh Imaging, University of Edinburgh, Edinburgh, UK
- UK Dementia Research Institute, University of Edinburgh, Edinburgh, UK
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Vedantam A, Stormes KM, Gadgil N, Kralik SF, Aldave G, Lam SK. Association between postoperative DTI metrics and neurological deficits after posterior fossa tumor resection in children. J Neurosurg Pediatr 2019; 24:364-370. [PMID: 31323626 DOI: 10.3171/2019.5.peds1912] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/20/2019] [Accepted: 05/09/2019] [Indexed: 12/11/2022]
Abstract
OBJECTIVE Resection of posterior fossa tumors in children may be associated with persistent neurological deficits. It is unclear if these neurological deficits are associated with persistent structural damage to the cerebellar pathways. The purpose of this research was to define longitudinal changes in diffusion tensor imaging (DTI) metrics in white matter cerebellar tracts and the clinical correlates of these metrics in children undergoing resection of posterior fossa tumors. METHODS Longitudinal brain DTI was performed in a cohort of pediatric patients who underwent resection of posterior fossa tumors. Fractional anisotropy (FA) of the superior cerebellar peduncles (SCPs) and middle cerebellar peduncles (MCPs) was measured on preoperative, postoperative, and follow-up DTI. Early postoperative (< 48 hours) and longer-term follow-up neurological deficits (mutism, ataxia, and extraocular movement dysfunction) were documented. Statistical analysis was performed to determine differences in FA values based on presence or absence of neurological deficits. Statistical significance was set at p < 0.05. RESULTS Twenty children (mean age 6.1 ± 4.1 years [SD], 12 males and 8 females) were included in this study. Follow-up DTI was performed at a median duration of 14.3 months after surgery, and the median duration of follow-up was 19.7 months. FA of the left SCP was significantly reduced on postoperative DTI in comparison with preoperative DTI (0.44 ± 0.07 vs 0.53 ± 0.1, p = 0.003). Presence of ataxia at follow-up was associated with a persistent reduction in the left SCP FA on follow-up DTI (0.43 ± 0.1 vs 0.55 ± 0.1, p = 0.016). Patients with early postoperative mutism who did not recover at follow-up had significantly decreased FA of the left SCP on early postoperative DTI in comparison with those who recovered (0.38 ± 0.05 vs 0.48 ± 0.06, p = 0.04). CONCLUSIONS DTI after resection of posterior fossa tumors in children shows that persistent reduction of SCP FA is associated with ataxia at follow-up.
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Affiliation(s)
- Aditya Vedantam
- 1Division of Pediatric Neurosurgery, Texas Children's Hospital, Department of Neurosurgery, Baylor College of Medicine, Houston, Texas; and
| | - Katie M Stormes
- 1Division of Pediatric Neurosurgery, Texas Children's Hospital, Department of Neurosurgery, Baylor College of Medicine, Houston, Texas; and
| | - Nisha Gadgil
- 1Division of Pediatric Neurosurgery, Texas Children's Hospital, Department of Neurosurgery, Baylor College of Medicine, Houston, Texas; and
| | - Stephen F Kralik
- 2Department of Radiology, Texas Children's Hospital, Houston, Texas
| | - Guillermo Aldave
- 1Division of Pediatric Neurosurgery, Texas Children's Hospital, Department of Neurosurgery, Baylor College of Medicine, Houston, Texas; and
| | - Sandi K Lam
- 1Division of Pediatric Neurosurgery, Texas Children's Hospital, Department of Neurosurgery, Baylor College of Medicine, Houston, Texas; and
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23
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Prohl AK, Scherrer B, Tomas-Fernandez X, Filip-Dhima R, Kapur K, Velasco-Annis C, Clancy S, Carmody E, Dean M, Valle M, Prabhu SP, Peters JM, Bebin EM, Krueger DA, Northrup H, Wu JY, Sahin M, Warfield SK. Reproducibility of Structural and Diffusion Tensor Imaging in the TACERN Multi-Center Study. Front Integr Neurosci 2019; 13:24. [PMID: 31417372 PMCID: PMC6650594 DOI: 10.3389/fnint.2019.00024] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2019] [Accepted: 06/24/2019] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Multi-site MRI studies are often necessary for recruiting sufficiently sized samples when studying rare conditions. However, they require pooling data from multiple scanners into a single data set, and therefore it is critical to evaluate the variability of quantitative MRI measures within and across scanners used in multi-site studies. The aim of this study was to evaluate the reproducibility of structural and diffusion weighted (DW) MRI measurements acquired on seven scanners at five medical centers as part of the Tuberous Sclerosis Complex Autism Center of Excellence Research Network (TACERN) multisite study. METHODS The American College of Radiology (ACR) phantom was imaged monthly to measure reproducibility of signal intensity and uniformity within and across seven 3T scanners from General Electric, Philips, and Siemens vendors. One healthy adult male volunteer was imaged repeatedly on all seven scanners under the TACERN structural and DW protocol (5 b = 0 s/mm2 and 30 b = 1000 s/mm2) over a period of 5 years (age 22-27 years). Reproducibility of inter- and intra-scanner brain segmentation volumes and diffusion tensor imaging metrics fractional anisotropy (FA) and mean diffusivity (MD) within white matter regions was quantified with coefficient of variation. RESULTS The American College of Radiology Phantom signal intensity and uniformity were similar across scanners and changed little over time, with a mean intra-scanner coefficient of variation of 3.6 and 1.8%, respectively. The mean inter- and intra-scanner coefficients of variation of brain structure volumes derived from T1-weighted (T1w) images of the human phantom were 3.3 and 1.1%, respectively. The mean inter- and intra-scanner coefficients of variation of FA in white matter regions were 4.5 and 2.5%, while the mean inter- and intra-scanner coefficients of variation of MD in white matter regions were 5.4 and 1.5%. CONCLUSION Our results suggest that volumetric and diffusion tensor imaging (DTI) measurements are highly reproducible between and within scanners and provide typical variation amplitudes that can be used as references to interpret future findings in the TACERN network.
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Affiliation(s)
- Anna K. Prohl
- Computational Radiology Laboratory, Department of Radiology, Boston Children’s Hospital, Harvard Medical School, Harvard University, Boston, MA, United States
| | - Benoit Scherrer
- Computational Radiology Laboratory, Department of Radiology, Boston Children’s Hospital, Harvard Medical School, Harvard University, Boston, MA, United States
| | - Xavier Tomas-Fernandez
- Computational Radiology Laboratory, Department of Radiology, Boston Children’s Hospital, Harvard Medical School, Harvard University, Boston, MA, United States
| | - Rajna Filip-Dhima
- Department of Neurology, Boston Children’s Hospital, Harvard Medical School, Harvard University, Boston, MA, United States
| | - Kush Kapur
- Department of Neurology, Boston Children’s Hospital, Harvard Medical School, Harvard University, Boston, MA, United States
| | - Clemente Velasco-Annis
- Computational Radiology Laboratory, Department of Radiology, Boston Children’s Hospital, Harvard Medical School, Harvard University, Boston, MA, United States
| | - Sean Clancy
- Computational Radiology Laboratory, Department of Radiology, Boston Children’s Hospital, Harvard Medical School, Harvard University, Boston, MA, United States
| | - Erin Carmody
- Department of Neurology, Boston Children’s Hospital, Harvard Medical School, Harvard University, Boston, MA, United States
| | - Meghan Dean
- Department of Neurology, Boston Children’s Hospital, Harvard Medical School, Harvard University, Boston, MA, United States
| | - Molly Valle
- Department of Neurology, Boston Children’s Hospital, Harvard Medical School, Harvard University, Boston, MA, United States
| | - Sanjay P. Prabhu
- Division of Neuroradiology, Department of Radiology, Boston Children’s Hospital, Harvard Medical School, Harvard University, Boston, MA, United States
| | - Jurriaan M. Peters
- Computational Radiology Laboratory, Department of Radiology, Boston Children’s Hospital, Harvard Medical School, Harvard University, Boston, MA, United States
- Department of Neurology, Boston Children’s Hospital, Harvard Medical School, Harvard University, Boston, MA, United States
| | - E. Martina Bebin
- Department of Neurology, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Darcy A. Krueger
- Department of Neurology and Rehabilitation Medicine, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, United States
| | - Hope Northrup
- Department of Pediatrics, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Joyce Y. Wu
- Division of Pediatric Neurology, UCLA Mattel Children’s Hospital, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Mustafa Sahin
- Department of Neurology, Boston Children’s Hospital, Harvard Medical School, Harvard University, Boston, MA, United States
- F.M. Kirby Neurobiology Center, Boston Children’s Hospital, Harvard Medical School, Harvard University, Boston, MA, United States
| | - Simon K. Warfield
- Computational Radiology Laboratory, Department of Radiology, Boston Children’s Hospital, Harvard Medical School, Harvard University, Boston, MA, United States
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24
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Stieb S, Elgohari B, Fuller CD. Repetitive MRI of organs at risk in head and neck cancer patients undergoing radiotherapy. Clin Transl Radiat Oncol 2019; 18:131-139. [PMID: 31341989 PMCID: PMC6630152 DOI: 10.1016/j.ctro.2019.04.014] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2019] [Revised: 04/16/2019] [Accepted: 04/16/2019] [Indexed: 02/07/2023] Open
Abstract
First review on MRI changes in head and neck organs at risk during radiotherapy. Focus on dynamics in salivary gland, muscle and bone in the head and neck region. Pointing out the limitations in implementing MRI in guiding radiation therapy.
With emerging technical advances like real-time MR imaging during radiotherapy (RT) with an integrated MR linear accelerator, it will soon be possible to analyze changes in the organs at risk (OARs) during radiotherapy without additional effort for the patients. Until then, patients have to undergo additional MR imaging and often without the same immobilization devices as used for radiotherapy. Consequently, studies with repetitive MRI during the course of radiotherapy are rare, with low patient numbers and with the challenge of registration between the different MR sequences and the varying imaging time points. This review focuses on studies with at least two MRIs, one before and another either during or post-RT, in order to report on RT-induced changes in normal tissues and their correlation with toxicity. We therefore included clinical studies published in English until March 2019, with repetitive MRI of OARs in head and neck cancer patients receiving external beam radiotherapy. OARs analyzed were salivary glands, musculoskeletal structures and bones. MR sequences used included T1, T2, dynamic contrast enhanced (DCE) imaging, diffusion-weighted imaging (DWI), DIXON and MR sialography.
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Affiliation(s)
- Sonja Stieb
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Baher Elgohari
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.,Department of Clinical Oncology and Nuclear Medicine, Mansoura University, Mansoura, Egypt
| | - Clifton David Fuller
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
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25
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Lock C, Kwok J, Kumar S, Ahmad-Annuar A, Narayanan V, Ng ASL, Tan YJ, Kandiah N, Tan EK, Czosnyka Z, Czosnyka M, Pickard JD, Keong NC. DTI Profiles for Rapid Description of Cohorts at the Clinical-Research Interface. Front Med (Lausanne) 2019; 5:357. [PMID: 30687707 PMCID: PMC6335243 DOI: 10.3389/fmed.2018.00357] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2018] [Accepted: 12/12/2018] [Indexed: 12/13/2022] Open
Abstract
Normal pressure hydrocephalus (NPH) is a syndrome comprising gait disturbance, cognitive decline and urinary incontinence that is an unique model of reversible brain injury, but it presents as a challenging spectrum of disease cohorts. Diffusion Tensor Imaging (DTI), with its ability to interrogate structural white matter patterns at a microarchitectural level, is a potentially useful tool for the confirmation and characterization of disease cohorts at the clinical-research interface. However, obstacles to its widespread use involve the need for consistent DTI analysis and interpretation tools across collaborator sites. We present the use of DTI profiles, a simplistic methodology to interpret white matter injury patterns based on the morphology of diffusivity parameters. We examined 13 patients with complex NPH, i.e., patients with NPH and overlay from multiple comorbidities, including vascular risk burden and neurodegenerative disease, undergoing extended CSF drainage, clinical assessments, and multi-modal MR imaging. Following appropriate exclusions, we compared the morphology of DTI profiles in such complex NPH patients (n = 12, comprising 4 responders and 8 non-responders) to exemplar DTI profiles from a cohort of classic NPH patients (n = 16) demonstrating responsiveness of white matter injury to ventriculo-peritoneal shunting. In the cohort of complex NPH patients, mean age was 71.3 ± 7.6 years (10 males, 2 females) with a mean MMSE score of 21.1. There were 5 age-matched healthy controls, mean age was 73.4 ± 7.2 years (1 male, 4 females) and mean MMSE score was 26.8. In the exemplar cohort of classic NPH patients, mean age was 74.7 ± 5.9 years (10 males, 6 females) and mean MMSE score was 24.1. There were 9 age-matched healthy controls, mean age was 69.4 ± 9.7 years (4 males, 5 females) and mean MMSE score was 28.6. We found that, despite the challenges of acquiring DTI metrics from differing scanners across collaborator sites and NPH patients presenting as differing cohorts along the spectrum of disease, DTI profiles for responsiveness to interventions were comparable. Distinct DTI characteristics were demonstrated for complex NPH responders vs. non-responders. The morphology of DTI profiles for complex NPH responders mimicked DTI patterns found in predominantly shunt-responsive patients undergoing intervention for classic NPH. However, DTI profiles for complex NPH non-responders was suggestive of atrophy. Our findings suggest that it is possible to use DTI profiles to provide a methodology for rapid description of differing cohorts of disease at the clinical-research interface. By describing DTI measures morphologically, it was possible to consistently compare white matter injury patterns across international collaborator datasets.
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Affiliation(s)
- Christine Lock
- Department of Neurosurgery, National Neuroscience Institute, Singapore, Singapore
| | - Janell Kwok
- Department of Neurosurgery, National Neuroscience Institute, Singapore, Singapore
| | - Sumeet Kumar
- Department of Neuroradiology, National Neuroscience Institute, Singapore, Singapore
| | - Azlina Ahmad-Annuar
- Department of Biomedical Science, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Vairavan Narayanan
- Division of Neurosurgery, Department of Surgery, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Adeline S L Ng
- Department of Neurology, National Neuroscience Institute, Singapore, Singapore
| | - Yi Jayne Tan
- Department of Neurology, National Neuroscience Institute, Singapore, Singapore
| | - Nagaendran Kandiah
- Department of Neurology, National Neuroscience Institute, Singapore, Singapore.,Duke-NUS Medical School, Singapore, Singapore
| | - Eng-King Tan
- Department of Neurology, National Neuroscience Institute, Singapore, Singapore.,Duke-NUS Medical School, Singapore, Singapore
| | - Zofia Czosnyka
- Neurosurgical Division, Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
| | - Marek Czosnyka
- Neurosurgical Division, Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
| | - John D Pickard
- Neurosurgical Division, Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
| | - Nicole C Keong
- Department of Neurosurgery, National Neuroscience Institute, Singapore, Singapore.,Duke-NUS Medical School, Singapore, Singapore
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