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Schneider N, Mainardi F, Budisavljevic S, Rolands M, Deoni S. Associations between Early Life Nutrient Intakes and Brain Maturation Show Developmental Dynamics from Infancy to Toddlerhood: A Neuroimaging Observation Study. J Nutr 2023; 153:897-908. [PMID: 36931756 PMCID: PMC10196598 DOI: 10.1016/j.tjnut.2023.01.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 12/23/2022] [Accepted: 01/10/2023] [Indexed: 01/15/2023] Open
Abstract
BACKGROUND Myelin imaging has increasingly been applied to study the impact of nutrition on brain development in recent years. Although individual dynamics for nutrient intakes and myelin trajectories previously have been investigated across childhood, the longitudinal interaction between both remains unclear in typically developed children. OBJECTIVES The objective of this work was to explore the developmental dynamics of nutrient-myelin interactions from infancy to early childhood using myelin imaging as a marker for brain maturation. METHODS Brain neuroimaging (1 scan per child) and dietary nutrient intake data were analyzed for 88 nutrients from 293 children (127 female, 62% White) from a longitudinal cohort study in the United States. A sliding window approach was used to investigate correlations between nutrient intakes and brain myelination over a continuous set of age windows. Image processing techniques (Sobel-filter vertical edge detection) were applied to determine age windows with unique association profiles, providing novel insight into how these relationships change with child age. RESULTS We identified 3 nutrient-myelin windows covering the age range of 1-5 y: window 1 from 6 to 20 mo with 60% positive nutrient correlations, window 2 from 20 to 30 mo with 20% positive correlations, and window 3 from 30 to 60 mo with 37% positive correlations. The windows are aligned with reported myelin and white matter dynamics that change in the first 5 y from fast and steep (window 1) to continued but slower growth (window 3), with window 2 possibly representing the inflection period. CONCLUSIONS To our knowledge, this is the first study in typically developing children demonstrating the developmental dynamics between early life nutrient intakes and brain maturation in toddlerhood. The knowledge can be applied for identifying targeted and brain-stage-appropriate nutritional interventions for this critical stage of brain development.
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Affiliation(s)
- Nora Schneider
- Brain Health Department, Nestlé Institute of Health Sciences, Société des Produits Nestlé SA, Vers-Chez-les-Blanc, Lausanne, Switzerland.
| | - Fabio Mainardi
- Applied Data Analytics Group, Nestlé Institute of Health Sciences, Société des Produits Nestlé SA, Vers-Chez-les-Blanc, Lausanne, Switzerland
| | - Sanja Budisavljevic
- Brain Health Department, Nestlé Institute of Health Sciences, Société des Produits Nestlé SA, Vers-Chez-les-Blanc, Lausanne, Switzerland
| | - Maryann Rolands
- Nutrition Science Group, Nestlé Institute of Health Sciences, Société des Produits Nestlé SA, Vers-Chez-les-Blanc, Lausanne, Switzerland
| | - Sean Deoni
- Advanced Baby Imaging Lab, Rhode Island Hospital, Providence, RI, USA; Department of Radiology, Warren Alpert Medical School at Brown University, Providence, RI, USA; Spinn Neuroscience, Seattle, WA, USA
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Ramos-Llordén G, Lobos RA, Kim TH, Tian Q, Witzel T, Lee HH, Scholz A, Keil B, Yendiki A, Bilgiç B, Haldar JP, Huang SY. High-fidelity, high-spatial-resolution diffusion magnetic resonance imaging of ex vivo whole human brain at ultra-high gradient strength with structured low-rank echo-planar imaging ghost correction. NMR IN BIOMEDICINE 2023; 36:e4831. [PMID: 36106429 PMCID: PMC9883835 DOI: 10.1002/nbm.4831] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 08/20/2022] [Accepted: 09/11/2022] [Indexed: 06/15/2023]
Abstract
Diffusion magnetic resonance imaging (dMRI) of whole ex vivo human brain specimens enables three-dimensional (3D) mapping of structural connectivity at the mesoscopic scale, providing detailed evaluation of fiber architecture and tissue microstructure at a spatial resolution that is difficult to access in vivo. To account for the short T2 and low diffusivity of fixed tissue, ex vivo dMRI is often acquired using strong diffusion-sensitizing gradients and multishot/segmented 3D echo-planar imaging (EPI) sequences to achieve high spatial resolution. However, the combination of strong diffusion-sensitizing gradients and multishot/segmented EPI readout can result in pronounced ghosting artifacts incurred by nonlinear spatiotemporal variations in the magnetic field produced by eddy currents. Such ghosting artifacts cannot be corrected with conventional correction solutions and pose a significant roadblock to leveraging human MRI scanners with ultrahigh gradients for ex vivo whole-brain dMRI. Here, we show that ghosting-correction approaches that correct for either polarity-related ghosting or shot-to-shot variations in a separate manner are suboptimal for 3D multishot diffusion-weighted EPI experiments in fixed human brain specimens using strong diffusion-sensitizing gradients on the 3-T Connectom MRI scanner, resulting in orientationally biased dMRI estimates. We apply a recently developed advanced k-space reconstruction method based on structured low-rank matrix (SLM) modeling that handles both polarity-related ghosting and shot-to-shot variation simultaneously, to mitigate artifacts in high-angular resolution multishot dMRI data acquired in several fixed human brain specimens at 0.7-0.8-mm isotropic spatial resolution using b-values up to 10,000 s/mm2 and gradient strengths up to 280 mT/m. We demonstrate the improved mapping of diffusion tensor imaging and fiber orientation distribution functions in key neuroanatomical areas distributed across the whole brain using SLM-based EPI ghost correction compared with alternative techniques.
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Affiliation(s)
- Gabriel Ramos-Llordén
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Rodrigo A. Lobos
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California, USA
| | - Tae Hyung Kim
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
- Computer Engineering, Hongik University, Seoul, Republic of Korea
| | - Qiyuan Tian
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | | | - Hong-Hsi Lee
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Alina Scholz
- Institute of Medical Physics and Radiation Protection, Mittelhessen University of Applied Sciences, Giessen, Germany
| | - Boris Keil
- Institute of Medical Physics and Radiation Protection, Mittelhessen University of Applied Sciences, Giessen, Germany
- Department of Diagnostic and Interventional Radiology, University Hospital Marburg, Philipps University of Marburg, Marburg, Germany
| | - Anastasia Yendiki
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Berkin Bilgiç
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
- Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Justin P. Haldar
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California, USA
| | - Susie Y. Huang
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
- Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
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Quantitative Relaxometry Metrics for Brain Metastases Compared to Normal Tissues: A Pilot MR Fingerprinting Study. Cancers (Basel) 2022; 14:cancers14225606. [PMID: 36428699 PMCID: PMC9688653 DOI: 10.3390/cancers14225606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 11/03/2022] [Accepted: 11/09/2022] [Indexed: 11/17/2022] Open
Abstract
The purpose of the present pilot study was to estimate T1 and T2 metric values derived simultaneously from a new, rapid Magnetic Resonance Fingerprinting (MRF) technique, as well as to assess their ability to characterize-brain metastases (BM) and normal-appearing brain tissues. Fourteen patients with BM underwent MRI, including prototype MRF, on a 3T scanner. In total, 108 measurements were analyzed: 42 from solid parts of BM's (21 each on T1 and T2 maps) and 66 from normal-appearing brain tissue (11 ROIs each on T1 and T2 maps for gray matter [GM], white matter [WM], and cerebrospinal fluid [CSF]). The BM's mean T1 and T2 values differed significantly from normal-appearing WM (p < 0.05). The mean T1 values from normal-appearing GM, WM, and CSF regions were 1205 ms, 840 ms, and 4233 ms, respectively. The mean T2 values were 108 ms, 78 ms, and 442 ms, respectively. The mean T1 and T2 values for untreated BM (n = 4) were 2035 ms and 168 ms, respectively. For treated BM (n = 17) the T1 and T2 values were 2163 ms and 141 ms, respectively. MRF technique appears to be a promising and rapid quantitative method for the characterization of free water content and tumor morphology in BMs.
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Boots EA, Castellanos KJ, Zhan L, Barnes LL, Tussing-Humphreys L, Deoni SCL, Lamar M. Inflammation, Cognition, and White Matter in Older Adults: An Examination by Race. Front Aging Neurosci 2020; 12:553998. [PMID: 33192454 PMCID: PMC7662133 DOI: 10.3389/fnagi.2020.553998] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Accepted: 10/12/2020] [Indexed: 11/18/2022] Open
Abstract
Objectives Non-Latino Black adults have greater risk for Alzheimer’s dementia compared to non-Latino White adults, possibly due to factors disproportionally affecting Black adults including cardiovascular disease (CVD). Chronic peripheral inflammation is implicated in both Alzheimer’s dementia and CVD and is known to impact cognition and cerebral white matter, yet little work has examined these associations by race. This study examined associations between inflammation, cognition, and cerebral white matter generally, and by race. Methods Eighty-six non-demented older Black and White participants (age = 69.03; 50% female; 45% Black participants) underwent fasting venipuncture, cognitive testing, and MRI. Serum was assayed for interleukin-6 (IL-6), C-reactive protein (CRP), and interleukin 1-beta. Cognitive domains included memory, executive function, and attention/information processing. MRI measures included white matter hyperintensity volumes (WMH) and quantification of white matter integrity in areas outside WMHs via DTI-derived fractional anisotropy (FA) and mean diffusivity, as well as multi-component relaxometry derived myelin water fraction (MWF). Results Black and White participants did not differ on age, sex, or CVD risk. Separate linear regression models adjusting for relevant confounders revealed that higher IL-6 associated with lower executive function and higher CRP levels associated with lower FA and MWF. Stratified analyses revealed that these association were significant for Black participants only. Discussion These findings suggest that peripheral inflammation is inversely associated with select cognitive domains and white matter integrity (but not WMHs), particularly in older Black adults. It is important to consider race when investigating inflammatory associates of brain and behavior.
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Affiliation(s)
- Elizabeth A Boots
- Department of Psychology, University of Illinois at Chicago, Chicago, IL, United States.,Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, United States
| | - Karla J Castellanos
- Institute for Health Research and Policy, University of Illinois at Chicago, Chicago, IL, United States
| | - Liang Zhan
- Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, United States
| | - Lisa L Barnes
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, United States.,Department of Psychiatry and Behavioral Sciences, Rush University Medical Center, Chicago, IL, United States
| | - Lisa Tussing-Humphreys
- Institute for Health Research and Policy, University of Illinois at Chicago, Chicago, IL, United States.,Division of Academic Internal Medicine and Geriatrics, College of Medicine, University of Illinois at Chicago, Chicago, IL, United States.,University of Illinois Cancer Center, Chicago, IL, United States
| | - Sean C L Deoni
- Advanced Baby Imaging Lab, Women and Infants Hospital, and Department of Pediatrics, Warren Alpert Medical School of Brown University, Providence, RI, United States
| | - Melissa Lamar
- Department of Psychology, University of Illinois at Chicago, Chicago, IL, United States.,Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, United States.,Department of Psychiatry and Behavioral Sciences, Rush University Medical Center, Chicago, IL, United States
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Hnilicová P, Štrbák O, Kolisek M, Kurča E, Zeleňák K, Sivák Š, Kantorová E. Current Methods of Magnetic Resonance for Noninvasive Assessment of Molecular Aspects of Pathoetiology in Multiple Sclerosis. Int J Mol Sci 2020; 21:E6117. [PMID: 32854318 PMCID: PMC7504207 DOI: 10.3390/ijms21176117] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Revised: 08/13/2020] [Accepted: 08/21/2020] [Indexed: 12/29/2022] Open
Abstract
Multiple sclerosis (MS) is an autoimmune disease with expanding axonal and neuronal degeneration in the central nervous system leading to motoric dysfunctions, psychical disability, and cognitive impairment during MS progression. The exact cascade of pathological processes (inflammation, demyelination, excitotoxicity, diffuse neuro-axonal degeneration, oxidative and metabolic stress, etc.) causing MS onset is still not fully understood, although several accompanying biomarkers are particularly suitable for the detection of early subclinical changes. Magnetic resonance (MR) methods are generally considered to be the most sensitive diagnostic tools. Their advantages include their noninvasive nature and their ability to image tissue in vivo. In particular, MR spectroscopy (proton 1H and phosphorus 31P MRS) is a powerful analytical tool for the detection and analysis of biomedically relevant metabolites, amino acids, and bioelements, and thus for providing information about neuro-axonal degradation, demyelination, reactive gliosis, mitochondrial and neurotransmitter failure, cellular energetic and membrane alternation, and the imbalance of magnesium homeostasis in specific tissues. Furthermore, the MR relaxometry-based detection of accumulated biogenic iron in the brain tissue is useful in disease evaluation. The early description and understanding of the developing pathological process might be critical for establishing clinically effective MS-modifying therapies.
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Affiliation(s)
- Petra Hnilicová
- Biomedical Center Martin, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, 036 01 Martin, Slovakia; (O.Š.); (M.K.)
| | - Oliver Štrbák
- Biomedical Center Martin, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, 036 01 Martin, Slovakia; (O.Š.); (M.K.)
| | - Martin Kolisek
- Biomedical Center Martin, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, 036 01 Martin, Slovakia; (O.Š.); (M.K.)
| | - Egon Kurča
- Clinic of Neurology, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, 036 01 Martin, Slovakia; (E.K.); (Š.S.); (E.K.)
| | - Kamil Zeleňák
- Clinic of Radiology, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, 036 01 Martin, Slovakia;
| | - Štefan Sivák
- Clinic of Neurology, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, 036 01 Martin, Slovakia; (E.K.); (Š.S.); (E.K.)
| | - Ema Kantorová
- Clinic of Neurology, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, 036 01 Martin, Slovakia; (E.K.); (Š.S.); (E.K.)
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Cao P, liu J, Tang S, Leynes AP, Lupo JM, Xu D, Larson PEZ. Technical Note: Simultaneous segmentation and relaxometry for MRI through multitask learning. Med Phys 2019; 46:4610-4621. [PMID: 31396973 PMCID: PMC6800607 DOI: 10.1002/mp.13756] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2018] [Revised: 07/26/2019] [Accepted: 07/30/2019] [Indexed: 11/09/2022] Open
Abstract
PURPOSE This study demonstrated a magnetic resonance (MR) signal multitask learning method for three-dimensional (3D) simultaneous segmentation and relaxometry of human brain tissues. MATERIALS AND METHODS A 3D inversion-prepared balanced steady-state free precession sequence was used for acquiring in vivo multicontrast brain images. The deep neural network contained three residual blocks, and each block had 8 fully connected layers with sigmoid activation, layer norm, and 256 neurons in each layer. Online-synthesized MR signal evolutions and labels were used to train the neural network batch-by-batch. Empirically defined ranges of T1 and T2 values for the normal gray matter, white matter, and cerebrospinal fluid (CSF) were used as the prior knowledge. MRI brain experiments were performed on three healthy volunteers. The mean and standard deviation for the T1 and T2 values in vivo were reported and compared to literature values. Additional animal (N = 6) and prostate patient (N = 1) experiments were performed to compare the estimated T1 and T2 values with those from gold standard methods and to demonstrate clinical applications of the proposed method. RESULTS In animal validation experiment, the differences/errors (mean difference ± standard deviation of difference) between the T1 and T2 values estimated from the proposed method and the ground truth were 113 ± 486 and 154 ± 512 ms for T1, and 5 ± 33 and 7 ± 41 ms for T2, respectively. In healthy volunteer experiments (N = 3), whole brain segmentation and relaxometry were finished within ~ 5 s. The estimated apparent T1 and T2 maps were in accordance with known brain anatomy, and not affected by coil sensitivity variation. Gray matter, white matter, and CSF were successfully segmented. The deep neural network can also generate synthetic T1- and T2-weighted images. CONCLUSION The proposed multitask learning method can directly generate brain apparent T1 and T2 maps, as well as synthetic T1- and T2-weighted images, in conjunction with segmentation of gray matter, white matter, and CSF.
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Affiliation(s)
- Peng Cao
- Department of Radiology and Biomedical Imaging, University of California at San Francisco, San Francisco, CA 94158, USA
| | - Jing liu
- Department of Radiology and Biomedical Imaging, University of California at San Francisco, San Francisco, CA 94158, USA
| | - Shuyu Tang
- Department of Radiology and Biomedical Imaging, University of California at San Francisco, San Francisco, CA 94158, USA
| | - Andrew P. Leynes
- Department of Radiology and Biomedical Imaging, University of California at San Francisco, San Francisco, CA 94158, USA
| | - Janine M. Lupo
- Department of Radiology and Biomedical Imaging, University of California at San Francisco, San Francisco, CA 94158, USA
| | - Duan Xu
- Department of Radiology and Biomedical Imaging, University of California at San Francisco, San Francisco, CA 94158, USA
| | - Peder E. Z. Larson
- Department of Radiology and Biomedical Imaging, University of California at San Francisco, San Francisco, CA 94158, USA
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Sphingomyelin in Brain and Cognitive Development: Preliminary Data. eNeuro 2019; 6:ENEURO.0421-18.2019. [PMID: 31324675 PMCID: PMC6709232 DOI: 10.1523/eneuro.0421-18.2019] [Citation(s) in RCA: 60] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2018] [Revised: 06/12/2019] [Accepted: 07/05/2019] [Indexed: 01/19/2023] Open
Abstract
Sphingomyelin (SM) supports brain myelination, a process closely associated with cognitive maturation. The presence of SM in breast milk suggests a role in infant nutrition; however, little is known about SM contribution to healthy cognitive development. We investigated the link between early life dietary SM, later cognitive development and myelination using an exploratory observational study of neurotypical children. SM levels were quantified in infant nutrition products fed in the first three months of life and associated with myelin content (brain MRI) as well as cognitive development (Mullen scales of early learning; MSEL). Higher levels of SM were significantly associated with higher rates of change in verbal development in the first two years of life (r = 0.65, p < 0.001), as well as, higher levels of myelin content at 12–24 months, delayed onset and/or more prolonged rates of myelination in different brain areas. Second, we explored mechanisms of action using in vitro models (Sprague Dawley rat pups). In vitro data showed SM treatment resulted in increased proliferation [p = 0.0133 and p = 0.0434 at 4 and 10 d in vitro (DIV)], maturation (p = 0.467 at 4 d DIV) and differentiation (p = 0.0123 and p = 0.0369 at 4 and 10 DIV) of oligodendrocyte precursor cells (OPCs), as well as increased axon myelination (p = 0.0005 at 32 DIV). These findings indicate an impact of dietary SM on cognitive development in healthy children, potentially modulated by oligodendrocytes and increased axon myelination. Future research should include randomized controlled trials to substantiate efficacy of SM for cognitive benefits together with preclinical studies examining SM bioavailability and brain uptake.
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Waterton JC, Hines CDG, Hockings PD, Laitinen I, Ziemian S, Campbell S, Gottschalk M, Green C, Haase M, Hassemer K, Juretschke HP, Koehler S, Lloyd W, Luo Y, Mahmutovic Persson I, O'Connor JPB, Olsson LE, Pindoria K, Schneider JE, Sourbron S, Steinmann D, Strobel K, Tadimalla S, Teh I, Veltien A, Zhang X, Schütz G. Repeatability and reproducibility of longitudinal relaxation rate in 12 small-animal MRI systems. Magn Reson Imaging 2019; 59:121-129. [PMID: 30872166 PMCID: PMC6477178 DOI: 10.1016/j.mri.2019.03.008] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2018] [Revised: 01/29/2019] [Accepted: 03/08/2019] [Indexed: 12/13/2022]
Abstract
BACKGROUND Many translational MR biomarkers derive from measurements of the water proton longitudinal relaxation rate R1, but evidence for between-site reproducibility of R1 in small-animal MRI is lacking. OBJECTIVE To assess R1 repeatability and multi-site reproducibility in phantoms for preclinical MRI. METHODS R1 was measured by saturation recovery in 2% agarose phantoms with five nickel chloride concentrations in 12 magnets at 5 field strengths in 11 centres on two different occasions within 1-13 days. R1 was analysed in three different regions of interest, giving 360 measurements in total. Root-mean-square repeatability and reproducibility coefficients of variation (CoV) were calculated. Propagation of reproducibility errors into 21 translational MR measurements and biomarkers was estimated. Relaxivities were calculated. Dynamic signal stability was also measured. RESULTS CoV for day-to-day repeatability (N = 180 regions of interest) was 2.34% and for between-centre reproducibility (N = 9 centres) was 1.43%. Mostly, these do not propagate to biologically significant between-centre error, although a few R1-based MR biomarkers were found to be quite sensitive even to such small errors in R1, notably in myocardial fibrosis, in white matter, and in oxygen-enhanced MRI. The relaxivity of aqueous Ni2+ in 2% agarose varied between 0.66 s-1 mM-1 at 3 T and 0.94 s-1 mM-1 at 11.7T. INTERPRETATION While several factors affect the reproducibility of R1-based MR biomarkers measured preclinically, between-centre propagation of errors arising from intrinsic equipment irreproducibility should in most cases be small. However, in a few specific cases exceptional efforts might be required to ensure R1-reproducibility.
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Affiliation(s)
- John C Waterton
- Bioxydyn Ltd, Manchester Science Park, Rutherford House, Pencroft Way, MANCHESTER M15 6SZ, United Kingdom; Centre for Imaging Sciences, Division of Informatics Imaging & Data Sciences, School of Health Sciences, Faculty of Biology Medicine & Health, University of Manchester, Manchester Academic Health Sciences Centre, MANCHESTER M13 9PL, United Kingdom.
| | | | - Paul D Hockings
- Antaros Medical, BioVenture Hub, 43183 Mölndal, Sweden; MedTech West, Chalmers University of Technology, Gothenburg, Sweden.
| | - Iina Laitinen
- Sanofi-Aventis Deutschland GmbH, R&D TIM - Bioimaging Germany, Industriepark Höchst, D-65926 Frankfurt am Main, Germany.
| | - Sabina Ziemian
- Bayer AG, Research and Development, Pharmaceuticals, MR and CT Contrast Media Research, Müllerstraße 178, D-13353 Berlin, Germany.
| | - Simon Campbell
- In-Vivo Bioimaging UK, RD Platform Technology & Science, GSK Medicines Research Centre, Gunnels Wood Road, STEVENAGE, Hertfordshire, SG1 2NY, United Kingdom.
| | - Michael Gottschalk
- Lund University BioImaging Center, Klinikgatan 32, SE-222-42 Lund, Sweden.
| | - Claudia Green
- Bayer AG, Research and Development, Pharmaceuticals, MR and CT Contrast Media Research, Müllerstraße 178, D-13353 Berlin, Germany.
| | - Michael Haase
- In-Vivo Bioimaging UK, RD Platform Technology & Science, GSK Medicines Research Centre, Gunnels Wood Road, STEVENAGE, Hertfordshire, SG1 2NY, United Kingdom.
| | - Katja Hassemer
- Sanofi-Aventis Deutschland GmbH, R&D TIM - Bioimaging Germany, Industriepark Höchst, D-65926 Frankfurt am Main, Germany.
| | - Hans-Paul Juretschke
- Sanofi-Aventis Deutschland GmbH, R&D TIM - Bioimaging Germany, Industriepark Höchst, D-65926 Frankfurt am Main, Germany
| | - Sascha Koehler
- Bruker BioSpin MRI GmbH, Rudolf-Plank-Straße 23, D-76275 Ettlingen, Germany.
| | - William Lloyd
- Centre for Imaging Sciences, Division of Informatics Imaging & Data Sciences, School of Health Sciences, Faculty of Biology Medicine & Health, University of Manchester, Manchester Academic Health Sciences Centre, MANCHESTER M13 9PL, United Kingdom.
| | - Yanping Luo
- iSAT Discovery, Abbvie, 1 North Waukegan Road, North Chicago, IL, 60064-1802, United States of America.
| | - Irma Mahmutovic Persson
- Department of Translational Sciences, Medical Radiation Physics, Lund University, Skåne University Hospital, SE-205 02 Malmö, Sweden.
| | - James P B O'Connor
- Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology Medicine & Health, University of Manchester, Manchester Academic Health Sciences Centre, MANCHESTER M20 4BX, United Kingdom. james.o'
| | - Lars E Olsson
- Department of Translational Sciences, Medical Radiation Physics, Lund University, Skåne University Hospital, SE-205 02 Malmö, Sweden.
| | - Kashmira Pindoria
- In-Vivo Bioimaging UK, RD Platform Technology & Science, GSK Medicines Research Centre, Gunnels Wood Road, STEVENAGE, Hertfordshire, SG1 2NY, United Kingdom.
| | - Jurgen E Schneider
- Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds LS2 9JT, United Kingdom.
| | - Steven Sourbron
- Leeds Imaging Biomarkers Group, Department of Biomedical Imaging Sciences, University of Leeds, LIGHT Labs, Clarendon Way, LEEDS LS2 9JT, United Kingdom.
| | - Denise Steinmann
- Sanofi-Aventis Deutschland GmbH, R&D TIM - Bioimaging Germany, Industriepark Höchst, D-65926 Frankfurt am Main, Germany.
| | - Klaus Strobel
- Bruker BioSpin MRI GmbH, Rudolf-Plank-Straße 23, D-76275 Ettlingen, Germany.
| | - Sirisha Tadimalla
- Leeds Imaging Biomarkers Group, Department of Biomedical Imaging Sciences, University of Leeds, LIGHT Labs, Clarendon Way, LEEDS LS2 9JT, United Kingdom.
| | - Irvin Teh
- Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds LS2 9JT, United Kingdom.
| | - Andor Veltien
- Radboud university medical center, Radiology (766), P.O.Box 9101, 6500, HB, Nijmegen, the Netherlands.
| | - Xiaomeng Zhang
- iSAT Discovery, Abbvie, 1 North Waukegan Road, North Chicago, IL, 60064-1802, United States of America.
| | - Gunnar Schütz
- Bayer AG, Research and Development, Pharmaceuticals, MR and CT Contrast Media Research, Müllerstraße 178, D-13353 Berlin, Germany.
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Cao X, Ye H, Liao C, Li Q, He H, Zhong J. Fast 3D brain MR fingerprinting based on multi-axis spiral projection trajectory. Magn Reson Med 2019; 82:289-301. [PMID: 30883867 DOI: 10.1002/mrm.27726] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2018] [Revised: 02/09/2019] [Accepted: 02/12/2019] [Indexed: 01/12/2023]
Abstract
PURPOSE To develop a fast, sub-millimeter 3D magnetic resonance fingerprinting (MRF) technique for whole-brain quantitative scans. METHODS An acquisition trajectory based on multi-axis spiral projection imaging (maSPI) was implemented for 3D MRF with steady-state precession and slab excitation. By appropriately assigning the in-plane and through-plane rotations of spiral interleaves in a novel acquisition scheme, an maSPI-based acquisition was implemented, and the total acquisition time was reduced by up to a factor of 8 compared to stack-of-spiral (SOS)-based acquisition. A sliding-window method was also used to further reduce the required number of time points for a faster acquisition. The experiments were conducted both on a phantom and in vivo. RESULTS The results from the phantom measurements with the proposed and gold standard methods were consistent with a good linear correlation and an R2 value approaching 0.99. The in vivo experiments achieved whole-brain parametric maps with isotropic resolutions of 1 mm and 0.8 mm in 5.0 and 6.0 min, respectively, with potential for further acceleration. An in vivo experiment with intentionally moving subjects demonstrated that the maSPI scheme largely outperforms the SOS scheme in terms of robustness to head motion. CONCLUSION 3D MRF with an maSPI acquisition scheme enables fast and robust scans for high-resolution parametric mapping.
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Affiliation(s)
- Xiaozhi Cao
- Center for Brain Imaging Science and Technology, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Huihui Ye
- Center for Brain Imaging Science and Technology, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, Zhejiang, China.,State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, Zhejiang, China
| | - Congyu Liao
- Center for Brain Imaging Science and Technology, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Qing Li
- Center for Brain Imaging Science and Technology, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Hongjian He
- Center for Brain Imaging Science and Technology, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Jianhui Zhong
- Center for Brain Imaging Science and Technology, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, Zhejiang, China.,Department of Imaging Sciences, University of Rochester, Rochester, New York
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10
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Bonnier G, Fischi-Gomez E, Roche A, Hilbert T, Kober T, Krueger G, Granziera C. Personalized pathology maps to quantify diffuse and focal brain damage. NEUROIMAGE-CLINICAL 2018; 21:101607. [PMID: 30502080 PMCID: PMC6413479 DOI: 10.1016/j.nicl.2018.11.017] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/14/2018] [Revised: 10/02/2018] [Accepted: 11/18/2018] [Indexed: 01/04/2023]
Abstract
Background and objectives Quantitative MRI (qMRI) permits the quantification of brain changes compatible with inflammation, degeneration and repair in multiple sclerosis (MS) patients. In this study, we propose a new method to provide personalized maps of tissue alterations and longitudinal brain changes based on different qMRI metrics, which provide complementary information about brain pathology. Methods We performed baseline and two-years follow-up on (i) 13 relapsing-remitting MS patients and (ii) four healthy controls. A group consisting of up to 65 healthy controls was used to compute the reference distribution of qMRI metrics in healthy tissue. All subjects underwent 3T MRI examinations including T1, T2, T2* relaxation and Magnetization Transfer Ratio (MTR) imaging. We used a recent partial volume estimation algorithm to estimate the concentration of different brain tissue types on T1 maps; then, we computed a deviation map (z-score map) for each contrast at both time-points. Finally, we subtracted those deviation maps only for voxels showing a significant difference with healthy tissue in one of the time points, to obtain a difference map for each subject. Results and conclusion Control subjects did not show any significant z-score deviations or longitudinal z-score changes. On the other hand, MS patients showed brain regions with cross-sectional and longitudinal concomitant increase in T1, T2, T2* z-scores and decrease of MTR z-scores, suggesting brain tissue degeneration/loss. In the lesion periphery, we observed areas with cross-sectional and longitudinal decreased T1/T2 and slight decrease in T2* most likely related to iron accumulation. Moreover, we measured longitudinal decrease in T1, T2 - and to a lesser extent in T2* - as well as a concomitant increase in MTR, suggesting remyelination/repair. In summary, we have developed a method that provides whole-brain personalized maps of cross-sectional and longitudinal changes in MS patients, which are computed in patient space. These maps may open new perspectives to complement and support radiological evaluation of brain damage for a given patient.
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Affiliation(s)
- G Bonnier
- MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, United States
| | - E Fischi-Gomez
- MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, United States; Signal Processing Laboratory 5 (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
| | - A Roche
- Advanced Clinical Imaging Technology (HC CEMEA SUI DI PI), Siemens Healthcare AG, Switzerland; Department of Radiology, University Hospital (CHUV), Lausanne, Switzerland; Signal Processing Laboratory 5 (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - T Hilbert
- Advanced Clinical Imaging Technology (HC CEMEA SUI DI PI), Siemens Healthcare AG, Switzerland; Department of Radiology, University Hospital (CHUV), Lausanne, Switzerland; Signal Processing Laboratory 5 (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - T Kober
- Advanced Clinical Imaging Technology (HC CEMEA SUI DI PI), Siemens Healthcare AG, Switzerland; Department of Radiology, University Hospital (CHUV), Lausanne, Switzerland; Signal Processing Laboratory 5 (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - G Krueger
- Siemens Healthcare AG (HC CEMEA DI), Zürich, Switzerland
| | - C Granziera
- MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, United States; 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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11
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Liao C, Wang K, Cao X, Li Y, Wu D, Ye H, Ding Q, He H, Zhong J. Detection of Lesions in Mesial Temporal Lobe Epilepsy by Using MR Fingerprinting. Radiology 2018; 288:804-812. [PMID: 29916782 DOI: 10.1148/radiol.2018172131] [Citation(s) in RCA: 47] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Purpose To improve diagnosis of hippocampal sclerosis (HS) in patients with mesial temporal lobe epilepsy (MTLE) by using MR fingerprinting and compare with visual assessment of T1- and T2-weighted MR images. Materials and Methods For this prospective study performed between April and November 2016, T1 and T2 maps were obtained and tissue segmentation performed in consecutive patients with drug-resistant MTLE with unilateral or bilateral HS. T1 and T2 maps were compared between 33 patients with MTLE (23 women and 10 men; mean age, 32.6 years; age range, 16-60 years) and 30 healthy participants (20 women and 10 men; mean age, 28.8 years; age range, 18-40 years). Differences in individual bilateral hippocampi were compared by using a Wilcoxon signed rank test, whereas the Wilcoxon rank-sum test was used for difference analysis between healthy control participants and patients with MTLE. Results The diagnosis rate (ie, ratio of HS diagnosed on the basis of a 2.5-minute MR fingerprinting examination compared with standard methods: MRI, electroencephalography, and PET) was 32 of 33 (96.9%; 95% confidence interval: 84.9%, 100%), reflecting improved accuracy of diagnosis (P = 1.92 × 10-12) over routine MR examinations that had a diagnostic rate of 23 of 33 (69.7%; 95% confidence interval: 51.5%, 81.6%). The comparison between atrophic and normal-appearing hippocampus in 33 patients with MTLE and healthy control participants demonstrated that both T1 and T2 values in HS lesions were higher than those of normal hippocampal tissue of healthy participants (T1: 1361 msec ± 85 vs 1249 msec ± 59, respectively; T2: 135 msec ± 15 vs 104 msec ± 9, respectively; P < .0001). Conclusion MR fingerprinting allowed for multiparametric mapping of temporal lobe within 2.5 minutes and helped to identify lesions suspicious for HS in patients with MTLE with improved accuracy.
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Affiliation(s)
- Congyu Liao
- From the Center for Brain Imaging Science and Technology, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrumental Science (C.L., X.C., Y.L., H.Y., Q.D., H.H., J.Z.), Department of Neurology, The First Affiliated Hospital (K.W., D.W.), State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering (H.Y.), and Center for Innovative and Collaborative Detection and Treatment of Infectious Diseases (J.Z.), Zhejiang University, 38 Zheda Rd, Hangzhou, Zhejiang 310027, China; and the Department of Imaging Sciences, University of Rochester, Rochester, NY (J.Z.)
| | - Kang Wang
- From the Center for Brain Imaging Science and Technology, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrumental Science (C.L., X.C., Y.L., H.Y., Q.D., H.H., J.Z.), Department of Neurology, The First Affiliated Hospital (K.W., D.W.), State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering (H.Y.), and Center for Innovative and Collaborative Detection and Treatment of Infectious Diseases (J.Z.), Zhejiang University, 38 Zheda Rd, Hangzhou, Zhejiang 310027, China; and the Department of Imaging Sciences, University of Rochester, Rochester, NY (J.Z.)
| | - Xiaozhi Cao
- From the Center for Brain Imaging Science and Technology, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrumental Science (C.L., X.C., Y.L., H.Y., Q.D., H.H., J.Z.), Department of Neurology, The First Affiliated Hospital (K.W., D.W.), State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering (H.Y.), and Center for Innovative and Collaborative Detection and Treatment of Infectious Diseases (J.Z.), Zhejiang University, 38 Zheda Rd, Hangzhou, Zhejiang 310027, China; and the Department of Imaging Sciences, University of Rochester, Rochester, NY (J.Z.)
| | - Yueping Li
- From the Center for Brain Imaging Science and Technology, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrumental Science (C.L., X.C., Y.L., H.Y., Q.D., H.H., J.Z.), Department of Neurology, The First Affiliated Hospital (K.W., D.W.), State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering (H.Y.), and Center for Innovative and Collaborative Detection and Treatment of Infectious Diseases (J.Z.), Zhejiang University, 38 Zheda Rd, Hangzhou, Zhejiang 310027, China; and the Department of Imaging Sciences, University of Rochester, Rochester, NY (J.Z.)
| | - Dengchang Wu
- From the Center for Brain Imaging Science and Technology, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrumental Science (C.L., X.C., Y.L., H.Y., Q.D., H.H., J.Z.), Department of Neurology, The First Affiliated Hospital (K.W., D.W.), State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering (H.Y.), and Center for Innovative and Collaborative Detection and Treatment of Infectious Diseases (J.Z.), Zhejiang University, 38 Zheda Rd, Hangzhou, Zhejiang 310027, China; and the Department of Imaging Sciences, University of Rochester, Rochester, NY (J.Z.)
| | - Huihui Ye
- From the Center for Brain Imaging Science and Technology, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrumental Science (C.L., X.C., Y.L., H.Y., Q.D., H.H., J.Z.), Department of Neurology, The First Affiliated Hospital (K.W., D.W.), State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering (H.Y.), and Center for Innovative and Collaborative Detection and Treatment of Infectious Diseases (J.Z.), Zhejiang University, 38 Zheda Rd, Hangzhou, Zhejiang 310027, China; and the Department of Imaging Sciences, University of Rochester, Rochester, NY (J.Z.)
| | - Qiuping Ding
- From the Center for Brain Imaging Science and Technology, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrumental Science (C.L., X.C., Y.L., H.Y., Q.D., H.H., J.Z.), Department of Neurology, The First Affiliated Hospital (K.W., D.W.), State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering (H.Y.), and Center for Innovative and Collaborative Detection and Treatment of Infectious Diseases (J.Z.), Zhejiang University, 38 Zheda Rd, Hangzhou, Zhejiang 310027, China; and the Department of Imaging Sciences, University of Rochester, Rochester, NY (J.Z.)
| | - Hongjian He
- From the Center for Brain Imaging Science and Technology, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrumental Science (C.L., X.C., Y.L., H.Y., Q.D., H.H., J.Z.), Department of Neurology, The First Affiliated Hospital (K.W., D.W.), State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering (H.Y.), and Center for Innovative and Collaborative Detection and Treatment of Infectious Diseases (J.Z.), Zhejiang University, 38 Zheda Rd, Hangzhou, Zhejiang 310027, China; and the Department of Imaging Sciences, University of Rochester, Rochester, NY (J.Z.)
| | - Jianhui Zhong
- From the Center for Brain Imaging Science and Technology, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrumental Science (C.L., X.C., Y.L., H.Y., Q.D., H.H., J.Z.), Department of Neurology, The First Affiliated Hospital (K.W., D.W.), State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering (H.Y.), and Center for Innovative and Collaborative Detection and Treatment of Infectious Diseases (J.Z.), Zhejiang University, 38 Zheda Rd, Hangzhou, Zhejiang 310027, China; and the Department of Imaging Sciences, University of Rochester, Rochester, NY (J.Z.)
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12
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Tang X, Cai F, Ding DX, Zhang LL, Cai XY, Fang Q. Magnetic resonance imaging relaxation time in Alzheimer's disease. Brain Res Bull 2018; 140:176-189. [PMID: 29738781 DOI: 10.1016/j.brainresbull.2018.05.004] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2018] [Revised: 04/18/2018] [Accepted: 05/04/2018] [Indexed: 12/26/2022]
Abstract
The magnetic resonance imaging (MRI) relaxation time constants, T1 and T2, are sensitive to changes in brain tissue microstructure integrity. Quantitative T1 and T2 relaxation times have been proposed to serve as non-invasive biomarkers of Alzheimer's disease (AD), in which alterations are believed to not only reflect AD-related neuropathology but also cognitive impairment. In this review, we summarize the applications and key findings of MRI techniques in the context of both AD subjects and AD transgenic mouse models. Furthermore, the possible mechanisms of relaxation time alterations in AD will be discussed. Future studies could focus on relaxation time alterations in the early stage of AD, and longitudinal studies are needed to further explore relaxation time alterations during disease progression.
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Affiliation(s)
- Xiang Tang
- Department of Neurology, The First Affiliated Hospital of Soochow University, No. 899, Pinghai Road, Suzhou, Jiangsu 215006, China
| | - Feng Cai
- Department of Orthopedics, The First Affiliated Hospital of Soochow University, No. 899, Pinghai Road, Suzhou, Jiangsu 215006, China
| | - Dong-Xue Ding
- Department of Neurology, The First Affiliated Hospital of Soochow University, No. 899, Pinghai Road, Suzhou, Jiangsu 215006, China
| | - Lu-Lu Zhang
- Department of Neurology, The First Affiliated Hospital of Soochow University, No. 899, Pinghai Road, Suzhou, Jiangsu 215006, China
| | - Xiu-Ying Cai
- Department of Neurology, The First Affiliated Hospital of Soochow University, No. 899, Pinghai Road, Suzhou, Jiangsu 215006, China.
| | - Qi Fang
- Department of Neurology, The First Affiliated Hospital of Soochow University, No. 899, Pinghai Road, Suzhou, Jiangsu 215006, China.
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13
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Cohen-Adad J. Microstructural imaging in the spinal cord and validation strategies. Neuroimage 2018; 182:169-183. [PMID: 29635029 DOI: 10.1016/j.neuroimage.2018.04.009] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2017] [Revised: 03/02/2018] [Accepted: 04/06/2018] [Indexed: 12/13/2022] Open
Abstract
In vivo histology using magnetic resonance imaging (MRI) is a newly emerging research field that aims to non-invasively characterize tissue microstructure. The implications of in vivo histology are many, from discovering novel biomarkers to studying human development, to providing tools for disease diagnosis and monitoring the effects of novel treatments on tissue. This review focuses on quantitative MRI (qMRI) techniques that are used to map spinal cord microstructure. Opening with a rationale for non-invasive imaging of the spinal cord, this article continues with a brief overview of the existing MRI techniques for axon and myelin imaging, followed by the specific challenges and potential solutions for acquiring and processing such data. The final part of this review focuses on histological validation, with suggested tissue preparation, acquisition and processing protocols for large-scale microscopy.
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Affiliation(s)
- J Cohen-Adad
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada; Functional Neuroimaging Unit, CRIUGM, Université de Montréal, Montreal, QC, Canada.
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14
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Liao C, Bilgic B, Manhard MK, Zhao B, Cao X, Zhong J, Wald LL, Setsompop K. 3D MR fingerprinting with accelerated stack-of-spirals and hybrid sliding-window and GRAPPA reconstruction. Neuroimage 2017; 162:13-22. [PMID: 28842384 PMCID: PMC6031129 DOI: 10.1016/j.neuroimage.2017.08.030] [Citation(s) in RCA: 78] [Impact Index Per Article: 11.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2017] [Revised: 07/02/2017] [Accepted: 08/09/2017] [Indexed: 12/13/2022] Open
Abstract
PURPOSE Whole-brain high-resolution quantitative imaging is extremely encoding intensive, and its rapid and robust acquisition remains a challenge. Here we present a 3D MR fingerprinting (MRF) acquisition with a hybrid sliding-window (SW) and GRAPPA reconstruction strategy to obtain high-resolution T1, T2 and proton density (PD) maps with whole brain coverage in a clinically feasible timeframe. METHODS 3D MRF data were acquired using a highly under-sampled stack-of-spirals trajectory with a steady-state precession (FISP) sequence. For data reconstruction, kx-ky under-sampling was mitigated using SW combination along the temporal axis. Non-uniform fast Fourier transform (NUFFT) was then applied to create Cartesian k-space data that are fully-sampled in the in-plane direction, and Cartesian GRAPPA was performed to resolve kz under-sampling to create an alias-free SW dataset. T1, T2 and PD maps were then obtained using dictionary matching. RESULTS Phantom study demonstrated that the proposed 3D-MRF acquisition/reconstruction method is able to produce quantitative maps that are consistent with conventional quantification techniques. Retrospectively under-sampled in vivo acquisition revealed that SW + GRAPPA substantially improves quantification accuracy over the current state-of-the-art accelerated 3D MRF. Prospectively under-sampled in vivo study showed that whole brain T1, T2 and PD maps with 1 mm3 resolution could be obtained in 7.5 min. CONCLUSIONS 3D MRF stack-of-spirals acquisition with hybrid SW + GRAPPA reconstruction may provide a feasible approach for rapid, high-resolution quantitative whole-brain imaging.
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Affiliation(s)
- Congyu Liao
- Center for Brain Imaging Science and Technology, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, Zhejiang, China; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - Berkin Bilgic
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Mary Kate Manhard
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Bo Zhao
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Xiaozhi Cao
- Center for Brain Imaging Science and Technology, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Jianhui Zhong
- Center for Brain Imaging Science and Technology, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, Zhejiang, China.
| | - Lawrence L Wald
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Kawin Setsompop
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; Department of Radiology, Harvard Medical School, Boston, MA, USA
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15
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Hawkins PCT, Wood TC, Vernon AC, Bertolino A, Sambataro F, Dukart J, Merlo-Pich E, Risterucci C, Silber-Baumann H, Walsh E, Mazibuko N, Zelaya FO, Mehta MA. An investigation of regional cerebral blood flow and tissue structure changes after acute administration of antipsychotics in healthy male volunteers. Hum Brain Mapp 2017; 39:319-331. [PMID: 29058358 DOI: 10.1002/hbm.23844] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2017] [Revised: 09/13/2017] [Accepted: 10/02/2017] [Indexed: 01/05/2023] Open
Abstract
Chronic administration of antipsychotic drugs has been linked to structural brain changes observed in patients with schizophrenia. Recent MRI studies have shown rapid changes in regional brain volume following just a single dose of these drugs. However, it is not clear if these changes represent real volume changes or are artefacts ("apparent" volume changes) due to drug-induced physiological changes, such as increased cerebral blood flow (CBF). To address this, we examined the effects of a single, clinical dose of three commonly prescribed antipsychotics on quantitative measures of T1 and regional blood flow of the healthy human brain. Males (n = 42) were randomly assigned to one of two parallel groups in a double-blind, placebo-controlled, randomized, three-period cross-over study design. One group received a single oral dose of either 0.5 or 2 mg of risperidone or placebo during each visit. The other received olanzapine (7.5 mg), haloperidol (3 mg), or placebo. MR measures of quantitative T1, CBF, and T1-weighted images were acquired at the estimated peak plasma concentration of the drug. All three drugs caused localized increases in striatal blood flow, although drug and region specific effects were also apparent. In contrast, all assessments of T1 and brain volume remained stable across sessions, even in those areas experiencing large changes in CBF. This illustrates that a single clinically relevant oral dose of an antipsychotic has no detectable acute effect on T1 in healthy volunteers. We further provide a methodology for applying quantitative imaging methods to assess the acute effects of other compounds on structural MRI metrics. Hum Brain Mapp 39:319-331, 2018. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Peter C T Hawkins
- Centre for Neuroimaging Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Tobias C Wood
- Centre for Neuroimaging Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Anthony C Vernon
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.,MRC Centre for Neurodevelopmental Disorders, King's College London, London, United Kingdom
| | - Alessandro Bertolino
- Department of Basic Medical Science, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari BA, Italy
| | - Fabio Sambataro
- Department of Experimental and Clinical Medical Sciences, University of Udine, Udine, Italy
| | - Juergen Dukart
- Translational Medicine Neuroscience and Biomarkers, F. Hoffmann-La Roche Ltd, Basel, Switzerland
| | - Emilio Merlo-Pich
- CNS Therapeutic Area Unit, Takeda Development Centre Europe, London, United Kingdom
| | - Celine Risterucci
- Pharma Research and Early Development, F. Hoffmann-La Roche Ltd, Basel, Switzerland
| | - Hanna Silber-Baumann
- Pharma Research and Early Development, F. Hoffmann-La Roche Ltd, Basel, Switzerland
| | - Eamonn Walsh
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Ndabezinhle Mazibuko
- Centre for Neuroimaging Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Fernando O Zelaya
- Centre for Neuroimaging Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Mitul A Mehta
- Centre for Neuroimaging Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
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16
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Bonnier G, Maréchal B, Fartaria MJ, Falkowskiy P, Marques JP, Simioni S, Schluep M, Du Pasquier R, Thiran JP, Krueger G, Granziera C. The Combined Quantification and Interpretation of Multiple Quantitative Magnetic Resonance Imaging Metrics Enlightens Longitudinal Changes Compatible with Brain Repair in Relapsing-Remitting Multiple Sclerosis Patients. Front Neurol 2017; 8:506. [PMID: 29021778 PMCID: PMC5623825 DOI: 10.3389/fneur.2017.00506] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2017] [Accepted: 09/08/2017] [Indexed: 12/25/2022] Open
Abstract
Objective Quantitative and semi-quantitative MRI (qMRI) metrics provide complementary specificity and differential sensitivity to pathological brain changes compatible with brain inflammation, degeneration, and repair. Moreover, advanced magnetic resonance imaging (MRI) metrics with overlapping elements amplify the true tissue-related information and limit measurement noise. In this work, we combined multiple advanced MRI parameters to assess focal and diffuse brain changes over 2 years in a group of early-stage relapsing-remitting MS patients. Methods Thirty relapsing-remitting MS patients with less than 5 years disease duration and nine healthy subjects underwent 3T MRI at baseline and after 2 years including T1, T2, T2* relaxometry, and magnetization transfer imaging. To assess longitudinal changes in normal-appearing (NA) tissue and lesions, we used analyses of variance and Bonferroni correction for multiple comparisons. Multivariate linear regression was used to assess the correlation between clinical outcome and multiparametric MRI changes in lesions and NA tissue. Results In patients, we measured a significant longitudinal decrease of mean T2 relaxation times in NA white matter (p = 0.005) and a decrease of T1 relaxation times in the pallidum (p < 0.05), which are compatible with edema reabsorption and/or iron deposition. No longitudinal changes in qMRI metrics were observed in controls. In MS lesions, we measured a decrease in T1 relaxation time (p-value < 2.2e−16) and a significant increase in MTR (p-value < 1e−6), suggesting repair mechanisms, such as remyelination, increased axonal density, and/or a gliosis. Last, the evolution of advanced MRI metrics—and not changes in lesions or brain volume—were correlated to motor and cognitive tests scores evolution (Adj-R2 > 0.4, p < 0.05). In summary, the combination of multiple advanced MRI provided evidence of changes compatible with focal and diffuse brain repair at early MS stages as suggested by histopathological studies.
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Affiliation(s)
- Guillaume Bonnier
- A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, United States
| | - Benedicte Maréchal
- Advanced Clinical Imaging Technology (HC CMEA SUI DI BM PI), Siemens Healthcare, Lausanne, Switzerland.,Signal Processing Laboratory 5 LTS5, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Mário João Fartaria
- Advanced Clinical Imaging Technology (HC CMEA SUI DI BM PI), Siemens Healthcare, Lausanne, Switzerland.,Signal Processing Laboratory 5 LTS5, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Pavel Falkowskiy
- Advanced Clinical Imaging Technology (HC CMEA SUI DI BM PI), Siemens Healthcare, Lausanne, Switzerland.,Signal Processing Laboratory 5 LTS5, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - José P Marques
- Donders Centre for Cognitive Neuroimaging, Radbound University, Nijmegen, Netherlands
| | - Samanta Simioni
- Neuropsychology, Institution de Lavigny, Denens, Switzerland
| | - Myriam Schluep
- Neurology Service and Neuroimmunology Laboratory, Department of Clinical Neurosciences, Centre Hospitalier Universitaire Vaudois, University of Lausanne, Lausanne, Switzerland
| | - Renaud Du Pasquier
- Neurology Service and Neuroimmunology Laboratory, Department of Clinical Neurosciences, Centre Hospitalier Universitaire Vaudois, University of Lausanne, Lausanne, Switzerland
| | - Jean-Philippe Thiran
- Signal Processing Laboratory 5 LTS5, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Gunnar Krueger
- Siemens Medical Solutions USA IM MR COL NEZ, Burlington, MA, United States
| | - Cristina Granziera
- A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, United States.,Neurology Service and Neuroimmunology Laboratory, Department of Clinical Neurosciences, Centre Hospitalier Universitaire Vaudois, University of Lausanne, Lausanne, Switzerland
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17
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Metzler-Baddeley C, Foley S, de Santis S, Charron C, Hampshire A, Caeyenberghs K, Jones DK. Dynamics of White Matter Plasticity Underlying Working Memory Training: Multimodal Evidence from Diffusion MRI and Relaxometry. J Cogn Neurosci 2017; 29:1509-1520. [PMID: 28358656 PMCID: PMC5881889 DOI: 10.1162/jocn_a_01127] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Adaptive working memory (WM) training may lead to cognitive benefits that are associated with white matter plasticity in parietofrontal networks, but the underlying mechanisms remain poorly understood. We investigated white matter microstructural changes after adaptive WM training relative to a nonadaptive comparison group. Microstructural changes were studied in the superior longitudinal fasciculus, the main parietofrontal connection, and the cingulum bundle as a comparison pathway. MRI-based metrics were the myelin water fraction and longitudinal relaxation rate R1 from multicomponent relaxometry (captured with the mcDESPOT approach) as proxy metrics of myelin, the restricted volume fraction from the composite hindered and restricted model of diffusion as an estimate of axon morphology, and fractional anisotropy and radial diffusivity from diffusion tensor imaging. PCA was used for dimensionality reduction. Adaptive training was associated with benefits in a “WM capacity” component and increases in a microstructural component (increases in R1, restricted volume fraction, fractional anisotropy, and reduced radial diffusivity) that predominantly loaded on changes in the right dorsolateral superior longitudinal fasciculus and the left parahippocampal cingulum. In contrast, nonadaptive comparison activities were associated with the opposite pattern of reductions in WM capacity and microstructure. No group differences were observed for the myelin water fraction metric suggesting that R1 was a more sensitive “myelin” index. These results demonstrate task complexity and location-specific white matter microstructural changes that are consistent with tissue alterations underlying myelination in response to training.
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Affiliation(s)
| | - Sonya Foley
- Cardiff University, Brain Research Imaging Centre (CUBRIC)
| | | | - Cyril Charron
- Cardiff University, Brain Research Imaging Centre (CUBRIC)
| | | | | | - Derek K Jones
- Cardiff University, Brain Research Imaging Centre (CUBRIC).,Australian Catholic University
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18
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Cao X, Liao C, Wang Z, Chen Y, Ye H, He H, Zhong J. Robust sliding-window reconstruction for Accelerating the acquisition of MR fingerprinting. Magn Reson Med 2016; 78:1579-1588. [PMID: 27851871 DOI: 10.1002/mrm.26521] [Citation(s) in RCA: 51] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2016] [Revised: 09/28/2016] [Accepted: 09/29/2016] [Indexed: 11/12/2022]
Abstract
PURPOSE To develop a method for accelerated and robust MR fingerprinting (MRF) with improved image reconstruction and parameter matching processes. THEORY AND METHODS A sliding-window (SW) strategy was applied to MRF, in which signal and dictionary matching was conducted between fingerprints consisting of mixed-contrast image series reconstructed from consecutive data frames segmented by a sliding window, and a precalculated mixed-contrast dictionary. The effectiveness and performance of this new method, dubbed SW-MRF, was evaluated in both phantom and in vivo. Error quantifications were conducted on results obtained with various settings of SW reconstruction parameters. RESULTS Compared with the original MRF strategy, the results of both phantom and in vivo experiments demonstrate that the proposed SW-MRF strategy either provided similar accuracy with reduced acquisition time, or improved accuracy with equal acquisition time. Parametric maps of T1 , T2 , and proton density of comparable quality could be achieved with a two-fold or more reduction in acquisition time. The effect of sliding-window width on dictionary sensitivity was also estimated. CONCLUSION The novel SW-MRF recovers high quality image frames from highly undersampled MRF data, which enables more robust dictionary matching with reduced numbers of data frames. This time efficiency may facilitate MRF applications in time-critical clinical settings. Magn Reson Med 78:1579-1588, 2017. © 2016 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Xiaozhi Cao
- Center for Brain Imaging Science and Technology, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Congyu Liao
- Center for Brain Imaging Science and Technology, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Zhixing Wang
- Center for Brain Imaging Science and Technology, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Ying Chen
- Center for Brain Imaging Science and Technology, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Huihui Ye
- Center for Brain Imaging Science and Technology, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Hongjian He
- Center for Brain Imaging Science and Technology, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Jianhui Zhong
- Center for Brain Imaging Science and Technology, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China.,Center for Innovative and Collaborative Detection and Treatment of Infectious Diseases, Zhejiang University, Hangzhou, Zhejiang, China.,Department of Imaging Sciences, University of Rochester, Rochester, New York, USA
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19
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Dean DC, Sojkova J, Hurley S, Kecskemeti S, Okonkwo O, Bendlin BB, Theisen F, Johnson SC, Alexander AL, Gallagher CL. Alterations of Myelin Content in Parkinson's Disease: A Cross-Sectional Neuroimaging Study. PLoS One 2016; 11:e0163774. [PMID: 27706215 PMCID: PMC5051727 DOI: 10.1371/journal.pone.0163774] [Citation(s) in RCA: 53] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2016] [Accepted: 09/14/2016] [Indexed: 12/11/2022] Open
Abstract
Alterations to myelin may be a core pathological feature of neurodegenerative diseases. Although white matter microstructural differences have been described in Parkinson's disease (PD), it is unknown whether such differences include alterations of the brain’s myelin content. Thus, the objective of the current study is to measure and compare brain myelin content between PD patients and age-matched controls. In this cross-sectional study, 63 participants from the Longitudinal MRI in Parkinson's Disease study underwent brain MRI, Unified Parkinson's Disease Rating Scale (UPDRS) scoring, and cognitive asessments. Subjects were imaged with the mcDEPSOT (multi-component driven equilibrium single pulse observation of T1 and T2), a multicomponent relaxometry technique that quantifies longitudinal and transverse relaxation rates (R1 and R2, respectively) and the myelin water fraction (VFM), a surrogate for myelin content. A voxel-wise approach was used to compare R1, R2, and VFM measures between PD and control groups, and to evaluate relationships with age as well as disease duration, UPDRS scores, and daily levodopa equivalent dose. PD subjects had higher VFM than controls in frontal and temporal white matter and bilateral thalamus. Greater age was strongly associated with lower VFM in both groups, while an age-by-group interaction suggested a slower rate of VFM decline in the left putamen with aging in PD. Within the PD group, measures of disease severity, including UPDRS, daily levodopa equivalent dose, and disease duration, were observed to be related with myelin content in diffuse brain regions. The age-by-group interaction suggests that either PD or dopaminergic therapies allay observed age-related myelin changes. The relationships between VFM and disease severity measures suggests that VFM may provide a surrogate marker for microstructural changes related to Parkinson’s disease.
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Affiliation(s)
- Douglas C Dean
- Waisman Center, University of Wisconsin Madison, Madison, Wisconsin, United States of America
| | - Jitka Sojkova
- William S. Middleton Memorial Veterans Hospital, Madison, Wisconsin, United States of America.,Department of Neurology, University of Wisconsin Madison, Madison, Wisconsin, United States of America
| | - Samuel Hurley
- Oxford Centre for Functional Magnetic Resonance Imaging of the Brain, University of Oxford, Oxford, Oxfordshire, United Kingdom
| | - Steven Kecskemeti
- Waisman Center, University of Wisconsin Madison, Madison, Wisconsin, United States of America
| | - Ozioma Okonkwo
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, United States of America
| | - Barbara B Bendlin
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, United States of America
| | - Frances Theisen
- William S. Middleton Memorial Veterans Hospital, Madison, Wisconsin, United States of America.,Department of Neurology, University of Wisconsin Madison, Madison, Wisconsin, United States of America
| | - Sterling C Johnson
- William S. Middleton Memorial Veterans Hospital, Madison, Wisconsin, United States of America.,Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, United States of America
| | - Andrew L Alexander
- Waisman Center, University of Wisconsin Madison, Madison, Wisconsin, United States of America.,Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, United States of America.,Department of Psychiatry, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
| | - Catherine L Gallagher
- William S. Middleton Memorial Veterans Hospital, Madison, Wisconsin, United States of America.,Department of Neurology, University of Wisconsin Madison, Madison, Wisconsin, United States of America.,Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, United States of America
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20
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Granziera C, Daducci A, Donati A, Bonnier G, Romascano D, Roche A, Bach Cuadra M, Schmitter D, Klöppel S, Meuli R, von Gunten A, Krueger G. A multi-contrast MRI study of microstructural brain damage in patients with mild cognitive impairment. NEUROIMAGE-CLINICAL 2015; 8:631-9. [PMID: 26236628 PMCID: PMC4511616 DOI: 10.1016/j.nicl.2015.06.003] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/02/2015] [Revised: 05/25/2015] [Accepted: 06/07/2015] [Indexed: 11/05/2022]
Abstract
Objectives The aim of this study was to investigate pathological mechanisms underlying brain tissue alterations in mild cognitive impairment (MCI) using multi-contrast 3 T magnetic resonance imaging (MRI). Methods Forty-two MCI patients and 77 healthy controls (HC) underwent T1/T2* relaxometry as well as Magnetization Transfer (MT) MRI. Between-groups comparisons in MRI metrics were performed using permutation-based tests. Using MRI data, a generalized linear model (GLM) was computed to predict clinical performance and a support-vector machine (SVM) classification was used to classify MCI and HC subjects. Results Multi-parametric MRI data showed microstructural brain alterations in MCI patients vs HC that might be interpreted as: (i) a broad loss of myelin/cellular proteins and tissue microstructure in the hippocampus (p ≤ 0.01) and global white matter (p < 0.05); and (ii) iron accumulation in the pallidus nucleus (p ≤ 0.05). MRI metrics accurately predicted memory and executive performances in patients (p ≤ 0.005). SVM classification reached an accuracy of 75% to separate MCI and HC, and performed best using both volumes and T1/T2*/MT metrics. Conclusion Multi-contrast MRI appears to be a promising approach to infer pathophysiological mechanisms leading to brain tissue alterations in MCI. Likewise, parametric MRI data provide powerful correlates of cognitive deficits and improve automatic disease classification based on morphometric features. Forty-two MCI patients and 77 HC underwent multi-contrast quantitative MRI. MCI patients showed T1/T2* increase and MTR decrease in the hippocampus. MCI patients exhibited T1 increase in WM and T2* decrease in the pallidus. MRI metrics accurately predicted memory and executive function in patients. SVM classified MCI patients with 75% accuracy using volumetric/parametric MRI.
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Affiliation(s)
- C Granziera
- Department of Clinical Neurosciences, CHUV, Lausanne, VD, Switzerland ; Advanced Clinical Imaging Technology, EPFL, Lausanne, VD, Switzerland
| | - A Daducci
- STI IEL LTS5, EPFL, Lausanne, VD, Switzerland
| | - A Donati
- Service of Old-Age Psychiatry, Department of Psychiatry, CHUV, Lausanne, VD, Switzerland
| | - G Bonnier
- Advanced Clinical Imaging Technology, EPFL, Lausanne, VD, Switzerland
| | - D Romascano
- Advanced Clinical Imaging Technology, EPFL, Lausanne, VD, Switzerland
| | - A Roche
- Advanced Clinical Imaging Technology, EPFL, Lausanne, VD, Switzerland
| | - M Bach Cuadra
- Department of Radiology, CHUV, Lausanne, VD, Switzerland ; Signal Processing Core, Center for Biomedical Imaging, CHUV, Lausanne, VD, Switzerland
| | - D Schmitter
- Advanced Clinical Imaging Technology, EPFL, Lausanne, VD, Switzerland
| | - S Klöppel
- Department of Psychiatry and Psychotherapy, Section of Gerontopsychiatry, Department of Neurology, University Medical Center, Freiburg, Germany
| | - R Meuli
- Department of Radiology, CHUV, Lausanne, VD, Switzerland
| | - A von Gunten
- Service of Old-Age Psychiatry, Department of Psychiatry, CHUV, Lausanne, VD, Switzerland
| | - G Krueger
- Advanced Clinical Imaging Technology, EPFL, Lausanne, VD, Switzerland ; Heathcare IM S AW, Siemens Schweiz AG, Renens, VD, Switzerland
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21
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Bonnier G, Roche A, Romascano D, Simioni S, Meskaldji D, Rotzinger D, Lin YC, Menegaz G, Schluep M, Du Pasquier R, Sumpf TJ, Frahm J, Thiran JP, Krueger G, Granziera C. Advanced MRI unravels the nature of tissue alterations in early multiple sclerosis. Ann Clin Transl Neurol 2014; 1:423-32. [PMID: 25356412 PMCID: PMC4184670 DOI: 10.1002/acn3.68] [Citation(s) in RCA: 58] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2014] [Revised: 03/27/2014] [Accepted: 04/28/2014] [Indexed: 01/16/2023] Open
Abstract
Introduction In patients with multiple sclerosis (MS), conventional magnetic resonance imaging (MRI) provides only limited insights into the nature of brain damage with modest clinic-radiological correlation. In this study, we applied recent advances in MRI techniques to study brain microstructural alterations in early relapsing-remitting MS (RRMS) patients with minor deficits. Further, we investigated the potential use of advanced MRI to predict functional performances in these patients. Methods Brain relaxometry (T1, T2, T2*) and magnetization transfer MRI were performed at 3T in 36 RRMS patients and 18 healthy controls (HC). Multicontrast analysis was used to assess for microstructural alterations in normal-appearing (NA) tissue and lesions. A generalized linear model was computed to predict clinical performance in patients using multicontrast MRI data, conventional MRI measures as well as demographic and behavioral data as covariates. Results Quantitative T2 and T2* relaxometry were significantly increased in temporal normal-appearing white matter (NAWM) of patients compared to HC, indicating subtle microedema (P = 0.03 and 0.004). Furthermore, significant T1 and magnetization transfer ratio (MTR) variations in lesions (mean T1 z-score: 4.42 and mean MTR z-score: −4.09) suggested substantial tissue loss. Combinations of multicontrast and conventional MRI data significantly predicted cognitive fatigue (P = 0.01, Adj-R2 = 0.4), attention (P = 0.0005, Adj-R2 = 0.6), and disability (P = 0.03, Adj-R2 = 0.4). Conclusion Advanced MRI techniques at 3T, unraveled the nature of brain tissue damage in early MS and substantially improved clinical–radiological correlations in patients with minor deficits, as compared to conventional measures of disease.
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Affiliation(s)
- Guillaume Bonnier
- Advanced Clinical Imaging Technology group, Siemens Healthcare IM BM PI Lausanne, Switzerland ; Neuro-immunology and Laboratoire de recherché en neuroimagérie, Neurology Division, Department of Clinical Neurosciences, Centre Hospitalier Universitaire Vaudois and University of Lausanne Lausanne, Switzerland ; LTS5, Ecole Polytechnique Fédérale de Lausanne Lausanne, Switzerland
| | - Alexis Roche
- Advanced Clinical Imaging Technology group, Siemens Healthcare IM BM PI Lausanne, Switzerland ; LTS5, Ecole Polytechnique Fédérale de Lausanne Lausanne, Switzerland ; Department of Radiology, Centre Hospitalier Universitaire Vaudois and University of Lausanne Lausanne, Switzerland
| | - David Romascano
- Advanced Clinical Imaging Technology group, Siemens Healthcare IM BM PI Lausanne, Switzerland ; LTS5, Ecole Polytechnique Fédérale de Lausanne Lausanne, Switzerland
| | - Samanta Simioni
- Neuro-immunology and Laboratoire de recherché en neuroimagérie, Neurology Division, Department of Clinical Neurosciences, Centre Hospitalier Universitaire Vaudois and University of Lausanne Lausanne, Switzerland
| | - Djalel Meskaldji
- LTS5, Ecole Polytechnique Fédérale de Lausanne Lausanne, Switzerland
| | - David Rotzinger
- Department of Radiology, Centre Hospitalier Universitaire Vaudois and University of Lausanne Lausanne, Switzerland
| | - Ying-Chia Lin
- Department of Computer Science, University of Verona Verona, Italy
| | - Gloria Menegaz
- Department of Computer Science, University of Verona Verona, Italy
| | - Myriam Schluep
- Neuro-immunology and Laboratoire de recherché en neuroimagérie, Neurology Division, Department of Clinical Neurosciences, Centre Hospitalier Universitaire Vaudois and University of Lausanne Lausanne, Switzerland
| | - Renaud Du Pasquier
- Neuro-immunology and Laboratoire de recherché en neuroimagérie, Neurology Division, Department of Clinical Neurosciences, Centre Hospitalier Universitaire Vaudois and University of Lausanne Lausanne, Switzerland
| | - Tilman Johannes Sumpf
- Biomedizinische NMR Forschungs GmbH, Max Planck Institute for Biophysical Chemistry Goettingen, Germany
| | - Jens Frahm
- Biomedizinische NMR Forschungs GmbH, Max Planck Institute for Biophysical Chemistry Goettingen, Germany
| | | | - Gunnar Krueger
- Advanced Clinical Imaging Technology group, Siemens Healthcare IM BM PI Lausanne, Switzerland ; Healthcare Sector IM&WS S, Siemens Schweiz AG Renens, Switzerland
| | - Cristina Granziera
- Advanced Clinical Imaging Technology group, Siemens Healthcare IM BM PI Lausanne, Switzerland ; Neuro-immunology and Laboratoire de recherché en neuroimagérie, Neurology Division, Department of Clinical Neurosciences, Centre Hospitalier Universitaire Vaudois and University of Lausanne Lausanne, Switzerland ; LTS5, Ecole Polytechnique Fédérale de Lausanne Lausanne, Switzerland
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22
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Paus T, Pesaresi M, French L. White matter as a transport system. Neuroscience 2014; 276:117-25. [PMID: 24508743 DOI: 10.1016/j.neuroscience.2014.01.055] [Citation(s) in RCA: 45] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2013] [Revised: 01/14/2014] [Accepted: 01/29/2014] [Indexed: 12/14/2022]
Abstract
There are two ways to picture white matter: as a grid of electrical wires or a network of roads. The first metaphor captures the classical function of an axon as conductor of action potentials (and information) from one brain region to another. The second one points to the important role of axons in a bi-directional transport of biological molecules and organelles between the cell body and synapse. Given the wide variety of such cargoes, a well-functioning axonal transport is critical for a number of processes, including neurotransmission, metabolism and viability of neurons. This selective review will emphasize the need for considering axonal transport when interpreting functional consequences of inter-individual variations in the structural properties of white matter. We start by describing the space occupied by white matter and techniques used in vivo for its characterization. We then provide examples of key features of maturation and aging of white matter, as well as some of the common abnormalities observed in neurodevelopmental and neurodegenerative disorders. Next, we review work that motivated our focus on axonal diameter, and explain the relationships between transport and cytoskeleton within the axon. We will conclude by describing molecular machinery of axonal transport and genes that may contribute to inter-individual variations in axonal diameter and axonal transport.
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Affiliation(s)
- T Paus
- Rotman Research Institute, University of Toronto, Toronto, Canada.
| | - M Pesaresi
- Rotman Research Institute, University of Toronto, Toronto, Canada
| | - L French
- Rotman Research Institute, University of Toronto, Toronto, Canada
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23
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Quantifying the local tissue volume and composition in individual brains with magnetic resonance imaging. Nat Med 2013; 19:1667-72. [PMID: 24185694 PMCID: PMC3855886 DOI: 10.1038/nm.3390] [Citation(s) in RCA: 196] [Impact Index Per Article: 17.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2012] [Accepted: 02/05/2013] [Indexed: 12/25/2022]
Abstract
We describe a quantitative neuroimaging method to estimate the macromolecular tissue volume (MTV), a fundamental measure of brain anatomy. By making measurements over a range of field strengths and scan parameters, we tested the key assumptions and the robustness of the method. The measurements confirm that a consistent, quantitative estimate of macromolecular volume can be obtained across a range of scanners. MTV estimates are sufficiently precise to enable a comparison between data obtained from an individual subject with control population data. We describe two applications. First, we show that MTV estimates can be combined with T1 and diffusion measurements to augment our understanding of the tissue properties. Second we show that MTV provides a sensitive measure of disease status in individual patients with multiple sclerosis. The MTV maps are obtained using short clinically appropriate scans that can reveal how tissue changes influence behavior and cognition.
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24
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Travis KE, Curran MM, Torres C, Leonard MK, Brown TT, Dale AM, Elman JL, Halgren E. Age-related changes in tissue signal properties within cortical areas important for word understanding in 12- to 19-month-old infants. ACTA ACUST UNITED AC 2013; 24:1948-55. [PMID: 23448869 DOI: 10.1093/cercor/bht052] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
Recently, our laboratory has shown that the neural mechanisms for encoding lexico-semantic information in adults operate functionally by 12-18 months of age within left frontotemporal cortices (Travis et al., 2011. Spatiotemporal neural dynamics of word understanding in 12- to 18-month-old-infants. Cereb Cortex. 8:1832-1839). However, there is minimal knowledge of the structural changes that occur within these and other cortical regions important for language development. To identify regional structural changes taking place during this important period in infant development, we examined age-related changes in tissue signal properties of gray matter (GM) and white matter (WM) intensity and contrast. T1-weighted surface-based measures were acquired from 12- to 19-month-old infants and analyzed using a general linear model. Significant age effects were observed for GM and WM intensity and contrast within bilateral inferior lateral and anterovental temporal regions, dorsomedial frontal, and superior parietal cortices. Region of interest (ROI) analyses revealed that GM and WM intensity and contrast significantly increased with age within the same left lateral temporal regions shown to generate lexico-semantic activity in infants and adults. These findings suggest that neurophysiological processes supporting linguistic and cognitive behaviors may develop before cellular and structural maturation is complete within associative cortices. These results have important implications for understanding the neurobiological mechanisms relating structural to functional brain development.
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Affiliation(s)
| | | | | | | | | | - Anders M Dale
- Department of Radiology, Multimodal Imaging Laboratory, Department of Neurosciences
| | - Jeffrey L Elman
- Kavli Institute for Brain and Mind, and Department of Cognitive Science, University of California, San Diego, CA, USA
| | - Eric Halgren
- Department of Radiology, Multimodal Imaging Laboratory, Kavli Institute for Brain and Mind, and
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25
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Cheng HLM, Stikov N, Ghugre NR, Wright GA. Practical medical applications of quantitative MR relaxometry. J Magn Reson Imaging 2013; 36:805-24. [PMID: 22987758 DOI: 10.1002/jmri.23718] [Citation(s) in RCA: 147] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
Conventional MR images are qualitative, and their signal intensity is dependent on several complementary contrast mechanisms that are manipulated by the MR hardware and software. In the absence of a quantitative metric for absolute interpretation of pixel signal intensities, one that is independent of scanner hardware and sequences, it is difficult to perform comparisons of MR images across subjects or longitudinally in the same subject. Quantitative relaxometry isolates the contributions of individual MR contrast mechanisms (T1, T2, T2) and provides maps, which are independent of the MR protocol and have a physical interpretation often expressed in absolute units. In addition to providing an unbiased metric for comparing MR scans, quantitative relaxometry uses the relationship between MR maps and physiology to provide a noninvasive surrogate for biopsy and histology. This study provides an overview of some promising clinical applications of quantitative relaxometry, followed by a description of the methods and challenges of acquiring accurate and precise quantitative MR maps. It concludes with three case studies of quantitative relaxometry applied to studying multiple sclerosis, liver iron, and acute myocardial infarction.
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Affiliation(s)
- Hai-Ling Margaret Cheng
- Physiology and Experimental Medicine, Research Institute, The Hospital for Sick Children, Toronto, ON, Canada
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26
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Neuroimaging in Epilepsy: Towards Structural Cellular Imaging. Can J Neurol Sci 2012. [DOI: 10.1017/s0317167100018102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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27
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Duyn JH, Koretsky AP. Novel frontiers in ultra-structural and molecular MRI of the brain. Curr Opin Neurol 2011; 24:386-93. [PMID: 21734576 DOI: 10.1097/wco.0b013e328348972a] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
PURPOSE OF REVIEW Recent developments in the MRI of the brain continue to expand its use in basic and clinical neuroscience. This review highlights some areas of recent progress. RECENT FINDINGS Higher magnetic field strengths and improved signal detectors have allowed improved visualization of the various properties of the brain, facilitating the anatomical definition of function-specific areas and their connections. For example, by sensitizing the MRI signal to the magnetic susceptibility of tissue, it is starting to become possible to reveal the laminar structure of the cortex and identify millimeter-scale fiber bundles. Using exogenous contrast agents, and innovative ways to manipulate contrast, it is becoming possible to highlight specific fiber tracts and cell populations. These techniques are bringing us closer to understanding the evolutionary blueprint of the brain, improving the detection and characterization of disease, and help to guide treatment. SUMMARY Recent MRI techniques are leading to more detailed and more specific contrast in the study of the brain.
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Affiliation(s)
- Jeff H Duyn
- Laboratory of Functional and Molecular Imaging, National Institutes of Health, Bethesda, Maryland 20892-1060, USA.
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