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Aye N, Lehmann N, Kaufmann J, Heinze HJ, Düzel E, Taubert M, Ziegler G. Test-retest reliability of multi-parametric maps (MPM) of brain microstructure. Neuroimage 2022; 256:119249. [PMID: 35487455 DOI: 10.1016/j.neuroimage.2022.119249] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 04/22/2022] [Accepted: 04/25/2022] [Indexed: 10/18/2022] Open
Abstract
Multiparameter mapping (MPM) is a quantitative MRI protocol that is promising for studying microstructural brain changes in vivo with high specificity. Reliability values are an important prior knowledge for efficient study design and facilitating replicable findings in development, aging and neuroplasticity research. To explore longitudinal reliability of MPM we acquired the protocol in 31 healthy young subjects twice over a rescan interval of 4 weeks. We assessed the within-subject coefficient of variation (WCV), the between-subject coefficient of variation (BCV), and the intraclass correlation coefficient (ICC). Using these metrics, we investigated the reliability of (semi-) quantitative magnetization transfer saturation (MTsat), proton density (PD), transversal relaxation (R2*) and longitudinal relaxation (R1). To increase relevance for explorative studies in development and training-induced plasticity, we assess reliability both on local voxel- as well as ROI-level. Finally, we disentangle contributions and interplay of within- and between-subject variability to ICC and assess the optimal degree of spatial smoothing applied to the data. We reveal evidence that voxelwise ICC reliability of MPMs is moderate to good with median values in cortex (subcortical GM): MT: 0.789 (0.447) PD: 0.553 (0.264) R1: 0.555 (0.369) R2*: 0.624 (0.477). The Gaussian smoothing kernel of 2 to 4 mm FWHM resulted in optimal reproducibility. We discuss these findings in the context of longitudinal intervention studies and the application to research designs in neuroimaging field.
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Affiliation(s)
- Norman Aye
- Faculty of Human Sciences, Institute III, Department of Sport Science, Otto von Guericke University, Zschokkestraße 32, 39104 Magdeburg, Germany.
| | - Nico Lehmann
- Faculty of Human Sciences, Institute III, Department of Sport Science, Otto von Guericke University, Zschokkestraße 32, 39104 Magdeburg, Germany; Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstraße 1a, 04103 Leipzig, Germany
| | - Jörn Kaufmann
- Department of Neurology, Otto von Guericke University, Leipziger Straße 44, 39120 Magdeburg, Germany
| | - Hans-Jochen Heinze
- Department of Neurology, Otto von Guericke University, Leipziger Straße 44, 39120 Magdeburg, Germany; German Center for Neurodegenerative Diseases (DZNE), Leipziger Straße 44, 39120 Magdeburg, Germany; Center for Behavioral and Brain Science (CBBS), Otto von Guericke University, Universitätsplatz 2, 39106 Magdeburg, Germany; Leibniz-Institute for Neurobiology (LIN), Brenneckestraße 6, 39118 Magdeburg, Germany
| | - Emrah Düzel
- German Center for Neurodegenerative Diseases (DZNE), Leipziger Straße 44, 39120 Magdeburg, Germany; Center for Behavioral and Brain Science (CBBS), Otto von Guericke University, Universitätsplatz 2, 39106 Magdeburg, Germany; Institute of Cognitive Neurology and Dementia Research, Otto von Guericke University, Leipziger Str. 44, 39120 Magdeburg, Germany; Institute of Cognitive Neuroscience, University College London, Alexandra House, 17-19 Queen Square, Bloomsbury, London, WC1N 3AZ United Kingdom
| | - Marco Taubert
- Faculty of Human Sciences, Institute III, Department of Sport Science, Otto von Guericke University, Zschokkestraße 32, 39104 Magdeburg, Germany; Center for Behavioral and Brain Science (CBBS), Otto von Guericke University, Universitätsplatz 2, 39106 Magdeburg, Germany
| | - Gabriel Ziegler
- German Center for Neurodegenerative Diseases (DZNE), Leipziger Straße 44, 39120 Magdeburg, Germany; Institute of Cognitive Neurology and Dementia Research, Otto von Guericke University, Leipziger Str. 44, 39120 Magdeburg, Germany
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Goto M, Abe O, Hagiwara A, Fujita S, Kamagata K, Hori M, Aoki S, Osada T, Konishi S, Masutani Y, Sakamoto H, Sakano Y, Kyogoku S, Daida H. Advantages of Using Both Voxel- and Surface-based Morphometry in Cortical Morphology Analysis: A Review of Various Applications. Magn Reson Med Sci 2022; 21:41-57. [PMID: 35185061 PMCID: PMC9199978 DOI: 10.2463/mrms.rev.2021-0096] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Surface-based morphometry (SBM) is extremely useful for estimating the indices of cortical morphology, such as volume, thickness, area, and gyrification, whereas voxel-based morphometry (VBM) is a typical method of gray matter (GM) volumetry that includes cortex measurement. In cases where SBM is used to estimate cortical morphology, it remains controversial as to whether VBM should be used in addition to estimate GM volume. Therefore, this review has two main goals. First, we summarize the differences between the two methods regarding preprocessing, statistical analysis, and reliability. Second, we review studies that estimate cortical morphological changes using VBM and/or SBM and discuss whether using VBM in conjunction with SBM produces additional values. We found cases in which detection of morphological change in either VBM or SBM was superior, and others that showed equivalent performance between the two methods. Therefore, we concluded that using VBM and SBM together can help researchers and clinicians obtain a better understanding of normal neurobiological processes of the brain. Moreover, the use of both methods may improve the accuracy of the detection of morphological changes when comparing the data of patients and controls. In addition, we introduce two other recent methods as future directions for estimating cortical morphological changes: a multi-modal parcellation method using structural and functional images, and a synthetic segmentation method using multi-contrast images (such as T1- and proton density-weighted images).
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Affiliation(s)
- Masami Goto
- Department of Radiological Technology, Faculty of Health Science, Juntendo University
| | - Osamu Abe
- Department of Radiology, Graduate School of Medicine, The University of Tokyo
| | | | - Shohei Fujita
- Department of Radiology, Graduate School of Medicine, The University of Tokyo
| | - Koji Kamagata
- Department of Radiology, Juntendo University School of Medicine
| | - Masaaki Hori
- Department of Radiology, Juntendo University School of Medicine
| | - Shigeki Aoki
- Department of Radiology, Juntendo University School of Medicine
| | - Takahiro Osada
- Department of Neurophysiology, Juntendo University School of Medicine
| | - Seiki Konishi
- Department of Neurophysiology, Juntendo University School of Medicine
| | | | - Hajime Sakamoto
- Department of Radiological Technology, Faculty of Health Science, Juntendo University
| | - Yasuaki Sakano
- Department of Radiological Technology, Faculty of Health Science, Juntendo University
| | - Shinsuke Kyogoku
- Department of Radiological Technology, Faculty of Health Science, Juntendo University
| | - Hiroyuki Daida
- Department of Radiological Technology, Faculty of Health Science, Juntendo University
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Stonnington CM, Chen Y, Savage CR, Lee W, Bauer RJ, Sharieff S, Thiyyagura P, Alexander GE, Caselli RJ, Locke DEC, Reiman EM, Chen K. Predicting Imminent Progression to Clinically Significant Memory Decline Using Volumetric MRI and FDG PET. J Alzheimers Dis 2019; 63:603-615. [PMID: 29630550 DOI: 10.3233/jad-170852] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
BACKGROUND Brain imaging measurements can provide evidence of possible preclinical Alzheimer's disease (AD). Their ability to predict individual imminent clinical conversion remains unclear. OBJECTIVE To investigate the ability of pre-specified volumetric magnetic resonance imaging (MRI) and fluorodeoxyglucose positron emission tomography (FDG-PET) measurements to predict which cognitively unimpaired older participants would subsequently progress to amnestic mild cognitive impairment (aMCI) within 2 years. METHODS From an apolipoprotein E4 (APOE4) enriched prospective cohort study, 18 participants subsequently progressed to the clinical diagnosis of aMCI or probable AD dementia within 1.8±0.8 years (progressors); 20 participants matched for sex, age, education, and APOE allele dose remained cognitively unimpaired for at least 4 years (nonprogressors). A complementary control group not matched for APOE allele dose included 35 nonprogressors. Groups were compared on baseline FDG-PET and MRI measures known to be preferentially affected in the preclinical and clinical stages of AD and by voxel-wise differences in regional gray matter volume and glucose metabolism. Receiver Operating Characteristic, binary logistic regression, and leave-one-out procedures were used to predict clinical outcome for the a priori measures. RESULTS Compared to non-progressors and regardless of APOE-matching, progressors had significantly reduced baseline MRI and PET measurements in brain regions preferentially affected by AD and reduced hippocampal volume was the strongest predictor of an individual's imminent progression to clinically significant memory decline (79% sensitivity/78% specificity among APOE-matched cohorts). CONCLUSION Regional MRI and FDG-PET measurements may be useful in predicting imminent progression to clinically significant memory decline.
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Affiliation(s)
- Cynthia M Stonnington
- Department of Psychiatry and Psychology, Mayo Clinic Arizona, Scottsdale, AZ, USA.,Arizona Alzheimer's Consortium, Phoenix, AZ, USA
| | - Yinghua Chen
- Banner Alzheimer's Institute and Banner Good Samaritan PET Center, Phoenix, AZ, USA.,Arizona Alzheimer's Consortium, Phoenix, AZ, USA
| | - Cary R Savage
- Banner Alzheimer's Institute and Banner Good Samaritan PET Center, Phoenix, AZ, USA.,Arizona Alzheimer's Consortium, Phoenix, AZ, USA
| | - Wendy Lee
- Banner Alzheimer's Institute and Banner Good Samaritan PET Center, Phoenix, AZ, USA.,Arizona Alzheimer's Consortium, Phoenix, AZ, USA
| | - Robert J Bauer
- Banner Alzheimer's Institute and Banner Good Samaritan PET Center, Phoenix, AZ, USA.,Arizona Alzheimer's Consortium, Phoenix, AZ, USA
| | - Sameen Sharieff
- Department of Psychiatry and Psychology, Mayo Clinic Arizona, Scottsdale, AZ, USA.,Midwestern University, Glendale, AZ, USA.,Arizona Alzheimer's Consortium, Phoenix, AZ, USA
| | - Pradeep Thiyyagura
- Banner Alzheimer's Institute and Banner Good Samaritan PET Center, Phoenix, AZ, USA.,Arizona Alzheimer's Consortium, Phoenix, AZ, USA
| | - Gene E Alexander
- Department of Psychology and Evelyn F. McKnight Brain Institute, University of Arizona, Tucson, AZ, USA.,Neuroscience and Physiological Science Interdisciplinary Graduate Programs, University of Arizona, Tucson, AZ, USA.,Arizona Alzheimer's Consortium, Phoenix, AZ, USA
| | - Richard J Caselli
- Department of Neurology, Mayo Clinic Arizona, Scottsdale, AZ, USA.,Arizona Alzheimer's Consortium, Phoenix, AZ, USA
| | - Dona E C Locke
- Department of Psychiatry and Psychology, Mayo Clinic Arizona, Scottsdale, AZ, USA.,Arizona Alzheimer's Consortium, Phoenix, AZ, USA
| | - Eric M Reiman
- Banner Alzheimer's Institute and Banner Good Samaritan PET Center, Phoenix, AZ, USA.,Translational Genomics Research Institute, Scottsdale, AZ, USA.,Department of Psychiatry, University of Arizona, Tucson, AZ, USA.,Arizona Alzheimer's Consortium, Phoenix, AZ, USA
| | - Kewei Chen
- Banner Alzheimer's Institute and Banner Good Samaritan PET Center, Phoenix, AZ, USA.,Arizona State University, Tempe, AZ, USA.,Department of Psychiatry, University of Arizona, Tucson, AZ, USA.,Arizona Alzheimer's Consortium, Phoenix, AZ, USA
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Griffanti L, Rolinski M, Szewczyk-Krolikowski K, Menke RA, Filippini N, Zamboni G, Jenkinson M, Hu MTM, Mackay CE. Challenges in the reproducibility of clinical studies with resting state fMRI: An example in early Parkinson's disease. Neuroimage 2015; 124:704-713. [PMID: 26386348 PMCID: PMC4655939 DOI: 10.1016/j.neuroimage.2015.09.021] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2015] [Revised: 09/07/2015] [Accepted: 09/09/2015] [Indexed: 11/06/2022] Open
Abstract
Resting state fMRI (rfMRI) is gaining in popularity, being easy to acquire and with promising clinical applications. However, rfMRI studies, especially those involving clinical groups, still lack reproducibility, largely due to the different analysis settings. This is particularly important for the development of imaging biomarkers. The aim of this work was to evaluate the reproducibility of our recent study regarding the functional connectivity of the basal ganglia network in early Parkinson's disease (PD) (Szewczyk-Krolikowski et al., 2014). In particular, we systematically analysed the influence of two rfMRI analysis steps on the results: the individual cleaning (artefact removal) of fMRI data and the choice of the set of independent components (template) used for dual regression. Our experience suggests that the use of a cleaning approach based on single-subject independent component analysis, which removes non neural-related sources of inter-individual variability, can help to increase the reproducibility of clinical findings. A template generated using an independent set of healthy controls is recommended for studies where the aim is to detect differences from a “healthy” brain, rather than an “average” template, derived from an equal number of patients and controls. While, exploratory analyses (e.g. testing multiple resting state networks) should be used to formulate new hypotheses, careful validation is necessary before promising findings can be translated into useful biomarkers. Reproducibility of clinical findings is crucial for imaging biomarker development. We addressed the impact on reproducibility of different analysis settings in rfMRI. ICA-based cleaning of rfMRI data increases reproducibility. The effect of the template choice for dual regression is evaluated.
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Affiliation(s)
- Ludovica Griffanti
- Centre for the functional MRI of the Brain (FMRIB), University of Oxford, Oxford, UK
| | - Michal Rolinski
- Oxford Parkinson's Disease Centre (OPDC), University of Oxford, Oxford, UK; Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Konrad Szewczyk-Krolikowski
- Oxford Parkinson's Disease Centre (OPDC), University of Oxford, Oxford, UK; Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Ricarda A Menke
- Centre for the functional MRI of the Brain (FMRIB), University of Oxford, Oxford, UK
| | - Nicola Filippini
- Centre for the functional MRI of the Brain (FMRIB), University of Oxford, Oxford, UK; Department of Psychiatry, University of Oxford, Oxford, UK
| | - Giovanna Zamboni
- Centre for the functional MRI of the Brain (FMRIB), University of Oxford, Oxford, UK
| | - Mark Jenkinson
- Centre for the functional MRI of the Brain (FMRIB), University of Oxford, Oxford, UK
| | - Michele T M Hu
- Oxford Parkinson's Disease Centre (OPDC), University of Oxford, Oxford, UK; Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Clare E Mackay
- Centre for the functional MRI of the Brain (FMRIB), University of Oxford, Oxford, UK; Oxford Parkinson's Disease Centre (OPDC), University of Oxford, Oxford, UK; Department of Psychiatry, University of Oxford, Oxford, UK.
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