Diffusion tensor magnetic resonance imaging of the brain in app transgenic mice: A cohort study.
Open accessAbstract
INTRODUCTION: Fast in-vivo high resolution diffusion tensor imaging (DTI) of the mouse brain has recently been shown to enable cohort studies by the combination of appropriate pulse sequences and cryogenically cooled resonators (CCR). The objective of this study was to apply this DTI approach at the group level to β-amyloid precursor protein (APP) transgenic mice. METHODS: Twelve mice (5 wild type, 7 APP transgenic tg2576) underwent DTI examination at 156(2) × 250 µm(3) spatial resolution with a CCR at ultrahigh field (11.7 T). Diffusion images were acquired along 30 gradient directions plus 5 references without diffusion encoding with a total acquisition time of 35 minutes. Fractional anisotropy (FA) maps were statistically compared by whole brain-based spatial statistics (WBSS) at the group level vs. wild type controls. RESULTS: FA-map comparison showed characteristic regional patterns of differences between the groups with localizations associated with Alzheimer's disease in humans, such as the hippocampus, the entorhinal cortex, and the caudoputamen. CONCLUSION: In this proof-of-principle study, regions associated with amyloid-β deposition could be identified by WBSS of FA maps in APP transgenic mice vs. wild type mice. Thus, DTI in the mouse brain acquired at 11.7 T by use of a CCR was demonstrated to be feasible for cohort studies.
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FA in the cortex was reported to increase in 3xTg AD mice, while FA was reduced in APP transgenic mice.
“For instance, FA in the cortex was reported to increase in 3xTg AD mice (Snow et al., 2017; Manno et al., 2019), while the FA was showed reduced in APP transgenic mice (Muller et al., 2013; Qin et al., 2013).”
From Age-dependent microstructure alterations in 5xFAD mice by high-resolution diffusion tensor imaging (2022) · cites this paper as Muller et al., 2013
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Cites
The Mouse Brain in Stereotaxic Coordinates
Paxinos, G. (2001) · Cited by 12,859 · see citing works
Tract-based spatial statistics: Voxelwise analysis of multi-subject diffusion data
Smith, S. M., Jenkinson, M., Johansen-Berg, H., Rueckert, D., Nichols, T. E., Mackay, C., Watkins, K., Ciccarelli, O., Cader, M., Matthews, P., et al. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2006.02.024 · Cited by 6,035 · see citing works
MR diffusion tensor spectroscopy and imaging
Basser, P. J., Mattiello, J., & LeBihan, D. (1994) · Biophysical Journal · doi:10.1016/s0006-3495(94)80775-1 · Cited by 5,970 · see citing works
Thresholding of statistical maps in functional neuroimaging using the false discovery rate
Genovese, C. R., Lazar, N. A., & Nichols, T. (2002) · NeuroImage · doi:10.1006/nimg.2001.1037 · Cited by 4,923 · see citing works
Correlative Memory Deficits, Aβ Elevation, and Amyloid Plaques in Transgenic Mice
Hsiao, K., Chapman, P., Nilsen, S., Eckman, C., Harigaya, Y., Younkin, S., Yang, F., & Cole, G. (1996) · Science · doi:10.1126/science.274.5284.99 · Cited by 4,210 · see citing works
Diffusion tensor imaging: Concepts and applications
Le Bihan, D., Mangin, J., Poupon, C., Clark, C. A., Pappata, S., Molko, N., & Chabriat, H. (2001) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.1076 · Cited by 3,547 · see citing works
Staging of alzheimer's disease-related neurofibrillary changes
Braak, H., & Braak, E. (1995) · Neurobiology of Aging · doi:10.1016/0197-4580(95)00021-6 · Cited by 2,357 · see citing works
Song, S.-K., Sun, S.-W., Ju, W.-K., Lin, S.-J., Cross, A. H., & Neufeld, A. H. (2003) · NeuroImage · doi:10.1016/j.neuroimage.2003.07.005 · Cited by 1,738 · see citing works
Diffusion‐tensor MRI: theory, experimental design and data analysis – a technical review
Basser, P. J., & Jones, D. K. (2002) · NMR in Biomedicine · doi:10.1002/nbm.783 · Cited by 1,484 · see citing works
Prediction of AD with MRI-based hippocampal volume in mild cognitive impairment
Jack, C. R., Petersen, R. C., Xu, Y. C., O’Brien, P. C., Smith, G. E., Ivnik, R. J., Boeve, B. F., Waring, S. C., Tangalos, E. G., & Kokmen, E. (1999) · Neurology · doi:10.1212/wnl.52.7.1397 · Cited by 1,404 · see citing works
Patterns of Brain Activation in People at Risk for Alzheimer's Disease
Bookheimer, S. Y., Strojwas, M. H., Cohen, M. S., Saunders, A. M., Pericak-Vance, M. A., Mazziotta, J. C., & Small, G. W. (2000) · New England Journal of Medicine · doi:10.1056/nejm200008173430701 · Cited by 1,328 · see citing works
The Alzheimer's Disease Neuroimaging Initiative: A review of papers published since its inception
Weiner, M. W., Veitch, D. P., Aisen, P. S. S., Beckett, L. A., Cairns, N. J., Green, R. C., Harvey, D. J., Jack, C. R., Jagust, W. J., Liu, E., et al. (2013) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2013.05.1769 · Cited by 1,011 · see citing works
The effect of filter size on VBM analyses of DT-MRI data
Jones, D. K., Symms, M. R., Cercignani, M., & Howard, R. J. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2005.02.013 · Cited by 562 · see citing works
The Alzheimer's Disease Neuroimaging Initiative: A review of papers published since its inception
Weiner, M. W., Veitch, D. P., Aisen, P. S. S., Beckett, L. A., Cairns, N. J., Green, R. C., Harvey, D. J., Jack, C. R., Jagust, W. J., Liu, E., et al. (2011) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2011.09.172 · Cited by 477 · see citing works
Diffusion tensor imaging in Alzheimer’s disease and mild cognitive impairment.
Stebbins, G. T., & Murphy, C. M. (2009) · Behavioural Neurology · doi:10.1155/2009/915041 · Cited by 245 · see citing works
Grinberg, L. T., Rüb, U., Ferretti, R. E. L., Nitríni, R., Farfel, J. M., Polichiso, L., Gierga, K., Jacob‐Filho, W., Heinsen, H., & Brazilian Brain Bank Study Group (2009) · Neuropathology and Applied Neurobiology · doi:10.1111/j.1365-2990.2008.00997.x · Cited by 244 · see citing works
Diffusion tensor imaging in Alzheimer's disease and mild cognitive impairment.
Stebbins, G. T., & Murphy, C. M. (2009) · PubMed · doi:10.3233/ben-2009-0234 · Cited by 210 · see citing works
Pihlajam�ki, M., Tanila, H., H�nninen, T., Könönen, M., Laakso, M. P., Partanen, K. P. L., Soininen, H. S., & Aronen, H. J. (2000) · Annals of Neurology · doi:10.1002/1531-8249(200004)47:4 · Cited by 210 · see citing works
Jack, C. R., Garwood, M., Wengenack, T. M., Borowski, B., Curran, G. L., Lin, J., Adriany, G., Gröhn, O. H. J., Grimm, R. C., & Poduslo, J. F. (2004) · Magnetic Resonance in Medicine · doi:10.1002/mrm.20266 · Cited by 196 · see citing works
Song, S.-K., Kim, J. H., Lin, S.-J., Brendza, R. P., & Holtzman, D. M. (2004) · Neurobiology of Disease · doi:10.1016/j.nbd.2003.12.003 · Cited by 170 · see citing works
Bennett, R. E., Donald, C. L. M., & Brody, D. L. (2012) · Neuroscience Letters · doi:10.1016/j.neulet.2012.02.024 · Cited by 136 · see citing works
Sun, S.-W., Song, S.-K., Harms, M. P., Lin, S.-J., Holtzman, D. M., Merchant, K. M., & Kotyk, J. J. (2005) · Experimental Neurology · doi:10.1016/j.expneurol.2004.09.006 · Cited by 121 · see citing works
Unrath, A., Müller, H., Riecker, A., Ludolph, A. C., Sperfeld, A., & Kassubek, J. (2010) · Human Brain Mapping · doi:10.1002/hbm.20971 · Cited by 119 · see citing works
Self-citationMüller, H.-P., Unrath, A., Ludolph, A. C., & Kassubek, J. (2007) · Physics in Medicine and Biology · doi:10.1088/0031-9155/52/6/n01 · Cited by 117 · see citing works
Self-citationIn vivo MRI and histological evaluation of brain atrophy in APP/PS1 transgenic mice
Delatour, B., Guégan, M., Volk, A., & Dhénain, M. (2005) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2005.04.011 · Cited by 106 · see citing works
Three-dimensional diffusion tensor microimaging for anatomical characterization of the mouse brain
Aggarwal, M., Mori, S., Shimogori, T., Blackshaw, S., & Zhang, J. (2010) · Magnetic Resonance in Medicine · doi:10.1002/mrm.22426 · Cited by 90 · see citing works
Gray and white matter degeneration revealed by diffusion in an Alzheimer mouse model
Zerbi, V., Kleinnijenhuis, M., Fang, X., Jansen, D., Veltien, A., Van Asten, J., Timmer, N., Dederen, P. J., Kiliaan, A. J., & Heerschap, A. (2013) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2012.11.017 · Cited by 74 · see citing works
Braunstein, K. E., Eschbach, J., Ròna-Vörös, K., Soylu-Kucharz, R., Mikrouli, E., Larmet, Y., René, F., de Aguilar, J. G., Loeffler, J., Müller, H.-P., et al. (2010) · Human Molecular Genetics · doi:10.1093/hmg/ddq361 · Cited by 62 · see citing works
Self-citationRelationship between apathy and diffusion tensor imaging metrics of the brain in Alzheimer's disease
Ota, M., Sato, N., Nakata, Y., Arima, K., & Uno, M. (2012) · International Journal of Geriatric Psychiatry · doi:10.1002/gps.2779 · Cited by 58 · see citing works
In vivo diffusion tensor magnetic resonance imaging and fiber tracking of the mouse brain
Harsan, L. A., Paul, D., Schnell, S., Kreher, B. W., Hennig, J., Staiger, J. F., & von Elverfeldt, D. (2010) · NMR in Biomedicine · doi:10.1002/nbm.1496 · Cited by 55 · see citing works
Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
Müller, H., & Kassubek, J. (2013) · Journal of Visualized Experiments · doi:10.3791/50427 · Cited by 47 · see citing works
Self-citationGutman, D. A., Keifer, O. P., Magnuson, M. E., Choi, D. C., Majeed, W., Keilholz, S. D., & Ressler, K. J. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.07.014 · Cited by 43 · see citing works
Mohammadi, S., Keller, S., Glauche, V., Kugel, H., Jansen, A., Hutton, C., Flöel, A., & Deppe, M. (2012) · PLoS ONE · doi:10.1371/journal.pone.0036851 · Cited by 41 · see citing works
Harms, M. P., Kotyk, J. J., & Merchant, K. M. (2006) · Experimental Neurology · doi:10.1016/j.expneurol.2006.01.002 · Cited by 38 · see citing works
Müller, H., Süßmuth, S. D., Landwehrmeyer, G. B., et al. (2011) · PLoS Currents · doi:10.1371/currents.rrn1292 · Cited by 21 · see citing works
Self-citationMüller, H., Vernikouskaya, I., Ludolph, A. C., Kassubek, J., & Rasche, V. (2012) · PLoS ONE · doi:10.1371/journal.pone.0053389 · Cited by 13 · see citing works
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Racine, A. M., Adluru, N., Alexander, A. L., Christian, B. T., Okonkwo, O. C., Oh, J., Cleary, C. A., Birdsill, A., Hillmer, A. T., Murali, D., et al. (2014) · NeuroImage: Clinical · doi:10.1016/j.nicl.2014.02.001 · Cited by 136 · see citing works
Colon-Perez, L. M., Ibanez, K. R., Suarez, M., Torroella, K., Acuna, K., Ofori, E., Levites, Y., Vaillancourt, D. E., Golde, T. E., Chakrabarty, P., et al. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116138 · Cited by 41 · see citing works
Badea, A., Kane, L., Anderson, R. J., Qi, Y., Foster, M., Cofer, G. P., Medvitz, N., Buckley, A. F., Badea, A. K., Wetsel, W. C., et al. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.08.014 · Cited by 39 · see citing works
Snow, W. M., Dale, R., O’Brien-Moran, Z., Buist, R., Peirson, D., Martin, M., & Albensi, B. C. (2017) · Journal of Alzheimer s Disease · doi:10.3233/jad-170136 · Cited by 29 · see citing works
Müller, H.-P., Roselli, F., Rasche, V., & Kassubek, J. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.00734 · Cited by 12 · see citing works
Self-citationAge-dependent microstructure alterations in 5xFAD mice by high-resolution diffusion tensor imaging
Maharjan, S., Tsai, A. P., Lin, P. B., Ingraham, C., Jewett, M. R., Landreth, G. E., Oblak, A. L., & Wang, N. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.964654 · Cited by 11 · see citing works
“, 2019), while the FA was showed reduced in APP transgenic mice (Muller et al., 2013; Qin et al., 2013).”
Background
- Authors
- Müller, H.-P., Kassubek, J., Vernikouskaya, I., Ludolph, A. C., Stiller, D., & Rasche, V.
- Affiliations
- Universität Ulm (DE)
- Boehringer Ingelheim (Germany) (DE)
- Corresponding author
- Müller, Hans-Peter
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2013-06-28
- Volume
- 8
- Issue
- 6
- Pages
- e67630
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC3695895
- OpenAlex
- W2054050985
- Cited by
- 22
- References
- 36
- Citations by year
- 2023: 1 · 2022: 1 · 2021: 1 · 2020: 2 · 2019: 5 · 2018: 4 · 2017: 3 · 2016: 1 · 2015: 2 · 2014: 1 · 2013: 1
- FWCI
- 1.29
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS ONE (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Keywords
- diffusion tensor imaging, APP transgenic mice, Tg2576 mice, amyloid-beta deposition, Alzheimer's disease model, fractional anisotropy, ultra-high field MRI, hippocampus, entorhinal cortex, caudoputamen, cohort studies
- MeSH terms
- Animals, Brain, Brain Mapping, Mice, Transgenic, Cohort Studies, Amyloid beta-Protein Precursor, Anisotropy, Diffusion Magnetic Resonance Imaging, Mice, Diffusion Tensor Imaging
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