Longitudinal Assessment of Amyloid Pathology in Transgenic ArcAβ Mice Using Multi-Parametric Magnetic Resonance Imaging
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Magnetic resonance imaging (MRI) can be used to monitor pathological changes in Alzheimer's disease (AD). The objective of this longitudinal study was to assess the effects of progressive amyloid-related pathology on multiple MRI parameters in transgenic arcAβ mice, a mouse model of cerebral amyloidosis. Diffusion-weighted imaging (DWI), T1-mapping and quantitative susceptibility mapping (QSM), a novel MRI based technique, were applied to monitor structural alterations and changes in tissue composition imposed by the pathology over time. Vascular function and integrity was studied by assessing blood-brain barrier integrity with dynamic contrast-enhanced MRI and cerebral microbleed (CMB) load with susceptibility weighted imaging and QSM. A linear mixed effects model was built for each MRI parameter to incorporate effects within and between groups (i.e. genotype) and to account for changes unrelated to the disease pathology. Linear mixed effects modelling revealed a strong association of all investigated MRI parameters with age. DWI and QSM in addition revealed differences between arcAβ and wt mice over time. CMBs became apparent in arcAβ mice with 9 month of age; and the CMB load reflected disease stage. This study demonstrates the benefits of linear mixed effects modelling of longitudinal imaging data. Moreover, the diagnostic utility of QSM and assessment of CMB load should be exploited further in studies of AD.
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Transgenic mice expressing different human APP mutations exhibit pronounced vascular pathology including severe BBB permeability changes, microbleeds, BBB leakages of blood-derived molecules, impaired Aβ clearance at the BBB, endothelial cell degeneration, loss of VSMCs and pericytes, and CAA.
“Similar to humans, transgenic mice expressing different human APP mutations exhibit pronounced vascular pathology including severe BBB permeability changes (322, 467, 579, 606), microbleeds (56, 321, 322, 336, 579), BBB leakages of blood-derived molecules (109, 331, 336, 467, 513), impaired A 𝛃 clearance at the BBB (57, 137, 464, 512), endothelial cell degeneration (336, 513), loss of VSMCs (336,”
From Blood-Brain Barrier: From Physiology to Disease and Back (2019) · cites this paper as [322]
The Discussion (page unavailable) describes pronounced vascular pathology in arcAβ mice, including CAA-affected vessels, fibrinogen deposition, loss of vascular smooth muscle cells, BBB leakage, and microbleeds. It does not establish impaired Aβ clearance at the BBB, endothelial degeneration, or pericyte loss. Its own DCE-MRI assessment found no evident BBB leakage, so it does not support the claim of severe permeability changes. (LLM, conf=0.88)
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Cites
R: A Language and Environment for Statistical Computing
R Core Team (2000) · doi:10.32614/r.manuals · Cited by 355,706 · see citing works
Modern Applied Statistics with S
Venables, W., & Ripley, B. (2010) · Statisctics and computing/Statistics and computing · doi:10.1007/978-0-387-21706-2 · Cited by 16,188 · see citing works
The Amyloid Hypothesis of Alzheimer’s Disease: Progress and Problems on the Road to Therapeutics
Hardy, J., & Selkoe, D. J. (2002) · Science · doi:10.1126/science.1072994 · Cited by 13,864 · see citing works
The Mouse Brain in Stereotaxic Coordinates
Paxinos, G. (2001) · Cited by 12,859 · see citing works
Modern Applied Statistics With S
Gloeckner, C. (2003) · Technometrics · doi:10.1198/tech.2003.s33 · Cited by 6,721 · see citing works
A reproducible evaluation of ANTs similarity metric performance in brain image registration
Avants, B. B., Tustison, N. J., Song, G., Cook, P. A., Klein, A., & Gee, J. C. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2010.09.025 · Cited by 4,796 · see citing works
mouse brain in stereotaxic coordinates
Franklin, K. B. J., & Paxinos, G. (1997) · Cited by 3,258 · see citing works
Springer Science+Business Media
Технология (2013) · Betascript Publishing eBooks · Cited by 3,087 · see citing works
Neurovascular regulation in the normal brain and in Alzheimer's disease
Iadecola, C. (2004) · Nature Reviews Neuroscience · doi:10.1038/nrn1387 · Cited by 2,213 · see citing works
Alzheimer’s Disease Neuroimaging Initiative
Mueller, S. G., Weiner, M. W., Thal, L. J., Petersen, R. C., Jack, C. R., Jagust, W. J., Trojanowski, J. Q., Toga, A. W., & Beckett, L. A. (2007) · Springer eBooks · doi:10.1007/978-0-387-72076-0_18 · Cited by 1,493 · see citing works
Schweser, F., Deistung, A., Lehr, B. W., & Reichenbach, J. R. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.10.070 · Cited by 713 · see citing works
Self-citationAnimal models of Alzheimer's disease and frontotemporal dementia
Götz, J., & Ittner, L. M. (2008) · Nature Reviews Neuroscience · doi:10.1038/nrn2420 · Cited by 676 · see citing works
de Rochefort, L., Liu, T., Kressler, B., Liu, J., Spincemaille, P., Lebon, V., Wu, J., & Wang, Y. (2009) · Magnetic Resonance in Medicine · doi:10.1002/mrm.22187 · Cited by 672 · see citing works
Small vessels in the human brain: MR venography with deoxyhemoglobin as an intrinsic contrast agent.
Reichenbach, J. R., Venkatesan, R., Schillinger, D. J., Kido, D. K., & Haacke, E. M. (1997) · Radiology · doi:10.1148/radiology.204.1.9205259 · Cited by 606 · see citing works
Self-citationMolecular diffusion, tissue microdynamics and microstructure
Bihan, D. L. (1995) · NMR in Biomedicine · doi:10.1002/nbm.1940080711 · Cited by 497 · see citing works
A novel background field removal method for MRI using projection onto dipole fields (PDF)
Liu, T., Khalidov, I., de Rochefort, L., Spincemaille, P., Liu, J., Tsiouris, A. J., & Wang, Y. (2011) · NMR in Biomedicine · doi:10.1002/nbm.1670 · Cited by 454 · see citing works
Statistical analysis of longitudinal neuroimage data with Linear Mixed Effects models
Bernal-Rusiel, J. L., Greve, D. N., Reuter, M., Fischl, B., & Sabuncu, M. R. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2012.10.065 · Cited by 360 · see citing works
Weller, R. O., Boche, D., & Nicoll, J. A. R. (2009) · Acta Neuropathologica · doi:10.1007/s00401-009-0498-z · Cited by 297 · see citing works
Fast and robust three-dimensional best path phase unwrapping algorithm
Abdul-Rahman, H. S., Gdeisat, M. A., Burton, D. R., Lalor, M. J., Lilley, F., & Moore, C. J. (2007) · Applied Optics · doi:10.1364/ao.46.006623 · Cited by 295 · see citing works
Spontaneous Hemorrhagic Stroke in a Mouse Model of Cerebral Amyloid Angiopathy
Winkler, D. T., Bondolfi, L., Herzig, M. C., Jann, L., Calhoun, M. E., Wiederhold, K., Tolnay, M., Staufenbiel, M., & Jucker, M. (2001) · Journal of Neuroscience · doi:10.1523/jneurosci.21-05-01619.2001 · Cited by 287 · see citing works
Gouw, A. A., Seewann, A., Vrenken, H., van der Flier, W. M., Rozemuller, J. M., Barkhof, F., Scheltens, P., & Geurts, J. J. G. (2008) · Brain · doi:10.1093/brain/awn265 · Cited by 281 · see citing works
Brunberg, J. A., Chenevert, T. L., McKeever, P. E., Ross, D. A., Junck, L., Muraszko, K. M., Dauser, R., Pipe, J. G., & Betley, A. T. (1995) · PubMed Central · Cited by 269 · see citing works
Intracellular Aβ and cognitive deficits precede β-amyloid deposition in transgenic arcAβ mice
Knobloch, M., Konietzko, U., Krebs, D. C., & Nitsch, R. M. (2007) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2006.06.019 · Cited by 259 · see citing works
Self-citationHigh‐resolution BOLD venographic imaging: a window into brain function
Reichenbach, J. R., & Haacke, E. M. (2001) · NMR in Biomedicine · doi:10.1002/nbm.722 · Cited by 248 · see citing works
Self-citationSchweser, F., Sommer, K., Deistung, A., & Reichenbach, J. R. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.05.067 · Cited by 243 · see citing works
Self-citationHigh-field (9.4T) MRI of brain dysmyelination by quantitative mapping of magnetic susceptibility
Liu, C., Li, W., Johnson, G. A., & Wu, B. H. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.02.024 · Cited by 231 · see citing works
Merlini, M., Meyer, E. P., Ulmann-Schuler, A., & Nitsch, R. M. (2011) · Acta Neuropathologica · doi:10.1007/s00401-011-0834-y · Cited by 225 · see citing works
Self-citationSpring, S., Lerch, J. P., & Henkelman, R. M. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.02.023 · Cited by 180 · 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
The concentration of glial fibrillary acidic protein increases with age in the mouse and rat brain
O'Callaghan, J. P., & Miller, D. B. (1991) · Neurobiology of Aging · doi:10.1016/0197-4580(91)90057-q · Cited by 152 · see citing works
Thakker, D. R., Weatherspoon, M. R., Harrison, J., Keene, T. E., Lane, D. S., Kaemmerer, W. F., Stewart, G. R., & Shafer, L. L. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0813404106 · Cited by 124 · see citing works
Syková, E., Voříšek, I., Antonova, T., Mazel, T., Meyer-Luehmann, M., Jucker, M., Hájek, M., Or, M., & Bureš, J. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0408235102 · Cited by 121 · see citing works
In 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
MRI assessment of neuropathology in a transgenic mouse model of Alzheimer's disease
Helpern, J. A., Lee, P., Falangola, M. F., Dyakin, V. V., Bogart, A., Ardekani, B. A., Duff, K., Branch, C. A., Wısnıewskı, T., de Leon, M. J., et al. (2004) · Magnetic Resonance in Medicine · doi:10.1002/mrm.20038 · Cited by 105 · see citing works
Tayara, N. E. T. E., Delatour, B., Cudennec, C. L., Guégan, M., Volk, A., & Dhénain, M. (2005) · Neurobiology of Disease · doi:10.1016/j.nbd.2005.10.013 · Cited by 85 · see citing works
Klohs, J., Deistung, A., Schweser, F., Grandjean, J., Dominietto, M., Waschkies, C., Nitsch, R. M., Knuesel, I., Reichenbach, J. R., & Rudin, M. (2011) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1038/jcbfm.2011.118 · Cited by 83 · see citing works
Self-citationCheng, Y. N., Neelavalli, J., & Haacke, E. M. (2009) · Physics in Medicine and Biology · doi:10.1088/0031-9155/54/5/005 · Cited by 79 · see citing works
Changes with age in copper and superoxide dismutase levels in brains of C57BL/6J mice
Massie, H. R., Aiello, V. R., & Iodice, A. A. (1979) · Mechanisms of Ageing and Development · doi:10.1016/0047-6374(79)90073-3 · Cited by 76 · see citing works
Klohs, J., Baltes, C., Princz-Kranz, F., Ratering, D., Nitsch, R. M., Knuesel, I., & Rudin, M. (2012) · The Journal of Neuroscience · doi:10.1523/jneurosci.5626-11.2012 · Cited by 73 · see citing works
Self-citationChamberlain, R., Reyes, D. A., Curran, G. L., Marjańska, M., Wengenack, T. M., Poduslo, J. F., Garwood, M., & Jack, C. R. (2009) · Magnetic Resonance in Medicine · doi:10.1002/mrm.21951 · Cited by 62 · see citing works
High-relaxivity contrast-enhanced magnetic resonance neuroimaging: a review
Giesel, F. L., Mehndiratta, A., & Essig, M. (2010) · European Radiology · doi:10.1007/s00330-010-1805-8 · Cited by 61 · see citing works
Restricted diffusion in the brain of transgenic mice with cerebral amyloidosis
Mueggler, T., Meyer‐Luehmann, M., Rausch, M., Staufenbiel, M., Jucker, M., & Rudin, M. (2004) · European Journal of Neuroscience · doi:10.1111/j.1460-9568.2004.03534.x · Cited by 59 · see citing works
Self-citationRudin, M., McSheehy, P. M. J., ALLEGRINI, P. R., Rausch, M., Kindler, D., Becquet, M., Brecht, K., Brueggen, J., Ferretti, S., Schaeffer, F., et al. (2005) · NMR in Biomedicine · doi:10.1002/nbm.961 · Cited by 58 · see citing works
Self-citationWeidensteiner, C., Metzger, F., Bohrmann, A. B. B., Kuennecke, B., & von Kienlin, M. (2009) · Magnetic Resonance in Medicine · doi:10.1002/mrm.21985 · Cited by 56 · see citing works
Quantitative MRI reveals aging‐associated T2 changes in mouse models of Alzheimer's disease
Falangola, M. F., Dyakin, V. V., Lee, P., Bogart, A., Babb, J. S., Duff, K., Nixon, R. A., & Helpern, J. A. (2007) · NMR in Biomedicine · doi:10.1002/nbm.1163 · Cited by 53 · see citing works
Badea, A., Johnson, G. A., & Jankowsky, J. L. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.12.070 · Cited by 47 · see citing works
Predicting missing biomarker data in a longitudinal study of Alzheimer disease
Lo, R. Y., Jagust, W. J., Aisen, P. S. S., Jack, C. R., Toga, A. W., Beckett, L. A., Gamst, A. C., Soares, H. D., Green, R. C., Montine, T., et al. (2012) · Neurology · doi:10.1212/wnl.0b013e318253d5b3 · Cited by 46 · see citing works
Integrity of blood-brain barrier to peroxidase in senescent mice
Rudick, R. A., & Buell, S. J. (1983) · Neurobiology of Aging · doi:10.1016/0197-4580(83)90004-0 · Cited by 38 · see citing works
VORBRODT, A. W., & Dobrogowska, D. H. (1994) · PubMed · Cited by 36 · see citing works
Princz-Kranz, F. L., Mueggler, T., Knobloch, M., Nitsch, R. M., & Rudin, M. (2010) · Neurobiology of Disease · doi:10.1016/j.nbd.2010.06.002 · Cited by 28 · see citing works
Self-citationUnveiling Molecular Events in the Brain by Noninvasive Imaging
Klohs, J., & Rudin, M. (2011) · The Neuroscientist · doi:10.1177/1073858410383433 · Cited by 22 · see citing works
Self-citationThiessen, J. D., Glazner, K. A. C., Nafez, S., Schellenberg, A. E., Buist, R. J., Martin, M., & Albensi, B. C. (2010) · Brain Structure and Function · doi:10.1007/s00429-010-0271-z · Cited by 20 · see citing works
Gouw, A. A., Seewann, A., Vrenken, H., van der Flier, W. M., Rozemüller, A. J. M., Barkhof, F., Scheltens, P., & Geurts, J. J. G. (2009) · The Neuroradiology Journal · doi:10.1177/19714009090220s110 · Cited by 11 · see citing works
Teipel, S. J., Peters, O., Heuser, I., Jessen, F., Maier, W., Froelich, L., Arlt, S., Hüll, M., Gertz, H., Kornhuber, J., et al. (2011) · The World Journal of Biological Psychiatry · doi:10.3109/15622975.2011.603222 · Cited by 8 · see citing works
The emerging utility of animal models of chronic neurodegenerative diseases
Kahle, P. J., & Haass, C. (2001) · Emerging Therapeutic Targets · doi:10.1517/14728222.5.1.125 · Cited by 7 · see citing works
Species, Strain, and Gender Issues in the Development and Validation of Animal Models of Dementia
Dijck, A. V., Dam, D. V., & Deyn, P. P. D. (2010) · Neuromethods · doi:10.1007/978-1-60761-898-0_4 · Cited by 2 · see citing works
Validation of Dementia Models Employing Neuroimaging Techniques
Vanhoutte, G., Campo, A., & der Linden, A. V. (2010) · Neuromethods · doi:10.1007/978-1-60761-898-0_11 · Cited by 1 · see citing works
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Blood-Brain Barrier: From Physiology to Disease and Back
Sweeney, M. D., Zhao, Z., Montagne, A., Nelson, A. R., & Zlokovic, B. V. (2019) · Physiological Reviews · doi:10.1152/physrev.00050.2017 · Cited by 2,294 · see citing works
Alzheimer’s disease: A matter of blood–brain barrier dysfunction?
Montagne, A., Zhao, Z., & Zlokovic, B. V. (2017) · Journal of Experimental Medicine · doi:10.1084/jem.20171406 · Cited by 672 · see citing works
Age and sex related differences in subcortical brain iron concentrations among healthy adults
Persson, N., Wu, J., Zhang, Q., Liu, T., Shen, J., Bao, R., Ni, M.-F., Liu, T., Wang, Y., & Spincemaille, P. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.07.050 · Cited by 135 · see citing works
An Integrated View on Vascular Dysfunction in Alzheimer’s Disease
Klohs, J. (2019) · Neurodegenerative Diseases · doi:10.1159/000505625 · Cited by 111 · see citing works
Self-citationUchida, Y., Kan, H., Sakurai, K., Oishi, K., & Matsukawa, N. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.938092 · Cited by 85 · see citing works
Multiscale optical and optoacoustic imaging of amyloid-β deposits in mice
Ni, R., Chen, Z., Deán‐Ben, X. L., Voigt, F. F., Kirschenbaum, D. S., Shi, G., Villois, A., Zhou, Q., Crimi, A., Arosio, P., et al. (2022) · Nature Biomedical Engineering · doi:10.1038/s41551-022-00906-1 · Cited by 84 · see citing works
Self-citationImaging of cerebrovascular pathology in animal models of Alzheimer's disease
Klohs, J., Rudin, M., Shimshek, D. R., & Beckmann, N. (2014) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2014.00032 · Cited by 82 · see citing works
Self-citationEcho time‐dependent quantitative susceptibility mapping contains information on tissue properties
Sood, S., Urriola, J., Reutens, D. C., O’Brien, K., Bollmann, S., Barth, M., & Vegh, V. (2016) · Magnetic Resonance in Medicine · doi:10.1002/mrm.26281 · Cited by 64 · see citing works
Tissue magnetic susceptibility mapping as a marker of tau pathology in Alzheimer's disease
O’Callaghan, J. M., Holmes, H. E., Powell, N. M., Wells, J. A., Ismail, O., Harrison, I. F., Siow, B. M., Johnson, R. A., Ahmed, Z., Fisher, A., et al. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.08.003 · Cited by 61 · see citing works
Ni, R., Villois, A., Deán‐Ben, X. L., Chen, Z., Vaas, M., Stavrakis, S., Shi, G., deMello, A. J., Ran, C., Razansky, D., et al. (2021) · Photoacoustics · doi:10.1016/j.pacs.2021.100285 · Cited by 55 · see citing works
Self-citationNi, R., Rudin, M., & Klohs, J. (2018) · Photoacoustics · doi:10.1016/j.pacs.2018.04.001 · Cited by 52 · see citing works
Self-citationGrandjean, J., Derungs, R., Kulic, L., Welt, T., Henkelman, M., Nitsch, R. M., & Rudin, M. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.03.042 · Cited by 48 · see citing works
Self-citationBlood-Brain Barrier Dysfunction in Small Vessel Disease Related Intracerebral Hemorrhage
Freeze, W. M., Jacobs, H. I. L., Schreuder, F. H. B. M., van Oostenbrugge, R. J., Backes, W. H., Verhey, F. R. J., & Klijn, C. J. M. (2018) · Frontiers in Neurology · doi:10.3389/fneur.2018.00926 · Cited by 45 · see citing works
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Kecheliev, V., Boss, L., Maheshwari, U., Konietzko, U., Keller, A., Razansky, D., Nitsch, R. M., Klohs, J., & Ni, R. (2023) · Life Sciences · doi:10.1016/j.lfs.2023.121593 · Cited by 34 · see citing works
Self-citationMagnetic Resonance Imaging in Animal Models of Alzheimer’s Disease Amyloidosis
Ni, R. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms222312768 · Cited by 33 · see citing works
Discerning the Role of Blood Brain Barrier Dysfunction in Alzheimer’s Disease
Tao, Q., Lin, R.-R., Chen, Y.-H., & Wu, Z. (2022) · Aging and Disease · doi:10.14336/ad.2022.0130-1 · Cited by 32 · see citing works
Zhukov, O., He, C., Soylu-Kucharz, R., Cai, C., Lauritzen, A. D., Aldana, B. I., Björkqvist, M., Lauritzen, M., & Kucharz, K. (2023) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2023.1089005 · Cited by 30 · see citing works
Quantitative assessment of microvasculopathy in arcAβ mice with USPIO-enhanced gradient echo MRI
Klohs, J., Deistung, A., Ielacqua, G. D., Seuwen, A., Kindler, D., Schweser, F., Vaas, M., Kipar, A., Reichenbach, J. R., & Rudin, M. (2015) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1177/0271678x15621500 · Cited by 29 · see citing works
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Nelson, A. R. (2022) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2022.858429 · Cited by 22 · see citing works
Schweser, F., & Zivadinov, R. (2018) · NMR in Biomedicine · doi:10.1002/nbm.3999 · Cited by 21 · see citing works
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Shmueli, K. (2020) · Advances in magnetic resonance technology and applications · doi:10.1016/b978-0-12-817057-1.00033-0 · Cited by 20 · see citing works
Ielacqua, G. D., Schlegel, F., Füchtemeier, M., Xandry, J., Rudin, M., & Klohs, J. (2016) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2015.00241 · Cited by 17 · see citing works
Self-citationNi, R., Deán‐Ben, X. L., Kirschenbaum, D. S., Rudin, M., Chen, Z., Crimi, A., Voigt, F. F., Nilsson, K. P. R., Helmchen, F., Nitsch, R. M., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.02.25.964064 · Cited by 17 · see citing works
Self-citationMouse models of neurodegenerative disease: preclinical imaging and neurovascular component
Albanese, S., Greco, A., Auletta, L., & Mancini, M. (2018) · Brain Imaging and Behavior · doi:10.1007/s11682-017-9770-3 · Cited by 16 · see citing works
Brain-targeted nanoparticles to overcome the blood-brain barrier
Seko, I., ŞAHİN, A., Tonbul, H., & Çapan, Y. (2020) · Journal of Pharmaceutical Technolgy · doi:10.37662/jpt.2020.4 · Cited by 16 · see citing works
Tang, Y., Qian, X., Lee, D. J., Zhou, Q., & Yao, J. (2020) · OBM Neurobiology · doi:10.21926/obm.neurobiol.2002056 · Cited by 14 · see citing works
Lorentzian effects in magnetic susceptibility mapping of anisotropic biological tissues
Yablonskiy, D. A., & Sukstanskii, A. L. (2018) · Journal of Magnetic Resonance · doi:10.1016/j.jmr.2018.04.014 · Cited by 11 · see citing works
Ni, R., Chen, Z., Shi, G., Villois, A., Zhou, Q., Arosio, P., Nitsch, R. M., Nilsson, K. P. R., Klohs, J., & Razansky, D. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.02.01.929844 · Cited by 10 · see citing works
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Lin, F., Prince, M. R., Spincemaille, P., & Wang, Y. (2018) · Recent Patents on Biotechnology · doi:10.2174/1872208313666181217112745 · Cited by 5 · see citing works
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Spencer, N. G., Lovell, D. P., Elderfield, K., Austen, B., & Howe, F. A. (2016) · Magnetic Resonance Materials in Physics Biology and Medicine · doi:10.1007/s10334-016-0593-9 · Cited by 5 · see citing works
Unschuld, P. G. (2018) · Neurodegenerative Diseases · doi:10.1159/000486565 · Cited by 5 · see citing works
Lauzon, M. L., McCreary, C. R., & Frayne, R. (2016) · NMR in Biomedicine · doi:10.1002/nbm.3566 · Cited by 3 · see citing works
Shahid, S. S., Li, X., Džemidžić, M., Jarvis, E., Takieldeen, Y., Wang, A., Wilcock, D. M., & Wu, Y. (2026) · NMR in Biomedicine · doi:10.1002/nbm.70262 · Cited by 1 · see citing works
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Hockings, P. D., & Beckmann, N. (2022) · Springer eBooks · doi:10.1007/978-3-030-73317-9_19-1 · Cited by 1 · see citing works
Development of magnetic resonance imaging techniques for mouse models of Alzheimer's Disease
O’Callaghan, J. M. (2015) · UCL Discovery (University College London)
Thomas, D. L., Shmueli, K., Rega, M., Torrealdea, F. J., van der Weerd, L., Lythgoe, M. F., & Thornton, J. S. (2017) · Springer eBooks · doi:10.1007/978-3-319-28275-6_97-1
Thomas, D. L., Shmueli, K., Rega, M., Torrealdea, F. J., van der Weerd, L., Lythgoe, M. F., & Thornton, J. S. (2018) · Springer eBooks · doi:10.1007/978-3-319-28388-3_97
Magnetic Resonance Imaging in Pharmaceutical Safety Assessment
Hockings, P. D., & Beckmann, N. (2024) · Springer eBooks · doi:10.1007/978-3-031-35529-5_19
Stas, E., Yang, M., Schultz, S. R., & Go, M. A. (2026) · Journal of Biomedical Optics · doi:10.1117/1.jbo.31.1.016004
Rouault, M., Kindler, D., Ni, R., Liberale, L., Seiler, R., Ielacqua, G. D., Nitsch, R. M., Pryce, C. R., Camici, G. G., Kulic, L., et al. (2026) · Aging Brain · doi:10.1016/j.nbas.2026.100167
Self-citation
- Authors
- Klohs, J., Politano, I. W., Deistung, A., Grandjean, J., Drewek, A., Dominietto, M., Keist, R., Schweser, F., Reichenbach, J. R., Nitsch, R. M., Knuesel, I., & Rudin, M.
- Affiliations
- University of Zurich (CH)
- Jena University Hospital (DE)
- ETH Zurich (CH)
- Corresponding author
- Klohs, Jan
- ORCID
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2013-06-19
- Volume
- 8
- Issue
- 6
- Pages
- e66097
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC3686820
- OpenAlex
- W2041824340
- Cited by
- 42
- References
- 57
- Citations by year
- 2026: 3 · 2024: 2 · 2023: 2 · 2022: 6 · 2021: 2 · 2020: 5 · 2019: 1 · 2018: 8 · 2017: 4 · 2016: 5 · 2015: 3 · 2014: 1
- FWCI
- 2.33
- 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)
- Zurich Open Repository and Archive (University of Zurich) (acceptedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- Universitätsbibliographie, Universität Duisburg-Essen (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Repository for Publications and Research Data (ETH Zurich) (submittedVersion, cc-by)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion, cc-by)
- ETH Zürich Research Collection (cc-by)
- Zurich Open Repository and Archive (University of Zurich)
- Funders
- National Science Foundation (310030)
- Carl-Zeiss-Stiftung
- Deutsche Forschungsgemeinschaft (RE1123/9-2)
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (132629, 112835, 310030, 3100A0, 126029)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Keywords
- Alzheimer's disease, cerebral amyloidosis, amyloid pathology, quantitative susceptibility mapping, cerebral microbleeds, blood-brain barrier integrity, diffusion-weighted imaging, T1 mapping, longitudinal MRI, disease staging
- MeSH terms
- Animals, Blood-Brain Barrier, Brain, Disease Models, Animal, Longitudinal Studies, Male, Mice, Transgenic, Linear Models, Cerebral Amyloid Angiopathy, Diffusion Magnetic Resonance Imaging, Mice
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