Neurite orientation dispersion and density imaging reveals white matter and hippocampal microstructure changes produced by Interleukin-6 in the TgCRND8 mouse model of amyloidosis
Open accessAbstract
Extracellular β-amyloid (Aβ) plaque deposits and inflammatory immune activation are thought to alter various aspects of tissue microstructure, such as extracellular free water, fractional anisotropy and diffusivity, as well as the density and geometric arrangement of axonal processes. Quantifying these microstructural changes in Alzheimer's disease and related neurodegenerative dementias could serve to monitor or predict disease course. In the present study we used high-field diffusion magnetic resonance imaging (dMRI) to investigate the effects of Aβ and inflammatory interleukin-6 (IL6), alone or in combination, on in vivo tissue microstructure in the TgCRND8 mouse model of Alzheimer's-type Aβ deposition. TgCRND8 and non-transgenic (nTg) mice expressing brain-targeted IL6 or enhanced glial fibrillary protein (EGFP controls) were scanned at 8 months of age using a 2-shell, 54-gradient direction dMRI sequence at 11.1 T. Images were processed using the diffusion tensor imaging (DTI) model or the neurite orientation dispersion and density imaging (NODDI) model. DTI and NODDI processing in TgCRND8 mice revealed a microstructure pattern in white matter (WM) and hippocampus consistent with radial and longitudinal diffusivity deficits along with an increase in density and geometric complexity of axonal and dendritic processes. This included reduced FA, mean, axial and radial diffusivity, and increased orientation dispersion (ODI) and intracellular volume fraction (ICVF) measured in WM and hippocampus. IL6 produced a 'protective-like' effect on WM FA in TgCRND8 mice, observed as an increased FA that counteracted a reduction in FA observed with endogenous Aβ production and accumulation. In addition, we found that ICVF and ODI had an inverse relationship with the functional connectome clustering coefficient. The relationship between NODDI and graph theory metrics suggests that currently unknown microstructure alterations in WM and hippocampus are associated with diminished functional network organization in the brain.
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NODDI provides enhanced sensitivity and specific metrics for detecting Alzheimer's disease-associated microstructural changes.
“In contrast, NODDI provides enhanced sensitivity and specific metrics for detecting AD‐associated microstructural changes. 31 , 32 , 33”
From Apolipoprotein ε4 exacerbates white matter impairment in a mouse model of Aβ amyloidosis by decreasing actively myelinating oligodendrocytes (2025) · cites this paper as [33]
The Introduction (page not available in our copy) says NODDI provides metrics of axonal and dendritic dispersion and density and reports changes in young-onset Alzheimer’s disease. The Discussion reports changes in those metrics in an amyloidosis mouse model. These passages support the specific-metrics and detection portions of the claim, but do not establish enhanced sensitivity relative to another method. (LLM, conf=0.84)
Experimental — accuracy not yet measuredIn another mouse model of Alzheimer's disease, greater NDI and ODI than controls were observed in WM and hippocampus.
“Similarly, in another mouse model of Alzheimer's disease, greater NDI and ODI than controls were observed in WM and hippocampus (Colon-Perez et al., 2019).”
From NODDI in clinical research (2020) · cites this paper as Colon-Perez et al., 2019
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Cites
The Structure and Function of Complex Networks
Newman, M. E. J. (2003) · SIAM Review · doi:10.1137/s003614450342480 · Cited by 18,719 · see citing works
Finding and evaluating community structure in networks
Newman, M. E. J., & Girvan, M. (2004) · Physical Review E · doi:10.1103/physreve.69.026113 · Cited by 13,931 · see citing works
Advances in functional and structural MR image analysis and implementation as FSL
Smith, S. M., Jenkinson, M., Woolrich, M. W., Beckmann, C. F., Behrens, T. E. J., Johansen-Berg, H., Bannister, P. R., De Luca, M., Drobnjak, I., Flitney, D. E., et al. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.07.051 · Cited by 13,630 · see citing works
Jenkinson, M., Bannister, P., Brady, M., & Smith, S. (2002) · NeuroImage · doi:10.1006/nimg.2002.1132 · Cited by 11,888 · see citing works
Complex network measures of brain connectivity: Uses and interpretations
Rubinov, M., & Sporns, O. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2009.10.003 · Cited by 11,522 · see citing works
Complex networks: structure and dynamics
BOCCALETTI, S., LATORA, V, MORENO, Y., CHAVEZ, M., & HWANG, D. (2006) · Physics Reports · doi:10.1016/j.physrep.2005.10.009 · Cited by 11,010 · see citing works
AFNI: Software for analysis and visualization of functional magnetic resonance neuroimages
Cox, R. W. (1996) · Computers and Biomedical Research · doi:10.1006/cbmr.1996.0014 · Cited by 10,913 · see citing works
Amyloid oligomers exacerbate tau pathology in a mouse model of tauopathy
Selenica, M.-L. B., Brownlow, M., Jimenez, J. P., Lee, D. C., Pena, G., Dickey, C. A., Gordon, M. N., & Morgan, D. (2013) · Neurodegenerative Diseases · doi:10.1159/000337230 · Cited by 10,359 · see citing works
Yushkevich, P. A., Piven, J., Hazlett, H. C., Smith, R. G., Ho, S., Gee, J. C., & Gerig, G. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2006.01.015 · Cited by 8,868 · cited 2× · see citing works
“ITKSNAP (Yushkevich et al., 2006) was used to manually segment hippocampus and WM areas such as anterior commissure, fimbria, genu and splenium of the corpus callosum, and cerebral peduncle (Figure 1, top panel).”
MethodN4ITK: Improved N3 Bias Correction
Tustison, N. J., Avants, B., Cook, P. A., Zheng, Y., Egan, A., Yushkevich, P. A., & Gee, J. C. (2010) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2010.2046908 · Cited by 6,119 · see citing works
Avants, B., Epstein, C. L., Grossman, M., & Gee, J. C. (2007) · Medical Image Analysis · doi:10.1016/j.media.2007.06.004 · Cited by 5,618 · see citing works
The basis of anisotropic water diffusion in the nervous system—A technical review.
Beaulieu, C. (2002) · NMR in Biomedicine · doi:10.1002/nbm.782 · Cited by 4,653 · see citing works
BrainNet Viewer: A Network Visualization Tool for Human Brain Connectomics
Xia, M., Wang, J., & He, Y. (2013) · PLoS ONE · doi:10.1371/journal.pone.0068910 · Cited by 4,106 · see citing works
NODDI: Practical in vivo neurite orientation dispersion and density imaging of the human brain
Zhang, H., Schneider, T., Wheeler-Kingshott, C. A., & Alexander, D. C. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.03.072 · Cited by 3,288 · see citing works
Characterization and propagation of uncertainty in diffusion-weighted MR imaging
Behrens, T. E. J., Woolrich, M. W., Jenkinson, M., Johansen‐Berg, H., Nunes, R. G., Clare, S., Matthews, P. M., Brady, J. M., & Smith, S. M. (2003) · Magnetic Resonance in Medicine · doi:10.1002/mrm.10609 · Cited by 3,038 · see citing works
Probabilistic Independent Component Analysis for Functional Magnetic Resonance Imaging
Beckmann, C. F., & Smith, S. M. (2004) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2003.822821 · Cited by 2,707 · see citing works
Tau Suppression in a Neurodegenerative Mouse Model Improves Memory Function
SantaCruz, K., Lewis, J., Spires, T., Paulson, J., Kotilinek, L., Ingelsson, M., Guimaraes, A., DeTure, M., Ramsden, M., McGowan, E., et al. (2005) · Science · doi:10.1126/science.1113694 · Cited by 2,014 · see citing works
Network ‘Small-World-Ness’: A Quantitative Method for Determining Canonical Network Equivalence
Humphries, M. D., & Gurney, K. (2008) · PLoS ONE · doi:10.1371/journal.pone.0002051 · Cited by 1,442 · see citing works
Aβ peptide immunization reduces behavioural impairment and plaques in a model of Alzheimer's disease
Janus, C., Pearson, J., McLaurin, J., Mathews, P. M., Jiang, Y., Schmidt, S. D., Chishti, M. A., Horne, P., Heslin, D., French, J., et al. (2000) · Nature · doi:10.1038/35050110 · Cited by 1,431 · see citing works
Free water elimination and mapping from diffusion MRI
Pasternak, O., Sochen, N., Gur, Y., Intrator, N., & Assaf, Y. (2009) · Magnetic Resonance in Medicine · doi:10.1002/mrm.22055 · Cited by 1,092 · see citing works
Chishti, M. A., Yang, D.-S., Janus, C., Phinney, A. L., Horne, P., Pearson, J., Strome, R., Zuker, N., Loukides, J., French, J., et al. (2001) · Journal of Biological Chemistry · doi:10.1074/jbc.m100710200 · Cited by 955 · see citing works
Generalizations of the clustering coefficient to weighted complex networks
Saramäki, J., Kivelä, M., Onnela, J.-P. “., Kaski, K. K., & Kertész, J. (2007) · Physical Review E · doi:10.1103/physreve.75.027105 · Cited by 727 · see citing works
Disruption of Functional Connectivity in Clinically Normal Older Adults Harboring Amyloid Burden
Hedden, T., Van Dijk, K. R. A., Becker, J. A., Mehta, A., Sperling, R. A., Johnson, K. A., & Buckner, R. L. (2009) · The Journal of Neuroscience · doi:10.1523/jneurosci.3189-09.2009 · Cited by 598 · see citing works
Accelerated microstructure imaging via Convex Optimization (AMICO) from diffusion MRI data
Daducci, A., Canales-Rodríguez, E. J., Zhang, H., Dyrby, T. B., Alexander, D. C., & Thiran, J.-P. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2014.10.026 · Cited by 593 · see citing works
Traumatic brain injury and amyloid-β pathology: a link to Alzheimer's disease?
Johnson, V. E., Stewart, W., & Smith, D. H. (2010) · Nature Reviews Neuroscience · doi:10.1038/nrn2808 · Cited by 587 · see citing works
Cascading network failure across the Alzheimer’s disease spectrum
Jones, D. T., Knopman, D. S., Gunter, J. L., Graff‐Radford, J., Vemuri, P., Boeve, B. F., Petersen, R. C., Weiner, M. W., & Jack, C. R. (2015) · Brain · doi:10.1093/brain/awv338 · Cited by 532 · see citing works
An early and late peak in microglial activation in Alzheimer’s disease trajectory
Fan, Z., Brooks, D. J., Okello, A., & Edison, P. (2017) · Brain · doi:10.1093/brain/aww349 · Cited by 422 · see citing works
Using Dual Regression to Investigate Network Shape and Amplitude in Functional Connectivity Analyses
Nickerson, L. D. H., Smith, S. M., Öngür, D., & Beckmann, C. F. (2017) · Frontiers in Neuroscience · doi:10.3389/fnins.2017.00115 · Cited by 409 · see citing works
Chakrabarty, P., Li, A., Ceballos-Diaz, C., Eddy, J. A., Funk, C. C., Moore, B., DiNunno, N., Rosario, A. M., Cruz, P. E., Verbeeck, C., et al. (2015) · Neuron · doi:10.1016/j.neuron.2014.11.020 · Cited by 354 · see citing works
Self-citationIn vivo radioligand binding to translocator protein correlates with severity of Alzheimer’s disease
Kreisl, W. C., Lyoo, C. H., McGwier, M., Snow, J., Jenko, K. J., Kimura, N., Corona, W., Morse, C. L., Zoghbi, S. S., Pike, V. W., et al. (2013) · Brain · doi:10.1093/brain/awt145 · Cited by 350 · see citing works
Chakrabarty, P., Jansen‐West, K., Beccard, A., Ceballos‐Diaz, C., Levites, Y., Verbeeck, C., Zubair, A. C., Dickson, D., Golde, T. E., & Das, P. (2010) · The FASEB Journal · doi:10.1096/fj.09-141754 · Cited by 328 · see citing works
Self-citationExcessive Extracellular Volume Reveals a Neurodegenerative Pattern in Schizophrenia Onset
Pasternak, O., Westin, C., Sylvain Bouix, A., Seidman, L. J., Goldstein, J. M., Woo, T.-U. W., Petryshen, T. L., Mesholam‐Gately, R. I., McCarley, R. W., Kikinis, R., et al. (2012) · Journal of Neuroscience · doi:10.1523/jneurosci.2904-12.2012 · Cited by 306 · see citing works
Sadleir, K. R., Kandalepas, P. C., Buggia-Prévot, V., Nicholson, D. A., Thinakaran, G., & Vassar, R. (2016) · Acta Neuropathologica · doi:10.1007/s00401-016-1558-9 · Cited by 294 · see citing works
Neurite dispersion: a new marker of multiple sclerosis spinal cord pathology?
Grussu, F., Schneider, T., Tur, C., Yates, R. L., Tachrount, M., Ianuş, A., Yiannakas, M. C., Newcombe, J., Zhang, H., Alexander, D. C., et al. (2017) · Annals of Clinical and Translational Neurology · doi:10.1002/acn3.445 · Cited by 291 · see citing works
Chen, G., Adleman, N. E., Saad, Z. S., Leibenluft, E., & Cox, R. W. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.06.027 · Cited by 248 · see citing works
Colgan, N., Siow, B., O'Callaghan, J. M., Harrison, I. F., Wells, J. A., Holmes, H. E., Ismail, O., Richardson, S., Alexander, D. C., Collins, E. C., et al. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2015.10.043 · Cited by 218 · see citing works
Chakrabarty, P., Ceballos-Diaz, C., Beccard, A., Janus, C., Dickson, D., Golde, T. E., & Das, P. (2010) · The Journal of Immunology · doi:10.4049/jimmunol.0903382 · Cited by 206 · see citing works
Self-citationNeuner, S. M., Heuer, S. E., Huentelman, M. J., O’Connell, K. M. S., & Kaczorowski, C. C. (2019) · Neuron · doi:10.1016/j.neuron.2018.11.040 · Cited by 202 · see citing works
Distributed BOLD and CBV-weighted resting-state networks in the mouse brain
Sforazzini, F., Schwarz, A. J., Galbusera, A., Bifone, A., & Gozzi, A. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.09.050 · Cited by 199 · see citing works
Parbo, P., Ismail, R., Hansen, K. V., Amidi, A., Mårup, F. H., Gottrup, H., Brændgaard, H., Eriksson, B. O., Eskildsen, S. F., Lund, T. E., et al. (2017) · Brain · doi:10.1093/brain/awx120 · Cited by 199 · see citing works
Bastin, M. E., Armitage, P. A., & Marshall, I. (1998) · Magnetic Resonance Imaging · doi:10.1016/s0730-725x(98)00098-8 · Cited by 188 · see citing works
Ofori, E., Pasternak, O., Planetta, P. J., Burciu, R. G., Snyder, A. F., Febo, M., Golde, T. E., Okun, M. S., & Vaillancourt, D. E. (2014) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2014.10.029 · Cited by 171 · see citing works
Self-citationBingham–NODDI: Mapping anisotropic orientation dispersion of neurites using diffusion MRI
Tariq, M., Schneider, T., Alexander, D. C., Gandini Wheeler-Kingshott, C. A., & Zhang, H. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.01.046 · Cited by 169 · see citing works
Microglial activation in Alzheimer's disease: an (R)-[11C]PK11195 positron emission tomography study
Schuitemaker, A., Kropholler, M. A., Boellaard, R., van der Flier, W. M., Kloet, R. W., van der Doef, T. F., Knol, D. L., Windhorst, A. D., Luurtsema, G., Barkhof, F., et al. (2012) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2012.04.021 · Cited by 167 · see citing works
Longitudinal changes in microstructural white matter metrics in Alzheimer's disease
Mayo, C. D., Mazerolle, E. L., Ritchie, L. J., Fisk, J. D., & Gawryluk, J. R. (2016) · NeuroImage Clinical · doi:10.1016/j.nicl.2016.12.012 · Cited by 159 · see citing works
Structural basis of large-scale functional connectivity in the mouse
Grandjean, J., Zerbi, V., Balsters, J. H., Wenderoth, N., & Rudin, M. (2017) · The Journal of Neuroscience · doi:10.1523/jneurosci.0438-17.2017 · Cited by 151 · see citing works
Brendel, M., Probst, F., Jaworska, A. M., Overhoff, F., Korzhova, V., Albert, N. L., Beck, R., Lindner, S., Gildehaus, F., Baumann, K., et al. (2016) · Journal of Nuclear Medicine · doi:10.2967/jnumed.115.167858 · Cited by 145 · see citing works
Sepehrband, F., Clark, K. A., Ullmann, J. F. P., Kurniawan, N. D., Leanage, G., Reutens, D. C., & Yang, Z. (2015) · Human Brain Mapping · doi:10.1002/hbm.22872 · Cited by 143 · see citing works
Robust Automatic Rodent Brain Extraction Using 3-D Pulse-Coupled Neural Networks (PCNN)
Chou, N., Wu, J., Bingren, J. B., Qiu, A., & Chuang, K. (2011) · IEEE Transactions on Image Processing · doi:10.1109/tip.2011.2126587 · Cited by 138 · see citing works
Pasternak, O., Westin, C., Dahlben, B., Sylvain Bouix, A., & Kubicki, M. R. (2014) · Schizophrenia Research · doi:10.1016/j.schres.2014.07.031 · Cited by 134 · see citing works
Praet, J., Manyakov, N. V., Muchene, L., Mai, Z., Terzopoulos, V., de Backer, S., Torremans, A., Guns, P.-J., Van De Casteele, T., Bottelbergs, A., et al. (2018) · Alzheimer's Research & Therapy · doi:10.1186/s13195-017-0329-8 · Cited by 133 · see citing works
Hoy, A. R., Ly, M., Carlsson, C. M., Okonkwo, O. C., Zetterberg, H., Blennow, K., Sager, M. A., Asthana, S., Johnson, S. C., Alexander, A. L., et al. (2017) · PLoS ONE · doi:10.1371/journal.pone.0173982 · Cited by 125 · see citing works
Parker, T. D., Slattery, C. F., Zhang, J., Nicholas, J. M., Paterson, R. W., Foulkes, A. J. M., Malone, I. B., Thomas, D. L., Modat, M., Cash, D. M., et al. (2018) · Human Brain Mapping · doi:10.1002/hbm.24056 · Cited by 122 · 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
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
Zhang, X., Cai, Y., Xiong, K., Cai, H., Luo, X., Feng, J., Clough, R. W., Struble, R. G., Patrylo, P. R., & Yan, X. (2009) · European Journal of Neuroscience · doi:10.1111/j.1460-9568.2009.07017.x · Cited by 117 · see citing works
Hippocampal expression of murine TNFα results in attenuation of amyloid deposition in vivo
Chakrabarty, P., Herring, A., Ceballos-Diaz, C., Das, P., & Golde, T. E. (2011) · Molecular Neurodegeneration · doi:10.1186/1750-1326-6-16 · Cited by 117 · see citing works
Self-citationHippocampal expression of murine IL-4 results in exacerbation of amyloid deposition
Chakrabarty, P., Tianbai, L., Herring, A., Ceballos-Diaz, C., Das, P., & Golde, T. E. (2012) · Molecular Neurodegeneration · doi:10.1186/1750-1326-7-36 · Cited by 116 · see citing works
Self-citationLiu, B., Le, K. X., Park, M.-A., Wang, S., Belanger, A. P., Dubey, S., Frost, J. L., Holton, P., Reiser, V., Jones, P. A., et al. (2015) · The Journal of Neuroscience · doi:10.1523/jneurosci.0996-15.2015 · Cited by 114 · see citing works
Ji, F., Pasternak, O., Liu, S., Loke, Y. M., Choo, B. L., Hilal, S., Xu, X., Ikram, M. K., Venketasubramanian, N., Chen, C., et al. (2017) · Alzheimer s Research & Therapy · doi:10.1186/s13195-017-0292-4 · Cited by 111 · see citing works
Neuronal Structure is Altered by Amyloid Plaques
Spires‐Jones, T. L., & Hyman, B. T. (2004) · Reviews in the Neurosciences · doi:10.1515/revneuro.2004.15.4.267 · Cited by 107 · see citing works
Ocklenburg, S., Friedrich, P., Fraenz, C., Schlüter, C., Beste, C., Güntürkün, O., & Genç, E. (2018) · Science Advances · doi:10.1126/sciadv.aar6830 · Cited by 105 · see citing works
ApoE influences regional white-matter axonal density loss in Alzheimer's disease
Slattery, C. F., Zhang, J., Paterson, R. W., Foulkes, A. J. M., Carton, A., Macpherson, K., Mancini, L., Thomas, D. L., Modat, M., Toussaint, N., et al. (2017) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2017.04.021 · Cited by 102 · see citing works
Shrivastava, A. N., Aperia, A. C., Melki, R., & Triller, A. (2017) · Neuron · doi:10.1016/j.neuron.2017.05.026 · Cited by 94 · see citing works
Widespread white matter degeneration preceding the onset of dementia
Maier-Hein, K. H., Westin, C., Shenton, M. E., Weiner, M. W., Raj, A., Thomann, P. A., Kikinis, R., Stieltjes, B., & Pasternak, O. (2014) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2014.04.518 · Cited by 89 · see citing works
Teipel, S. J., the EDSD study group, Grothe, M. J., Filippi, M., Fellgiebel, A., Dyrba, M., Frisoni, G. B., Meindl, T., Bokde, A. L. W., Hampel, H., et al. (2014) · Journal of Alzheimer s Disease · doi:10.3233/jad-131829 · Cited by 82 · see citing works
Mirzaei, N., Tang, S. P., Ashworth, S., Coello, C., Plisson, C., Passchier, J., Selvaraj, V., Tyacke, R. J., Nutt, D. J., & Sastre, M. (2016) · Glia · doi:10.1002/glia.22978 · Cited by 82 · see citing works
Interferon-γ induces progressive nigrostriatal degeneration and basal ganglia calcification
Chakrabarty, P., Ceballos‐Diaz, C., Lin, W.-L., Beccard, A., Jansen‐West, K. R., McFarland, N. R., Janus, C., Dickson, D. W., Das, P., & Golde, T. E. (2011) · Nature Neuroscience · doi:10.1038/nn.2829 · Cited by 81 · see citing works
Self-citationAutomatic Structural Parcellation of Mouse Brain MRI Using Multi-Atlas Label Fusion
Ma, D., Cardoso, M. J., Marc Modat, A., Powell, N. M., Wells, J. A., Holmes, H. E., Wiseman, F. K., Tybulewicz, V. L. J., Fisher, E., Lythgoe, M. F., et al. (2014) · PLoS ONE · doi:10.1371/journal.pone.0086576 · Cited by 81 · see citing works
Cummings, D. M., Liu, W., Portelius, E., Bayram, S., Yasvoina, M. V., Ho, H., Smits, H., Ali, S. S., Steinberg, R., Pegasiou, C.-M., et al. (2015) · Brain · doi:10.1093/brain/awv127 · Cited by 81 · see citing works
Sahara, N., Perez, P. D., Lin, W.-L., Dickson, D. W., Ren, Y., Zeng, H., Lewis, J., & Febo, M. (2014) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2013.12.009 · Cited by 70 · see citing works
Self-citationKim, J., Choi, I.-Y., Michaelis, M. L., & Lee, P. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.02.039 · Cited by 65 · see citing works
Biscaro, B., Lindvall, O., Höck, C., Ekdahl, C. T., & Nitsch, R. M. (2009) · Journal of Neuroscience · doi:10.1523/jneurosci.2055-09.2009 · Cited by 64 · 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
Febo, M., Pérez, P. D., Ceballos‐Diaz, C., Colón-Pérez, L. M., Zeng, H., Ofori, E., Golde, T. E., Vaillancourt, D. E., & Chakrabarty, P. (2020) · Brain Structure and Function · doi:10.1007/s00429-019-02017-1 · Cited by 54 · see citing works
Self-citationAmyloid-β1-42 Disrupts Synaptic Plasticity by Altering Glutamate Recycling at the Synapse
Varga, E., Juhász, G., Bozsó, Z., Penke, B., Fülöp, L., & Szegedi, V. (2015) · Journal of Alzheimer s Disease · doi:10.3233/jad-142367 · Cited by 51 · see citing works
Amyloid load and translocator protein 18 kDa in APPswePS1-dE9 mice: a longitudinal study
Sérrière, S., Tauber, C., Vercouillie, J., Mothes, C., Pruckner, C., Guilloteau, D., Kassiou, M., Doméné, A., Garreau, L., Page, G., et al. (2015) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2014.11.023 · Cited by 49 · see citing works
Grandjean, 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
Qin, Y.-Y., Li, M.-W., Zhang, S., Zhang, Y., Zhao, L.-Y., Lei, H., Oishi, K., & Zhu, W.-Z. (2013) · Neuroradiology · doi:10.1007/s00234-013-1195-0 · Cited by 42 · see citing works
Voxel-based diffusion tensor imaging of an APP/PS1 mouse model of Alzheimer’s disease.
Shu, X., Qin, Y.-Y., Zhang, S., Jiang, J.-J., Zhang, Y., Zhao, L.-Y., Shan, D., & Zhu, W.-Z. (2013) · Molecular Neurobiology · doi:10.1007/s12035-013-8418-6 · Cited by 41 · see citing works
O’Dwyer, L., Lamberton, F., Bokde, A. L. W., Ewers, M., Faluyi, Y. O., Tanner, C. J., Mazoyer, B. M., O’Neill, D. J., Bartley, M. M., Collins, R., et al. (2011) · Journal of Alzheimer s Disease · doi:10.3233/jad-2011-110137 · Cited by 38 · see citing works
Moore, K., Madularu, D., Iriah, S. C., Yee, J. R., Kulkarni, P. P., Darcq, E., Kieffer, B. L., & Ferris, C. F. (2016) · Frontiers in Neuroscience · doi:10.3389/fnins.2016.00471 · Cited by 37 · see citing works
Zhang, L., Chao, F., Luo, Y., Xiao, Q., Jiang, L. H., Zhou, C., Zhang, Y., Lv, F., He, Q., Ma, J., et al. (2016) · Current Alzheimer Research · doi:10.2174/1567205014666161213121353 · Cited by 33 · see citing works
Brown, C., Jiang, Y., Smith, C. D., & Gold, B. T. (2018) · Cortex · doi:10.1016/j.cortex.2018.04.006 · Cited by 33 · see citing works
Shah, D., Deleye, S., Verhoye, M., Staelens, S. G., & der Linden, A. V. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.10.073 · Cited by 32 · 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
Adalbert, R., Milde, S., Durrant, C. S., Ando, K., Stygelbout, V., Yilmaz, Z., Gould, S. A., Brion, J., & Coleman, M. P. (2018) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2018.03.033 · Cited by 24 · see citing works
Immunogenic properties of amyloid beta oligomers
Dalgediene, I., Lasickiene, R., Budvytyte, R., Valincius, G., Morkuniene, R., Borutaite, V., & Zvirbliene, A. (2013) · Journal of Biomedical Science · doi:10.1186/1423-0127-20-10 · Cited by 22 · see citing works
Diffusion tensor magnetic resonance imaging of the brain in app transgenic mice: A cohort study.
Müller, H.-P., Kassubek, J., Vernikouskaya, I., Ludolph, A. C., Stiller, D., & Rasche, V. (2013) · PLoS ONE · doi:10.1371/journal.pone.0067630 · Cited by 22 · see citing works
Colón-Pérez, L. M., Turner, S. M., Lubke, K. N., Pompilus, M., Febo, M., & Burke, S. N. (2019) · eNeuro · doi:10.1523/eneuro.0047-19.2019 · Cited by 21 · 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
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Research works printed but not linked: 2
- Chakrabarty P, Ceballos-Diaz C, Beccard A, Janus C, Dickson D, Golde TE, Das P, 2010b IFN-gamma promotes complement expression and attenuates amyloid plaque deposition in amyloid beta precursor protein transgenic mice. Journal of immunology (Baltimore, Md: 1950) 184, 5333– 5343. PMC3798002 20368278
- Mirzaei N, Tang SP, Ashworth S, Coello C, Plisson C, Passchier J, Selvaraj V, Tyacke RJ, Nutt DJ, Sastre M, 2016 In vivo imaging of microglial activation by positron emission tomography with [(11)C]PBR28 in the 5XFAD model of Alzheimer’s disease. Glia 64, 993– 1006. 26959396
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Sorted by citations
Kamiya, K., Hori, M., & Aoki, S. (2020) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2020.108908 · Cited by 283 · see citing works
“Similarly, in another mouse model of Alzheimer’s disease, greater NDI and ODI than controls were observed in WM and hippocampus (Colon-Perez et al., 2019).”
BackgroundFebo, M., Pérez, P. D., Ceballos‐Diaz, C., Colón-Pérez, L. M., Zeng, H., Ofori, E., Golde, T. E., Vaillancourt, D. E., & Chakrabarty, P. (2020) · Brain Structure and Function · doi:10.1007/s00429-019-02017-1 · Cited by 54 · see citing works
Self-citationRay, N. J., Lawson, R. A., Martin, S. L., Sigurdsson, H. P., Wilson, J., Galna, B., Lord, S., Alcock, L., Duncan, G. W., Khoo, T. K., et al. (2022) · Brain · doi:10.1093/brain/awac127 · Cited by 45 · see citing works
Interaction of amyloid and tau on cortical microstructure in cognitively unimpaired adults
Vogt, N. M., Hunt, J. F. V., Adluru, N., Ma, Y., Hulle, C. A. V., Dean, D., Kecskemeti, S., Chin, N. A., Carlsson, C. M., Asthana, S., et al. (2021) · Alzheimer s & Dementia · doi:10.1002/alz.12364 · Cited by 41 · see citing works
Neuroimaging of mouse models of Alzheimer’s disease
Jullienne, A., Trinh, M. V., & Obenaus, A. (2022) · Biomedicines · doi:10.3390/biomedicines10020305 · Cited by 32 · see citing works
Magnetic 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
Microglia show differential transcriptomic response to Aβ peptide aggregates ex vivo and in vivo
McFarland, K. N., Ceballos, C., Rosario, A. M., Ladd, T. B., Moore, B. D., Golde, G., Wang, X., Allen, M., Ertekin‐Taner, N., Funk, C. C., et al. (2021) · Life Science Alliance · doi:10.26508/lsa.202101108 · Cited by 27 · see citing works
Self-citationChu, W. T., DeSimone, J. C., Riffe, C. J., Liu, H., Chakrabarty, P., Giasson, B. I., Vedam‐Mai, V., & Vaillancourt, D. E. (2020) · Journal of Neuroscience · doi:10.1523/jneurosci.0189-20.2020 · Cited by 28 · see citing works
Self-citationScanning ultrasound-mediated memory and functional improvements do not require amyloid-β reduction
Leinenga, G., To, X. V., Bodea, L., Yousef, J. M., Richter-Stretton, G. L., Palliyaguru, T., Chicoteau, A., Dagley, L. F., Nasrallah, F. A., & Götz, J. (2024) · Molecular Psychiatry · doi:10.1038/s41380-024-02509-5 · Cited by 27 · see citing works
Luca, A. D., Karayumak, S. C., Leemans, A., Yogesh Rathi, A., Swinnen, S. P., Gooijers, J., Clauwaert, A., Bahr, R., Sandmo, S. B., Sochen, N., et al. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119439 · Cited by 22 · see citing works
Network analysis of neuroimaging in mice
Scharwächter, L., Schmitt, F. J., Pallast, N., Fink, G. R., & Aswendt, M. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119110 · Cited by 21 · see citing works
Colón-Pérez, L. M., Turner, S. M., Lubke, K. N., Pompilus, M., Febo, M., & Burke, S. N. (2019) · eNeuro · doi:10.1523/eneuro.0047-19.2019 · Cited by 21 · see citing works
Self-citationRavi, S., Criado‐Marrero, M., Barroso, D., Braga, I., Bolen, M. L., Rubinovich, U., Hery, G. P., Grudny, M. M., Koren, J., Prokop, S., et al. (2023) · Journal of Neurotrauma · doi:10.1089/neu.2022.0464 · Cited by 20 · see citing works
Self-citationGatto, R. G., Weissmann, C., Amin, M., Angeles-López, Q. D., García‐Lara, L., Castellanos, L. C. S., Deyoung, D., Segovia, J., Mareci, T. H., Uchitel, O. D., et al. (2021) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2021.02.006 · Cited by 20 · see citing works
Pursuit of precision medicine: Systems biology approaches in Alzheimer's disease mouse models
Gurdon, B., & Kaczorowski, C. C. (2021) · Neurobiology of Disease · doi:10.1016/j.nbd.2021.105558 · Cited by 16 · see citing works
Criado‐Marrero, M., Ravi, S., Bhaskar, E., Barroso, D., Pizzi, M. A., Williams, L. N., Wellington, C. L., Febo, M., & Abisambra, J. F. (2024) · NeuroImage · doi:10.1016/j.neuroimage.2024.120764 · Cited by 13 · see citing works
Self-citationKraguljac, N. V., Monroe, W. S., Anthony, T., Jindal, R. D., Hill, H., & Lahti, A. C. (2021) · Neuroimage Reports · doi:10.1016/j.ynirp.2021.100005 · Cited by 13 · 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
The future of neuroimaging and gut-brain axis research for substance use disorders
Colón-Pérez, L. M., Montesinos, J. S., & Monsivais, M. (2022) · Brain Research · doi:10.1016/j.brainres.2022.147835 · Cited by 11 · see citing works
Connectomic analysis of Alzheimer’s disease using percolation theory
Kotlarz, P. L., Nino, J. C., & Febo, M. (2021) · Network Neuroscience · doi:10.1162/netn_a_00221 · Cited by 11 · see citing works
Self-citationFebo, M., Mahar, R., Rodriguez, N. A., Buraima, J., Pompilus, M., Pinto, A. M., Grudny, M. M., Bruijnzeel, A. W., & Merritt, M. E. (2024) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2024.1356086 · Cited by 9 · see citing works
Self-citationWilkes, B. J., DeSimone, J. C., Liu, Y., Chu, W. T., Coombes, S. A., Li, Y., & Vaillancourt, D. E. (2021) · Experimental Neurology · doi:10.1016/j.expneurol.2021.113783 · Cited by 8 · see citing works
Self-citationWilkes, B. J., Adury, R. Z., Berryman, D., Concepcion, L. R., Liu, Y., Yokoi, F., Maugee, C., Li, Y., & Vaillancourt, D. E. (2023) · Experimental Neurology · doi:10.1016/j.expneurol.2023.114471 · Cited by 7 · see citing works
Self-citationBrain Network Modularity and Resilience Signaled by Betweenness Centrality Percolation Spiking
Kotlarz, P. L., Febo, M., Nino, J. C., & Initiative, O. B. O. T. A. D. N. (2024) · Applied Sciences · doi:10.3390/app14104197 · Cited by 7 · see citing works
Self-citationCruz‐Almeida, Y., Coombes, S. A., & Febo, M. (2021) · Experimental Gerontology · doi:10.1016/j.exger.2021.111520 · Cited by 6 · see citing works
Self-citationMurdy, T. J., Dunn, A. R., Singh, S., Telpoukhovskaia, M. A., Zhang, S., White, J. K., Kahn, I., Febo, M., & Kaczorowski, C. C. (2023) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2022.1033975 · Cited by 6 · see citing works
Self-citationAl‐Amin, M. M., Kim, B., Karahan, H., Tate, M. D., Walsh, S. P., Puntambekar, S. S., Bissel, S. J., Lamb, B. T., Wang, N., & Kim, J. (2025) · Alzheimer's & Dementia · doi:10.1002/alz.70791 · Cited by 6 · see citing works
Zamani, A., Walker, A. K., Rollo, B., Ayers, K. L., Farah, R., O'Brien, T. J., & Wright, D. K. (2022) · NeuroImage Clinical · doi:10.1016/j.nicl.2022.103016 · Cited by 6 · see citing works
Granger, S. J., Colón-Pérez, L. M., Larson, M. S., Phelan, M. J., Keator, D. B., Janecek, J. T., Sathishkumar, M. T., Smith, A. P., McMillan, L. C., Greenia, D., et al. (2022) · Hippocampus · doi:10.1002/hipo.23456 · Cited by 6 · see citing works
Effects of Body Mass Index on Brain Structures in the Elderly: Longitudinal Analyses
Takeuchi, H., & Kawashima, R. (2022) · Frontiers in Endocrinology · doi:10.3389/fendo.2022.824661 · Cited by 4 · see citing works
Sun, Y., Wu, D., Yang, X., Tang, B., Xia, C., Luo, C., Gong, Q., Lui, S., & Hu, N. (2024) · Cerebral Cortex · doi:10.1093/cercor/bhae308 · Cited by 3 · see citing works
Disrupted structural connectivity in ArcAβ mouse model of Aβ amyloidosis
Al-Amin, M. M., Grandjean, J., Klohs, J., & Kim, J. (2020) · doi:10.1101/2020.04.27.064634 · Cited by 2 · see citing works
“…such as neurite orientation dispersion and density imaging (NODDI), have been used to profile neurite density indexes (NDI) and orientation dispersion indexes (ODI) (Wang et al., 2019) in the mouse models of Aβ amyloidosis (Colon-Perez et al., 2019) and tau pathology (Colgan et al., 2016).”
MethodCoad, B. M., Ghomroudi, P. A., Sims, R., Aggleton, J. P., Vann, S. D., & Metzler‐Baddeley, C. (2022) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2022.02.004 · Cited by 2 · see citing works
Ravi, S., Criado‐Marrero, M., Barroso, D., Braga, I., Bolen, M. L., Rubinovich, U., Hery, G. P., Grudny, M. M., Koren, J., Prokop, S., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.09.21.508917 · Cited by 1 · see citing works
Self-citationCriado‐Marrero, M., Ravi, S., Bhaskar, E., Barroso, D., Pizzi, M. A., Williams, L. N., Wellington, C. L., Febo, M., & Abisambra, J. F. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.01.25.577316 · Cited by 1 · see citing works
Self-citationHan, X., Hu, R., Ping, X., Chen, J., Liu, J., Liu, Z., Jin, X., Gao, X., Lin, M., Zhao, Y., et al. (2026) · Alzheimer's & Dementia · doi:10.1002/alz.71908
Models: Signal Representation vs. Microstructural Models
Luca, A. D., Froeling, M., & Leemans, A. (2024) · Springer eBooks · doi:10.1007/978-3-031-70337-9_4
Simon, Z. D., McFarland, K. N., Golde, T. E., Chakrabarty, P., & Febo, M. (2025) · Journal of Alzheimer s Disease · doi:10.1177/13872877251378778
Self-citationWang, S., Wang, C., Song, W., & Lin, J. (2026) · Frontiers in Neuroscience · doi:10.3389/fnins.2026.1806164
Association of C-reactive protein with brain micro- and macro-structure among older adult men
Bell, T. R., Pearce, R. C., Puckett, O. K., Hagler, D. J., Dale, A. M., Elman, J. A., Fennema‐Notestine, C., Franz, C. E., Lyons, M. J., Panizzon, M. S., et al. (2026) · Brain Behavior and Immunity · doi:10.1016/j.bbi.2026.106798
Bai, L., Zhang, Q., 朱静媛, Zhang, W., Cui, Z., Jia, T., Li, C., & Guo, Z. (2026) · Frontiers in Microbiology · doi:10.3389/fmicb.2026.1939532
- Authors
- 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., & Febo, M.
- Affiliations
- University of Florida (US)
- University of Florida Health (US)
- Corresponding author
- Chakrabarty, Paramita
- Febo, Marcelo
- ORCID
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2019-11
- Volume
- 202
- Pages
- 116138
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC6894485
- OpenAlex
- W2971088730
- Cited by
- 41
- References
- 90
- Citations by year
- 2026: 4 · 2025: 2 · 2024: 7 · 2023: 3 · 2022: 10 · 2021: 9 · 2020: 5 · 2019: 1
- FWCI
- 4.13
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Life Sciences › Neuroscience › Neurology › Neuroinflammation and Neurodegeneration Mechanisms
- Keywords
- NODDI, interleukin-6, Alzheimer's disease, amyloidosis, white matter microstructure, hippocampus, intracellular volume fraction, orientation dispersion index, fractional anisotropy, diffusion tensor imaging, graph theory, neuroinflammation, brain microstructure
- MeSH terms
- Alzheimer Disease, Animals, Disease Models, Animal, Hippocampus, Mice, Transgenic, Nerve Net, Interleukin-6, Amyloid beta-Peptides, Neurites, Mice, Diffusion Tensor Imaging, White Matter, ErbB Receptors
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