In vivo Volumetric, DTI and ¹H MRS Rat Brain Protocol for Monitoring Early Neurodegeneration and Efficacy of the Used Therapy

[1] Sadigh-Eteghad, S., Majdi, A., McCann, S. K., Mahmoudi, J., Vafaee, M. S., Macleod, M. R. (2017). Correction: D-galactose-induced brain ageing model: A systematic review and meta-analysis on cognitive outcomes and oxidative stress indices. PloS One, 12 (12), e0190328. https://doi.org/10.1371/journal.pone.0190328 Search in Google Scholar

[2] Damar, U., Gersner, R., Johnstone, J. T., Schachter, S., Rotenberg, A. (2016). Huperzine A as a neuroprotective and antiepileptic drug: A review of preclinical research. Expert Review of Neurotherapeutics, 16 (6), 671-680. https://doi.org/10.1080/14737175.2016.1175303 Search in Google Scholar

[3] Ruan, Q., Hu, X., Ao, H., Ma, H., Gao, Z., Liu, F., Kong, D., Bao, Z., Yu, Z. (2014). The neurovascular protective effects of huperzine A on D-galactose-induced inflammatory damage in the rat hippocampus. Gerontology, 60 (5), 424-439. https://doi.org/10.1159/000358235 Search in Google Scholar

[4] Prince, M., Bryce, R., Albanese, E., Wimo, A., Ribeiro, W., Ferri, C. P. (2013). The global prevalence of dementia: A systematic review and metaanalysis. Alzheimer’s & Dementia, 9 (1), 63-75.e2. https://doi.org/10.1016/j.jalz.2012.11.007 Search in Google Scholar

[5] Hort, J., O’Brien, J. T., Gainotti, G., Pirttila, T., Popescu, B. O., Rektorova, I., Sorbi, S., Scheltens, P. (2010). EFNS guidelines for the diagnosis and management of Alzheimer’s disease. European Journal of Neurology, 17 (10), 1236-1248. https://doi.org/10.1111/j.1468-1331.2010.03040.x Search in Google Scholar

[6] Schroeter, M. L., Stein, T., Maslowski, N., Neumann, J. (2009). Neural correlates of Alzheimer’s disease and mild cognitive impairment: A systematic and quantitative meta-analysis involving 1351 patients. Neuroimage, 47 (4), 1196-1206. https://doi.org/10.1016/j.neuroimage.2009.05.037 Search in Google Scholar

[7] Wattjes, M. P., Henneman, W. J., van der Flier, W. M., de Vries, O., Träber, F., Geurts, J. J., Scheltens, P., Vrenken, H., Barkhof, F. (2009). Diagnostic imaging of patients in a memory clinic: Comparison of MR imaging and 64-detector row CT. Radiology, 253 (1), 174-183. https://doi.org/10.1148/radiol.2531082262 Search in Google Scholar

[8] Wahlund, L.-O., Julin, P., Johansson, S.-E., Scheltens, P. (2000). Visual rating and volumetry of the medial temporal lobe on magnetic resonance imaging in dementia: a comparative study. Journal of Neurology, Neurosurgery & Psychiatry, 69 (5), 630-635. http://dx.doi.org/10.1136/jnnp.69.5.630 Search in Google Scholar

[9] O’Brien, J. T. (2007). Role of imaging techniques in the diagnosis of dementia. The British Journal of Radiology, 80 (special_issue_2), S71-S77. https://doi.org/10.1259/bjr/33117326 Search in Google Scholar

[10] Patel, K. P., Wymer, D. T., Bhatia, V. K., Duara, R., Rajadhyaksha, C. D. (2020). Multimodality imaging of dementia: Clinical importance and role of integrated anatomic and molecular imaging. RadioGraphics, 40 (1), 200-222. https://doi.org/10.1148/rg.2020190070 Search in Google Scholar

[11] Loos, C., Achten, E., Santens, P. (2010). Proton magnetic resonance spectroscopy in Alzheimer’s disease, a review. Acta Neurologica Belgica, 110 (4), 291-298. https://www.actaneurologica.be/pdfs/2010-4/01-Loos et al.pdf Search in Google Scholar

[12] Waller, A. (1850). Experiments on the frog, and observations of the alterations produced thereby in the structure of their primitive fibers. Philosophical Transactions of the Royal Society of London, Series B: Biological Sciences, 140, 423-429. Search in Google Scholar

[13] Amlien, I. K., Fjell, A. M. (2014). Diffusion tensor imaging of white matter degeneration in Alzheimer’s disease and mild cognitive impairment. Neuroscience, 276, 206-215. https://doi.org/10.1016/j.neuroscience.2014.02.017 Search in Google Scholar

[14] Delano-Wood, L., Stricker, N. H., Sorg, S. F., Nation, D. A., Jak, A. J., Woods, S. P., Libon, D. J., Delis, D. C., Frank, L. R., Bondi, M. W. (2012). Posterior cingulum white matter disruption and its associations with verbal memory and stroke risk in mild cognitive impairment. Journal of Alzheimer’s Disease, 29 (3), 589-603. https://doi.org/10.3233/JAD-2012-102103 Search in Google Scholar

[15] Head, D., Buckner, R. L., Shimony, J. S., Williams, L. E., Akbudak, E., Conturo, T. E., McAvoy, M., Morris, J. C., Snyder, A. Z. (2004). Differential vulnerability of anterior white matter in nondemented aging with minimal acceleration in dementia of the Alzheimer type: Evidence from diffusion tensor imaging. Cerebral Cortex, 14 (4), 410-423. https://doi.org/10.1093/cercor/bhh003 Search in Google Scholar

[16] Catheline, G., Periot, O., Amirault, M., Braun, M., Dartigues, J.-F., Auriacombe, S., Allard, M. (2010). Distinctive alterations of the cingulum bundle during aging and Alzheimer’s disease. Neurobiology of Aging, 31 (9), 1582-1592. https://doi.org/10.1016/j.neurobiolaging.2008.08.012 Search in Google Scholar

[17] Basser, P. J. (1995). Inferring microstructural features and the physiological state of tissues from diffusion-weighted images. NMR in Biomedicine, 8 (7), 333-344. https://doi.org/10.1002/nbm.1940080707 Search in Google Scholar

[18] Jeurissen, B., Leemans, A., Tournier, J.-D., Jones, D. K., Sijbers, J. (2013). Investigating the prevalence of complex fiber configurations in white matter tissue with diffusion magnetic resonance imaging. Human Brain Mapping, 34 (11), 2747-2766. https://doi.org/10.1002/hbm.22099 Search in Google Scholar

[19] Jones, D. K., Knösche, T. R., Turner, R. (2013). White matter integrity, fiber count, and other fallacies: The do’s and don’ts of diffusion MRI. Neuroimage, 73, 239-254. https://doi.org/10.1016/j.neuroimage.2012.06.081 Search in Google Scholar

[20] Foster, T. C. (2006). Biological markers of age-related memory deficits. CNS Drugs, 20 (2), 153-166. https://doi.org/10.2165/00023210-200620020-00006 Search in Google Scholar

[21] Holden, H. M., Rayment, I., Thoden, J. B. (2003). Structure and function of enzymes of the Leloir pathway for galactose metabolism. Journal of Biological Chemistry, 278 (45), 43885-43888. https://doi.org/10.1074/jbc.R300025200 Search in Google Scholar

[22] Song, X. U., Bao, M., Li, D., Li, Y. M. (1999). Advanced glycation in D-galactose induced mouse aging model. Mechanisms of Ageing and Development, 108 (3), 239-251. https://doi.org/10.1016/S0047-6374(99)00022-6 Search in Google Scholar

[23] Gasparotto, J., Girardi, C. S., Somensi, N., Ribeiro, C. T., Moreira, J. C., Michels, M., Sonai, B., Rocha, M., Steckert, A. V., Barichello, T., Quevedo, J., Dal-Pizzol, F., Gelain, D. P. (2018). Receptor for advanced glycation end products mediates sepsis-triggered amyloid-β accumulation, Tau phosphorylation, and cognitive impairment. Journal of Biological Chemistry, 293 (1), 226-244. https://doi.org/10.1074/jbc.M117.786756 Search in Google Scholar

[24] Tobon-Velasco, J. C., Cuevas, E., Torres-Ramos, M. A. (2014). Receptor for AGEs (RAGE) as mediator of NF-kB pathway activation in neuroinflammation and oxidative stress. CNS & Neurological Disorders: Drug Targets, 13 (9), 1615-1626. http://dx.doi.org/10.2174/1871527313666140806144831 Search in Google Scholar

[25] Hua, X., Lei, M., Zhang, Y., Ding, J., Han, Q., Hu, G., Xiao, M. (2007). Long-term D-galactose injection combined with ovariectomy serves as a new rodent model for Alzheimer’s disease. Life Sciences, 80 (20), 1897-1905. https://doi.org/10.1016/j.lfs.2007.02.030 Search in Google Scholar

[26] Schmidt, R., Hofer, E., Bouwman, F. H., Buerger, K., Cordonnier, C., Fladby, T., Galimberti, D., Georges, J., Heneka, M. T., Hort, J., Laczó, J., Molinuevo, J. L., O’Brien, J. T., Religa, D., Scheltens, P., Schott, J. M., Sorbi, S. (2015). EFNS-ENS/EAN Guideline on concomitant use of cholinesterase inhibitors and memantine in moderate to severe Alzheimer’s disease. European Journal of Neurology, 22 (6), 889-898. https://doi.org/10.1111/ene.12707 Search in Google Scholar

[27] Gruetter, R. (1993). Automatic, localized in Vivo adjustment of all first-and second-order shim coils. Magnetic Resonance in Medicine, 29 (6), 804-811. https://doi.org/10.1002/mrm.1910290613 Search in Google Scholar

[28] Tkáč, I., Starčuk, Z., Choi, I.-Y., Gruetter, R. (1999). In vivo ¹H NMR spectroscopy of rat brain at 1 ms echo time. Magnetic Resonance in Medicine, 41, 649-656. https://doi.org/10.1002/(SICI)1522-2594(199904)41:4%3C649::AIDMRM2%3E3.0.CO;2-G Search in Google Scholar

[29] Mlynárik, V., Gambarota, G., Frenkel, H., Gruetter, R. (2006). Localized short-echo-time proton MR spectroscopy with full signal-intensity acquisition. Magnetic Resonance in Medicine, 56 (5), 965-970. https://doi.org/10.1002/mrm.21043 Search in Google Scholar

[30] Basser, P. J., Mattiello, J., Lebihan, D. (1994). Estimation of the effective self-diffusion tensor from the NMR spin echo. Journal of Magnetic Resonance, Series B, 103 (3), 247-254. Search in Google Scholar

[31] Jiang, H., Van Zijl, P. C., Kim, J., Pearlson, G. D., Mori, S. (2006). DtiStudio: Resource program for diffusion tensor computation and fiber bundle tracking. Computer Methods and Programs in Biomedicine, 81 (2), 106-116. https://doi.org/10.1016/j.cmpb.2005.08.004 Search in Google Scholar

[32] Paxinos, G., Watson, C. (2007). The Rat Brain in Stereotaxic Coordinates. Elsevier, ISBN 978-0-12-547620-1. Search in Google Scholar

[33] Yeh, F.-C., Verstynen, T. D., Wang, Y., Fernández-Miranda, J. C., Tseng, W.-Y. I. (2013). Deterministic diffusion fiber tracking improved by quantitative anisotropy. PloS One, 8 (11). https://doi.org/10.1371/journal.pone.0080713 Search in Google Scholar

[34] Yeh, F.-C., Panesar, S., Barrios, J., Fernandes, D., Abhinav, K., Meola, A., Fernandez-Miranda, J. C. (2019). Automatic removal of false connections in diffusion mri tractography using topology-informed pruning (TIP). Neurotherapeutics, 16 (1), 52-58. https://doi.org/10.1007/s13311-018-0663-y Search in Google Scholar

[35] Veraart, J., Leergaard, T. B., Antonsen, B. T., Van Hecke, W., Blockx, I., Jeurissen, B., Jiang, Y., Van der Linden, A., Johnson, G. A., Verhoye, M., Sijbers, J. (2011). Population-averaged diffusion tensor imaging atlas of the Sprague Dawley rat brain. Neuroimage, 58 (4), 975-983. https://doi.org/10.1016/j.neuroimage.2011.06.063 Search in Google Scholar

[36] Papp, E. A., Leergaard, T. B., Calabrese, E., Johnson, G. A., Bjaalie, J. G. (2014). Waxholm Space atlas of the Sprague Dawley rat brain. NeuroImage, 97, 374-386. https://doi.org/10.1016/j.neuroimage.2014.04.001 Search in Google Scholar

[37] Yushkevich, P. A., Piven, J., Hazlett, H. C., Smith, R. G., Ho, S., Gee, J. C., Gerig, G. (2006). User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage, 31 (3), 1116-1128. https://doi.org/10.1016/j.neuroimage.2006.01.015 Search in Google Scholar

[38] Pluta, J., Avants, B. B., Glynn, S., Awate, S., Gee, J. C., Detre, J. A. (2009). Appearance and incomplete label matching for diffeomorphic template based hippocampus segmentation. Hippocampus, 19 (6), 565-571. https://doi.org/10.1002/hipo.20619 Search in Google Scholar

[39] Tournier, J.-D., Smith, R., Raffelt, D., Tabbara, R., Dhollander, T., Pietsch, M., Christiaens, D., Jeurissen, B., Yeh, C.-H., Connelly, A. (2019). MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation. Neuroimage, 202 116137. https://doi.org/10.1016/j.neuroimage.2019.116137 Search in Google Scholar

[40] Provencher, S. W. (1993). Estimation of metabolite concentrations from localized in vivo proton NMR spectra. Magnetic Resonance in Medicine, 30 (6), 672-679. https://doi.org/10.1002/mrm.1910300604 Search in Google Scholar

[41] Xie, Z., Dong, Y., Maeda, U., Moir, R. D., Xia, W., Culley, D. J., Crosby, G., Tanzi, R. E. (2007). The inhalation anesthetic isoflurane induces a vicious cycle of apoptosis and amyloid β-protein accumulation. Journal of Neuroscience, 27 (6), 1247-1254. https://doi.org/10.1523/JNEUROSCI.5320-06.2007 Search in Google Scholar

[42] McEwen, B. S. (2007). Physiology and neurobiology of stress and adaptation: Central role of the brain. Physiological Reviews, 87 (3), 873-904. https://doi.org/10.1152/physrev.00041.2006 Search in Google Scholar

[43] Petersen, R. C., Jack, C. R., Xu, Y.-C., Waring, S. C., O’Brien, P. C., Smith, G. E., Ivnik, R. J., Tangalos, E. G., Boeve, B. F., Kokmen, E. (2000). Memory and MRI-based hippocampal volumes in aging and AD. Neurology, 54 (3), 581-581. https://doi.org/10.1212/WNL.54.3.581 Search in Google Scholar

[44] Morrison, J. H., Hof, P. R. (1997). Life and death of neurons in the aging brain. Science, 278 (5337), 412-419. https://doi.org/10.1126/science.278.5337.412 Search in Google Scholar

[45] Voevodskaya, O., Poulakis, K., Sundgren, P., Westen, D. V., Palmqvist, S., Wahlund, L., Stomrud, E., Hansson, O., Westmanet, E. (2019). Brain myoinositol as a potential marker of amyloid-related pathology. Neurology, 92 (5). https://doi.org/10.1212/WNL.0000000000006852 Search in Google Scholar

[46] Mlynárik, V., Cacquevel, M., Sun-Reimer, L., Janssens, S., Cudalbu, C., Lei, H., Schneider, B. L., Aebischer, P., Gruetteret, R. (2012). Proton and phosphorus magnetic resonance spectroscopy of a mouse model of Alzheimer’s disease. Journal of Alzheimer’s Disease, 31 (s3), S87-S99. https://doi.org/10.3233/jad-2012-112072 Search in Google Scholar

[47] Watanabe, T., Shiino, A., Akiguchi, I. (2012). Hippocampal metabolites and memory performances in patients with amnestic mild cognitive impairment and Alzheimer’s disease. Neurobiology of Learning and Memory, 97 (3), 289-293. https://doi.org/10.1016/j.nlm.2012.01.006 Search in Google Scholar

[48] Van Hecke, W., Emsell, L., Sunaert, S. (eds.) (2016). Diffusion Tensor Imaging: A Practical Handbook. Springer. https://doi.org/10.1007/978-1-4939-3118-7 Search in Google Scholar

[49] Descoteaux, M., Deriche, R., Knosche, T. R., Anwander, A. (2008). Deterministic and probabilistic tractography based on complex fibre orientation distributions. IEEE Transactions on Medical Imaging, 28 (2), 269-286. https://doi.org/10.1109/TMI.2008.2004424 Search in Google Scholar

[50] Budde, M. D., Janes, L., Gold, E., Turtzo, L. C., Frank, J. A. (2011). The contribution of gliosis to diffusion tensor anisotropy and tractography following traumatic brain injury: Validation in the rat using Fourier analysis of stained tissue sections. Brain, 134 (8), 2248-2260. https://doi.org/10.1093/brain/awr161 Search in Google Scholar

[51] Xi, G., Hui, J., Zhang, Z., Liu, S., Zhang, X., Teng, G., Chan, K. C., Wu, E. X., Nie, B., Shan, B., Li, L., Reynolds, G. P. (2011). Learning and memory alterations are associated with hippocampal N-acetylaspartate in a rat model of depression as measured by 1H-MRS. PloS One, 6 (12), e28686. https://doi.org/10.1371/journal.pone.0028686 Search in Google Scholar

[52] Marjanska, M., Curran, G. L., Wengenack, T. M., Henry, P.-G., Bliss, R. L., Poduslo, J. F., Jack Jr, C. R., Ugurbil, K., Garwood, M. (2005). Monitoring disease progression in transgenic mouse models of Alzheimer’s disease with proton magnetic resonance spectroscopy. Proceedings of the National Academy of Sciences, 102 (33), 11906-11910. https://doi.org/10.1073/pnas.0505513102 Search in Google Scholar

[53] Márquez, F., Yassa, M. A. (2019). Neuroimaging biomarkers for Alzheimer’s disease. Molecular Neurodegeneration, 14 (1), 21. https://doi.org/10.1186/s13024-019-0325-5 Search in Google Scholar

[54] Veeraiah, P., Jansen, J. F. (2023). Multinuclear magnetic resonance spectroscopy at ultra-high-field: Assessing human cerebral metabolism in healthy and diseased states. Metabolites, 13 (4), 577. https://doi.org/10.3390/metabo13040577 Search in Google Scholar

[55] Block, W., Jessen, F., Träber, F., Flacke, S., Manka, C., Lamerichs, R., Keller, E., Heun, R., Schild, H. (2002). Regional N-acetylaspartate reduction in the hippocampus detected with fast proton magnetic resonance spectroscopic imaging in patients with Alzheimer disease. Archives of Neurology, 59 (5), 828-834. https://doi.org/10.1001/archneur.59.5.828 Search in Google Scholar

[56] Marjańska, M., McCarten, J. R., Hodges, J. S., Hemmy, L. S., Terpstra, M. (2019). Distinctive neurochemistry in Alzheimer’s disease via 7 T in vivo magnetic resonance spectroscopy. Journal of Alzheimer’s Disease, 68 (2), 559-569. https://doi.org/10.3233%2FJAD-180861 Search in Google Scholar

[57] Sumbalová, Z., Uličná, O., Kucharská, J., Rausová, Z., Vančová, O., Melicherčík, L., Tvrdík, T., Nemec, M., Kašparová, S. (2022). D-galactose-induced aging in rats - The effect of metformin on bioenergetics of brain, skeletal muscle and liver. Experimental Gerontology, 163, 111770. https://doi.org/10.1016/j.exger.2022.111770 Search in Google Scholar

[58] Melichercik, L., Tvrdik, T., Novakova, K., Nemec, M., Kalinak, M., Baciak, L., Kasparova, S. (2022). Huperzine aggravated neurochemical and volumetric changes induced by D-galactose in the model of neurodegeneration in rats. Neurochemistry International, 158, 105365. https://doi.org/10.1016/j.neuint.2022.105365 Search in Google Scholar