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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.0190328Search in Google Scholar
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/10.1080/14737175.2016.1175303Search in Google Scholar
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/10.1159/000358235Search in Google Scholar
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.2012.11.007Search in Google Scholar
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.xSearch in Google Scholar
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.2009.05.037Search in Google Scholar
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.2531082262Search in Google Scholar
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.69.5.630Search in Google Scholar
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/33117326Search in Google Scholar
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.2020190070Search in Google Scholar
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.pdfSearch in Google Scholar
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
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.2014.02.017Search in Google Scholar
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-2012-102103Search in Google Scholar
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/bhh003Search in Google Scholar
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.2008.08.012Search in Google Scholar
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.1940080707Search in Google Scholar
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.22099Search in Google Scholar
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.2012.06.081Search in Google Scholar
Foster, T. C. (2006). Biological markers of age-related memory deficits. CNS Drugs, 20 (2), 153-166. https://doi.org/10.2165/00023210-200620020-00006Search in Google Scholar
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.R300025200Search in Google Scholar
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)00022-6Search in Google Scholar
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.M117.786756Search in Google Scholar
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/10.2174/1871527313666140806144831Search in Google Scholar
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.2007.02.030Search in Google Scholar
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.12707Search in Google Scholar
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.1910290613Search in Google Scholar
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-GSearch in Google Scholar
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.21043Search in Google Scholar
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
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.2005.08.004Search in Google Scholar
Paxinos, G., Watson, C. (2007). The Rat Brain in Stereotaxic Coordinates. Elsevier, ISBN 978-0-12-547620-1.Search in Google Scholar
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.0080713Search in Google Scholar
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-ySearch in Google Scholar
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.2011.06.063Search in Google Scholar
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.2014.04.001Search in Google Scholar
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.2006.01.015Search in Google Scholar
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.20619Search in Google Scholar
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.2019.116137Search in Google Scholar
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.1910300604Search in Google Scholar
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.5320-06.2007Search in Google Scholar
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.00041.2006Search in Google Scholar
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.54.3.581Search in Google Scholar
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.278.5337.412Search in Google Scholar
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.0000000000006852Search in Google Scholar
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-2012-112072Search in Google Scholar
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.2012.01.006Search in Google Scholar
Van Hecke, W., Emsell, L., Sunaert, S. (eds.) (2016). Diffusion Tensor Imaging: A Practical Handbook. Springer. https://doi.org/10.1007/978-1-4939-3118-7Search in Google Scholar
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.2008.2004424Search in Google Scholar
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/awr161Search in Google Scholar
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.0028686Search in Google Scholar
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.0505513102Search in Google Scholar
Márquez, F., Yassa, M. A. (2019). Neuroimaging biomarkers for Alzheimer’s disease. Molecular Neurodegeneration, 14 (1), 21. https://doi.org/10.1186/s13024-019-0325-5Search in Google Scholar
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/metabo13040577Search in Google Scholar
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.59.5.828Search in Google Scholar
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-180861Search in Google Scholar
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.2022.111770Search in Google Scholar
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.2022.105365Search in Google Scholar