The DOI matches its Crossref record; 85 of the 85 references it cites were found in a registry or an index.
Gene Microarrays in Hippocampal Aging: Statistical Profiling Identifies Novel Processes Correlated with Cognitive Impairment
Open accessAbstract
Gene expression microarrays provide a powerful new tool for studying complex processes such as brain aging. However, inferences from microarray data are often hindered by multiple comparisons, small sample sizes, and uncertain relationships to functional endpoints. Here we sought gene expression correlates of aging-dependent cognitive decline, using statistical profiling of gene microarrays in well powered groups of young, mid-aged, and aged rats (n = 10 per group). Animals were trained on two memory tasks, and the hippocampal CA1 region of each was analyzed on an individual microarray (one chip per animal). Aging- and cognition-related genes were identified by testing each gene by ANOVA (for aging effects) and then by Pearson's test (correlating expression with memory). Genes identified by this algorithm were associated with several phenomena known to be aging-dependent, including inflammation, oxidative stress, altered protein processing, and decreased mitochondrial function, but also with multiple processes not previously linked to functional brain aging. These novel processes included downregulated early response signaling, biosynthesis and activity-regulated synaptogenesis, and upregulated myelin turnover, cholesterol synthesis, lipid and monoamine metabolism, iron utilization, structural reorganization, and intracellular Ca2+ release pathways. Multiple transcriptional regulators and cytokines also were identified. Although most gene expression changes began by mid-life, cognition was not clearly impaired until late life. Collectively, these results suggest a new integrative model of brain aging in which genomic alterations in early adulthood initiate interacting cascades of decreased signaling and synaptic plasticity in neurons, extracellular changes, and increased myelin turnover-fueled inflammation in glia that cumulatively induce aging-related cognitive impairment.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Gene expression profiling studies of ageing mouse, rat, monkey and human brains have shown significant changes in the expression of synaptic genes (7–13).”
From Neural mechanisms of ageing and cognitive decline (2010) · cites this paper as [10]
In the citing paper
“In particular, organ-specific analysis of brain ageing has revealed a progressive decline in mitochondrial gene expression in rats, rhesus macaques and humans (7,10,13).”
From Neural mechanisms of ageing and cognitive decline (2010) · cites this paper as [10]
In the citing paper
“Markers of inflammation in the blood and in specific tissues are associated with impaired motor and cognitive function (Blalock, Chen, Sharrow, Herman, Porter, Foster, and Landfield 2003;Speisman, Kumar, Rani, Foster, and Ormerod 2013;Zeier, Madorsky, Xu, Ogle, Notterpek, and Foster 2011) suggesting that the pro-inflammatory phenotype impairs the ability of older individuals to cope with age”
From Successful aging: Advancing the science of physical independence in older adults (2015) · cites this paper as Blalock et al. 2003
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Memory - A century of consolidation
McGaugh, J. L. (2000) · Science · doi:10.1126/science.287.5451.248 · Cited by 4,250
Controlling the false discovery rate in behavior genetics research
Benjamini, Y., Drai, D., Elmer, G., Kafkafi, N., & Golani, I. (2001) · Behavioural Brain Research · doi:10.1016/s0166-4328(01)00297-2 · Cited by 4,146
The molecular biology of memory storage: a dialogue between genes and synapses
Kandel, E. R. (2001) · Science · doi:10.1126/science.1067020 · Cited by 3,917
Excitatory Amino Acids as a Final Common Pathway for Neurologic Disorders
Epstein, F. H., Lipton, S. A., & Rosenberg, P. A. (1994) · New England Journal of Medicine · doi:10.1056/nejm199403033300907 · Cited by 2,676
Parallel human genome analysis: microarray-based expression monitoring of 1000 genes.
Schena, M., Shalon, D., Heller, R. A., Chai, A., Brown, P. O., & Davis, R. W. (1996) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.93.20.10614 · Cited by 1,599
Inflammation in Neurodegenerative Disease—A Double-Edged Sword
Wyss-Coray, T., & Mucke, L. (2002) · Neuron · doi:10.1016/s0896-6273(02)00794-8 · Cited by 1,235
Gene-expression profile of the ageing brain in mice
Lee, C.-K., Weindruch, R., & Prolla, T. A. (2000) · Nature Genetics · doi:10.1038/77046 · Cited by 1,055
Guzowski, J. F., Lyford, G. L., Stevenson, G. D., Houston, F. P., McGaugh, J. L., Worley, P. F., & Barnes, C. A. (2000) · The Journal of Neuroscience · doi:10.1523/jneurosci.20-11-03993.2000 · Cited by 992
Mirnics, K., Middleton, F. A., Marquez, A., Lewis, D. A., & Levitt, P. (2000) · Neuron · doi:10.1016/s0896-6273(00)00085-4 · Cited by 882
Steward, O., Wallace, C. S., Lyford, G., & Worley, P. (1998) · Neuron · doi:10.1016/s0896-6273(00)80591-7 · Cited by 845
Cited by
Sorted by citations
Items 401–500 of 561
Ashraf, A., Dani, M., & So, P. (2020) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2020.590979 · Cited by 16
Genomic transcriptional profiling in LOU/C/Jall rats identifies genes for successful aging
Paban, V., Billard, J., Bouët, V., Fréret, T., Boulouard, M., Chambon, C., Loriod, B., & Alescio‐Lautier, B. (2012) · Brain Structure and Function · doi:10.1007/s00429-012-0472-8 · Cited by 16
Persson, N., Lavebratt, C., & Wåhlin, Å. (2013) · Neurobiology of Learning and Memory · doi:10.1016/j.nlm.2013.08.017 · Cited by 15
Krueger, J. N., Moore, S. J., Parent, R., McKinney, B. C., Lee, A., & Murphy, G. G. (2016) · Behavioural Brain Research · doi:10.1016/j.bbr.2016.06.054 · Cited by 15
Age- and Nicotine-Associated Gene Expression Changes in the Hippocampus of APP/PS1 Mice
Yang, J., Long, Y., Xu, D., Zhu, B., Deng, X., Yan, Z., Sun, F., & Chen, G.-J. (2019) · Journal of Molecular Neuroscience · doi:10.1007/s12031-019-01389-7 · Cited by 15
Phospho-Tau and Chromatin Landscapes in Early and Late Alzheimer’s Disease
Gil, L., Niño, S. A., Guerrero, C., & Jiménez-Capdeville, M. E. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms221910283 · Cited by 16
Expression Profile Analysis of Brain Aging
Ginsberg, S. D. (2007) · CRC Press eBooks · doi:10.1201/9781420005523-7 · Cited by 15
Yu, L., Haverty, P. M., Mariani, J., Wang, Y., Shen, H., Schwarzschild, M. A., Weng, Z., & Chen, J. (2005) · Physiological Genomics · doi:10.1152/physiolgenomics.00068.2005 · Cited by 15
Zhang, J., Moseley, A. E., Jegga, A. G., Gupta, A., Witte, D. P., Sartor, M. A., Medvedovic, M., Williams, S. S., Ebert, C., Coolen, L. M., et al. (2004) · Physiological Genomics · doi:10.1152/physiolgenomics.00220.2003 · Cited by 15
Aging, Neuroinflammation, and Behavior
Johnson, R. W., & Godbout, J. P. (2007) · Elsevier eBooks · doi:10.1016/b978-012088576-3/50022-8 · Cited by 15
Cognitive rejuvenation in old rats by hippocampal OSKM gene therapy
Horvath, S., Lacunza, E., Canatelli-Mallat, M., Portiansky, E. L., Gallardo, M. D., Brooke, R. T., Chiavellini, P., Pasquini, D. C., Girard, M., Lehmann, M., et al. (2024) · GeroScience · doi:10.1007/s11357-024-01269-y · Cited by 14
Survey of the Arc Epigenetic Landscape in Normal Cognitive Aging
Myrum, C., Kittleson, J. T., De, S., Fletcher, B. R., Castellano, J. F., Kundu, G., Becker, K. G., & Rapp, P. R. (2020) · Molecular Neurobiology · doi:10.1007/s12035-020-01915-4 · Cited by 14
Crivello, N. A., Rosenberg, I. H., Shukitt‐Hale, B., Bielinski, D. F., Dallal, G. E., & Joseph, J. A. (2007) · AGE · doi:10.1007/s11357-007-9039-7 · Cited by 14
Across sex and age: Learning and memory and patterns of avian hippocampal gene expression.
Kosarussavadi, S., Pennington, Z. T., Covell, J. J., Blaisdell, A. P., & Schlinger, B. A. (2017) · Behavioral Neuroscience · doi:10.1037/bne0000222 · Cited by 15
Gant, J. C., Kadish, I., Chen, K.-C., Thibault, O., Blalock, E. M., Porter, N. M., & Landfield, P. W. (2018) · Journal of Alzheimer s Disease · doi:10.3233/jad-180618 · Cited by 14
Self-citationTests for differential gene expression using weights in oligonucleotide microarray experiments
Hu, P., Beyene, J., & Greenwood, C. M. T. (2006) · BMC Genomics · doi:10.1186/1471-2164-7-33 · Cited by 14
Beyer, K., Lao, J. I., Latorre, P., & Ariza, A. (2005) · Annals of the New York Academy of Sciences · doi:10.1111/j.1749-6632.2005.tb06138.x · Cited by 14
Amyloid Precursor Protein: From Synaptic Plasticity to Alzheimer's Disease
Mileusnić, R., Lancashire, C. L., & Rose, S. P. R. (2005) · Annals of the New York Academy of Sciences · doi:10.1196/annals.1342.014 · Cited by 14
Xanthine-induced deficits in hippocampal behavior and abnormal expression of hemoglobin genes
Kambe, J., Miyata, S., Li, C., Yamamoto, Y., & Nagaoka, K. (2023) · Behavioural Brain Research · doi:10.1016/j.bbr.2023.114476 · Cited by 13
Neuroinflammation and oxidative injury in developmental neurotoxicity
Milatović, D., Zaja‐Milatovic, S., BREYER, R. M., Aschner, M., & Montine, T. J. (2011) · Elsevier eBooks · doi:10.1016/b978-0-12-382032-7.10064-5 · Cited by 13
Kong, W., Mou, X., Xing, Z., Zhang, X., & Yang, Y. (2014) · Computational and Mathematical Methods in Medicine · doi:10.1155/2014/891761 · Cited by 13
Apomyoglobin Sequesters Heme from Heme Bound Aβ Peptides
Pramanik, D., Mukherjee, S., & Dey, S. G. (2013) · Inorganic Chemistry · doi:10.1021/ic401771j · Cited by 13
Schrötter, A., Oberhaus, A., Kolbe, K., Seger, S., Mastalski, T., Magraoui, F. E., Hoffmann-Posorske, E., Bohnert, M., Deckert, J., Braun, C., et al. (2017) · Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics · doi:10.1016/j.bbapap.2017.03.013 · Cited by 14
Molecular and histological correlates of cognitive decline across age in male C57BL/6J mice
Britton, R., Liu, A. T., Rege, S. V., Adams, J. M., Akrapongpisak, L., Le, D., Alcantara‐Lee, R., Estrada, R. A., Ray, R. D., Ahadi, S., et al. (2022) · Brain and Behavior · doi:10.1002/brb3.2736 · Cited by 14
Yassa, H. D. (2014) · Acta Histochemica · doi:10.1016/j.acthis.2014.05.001 · Cited by 12
Identification of significant features in DNA microarray data
Bair, E. (2013) · Wiley Interdisciplinary Reviews Computational Statistics · doi:10.1002/wics.1260 · Cited by 12
Untreated Hypertension Decreases Heritability of Cognition in Late Middle Age
Vasilopoulos, T., Kremen, W. S., Kim, K., Panizzon, M. S., Stein, P. K., Xian, H., Grant, M. D., Lyons, M. J., Toomey, R., Eaves, L. J., et al. (2011) · Behavior Genetics · doi:10.1007/s10519-011-9479-9 · Cited by 13
Accelerated midlife endocrine and bioenergetic brain aging in APOE4 females
Wang, T., Mao, Z., Shang, Y., Merlini, S., Vitali, F., Wiegand, J. L., & Brinton, R. D. (2025) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2025.1632877 · Cited by 12
Structural aspects of the aging invertebrate brain
Koch, S. C., Nelson, A., & Hartenstein, V. (2021) · Cell and Tissue Research · doi:10.1007/s00441-020-03314-6 · Cited by 12
Glial glucocorticoid receptors in aged Fisher 344 (F344) and F344/Brown Norway rats
Kasckow, J. W., Xiao, C. Y., & Herman, J. P. (2009) · Experimental Gerontology · doi:10.1016/j.exger.2009.02.003 · Cited by 11
Self-citationRathod, P., Hemnani, T., & Parihar, M. S. (2011) · PubMed · Cited by 11
CART variance stabilization and regularization for high-throughput genomic data
Papana, A., & Ishwaran, H. (2006) · Bioinformatics · doi:10.1093/bioinformatics/btl384 · Cited by 11
Cellular and Molecular Mechanisms for Age-Related Cognitive Decline
Barter, J., & Foster, T. C. (2019) · Cambridge University Press eBooks · doi:10.1017/9781108554350.003 · Cited by 11
Self-citationNon-canonical hemoglobin: An updated review on its ubiquitous expression
Reed, E. C., Kim, J. D., & Case, A. J. (2025) · Redox Biology · doi:10.1016/j.redox.2025.103602 · Cited by 10
Linial, M., Stern, A., & Weinstock, M. (2020) · Neuropharmacology · doi:10.1016/j.neuropharm.2020.108229 · Cited by 10
Gene expression profile in rat hippocampus with and without memory deficit
Paban, V., Farioli, F., Romier, B., Chambon, C., & Alescio‐Lautier, B. (2010) · Neurobiology of Learning and Memory · doi:10.1016/j.nlm.2010.03.005 · Cited by 11
New cellular and molecular approaches to ageing brain
Tripathi, A. (2012) · Annals of Neurosciences · doi:10.5214/ans.0972.7531.190410 · Cited by 11
Brinton, R. D. (2010) · Elsevier eBooks · doi:10.1016/b978-1-4160-6231-8.10027-3 · Cited by 10
Behavioral and Cellular Tagging in Young and in Early Cognitive Aging
Gros, A., Lim, A. W. H., Hohendorf, V., White, N. C., Eckert, M. J., McHugh, T. J., & Wang, S. (2022) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2022.809879 · Cited by 11
Reproductive Aging of Females: Neural Systems
Brinton, R. D., Gore, A. C., Schmidt, P. J., & Morrison, J. H. (2009) · Elsevier eBooks · doi:10.1016/b978-008088783-8.00068-1 · Cited by 10
Kai, N., Iwase, K., Imai, K., Nakahira, E., Soma, M., Ohtsuka, S., Yagi, T., Kobayashi, K., Koga, H., Takiguchi, M., et al. (2006) · Brain Research · doi:10.1016/j.brainres.2005.12.042 · Cited by 10
Uddin, R., & Singh, S. M. (2017) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2017.00075 · Cited by 9
Neuroadaptive Trajectories of Healthy Mindspan: From Genes to Neural Networks
Rapp, P. R., Bañuelos, C., & Myrum, C. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108552684.005 · Cited by 9
McShea, A., Marlatt, M. W., Lee, H., Tarkowsky, S. M., Smit, M., & Smith, M. A. (2006) · Journal of Neuropathology & Experimental Neurology · doi:10.1097/01.jnen.0000240471.04920.3c · Cited by 9
Jee, S. W., Cho, J. S., Kim, C. K., Hwang, D. Y., Shim, S. B., Lee, S. H., Sin, J. S., Park, J. H., Kim, Y. S., Choi, S. Y., et al. (2006) · Neurochemical Research · doi:10.1007/s11064-006-9117-8 · Cited by 9
Hargis, K., Buechel, H. M., Popović, J., & Blalock, E. M. (2018) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2018.06.009 · Cited by 9
Self-citationFu, C., Liang, X., Wu, Y., McCarter, R., Richardson, A. G., Hickey, M., & Han, E.-S. (2004) · Journal of Nutrition · doi:10.1093/jn/134.11.2965 · Cited by 9
Fluoxetine Ameliorates Cognitive Deficits in High-Fat Diet Mice by Regulating BDNF Expression
Zuo, X., Zhu, Z., Liu, M., Zhao, Q., Li, X., Zhao, X., & Feng, X. (2024) · ACS Chemical Neuroscience · doi:10.1021/acschemneuro.4c00540 · Cited by 9
Chronological Pattern Exploration Algorithm for Gene Expression Data Clustering and Classification
Sharmila, L., & Sakthi, U. (2017) · Wireless Personal Communications · doi:10.1007/s11277-017-5208-x · Cited by 8
Chronic oral d-galactose intake provokes age-related changes in the rat prefrontal cortex
Martinović, J., Kontić, M. Z., Dragić, M., Todorović, A., Stojanović, I. G., Mitrović, N., Grković, I., & Drakulić, D. (2022) · Behavioural Brain Research · doi:10.1016/j.bbr.2022.114072 · Cited by 8
Kremsky, I., Morgan, T. E., Hou, X., Li, L., & Finch, C. E. (2011) · Brain Behavior and Immunity · doi:10.1016/j.bbi.2011.12.008 · Cited by 10
Kedmi, M., & Orr‐Urtreger, A. (2010) · AGE · doi:10.1007/s11357-010-9155-7 · Cited by 8
Recovery of Age-Related Memory Loss: Hopes and Challenges
Thakur, M. K., Konar, A., Kumar, D., Baghel, M. S., & Singh, P. (2016) · Springer eBooks · doi:10.1007/978-981-10-2155-8_14 · Cited by 8
Sex as a determinant of age-related changes in rat spinal cord inflammation-oxidation state
Nacka-Aleksić, M., Stojanović, M., Simic, L., Bufan, B., Kotur‐Stevuljević, J., Stojić-Vukanić, Z., Dimitrijević, M., Ražić, S., & Leposavić, G. (2017) · Biogerontology · doi:10.1007/s10522-017-9726-4 · Cited by 9
Application of DNA Microarray Technology to Gerontological Studies
Anisimov, S. V. (2007) · Methods in molecular biology · doi:10.1007/978-1-59745-361-5_19 · Cited by 8
Balivada, S., Ganta, C. K., Zhang, Y., Pawar, H. N., Ortiz, R. J., Becker, K. G., Khan, A. M., & Kenney, M. (2017) · Physiological Genomics · doi:10.1152/physiolgenomics.00131.2016 · Cited by 7
Iqbal, J. (2018) · Journal of Behavioral and Brain Science · doi:10.4236/jbbs.2018.82005 · Cited by 7
Neuroinflammation and oxidative injury in developmental neurotoxicity
Milatović, D., Zaja‐Milatovic, S., Brockett, M. M., BREYER, R. M., Aschner, M., & Montine, T. J. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-89773-0.00056-4 · Cited by 7
Disease Modeling of Neurodegenerative Disorders Using Direct Neural Reprogramming
Legault, E. M., Bouquety, J., & Drouin‐Ouellet, J. (2022) · Cellular Reprogramming · doi:10.1089/cell.2021.0172 · Cited by 7
HDAC Inhibitors as Novel Therapeutics in Aging and Alzheimer’s Disease
Nott, A., Fass, D. M., Haggarty, S. J., & Tsai, L. (2013) · Elsevier eBooks · doi:10.1016/b978-0-12-391494-1.00008-2 · Cited by 8
Duggan, M. R., Joshi, S., Tan, Y., Slifker, M. J., Ross, E. A., Wimmer, M. E., & Parikh, V. (2019) · Neurobiology of Learning and Memory · doi:10.1016/j.nlm.2019.107035 · Cited by 7
Unni, P. A., Pillai, G. G., & Sajithalulu, S. (2021) · Life Sciences · doi:10.1016/j.lfs.2021.119079 · Cited by 7
Fletcher, B. R., & Rapp, P. R. (2012) · Wiley eBooks · doi:10.1002/9781118133880.hop203024 · Cited by 8
Human Reproductive Costs and the Predicted Response to Dietary Restriction
Arking, R. (2007) · Rejuvenation Research · doi:10.1089/rej.2007.0519 · Cited by 7
Dietary Effects of Arachidonate-Rich Fungal Oil and Fish Oil on Murine Hippocampal Gene Expression
Vergnes, L., Phan, J., & Reue, K. (2004) · CRC Press eBooks · doi:10.1201/9781420030921-8 · Cited by 6
Davinelli, S., Vasto, S., Caruso, C., Zella, D., & Scapagnini, G. (2012) · InTech eBooks · doi:10.5772/32944 · Cited by 6
Experience-dependent persistent Arc expression is reduced in the aged hippocampus
Gheidi, A., Damphousse, C. C., & Marrone, D. F. (2020) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2020.07.032 · Cited by 6
New Insight in The Molecular Mechanisms of Neurodegenerative Disease
Meiliana, A., Dewi, N. M., & Wijaya, A. (2018) · The Indonesian Biomedical Journal · doi:10.18585/inabj.v10i1.448 · Cited by 6
Ageing, Neurodegeneration and the Endocannabinoid System
Fernández‐Ruíz, J., Sagredo, O., Gómez‐Ruíz, M., & de Lago, E. (2025) · Current topics in behavioral neurosciences · doi:10.1007/7854_2025_597 · Cited by 5
Age‐Associated Transcriptomic and Epigenetic Alterations in Mouse Hippocampus
Bilgic, M., Obata, R., Panfil, V., Zhu, Z., Saeki, M., Gotoh, Y., & Kishi, Y. (2025) · Aging Cell · doi:10.1111/acel.70233 · Cited by 5
Park, C. S., Valomon, A., & Welzl, H. (2015) · PLoS ONE · doi:10.1371/journal.pone.0130891 · Cited by 5
Unno, K., Yamamoto, H., Ohtaki, T., Ishikawa, Y., Noda, S., Maeda, K., Fujitani, K., Miyazaki, H., Takabayashi, F., Sasaki, T., et al. (2011) · Anti-aging Medicine · doi:10.3793/jaam.8.75 · Cited by 5
In vivo and ex vivo analyses of amyloid toxicity in the Tc1 mouse model of Down syndrome
Naert, G., Ferré, V. M., Keller, E., Slender, A., Gibbins, D., Fisher, E., Tybulewicz, V. L. J., & Maurice, T. (2017) · Journal of Psychopharmacology · doi:10.1177/0269881117743484 · Cited by 5
Network-Based Analysis of Cognitive Impairment and Memory Deficits from Transcriptome Data
Emanetci, E., & Çakır, T. (2021) · Journal of Molecular Neuroscience · doi:10.1007/s12031-021-01807-9 · Cited by 5
Microarray Analysis of Gene Expression Changes in Aging
Hudson, F. N., Kaeberlein, M. R., Linford, N. J., Pritchard, D. K., Beyer, R., & Rabinovitch, P. S. (2005) · Elsevier eBooks · doi:10.1016/b978-012088387-5/50014-5 · Cited by 4
Anticipated Brain Molecular Aging in Major Depression
Douillard‐Guilloux, G., Guilloux, J., Lewis, D. A., & Sibille, E. L. (2012) · American Journal of Geriatric Psychiatry · doi:10.1097/jgp.0b013e318266b7ad · Cited by 4
Srivas, S., & Thakur, M. K. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-801238-3.11398-4 · Cited by 3
Study DNA Microarray Gene Expression Data of Alzheimer's Disease by Independent Component Analysis
Kong, W., Mou, X., & Yang, B. (2009) · International Joint Conferences on Bioinformatics, Systems Biology and Intelligent Computing · doi:10.1109/ijcbs.2009.106 · Cited by 3
Mei, H., Simino, J. M., Li, L., Jiang, F., Bis, J. C., Davies, G., Hill, W. D., Xia, C., Gudnason, V. G., Yang, Q., et al. (2024) · Alzheimer s Research & Therapy · doi:10.1186/s13195-023-01376-6 · Cited by 3
Effect of growth hormone and melatonin on the brain: from molecular mechanisms to structural changes
Kireev, R. A., Cuesta‐Sancho, S., Vara, E., & Tresguerres, J. A. F. (2011) · Hormone Molecular Biology and Clinical Investigation · doi:10.1515/hmbci.2011.115 · Cited by 3
Fish Oil Supplementation Prevents Age-Related Memory Decline
Alfos, S. (2014) · Elsevier eBooks · doi:10.1016/b978-0-12-410527-0.00013-2 · Cited by 3
Yang, Y., Teng, Z., Meng, S., & Yu, W. (2016) · BioMed Research International · doi:10.1155/2016/9181702 · Cited by 3
Preparing Acute Brain Slices from the Dorsal Pole of the Hippocampus from Adult Rodents
Booker, S. A. (2020) · Journal of Visualized Experiments · doi:10.3791/61699 · Cited by 3
9 Genome-wide Views of Aging Gene NetworksPreprint
Kim, S. K. (2008) · Cold Spring Harbor Monograph Archive · doi:10.1101/087969824.51.215 · Cited by 3
The Ageless Question--What Accounts for Age-Related Cognitive Decline?
Nagahara, A. H., & Tuszynski, M. H. (2004) · Science of Aging Knowledge Environment · doi:10.1126/sageke.2004.19.pe20 · Cited by 3
Roles of Innate Immunity and Inflammation in the Aging Brain
Okun, E., Chen, X., Bašta, M., & Mattson, M. P. (2011) · Oxidative stress and disease · doi:10.1201/b11014-2 · Cited by 2
Molecular and Cellular Evidence for Age by Disease Interactions: Updates and Path Forward
Shukla, R., Oh, H., & Sibille, E. L. (2019) · American Journal of Geriatric Psychiatry · doi:10.1016/j.jagp.2019.06.001 · Cited by 2
Classical Models of Senile Osteoporosis
Watanabe, K. (2011) · Springer eBooks · doi:10.1007/978-0-85729-293-3_10 · Cited by 2
DNA Microarrays and Animal Models of Learning and Memory
Cavallaro, S. (2004) · International review of neurobiology · doi:10.1016/s0074-7742(04)60004-3 · Cited by 2
Sen, T., Jana, S., Sreetama, S. C., Chatterjee, U., & Chakrabarti, S. (2006) · Free Radical Research · doi:10.1080/10715760601083722 · Cited by 2
Neurological Effects of Dietary n-3 Fatty Acids in Rat Brain
Barceló‐Coblijn, G., Farkas, T., Pusk√°s, L., & Kitajka, K. (2004) · CRC Press eBooks · doi:10.1201/9781420030921.ch5 · Cited by 2
A modified F-test for hypothesis testing in large-scale data
Salehi, M., Mohammadpour, A., Mohammadi, M., & Aminghafari, M. (2018) · Journal of Biopharmaceutical Statistics · doi:10.1080/10543406.2018.1436557 · Cited by 2
Chataigner, M., Dinel, A., Pallet, V., Layé, S., & Joffre, C. (2018) · Journal of Alzheimer’s Disease & Parkinsonism · doi:10.4172/2161-0460.1000438 · Cited by 2
The Principal Inferior Olivary Nucleus in Aging and Alzheimer's Disease
Lasn, H. (2006) · Figshare · Cited by 2
Dietary Effects of Arachidonate-Rich Fungal Oil and Fish Oil on Murine Hippocampal Gene Expression
Roberts, M., & Berger, A. (2004) · CRC Press eBooks · doi:10.1201/9781420030921.ch3 · Cited by 2
Mennenga, S. E., Hanson, T., Semple, M. G., Lifshitz, D., Flores, B., Balducci, J., Harker, S. A., Ford, A., & Lewis, C. R. (2025) · Research Square · doi:10.21203/rs.3.rs-7890051/v1 · Cited by 2
Burke, S. N., & Barnes, C. A. (2025) · Oxford University Press eBooks · doi:10.1093/med/9780190065324.003.0015 · Cited by 1
Kreymerman, A., Weinstein, J. E., Vadgama, N., Shah, S. H., Nahmou, M. M., Belle, K. C., Ji, M. H., Xia, X., Faust, A. E., van der Merwe, Y., et al. (2025) · iScience · doi:10.1016/j.isci.2025.113563 · Cited by 1
McQuail, J. A., Barnes, C. A., Bizon, J. L., Burke, S. N., Disterhoft, J. F., Foster, T. C., Huentelman, M. J., Kaczorowski, C. C., Long, J. M., Moore, S. J., et al. (2026) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2026.09.003 · Cited by 1
Self-citationLearning the local molecular pattern of Alzheimer's disease by non-negative matrix factorization
Kong, W., Mou, X., Li, Q., & Song, Y. (2010) · International Conference on Green Circuits and Systems · doi:10.1109/icgcs.2010.5542987 · Cited by 1
Metadata
- Authors
- Blalock, E. M., Chen, K.-C., Sharrow, K., Herman, J. P., Porter, N. M., Foster, T. C., & Landfield, P. W.
- Affiliations
- University of Kentucky (US)
- University of Cincinnati (US)
- Venue
- The Journal of Neuroscience
- Publisher
- Society for Neuroscience
- Published
- 2003-05-01
- Volume
- 23
- Issue
- 9
- Pages
- 3807-3819
- ISSN
- 0270-6474 (ISSN-L)
- 1529-2401
- PMC
- PMC6742177
- OpenAlex
- W2132977458
- Cited by
- 570
- References
- 85
- Citations by year
- 2026: 2 · 2025: 7 · 2024: 8 · 2023: 5 · 2022: 23 · 2021: 15 · 2020: 17 · 2019: 27 · 2018: 22 · 2017: 28 · 2016: 28 · 2015: 19 · 2014: 22 · 2013: 32 · 2012: 39
- FWCI
- 22.81
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of Neuroscience (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health (5r01ag018228-02)
- National Institute on Aging (P01AG010836, R01AG014979, AG 14979, AG 10836, AG18228, R37AG004542, AG 04542)
- National Institute of Mental Health (MH 59891, R01MH059891)
- Topics
- Life Sciences › Neuroscience › Neurology › Neuroinflammation and Neurodegeneration Mechanisms
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Aging › Genetics, Aging, and Longevity in Model Organisms
- Life Sciences › Neuroscience › Neurology › Neurological Disease Mechanisms and Treatments
- Keywords
- brain aging, hippocampal CA1, cognitive impairment, gene expression profiling, memory, inflammation, oxidative stress, mitochondrial function, myelin turnover, synaptogenesis, cholesterol biosynthesis, lipid metabolism, monoamine metabolism, iron utilization, intracellular calcium release, transcription factors, cytokines, synaptic plasticity, rats
- MeSH terms
- Aging, Algorithms, Animals, Behavior, Animal, Cognition, Cognition Disorders, Exploratory Behavior, Gene Expression Regulation, Hippocampus, Male, Memory, Psychomotor Performance, Rats, Inbred F344, Sensitivity and Specificity, Reproducibility of Results, DNA Probes, Biomarkers, Maze Learning, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling, Rats
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review