The DOI matches its Crossref record; 58 of the 58 references it cites were found in a registry or an index.
Molecular aging in human prefrontal cortex is selective and continuous throughout adult life
Abstract
Background Aging leads to morphologic and functional changes in the brain and is associated with increased risk for psychiatric and neurological disorders.Methods To identify age-related transcriptional changes in the human brain, we profiled gene expression in two prefrontal cortex (PFC) areas in postmortem samples from 39 subjects, ranging in age from 13 to 79 years.Results Robust transcriptional age-related changes were identified for at least 540 genes. Gene expression correlates of aging were highly specific, and the large majority of the 22,000 transcripts investigated were unaffected by age. Across subjects, changes were progressive throughout adult life and accurately predicted chronological age. Age-upregulated transcripts were mostly of glial origin and related to inflammation and cellular defenses, whereas downregulated genes displayed mostly neuron-enriched transcripts relating to cellular communication and signaling.Conclusions Continuous changes in gene expression with increasing age revealed a "molecular profile" of aging in human PFC. The restricted scope of the transcript changes suggests cellular populations or functions that are selectively vulnerable during aging. Because age-related gene expression changes begin early in adulthood and are continuous throughout life, our results suggest the possibility of identifying early cellular mechanisms that may be engaged in preventive or detrimental age-related brain functions.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
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 [12]
Citations
Cites
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482
Gene Ontology: tool for the unification of biology
Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T., et al. (2000) · Nature Genetics · doi:10.1038/75556 · Cited by 43,206
Cluster analysis and display of genome-wide expression patterns
Eisen, M. B., Spellman, P. T., Brown, P. O., & Botstein, D. (1998) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.95.25.14863 · Cited by 16,184
Summaries of Affymetrix GeneChip probe level data
Irizarry, R. A. (2003) · Nucleic Acids Research · doi:10.1093/nar/gng015 · Cited by 4,837
Mapping cortical change across the human life span
Sowell, E. R., Peterson, B. S., Thompson, P. M., Welcome, S. E., Henkenius, A. L., & Toga, A. W. (2003) · Nature Neuroscience · doi:10.1038/nn1008 · Cited by 2,328
Insights into the ageing mind: a view from cognitive neuroscience
Hedden, T., & Gabrieli, J. D. E. (2004) · Nature Reviews Neuroscience · doi:10.1038/nrn1323 · Cited by 2,223
Models of visuospatial and verbal memory across the adult life span.
Park, D. C., Lautenschlager, G. J., Hedden, T., Davidson, N. S., Smith, A. D., & Smith, P. K. (2002) · Psychology and Aging · doi:10.1037/0882-7974.17.2.299 · Cited by 1,929
Gene regulation and DNA damage in the ageing human brain
Lu, T., Pan, Y., Kao, S.-Y., Li, C., Kohane, I., Chan, J., & Yankner, B. A. (2004) · Nature · doi:10.1038/nature02661 · Cited by 1,874
Gene expression profile of aging and its retardation by caloric restriction
Lee, C.-K., Klopp, R. G., Weindruch, R., & Prolla, T. A. (1999) · Science · doi:10.1126/science.285.5432.1390 · Cited by 1,473
Longitudinal Magnetic Resonance Imaging Studies of Older Adults: A Shrinking Brain
Resnick, S. M., Pham, D. L., Kraut, M. A., Zonderman, A. B., & Davatzikos, C. (2003) · The Journal of Neuroscience · doi:10.1523/jneurosci.23-08-03295.2003 · Cited by 1,425
Research works printed but not linked: 1
- Sastre, 1994, Density of alpha-2A adrenoceptors and Gi proteins in the human brain, J Pharmacol Exp Ther, 269, 1062
Cited by
Sorted by citations
Items 201–236 of 236
CaMK modulates sensory neural activity to control longevity and proteostasis
Zhao, R., Ge, W., Xue, W., Deng, Z., Liu, J., Wang, K., Jin, Y. N., & Yu, Y. V. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2423428122 · Cited by 2
Global view of transcriptome in the brains of aged NR2B transgenic mice.
Li, C., Su, M., Wang, H., & Hu, Y. (2013) · PubMed Central · doi:10.3969/j.issn.1673-5374.2013.29.005 · Cited by 1
Epigenetic Regulation in Autism
Peter, C. J., Reichenberg, A., & Akbarian, S. (2015) · Contemporary clinical neuroscience · doi:10.1007/978-1-4939-2190-4_5 · Cited by 1
Rensing, L., & Rippe, V. (2013) · Springer eBooks · doi:10.1007/978-3-642-37733-4_12 · Cited by 1
Insights into the Brain Effects of a Bacterial Nicotine Derivative – 6-Hydroxy-L-NicotinePreprint
Boiangiu, R. Ş., Brinza, I., Honceriu, I., Mihăşan, M., & Hriţcu, L. (2023) · Preprints.org · doi:10.20944/preprints202310.0340.v1 · Cited by 1
Electrophysiology and Morphology of Human Cortical Supragranular Pyramidal Cells in a Wide Age RangePreprint
Barzó, P., Szöts, I., Tóth, M., Csajbók, É., Molnár, G., & Tamás, G. (2024) · eLife · doi:10.7554/elife.100390.1 · Cited by 1
Electrophysiology and Morphology of Human Cortical Supragranular Pyramidal Cells in a Wide Age RangePreprint
Barzó, P., Szöts, I., Tóth, M., Csajbók, É., Molnár, G., & Tamás, G. (2025) · eLife · doi:10.7554/elife.100390.2 · Cited by 1
Selected Age-Related Disorders
Behl, C., & Ziegler, C. (2013) · SpringerBriefs in molecular medicine · doi:10.1007/978-3-642-45179-9_4
The effects of aging and cognitive decrements on simulated driving performance
Sifrit, K. J. (2005) · Holmes Museum Of Anthropology (Wichita State University)
Exploring the transcriptome of the brain
Dillman, A. (2014) · Figshare
Coding and Non-Coding RNA in Age-Associated Memory Impairment and Alzheimer's Disease
Rao, P. (2014) · eDiss (Georg-August-Universität Göttingen) · doi:10.53846/goediss-4498
CONTRIBUTION OF SOMATOSTATIN DEFICITS AND EIF2 SIGNALING TO ANXIETY/DEPRESSIVE-LIKE BEHAVIORS
Lin, L. (2014) · D-Scholarship@Pitt (University of Pittsburgh)
Genomics to Identify Biomarkers of Normal Brain Aging
Erraji-Benchekroun, L., Arango, V., Mann, J. J., & Underwood, M. D. (2007) · Humana Press eBooks · doi:10.1007/978-1-59745-328-8_6
Self-citationMOLECULAR MECHANISMS OF AGING IN THE PERIPHERAL NOCICEPTIVE SYSTEM
Wang, S. (2007) · D-Scholarship@Pitt (University of Pittsburgh)
Sibille, E. L. (2013) · American Journal of Geriatric Psychiatry · doi:10.1016/j.jagp.2013.02.016
Self-citationKirkwood, C. M. (2015) · D-Scholarship@Pitt (University of Pittsburgh)
THE ROLE OF DENDRITIC BDNF TRANSCRIPTS ON ALTERED INHIBITORY CIRCUITRY IN AGING AND DEPRESSION
Oh, H. (2015) · D-Scholarship@Pitt (University of Pittsburgh)
Age-Related Changes in Neurons and S100-, GFAP-Immunoreactive Cells in the Motor Cortex of Cats
Lin, S. H. E. N. (2008) · Zoological Research · doi:10.3724/sp.j.1141.2008.00056
Enhanced Longevity and Role of Omega-3 Fatty Acids
Singh, V. K., & WATSON, R. R. (2014) · Elsevier eBooks · doi:10.1016/b978-0-12-410527-0.00001-6
Kapadia, F. (2016) · TSpace (University of Toronto)
Prefrontal gene expression changes in mood disorders and suicide
Zhao, J. (2018) · Data Archiving and Networked Services (DANS)
Accelerated brain molecular aging in depression
Shukla, R., & Sibille, E. L. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-800136-3.00008-9
Self-citationLos peregrinos noruegos también se dirigieron a Santiago
Njerve, E. (1996) · Peregrino: revista del Camino de Santiago
Molecular regulation of fetal brain development in pigs
Strawn, M. (2021) · MOspace Institutional Repository (University of Missouri) · doi:10.32469/10355/88112
GABA: A critical player for regulating synaptic plasticity and adult neurogenesis
Mishra, M., & Seth, P. (2022) · IP Indian Journal of Neurosciences · doi:10.18231/j.ijn.2021.050
Effect of Somatostatin-Expressing Interneuron Deficits in Depression
郭, 沉. (2019) · Advances in Psychology · doi:10.12677/ap.2019.910214
Pathophysiology of Brain Aging: A Brief Account on Molecular Changes
Chikkanarayanappa, V., & Gangadharappa, H. (2015) · JOURNAL OF CLINICAL AND BIOMEDICAL SCIENCES · doi:10.58739/jcbs/v05i2.11
Wang, J., Li, Y., Li, R., Yang, J. N., Ma, Y., Su, J., Yu, X., Chen, K., Lu, F., & Chen, Y. (2023) · Research Square · doi:10.21203/rs.3.rs-2610086/v1
Biology of cognitive aging across species
Kakizawa, S., Park, J., & Tonoki, A. (2023) · Geriatrics and gerontology international/Geriatrics & gerontology international · doi:10.1111/ggi.14782
Dynamic changes in neuronal and glial GAL4 driver expression during Drosophila ageingPreprint
Delandre, C., McMullen, J. P. D., & Marshall, O. J. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.06.16.599238
Neurostructural and cognitive signatures of novel polygenic risk scores for molecular brain agingPreprint
Clifford, K. P., Santos, F. C. D., Abdelhack, M., Felsky, D., Sibille, E. L., & Nikolova, Y. S. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.11.17.624029
Self-citationNeurological aging and neurodegenerative disease in humans and great apes
Edler, M. K., Hearn, C. J., Guevara, E. E., Mhatre‐Winters, I., Varghese, M., Freire‐Cobo, C., Sherwood, C. C., Richardson, J. R., Raghanti, M. A., & Hof, P. R. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-27380-3.00074-9
Ibargüen-Vargas, Y. (2008) · theses.fr (ABES) · doi:10.70675/2d73c017z46fcz4c35z8397zb504072e069a
Loss of calbindin and rise in pT217-tau in aging monkey prefrontal cortical dendritesPreprint
Perone, I., Bolat, D., Gu, Z., Zeiss, C. J., Bliss‐Moreau, E., Duque, A., Arellano, J. I., Zhao, Y., Datta, D., & Arnsten, A. F. T. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.08.18.745599
DNA Microarray Analysis of Gene Expression Profiles in Aging process of Mouse Brain
Mi-Suk, L., Heo, J., Kim, J., Park, J., Lee, J.-Y., Han, J.-A., & Jong-Il, K. (2006) · Seoul National University Open Repository (Seoul National University)
Metadata
- Authors
- Erraji-Benchekroun, L., Underwood, M. D., Arango, V., Galfalvy, H., Pavlidis, P., Smyrniotopoulos, P., Mann, J. J., & Sibille, E.
- Affiliations
- New York State Psychiatric Institute (US)
- Columbia University (US)
- Corresponding author
- Sibille, Etienne
- Venue
- Biological Psychiatry
- Publisher
- Elsevier BV
- Published
- 2005-03
- Volume
- 57
- Issue
- 5
- Pages
- 549-558
- ISSN
- 0006-3223 (ISSN-L)
- OpenAlex
- W2151431569
- Cited by
- 236
- References
- 58
- Citations by year
- 2026: 3 · 2025: 11 · 2024: 10 · 2023: 13 · 2022: 6 · 2021: 3 · 2020: 10 · 2019: 15 · 2018: 11 · 2017: 12 · 2016: 12 · 2015: 13 · 2014: 13 · 2013: 18 · 2012: 14
- FWCI
- 3.39
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Biological Psychiatry (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institute of Mental Health (T32MH020004, MH 62185, R01MH064168, MH64168, R01 MH40210, R01MH040210, F32MH63559, F32MH063559, K01MH067721, P50MH062185)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Aging › Genetics, Aging, and Longevity in Model Organisms
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Neuroscience › Neurology › Neuroinflammation and Neurodegeneration Mechanisms
- Keywords
- brain aging, prefrontal cortex, age-related gene expression, transcriptome, neuroinflammation, neuronal gene expression, age prediction, postmortem brain, adult neuroplasticity
- MeSH terms
- Adolescent, Adult, Age Factors, Aged, Aging, Female, Humans, Longevity, Male, Middle Aged, Neuroglia, Neurons, Postmortem Changes, RNA, Messenger, Gene Expression, Prefrontal Cortex, Gene Expression Regulation, Developmental, Reverse Transcriptase Polymerase Chain Reaction, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling
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