Age-related brain atrophy is not a homogenous process Different functional brain networks associate differentially with aging and blood factors
Aging is characterized by a progressive loss of brain volume at an estimated rate of 5% per decade after age 40. While these morphometric changes, especially those affecting gray matter and atrophy of the temporal lobe, are predictors of cognitive performance, the strong association with aging obscu...
Saved in:
Published in | Nevada RNformation Vol. 119; no. 49; pp. 1 - 11 |
---|---|
Main Authors | , , , , , , , , , , |
Format | Journal Article Newsletter |
Language | English |
Published |
United States
National Academy of Sciences
06.12.2022
Nevada Nurses Association |
Subjects | |
Online Access | Get full text |
ISSN | 0027-8424 0273-4117 1091-6490 1091-6490 |
DOI | 10.1073/pnas.2207181119 |
Cover
Abstract | Aging is characterized by a progressive loss of brain volume at an estimated rate of 5% per decade after age 40. While these morphometric changes, especially those affecting gray matter and atrophy of the temporal lobe, are predictors of cognitive performance, the strong association with aging obscures the potential parallel, but more specific role, of individual subject physiology. Here, we studied a cohort of 554 human subjects who were monitored using structural MRI scans and blood immune protein concentrations. Using machine learning, we derived a cytokine clock (CyClo), which predicted age with good accuracy (Mean Absolute Error = 6 y) based on the expression of a subset of immune proteins. These proteins included, among others, Placenta Growth Factor (PLGF) and Vascular Endothelial Growth Factor (VEGF), both involved in angiogenesis, the chemoattractant vascular cell adhesion molecule 1 (VCAM-1), the canonical inflammatory proteins interleukin-6 (IL-6) and tumor necrosis factor alpha (TNFα), the chemoattractant IP-10 (CXCL10), and eotaxin-1 (CCL11), previously involved in brain disorders. Age, sex, and the CyClo were independently associated with different functionally defined cortical networks in the brain. While age was mostly correlated with changes in the somatomotor system, sex was associated with variability in the frontoparietal, ventral attention, and visual networks. Significant canonical correlation was observed for the CyClo and the default mode, limbic, and dorsal attention networks, indicating that immune circulating proteins preferentially affect brain processes such as focused attention, emotion, memory, response to social stress, internal evaluation, and access to consciousness. Thus, we identified immune biomarkers of brain aging which could be potential therapeutic targets for the prevention of age-related cognitive decline. |
---|---|
AbstractList | Aging is characterized by a progressive loss of brain volume at an estimated rate of 5% per decade after age 40. While these morphometric changes, especially those affecting gray matter and atrophy of the temporal lobe, are predictors of cognitive performance, the strong association with aging obscures the potential parallel, but more specific role, of individual subject physiology. Here, we studied a cohort of 554 human subjects who were monitored using structural MRI scans and blood immune protein concentrations. Using machine learning, we derived a cytokine clock (CyClo), which predicted age with good accuracy (Mean Absolute Error = 6 y) based on the expression of a subset of immune proteins. These proteins included, among others, Placenta Growth Factor (PLGF) and Vascular Endothelial Growth Factor (VEGF), both involved in angiogenesis, the chemoattractant vascular cell adhesion molecule 1 (VCAM-1), the canonical inflammatory proteins interleukin-6 (IL-6) and tumor necrosis factor alpha (TNFα), the chemoattractant IP-10 (CXCL10), and eotaxin-1 (CCL11), previously involved in brain disorders. Age, sex, and the CyClo were independently associated with different functionally defined cortical networks in the brain. While age was mostly correlated with changes in the somatomotor system, sex was associated with variability in the frontoparietal, ventral attention, and visual networks. Significant canonical correlation was observed for the CyClo and the default mode, limbic, and dorsal attention networks, indicating that immune circulating proteins preferentially affect brain processes such as focused attention, emotion, memory, response to social stress, internal evaluation, and access to consciousness. Thus, we identified immune biomarkers of brain aging which could be potential therapeutic targets for the prevention of age-related cognitive decline.Aging is characterized by a progressive loss of brain volume at an estimated rate of 5% per decade after age 40. While these morphometric changes, especially those affecting gray matter and atrophy of the temporal lobe, are predictors of cognitive performance, the strong association with aging obscures the potential parallel, but more specific role, of individual subject physiology. Here, we studied a cohort of 554 human subjects who were monitored using structural MRI scans and blood immune protein concentrations. Using machine learning, we derived a cytokine clock (CyClo), which predicted age with good accuracy (Mean Absolute Error = 6 y) based on the expression of a subset of immune proteins. These proteins included, among others, Placenta Growth Factor (PLGF) and Vascular Endothelial Growth Factor (VEGF), both involved in angiogenesis, the chemoattractant vascular cell adhesion molecule 1 (VCAM-1), the canonical inflammatory proteins interleukin-6 (IL-6) and tumor necrosis factor alpha (TNFα), the chemoattractant IP-10 (CXCL10), and eotaxin-1 (CCL11), previously involved in brain disorders. Age, sex, and the CyClo were independently associated with different functionally defined cortical networks in the brain. While age was mostly correlated with changes in the somatomotor system, sex was associated with variability in the frontoparietal, ventral attention, and visual networks. Significant canonical correlation was observed for the CyClo and the default mode, limbic, and dorsal attention networks, indicating that immune circulating proteins preferentially affect brain processes such as focused attention, emotion, memory, response to social stress, internal evaluation, and access to consciousness. Thus, we identified immune biomarkers of brain aging which could be potential therapeutic targets for the prevention of age-related cognitive decline. The fields of immunology and neuroscience have evolved in isolation, partially justified by the view that the brain–blood barrier is impermeable, however, the newly developing science of aging has revealed that chronic, high levels of proinflammatory immune factors accelerate the aging process in the brain. MRI scans identify a decline in cortical volume as a marker for aging. We extracted a cytokine clock (CyClo) that was able to estimate physiological age based on the concentrations of a set of blood proteins that change throughout life. Canonical correlation analysis reveals that the variability in the volume of different functional cortical networks associates differentially with age, sex, and CyClo, suggesting selective vulnerabilities of certain functional networks to circulating levels of immune markers of aging. Aging is characterized by a progressive loss of brain volume at an estimated rate of 5% per decade after age 40. While these morphometric changes, especially those affecting gray matter and atrophy of the temporal lobe, are predictors of cognitive performance, the strong association with aging obscures the potential parallel, but more specific role, of individual subject physiology. Here, we studied a cohort of 554 human subjects who were monitored using structural MRI scans and blood immune protein concentrations. Using machine learning, we derived a cytokine clock (CyClo), which predicted age with good accuracy (Mean Absolute Error = 6 y) based on the expression of a subset of immune proteins. These proteins included, among others, Placenta Growth Factor (PLGF) and Vascular Endothelial Growth Factor (VEGF), both involved in angiogenesis, the chemoattractant vascular cell adhesion molecule 1 (VCAM-1), the canonical inflammatory proteins interleukin-6 (IL-6) and tumor necrosis factor alpha (TNFα), the chemoattractant IP-10 (CXCL10), and eotaxin-1 (CCL11), previously involved in brain disorders. Age, sex, and the CyClo were independently associated with different functionally defined cortical networks in the brain. While age was mostly correlated with changes in the somatomotor system, sex was associated with variability in the frontoparietal, ventral attention, and visual networks. Significant canonical correlation was observed for the CyClo and the default mode, limbic, and dorsal attention networks, indicating that immune circulating proteins preferentially affect brain processes such as focused attention, emotion, memory, response to social stress, internal evaluation, and access to consciousness. Thus, we identified immune biomarkers of brain aging which could be potential therapeutic targets for the prevention of age-related cognitive decline. Aging is characterized by a progressive loss of brain volume at an estimated rate of 5% per decade after age 40. While these morphometric changes, especially those affecting gray matter and atrophy of the temporal lobe, are predictors of cognitive performance, the strong association with aging obscures the potential parallel, but more specific role, of individual subject physiology. Here, we studied a cohort of 554 human subjects who were monitored using structural MRI scans and blood immune protein concentrations. Using machine learning, we derived a cytokine clock (CyClo), which predicted age with good accuracy (Mean Absolute Error = 6 y) based on the expression of a subset of immune proteins. These proteins included, among others, Placenta Growth Factor (PLGF) and Vascular Endothelial Growth Factor (VEGF), both involved in angiogenesis, the chemoattractant vascular cell adhesion molecule 1 (VCAM-1), the canonical inflammatory proteins interleukin-6 (IL-6) and tumor necrosis factor alpha (TNF[alpha]), the chemoattractant IP-10 (CXCL10), and eotaxin-1 (CCL11), previously involved in brain disorders. Age, sex, and the CyClo were independently associated with different functionally defined cortical networks in the brain. While age was mostly correlated with changes in the somatomotor system, sex was associated with variability in the frontoparietal, ventral attention, and visual networks. Significant canonical correlation was observed for the CyClo and the default mode, limbic, and dorsal attention networks, indicating that immune circulating proteins preferentially affect brain processes such as focused attention, emotion, memory, response to social stress, internal evaluation, and access to consciousness. Thus, we identified immune biomarkers of brain aging which could be potential therapeutic targets for the prevention of age-related cognitive decline. brain aging | cytokines | cytokine clock | aging | gray matter volume Aging is characterized by a progressive loss of brain volume at an estimated rate of 5% per decade after age 40. While these morphometric changes, especially those affecting gray matter and atrophy of the temporal lobe, are predictors of cognitive performance, the strong association with aging obscures the potential parallel, but more specific role, of individual subject physiology. Here, we studied a cohort of 554 human subjects who were monitored using structural MRI scans and blood immune protein concentrations. Using machine learning, we derived a cytokine clock (CyClo), which predicted age with good accuracy (Mean Absolute Error = 6 y) based on the expression of a subset of immune proteins. These proteins included, among others, Placenta Growth Factor (PLGF) and Vascular Endothelial Growth Factor (VEGF), both involved in angiogenesis, the chemoattractant vascular cell adhesion molecule 1 (VCAM-1), the canonical inflammatory proteins interleukin-6 (IL-6) and tumor necrosis factor alpha (TNFα), the chemoattractant IP-10 (CXCL10), and eotaxin-1 (CCL11), previously involved in brain disorders. Age, sex, and the CyClo were independently associated with different functionally defined cortical networks in the brain. While age was mostly correlated with changes in the somatomotor system, sex was associated with variability in the frontoparietal, ventral attention, and visual networks. Significant canonical correlation was observed for the CyClo and the default mode, limbic, and dorsal attention networks, indicating that immune circulating proteins preferentially affect brain processes such as focused attention, emotion, memory, response to social stress, internal evaluation, and access to consciousness. Thus, we identified immune biomarkers of brain aging which could be potential therapeutic targets for the prevention of age-related cognitive decline. Aging is characterized by a progressive loss of brain volume at an estimated rate of 5% per decade after age 40. While these morphometric changes, especially those affecting gray matter and atrophy of the temporal lobe, are predictors of cognitive performance, the strong association with aging obscures the potential parallel, but more specific role, of individual subject physiology. Here, we studied a cohort of 554 human subjects who were monitored using structural MRI scans and blood immune protein concentrations. Using machine learning, we derived a cytokine clock (CyClo), which predicted age with good accuracy (Mean Absolute Error = 6 y) based on the expression of a subset of immune proteins. These proteins included, among others, Placenta Growth Factor (PLGF) and Vascular Endothelial Growth Factor (VEGF), both involved in angiogenesis, the chemoattractant vascular cell adhesion molecule 1 (VCAM-1), the canonical inflammatory proteins interleukin-6 (IL-6) and tumor necrosis factor alpha (TNF[alpha]), the chemoattractant IP-10 (CXCL10), and eotaxin-1 (CCL11), previously involved in brain disorders. Age, sex, and the CyClo were independently associated with different functionally defined cortical networks in the brain. While age was mostly correlated with changes in the somatomotor system, sex was associated with variability in the frontoparietal, ventral attention, and visual networks. Significant canonical correlation was observed for the CyClo and the default mode, limbic, and dorsal attention networks, indicating that immune circulating proteins preferentially affect brain processes such as focused attention, emotion, memory, response to social stress, internal evaluation, and access to consciousness. Thus, we identified immune biomarkers of brain aging which could be potential therapeutic targets for the prevention of age-related cognitive decline. Aging is characterized by a progressive loss of brain volume at an estimated rate of 5% per decade after age 40. While these morphometric changes, especially those affecting gray matter and atrophy of the temporal lobe, are predictors of cognitive performance, the strong association with aging obscures the potential parallel, but more specific role, of individual subject physiology. Here, we studied a cohort of 554 human subjects who were monitored using structural MRI scans and blood immune protein concentrations. Using machine learning, we derived a cytokine clock (CyClo), which predicted age with good accuracy (Mean Absolute Error = 6 y) based on the expression of a subset of immune proteins. These proteins included, among others, Placenta Growth Factor (PLGF) and Vascular Endothelial Growth Factor (VEGF), both involved in angiogenesis, the chemoattractant vascular cell adhesion molecule 1 (VCAM-1), the canonical inflammatory proteins interleukin-6 (IL-6) and tumor necrosis factor alpha (TNFα), the chemoattractant IP-10 (CXCL10), and eotaxin-1 (CCL11), previously involved in brain disorders. Age, sex, and the CyClo were independently associated with different functionally defined cortical networks in the brain. While age was mostly correlated with changes in the somatomotor system, sex was associated with variability in the frontoparietal, ventral attention, and visual networks. Significant canonical correlation was observed for the CyClo and the default mode, limbic, and dorsal attention networks, indicating that immune circulating proteins preferentially affect brain processes such as focused attention, emotion, memory, response to social stress, internal evaluation, and access to consciousness. Thus, we identified immune biomarkers of brain aging which could be potential therapeutic targets for the prevention of age-related cognitive decline. |
Audience | Professional Trade |
Author | Campisi, Judith Furman, David Markov, Nikola T. Kramer, Joel Lindbergh, Cutter A. Perez, Kevin Murad, Natalia F. Stevens, Michael Nguyen, Khiem Staffaroni, Adam M. Fonseca, Corrina |
Author_xml | – sequence: 1 givenname: Nikola T. surname: Markov fullname: Markov, Nikola T. – sequence: 2 givenname: Cutter A. surname: Lindbergh fullname: Lindbergh, Cutter A. – sequence: 3 givenname: Adam M. surname: Staffaroni fullname: Staffaroni, Adam M. – sequence: 4 givenname: Kevin surname: Perez fullname: Perez, Kevin – sequence: 5 givenname: Michael surname: Stevens fullname: Stevens, Michael – sequence: 6 givenname: Khiem surname: Nguyen fullname: Nguyen, Khiem – sequence: 7 givenname: Natalia F. surname: Murad fullname: Murad, Natalia F. – sequence: 8 givenname: Corrina surname: Fonseca fullname: Fonseca, Corrina – sequence: 9 givenname: Judith surname: Campisi fullname: Campisi, Judith – sequence: 10 givenname: Joel surname: Kramer fullname: Kramer, Joel – sequence: 11 givenname: David surname: Furman fullname: Furman, David |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/36459652$$D View this record in MEDLINE/PubMed |
BookMark | eNp9ks9rFDEUx4NU7LZ69qQMePEy25dfk-QiLMWqUPCi55DJJLtZZpI1mRX635tl260tIjkE8j7f7-Ob9y7QWUzRIfQWwxKDoFe7aMqSEBBYYozVC7TAoHDbMQVnaAFARCsZYefoopQtACgu4RU6px3jquNkgfhq7drsRjO7oemzCbExc067zV0TShPT3Jhmk6a0djHtS7PLybpSXqOX3ozFvbm_L9HPm88_rr-2t9-_fLte3baWY5hbIRnzlnnjBwoG-oGZflADMVz6DssBek-Yclja3lCgXhknqHHgMbF8wJheok9H392-n9xgXZyzGfUuh8nkO51M0E8rMWz0Ov3WSipGMKkGH-8Ncvq1d2XWUyjWjaOJrubRRLCOKq54V9EPz9Bt2udY41WKc8w7JugjtTaj0yH6VPvag6leCaoYJtCJSi3_QdUzuCnYOkIf6vsTwfu_g54SPgyqAldHwOZUSnb-hGDQh1XQh1XQj6tQFfyZwobZzCEdfiqM_9G9O-q2ZU751IYIAopJSf8Aq3fAdQ |
CitedBy_id | crossref_primary_10_1097_PAF_0000000000001010 crossref_primary_10_1162_imag_a_00503 crossref_primary_10_3390_ijms252413741 crossref_primary_10_1186_s13195_024_01603_8 crossref_primary_10_1038_s41398_024_02958_0 crossref_primary_10_3389_fnana_2025_1431128 crossref_primary_10_1007_s00276_023_03205_9 crossref_primary_10_1111_adb_13278 crossref_primary_10_1038_s43587_024_00716_x crossref_primary_10_1016_j_arr_2024_102653 crossref_primary_10_1111_jnc_15999 crossref_primary_10_1016_j_bandl_2023_105343 crossref_primary_10_14336_AD_2024_0961 crossref_primary_10_1038_s41598_024_60286_1 crossref_primary_10_1016_j_neuron_2024_12_004 crossref_primary_10_1515_revneuro_2024_0025 crossref_primary_10_3390_jcm13237031 |
Cites_doi | 10.1111/j.1749-6632.2009.04421.x 10.1038/mp.2015.131 10.1016/S0197-4580(01)00276-7 10.1109/42.668698 10.1016/j.tins.2004.07.011 10.1016/j.tics.2013.09.017 10.1186/gb-2013-14-10-r115 10.1111/acel.12271 10.1038/nature20411 10.1093/gerona/glu057 10.1001/archneur.60.7.989 10.1037/neu0000696 10.1016/j.neuron.2008.01.003 10.1038/s43587-021-00082-y 10.1523/JNEUROSCI.3252-09.2009 10.1111/1440-1681.12758 10.1111/j.1749-6632.2009.05118.x 10.1126/science.278.5337.412 10.1371/journal.pcbi.1000808 10.3727/000000007783464731 10.1016/j.jamda.2013.05.009 10.1212/WNL.0b013e318248e50f 10.1177/1359105305055332 10.1006/nimg.1995.1012 10.18637/jss.v033.i01 10.1097/JNN.0b013e3182527690 10.1523/JNEUROSCI.23-08-03295.2003 10.1007/s10548-018-0675-2 10.1016/j.celrep.2020.03.012 10.1093/gerona/glab162 10.1016/j.cub.2012.07.024 10.1093/gerona/glz209 10.1038/s41591-019-0440-4 10.1136/pgmj.2005.036665 10.1212/WNL.0b013e318266fa70 10.1016/j.cmet.2018.05.011 10.3389/fnagi.2020.603854 10.1016/j.bbi.2020.05.014 10.1016/j.neuron.2007.10.038 10.1152/physrev.00004.2002 10.1007/s12035-014-8657-1 10.1093/nar/gkv007 10.1523/JNEUROSCI.3067-17.2018 10.1016/j.ajpath.2011.10.024 10.1016/j.jagp.2018.05.004 10.1371/journal.pone.0084093 10.4103/1673-5374.238608 10.1037/neu0000447 10.1016/j.neuroimage.2007.07.007 10.1016/j.neuroimage.2012.01.120 10.1093/bioinformatics/btp491 10.1093/gerona/glu121 10.1093/gerona/gly028 10.1038/nrn1886 10.1038/s41569-018-0064-2 10.1111/j.1749-6632.2000.tb06651.x 10.1177/0891988708328216 10.1016/j.pneurobio.2014.02.004 10.1016/j.ynstr.2018.09.006 10.1038/nm1653 10.1186/1742-2094-2-9 10.3389/fgene.2021.706986 10.1056/NEJM2003ra020003 10.1016/0197-4580(86)90086-2 10.1152/jn.00338.2011 10.1016/j.arr.2014.01.004 10.1038/s41583-019-0255-9 10.1038/s41380-018-0098-1 10.1016/j.neurobiolaging.2021.09.010 10.1016/j.neurobiolaging.2020.01.006 10.1016/j.tins.2022.08.001 10.1038/s41591-019-0675-0 10.4135/9781412983570 10.1002/hbm.25090 10.1016/j.cyto.2018.05.032 |
ContentType | Journal Article Newsletter |
Copyright | Copyright © 2022 the Author(s) COPYRIGHT 2022 Nevada Nurses Association Copyright National Academy of Sciences Dec 6, 2022 Copyright © 2022 the Author(s). Published by PNAS. 2022 |
Copyright_xml | – notice: Copyright © 2022 the Author(s) – notice: COPYRIGHT 2022 Nevada Nurses Association – notice: Copyright National Academy of Sciences Dec 6, 2022 – notice: Copyright © 2022 the Author(s). Published by PNAS. 2022 |
DBID | AAYXX CITATION CGR CUY CVF ECM EIF NPM 7QG 7QL 7QP 7QR 7SN 7SS 7T5 7TK 7TM 7TO 7U9 8FD C1K FR3 H94 M7N P64 RC3 7X8 5PM |
DOI | 10.1073/pnas.2207181119 |
DatabaseName | CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed Animal Behavior Abstracts Bacteriology Abstracts (Microbiology B) Calcium & Calcified Tissue Abstracts Chemoreception Abstracts Ecology Abstracts Entomology Abstracts (Full archive) Immunology Abstracts Neurosciences Abstracts Nucleic Acids Abstracts Oncogenes and Growth Factors Abstracts Virology and AIDS Abstracts Technology Research Database Environmental Sciences and Pollution Management Engineering Research Database AIDS and Cancer Research Abstracts Algology Mycology and Protozoology Abstracts (Microbiology C) Biotechnology and BioEngineering Abstracts Genetics Abstracts MEDLINE - Academic PubMed Central (Full Participant titles) |
DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Virology and AIDS Abstracts Oncogenes and Growth Factors Abstracts Technology Research Database Nucleic Acids Abstracts Ecology Abstracts Neurosciences Abstracts Biotechnology and BioEngineering Abstracts Environmental Sciences and Pollution Management Entomology Abstracts Genetics Abstracts Animal Behavior Abstracts Bacteriology Abstracts (Microbiology B) Algology Mycology and Protozoology Abstracts (Microbiology C) AIDS and Cancer Research Abstracts Chemoreception Abstracts Immunology Abstracts Engineering Research Database Calcium & Calcified Tissue Abstracts MEDLINE - Academic |
DatabaseTitleList | MEDLINE - Academic MEDLINE CrossRef Virology and AIDS Abstracts |
Database_xml | – sequence: 1 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: EIF name: MEDLINE url: https://proxy.k.utb.cz/login?url=https://www.webofscience.com/wos/medline/basic-search sourceTypes: Index Database |
DeliveryMethod | fulltext_linktorsrc |
Discipline | Sciences (General) Nursing |
EISSN | 1091-6490 |
EndPage | 11 |
ExternalDocumentID | PMC9894212 A739412067 36459652 10_1073_pnas_2207181119 27209488 |
Genre | Journal Article Research Support, N.I.H., Extramural |
GeographicLocations | California |
GeographicLocations_xml | – name: California |
GrantInformation_xml | – fundername: NIA NIH HHS grantid: P01 AG066591 – fundername: NIA NIH HHS grantid: P30 AG062422 – fundername: NIA NIH HHS grantid: R01 AG032289 – fundername: NIA NIH HHS grantid: R01 AG048234 – fundername: NIA NIH HHS grantid: K23 AG061253 – fundername: NIA NIH HHS grantid: P50 AG023501 – fundername: ; grantid: 1P01AG066591-01A1 – fundername: ; grantid: K23AG061253 – fundername: ; grantid: ADRC P50 AG023501 – fundername: ; grantid: R01AG032289 – fundername: ; grantid: 2014-A-004-NET – fundername: ; grantid: ADRC P30 AG062422 – fundername: ; grantid: R01AG048234 |
GroupedDBID | --- -DZ -~X .55 0R~ 123 29P 2FS 2WC 4.4 53G 5RE 5VS 85S AACGO AAFWJ AANCE ABOCM ABPLY ABPPZ ABTLG ABZEH ACGOD ACIWK ACNCT ACPRK AENEX AFFNX AFOSN AFRAH ALMA_UNASSIGNED_HOLDINGS BKOMP CS3 D0L DIK DU5 E3Z EBS F5P FRP GX1 H13 HH5 HYE JENOY JLS JSG JST KQ8 L7B LU7 N9A N~3 O9- OK1 PNE PQQKQ R.V RHI RNA RNS RPM RXW SJN TAE TN5 UKR W8F WH7 WOQ WOW X7M XSW Y6R YBH YKV YSK ZCA ~02 ~KM AAYXX CITATION CGR CUY CVF ECM EIF NPM 04C 29N 36B 5Q2 6I6 6PF 7RV 7X7 88E 8AO 8FI 8FJ 8FW 8R4 8R5 AAWTL ABUWG ADBBV ADFRT ADOJX AFKRA ALIPV AXR BENPR BKEYQ BMSDO BPHCQ BVXVI CCPQU ECF ECI ECP ECT EIHBH EJD EX3 FYUFA HMCUK IAO IHR INH INR ITC M1P NAPCQ PHGZM PHGZT PMFND PROAC PSQYO PV9 Q2X RWL RZL S0X UKHRP WQ9 XWR ZAC 7QG 7QL 7QP 7QR 7SN 7SS 7T5 7TK 7TM 7TO 7U9 8FD C1K FR3 H94 M7N P64 RC3 7X8 5PM |
ID | FETCH-LOGICAL-c510t-7844fc4fafd30a0bd4abd9d2a58f618d0bf249e18cba303f9ae73ae0f12c5d113 |
ISSN | 0027-8424 0273-4117 1091-6490 |
IngestDate | Thu Aug 21 18:38:05 EDT 2025 Fri Sep 05 12:03:08 EDT 2025 Mon Jun 30 09:53:38 EDT 2025 Tue Jun 17 21:42:51 EDT 2025 Tue Jun 10 20:25:08 EDT 2025 Thu Apr 03 06:50:19 EDT 2025 Thu Apr 24 22:55:33 EDT 2025 Tue Jul 01 01:03:29 EDT 2025 Thu May 29 08:48:22 EDT 2025 |
IsDoiOpenAccess | true |
IsOpenAccess | true |
IsPeerReviewed | true |
IsScholarly | true |
Issue | 49 |
Keywords | brain aging gray matter volume cytokine clock cytokines aging |
Language | English |
License | This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY). |
LinkModel | OpenURL |
MergedId | FETCHMERGED-LOGICAL-c510t-7844fc4fafd30a0bd4abd9d2a58f618d0bf249e18cba303f9ae73ae0f12c5d113 |
Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Contributed by Judith Campisi; received April 28, 2022; accepted October 4, 2022; reviewed by John H. Morrison and Adriana Tomic |
ORCID | 0000-0002-7306-588X 0000-0003-1417-1161 0000-0002-3654-9519 |
OpenAccessLink | https://pubmed.ncbi.nlm.nih.gov/PMC9894212 |
PMID | 36459652 |
PQID | 2755156473 |
PQPubID | 42026 |
PageCount | 11 |
ParticipantIDs | pubmedcentral_primary_oai_pubmedcentral_nih_gov_9894212 proquest_miscellaneous_2746395956 proquest_journals_2755156473 gale_infotracmisc_A739412067 gale_infotracacademiconefile_A739412067 pubmed_primary_36459652 crossref_primary_10_1073_pnas_2207181119 crossref_citationtrail_10_1073_pnas_2207181119 jstor_primary_27209488 |
ProviderPackageCode | CITATION AAYXX |
PublicationCentury | 2000 |
PublicationDate | 2022-12-06 |
PublicationDateYYYYMMDD | 2022-12-06 |
PublicationDate_xml | – month: 12 year: 2022 text: 2022-12-06 day: 06 |
PublicationDecade | 2020 |
PublicationPlace | United States |
PublicationPlace_xml | – name: United States – name: Washington |
PublicationTitle | Nevada RNformation |
PublicationTitleAlternate | Proc Natl Acad Sci U S A |
PublicationYear | 2022 |
Publisher | National Academy of Sciences Nevada Nurses Association |
Publisher_xml | – name: National Academy of Sciences – name: Nevada Nurses Association |
References | e_1_3_4_3_2 e_1_3_4_1_2 e_1_3_4_61_2 e_1_3_4_9_2 e_1_3_4_63_2 e_1_3_4_7_2 e_1_3_4_40_2 e_1_3_4_5_2 e_1_3_4_80_2 e_1_3_4_23_2 e_1_3_4_69_2 e_1_3_4_21_2 e_1_3_4_42_2 e_1_3_4_27_2 e_1_3_4_48_2 e_1_3_4_65_2 e_1_3_4_25_2 e_1_3_4_46_2 e_1_3_4_67_2 e_1_3_4_29_2 Thompson B. (e_1_3_4_82_2) 1984 e_1_3_4_72_2 e_1_3_4_74_2 e_1_3_4_30_2 e_1_3_4_51_2 van Buuren S. (e_1_3_4_78_2) 2011; 45 Krubitzer L. (e_1_3_4_44_2) 2007; 56 e_1_3_4_70_2 e_1_3_4_11_2 e_1_3_4_34_2 e_1_3_4_57_2 e_1_3_4_55_2 e_1_3_4_32_2 e_1_3_4_59_2 e_1_3_4_53_2 e_1_3_4_15_2 e_1_3_4_38_2 e_1_3_4_76_2 e_1_3_4_13_2 e_1_3_4_36_2 e_1_3_4_19_2 e_1_3_4_17_2 e_1_3_4_2_2 e_1_3_4_60_2 e_1_3_4_62_2 e_1_3_4_8_2 e_1_3_4_41_2 e_1_3_4_6_2 e_1_3_4_81_2 e_1_3_4_4_2 e_1_3_4_22_2 e_1_3_4_45_2 e_1_3_4_20_2 e_1_3_4_43_2 e_1_3_4_26_2 e_1_3_4_49_2 e_1_3_4_64_2 e_1_3_4_24_2 e_1_3_4_47_2 e_1_3_4_66_2 e_1_3_4_28_2 Penny W. (e_1_3_4_68_2) 2007 e_1_3_4_71_2 e_1_3_4_73_2 e_1_3_4_52_2 e_1_3_4_50_2 e_1_3_4_79_2 e_1_3_4_12_2 e_1_3_4_33_2 e_1_3_4_58_2 e_1_3_4_54_2 e_1_3_4_10_2 e_1_3_4_31_2 e_1_3_4_75_2 e_1_3_4_16_2 e_1_3_4_37_2 e_1_3_4_77_2 e_1_3_4_14_2 e_1_3_4_35_2 e_1_3_4_56_2 e_1_3_4_18_2 e_1_3_4_39_2 |
References_xml | – ident: e_1_3_4_42_2 doi: 10.1111/j.1749-6632.2009.04421.x – ident: e_1_3_4_63_2 doi: 10.1038/mp.2015.131 – ident: e_1_3_4_65_2 – ident: e_1_3_4_48_2 doi: 10.1016/S0197-4580(01)00276-7 – ident: e_1_3_4_67_2 doi: 10.1109/42.668698 – ident: e_1_3_4_39_2 doi: 10.1016/j.tins.2004.07.011 – ident: e_1_3_4_45_2 doi: 10.1016/j.tics.2013.09.017 – ident: e_1_3_4_2_2 doi: 10.1186/gb-2013-14-10-r115 – ident: e_1_3_4_22_2 doi: 10.1111/acel.12271 – ident: e_1_3_4_15_2 doi: 10.1038/nature20411 – ident: e_1_3_4_8_2 doi: 10.1093/gerona/glu057 – ident: e_1_3_4_17_2 doi: 10.1001/archneur.60.7.989 – ident: e_1_3_4_27_2 doi: 10.1037/neu0000696 – ident: e_1_3_4_10_2 doi: 10.1016/j.neuron.2008.01.003 – ident: e_1_3_4_3_2 doi: 10.1038/s43587-021-00082-y – ident: e_1_3_4_19_2 doi: 10.1523/JNEUROSCI.3252-09.2009 – ident: e_1_3_4_75_2 doi: 10.1111/1440-1681.12758 – ident: e_1_3_4_40_2 doi: 10.1111/j.1749-6632.2009.05118.x – ident: e_1_3_4_23_2 doi: 10.1126/science.278.5337.412 – ident: e_1_3_4_43_2 doi: 10.1371/journal.pcbi.1000808 – ident: e_1_3_4_80_2 – ident: e_1_3_4_59_2 doi: 10.3727/000000007783464731 – ident: e_1_3_4_76_2 doi: 10.1016/j.jamda.2013.05.009 – ident: e_1_3_4_30_2 doi: 10.1212/WNL.0b013e318248e50f – ident: e_1_3_4_53_2 doi: 10.1177/1359105305055332 – ident: e_1_3_4_66_2 – ident: e_1_3_4_69_2 doi: 10.1006/nimg.1995.1012 – ident: e_1_3_4_79_2 doi: 10.18637/jss.v033.i01 – ident: e_1_3_4_6_2 doi: 10.1097/JNN.0b013e3182527690 – ident: e_1_3_4_16_2 doi: 10.1523/JNEUROSCI.23-08-03295.2003 – ident: e_1_3_4_21_2 doi: 10.1007/s10548-018-0675-2 – volume: 45 start-page: 1 year: 2011 ident: e_1_3_4_78_2 article-title: mice: Multivariate imputation by chained equations in R publication-title: J. Stat. Softw. – ident: e_1_3_4_11_2 doi: 10.1016/j.celrep.2020.03.012 – ident: e_1_3_4_28_2 doi: 10.1093/gerona/glab162 – ident: e_1_3_4_1_2 doi: 10.1016/j.cub.2012.07.024 – ident: e_1_3_4_70_2 doi: 10.1093/gerona/glz209 – ident: e_1_3_4_58_2 doi: 10.1038/s41591-019-0440-4 – ident: e_1_3_4_36_2 doi: 10.1136/pgmj.2005.036665 – ident: e_1_3_4_61_2 doi: 10.1212/WNL.0b013e318266fa70 – ident: e_1_3_4_14_2 doi: 10.1016/j.cmet.2018.05.011 – ident: e_1_3_4_33_2 doi: 10.3389/fnagi.2020.603854 – ident: e_1_3_4_72_2 doi: 10.1016/j.bbi.2020.05.014 – ident: e_1_3_4_25_2 doi: 10.1016/j.neuron.2007.10.038 – ident: e_1_3_4_38_2 doi: 10.1152/physrev.00004.2002 – ident: e_1_3_4_50_2 doi: 10.1007/s12035-014-8657-1 – ident: e_1_3_4_77_2 doi: 10.1093/nar/gkv007 – ident: e_1_3_4_24_2 doi: 10.1523/JNEUROSCI.3067-17.2018 – ident: e_1_3_4_60_2 doi: 10.1016/j.ajpath.2011.10.024 – ident: e_1_3_4_71_2 doi: 10.1016/j.jagp.2018.05.004 – ident: e_1_3_4_18_2 doi: 10.1371/journal.pone.0084093 – ident: e_1_3_4_57_2 doi: 10.4103/1673-5374.238608 – ident: e_1_3_4_5_2 doi: 10.1037/neu0000447 – volume-title: Statistical Parametric Mapping: The Analysis of Functional Brain Images year: 2007 ident: e_1_3_4_68_2 – ident: e_1_3_4_29_2 doi: 10.1016/j.neuroimage.2007.07.007 – ident: e_1_3_4_52_2 doi: 10.1016/j.neuroimage.2012.01.120 – ident: e_1_3_4_55_2 doi: 10.1093/bioinformatics/btp491 – ident: e_1_3_4_62_2 doi: 10.1093/gerona/glu121 – ident: e_1_3_4_74_2 doi: 10.1093/gerona/gly028 – ident: e_1_3_4_13_2 doi: 10.1038/nrn1886 – ident: e_1_3_4_9_2 doi: 10.1038/s41569-018-0064-2 – ident: e_1_3_4_7_2 doi: 10.1111/j.1749-6632.2000.tb06651.x – ident: e_1_3_4_46_2 doi: 10.1177/0891988708328216 – ident: e_1_3_4_20_2 doi: 10.1016/j.pneurobio.2014.02.004 – ident: e_1_3_4_51_2 doi: 10.1016/j.ynstr.2018.09.006 – ident: e_1_3_4_47_2 doi: 10.1038/nm1653 – volume: 56 start-page: 201 year: 2007 ident: e_1_3_4_44_2 article-title: Minireview the magnificent compromise: Cortical field evolution in mammals minireview publication-title: Brain – ident: e_1_3_4_49_2 doi: 10.1186/1742-2094-2-9 – ident: e_1_3_4_56_2 doi: 10.3389/fgene.2021.706986 – ident: e_1_3_4_34_2 doi: 10.1056/NEJM2003ra020003 – ident: e_1_3_4_41_2 doi: 10.1016/0197-4580(86)90086-2 – ident: e_1_3_4_26_2 doi: 10.1152/jn.00338.2011 – ident: e_1_3_4_35_2 doi: 10.1016/j.arr.2014.01.004 – ident: e_1_3_4_12_2 doi: 10.1038/s41583-019-0255-9 – ident: e_1_3_4_37_2 doi: 10.1038/s41380-018-0098-1 – ident: e_1_3_4_81_2 – ident: e_1_3_4_32_2 doi: 10.1016/j.neurobiolaging.2021.09.010 – ident: e_1_3_4_31_2 doi: 10.1016/j.neurobiolaging.2020.01.006 – ident: e_1_3_4_64_2 doi: 10.1016/j.tins.2022.08.001 – ident: e_1_3_4_4_2 doi: 10.1038/s41591-019-0675-0 – start-page: 71 volume-title: Canonical Correlation Analysis: Uses and Interpretation year: 1984 ident: e_1_3_4_82_2 doi: 10.4135/9781412983570 – ident: e_1_3_4_54_2 doi: 10.1002/hbm.25090 – ident: e_1_3_4_73_2 doi: 10.1016/j.cyto.2018.05.032 |
SSID | ssj0009580 ssj0014689 |
Score | 2.5152214 |
Snippet | Aging is characterized by a progressive loss of brain volume at an estimated rate of 5% per decade after age 40. While these morphometric changes, especially... Aging is characterized by a progressive loss of brain volume at an estimated rate of 5% per decade after age 40. While these morphometric changes, especially... The fields of immunology and neuroscience have evolved in isolation, partially justified by the view that the brain–blood barrier is impermeable, however, the... |
SourceID | pubmedcentral proquest gale pubmed crossref jstor |
SourceType | Open Access Repository Aggregation Database Index Database Enrichment Source Publisher |
StartPage | 1 |
SubjectTerms | Adult Age Aging Angiogenesis Atrophy Attention Biological Sciences Biomarkers Blood Brain Brain - diagnostic imaging Cell adhesion Cell adhesion molecules Cognitive ability Cognitive tasks CXCL10 protein Cytokines Eotaxin Growth factors Humans Inflammation Interleukin 6 IP-10 protein Machine learning Networks Physiological aspects Placenta Placenta growth factor Proteins Research Personnel Sex Social interactions Substantia grisea Temporal lobe Therapeutic targets Tumor necrosis factor-α Vascular cell adhesion molecule 1 Vascular endothelial growth factor Vascular Endothelial Growth Factor A Visual observation Visual perception |
Subtitle | Different functional brain networks associate differentially with aging and blood factors |
Title | Age-related brain atrophy is not a homogenous process |
URI | https://www.jstor.org/stable/27209488 https://www.ncbi.nlm.nih.gov/pubmed/36459652 https://www.proquest.com/docview/2755156473 https://www.proquest.com/docview/2746395956 https://pubmed.ncbi.nlm.nih.gov/PMC9894212 |
Volume | 119 |
hasFullText | 1 |
inHoldings | 1 |
isFullTextHit | |
isPrint | |
link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3bbtNAEF2VIqG-IAoUAqVaJCSKLAdf1l6bt6hcKlCrPKRS3qz1ZduoiRMlKRJ8BZ_KJzCzu147pSDoixVlL155jmdm1zNzCHlVMJmXscxdr_SFy0JRgB6MhJsHHpdBARuCHL_onpzGx2fs8zgab2397EQtXa3zfvH9xryS20gV_gO5Ypbsf0jWTgp_wG-QL1xBwnD9JxkPzitXJaOgG4lcDw6ebMODQ5ryer52hHMxn81NHdaFzgnAM4D3hhZl7aBdM8eBeoZax4WvHGEEV1kSFVAG0-k3kw533mQ3qtD3hren6-sOrW1cNZEIp83R46BNZDHaZeW4zvB00DkiX17Ov2qwXsL-2xn1bfDQpC4xLE1_HVJM287AtiIhsRRY8lfpvVLMnJN-awGW-sj8CzgEdffII1DsK17cRoT8Za1dXR-A_WU6Q7tfafUO3pEbM01QavW_0dka6LqA6m-GBTQhsiHXYtUPAMbgF5lhHZgtZgpn-GE3jXVd3mu1vIcnR1jwPkBa7LsB5yqw4NPY75SJTnTSlFl7U4yKh2-v3XuH3GtutOFSGcdCB9fetG26Hv3bcadGD8h9sw-iAw3qXbJV1Q_JbvN86aEph_7mEfnRQTlVGKUG5XSyooByKmiLcmpQ_o5ajNMW42Z8g3FqMU43MU4R41RhnALGqcI4NRh_TM4-fhgdHbuGR8QtwOKsXZ4wJkEpCVmGnvDykom8TMtARImM_aT0chmwtPKTIhfg0clUVDwUlSf9oIhK3w_3yHY9r6unhCI9RM6qJJXI7-X7wpepzJNKiiiQ4E73SL8RRVaYIvvI9TLNVLAHDzMUY9aKsUcO7YCFri_z566vUbYZog3mLIRJoIGVYQ23bMDDlPlIx9Aj-xs9wWIUG817Ch32hhiLkYIlh3ENXDKj5mABHDZVUcx42CMvbTNOieGZdQWihT4MdjpRGsU98kSjy07eoLRH-AbubAcscL_ZUk8uVKF78648u_XI52Sn1R_7ZHu9vKpewCZinR-QO3zMD9Tb9wuHqSXQ |
linkProvider | Geneva Foundation for Medical Education and Research |
openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Age-related+brain+atrophy+is+not+a+homogenous+process%3A+Different+functional+brain+networks+associate+differentially+with+aging+and+blood+factors&rft.jtitle=Proceedings+of+the+National+Academy+of+Sciences+-+PNAS&rft.au=Markov%2C+Nikola+T.&rft.au=Lindbergh%2C+Cutter+A.&rft.au=Staffaroni%2C+Adam+M.&rft.au=Perez%2C+Kevin&rft.date=2022-12-06&rft.pub=National+Academy+of+Sciences&rft.issn=0027-8424&rft.eissn=1091-6490&rft.volume=119&rft.issue=49&rft_id=info:doi/10.1073%2Fpnas.2207181119&rft_id=info%3Apmid%2F36459652&rft.externalDocID=PMC9894212 |
thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0027-8424&client=summon |
thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0027-8424&client=summon |
thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0027-8424&client=summon |