Age-related magnetic susceptibility changes in deep grey matter and cerebral cortex of normal young and middle-aged adults depicted by whole brain analysis
Iron accumulates in brain tissue in healthy subjects during aging. Our goal was to conduct a detailed analysis of iron deposition patterns in the cerebral deep grey matter and cortex using region-based and whole-brain analyses of brain magnetic susceptibility. Brain MRI was performed in 95 healthy i...
Saved in:
Published in | Quantitative imaging in medicine and surgery Vol. 11; no. 9; pp. 3906 - 3919 |
---|---|
Main Authors | , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
China
AME Publishing Company
01.09.2021
|
Subjects | |
Online Access | Get full text |
ISSN | 2223-4292 2223-4306 |
DOI | 10.21037/qims-21-87 |
Cover
Abstract | Iron accumulates in brain tissue in healthy subjects during aging. Our goal was to conduct a detailed analysis of iron deposition patterns in the cerebral deep grey matter and cortex using region-based and whole-brain analyses of brain magnetic susceptibility.
Brain MRI was performed in 95 healthy individuals aged between 21 and 58 years on a 3T scanner. MRI protocol included T1-weighted (T1W) magnetization-prepared rapid acquisition with gradient echo images and 3D flow-compensated multi-echo gradient-echo images for quantitative susceptibility mapping (QSM). In the region-based analysis, QSM and T1W images entered an automated multi-atlas segmentation pipeline and regional mean bulk susceptibility values were calculated. The whole-brain analysis included a non-linear transformation of QSM images to the standard MNI template. For the whole-brain analysis voxel-wise maps of linear regression slopes β and P values were calculated. Regional masks of cortical voxels with a significant association between susceptibility and age were created and further analyzed.
In cortical regions, the highest increase of susceptibility values with age was found in areas involved in motor functions (precentral and postcentral areas, premotor cortex), in cognitive processing (prefrontal cortex, superior temporal gyrus, insula, precuneus), and visual processing (occipital gyri, cuneus, posterior cingulum, fusiform, calcarine and lingual gyrus). Thalamic susceptibility increased until the fourth decade and decreased thereafter with the exception of the pulvinar where susceptibility increase was observed throughout the adult lifespan. Deep grey matter structures with the highest increase of susceptibility values with age included the red nucleus, putamen, substantia nigra, dentate nucleus, external globus pallidus, caudate nucleus, and the subthalamic nucleus in decreasing order.
Accumulation of iron in basal ganglia follows a linear pattern whereas in the thalamus, pulvinar, precentral cortex, and precuneus, it follows a quadratic or exponential pattern. Age-related changes of iron content are different in the pulvinar and the rest of the thalamus as well as in internal and external globus pallidus. In the cortex, areas involved in motor and cognitive functions and visual processing show the highest iron increase with aging. We suggest that the departure from normal patterns of regional brain iron trajectories during aging may be helpful in the detection of subtle neurodegenerative and neuroinflammatory processes. |
---|---|
AbstractList | Iron accumulates in brain tissue in healthy subjects during aging. Our goal was to conduct a detailed analysis of iron deposition patterns in the cerebral deep grey matter and cortex using region-based and whole-brain analyses of brain magnetic susceptibility.BACKGROUNDIron accumulates in brain tissue in healthy subjects during aging. Our goal was to conduct a detailed analysis of iron deposition patterns in the cerebral deep grey matter and cortex using region-based and whole-brain analyses of brain magnetic susceptibility.Brain MRI was performed in 95 healthy individuals aged between 21 and 58 years on a 3T scanner. MRI protocol included T1-weighted (T1W) magnetization-prepared rapid acquisition with gradient echo images and 3D flow-compensated multi-echo gradient-echo images for quantitative susceptibility mapping (QSM). In the region-based analysis, QSM and T1W images entered an automated multi-atlas segmentation pipeline and regional mean bulk susceptibility values were calculated. The whole-brain analysis included a non-linear transformation of QSM images to the standard MNI template. For the whole-brain analysis voxel-wise maps of linear regression slopes β and P values were calculated. Regional masks of cortical voxels with a significant association between susceptibility and age were created and further analyzed.METHODSBrain MRI was performed in 95 healthy individuals aged between 21 and 58 years on a 3T scanner. MRI protocol included T1-weighted (T1W) magnetization-prepared rapid acquisition with gradient echo images and 3D flow-compensated multi-echo gradient-echo images for quantitative susceptibility mapping (QSM). In the region-based analysis, QSM and T1W images entered an automated multi-atlas segmentation pipeline and regional mean bulk susceptibility values were calculated. The whole-brain analysis included a non-linear transformation of QSM images to the standard MNI template. For the whole-brain analysis voxel-wise maps of linear regression slopes β and P values were calculated. Regional masks of cortical voxels with a significant association between susceptibility and age were created and further analyzed.In cortical regions, the highest increase of susceptibility values with age was found in areas involved in motor functions (precentral and postcentral areas, premotor cortex), in cognitive processing (prefrontal cortex, superior temporal gyrus, insula, precuneus), and visual processing (occipital gyri, cuneus, posterior cingulum, fusiform, calcarine and lingual gyrus). Thalamic susceptibility increased until the fourth decade and decreased thereafter with the exception of the pulvinar where susceptibility increase was observed throughout the adult lifespan. Deep grey matter structures with the highest increase of susceptibility values with age included the red nucleus, putamen, substantia nigra, dentate nucleus, external globus pallidus, caudate nucleus, and the subthalamic nucleus in decreasing order.RESULTSIn cortical regions, the highest increase of susceptibility values with age was found in areas involved in motor functions (precentral and postcentral areas, premotor cortex), in cognitive processing (prefrontal cortex, superior temporal gyrus, insula, precuneus), and visual processing (occipital gyri, cuneus, posterior cingulum, fusiform, calcarine and lingual gyrus). Thalamic susceptibility increased until the fourth decade and decreased thereafter with the exception of the pulvinar where susceptibility increase was observed throughout the adult lifespan. Deep grey matter structures with the highest increase of susceptibility values with age included the red nucleus, putamen, substantia nigra, dentate nucleus, external globus pallidus, caudate nucleus, and the subthalamic nucleus in decreasing order.Accumulation of iron in basal ganglia follows a linear pattern whereas in the thalamus, pulvinar, precentral cortex, and precuneus, it follows a quadratic or exponential pattern. Age-related changes of iron content are different in the pulvinar and the rest of the thalamus as well as in internal and external globus pallidus. In the cortex, areas involved in motor and cognitive functions and visual processing show the highest iron increase with aging. We suggest that the departure from normal patterns of regional brain iron trajectories during aging may be helpful in the detection of subtle neurodegenerative and neuroinflammatory processes.CONCLUSIONSAccumulation of iron in basal ganglia follows a linear pattern whereas in the thalamus, pulvinar, precentral cortex, and precuneus, it follows a quadratic or exponential pattern. Age-related changes of iron content are different in the pulvinar and the rest of the thalamus as well as in internal and external globus pallidus. In the cortex, areas involved in motor and cognitive functions and visual processing show the highest iron increase with aging. We suggest that the departure from normal patterns of regional brain iron trajectories during aging may be helpful in the detection of subtle neurodegenerative and neuroinflammatory processes. Iron accumulates in brain tissue in healthy subjects during aging. Our goal was to conduct a detailed analysis of iron deposition patterns in the cerebral deep grey matter and cortex using region-based and whole-brain analyses of brain magnetic susceptibility. Brain MRI was performed in 95 healthy individuals aged between 21 and 58 years on a 3T scanner. MRI protocol included T1-weighted (T1W) magnetization-prepared rapid acquisition with gradient echo images and 3D flow-compensated multi-echo gradient-echo images for quantitative susceptibility mapping (QSM). In the region-based analysis, QSM and T1W images entered an automated multi-atlas segmentation pipeline and regional mean bulk susceptibility values were calculated. The whole-brain analysis included a non-linear transformation of QSM images to the standard MNI template. For the whole-brain analysis voxel-wise maps of linear regression slopes β and P values were calculated. Regional masks of cortical voxels with a significant association between susceptibility and age were created and further analyzed. In cortical regions, the highest increase of susceptibility values with age was found in areas involved in motor functions (precentral and postcentral areas, premotor cortex), in cognitive processing (prefrontal cortex, superior temporal gyrus, insula, precuneus), and visual processing (occipital gyri, cuneus, posterior cingulum, fusiform, calcarine and lingual gyrus). Thalamic susceptibility increased until the fourth decade and decreased thereafter with the exception of the pulvinar where susceptibility increase was observed throughout the adult lifespan. Deep grey matter structures with the highest increase of susceptibility values with age included the red nucleus, putamen, substantia nigra, dentate nucleus, external globus pallidus, caudate nucleus, and the subthalamic nucleus in decreasing order. Accumulation of iron in basal ganglia follows a linear pattern whereas in the thalamus, pulvinar, precentral cortex, and precuneus, it follows a quadratic or exponential pattern. Age-related changes of iron content are different in the pulvinar and the rest of the thalamus as well as in internal and external globus pallidus. In the cortex, areas involved in motor and cognitive functions and visual processing show the highest iron increase with aging. We suggest that the departure from normal patterns of regional brain iron trajectories during aging may be helpful in the detection of subtle neurodegenerative and neuroinflammatory processes. |
Author | Dusek, Petr Burgetova, Andrea Vaneckova, Manuela Pudlac, Adam Lambert, Lukas Burgetova, Romana Horakova, Dana Krasensky, Jan Varga, Zsoka |
Author_xml | – sequence: 1 givenname: Romana surname: Burgetova fullname: Burgetova, Romana – sequence: 2 givenname: Petr surname: Dusek fullname: Dusek, Petr – sequence: 3 givenname: Andrea surname: Burgetova fullname: Burgetova, Andrea – sequence: 4 givenname: Adam surname: Pudlac fullname: Pudlac, Adam – sequence: 5 givenname: Manuela surname: Vaneckova fullname: Vaneckova, Manuela – sequence: 6 givenname: Dana surname: Horakova fullname: Horakova, Dana – sequence: 7 givenname: Jan surname: Krasensky fullname: Krasensky, Jan – sequence: 8 givenname: Zsoka surname: Varga fullname: Varga, Zsoka – sequence: 9 givenname: Lukas surname: Lambert fullname: Lambert, Lukas |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/34476177$$D View this record in MEDLINE/PubMed |
BookMark | eNptkUtvFiEUhompsbV25d6wNGlGucwMMxuTpvGWNHGja8LAYT4MA1-BUee3-GfL11vUyIYTeM77wnmfo6MQAyD0kpI3jBIu3l67JTeMNoN4gk4YY7xpOemPHmo2smN0lvN3UpcYqKDkGTrmbSt6KsQJ-n0xQ5PAqwIGL2oOUJzGec0a9sVNzruyYb1TYYaMXcAGYI_nBFuFS4GEVTBYQ4IpKY91TAV-4WhxiGmpB1tcw3zLLM4YD42aq48yqy-5au2dPvhOG_65ix5wFakeKii_ZZdfoKdW-Qxn9_sp-vbh_dfLT83Vl4-fLy-uGs0HWhoztMQoSykxbdfrXlve9dQyMLrn3E5iNBR6zuzITTcQRSbSWQt1PN3UCab4KXp3p7tfp6V2QSj1M3Kf3KLSJqNy8u-b4HZyjj_kwPnYd2MVeH0vkOL1CrnIxdUBeq8CxDVL1vUjFy3rhoq--tPr0eQhkQqc3wE6xZwT2EeEEnkbuTxEXks5HGj6D61dUcXFw0Od_2_PDZIWs_I |
CitedBy_id | crossref_primary_10_3390_ijms242216353 crossref_primary_10_1162_imag_a_00304 crossref_primary_10_1002_jmri_29698 crossref_primary_10_3390_biom12050714 crossref_primary_10_3390_ijms231710018 crossref_primary_10_1016_j_neuroimage_2022_119788 crossref_primary_10_1002_jor_25785 crossref_primary_10_1016_j_neuroimage_2023_119923 crossref_primary_10_1002_jmri_29419 crossref_primary_10_3390_ijms241210048 crossref_primary_10_3389_fnimg_2024_1359630 crossref_primary_10_1002_mds_29702 crossref_primary_10_1111_ejn_16282 crossref_primary_10_1002_agm2_12363 crossref_primary_10_3390_diagnostics12061365 crossref_primary_10_1016_j_jns_2023_120816 crossref_primary_10_1016_j_sleep_2024_09_019 crossref_primary_10_1002_hbm_26178 crossref_primary_10_1080_07853890_2025_2472871 crossref_primary_10_5498_wjp_v14_i7_1106 crossref_primary_10_1186_s13195_024_01575_9 crossref_primary_10_3390_ijms242115721 crossref_primary_10_3389_fpubh_2024_1447290 crossref_primary_10_1162_imag_a_00456 crossref_primary_10_1212_WNL_0000000000209478 |
ContentType | Journal Article |
Copyright | 2021 Quantitative Imaging in Medicine and Surgery. All rights reserved. 2021 Quantitative Imaging in Medicine and Surgery. All rights reserved. 2021 Quantitative Imaging in Medicine and Surgery. |
Copyright_xml | – notice: 2021 Quantitative Imaging in Medicine and Surgery. All rights reserved. – notice: 2021 Quantitative Imaging in Medicine and Surgery. All rights reserved. 2021 Quantitative Imaging in Medicine and Surgery. |
DBID | AAYXX CITATION NPM 7X8 5PM |
DOI | 10.21037/qims-21-87 |
DatabaseName | CrossRef PubMed MEDLINE - Academic PubMed Central (Full Participant titles) |
DatabaseTitle | CrossRef PubMed MEDLINE - Academic |
DatabaseTitleList | MEDLINE - Academic PubMed |
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 |
DeliveryMethod | fulltext_linktorsrc |
Discipline | Medicine |
EISSN | 2223-4306 |
EndPage | 3919 |
ExternalDocumentID | PMC8339659 34476177 10_21037_qims_21_87 |
Genre | Journal Article |
GroupedDBID | 53G AAKDD AAYXX ALMA_UNASSIGNED_HOLDINGS CITATION DIK HYE OK1 RPM M~E NPM 7X8 5PM |
ID | FETCH-LOGICAL-c381t-d840daf110d456c6cf3561f2edc633fb79d1e632f93d580a0b05ffe2235b572a3 |
ISSN | 2223-4292 |
IngestDate | Thu Aug 21 14:35:17 EDT 2025 Fri Sep 05 13:22:09 EDT 2025 Thu Jan 02 22:55:28 EST 2025 Tue Jul 01 02:30:34 EDT 2025 Thu Apr 24 23:10:15 EDT 2025 |
IsDoiOpenAccess | false |
IsOpenAccess | true |
IsPeerReviewed | true |
IsScholarly | true |
Issue | 9 |
Keywords | deep grey matter cerebral cortex iron aging brain Magnetic susceptibility |
Language | English |
License | 2021 Quantitative Imaging in Medicine and Surgery. All rights reserved. |
LinkModel | OpenURL |
MergedId | FETCHMERGED-LOGICAL-c381t-d840daf110d456c6cf3561f2edc633fb79d1e632f93d580a0b05ffe2235b572a3 |
Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Contributions: (I) Conception and design: P Dusek, A Burgetova, L Lambert; (II) Administrative support: A Burgetova, L Lambert, A Pudlac; (III) Provision of study materials or patients: D Horakova, Z Varga, P Dusek, M Vaneckova, J Krasensky; (IV) Collection and assembly of data: R Burgetova, M Vaneckova, P Dusek, J Krasensky, D Horakova; (V) Data analysis and interpretation: P Dusek, L Lambert, R Burgetova, A Burgetova; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors. ORCID: 0000-0002-9975-2338. |
OpenAccessLink | https://qims.amegroups.com/article/viewFile/69960/pdf |
PMID | 34476177 |
PQID | 2569374258 |
PQPubID | 23479 |
PageCount | 14 |
ParticipantIDs | pubmedcentral_primary_oai_pubmedcentral_nih_gov_8339659 proquest_miscellaneous_2569374258 pubmed_primary_34476177 crossref_primary_10_21037_qims_21_87 crossref_citationtrail_10_21037_qims_21_87 |
ProviderPackageCode | CITATION AAYXX |
PublicationCentury | 2000 |
PublicationDate | 2021-9-00 2021-Sep 20210901 |
PublicationDateYYYYMMDD | 2021-09-01 |
PublicationDate_xml | – month: 09 year: 2021 text: 2021-9-00 |
PublicationDecade | 2020 |
PublicationPlace | China |
PublicationPlace_xml | – name: China |
PublicationTitle | Quantitative imaging in medicine and surgery |
PublicationTitleAlternate | Quant Imaging Med Surg |
PublicationYear | 2021 |
Publisher | AME Publishing Company |
Publisher_xml | – name: AME Publishing Company |
SSID | ssj0000781710 |
Score | 2.3230221 |
Snippet | Iron accumulates in brain tissue in healthy subjects during aging. Our goal was to conduct a detailed analysis of iron deposition patterns in the cerebral deep... |
SourceID | pubmedcentral proquest pubmed crossref |
SourceType | Open Access Repository Aggregation Database Index Database Enrichment Source |
StartPage | 3906 |
SubjectTerms | Original |
Title | Age-related magnetic susceptibility changes in deep grey matter and cerebral cortex of normal young and middle-aged adults depicted by whole brain analysis |
URI | https://www.ncbi.nlm.nih.gov/pubmed/34476177 https://www.proquest.com/docview/2569374258 https://pubmed.ncbi.nlm.nih.gov/PMC8339659 |
Volume | 11 |
hasFullText | 1 |
inHoldings | 1 |
isFullTextHit | |
isPrint | |
link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1ti9NAEF7qCcd9Ed_t-cIK98kSTfO2ycciyiGcKNzBfQubZFOLTVqb5LT-Ff-Fv9CZ2WyaeBXULyE0w27JPNl5NnlmhrET8KqDaLBUGPiWl0TwzOXT1IpsJTBcOcLG3OGz98Hphffu0r8cjX72VEtNnbxMv-_NK_kfr8Jv4FfMkv0Hz3aDwg9wDv6FI3gYjn_l49lcWZSMAqyxkPMSExInVVORVIVUr9s2s7dVvar1BPbXWzDGJB6d06Y2-O0Yq4RsavWNhB3IY5eTLS4EZFPQWwwLlp5MF-yoUEG7SGtNX79ij91Jgs0mwFwXOemT3o-NLCmXDVVKi0L3RQJb812f5qh6-dk6vWKu6tWV5rYrFNnuOHelPrfa4s1ee1JpdvYfmmwpqZH7LJNF_zWHM-10XO1qiDzGwt5ag6V72oNo1FuH3YjqGFwLEE5bY-DLoqgsmEQH-x5U1gVhBesgArcTuyjZaRfNpRvspiMESQPMGyKK_iKcCiqC0f1jnRZKU7_aTXzEDs1QQ050baPzu163R4DOb7Nb7c6FzzQM77CRKu-yw7PWh_fYjx4auUEjH6KRt2jki5IjGjmikWs0ckABN2jkGo18lXONRk5oJJseGrlGIzdo5MmWExo5oZEbNN5nF2_fnL8-tdrWH1YKFLK2stCzM5kDN82A4adBmrtA9HMH7kHgunkiomyqAtfJIzfzQ1vaie3nuYI77ie-cKT7gB2Uq1I9YtxLJVCw0FOuLT1f5BLCf6RSYWc-lmsUY_bC3Pw4beviY3uWZQz7Y3JajE6D0zgE45POeK3Lwew3e268GMNyjd_gZKlWDVz2A9gQQKAMx-yh9mo3kIHDmImBvzsDLAU_vFIuPlFJ-NB1sTLo8R_HfMyOdk_VE3ZQbxr1FOh0nTwj_P4Cm3TT0w |
linkProvider | National Library of Medicine |
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+magnetic+susceptibility+changes+in+deep+grey+matter+and+cerebral+cortex+of+normal+young+and+middle-aged+adults+depicted+by+whole+brain+analysis&rft.jtitle=Quantitative+imaging+in+medicine+and+surgery&rft.au=Burgetova%2C+Romana&rft.au=Dusek%2C+Petr&rft.au=Burgetova%2C+Andrea&rft.au=Pudlac%2C+Adam&rft.date=2021-09-01&rft.issn=2223-4292&rft.volume=11&rft.issue=9&rft.spage=3906&rft_id=info:doi/10.21037%2Fqims-21-87&rft_id=info%3Apmid%2F34476177&rft.externalDocID=34476177 |
thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2223-4292&client=summon |
thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2223-4292&client=summon |
thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2223-4292&client=summon |