Gast ::
| Hauptseite > Publikationsdatenbank > Systematic cross-sectional age-associations in global fMRI signal topography > print |
| Hauptseite > Publikationsdatenbank > Systematic cross-sectional age-associations in global fMRI signal topography > print |
| 001 | 1030932 | ||
| 005 | 20250912110145.0 | ||
| 024 | 7 | _ | |a 10.1162/imag_a_00101 |2 doi |
| 024 | 7 | _ | |a 10.34734/FZJ-2024-05524 |2 datacite_doi |
| 024 | 7 | _ | |a 40800417 |2 pmid |
| 024 | 7 | _ | |a WOS:001531547100003 |2 WOS |
| 037 | _ | _ | |a FZJ-2024-05524 |
| 082 | _ | _ | |a 050 |
| 100 | 1 | _ | |a Nomi, Jason S. |0 P:(DE-HGF)0 |b 0 |e Corresponding author |
| 245 | _ | _ | |a Systematic cross-sectional age-associations in global fMRI signal topography |
| 260 | _ | _ | |a Cambridge, MA |c 2024 |b MIT Press |
| 336 | 7 | _ | |a article |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Journal article |2 DataCite |
| 336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1737100648_6866 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a JOURNAL_ARTICLE |2 ORCID |
| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 520 | _ | _ | |a The global signal (GS) in resting-state functional MRI (fMRI), known to contain artifacts and non-neuronal physiological signals, also contains important neural information related to individual state and trait characteristics. Here, we show distinct linear and curvilinear relationships between GS topography and age in a cross-sectional sample of individuals (6-85 years old) representing a significant portion of the lifespan. Subcortical brain regions such as the thalamus and putamen show linear associations with the GS across age. The thalamus has stronger contributions to the GS in older-age individuals compared with younger-aged individuals, while the putamen has stronger contributions in younger individuals compared with older individuals. The subcortical nucleus basalis of Meynert shows a u-shaped pattern similar to cortical regions within the lateral frontoparietal network and dorsal attention network, where contributions of the GS are stronger at early and old age, and weaker in middle age. This differentiation between subcortical and cortical brain activity across age supports a dual-layer model of GS composition, where subcortical aspects of the GS are differentiated from cortical aspects of the GS. We find that these subcortical-cortical contributions to the GS depend strongly on age across the lifespan of human development. Our findings demonstrate how neurobiological information within the GS differs across development and highlight the need to carefully consider whether or not to remove this signal when investigating age-related functional differences in the brain. |
| 536 | _ | _ | |a 5251 - Multilevel Brain Organization and Variability (POF4-525) |0 G:(DE-HGF)POF4-5251 |c POF4-525 |f POF IV |x 0 |
| 536 | _ | _ | |a 5252 - Brain Dysfunction and Plasticity (POF4-525) |0 G:(DE-HGF)POF4-5252 |c POF4-525 |f POF IV |x 1 |
| 588 | _ | _ | |a Dataset connected to CrossRef, Journals: juser.fz-juelich.de |
| 700 | 1 | _ | |a Bzdok, Danilo |0 P:(DE-HGF)0 |b 1 |
| 700 | 1 | _ | |a Li, Jingwei |0 P:(DE-Juel1)164828 |b 2 |u fzj |
| 700 | 1 | _ | |a Bolt, Taylor |0 P:(DE-HGF)0 |b 3 |
| 700 | 1 | _ | |a Chang, Catie |0 P:(DE-HGF)0 |b 4 |
| 700 | 1 | _ | |a Kornfeld, Salome |0 P:(DE-HGF)0 |b 5 |
| 700 | 1 | _ | |a Goodman, Zachary T. |0 P:(DE-HGF)0 |b 6 |
| 700 | 1 | _ | |a Yeo, B. T. Thomas |0 P:(DE-HGF)0 |b 7 |
| 700 | 1 | _ | |a Spreng, R. Nathan |0 P:(DE-HGF)0 |b 8 |
| 700 | 1 | _ | |a Uddin, Lucina Q. |0 P:(DE-HGF)0 |b 9 |
| 773 | _ | _ | |a 10.1162/imag_a_00101 |g Vol. 2, p. 1 - 13 |0 PERI:(DE-600)3167925-0 |p 1 - 13 |t Imaging neuroscience |v 2 |y 2024 |x 2837-6056 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/1030932/files/imag_a_00101.pdf |y OpenAccess |
| 909 | C | O | |o oai:juser.fz-juelich.de:1030932 |p openaire |p open_access |p VDB |p driver |p dnbdelivery |
| 910 | 1 | _ | |a Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA, United States †Corresponding Author: Jason S. Nomi (jnomi@mednet.ucla.edu) |0 I:(DE-HGF)0 |b 0 |6 P:(DE-HGF)0 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 2 |6 P:(DE-Juel1)164828 |
| 910 | 1 | _ | |a HHU Düsseldorf |0 I:(DE-HGF)0 |b 2 |6 P:(DE-Juel1)164828 |
| 913 | 1 | _ | |a DE-HGF |b Key Technologies |l Natural, Artificial and Cognitive Information Processing |1 G:(DE-HGF)POF4-520 |0 G:(DE-HGF)POF4-525 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-500 |4 G:(DE-HGF)POF |v Decoding Brain Organization and Dysfunction |9 G:(DE-HGF)POF4-5251 |x 0 |
| 913 | 1 | _ | |a DE-HGF |b Key Technologies |l Natural, Artificial and Cognitive Information Processing |1 G:(DE-HGF)POF4-520 |0 G:(DE-HGF)POF4-525 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-500 |4 G:(DE-HGF)POF |v Decoding Brain Organization and Dysfunction |9 G:(DE-HGF)POF4-5252 |x 1 |
| 914 | 1 | _ | |y 2024 |
| 915 | _ | _ | |a OpenAccess |0 StatID:(DE-HGF)0510 |2 StatID |
| 915 | _ | _ | |a Creative Commons Attribution CC BY 4.0 |0 LIC:(DE-HGF)CCBY4 |2 HGFVOC |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0300 |2 StatID |b Medline |d 2025-01-02 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0501 |2 StatID |b DOAJ Seal |d 2024-09-26T09:40:26Z |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0500 |2 StatID |b DOAJ |d 2024-09-26T09:40:26Z |
| 915 | _ | _ | |a Peer Review |0 StatID:(DE-HGF)0030 |2 StatID |b DOAJ : Anonymous peer review |d 2024-09-26T09:40:26Z |
| 920 | 1 | _ | |0 I:(DE-Juel1)INM-7-20090406 |k INM-7 |l Gehirn & Verhalten |x 0 |
| 980 | 1 | _ | |a FullTexts |
| 980 | _ | _ | |a journal |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a UNRESTRICTED |
| 980 | _ | _ | |a I:(DE-Juel1)INM-7-20090406 |
| Library | Collection | CLSMajor | CLSMinor | Language | Author |
|---|