umu.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Longitudinal evidence for diminished frontal cortex function in aging
Umeå University, Faculty of Medicine, Department of Radiation Sciences, Diagnostic Radiology. Umeå University, Faculty of Medicine, Department of Integrative Medical Biology (IMB), Physiology. Umeå University, Faculty of Medicine, Umeå Centre for Functional Brain Imaging (UFBI).
Umeå University, Faculty of Medicine, Department of Integrative Medical Biology (IMB), Physiology. Umeå University, Faculty of Medicine, Umeå Centre for Functional Brain Imaging (UFBI).
Umeå University, Faculty of Medicine, Department of Integrative Medical Biology (IMB), Physiology. Umeå University, Faculty of Medicine, Umeå Centre for Functional Brain Imaging (UFBI).
Umeå University, Faculty of Medicine, Department of Integrative Medical Biology (IMB), Physiology. Umeå University, Faculty of Medicine, Umeå Centre for Functional Brain Imaging (UFBI).
Show others and affiliations
2010 (English)In: Proceedings of the National Academy of Sciences of the United States of America, ISSN 0027-8424, E-ISSN 1091-6490, Vol. 107, no 52, 22682-22686 p.Article in journal (Refereed) Published
Description
Abstract [en]

Cross-sectional estimates of age-related changes in brain structure and function were compared with 6-y longitudinal estimates. The results indicated increased sensitivity of the longitudinal approach as well as qualitative differences. Critically, the cross-sectional analyses were suggestive of age-related frontal overrecruitment, whereas the longitudinal analyses revealed frontal underrecruitment with advancing age. The cross-sectional observation of overrecruitment reflected a select elderly sample. However, when followed over time, this sample showed reduced frontal recruitment. These findings dispute inferences of true age changes on the basis of age differences, hence challenging some contemporary models of neurocognitive aging, and demonstrate age-related decline in frontal brain volume as well as functional response.

Place, publisher, year, edition, pages
2010. Vol. 107, no 52, 22682-22686 p.
Keyword [en]
attrition, frontal lobe, multimodal, reorganization
National Category
Physiology Neurosciences
Identifiers
URN: urn:nbn:se:umu:diva-38932DOI: 10.1073/pnas.1012651108ISI: 000285684200062PubMedID: 21156826OAI: oai:DiVA.org:umu-38932DiVA: diva2:385191
Available from: 2011-01-11 Created: 2011-01-11 Last updated: 2017-12-11Bibliographically approved
In thesis
1. Decoding the complex brain: multivariate and multimodal analyses of neuroimaging data
Open this publication in new window or tab >>Decoding the complex brain: multivariate and multimodal analyses of neuroimaging data
2012 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Functional brain images are extraordinarily rich data sets that reveal distributed brain networks engaged in a wide variety of cognitive operations. It is a substantial challenge both to create models of cognition that mimic behavior and underlying cognitive processes and to choose a suitable analytic method to identify underlying brain networks.

Most of the contemporary techniques used in analyses of functional neuroimaging data are based on univariate approaches in which single image elements (i.e. voxels) are considered to be computationally independent measures. Beyond univariate methods (e.g. statistical parametric mapping), multivariate approaches, which identify a network across all regions of the brain rather than a tessellation of regions, are potentially well suited for analyses of brain imaging data. A multivariate method (e.g. partial least squares) is a computational strategy that determines time-varying distributed patterns of the brain (as a function of a cognitive task). Compared to its univariate counterparts, a multivariate approach provides greater levels of sensitivity and reflects cooperative interactions among brain regions. Thus, by considering information across more than one measuring point, additional information on brain function can be revealed.

Similarly, by considering information across more than one measuring technique, the nature of underlying cognitive processes become well-understood. Cognitive processes have been investigated in conjunction with multiple neuroimaging modalities (e.g. fMRI, sMRI, EEG, DTI), whereas the typical method has been to analyze each modality separately. Accordingly, little work has been carried out to examine the relation between different modalities. Indeed, due to the interconnected nature of brain processing, it is plausible that changes in one modality locally or distally modulate changes in another modality.

This thesis focuses on multivariate and multimodal methods of image analysis applied to various cognitive questions. These methods are used in order to extract features that are inaccessible using univariate / unimodal analytic approaches. To this end, I implemented multivariate partial least squares analysis in study I and II in order to identify neural commonalities and differences between the available and accessible information in memory (study I), and also between episodic encoding and episodic retrieval (study II). Study I provided evidence of a qualitative differences between availability and accessibility signals in memory by linking memory access to modality-independent brain regions, and availability in memory to elevated activity in modality-specific brain regions. Study II provided evidence in support of general and specific memory operations during encoding and retrieval by linking general processes to the joint demands on attentional, executive, and strategic processing, and a process-specific network to core episodic memory function. In study II, III, and IV, I explored whether the age-related changes/differences in one modality were driven by age-related changes/differences in another modality. To this end, study II investigated whether age-related functional differences in hippocampus during an episodic memory task could be accounted for by age-related structural differences. I found that age-related local structural deterioration could partially but not entirely account for age-related diminished hippocampal activation. In study III, I sought to explore whether age-related changes in the prefrontal and occipital cortex during a semantic memory task were driven by local and/or distal gray matter loss. I found that age-related diminished prefrontal activation was driven, at least in part, by local gray matter atrophy, whereas the age-related decline in occipital cortex was accounted for by distal gray matter atrophy. Finally, in study IV, I investigated whether white matter (WM) microstructural differences mediated age-related decline in different cognitive domains. The findings implicated WM as one source of age-related decline on tasks measuring processing speed, but they did not support the view that age-related differences in episodic memory, visuospatial ability, or fluency were strongly driven by age-related differences in white-matter pathways.

Taken together, the architecture of different aspects of episodic memory (e.g. encoding vs. retrieval; availability vs. accessibility) was characterized using a multivariate partial least squares. This finding highlights usefulness of multivariate techniques in guiding cognitive theories of episodic memory. Additionally, competing theories of cognitive aging were investigated by multimodal integration of age-related changes in brain structure, function, and behavior. The structure-function relationships were specific to brain regions and cognitive domains. Finally, we urged that contemporary theories on cognitive aging need to be extended to longitudinal measures to be further validated.

Place, publisher, year, edition, pages
Umeå: Umeå University, 2012. 94 p.
Series
Umeå University medical dissertations, ISSN 0346-6612 ; 1479
Keyword
multivariate analysis, univariate analysis, multimodal imaging, episodic memory, aging, functional magnetic resonance imaging, diffusion tensor imaging
National Category
Neurosciences
Research subject
biology; Psychology; Computerized Image Analysis
Identifiers
urn:nbn:se:umu:diva-51842 (URN)978-91-7459-362-4 (ISBN)
Public defence
2012-02-24, BiA 201, Biologihuset, Umeå, 09:00 (English)
Opponent
Supervisors
Available from: 2012-02-03 Created: 2012-02-03 Last updated: 2012-10-19Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full textPubMed

Search in DiVA

By author/editor
Nyberg, LarsSalami, AlirezaAndersson, MikaelEriksson, JohanKalpouzos, GrégoriaKauppi, KarolinaPudas, Sara
By organisation
Diagnostic RadiologyPhysiologyUmeå Centre for Functional Brain Imaging (UFBI)Department of Integrative Medical Biology (IMB)
In the same journal
Proceedings of the National Academy of Sciences of the United States of America
PhysiologyNeurosciences

Search outside of DiVA

GoogleGoogle Scholar

doi
pubmed
urn-nbn

Altmetric score

doi
pubmed
urn-nbn
Total: 146 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf