GLORIA

GEOMAR Library Ocean Research Information Access

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
  • 1
    In: Journal of Cognitive Neuroscience, MIT Press, Vol. 33, No. 9 ( 2021-08-01), p. 1716-1752
    Abstract: Healthy aging is associated with changes in cognitive performance, including executive functions (EFs) and their associated brain activation patterns. However, it has remained unclear which EF-related brain regions are affected consistently, because the results of pertinent neuroimaging studies and earlier meta-analyses vary considerably. We, therefore, conducted new rigorous meta-analyses of published age differences in EF-related brain activity. Out of a larger set of regions associated with EFs, only left inferior frontal junction and left anterior cuneus/precuneus were found to show consistent age differences. To further characterize these two age-sensitive regions, we performed seed-based resting-state functional connectivity (RS-FC) analyses using fMRI data from a large adult sample with a wide age range. We also assessed associations of the two regions' whole-brain RS-FC patterns with age and EF performance. Although our results largely point toward a domain-general role of left inferior frontal junction in EFs, the pattern of individual study contributions to the meta-analytic results suggests process-specific modulations by age. Our analyses further indicate that the left anterior cuneus/precuneus is recruited differently by older (compared with younger) adults during EF tasks, potentially reflecting inefficiencies in switching the attentional focus. Overall, our findings question earlier meta-analytic results and suggest a larger heterogeneity of age-related differences in brain activity associated with EFs. Hence, they encourage future research that pays greater attention to replicability, investigates age-related differences in deactivation, and focuses on more narrowly defined EF subprocesses, combining multiple behavioral assessments with multimodal imaging.
    Type of Medium: Online Resource
    ISSN: 0898-929X , 1530-8898
    Language: English
    Publisher: MIT Press
    Publication Date: 2021
    detail.hit.zdb_id: 1007410-7
    detail.hit.zdb_id: 1475255-4
    SSG: 5,2
    SSG: 7,11
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
  • 2
    In: Imaging Neuroscience, MIT Press, Vol. 2 ( 2024-01-25), p. 1-19
    Abstract: Neuroimaging research faces a crisis of reproducibility. With massive sample sizes and greater data complexity, this problem becomes more acute. Software that operates on imaging data defined using the Brain Imaging Data Structure (BIDS)—the BIDS App—has provided a substantial advance. However, even using BIDS Apps, a full audit trail of data processing is a necessary prerequisite for fully reproducible research. Obtaining a faithful record of the audit trail is challenging—especially for large datasets. Recently, the FAIRly big framework was introduced as a way to facilitate reproducible processing of large-scale data by leveraging DataLad—a version control system for data management. However, the current implementation of this framework was more of a proof of concept, and could not be immediately reused by other investigators for different use cases. Here, we introduce the BIDS App Bootstrap (BABS), a user-friendly and generalizable Python package for reproducible image processing at scale. BABS facilitates the reproducible application of BIDS Apps to large-scale datasets. Leveraging DataLad and the FAIRly big framework, BABS tracks the full audit trail of data processing in a scalable way by automatically preparing all scripts necessary for data processing and version tracking on high performance computing (HPC) systems. Currently, BABS supports jobs submissions and audits on Sun Grid Engine (SGE) and Slurm HPCs with a parsimonious set of programs. To demonstrate its scalability, we applied BABS to data from the Healthy Brain Network (HBN; n = 2,565). Taken together, BABS allows reproducible and scalable image processing and is broadly extensible via an open-source development model.
    Type of Medium: Online Resource
    ISSN: 2837-6056
    Language: English
    Publisher: MIT Press
    Publication Date: 2024
    detail.hit.zdb_id: 3167925-0
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...