Task-evoked metabolic demands of the posteromedial default mode network are shaped by dorsal attention and frontoparietal control networks
- Medical University of Vienna, Austria
- University of Vienna, Austria
- Umeå University, Sweden
- QIMR Berghofer Medical Research Institute, Australia
- University of Newcastle Australia, Australia
Abstract
External tasks evoke characteristic fMRI BOLD signal deactivations in the default mode network (DMN). However, for the corresponding metabolic glucose demands both decreases and increases have been reported. To resolve this discrepancy, functional PET/MRI data from 50 healthy subjects performing Tetris® were combined with previously published data sets of working memory, visual and motor stimulation. We show that the glucose metabolism of the posteromedial DMN is dependent on the metabolic demands of the correspondingly engaged task-positive networks. Specifically, the dorsal attention and frontoparietal network shape the glucose metabolism of the posteromedial DMN in opposing directions. While tasks that mainly require an external focus of attention lead to a consistent downregulation of both metabolism and the BOLD signal in the posteromedial DMN, cognitive control during working memory requires a metabolically expensive BOLD suppression. This indicates that two types of BOLD deactivations with different-oxygen-to-glucose index may occur in this region. We further speculate that consistent downregulation of the two signals is mediated by decreased glutamate signaling, while divergence may be subject to active GABAergic inhibition. The results demonstrate that the DMN relates to cognitive processing in a flexible manner and does not always act as a cohesive task-negative network in isolation.
Data availability
Raw data will not be publicly available due to reasons of data protection. Sharing of raw data requires a data sharing agreement, approved by the departments of legal affairs and data clearing of the Medical University of Vienna. Details about this process can be obtained from the corresponding author. Processed data are available at Dryad https://doi.org/10.5061/dryad.5qfttdzbd. Custom code is available at GitHub https://github.com/NeuroimagingLabsMUV/Godbersen2023_eLife.
-
Data from: Task-evoked metabolic demands of the posteromedial default mode network are shaped by dorsal attention and frontoparietal control networksDryad Digital Repository, doi:10.5061/dryad.5qfttdzbd.
Article and author information
Author details
Rupert Lanzenberger
Funding
Austrian Science Fund (KLI610)
- Andreas Hahn
Medical University of Vienna (MDPhD Excellence Programm)
- Sebastian Klug
European Research Council (ERC-STG-716065)
- Anna Rieckmann
- Lars Stiernman
National Health and Medical Research Council (GN2001283)
- Luca Cocchi
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Human subjects: All participants provided written informed consent after a detailed explanation of the study protocol, they were insured and reimbursed for participation. The study was approved by the Ethics Committee of the Medical University of Vienna (ethics number 1479/2015) and procedures were carried out according to the Declaration of Helsinki. The study was pre-registered at ClinicalTrials.gov (NCT03485066).
Copyright
© 2023, Godbersen et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 824
- views
-
- 124
- downloads
-
- 12
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
A two-part list of links to download the article, or parts of the article, in various formats.
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Task-evoked metabolic demands of the posteromedial default mode network are shaped by dorsal attention and frontoparietal control networks
eLife 12:e84683.
https://doi.org/10.7554/eLife.84683
Further reading
-
- Neuroscience
Modern neurophysiological recordings are performed using multichannel sensor arrays that are able to record activity in an increasingly high number of channels numbering in the 100s to 1000s. Often, underlying lower-dimensional patterns of activity are responsible for the observed dynamics, but these representations are difficult to reliably identify using existing methods that attempt to summarize multivariate relationships in a post hoc manner from univariate analyses or using current blind source separation methods. While such methods can reveal appealing patterns of activity, determining the number of components to include, assessing their statistical significance, and interpreting them requires extensive manual intervention and subjective judgment in practice. These difficulties with component selection and interpretation occur in large part because these methods lack a generative model for the underlying spatio-temporal dynamics. Here, we describe a novel component analysis method anchored by a generative model where each source is described by a bio-physically inspired state-space representation. The parameters governing this representation readily capture the oscillatory temporal dynamics of the components, so we refer to it as oscillation component analysis. These parameters – the oscillatory properties, the component mixing weights at the sensors, and the number of oscillations – all are inferred in a data-driven fashion within a Bayesian framework employing an instance of the expectation maximization algorithm. We analyze high-dimensional electroencephalography and magnetoencephalography recordings from human studies to illustrate the potential utility of this method for neuroscience data.
-
- Neuroscience
A neural signature of serial dependence has been found, which mirrors the attractive bias of visual information seen in behavioral experiments.
