Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2021 Feb;25(2):556-565.
doi: 10.1177/1362361320969258. Epub 2020 Nov 27.

Difference in default mode network subsystems in autism across childhood and adolescence

Joe Bathelt  1 , Hilde M Geurts  2
Affiliations

Difference in default mode network subsystems in autism across childhood and adolescence

Joe Bathelt et al. Autism. 2021 Feb.

Abstract

Neuroimaging research has identified a network of brain regions that are more active when we daydream compared to when we are engaged in a task. This network has been named the default mode network. Furthermore, differences in the default mode network are the most consistent findings in neuroimaging research in autism. Recent studies suggest that the default mode network is composed of subnetworks that are tied to different functions, namely memory and understanding others' minds. In this study, we investigated if default mode network differences in autism are related to specific subnetworks of the default mode network and if these differences change across childhood and adolescence. Our results suggest that the subnetworks of the default mode network are less differentiated in autism in middle childhood compared to neurotypicals. By late adolescence, the default mode network subnetwork organisation was similar in the autistic and neurotypical groups. These findings provide a foundation for future studies to investigate if this developmental pattern relates to improvements in the integration of memory and social understanding as autistic children grow up.

Keywords: autism spectrum disorders; brain development; default mode network; functional connectivity; modularity.

PubMed Disclaimer

Conflict of interest statement

Declaration of conflicting interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Figures

Figure 1.
Figure 1.
Illustration of DMN ROIs. The colour indicates the subsystem associated with each node. aMPFC: anterior medial prefrontal cortex; dMPFC: dorsal medial prefrontal cortex; HF: hippocampal formation; LTC: lateral temporal cortex; MTL: medial temporal lobe; PCC: posterior cingulate cortex; PHC: parahippocampal cortex; pIPL: posterior inferior parietal lobe; RSP: retrosplenial cortex; TempP: temporal pole; TPJ: temporal parietal junction; vMPFC: ventral medial prefrontal cortex.
Figure 2.
Figure 2.
Differences in DMN connections. (a) Average adjacency matrix in younger (left) and older (right) participants in the ASC (top) or CMP (bottom) group. White boxes indicate the boundaries of subnetworks (green: MTL subsystem; yellow: PCC-aMPFC core; blue: dMPFC subsystem). Please note that age was treated as a continuous variable in the main analysis. The age split is only shown for illustration purposes. (b) Results of the statistical analysis. The upper matrix shows significant age effects with FDR-corrected p values in the lower triangle and the standardised regression coefficient (β) in the upper triangle. The lower matrix shows the age × group interaction. (c) Regression results for the association between age and connection strength in the ASC (red) and CMP (grey) group per region. The regression lines and confidence intervals were based on 5000 bootstrap samples drawn from the original data. (d) Regression results for the association between age and modularity in the ASC (red) and CMP (grey) group. btw: between module; MTL: MTL subsystem; dMPFC: dMPFC subsystem; core: PCC-aMPFC subsystem.
See this image and copyright information in PMC

References

    1. Abraham A., Milham M. P., Martino A. D., Craddock R. C., Samaras D., Thirion B., Varoquaux G. (2017). Deriving reproducible biomarkers from multi-site resting-state data: An autism-based example. NeuroImage, 147, 736–745. 10.1016/j.neuroimage.2016.10.045 - DOI - PubMed
    1. Abraham A., Pedregosa F., Eickenberg M., Gervais P., Mueller A., Kossaifi J., . . . Varoquaux G. (2014). Machine learning for neuroimaging with scikit-learn. Frontiers in Neuroinformatics, 8, Article 14. 10.3389/fninf.2014.00014 - DOI - PMC - PubMed
    1. Alaerts K., Swinnen S. P., Wenderoth N. (2016). Sex differences in autism: A resting-state fMRI investigation of functional brain connectivity in males and females. Social Cognitive and Affective Neuroscience, 11(6), 1002–1016. 10.1093/scan/nsw027 - DOI - PMC - PubMed
    1. Andrews-Hanna J. R., Reidler J. S., Sepulcre J., Poulin R., Buckner R. L. (2010). Functional-anatomic fractionation of the brain’s default network. Neuron, 65(4), 550–562. 10.1016/j.neuron.2010.02.005 - DOI - PMC - PubMed
    1. Andrews-Hanna J. R., Saxe R., Yarkoni T. (2014). Contributions of episodic retrieval and mentalizing to autobiographical thought: Evidence from functional neuroimaging, resting-state connectivity, and fMRI meta-analyses. NeuroImage, 91, 324–335. 10.1016/j.neuroimage.2014.01.032 - DOI - PMC - PubMed

Publication types