Brain structure and function link to variation in biobehavioral dimensions across the psychopathological continuum

Abstract

In line with the Research Domain Criteria (RDoC), we set out to investigate the brain basis of psychopathology within a transdiagnostic, dimensional framework. We performed an integrative structural-functional linked independent component analysis, to study the relationship between brain measures and a broad set of biobehavioral measures in a sample (n = 295) with both mentally healthy participants and patients with diverse non-psychotic psychiatric disorders (i.e. mood, anxiety, addiction, and neurodevelopmental disorders). To get a more complete understanding of the underlying brain mechanisms, we used gray and white matter measures for brain structure and both resting-state and stress scans for brain function. The results emphasize the importance of the executive control network (ECN) during the functional scans, for the understanding of transdiagnostic symptom dimensions. The connectivity between the ECN and the frontoparietal network in the aftermath of stress, was correlated with symptom dimensions across both the cognitive and negative valence domains, and also with various other health related biological and behavioral measures. Finally, we identified a multimodal component that was specifically associated with the diagnosis of autism spectrum disorder (ASD). The involvement of the default mode network, precentral gyrus and thalamus across the different modalities of this component, may reflect the broad functional domains that may be affected in ASD, like theory of mind, motor problems and sensitivity to sensory stimuli respectively. Taken together, the findings from our extentensive, exploratory analyses emphasize the importance of a dimensional and more integrative approach for getting a better understanding of the brain basis of psychopathology.

Data availability

All data analysed in this study is stored in the institutional repository of the Donders Institute for Brain, Cognition and Behavior, and is available on request in line with the institutional ethics guidelines (https://data.donders.ru.nl/). Relevant data generated by the analyses we performed are included in the manuscript and supporting files. The linked ICA decomposition was performed using the Linked ICA toolbox, which was made available earlier by Llera and colleagues (2019) (https://github.com/allera/Llera_elife_2019_1/tree/master/matlab_flica_toolbox).

Article and author information

Author details

  1. Jasper van Oort

    Department of Psychiatry, Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands
    For correspondence
    jasper.vanoort@radboudumc.nl
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2281-0349
  2. Alberto Llera

    Donders Institute for Brain, Cognition and Behavior, Radboud University Nijmegen, Nijmegen, Netherlands
    Competing interests
    The authors declare that no competing interests exist.
  3. Nils Kohn

    Donders Institute for Brain, Cognition and Behavior, Radboud University Nijmegen, Nijmegen, Netherlands
    Competing interests
    The authors declare that no competing interests exist.
  4. Ting Mei

    Donders Institute for Brain, Cognition and Behavior, Radboud University Nijmegen, Nijmegen, Netherlands
    Competing interests
    The authors declare that no competing interests exist.
  5. Rose M Collard

    Department of Psychiatry, Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands
    Competing interests
    The authors declare that no competing interests exist.
  6. Fleur A Duyser

    Department of Psychiatry, Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands
    Competing interests
    The authors declare that no competing interests exist.
  7. Janna N Vrijsen

    Department of Cognitive Neuroscience, Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands
    Competing interests
    The authors declare that no competing interests exist.
  8. Christian F Beckmann

    Department of Cognitive Neuroscience, Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands
    Competing interests
    The authors declare that no competing interests exist.
  9. Aart H Schene

    Department of Psychiatry, Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands
    Competing interests
    The authors declare that no competing interests exist.
  10. Guillén Fernández

    Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands
    Competing interests
    The authors declare that no competing interests exist.
  11. Indira Tendolkar

    Department of Psychiatry, Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands
    Competing interests
    The authors declare that no competing interests exist.
  12. Philip FP van Eijndhoven

    Department of Psychiatry, Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands
    Competing interests
    The authors declare that no competing interests exist.

Funding

No external funding was received for this work.

Ethics

Human subjects: The MIND-Set study has been approved by the Ethical Review Board of the Radboudumc and all participants signed informed consent before participation.

Copyright

© 2023, van Oort 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

  • 923
    views
  • 123
    downloads
  • 0
    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)

  1. Jasper van Oort
  2. Alberto Llera
  3. Nils Kohn
  4. Ting Mei
  5. Rose M Collard
  6. Fleur A Duyser
  7. Janna N Vrijsen
  8. Christian F Beckmann
  9. Aart H Schene
  10. Guillén Fernández
  11. Indira Tendolkar
  12. Philip FP van Eijndhoven
(2023)
Brain structure and function link to variation in biobehavioral dimensions across the psychopathological continuum
eLife 12:e85006.
https://doi.org/10.7554/eLife.85006

Share this article

https://doi.org/10.7554/eLife.85006

Further reading

    1. Neuroscience
    Geoffrey W Meissner, Allison Vannan ... FlyLight Project Team
    Research Article

    Techniques that enable precise manipulations of subsets of neurons in the fly central nervous system (CNS) have greatly facilitated our understanding of the neural basis of behavior. Split-GAL4 driver lines allow specific targeting of cell types in Drosophila melanogaster and other species. We describe here a collection of 3060 lines targeting a range of cell types in the adult Drosophila CNS and 1373 lines characterized in third-instar larvae. These tools enable functional, transcriptomic, and proteomic studies based on precise anatomical targeting. NeuronBridge and other search tools relate light microscopy images of these split-GAL4 lines to connectomes reconstructed from electron microscopy images. The collections are the result of screening over 77,000 split hemidriver combinations. Previously published and new lines are included, all validated for driver expression and curated for optimal cell-type specificity across diverse cell types. In addition to images and fly stocks for these well-characterized lines, we make available 300,000 new 3D images of other split-GAL4 lines.

    1. Neuroscience
    Hyun Jee Lee, Jingting Liang ... Hang Lu
    Research Advance

    Cell identification is an important yet difficult process in data analysis of biological images. Previously, we developed an automated cell identification method called CRF_ID and demonstrated its high performance in Caenorhabditis elegans whole-brain images (Chaudhary et al., 2021). However, because the method was optimized for whole-brain imaging, comparable performance could not be guaranteed for application in commonly used C. elegans multi-cell images that display a subpopulation of cells. Here, we present an advancement, CRF_ID 2.0, that expands the generalizability of the method to multi-cell imaging beyond whole-brain imaging. To illustrate the application of the advance, we show the characterization of CRF_ID 2.0 in multi-cell imaging and cell-specific gene expression analysis in C. elegans. This work demonstrates that high-accuracy automated cell annotation in multi-cell imaging can expedite cell identification and reduce its subjectivity in C. elegans and potentially other biological images of various origins.