The BigBrainWarp toolbox for integration of BigBrain 3D histology with multimodal neuroimaging

  1. Casey Paquola  Is a corresponding author
  2. Jessica Royer
  3. Lindsay B Lewis
  4. Claude Lepage
  5. Tristan Glatard
  6. Konrad Wagstyl
  7. Jordan DeKraker
  8. Paule-Joanne Toussaint
  9. Sofie Louise Valk
  10. D Louis Collins
  11. Ali Khan
  12. Katrin Amunts
  13. Alan C Evans
  14. Timo Dickscheid
  15. Boris C Bernhardt  Is a corresponding author
  1. McGill University, Canada
  2. Concordia University, Canada
  3. UCL, United Kingdom
  4. University of Western Ontario, Canada
  5. Max Planck Institute Leipzig, Germany
  6. Montreal Neurological Institute and Hospital, Canada
  7. Heinrich Heine University, Germany
  8. Forschungszentrum Jülich, Germany

Abstract

Neuroimaging stands to benefit from emerging ultrahigh-resolution 3D histological atlases of the human brain; the first of which is 'BigBrain'. Here, we review recent methodological advances for the integration of BigBrain with multi-modal neuroimaging and introduce a toolbox, 'BigBrainWarp', that combines these developments. The aim of BigBrainWarp is to simplify workflows and support the adoption of best practices. This is accomplished with a simple wrapper function that allows users to easily map data between BigBrain and standard MRI spaces. The function automatically pulls specialised transformation procedures, based on ongoing research from a wide collaborative network of researchers. Additionally, the toolbox improves accessibility of histological information through dissemination of ready-to-use cytoarchitectural features. Finally, we demonstrate the utility of BigBrainWarp with three tutorials and discuss the potential of the toolbox to support multi-scale investigations of brain organisation.

Data availability

All data generated or analysed during this study are included in the BigBrainWarp repository (https://github.com/caseypaquola/BigBrainWarp).

The following previously published data sets were used

Article and author information

Author details

  1. Casey Paquola

    Neurology and Neurosurgery, McGill University, Montréal, Canada
    For correspondence
    casey.paquola@gmail.com
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0190-4103
  2. Jessica Royer

    Neurology and Neurosurgery, McGill University, Montreal, Canada
    Competing interests
    The authors declare that no competing interests exist.
  3. Lindsay B Lewis

    Neurology and Neurosurgery, McGill University, Montreal, Canada
    Competing interests
    The authors declare that no competing interests exist.
  4. Claude Lepage

    Neurology and Neurosurgery, McGill University, Montreal, Canada
    Competing interests
    The authors declare that no competing interests exist.
  5. Tristan Glatard

    Concordia University, Montreal, Canada
    Competing interests
    The authors declare that no competing interests exist.
  6. Konrad Wagstyl

    Wellcome Centre for Human Neuroimaging, UCL, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  7. Jordan DeKraker

    Brain and Mind Institute, University of Western Ontario, London, Canada
    Competing interests
    The authors declare that no competing interests exist.
  8. Paule-Joanne Toussaint

    Neurology and Neurosurgery, McGill University, Montreal, Canada
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7446-150X
  9. Sofie Louise Valk

    Cognitive Neurogenetics, Max Planck Institute Leipzig, Leipzig, Germany
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2998-6849
  10. D Louis Collins

    McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal Neurological Institute and Hospital, Montreal, Canada
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8432-7021
  11. Ali Khan

    Brain and Mind Institute, University of Western Ontario, London, Canada
    Competing interests
    The authors declare that no competing interests exist.
  12. Katrin Amunts

    Heinrich Heine University, Düsseldorf, Germany
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5828-0867
  13. Alan C Evans

    Neurology and Neurosurgery, McGill University, Montreal, Canada
    Competing interests
    The authors declare that no competing interests exist.
  14. Timo Dickscheid

    Forschungszentrum Jülich, Jülich, Germany
    Competing interests
    The authors declare that no competing interests exist.
  15. Boris C Bernhardt

    Neurology and Neurosurgery, McGill University, Montreal, Canada
    For correspondence
    boris.bernhardt@mcgill.ca
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9256-6041

Funding

Helmholtz Association

  • Casey Paquola
  • Lindsay B Lewis
  • Claude Lepage
  • Jordan DeKraker
  • Paule-Joanne Toussaint
  • Sofie Louise Valk
  • D Louis Collins
  • Katrin Amunts
  • Alan C Evans
  • Timo Dickscheid
  • Boris C Bernhardt

Fonds de Recherche du Québec - Santé

  • Casey Paquola
  • Boris C Bernhardt

National Science and Engineering Research Council of Canada

  • Ali Khan
  • Boris C Bernhardt

Canadian Institutes of Health Research

  • Jessica Royer
  • Ali Khan
  • Boris C Bernhardt

SickKids Foundation

  • Boris C Bernhardt

Azrieli Center for Autism Research

  • Boris C Bernhardt

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Copyright

© 2021, Paquola 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

  • 2,673
    views
  • 370
    downloads
  • 45
    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. Casey Paquola
  2. Jessica Royer
  3. Lindsay B Lewis
  4. Claude Lepage
  5. Tristan Glatard
  6. Konrad Wagstyl
  7. Jordan DeKraker
  8. Paule-Joanne Toussaint
  9. Sofie Louise Valk
  10. D Louis Collins
  11. Ali Khan
  12. Katrin Amunts
  13. Alan C Evans
  14. Timo Dickscheid
  15. Boris C Bernhardt
(2021)
The BigBrainWarp toolbox for integration of BigBrain 3D histology with multimodal neuroimaging
eLife 10:e70119.
https://doi.org/10.7554/eLife.70119

Share this article

https://doi.org/10.7554/eLife.70119

Further reading

    1. Neuroscience
    Jing Li, Chao Ning ... Chuan Zhou
    Research Article

    Female sexual receptivity is essential for reproduction of a species. Neuropeptides play the main role in regulating female receptivity. However, whether neuropeptides regulate female sexual receptivity during the neurodevelopment is unknown. Here, we found the peptide hormone prothoracicotropic hormone (PTTH), which belongs to the insect PG (prothoracic gland) axis, negatively regulated virgin female receptivity through ecdysone during neurodevelopment in Drosophila melanogaster. We identified PTTH neurons as doublesex-positive neurons, they regulated virgin female receptivity before the metamorphosis during the third-instar larval stage. PTTH deletion resulted in the increased EcR-A expression in the whole newly formed prepupae. Furthermore, the ecdysone receptor EcR-A in pC1 neurons positively regulated virgin female receptivity during metamorphosis. The decreased EcR-A in pC1 neurons induced abnormal morphological development of pC1 neurons without changing neural activity. Among all subtypes of pC1 neurons, the function of EcR-A in pC1b neurons was necessary for virgin female copulation rate. These suggested that the changes of synaptic connections between pC1b and other neurons decreased female copulation rate. Moreover, female receptivity significantly decreased when the expression of PTTH receptor Torso was reduced in pC1 neurons. This suggested that PTTH not only regulates female receptivity through ecdysone but also through affecting female receptivity associated neurons directly. The PG axis has similar functional strategy as the hypothalamic–pituitary–gonadal axis in mammals to trigger the juvenile–adult transition. Our work suggests a general mechanism underlying which the neurodevelopment during maturation regulates female sexual receptivity.

    1. Neuroscience
    Yoav Ger, Moni Shahar, Nitzan Shahar
    Research Article

    Theoretical computational models are widely used to describe latent cognitive processes. However, these models do not equally explain data across participants, with some individuals showing a bigger predictive gap than others. In the current study, we examined the use of theory-independent models, specifically recurrent neural networks (RNNs), to classify the source of a predictive gap in the observed data of a single individual. This approach aims to identify whether the low predictability of behavioral data is mainly due to noisy decision-making or misspecification of the theoretical model. First, we used computer simulation in the context of reinforcement learning to demonstrate that RNNs can be used to identify model misspecification in simulated agents with varying degrees of behavioral noise. Specifically, both prediction performance and the number of RNN training epochs (i.e., the point of early stopping) can be used to estimate the amount of stochasticity in the data. Second, we applied our approach to an empirical dataset where the actions of low IQ participants, compared with high IQ participants, showed lower predictability by a well-known theoretical model (i.e., Daw’s hybrid model for the two-step task). Both the predictive gap and the point of early stopping of the RNN suggested that model misspecification is similar across individuals. This led us to a provisional conclusion that low IQ subjects are mostly noisier compared to their high IQ peers, rather than being more misspecified by the theoretical model. We discuss the implications and limitations of this approach, considering the growing literature in both theoretical and data-driven computational modeling in decision-making science.