1. Computational and Systems Biology
  2. Neuroscience

A generalizable brain extraction net (BEN) for multimodal MRI data from rodents, nonhuman primates, and humans

  1. Ziqi Yu
  2. Xiaoyang Han
  3. Wenjing Xu
  4. Jie Zhang
  5. Carsten Marr
  6. Dinggang Shen
  7. Tingying Peng  Is a corresponding author
  8. Xiao-Yong Zhang  Is a corresponding author
  9. Jianfeng Feng
  1. Fudan University, China
  2. Helmholtz Zentrum München, Germany
  3. ShanghaiTech University, China
https://doi.org/10.7554/eLife.81217
Share this article
Cite this article
  1. Ziqi Yu
  2. Xiaoyang Han
  3. Wenjing Xu
  4. Jie Zhang
  5. Carsten Marr
  6. Dinggang Shen
  7. Tingying Peng
  8. Xiao-Yong Zhang
  9. Jianfeng Feng
(2022)
A generalizable brain extraction net (BEN) for multimodal MRI data from rodents, nonhuman primates, and humans
eLife 11:e81217.
https://doi.org/10.7554/eLife.81217
  2. Download BibTeX
  3. Download .RIS

Abstract

Accurate brain tissue extraction on magnetic resonance imaging (MRI) data is crucial for analyzing brain structure and function. While several conventional tools have been optimized to handle human brain data, there have been no generalizable methods to extract brain tissues for multimodal MRI data from rodents, nonhuman primates, and humans. Therefore, developing a flexible and generalizable method for extracting whole brain tissue across species would allow researchers to analyze and compare experiment results more efficiently. Here, we propose a domain-adaptive and semi-supervised deep neural network, named the Brain Extraction Net (BEN), to extract brain tissues across species, MRI modalities, and MR scanners. We have evaluated BEN on 18 independent datasets, including 783 rodent MRI scans, 246 nonhuman primate MRI scans, and 4,601 human MRI scans, covering five species, four modalities, and six MR scanners with various magnetic field strengths. Compared to conventional toolboxes, the superiority of BEN is illustrated by its robustness, accuracy, and generalizability. Our proposed method not only provides a generalized solution for extracting brain tissue across species but also significantly improves the accuracy of atlas registration, thereby benefiting the downstream processing tasks. As a novel fully automated deep-learning method, BEN is designed as an open-source software to enable high-throughput processing of neuroimaging data across species in preclinical and clinical applications.

Data availability

All data (MRI data, source codes, pretrained weights and replicate demo notebooks for Figure 1-7) are included in the manuscript or available at https://github.com/yu02019/BEN.

The following data sets were generated
The following previously published data sets were used

Article and author information

Author details

  1. Ziqi Yu

    Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8201-5481

  2. Xiaoyang Han

    Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3007-6079

  3. Wenjing Xu

    Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
    Competing interests
    No competing interests declared.

  4. Jie Zhang

    Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
    Competing interests
    No competing interests declared.

  5. Carsten Marr

    Institute of AI for Health, Helmholtz Zentrum München, Neuherberg, Germany
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2154-4552

  6. Dinggang Shen

    School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
    Competing interests
    Dinggang Shen, is affiliated with Shanghai United Imaging Intelligence Co., Ltd. He has financial interests to declare..

  7. Tingying Peng

    Helmholtz AI, Helmholtz Zentrum München, Neuherberg, Germany
    For correspondence
    tingying.peng@helmholtz-muenchen.de
    Competing interests
    No competing interests declared.

  8. Xiao-Yong Zhang

    Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
    For correspondence
    xiaoyong_zhang@fudan.edu.cn
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8965-1077

  9. Jianfeng Feng

    Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5987-2258

Funding

National Natural Science Foundation of China (81873893,82171903,92043301)

  • Xiao-Yong Zhang

Fudan University (the Office of Global Partnerships (Key Projects Development Fund))

  • Xiao-Yong Zhang

Shanghai Municipal Science and Technology Major Project (No.2018SHZDZX01)

  • Xiao-Yong Zhang

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

Ethics

Animal experimentation: Partial rodent MRI data collection were approved by the Animal Care and Use Committee of Fudan University, China. The rest rodent data (Rat-T2WI-9.4T and Rat-EPI-9.4T datasets) are publicly available (CARMI: https://openneuro.org/datasets/ds002870/versions/1.0.0). Marmoset MRI data collection were approved by the Animal Care and Use Committee of the Institute of Neuroscience, Chinese Academy of Sciences, China. Macaque MRI data are publicly available from the nonhuman PRIMatE Data Exchange (PRIME-DE) (https://fcon_1000.projects.nitrc.org/indi/indiPRIME.html).

Human subjects: The Zhangjiang International Brain Biobank (ZIB) protocols were approved by the Ethics Committee of Fudan University (AF/SC-03/20200722) and written informed consents were obtained from all volunteers. UK Biobank (UKB) and Adolescent Brain Cognitive Development (ABCD) are publicly available.

Reviewing Editor

  1. Saad Jbabdi, University of Oxford, United Kingdom

Version history

  1. Preprint posted: May 26, 2022 (view preprint)
  2. Received: June 20, 2022
  3. Accepted: December 21, 2022
  4. Accepted Manuscript published: December 22, 2022 (version 1)
  5. Version of Record published: February 17, 2023 (version 2)
  6. Version of Record updated: February 21, 2023 (version 3)

Copyright

© 2022, Yu 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

  • 977
    Page views
  • 142
    Downloads
  • 1
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.

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. Ziqi Yu
  2. Xiaoyang Han
  3. Wenjing Xu
  4. Jie Zhang
  5. Carsten Marr
  6. Dinggang Shen
  7. Tingying Peng
  8. Xiao-Yong Zhang
  9. Jianfeng Feng
(2022)
A generalizable brain extraction net (BEN) for multimodal MRI data from rodents, nonhuman primates, and humans
eLife 11:e81217.
https://doi.org/10.7554/eLife.81217

Categories and tags

Research organisms

Further reading

    1. Computational and Systems Biology
    2. Neuroscience

    Task-dependent optimal representations for cerebellar learning

    Marjorie Xie, Samuel P Muscinelli ... Ashok Litwin-Kumar
    Research Article Updated

    The cerebellar granule cell layer has inspired numerous theoretical models of neural representations that support learned behaviors, beginning with the work of Marr and Albus. In these models, granule cells form a sparse, combinatorial encoding of diverse sensorimotor inputs. Such sparse representations are optimal for learning to discriminate random stimuli. However, recent observations of dense, low-dimensional activity across granule cells have called into question the role of sparse coding in these neurons. Here, we generalize theories of cerebellar learning to determine the optimal granule cell representation for tasks beyond random stimulus discrimination, including continuous input-output transformations as required for smooth motor control. We show that for such tasks, the optimal granule cell representation is substantially denser than predicted by classical theories. Our results provide a general theory of learning in cerebellum-like systems and suggest that optimal cerebellar representations are task-dependent.

    1. Computational and Systems Biology
    2. Neuroscience

    Dissecting the chain of information processing and its interplay with neurochemicals and fluid intelligence across development

    George Zacharopoulos, Francesco Sella ... Roi Cohen Kadosh
    Research Article

    Previous research has highlighted the role of glutamate and gamma-aminobutyric acid (GABA) in perceptual, cognitive, and motor tasks. However, the exact involvement of these neurochemical mechanisms in the chain of information processing, and across human development, is unclear. In a cross-sectional longitudinal design, we used a computational approach to dissociate cognitive, decision, and visuomotor processing in 293 individuals spanning early childhood to adulthood. We found that glutamate and GABA within the intraparietal sulcus (IPS) explained unique variance in visuomotor processing, with higher glutamate predicting poorer visuomotor processing in younger participants but better visuomotor processing in mature participants, while GABA showed the opposite pattern. These findings, which were neurochemically, neuroanatomically and functionally specific, were replicated ~21 mo later and were generalized in two further different behavioral tasks. Using resting functional MRI, we revealed that the relationship between IPS neurochemicals and visuomotor processing is mediated by functional connectivity in the visuomotor network. We then extended our findings to high-level cognitive behavior by predicting fluid intelligence performance. We present evidence that fluid intelligence performance is explained by IPS GABA and glutamate and is mediated by visuomotor processing. However, this evidence was obtained using an uncorrected alpha and needs to be replicated in future studies. These results provide an integrative biological and psychological mechanistic explanation that links cognitive processes and neurotransmitters across human development and establishes their potential involvement in intelligent behavior.

    1. Computational and Systems Biology
    2. Neuroscience

    Dynamic organization of cerebellar climbing fiber response and synchrony in multiple functional components reduces dimensions for reinforcement learning

    Huu Hoang, Shinichiro Tsutsumi ... Keisuke Toyama
    Research Article Updated

    Cerebellar climbing fibers convey diverse signals, but how they are organized in the compartmental structure of the cerebellar cortex during learning remains largely unclear. We analyzed a large amount of coordinate-localized two-photon imaging data from cerebellar Crus II in mice undergoing ‘Go/No-go’ reinforcement learning. Tensor component analysis revealed that a majority of climbing fiber inputs to Purkinje cells were reduced to only four functional components, corresponding to accurate timing control of motor initiation related to a Go cue, cognitive error-based learning, reward processing, and inhibition of erroneous behaviors after a No-go cue. Changes in neural activities during learning of the first two components were correlated with corresponding changes in timing control and error learning across animals, indirectly suggesting causal relationships. Spatial distribution of these components coincided well with boundaries of Aldolase-C/zebrin II expression in Purkinje cells, whereas several components are mixed in single neurons. Synchronization within individual components was bidirectionally regulated according to specific task contexts and learning stages. These findings suggest that, in close collaborations with other brain regions including the inferior olive nucleus, the cerebellum, based on anatomical compartments, reduces dimensions of the learning space by dynamically organizing multiple functional components, a feature that may inspire new-generation AI designs.