Direct modulation of aberrant brain network connectivity through real-time neurofeedback

  1. Michal Ramot  Is a corresponding author
  2. Sara Kimmich
  3. Javier Gonzalez-Castillo
  4. Vinai Roopchansingh
  5. Haroon Popal
  6. Emily White
  7. Stephen J Gotts
  8. Alex Martin
  1. National Institute of Mental Health, National Institutes of Health, United States

Abstract

The existence of abnormal connectivity patterns between resting state networks in neuropsychiatric disorders, including Autism Spectrum Disorder (ASD), has been well established. Traditional treatment methods in ASD are limited, and do not address the aberrant network structure. Using real-time fMRI neurofeedback, we directly trained 3 brain nodes in participants with ASD, in which the aberrant connectivity has been shown to correlate with symptom severity. Desired network connectivity patterns were reinforced in real-time, without participants' awareness of the training taking place. This training regimen produced large, significant long-term changes in correlations at the network level, and whole brain analysis revealed that the greatest changes were focused on the areas being trained. These changes were not found in the control group. Moreover, changes in ASD resting state connectivity following the training were correlated to changes in behavior, suggesting that neurofeedback can be used to directly alter complex, clinically relevant network connectivity patterns.

Data availability

The following data sets were generated

Article and author information

Author details

  1. Michal Ramot

    Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, United States
    For correspondence
    michal.ramot@nih.gov
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9716-6469
  2. Sara Kimmich

    Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Javier Gonzalez-Castillo

    Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Vinai Roopchansingh

    Functional MRI Facility, National Institute of Mental Health, National Institutes of Health, Bethesda, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Haroon Popal

    Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, United States
    Competing interests
    The authors declare that no competing interests exist.
  6. Emily White

    Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, United States
    Competing interests
    The authors declare that no competing interests exist.
  7. Stephen J Gotts

    Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, United States
    Competing interests
    The authors declare that no competing interests exist.
  8. Alex Martin

    Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, United States
    Competing interests
    The authors declare that no competing interests exist.

Funding

National Institute of Mental Health (ZIAMH002920)

  • Michal Ramot
  • Sara Kimmich
  • Haroon Popal
  • Emily White
  • Stephen J Gotts
  • Alex Martin

National Institute of Mental Health (ZIAMH002783)

  • Javier Gonzalez-Castillo
  • Vinai Roopchansingh

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

Ethics

Human subjects: The experiment was approved by the NIMH Institutional Review Board, protocol number 10-M-0027, clinical trials number NCT01031407. Written informed consent and consent to publish were obtained from all participants. All procedures performed were in accordance with ethical standards set out by the Federal Policy for the Protection of Human Subjects (or 'Common Rule', U.S. Department of Health and Human Services Title 45 DFR 46).

Copyright

This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

Metrics

  • 4,342
    views
  • 694
    downloads
  • 97
    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. Michal Ramot
  2. Sara Kimmich
  3. Javier Gonzalez-Castillo
  4. Vinai Roopchansingh
  5. Haroon Popal
  6. Emily White
  7. Stephen J Gotts
  8. Alex Martin
(2017)
Direct modulation of aberrant brain network connectivity through real-time neurofeedback
eLife 6:e28974.
https://doi.org/10.7554/eLife.28974

Share this article

https://doi.org/10.7554/eLife.28974

Further reading

    1. Neuroscience
    Rongxin Fang, Aaron Halpern ... Xiaowei Zhuang
    Tools and Resources

    Multiplexed error-robust fluorescence in situ hybridization (MERFISH) allows genome-scale imaging of RNAs in individual cells in intact tissues. To date, MERFISH has been applied to image thin-tissue samples of ~10 µm thickness. Here, we present a thick-tissue three-dimensional (3D) MERFISH imaging method, which uses confocal microscopy for optical sectioning, deep learning for increasing imaging speed and quality, as well as sample preparation and imaging protocol optimized for thick samples. We demonstrated 3D MERFISH on mouse brain tissue sections of up to 200 µm thickness with high detection efficiency and accuracy. We anticipate that 3D thick-tissue MERFISH imaging will broaden the scope of questions that can be addressed by spatial genomics.

    1. Neuroscience
    Sam E Benezra, Kripa B Patel ... Randy M Bruno
    Research Article

    Learning alters cortical representations and improves perception. Apical tuft dendrites in cortical layer 1, which are unique in their connectivity and biophysical properties, may be a key site of learning-induced plasticity. We used both two-photon and SCAPE microscopy to longitudinally track tuft-wide calcium spikes in apical dendrites of layer 5 pyramidal neurons in barrel cortex as mice learned a tactile behavior. Mice were trained to discriminate two orthogonal directions of whisker stimulation. Reinforcement learning, but not repeated stimulus exposure, enhanced tuft selectivity for both directions equally, even though only one was associated with reward. Selective tufts emerged from initially unresponsive or low-selectivity populations. Animal movement and choice did not account for changes in stimulus selectivity. Enhanced selectivity persisted even after rewards were removed and animals ceased performing the task. We conclude that learning produces long-lasting realignment of apical dendrite tuft responses to behaviorally relevant dimensions of a task.