1. Neuroscience

The impact of task context on predicting finger movements in a brain-machine interface

  1. Matthew J Mender
  2. Samuel R Nason-Tomaszewski
  3. Hisham Temmar
  4. Joseph T Costello
  5. Dylan M Wallace
  6. Matthew S Willsey
  7. Nishant Ganesh Kumar
  8. Theodore A Kung
  9. Parag Patil  Is a corresponding author
  10. Cynthia A Chestek  Is a corresponding author
  1. University of Michigan-Ann Arbor, United States
https://doi.org/10.7554/eLife.82598
Share this article
Cite this article
  1. Matthew J Mender
  2. Samuel R Nason-Tomaszewski
  3. Hisham Temmar
  4. Joseph T Costello
  5. Dylan M Wallace
  6. Matthew S Willsey
  7. Nishant Ganesh Kumar
  8. Theodore A Kung
  9. Parag Patil
  10. Cynthia A Chestek
(2023)
The impact of task context on predicting finger movements in a brain-machine interface
eLife 12:e82598.
https://doi.org/10.7554/eLife.82598
  2. Download BibTeX
  3. Download .RIS

Abstract

A key factor in the clinical translation of brain-machine interfaces (BMIs) for restoring hand motor function will be their robustness to changes in a task. With functional electrical stimulation (FES) for example, the patient's own hand will be used to produce a wide range of forces in otherwise similar movements. To investigate the impact of task changes on BMI performance, we trained two rhesus macaques to control a virtual hand with their physical hand while we added springs to each finger group (index or middle-ring-small) or altered their wrist posture. Using simultaneously recorded intracortical neural activity, finger positions, and electromyography, we found that decoders trained in one context did not generalize well to other contexts, leading to significant increases in prediction error, especially for muscle activations. However, with respect to online BMI control of the virtual hand, changing either the decoder training task context or the hand's physical context during online control had little effect on online performance. We explain this dichotomy by showing that the structure of neural population activity remained similar in new contexts, which could allow for fast adjustment online. Additionally, we found that neural activity shifted trajectories proportional to the required muscle activation in new contexts. This shift in neural activity possibly explains biases to off-context kinematic predictions and suggests a feature that could help predict different magnitude muscle activations while producing similar kinematics.

Data availability

Neural, behavioral, EMG, and online BMI performance data has been deposited in the Dryad repository (https://doi.org/10.5061/dryad.p2ngf1vtn).

The following data sets were generated

Article and author information

Author details

  1. Matthew J Mender

    Department of Biomedical Engineering, University of Michigan-Ann Arbor, Ann Arbor, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1562-3289

  2. Samuel R Nason-Tomaszewski

    Department of Biomedical Engineering, University of Michigan-Ann Arbor, Ann Arbor, United States
    Competing interests
    The authors declare that no competing interests exist.

  3. Hisham Temmar

    Department of Biomedical Engineering, University of Michigan-Ann Arbor, Ann Arbor, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4464-4911

  4. Joseph T Costello

    Department of Electrical Engineering and Computer Science, University of Michigan-Ann Arbor, Ann Arbor, United States
    Competing interests
    The authors declare that no competing interests exist.

  5. Dylan M Wallace

    Department of Robotics, University of Michigan-Ann Arbor, Ann Arbor, United States
    Competing interests
    The authors declare that no competing interests exist.

  6. Matthew S Willsey

    Department of Biomedical Engineering, University of Michigan-Ann Arbor, Ann Arbor, United States
    Competing interests
    The authors declare that no competing interests exist.

  7. Nishant Ganesh Kumar

    Department of Surgery, University of Michigan-Ann Arbor, Ann Arbor, United States
    Competing interests
    The authors declare that no competing interests exist.

  8. Theodore A Kung

    Department of Surgery, University of Michigan-Ann Arbor, Ann Arbor, United States
    Competing interests
    The authors declare that no competing interests exist.

  9. Parag Patil

    Department of Biomedical Engineering, University of Michigan-Ann Arbor, Ann Arbor, United States
    For correspondence
    pgpatil@med.umich.edu
    Competing interests
    The authors declare that no competing interests exist.

  10. Cynthia A Chestek

    Department of Biomedical Engineering, University of Michigan-Ann Arbor, Ann Arbor, United States
    For correspondence
    cchestek@umich.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9671-7051

Funding

National Science Foundation (Grant Number 1926576)

  • Matthew J Mender
  • Hisham Temmar
  • Parag Patil
  • Cynthia A Chestek

National Institute of General Medical Sciences (Grant Number R01GM111293)

  • Parag Patil
  • Cynthia A Chestek

National Science Foundation (Graduate Research Fellowship Program)

  • Joseph T Costello

Eunice Kennedy Shriver National Institute of Child Health and Human Development (Grant Number F31HD098804)

  • Samuel R Nason-Tomaszewski

National Institute of Neurological Disorders and Stroke (Grant Number T32NS007222)

  • Matthew S Willsey

National Institute of Neurological Disorders and Stroke (Grant Number R01NS105132)

  • Nishant Ganesh Kumar
  • Theodore A Kung

The D. Dan and Betty Kahn Foundation (Grant AWD011321)

  • Dylan M Wallace

University of Michigan Robotics Institute (Graduate student fellowship)

  • Dylan M Wallace

A. Alfred Taubman Medical Research Institute

  • Parag Patil

Craig H. Neilsen Foundation (Project 315108)

  • Cynthia A Chestek

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

Ethics

Animal experimentation: All protocols were in accord with the National Institutes of Health guidelines and approved by the University of Michigan Institutional Animal Care and Use Committee (protocol numbers PRO00010076 and PRO00008138).

Copyright

© 2023, Mender 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

  • 677
    views
  • 116
    downloads
  • 4
    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. Matthew J Mender
  2. Samuel R Nason-Tomaszewski
  3. Hisham Temmar
  4. Joseph T Costello
  5. Dylan M Wallace
  6. Matthew S Willsey
  7. Nishant Ganesh Kumar
  8. Theodore A Kung
  9. Parag Patil
  10. Cynthia A Chestek
(2023)
The impact of task context on predicting finger movements in a brain-machine interface
eLife 12:e82598.
https://doi.org/10.7554/eLife.82598

Share this article

https://doi.org/10.7554/eLife.82598

Categories and tags

Research organism

Further reading

    1. Neuroscience

    A novel method (RIM-Deep) for enhancing imaging depth and resolution stability of deep cleared tissue in inverted confocal microscopy

    Yisi Liu, Pu Wang ... Hongwei Zhou
    Short Report

    The increasing use of tissue clearing techniques underscores the urgent need for cost-effective and simplified deep imaging methods. While traditional inverted confocal microscopes excel in high-resolution imaging of tissue sections and cultured cells, they face limitations in deep imaging of cleared tissues due to refractive index mismatches between the immersion media of objectives and sample container. To overcome these challenges, the RIM-Deep was developed to significantly improve deep imaging capabilities without compromising the normal function of the confocal microscope. This system facilitates deep immunofluorescence imaging of the prefrontal cortex in cleared macaque tissue, extending imaging depth from 2 mm to 5 mm. Applied to an intact and cleared Thy1-EGFP mouse brain, the system allowed for clear axonal visualization at high imaging depth. Moreover, this advancement enables large-scale, deep 3D imaging of intact tissues. In principle, this concept can be extended to any imaging modality, including existing inverted wide-field, confocal, and two-photon microscopy. This would significantly upgrade traditional laboratory configurations and facilitate the study of connectomes in the brain and other tissues.

    1. Neuroscience
    2. Physics of Living Systems

    Annihilation of action potentials induces electrical coupling between neurons

    Moritz Schloetter, Georg U Maret, Christoph J Kleineidam
    Research Article

    Neurons generate and propagate electrical pulses called action potentials which annihilate on arrival at the axon terminal. We measure the extracellular electric field generated by propagating and annihilating action potentials and find that on annihilation, action potentials expel a local discharge. The discharge at the axon terminal generates an inhomogeneous electric field that immediately influences target neurons and thus provokes ephaptic coupling. Our measurements are quantitatively verified by a powerful analytical model which reveals excitation and inhibition in target neurons, depending on position and morphology of the source-target arrangement. Our model is in full agreement with experimental findings on ephaptic coupling at the well-studied Basket cell-Purkinje cell synapse. It is able to predict ephaptic coupling for any other synaptic geometry as illustrated by a few examples.

    1. Neuroscience

    Synaptic deregulation of cholinergic projection neurons causes olfactory dysfunction across five fly Parkinsonism models

    Ulrike Pech, Jasper Janssens ... Patrik Verstreken
    Research Article

    The classical diagnosis of Parkinsonism is based on motor symptoms that are the consequence of nigrostriatal pathway dysfunction and reduced dopaminergic output. However, a decade prior to the emergence of motor issues, patients frequently experience non-motor symptoms, such as a reduced sense of smell (hyposmia). The cellular and molecular bases for these early defects remain enigmatic. To explore this, we developed a new collection of five fruit fly models of familial Parkinsonism and conducted single-cell RNA sequencing on young brains of these models. Interestingly, cholinergic projection neurons are the most vulnerable cells, and genes associated with presynaptic function are the most deregulated. Additional single nucleus sequencing of three specific brain regions of Parkinson’s disease patients confirms these findings. Indeed, the disturbances lead to early synaptic dysfunction, notably affecting cholinergic olfactory projection neurons crucial for olfactory function in flies. Correcting these defects specifically in olfactory cholinergic interneurons in flies or inducing cholinergic signaling in Parkinson mutant human induced dopaminergic neurons in vitro using nicotine, both rescue age-dependent dopaminergic neuron decline. Hence, our research uncovers that one of the earliest indicators of disease in five different models of familial Parkinsonism is synaptic dysfunction in higher-order cholinergic projection neurons and this contributes to the development of hyposmia. Furthermore, the shared pathways of synaptic failure in these cholinergic neurons ultimately contribute to dopaminergic dysfunction later in life.