Resting state neurophysiology of agonist-antagonist myoneural interface in persons with transtibial amputation.


Journal

Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288

Informations de publication

Date de publication:
12 Jun 2024
Historique:
received: 06 04 2024
accepted: 24 05 2024
medline: 12 6 2024
pubmed: 12 6 2024
entrez: 11 6 2024
Statut: epublish

Résumé

The agonist-antagonist myoneural interface (AMI) is an amputation surgery that preserves sensorimotor signaling mechanisms of the central-peripheral nervous systems. Our first neuroimaging study investigating AMI subjects conducted by Srinivasan et al. (2020) focused on task-based neural signatures, and showed evidence of proprioceptive feedback to the central nervous system. The study of resting state neural activity helps non-invasively characterize the neural patterns that prime task response. In this study on resting state functional magnetic resonance imaging in AMI subjects, we compared functional connectivity in patients with transtibial AMI (n = 12) and traditional (n = 7) amputations (TA). To test our hypothesis that we would find significant neurophysiological differences between AMI and TA subjects, we performed a whole-brain exploratory analysis to identify a seed region; namely, we conducted ANOVA, followed by t-test statistics to locate a seed in the salience network. Then, we implemented a seed-based connectivity analysis to gather cluster-level inferences contrasting our subject groups. We show evidence supporting our hypothesis that the AMI surgery induces functional network reorganization resulting in a neural configuration that significantly differs from the neural configuration after TA surgery. AMI subjects show significantly less coupling with regions functionally dedicated to selecting where to focus attention when it comes to salient stimuli. Our findings provide researchers and clinicians with a critical mechanistic understanding of the effect of AMI amputation on brain networks at rest, which has promising implications for improved neurorehabilitation and prosthetic control.

Identifiants

pubmed: 38862558
doi: 10.1038/s41598-024-63134-4
pii: 10.1038/s41598-024-63134-4
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

13456

Subventions

Organisme : Center for Functional Neuroimaging Technologies
ID : P41EB015896
Organisme : Center for Mesoscale Mapping
ID : P41EB030006
Organisme : NIH HHS
ID : R00EB016689
Pays : United States

Informations de copyright

© 2024. The Author(s).

Références

Molina, C., Faulk, J. Lower Extremity Amputation. (StatPearls Publishing LLC, 2022), pp. 1–23.
Dillingham, T., Pezzin, L. & Shore, A. Reamputation, mortality, and health care costs among persons with dysvascular lower-limb amputations. Arch. Phys. Med. Rehabil. 86, 480–486 (2005).
pubmed: 15759232 doi: 10.1016/j.apmr.2004.06.072
Sauter, C. N., Pezzin, L. E. & Dillingham, T. R. Functional outcomes of persons who underwent dysvascular lower extremity amputations: effect of postacute rehabilitation setting. Am. J. Phys. Med. Rehabil. 92(4), 287–296 (2013).
pubmed: 23291599 pmcid: 3604129 doi: 10.1097/PHM.0b013e31827d620d
M. Edwards, Clinician's Guide to Assistive Technology. (2002), pp. 297–310.
List, E., Krijgh, D., Enrico, M. & Coert, J. Prevalence of residual limb pain and symptomatic neuromas after lower extremity amputation: a systematic review and meta-analysis. Pain. 162(7), 1906–1913 (2021).
pubmed: 33470746 doi: 10.1097/j.pain.0000000000002202
Penna, A., Konstantatos, A., Cranwell, W., Paul, E. & Bruscino-Raiola, F. Incidence and associations of painful neuroma in a contemporary cohort of lower-limb amputees. ANZ J. Surg. 88(5), 491–496 (2018).
pubmed: 29654613 doi: 10.1111/ans.14293
Flor, H., Nikolajsen, L. & Jensen, T. Phantom limb pain: A case of maladaptive CNS plasticity?. Nat. Rev. Neurosci. 7, 873–771 (2006).
pubmed: 17053811 doi: 10.1038/nrn1991
Schone, H. et al. Making sense of phantom limb pain. J. Neurol. Neurosurg. Psychiatry. 93, 833–843 (2022).
pubmed: 35609964 doi: 10.1136/jnnp-2021-328428
Srinivasan, S. et al. On prosthetic control: A regenerative agonist-antagonist myoneural interface. Sci. Robot. 2, 6 (2017).
doi: 10.1126/scirobotics.aan2971
Srinivasan, S. et al. Neural interfacing architecture enables enhanced motor control and residual limb functionality postamputation. Proc. Natl. Acad. Sci. U.S.A. 118(9), e2019555118 (2021).
pubmed: 33593940 pmcid: 7936324 doi: 10.1073/pnas.2019555118
Srinivasan, S. et al. Agonist-antagonist myoneural interface amputation preserves proprioceptive sensorimotor neurophysiology in lower limbs. Sci. Trans. Med. 12, 573 (2020).
doi: 10.1126/scitranslmed.abc5926
Whitfield-Gabrieli, S. & Nieto-Castanon, A. Conn: A functional connectivity toolbox for correlated and anticorrelated brain networks. Brain Conn. 2, 125–141 (2012).
doi: 10.1089/brain.2012.0073
Power, J., Schlaggar, B. & Petersen, S. Studying brain organization via spontaneous fMRI signal. Neuron. 84, 681–696 (2014).
pubmed: 25459408 pmcid: 4254503 doi: 10.1016/j.neuron.2014.09.007
Zhang, J. et al. Brain functional connectivity plasticity within and beyond the sensorimotor network in lower-limb amputees. Front. Hum. Neurosci. 12, 403 (2018).
pubmed: 30356798 pmcid: 6189475 doi: 10.3389/fnhum.2018.00403
Bramati, I. et al. Lower limb amputees undergo long-distance plasticity in sensorimotor functional connectivity. Sci Rep. 9, 2518 (2019).
pubmed: 30792514 pmcid: 6384924 doi: 10.1038/s41598-019-39696-z
Menon, V. & Uddin, L. Saliency, switching, attention and control: a network model of insula function. Brain Struct. Function. 214, 655–667 (2010).
doi: 10.1007/s00429-010-0262-0
Seeley, W. The salience network: a neural system for perceiving and responding to homeostatic demands. J. Neurosci. 39, 9878–9882 (2019).
pubmed: 31676604 pmcid: 6978945 doi: 10.1523/JNEUROSCI.1138-17.2019
C. Henley, Foundations of Neuroscience. (Michigan State University Libraries, 2021), Ch. 26.
Claret, C. R. et al. Neuromuscular adaptations and sensorimotor integration following a unilateral transfemoral amputation. J. Neuroeng. and Rehabil. 16, 115 (2019).
doi: 10.1186/s12984-019-0586-9
Geurts, A. & Mulder, T. Reorganisation of postural control following lower limb amputation: Theoretical considerations and implications for rehabilitation. Physiother. Theory Pract. 8, 145–157 (1992).
doi: 10.3109/09593989209108094
Hlavackova, P., Franco, C., Diot, B. & Vuillerme, N. Contribution of each leg to the control of unperturbed bipedal stance in lower limb amputees: New insights using entropy. PLoS One. 6(5), e19661 (2011).
pubmed: 21603630 pmcid: 3094383 doi: 10.1371/journal.pone.0019661
N. Carlson, Physiology of Behavior. (Pearson, ed. 11, 2014), pp. 255–288.
Reed, C. & Caselli, R. The nature of tactile agnosia: A case study. Neuropsychologia. 32, 527–539 (1994).
pubmed: 8084412 doi: 10.1016/0028-3932(94)90142-2
Wijk, U. & Carlsson, I. Forearm amputees’ views of prosthesis use and sensory feedback. J. Hand. Ther. 28, 269–278 (2015).
pubmed: 25990442 doi: 10.1016/j.jht.2015.01.013
Smail, L., Neal, C., Wilkins, C. & Packham, T. Comfort and function remain key factors in upper limb prosthetic abandonment: findings of a scoping review. Disabil. Rehabil. Assist. Technol. 16, 821–830 (2021).
pubmed: 32189537 doi: 10.1080/17483107.2020.1738567
Rackerby, R., Lukosch, S. & Munro, D. Understanding and measuring the cognitive load of amputees for rehabilitation and prosthesis development. Arch. Rehabil. Res. Clin. Transl. 4, 100216 (2022).
pubmed: 36123983 pmcid: 9482031
Swerdloff, M., Hargrove, L. Quantifying cognitive load using EEG during ambulation and postural tasks. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. 2849–2852 (2020)
Mohan, A. & Vanneste, S. Adaptive and maladaptive neural compensatory consequences of sensory deprivation—from a phantom percept perspective. Prog. Neurobiol. 153, 1–17 (2017).
pubmed: 28408150 doi: 10.1016/j.pneurobio.2017.03.010
Perez, D., Dwortesky, A., Braga, R., Beeman, M. & Gratton, C. Hemispheric asymmetries of individual differences in functional connectivity. J. Cogn. Neurosci. 35, 200–225 (2023).
pubmed: 36378901 pmcid: 10029817 doi: 10.1162/jocn_a_01945
Wan, B. et al. Heritability and cross-species comparisons of human cortical functional organization asymmetry. elife. 11, e77215 (2022).
pubmed: 35904242 pmcid: 9381036 doi: 10.7554/eLife.77215
Bekrater-Bodmann, R. Perceptual correlates of successful body–prosthesis interaction in lower limb amputees: psychometric characterisation and development of the Prosthesis Embodiment Scale. Sci. Rep. 10, 14203 (2020).
pubmed: 32848166 pmcid: 7450092 doi: 10.1038/s41598-020-70828-y
Bekrater-Bodmann, R. Factors associated with prosthesis embodiment and its importance for prosthetic satisfaction in lower limb amputees. Front. Neurorobot. 14, 604376 (2021).
pubmed: 33519413 pmcid: 7843383 doi: 10.3389/fnbot.2020.604376
Akselrod, M. et al. Anatomical and functional properties of the foot and leg representation in areas 3b, 1 and 2 of primary somatosensory cortex in humans: a 7T fMRI study. NeuroImage. 159, 473–487 (2017).
pubmed: 28629975 doi: 10.1016/j.neuroimage.2017.06.021
Makin, T. R. et al. Network-level reorganisation of functional connectivity following arm amputation. Neuroimage. 1(114), 217–25 (2015).
doi: 10.1016/j.neuroimage.2015.02.067
Jiang, G. et al. The plasticity of brain gray matter and white matter following lower limb amputation. Neural Plast. 2015, 1–10 (2015).
Pazzaglia, M. & Zantedeschi, M. Plasticity and awareness of bodily distortion. Neural Plast. 2016, 1–7 (2016).
doi: 10.1155/2016/9834340
Clites, T., Herr, H., Srinivasan, S., Zorzos, A. & Carty, M. The ewing amputation: The first human implementation of the agonist-antagonist myoneural interface. Plast. Reconstr. Surg. Glob. 6, e1997 (2018).
Clites, T. et al. Proprioception from a neurally controlled lower-extremity prosthesis. Sci. Transl. Med. 10, eaap8373 (2018).
pubmed: 29848665 doi: 10.1126/scitranslmed.aap8373
Vizioli, L. et al. Lowering the thermal noise barrier in functional brain mapping with magnetic resonance imaging. Nat. Commun. 12, 5181 (2021).
pubmed: 34462435 pmcid: 8405721 doi: 10.1038/s41467-021-25431-8
Friston, K., Büchel, C. in Statistical Parametric Mapping, K. Friston, J. Ashburner, S. Kiebel, T. Nichols, and W. Penny, Eds. (Elsevier LTD, Oxford, 2007), pp. 492–508.
MATLAB and Statistics Toolbox Release, The MathWorks, Inc., Natick, Massachusetts, United States (2012b).
Wu, G. R. et al. A blind deconvolution approach to recover effective connectivity brain networks from resting state fMRI data. Med. Image Anal. 17, 365–374 (2013).
pubmed: 23422254 doi: 10.1016/j.media.2013.01.003
Rangaprakash, D., Wu, G. R., Marinazzo, D., Hu, X. & Deshpande, G. Hemodynamic response function (HRF) variability confounds resting-state fMRI functional connectivity. Magn. Reson. Med. 80, 1697–1713 (2018).
pubmed: 29656446 doi: 10.1002/mrm.27146
Rangaprakash, D. et al. Hemodynamic variability in soldiers with trauma: Implications for functional MRI connectivity studies. Neuroimage Clin. 16, 409–417 (2017).
pubmed: 28879082 pmcid: 5574840 doi: 10.1016/j.nicl.2017.07.016
Yan, W., Rangaprakash, D. & Deshpande, G. Aberrant hemodynamic responses in autism: Implications for resting State fMRI functional connectivity studies. Neuroimage Clin. 19, 320–330 (2018).
pubmed: 30013915 pmcid: 6044186 doi: 10.1016/j.nicl.2018.04.013
Zhi, D., King, M., Hernandez-Castillo, C. & Diedrichsen, J. Evaluating brain parcellations using the distance controlled boundary coefficient. Hum. Brain Mapp. 43, 3706–3720 (2022).
pubmed: 35451538 pmcid: 9294308 doi: 10.1002/hbm.25878
Power, J. et al. Functional network organization of the human brain. Neuron. 72, 665–678 (2011).
pubmed: 22099467 pmcid: 3222858 doi: 10.1016/j.neuron.2011.09.006
Makris, N. et al. Decreased Volume of left and total anterior insular lobule in schizophrenia. Schizophr. Res. 83, 155–171 (2006).
pubmed: 16448806 doi: 10.1016/j.schres.2005.11.020
Frazier, J. et al. Structural brain magnetic resonance imaging of limbic and thalamic volumes in pediatric bipolar disorder. Am. J. Psychiatry. 162, 1256–1265 (2005).
pubmed: 15994707 doi: 10.1176/appi.ajp.162.7.1256
Desikan, R. et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage. 31, 968–980 (2006).
pubmed: 16530430 doi: 10.1016/j.neuroimage.2006.01.021
Goldstein, J. et al. Hypothalamic abnormalities in schizophrenia: Sex effects and genetic vulnerability. Biol. Psychiatry. 61, 935–945 (2007).
pubmed: 17046727 doi: 10.1016/j.biopsych.2006.06.027
Buckner, R., Krienen, F., Castellanos, A., Diaz, J. & Yeo, B. The organization of the human cerebellum estimated by intrinsic functional connectivity. J. Neurophys. 106, 2322–2345 (2011).
doi: 10.1152/jn.00339.2011
Rubinov, M. & Sporns, O. Complex network measures of brain connectivity: Uses and interpretations. Neuroimage. 52, 1059–1069 (2010).
pubmed: 19819337 doi: 10.1016/j.neuroimage.2009.10.003
Xia, M., Wang, J. & He, Y. BrainNet viewer: A network visualization tool for human brain connectomics. PLoS One. 8, e68910 (2013).
pubmed: 23861951 pmcid: 3701683 doi: 10.1371/journal.pone.0068910

Auteurs

Laura A Chicos (LA)

Biomechatronics Group, Massachusetts Institute of Technology, Media Lab, Cambridge, MA, 02139, USA. laurachi@media.mit.edu.
K. Lisa Yang Center for Bionics, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA. laurachi@media.mit.edu.

D Rangaprakash (D)

Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, 02129, USA.
Department of Radiology, Harvard Medical School, Boston, MA, 02115, USA.

Shriya S Srinivasan (SS)

Harvard-MA Institute of Technology Division of Health Sciences and Technology, Cambridge, MA, 02139, USA.
John A. Paulson School of Engineering and Applied Sciences, Harvard University, Allston, MA, 02134, USA.

Samantha Gutierrez-Arango (S)

Biomechatronics Group, Massachusetts Institute of Technology, Media Lab, Cambridge, MA, 02139, USA.
K. Lisa Yang Center for Bionics, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.

Hyungeun Song (H)

Biomechatronics Group, Massachusetts Institute of Technology, Media Lab, Cambridge, MA, 02139, USA.
K. Lisa Yang Center for Bionics, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
Harvard-MA Institute of Technology Division of Health Sciences and Technology, Cambridge, MA, 02139, USA.

Robert L Barry (RL)

Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, 02129, USA.
Department of Radiology, Harvard Medical School, Boston, MA, 02115, USA.
Harvard-MA Institute of Technology Division of Health Sciences and Technology, Cambridge, MA, 02139, USA.

Hugh M Herr (HM)

Biomechatronics Group, Massachusetts Institute of Technology, Media Lab, Cambridge, MA, 02139, USA.
K. Lisa Yang Center for Bionics, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.

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