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Decoding cerebro-spinal signatures of human behavior: Application to motor sequence learning.

N Kinany1, A Khatibi2, O Lungu3

  • 1Department of Radiology and Medical Informatics, University of Geneva, Geneva 1211, Switzerland; Neuro-X Institute, École Polytechnique Fédérale de Lausanne (EPFL), Geneva 1202, Switzerland.

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Neuroscience research now examines brain and spinal cord activity together. This study used a new method to reveal how these combined neural signals change during motor learning.

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Area of Science:

  • Neuroscience
  • Neuroimaging
  • Motor Control

Background:

  • Human behavior arises from complex interactions within the central nervous system (CNS), involving both brain and spinal cord.
  • Traditional neuroimaging often overlooks spinal cord contributions, focusing primarily on cerebral mechanisms.
  • Simultaneous brain-spinal cord functional magnetic resonance imaging (fMRI) offers new possibilities, but analysis has been limited.

Purpose of the Study:

  • To introduce a novel multivariate, data-driven approach using innovation-driven coactivation patterns (iCAPs) to analyze dynamic cerebro-spinal signals.
  • To investigate the role of large-scale central nervous system (CNS) plasticity in motor sequence learning (MSL).
  • To identify cerebro-spinal signatures associated with different stages of motor skill acquisition and consolidation.

Main Methods:

  • Utilized simultaneous brain-spinal cord fMRI data acquired during a motor sequence learning (MSL) task.
  • Applied a data-driven, multivariate approach leveraging innovation-driven coactivation patterns (iCAPs) to analyze dynamic cerebro-spinal signals.
  • Developed methods to decode learning stages using identified functional networks.

Main Results:

  • Uncovered distinct cortical, subcortical, and spinal functional networks involved in MSL.
  • Successfully decoded different stages of motor learning with high accuracy using these cerebro-spinal networks.
  • Delineated specific cerebro-spinal signatures that track learning progression.

Conclusions:

  • The proposed iCAPs framework effectively analyzes dynamic cerebro-spinal signals to understand CNS organization.
  • This approach reveals large-scale CNS plasticity underlying motor learning, from initial acquisition to consolidation.
  • The versatile framework has broad applications for studying cerebro-spinal networks in various conditions.