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Trial-by-trial source-resolved EEG responses to gait task challenges predict subsequent step adaptation.

Johanna Wagner1, Ramón Martínez-Cancino1, Scott Makeig1

  • 1Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San Diego, 9500 Gilman Drive, La Jolla, CA, 92093-0559, USA.

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Researchers identified a new brain signal, the step-cue delay negativity (SDN), linked to gait adaptation and fall prevention. This electroencephalography (EEG) finding may help predict fall risk in conditions like Parkinson's disease.

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Area of Science:

  • Neuroscience
  • Motor Control
  • Cognitive Science

Background:

  • Executive functions, particularly inhibitory control, are crucial for gait regulation and preventing falls.
  • Previous research suggests a link between cognitive processes and gait adaptation, but direct neural evidence has been limited.
  • Error-related potentials (ERPs) are electroencephalographic (EEG) signatures associated with performance monitoring and response adjustment.

Purpose of the Study:

  • To investigate whether error-related potential (ERP) features can serve as indicators of executive control during gait adaptation.
  • To identify and characterize a specific neural signal associated with gait adjustments in response to altered auditory cues.
  • To explore the relationship between this neural signal and the magnitude of gait adaptation.

Main Methods:

  • Analysis of high-density (108-channel) EEG data from 18 participants performing an auditory gait pacing task on a treadmill.
  • Participants adapted their step length and rate to unexpected shifts in auditory cue tempo.
  • Independent component analysis (ICA) was used to decompose EEG data and identify source-resolved ERPs, including the novel step-cue delay negativity (SDN).

Main Results:

  • A distinct vertex-negative ERP component, termed step-cue delay negativity (SDN), was identified, time-locked to cues signaling a slower tempo shift.
  • The SDN was localized to the posterior medial frontal cortex (pMFC) and peaked approximately 250 ms after the cue onset.
  • SDN amplitude correlated with the magnitude of the cue latency deviation and predicted the speed of subsequent gait tempo adaptation.

Conclusions:

  • The SDN appears to reflect the perception of the need for gait adjustment and the magnitude of the subsequent motor response, aligning with performance-monitoring models.
  • This finding provides direct neural evidence for cognitive control mechanisms underlying gait adaptation.
  • The SDN may hold potential as a biomarker for assessing gait control deficits and predicting fall risk in neurodegenerative conditions and cognitive decline.