Abstract

The mechanisms of learning involved in brain self-regulation have still to be unveiled to exploit the full potential of this methodology for therapeutic interventions. This skill of volitionally changing brain activity presumably resembles motor skill learning which in turn is accompanied by plastic changes modulating resting state networks. Along these lines, we hypothesized that brain regulation and neurofeedback would similarly modify intrinsic networks at rest while presenting a distinct spatio-temporal pattern. High-resolution electroencephalography preceded and followed a single neurofeedback training intervention of modulating circumscribed sensorimotor low β-activity by kinesthetic motor imagery in eleven healthy participants. The participants were kept in the deliberative phase of skill acquisition with high demands for learning self-regulation through stepwise increases of task difficulty. By applying the corrected imaginary part of the coherency function, we observed increased functional connectivity of both the primary motor and the primary somatosensory cortex with their respective contralateral homologous cortices in the low β-frequency band which was self-regulated during feedback. At the same time, the primary motor cortex—but none of the surrounding cortical areas—showed connectivity to contralateral supplementary motor and dorsal premotor areas in the high β-band. Simultaneously, the neurofeedback target displayed a specific increase of functional connectivity with an ipsilateral fronto-parietal network in the α-band while presenting a de-coupling with contralateral primary and secondary sensorimotor areas in the very same frequency band. Brain self-regulation modifies resting state connections spatially selective to the neurofeedback target of the dominant hemisphere. These are anatomically distinct with regard to the cortico-cortical connectivity pattern and are functionally specific with regard to the time domain of coherent activity consistent with a Hebbian-like sharpening concept.

Highlights

  • Brain-computer interfaces are currently being applied in neurofeedback training for a variety of brain-related pathological conditions to alleviate related symptoms (Wyckoff and Birbaumer, 2014)

  • The current data were collected as part of a larger research project investigating the neurophysiology of neurofeedback; whereas previous work analyzed the cortical physiology during neurofeedback training (Vukelic and Gharabaghi, 2015), this evaluation focused on the resting state networks after the interventions

  • We evaluated the functional connectivity within predefined frequency bands of interest (FOI): α (8–14 Hz), low β (15–25 Hz), and high β (26–40 Hz)

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Summary

Introduction

Brain-computer interfaces are currently being applied in neurofeedback training for a variety of brain-related pathological conditions to alleviate related symptoms (Wyckoff and Birbaumer, 2014) In such an environment, contingent feedback of the neuronal state is provided to enhance self-regulation of brain activity via operant conditioning. Motor learning resulted in functionally distinct changes in subsequent intrinsic networks, revealing a distributed pattern of sensory and motor plasticity (Vahdat et al, 2011). These studies suggested that intrinsic resting state activity may reflect the processing of memory during consolidation, thereby resembling functional neuronal networks involved in skill learning (Albert et al, 2009)

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