Abstract
Humans differ in their ability to learn how to control their own brain activity by neurofeedback. However, neural mechanisms underlying these inter-individual differences, which may determine training success and associated cognitive enhancement, are not well-understood. Here, it is asked whether neurofeedback success of frontal-midline (fm) theta, an oscillation related to higher cognitive functions, could be predicted by the morphology of brain structures known to be critically involved in fm-theta generation. Nineteen young, right-handed participants underwent magnetic resonance imaging of T1-weighted brain images, and took part in an individualized, eight-session neurofeedback training in order to learn how to enhance activity in their fm-theta frequency band. Initial training success, measured at the second training session, was correlated with the final outcome measure. We found that the inferior, superior, and middle frontal cortices were not associated with training success. However, volume of the midcingulate cortex as well as volume and concentration of the underlying white matter structures act as predictor variables for the general responsiveness to training. These findings suggest a neuroanatomical foundation for the ability to learn to control one's own brain activity.
Highlights
One of the most promising neuroscientific approaches for the enhancement of cognition and task performance, and eventually even for the treatment of psychiatric mental disorders, is neurofeedback (e.g., Enriquez-Geppert et al, 2013a)
Neural mechanisms underlying these inter-individual differences, which may determine training success and associated cognitive enhancement, are not well-understood. It is asked whether neurofeedback success of frontal-midline theta, an oscillation related to higher cognitive functions, could be predicted by the morphology of brain structures known to be critically involved in fm-theta generation
Neurofeedback has been shown to be effective in the therapy of, among other conditions, attention deficit hyperactivity disorder (ADHD; e.g., Birbaumer et al, 2009) leading to a significant reduction in symptom severity even lasting for more than 2 years (Gani et al, 2008)
Summary
One of the most promising neuroscientific approaches for the enhancement of cognition and task performance, and eventually even for the treatment of psychiatric mental disorders, is neurofeedback (e.g., Enriquez-Geppert et al, 2013a). Neurofeedback is based on trial-to-trial modulations of the ongoing neural activity, for instance on the modification of cortical oscillations (e.g., Enriquez-Geppert et al, 2013b). Participants play an active role while utilizing the real-time feedback based on the online-analysis of the brain activity in order to learn how to influence their own brain functions. This feedback loop adheres to operant conditioning principles. Neurofeedback constitutes an inexpensive and non-invasive intervention tool
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