Abstract
Two groups of inexperienced brain-computer interface users underwent a purely mental EEG-BCI session that rapidly impacted on their brain. Modulations in structural and functional MRI were found after only 1h of BCI training. Two different types of BCI (based on motor imagery or visually evoked potentials) were employed and analyses showed that the brain plastic changes are spatially specific for the respective neurofeedback. This spatial specificity promises tailored therapeutic interventions (e.g. for stroke patients). A brain-computer-interface (BCI) allows humans to control computational devices using only neural signals. However, it is still an open question, whether performing BCI also impacts on the brain itself, i.e. whether brain plasticity is induced. Here, we show rapid and spatially specific signs of brain plasticity measured with functional and structural MRI after only 1h of purely mental BCI training in BCI-naive subjects. We employed two BCI approaches with neurofeedback based on (i) modulations of EEG rhythms by motor imagery (MI-BCI) or (ii) event-related potentials elicited by visually targeting flashing letters (ERP-BCI). Before and after the BCI session we performed structural and functional MRI. For both BCI approaches we found increased T1-weighted MR signal in the grey matter of the respective target brain regions, such as occipital/parietal areas after ERP-BCI and precuneus and sensorimotor regions after MI-BCI. The latter also showed increased functional connectivity and higher task-evoked BOLD activity in the same areas. Our results demonstrate for the first time that BCI by means of targeted neurofeedback rapidly impacts on MRI measures of brain structure and function. The spatial specificity of BCI-induced brain plasticity promises therapeutic interventions tailored to individual functional deficits, for example in patients after stroke.
Highlights
A brain–computer-interface (BCI), proposed for the first time by Vidal (Vidal, 1973), translates brain activity that reflects the subject’s intention into control commands for a device, bypassing the physiological motor output system
For the MI-BCI group, we observed increased central background rhythm activity, and the mu-rhythm change as well as the MI-BCI task discriminability shows a strong correlation with T1-weighted and eigenvector centrality mapping (ECM) change in sensorimotor cortices
When using the subject specific features of run 4 and run 1, we observed a similar correlation with T1-weighted MRI data in sensorimotor cortex, but no correlation for ECM
Summary
A brain–computer-interface (BCI), proposed for the first time by Vidal (Vidal, 1973), translates brain activity that reflects the subject’s intention into control commands for a device, bypassing the physiological motor output system. Common types of BCI are based on sensorimotor rhythms modulated by a motor-imagery task (MI-BCI), or event-related-potentials (ERP) elicited by visual paradigms. The modulation of the visual-evokedpotential (ERP-BCI) is usually induced by observing and counting flashing letters on a screen. The modulation of sensorimotor rhythms by imagining limb movements (MI-BCI) (Pfurtscheller & Lopes da Silva, 1999) is a more active and complex task where, if the system is adequately tuned to the subject, increased performance is often observed (Blankertz et al 2010; Lorenz et al 2014; Sannelli et al 2016). While there is a large fraction (about 30%) of MI-BCI users that are unable to gain control (Guger et al 2003), recent progress has been made to drastically reduce this percentage by using co-adaptive approaches, i.e. do the MI-BCI users modulate their sensorimotor rhythms to gain BCI control, but the algorithm adapts to the user to increase the system’s accuracy (Vidaurre et al 2011a, Acqualagna et al 2016)
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