Abstract
Brain-computer interfaces (BCIs) based on real-time functional magnetic resonance imaging (rtfMRI) are currently explored in the context of developing alternative (motor-independent) communication and control means for the severely disabled. In such BCI systems, the user encodes a particular intention (e.g., an answer to a question or an intended action) by evoking specific mental activity resulting in a distinct brain state that can be decoded from fMRI activation. One goal in this context is to increase the degrees of freedom in encoding different intentions, i.e., to allow the BCI user to choose from as many options as possible. Recently, the ability to voluntarily modulate spatial and/or temporal blood oxygenation level-dependent (BOLD)-signal features has been explored implementing different mental tasks and/or different encoding time intervals, respectively. Our two-session fMRI feasibility study systematically investigated for the first time the possibility of using magnitudinal BOLD-signal features for intention encoding. Particularly, in our novel paradigm, participants (n=10) were asked to alternately self-regulate their regional brain-activation level to 30%, 60% or 90% of their maximal capacity by applying a selected activation strategy (i.e., performing a mental task, e.g., inner speech) and modulation strategies (e.g., using different speech rates) suggested by the experimenters. In a second step, we tested the hypothesis that the additional availability of feedback information on the current BOLD-signal level within a region of interest improves the gradual-self regulation performance. Therefore, participants were provided with neurofeedback in one of the two fMRI sessions. Our results show that the majority of the participants were able to gradually self-regulate regional brain activation to at least two different target levels even in the absence of neurofeedback. When provided with continuous feedback on their current BOLD-signal level, most participants further enhanced their gradual self-regulation ability. Our findings were observed across a wide variety of mental tasks and across clinical MR field strengths (i.e., at 1.5T and 3T), indicating that these findings are robust and can be generalized across mental tasks and scanner types. The suggested novel parametric activation paradigm enriches the spectrum of current rtfMRI-neurofeedback and BCI methodology and has considerable potential for fundamental and clinical neuroscience applications.
Highlights
IntroductionTranslational studies explored the feasibility of real-time functional magnetic resonance imaging (rtfMRI) neurofeedback to remediate pathological brain activation associated with symptoms of various (mostly neurological and psychiatric) disorders including major depressive disorder (Linden et al, 2012; Young et al, 2014b; Hamilton et al, 2016; Zotev et al, 2016), schizophrenia (Ruiz et al, 2013a; Cordes et al, 2015), Parkinson’s disease (Subramanian et al, 2011), spider phobia (Zilverstand et al, 2015), chronic pain (deCharms et al, 2005; Guan et al, 2015), tinnitus (Haller et al, 2010), addiction (Canterberry et al, 2013; Li et al, 2013; Karch et al, 2015; Kirsch et al, 2015; Hartwell et al, 2016), obesity (Frank et al, 2012), autism (Caria and de Falco, 2015), and stroke (Chiew et al, 2012; Young et al, 2014a)
Real-time functional magnetic resonance imaging allows for brain-computer interfacing – therewith, providing a tool to monitor and alter current a) brain activation
Participants received no feedback in one fMRI session, whereas in the other session they were provided with neurofeedback information on the current blood oxygenation level-dependent (BOLD)-signal level in a pre-defined mental task-related brain region
Summary
Translational studies explored the feasibility of rtfMRI neurofeedback to remediate pathological brain activation associated with symptoms of various (mostly neurological and psychiatric) disorders including major depressive disorder (Linden et al, 2012; Young et al, 2014b; Hamilton et al, 2016; Zotev et al, 2016), schizophrenia (Ruiz et al, 2013a; Cordes et al, 2015), Parkinson’s disease (Subramanian et al, 2011), spider phobia (Zilverstand et al, 2015), chronic pain (deCharms et al, 2005; Guan et al, 2015), tinnitus (Haller et al, 2010), addiction (Canterberry et al, 2013; Li et al, 2013; Karch et al, 2015; Kirsch et al, 2015; Hartwell et al, 2016), obesity (Frank et al, 2012), autism (Caria and de Falco, 2015), and stroke (Chiew et al, 2012; Young et al, 2014a)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
