Functional Magnetic Resonance Imaging Neurofeedback-guided Motor Imagery Training and Motor Training for Parkinson’s Disease: Randomized Trial

Abstract

Objective: Real-time functional magnetic resonance imaging (rt-fMRI) neurofeedback (NF) uses feedback of the patient’s own brain activity to self-regulate brain networks which in turn could lead to a change in behavior and clinical symptoms. The objective was to determine the effect of NF and motor training (MOT) alone on motor and non-motor functions in Parkinson’s Disease (PD) in a 10-week small Phase I randomized controlled trial.Methods: Thirty patients with Parkinson’s disease (PD; Hoehn and Yahr I-III) and no significant comorbidity took part in the trial with random allocation to two groups. Group 1 (NF: 15 patients) received rt-fMRI-NF with MOT. Group 2 (MOT: 15 patients) received MOT alone. The primary outcome measure was the Movement Disorder Society—Unified PD Rating Scale-Motor scale (MDS-UPDRS-MS), administered pre- and post-intervention “off-medication”. The secondary outcome measures were the “on-medication” MDS-UPDRS, the PD Questionnaire-39, and quantitative motor assessments after 4 and 10 weeks.Results: Patients in the NF group were able to upregulate activity in the supplementary motor area (SMA) by using motor imagery. They improved by an average of 4.5 points on the MDS-UPDRS-MS in the “off-medication” state (95% confidence interval: −2.5 to −6.6), whereas the MOT group improved only by 1.9 points (95% confidence interval +3.2 to −6.8). The improvement in the intervention group meets the minimal clinically important difference which is also on par with other non-invasive therapies such as repetitive Transcranial Magnetic Stimulation (rTMS). However, the improvement did not differ significantly between the groups. No adverse events were reported in either group.Interpretation: This Phase I study suggests that NF combined with MOT is safe and improves motor symptoms immediately after treatment, but larger trials are needed to explore its superiority over active control conditions.

The many facets of motor learning and their relevance for Parkinson's disease

“…Other mechanisms by which exercise leads to beneficial effects include the decrease of the pathological hyperexcitability in the basal ganglia-cortical circuits and the induction of compensatory changes in dopamine handling and neurotransmission, in particular in the dorsolateral striatum (Petzinger et al 2007). Finally, rTMS and neurofeedback are promising add-on treatments that may increase the persistence of the exercise-related benefits (Yang et al 2013; Subramanian et al 2016). …”

Stimulating neural plasticity with real‐time fMRI neurofeedback in Huntington's disease: A proof of concept study

“…As part of the training, patients used motor imagery and received continuous feedback reflecting the level of BOLD activity of the SMA (including pre‐SMA). We selected the SMA, because its function and connectivity with the striatum is affected by HD (Klöppel et al, ) and can provide reliable, continuous signal estimates suitable for real‐time fMRI neurofeedback training (Subramanian et al, ). We expected that patients' SMA activity would increase during the training and after the training patients would be able to volitionally regulate their SMA activity, even in the absence of neurofeedback (Haller et al, ; Scharnowski et al, ).…”

“…Real-time fMRI neurofeedback training is a novel approach that uses real-time fMRI and reinforcement learning to induce changes in brain activity (Caria, Sitaram, & Birbaumer, 2012;De Charms, 2008;Linden and Turner, 2016;MacInnes, Dickerson, Chen, & Adcock, 2016;Shibata, Watanabe, Sasaki, & Kawato, 2011;Sulzer et al, 2013;Weiskopf, 2012). It improves cognitive function in healthy individuals (De Bettencourt, Cohen, Lee, Norman, & Turk-Browne, 2015;Scharnowski, Hutton, Josephs, Weiskopf, & Rees, 2012) and motor function in patients with Parkinson's disease (Subramanian et al, 2011(Subramanian et al, , 2016. By providing participants with feedback of their own neural activity in a closed-loop experimental design, participants gradually learn to control it, thereby inducing structural and functional changes to target regions and their associated networks (Greer, Trujillo, Glover, & Knutson, 2014;Haller et al, 2013;Horovitz, Berman, & Hallett, 2010;MacInnes et al, 2016;Megumi, Yamashita, Kawato, & Imamizu, 2015;Ruiz et al, 2013).…”

“…As part of the training, patients used motor imagery and received continuous feedback reflecting the level of BOLD activity of the SMA (including pre-SMA). We selected the SMA, because its function and connectivity with the striatum is affected by HD (Kl€ oppel et al, 2009) and can provide reliable, continuous signal estimates suitable for realtime fMRI neurofeedback training (Subramanian et al, 2011(Subramanian et al, , 2016.…”

Activity or Connectivity? Evaluating neurofeedback training in Huntington’s disease

“…In a recent proof-of-concept study we used the supplementary motor area (SMA) as a target for real-time fMRI NFT in HD patients (Papoutsi et al, 2018). We selected BOLD fMRI signal from the SMA because it can be reliably measured in real-time (Subramanian et al, 2011(Subramanian et al, , 2016, and its function and connectivity to the striatum is disrupted by HD (Klöppel et al, 2009).…”