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

Abstract

Novel methods that stimulate neuroplasticity are increasingly being studied to treat neurological and psychiatric conditions. We sought to determine whether real‐time fMRI neurofeedback training is feasible in Huntington's disease (HD), and assess any factors that contribute to its effectiveness. In this proof‐of‐concept study, we used this technique to train 10 patients with HD to volitionally regulate the activity of their supplementary motor area (SMA). We collected detailed behavioral and neuroimaging data before and after training to examine changes of brain function and structure, and cognitive and motor performance. We found that patients overall learned to increase activity of the target region during training with variable effects on cognitive and motor behavior. Improved cognitive and motor performance after training predicted increases in pre‐SMA grey matter volume, fMRI activity in the left putamen, and increased SMA–left putamen functional connectivity. Although we did not directly target the putamen and corticostriatal connectivity during neurofeedback training, our results suggest that training the SMA can lead to regulation of associated networks with beneficial effects in behavior. We conclude that neurofeedback training can induce plasticity in patients with Huntington's disease despite the presence of neurodegeneration, and the effects of training a single region may engage other regions and circuits implicated in disease pathology.

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

“…Similar to our previous study (Papoutsi et al, 2018) and comparable to other studies (Subramanian et al, 2011;Paret et al, 2014Paret et al, , 2016Nicholson et al, 2017), the ROIs were re-drawn at each session ensuring that only voxels with high activation are selected. The ROIs from the first visit were used as a reference, when drawing the ROIs for the subsequent visits to ensure that the position was similar, although the exact voxels selected might be different.…”

“…To assess change in cognitive and psychomotor function following NFT we calculated a composite score using the same procedure and measures as in our previous study (Papoutsi et al, 2018). In summary, these measures were selected a-priori based on previous work showing that they are sensitive to disease progression(ref), they were converted to z-scores and summed to create the composite score.…”

“…The fMRI runs consisted of 5 upregulation blocks (30s each), 6 rest blocks (30s each) and 5 response blocks (18s each; see Supplementary Figure 2B). Similar to our previous study (Papoutsi et al, 2018) we used a simple attention task during the rest blocks, whereby participants monitored changes in the luminance of a white bar. After the baseline session participants were randomized to one of four groups: activity NFT treatment and control groups, and connectivity NFT treatment and control groups.…”

“…To assess the effect that NFT had on participants' performance in tasks unrelated to the training (far transfer), we examined change from baseline after training in the composite score comprising of measures previously shown to be sensitive to HD progression (Tabrizi et al, 2011;Papoutsi et al, 2018). We performed a similar mixed linear model analysis to the one described in the near transfer section above.…”

“…while levels of atrophy are still low. 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).…”