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

Abstract

Non-invasive methods, such as neurofeedback training (NFT), could support cognitive symptom management in Huntington's disease (HD) by targeting brain regions whose function is impaired.The aim of our single-blind, sham-controlled study was to collect rigorous evidence regarding the feasibility of NFT in HD by examining two different methods, activity and connectivity real-time fMRI NFT. Thirty-two HD gene-carriers completed 16 runs of NFT training, using an optimized real-time fMRI protocol. Participants were randomized into four groups, two treatment groups, one receiving neurofeedback derived from the activity of the Supplementary Motor Area (SMA), and another receiving neurofeedback based on the correlation of SMA and left striatum activity (connectivity NFT), and two sham control groups, matched to each of the treatment groups. We examined differences between the groups during NFT training sessions and after training at follow-up sessions.Transfer of training was measured by measuring the participants' ability to upregulate NFT target levels without feedback (near transfer), as well as by examining change in objective, a-priori defined, behavioural measures of cognitive and psychomotor function (far transfer) before and at 2 months after training. We found that the treatment group had significantly higher NFT target levels during the training sessions compared to the control group. However, we did not find robust evidence of better transfer in the treatment group compared to controls, or a difference between the two NFT methods. We also did not find evidence in support of a relationship between change in cognitive and psychomotor function and NFT learning success. We conclude that although there is evidence that NFT can be used to guide participants to regulate the activity and connectivity of specific regions in the brain, evidence regarding transfer of learning and clinical benefit was not robust. Although the intervention is non-invasive, given the costs and absence of reliable evidence of clinical benefit, we cannot recommend real-time fMRI NFT as a potential intervention in HD.

Can we predict real-time fMRI neurofeedback learning success from pre-training brain activity?

“…Brain areas that were targeted in these studies include the amygdala, anterior cingulate cortex (ACC), anterior insula, auditory cortex, dorsolateral prefrontal cortex (dlPFC), dorsomedial prefrontal cortex (dmPFC), medial prefrontal cortex (mPFC), orbitofrontal cortex (OFC), parahippocampal gyrus (PHG), posterior cingulate cortex (PCC), precuneus, premotor cortex (PMC), primary motor cortex (M1), somatomotor cortex (SMC), superior parietal lobule (SPL), supplementary motor area (SMA), ventral tegmental area (VTA), ventromedial prefrontal cortex (vmPFC), and the visual cortex ( Figure 1). Table 1 provides an overview over all studies (Auer et al, 2015;Blefari, Sulzer, Hepp-Reymond, Kollias, & Gassert, 2015b;Kim et al, 2015;Kirschner et al, 2018;Koush et al, 2015Koush et al, , 2013MacInnes et al, 2016;Marins et al, 2015;McDonald et al, 2017;Megumi et al, 2015;Papoutsi et al, 2019Papoutsi et al, , 2018Scharnowski et al, 2012Scharnowski et al, , 2015Skouras & Scharnowski, 2019;Sorger, Kamp, Weiskopf, Peters, & Goebel, 2018;Spetter et al, 2017;Yao et al, 2016;Young et al, 2017). ******** INSERT FIGURE 1 ********** ******** INSERT TABLE 1 **********…”

A randomised feasibility study of computerised cognitive training as a therapeutic intervention for people with Huntington’s disease (CogTrainHD)

“…Studies in mouse models of HD, which replicate the genetic cause of the disease and share strong phenotypic similarities with the human condition of HD, have indicated that cognitive training has the potential to modify both cognitive and motor symptoms of HD [25][26][27]. Studies in HD clinical populations have suggested that neurofeedback (where by the participant can see a visual representation of their neural activity and is required to respond to a task in real time) may have some benefit in comparison to activity based interventions [28]. A single arm study (with no comparator group) which specifically considered computerised working memory training in HD [29] reported beneficial effects in relation to improving working memory, although this study did not include a randomisation procedure or control group.…”

Relating quantitative 7T MRI across cortical depths to cytoarchitectonics, gene expression and connectomics: a framework for tracking neurodegenerative disease

“…Motion was monitored and corrected prospectively by an optical tracking system (Kineticor, Honolulu, HI, USA) (Callaghan et al, 2015b). For the purposes of prospective motion correction of the high resolution MPM acquisitions, each volunteer was scanned while wearing a mouth guard assembly (with attached passive Moiré pattern markers) moulded to their front teeth (manufactured by the Department of Cardiology, Endodontology and Periodontology, University Medical Center Leipzig; (Papoutsi et al, 2018)). R1, R2* and PD parameter maps were computed using the hMRI toolbox (Tabelow et al, 2019).…”