Intermittent compared to continuous real-time fMRI neurofeedback boosts control over amygdala activation

Abstract

Real-time fMRI neurofeedback is a feasible tool to learn the volitional regulation of brain activity. So far, most studies provide continuous feedback information that is presented upon every volume acquisition. Although this maximizes the temporal resolution of feedback information, it may be accompanied by some disadvantages. Participants can be distracted from the regulation task due to (1) the intrinsic delay of the hemodynamic response and associated feedback and (2) limited cognitive resources available to simultaneously evaluate feedback information and stay engaged with the task. Here, we systematically investigate differences between groups presented with different variants of feedback (continuous vs. intermittent) and a control group receiving no feedback on their ability to regulate amygdala activity using positive memories and feelings. In contrast to the feedback groups, no learning effect was observed in the group without any feedback presentation. The group receiving intermittent feedback exhibited better amygdala regulation performance when compared with the group receiving continuous feedback. Behavioural measurements show that these effects were reflected in differences in task engagement. Overall, we not only demonstrate that the presentation of feedback is a prerequisite to learn volitional control of amygdala activity but also that intermittent feedback is superior to continuous feedback presentation.

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

“…Therefore, the two elements, frequency of feedback presentation and NFT type, were intertwined and could not be separated. A previous study comparing continuous vs intermittent feedback using percent signal change in the amygdala in healthy young adults showed that participants were able to learn to increase the target NFT levels using both approaches, although intermittent feedback was more effective than continuous in that study (Hellrung et al, 2018). In our study we did not find any evidence for a difference between the two approaches.…”

“…Although targeting SMA-striatal connectivity is theoretically motivated by knowledge regarding the disease mechanism and was identified as a potential target in our previous study (Papoutsi et al, 2018), we did not find any reliable evidence that it was better or worse than activity NFT. Both activity (Young et al, 2017;Hellrung et al, 2018) and connectivity (Megumi et al, 2015;Ramot et al, 2017;Yamashita et al, 2017) NFT have been used successfully in other studies, suggesting that both methods are effective.…”

“…To examine differences in NFT learning between the treatment and control groups and the two different types of NFT we used repeated-measures ANCOVA testing for between group differences across all NFT sessions, as well as a linear increase in the target NFT measure (Hellrung et al, 2018) across sessions. The dependent variable was the standardized NFT target estimates and the model included as factors session (modelled as a continuous variable), group (treatment vs control), NFT type (activity vs connectivity) and all their interactions.…”

Neurofeedback: Principles, appraisal, and outstanding issues

“…This is perfectly justified in regard to the fact that neuronal network rhythmic activity at specific frequency bands is thought to contribute to information transfer and processing in the brain (Engel, Fries, & Singer, 2001;Fries, 2005). In fMRI-guided NF protocols, the target function is often the BOLD signal amplitude in well-defined brain regions (Caria & de Falco, 2015;Hellrung et al, 2018;Young et al, 2014;Zotev, Misaki, Phillips, Wong, & Bodurka, 2018;Zotev et al, 2011). However, a target could in principle be any function of possibly spatially (Koush et al, 2015;Ruiz, Buyukturkoglu, Rana, Birbaumer, & Sitaram, 2014) and temporally (Diaz Hernandez, Rieger, Baenninger, Brandeis, & Koenig, 2016;Ros et al, 2017) non-local dynamics.…”

Striving for cognitive enhancement with RT-FMRI neurofeedback

“…As a consequence, future studies investigating neurofeedback applications within the DMN or trying to regulate input-sensitive regions should consider to use intermittent neurofeedback. In two recent studies, it has been found that intermittent neurofeedback was even more efficient for participants in the regulation process than continuous neurofeedback information (Hellrung et al, 2018;Johnson et al, 2012). Advantages of intermittent feedback are that it is robust against noise present in the data, and participants do not have to get used to the inherent delay of the BOLD signal.…”