Does priming subjects, with not only resting state but also with mindfulness or/and guided imagery, affect self-regulation of SMR neurofeedback? Framework to improve brain self-regulation and support the rehabilitation of disorders such as depression, anxiety, stress and attention control.
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1
Massachusetts Institute of Technology (Portugal), Portugal
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2
ALGORITMI Center, University of Minho, School of Engineering, University of Minho, Portugal
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3
Life and Health Sciences Research Institute, School of Health Sciences, University of Minho, Portugal
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4
2AI, Polytechnic Institute of Cávado and Ave, Portugal
Introduction:
Self-regulation (SR) is the capacity to voluntary regulate, alter or inhibit certain behaviors. In fact, SR deficits are associated with diverse behavioral problems and mental disorders such as depression, rumination, distraction, anxiety, stress, and attention control. Therefore, understanding how to influence individual self-regulation learning ability through training methods could be the key to unlock predictors and optimize the success of Neurofeedback, and vice-versa (Heatherton 2011).
Neurofeedback Training (NFT) is one of the techniques to train brain self-regulation (BSR). In the current protocols, the benefits from NFT greatly differ between subjects, with high percentage of inefficacy, leading to frustration of potential users, economic costs, and discredit in NFT and its professionals (Sitaram et al. 2016; Enriquez-Geppert and Huster 2017).
BSR practice is closely related to mindfulness meditation and they both seem to depend upon 3 core components: attention control, self-awareness and emotional regulation, (Wood et al. 2014; Tang et al. 2015; S. Kober et al. 2017).
In literature, it has been hypothesized that an “optimal” self-regulation state is necessary to achieve greater performance in voluntary modulating Neurofeedback. In this state the learner should be more: engaged, focused, undistracted and mindful of the experiment. Contrarily, learner should avoid: self-related thinking (self-monitoring), ruminating and distractive and task-unrelated thoughts(Witte et al. 2013; Ninaus et al. 2013; Wood et al. 2014). Suggesting again, a close relation to focused attention forms of mindfulness meditation (MM), that involve training an individual to better sustain their attention toward an intended object (e.g., the sensation of respiration) and away from external (e.g., sounds) or internal sources of distraction (e.g., thoughts, mind wandering) (Chow et al. 2017).
Regarding electroencephalography (EEG) correlations with the “optimal” state, current literature potentially relates this state with electrophysiological sensory motor rhythm (SMR) or/and upper alpha rhythm (UA). Interventions on Mindful (Ros et al. 2014; S. E. Kober et al. 2015; S. Kober et al. 2017; Chow et al. 2017).
Methods:
The intervention consisted in a single session within-group design to investigate the effects on the self-regulation of SMR neurofeedback after priming vs no priming. Therefore, the protocol was set up including an active control group receiving NFT without priming. EEG signals were acquired from 33 healthy young adult participants (20-31 years old). 16 Participants of the control group (CG) were pseudo-randomly matched to 17 participants of the experimental group (EG). The experimental group received sequentially the pre-training priming protocol (PRET) then the SMR NFT.
PRET consists in a Brain State Dependent Stimulation (BSDS) training design to investigate the functional role of specific target oscillations (mental states). Control condition, within-subject ABA design, to simplify the analysis of stimulus dependent oscillations. A, target condition: mindfulness and guided imagery audio stimulus, promote less self-related thinking. B, inverse condition: evaluation emotion questionnaire, promote more self-related thinking. MM guidelines and guided imagery were adapted from (Chow et al. 2017).
SMR NFT protocol was based on (S. Kober et al. 2017; Chow et al. 2017). Consists in a Pre-post training design to investigate single-session self-regulation performance of SMR upregulation (12-15Hz), and simultaneous downregulation of Theta (4-7Hz) and Beta (21-35Hz). EEG spectral densities estimated using a discrete Fourier transform with Hanning windows of 1s segment of 1024 samples. From 32 Active channel electrodes with a sampling rate of 1kHz. Reward band (SMR): mean power of Cz channel during resting state. Inhibit bands (Theta and Beta): mean power + 1 standard deviation of Cz channel – objective, implicit cue to reduce involuntary artifacts, like eye blinks and muscle movements. No individual alpha frequency personalization of bands, because we want to compare stimulus-response of same band frequencies in all the subjects.
In a single session, first the subject fills the sociodemographic and the Five Facet Mindfulness Questionnaire. Then, the training starts, like the representation in the diagram in Figure 1. There are 3 block runs in total, each begins with the rest state with eyes open for 2 min and ends SMR NFT for 3 mins. For the experimental group, in block 2 and 3 the subject is submitted to PRET, after the rest and before the NFT. The control group does not receive PRET. PRET follows ABA design, where stimuli A are randomized (double blind) between mindfulness or imagery guided practice, while B are Likert scale questions in random order.
The session goes at the user own pace, before each task a window appears providing the instructions and asking the subject if she/he is ready to start. Feedback presentation consists in three vertically moving bars, depicting the changes of power in the 3 bands, as one can see in the graphic user interface presented in Figure 2. The SMR bar in the center, and the small bars, from left to right being Theta and Beta, respectively. NFT bars represent the neurofeedback presented to the subject, and the blue line represents the threshold. Reward function: positive feedback (green and sound cues) when reward frequency above threshold (Power/threshold >1) while simultaneously inhibit frequencies below threshold (Power/threshold <1). Points, visual (color change between green and red) and sound cues are presented as reward cues.
Regarding this intervention, ethical approval was obtained from the ethics committee of the University of Minho, Portugal.
Results:
The analysis presented in this abstract makes use of the online NFT processing algorithm. As explained before, a sliding window of 1024 samples, updated every 1024 samples (~1s), is used to extract the mean power spectrum density of SMR band. Using the same strategy to segment the data into 1.024 s epochs, we extract the SMR mean power for each epoch in the rest and NFT tasks in each of the 3 blocks, represented in Figure 3 as the dots in the graph. The red line in the figure represents the absolute mean PSD of rest state. Also, it corresponds to the threshold used in the NFT. Blue is the absolute mean PSD during the NFT task. Green and Grey dots correspond to 1s epochs of SMR. Green epochs are above rest state absolute power, to indicate that the subject surpass the threshold.
After having the relative NFT mean and the rest absolute mean we calculate the percentage of time above target (%tBAT), one of the features to inform learning performance during training (Dempster and Vernon 2009). It’s described as the percent of time spent with positive feedback, which, in this case, equals the ratio between the number of epochs above rest threshold and the total number of epochs. In Figure 3 is illustrated an example of a subject from each group where is possible to follow the within-session dynamics of the SMR.
To understand the effects of priming vs no priming in this preliminary study, an absolute group mean of the %tBAT in each block was calculated. The graphic in Figure 4 illustrates the results. Red represents the control group. Blue, the experimental group. Blue bold line, represents the absolute mean of the EG. Blue dashed lines represent the NFT block 2 after mindfulness and NFT block 3 after guided imagery. Blue points graph represents the NFT block 2 after guided imagery and NFT block 3 after mindfulness. The error bars are presented as vertical standard deviations (std). Stronger increases in the PRET intervention are visible compared to the no priming neurofeedback intervention in block 2 and 3. Additionally, the group with imagery in block 2 and mindfulness in the block 3 show the stronger increase. Nonetheless, this preliminary data is not conclusive nor significant due to standard deviation overlapping (see Table 1 with %tBAT in the supplementary material).
Conclusion:
In conclusion, the preliminary analysis presented in this abstract follows the online data analysis, showing the perspective of the user. This analysis seems to point against the null hypothesis: priming seems to have effects in the NFT SMR protocol developed. Nonetheless, working with EEG online has its limitations because of noise in the signal. Any small movements, like eye blinks or muscle contraction can add artifacts to the signal. Attempts were made to prevent and detect such artifacts in real time and to not include those samples when calculating the Neurofeedback. This process is not perfect and can sometimes lead to a number of false positives leading to variations in the number of epochs included. Therefore, the error range presented in this analysis is still big, deeper offline analysis is being done to validate these preliminary behavioral results.
Figure 1 – Experimental design diagram. Time flows from left to right, top to bottom.
Figure 2 – Graphic user interface of training session. Time flows from left to right, top to bottom.
Figure 3 – SMR PSD of Cz channel. Behavioral depiction of subjects during experiment. (A) Example of Experimental Group Subject. (B) Example of Control Group Subject.
Figure 4 – Group % of time in brain activity target. Representation of mean %tBAT of subjects from each group (Y-axis), during each NFT trial (X-axis). The error bars are presented as vertical standard deviations, but because the std is big (~25%) they overlap and are outside graph boundaries.
Acknowledgements
ME’s Researcher supported by Fundação Para a Ciência e tecnologia (FCT) grant number PD/BD/114033/2015 (in the scope of the MIT PhD Program in Bioengineering Systems). This work has been partially supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013.
References
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Dempster, T, and D Vernon. 2009. “Identifying Indices of Learning for Alpha Neurofeedback Training.” Applied Psychophysiology and Biofeedback. http://link.springer.com/article/10.1007/s10484-009-9112-3.
Enriquez-Geppert, S, and RJ Huster. 2017. “EEG-Neurofeedback as a Tool to Modulate Cognition and Behavior: A Review Tutorial.” Frontiers in Human. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5319996/.
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Keywords:
priming mental states,
decode mental states,
Self-regulation,
Neurofeedback,
mindfulness,
guided imagery,
stimulus
Conference:
XVI Meeting of the Portuguese Society for Neuroscience (SPN2019), Lisboa, Portugal, 30 May - 1 Jun, 2019.
Presentation Type:
Poster presentation
Topic:
Psychiatric Disorders / Addiction
Citation:
Da Costa
NC,
Bicho
E and
Dias
N
(2019). Does priming subjects, with not only resting state but also with mindfulness or/and guided imagery, affect self-regulation of SMR neurofeedback? Framework to improve brain self-regulation and support the rehabilitation of disorders such as depression, anxiety, stress and attention control..
Front. Cell. Neurosci.
Conference Abstract:
XVI Meeting of the Portuguese Society for Neuroscience (SPN2019).
doi: 10.3389/conf.fncel.2019.01.00050
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Received:
16 Apr 2019;
Published Online:
27 Sep 2019.
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Correspondence:
Mr. Nuno Miguel C Da Costa, Massachusetts Institute of Technology (Portugal), Guimarães, Portugal, nbugz@hotmail.com