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Comment
. 2017 Oct 1;140(10):e64.
doi: 10.1093/brain/awx212.

Reply: On assessing neurofeedback effects: should double-blind replace neurophysiological mechanisms?

Manuel Schabus  1   2
Affiliations
Comment

Reply: On assessing neurofeedback effects: should double-blind replace neurophysiological mechanisms?

Manuel Schabus. Brain. .
No abstract available

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Figures

Figure 1
Figure 1
Comparison of the subjective quality of life data from our earlier single-blind (Schabus et al., 2014) and current double-blind (Schabus et al., 2017) study. (A) Subjective quality of life effects (WHOQOL) are plotted for the sub-domain physical quality of life (including activities of daily living, energy and fatigue, pain and discomfort, sleep and rest or work capacity) and social quality of life (including personal relationships and perceived social support) for our earlier single-blind study. Note that physical quality of life changes over time but irrespective of placebo-feedback training (PFT) or NFT. Data for social quality of life indicate a trend towards increased perceived social support only between sessions with real NFT training [dashed circles; i.e. entrance to LPSG2 in NFT-first group (n = 12); and LPSG2 to LPSG3 in the PFT-first group (n = 11)]. (B) The same subjective data for our double-blind study (Schabus et al., 2017) do not indicate differences in perceived social support (for NFT versus PFT training). Yet we found again a non-specific increase in physical quality of life from study entrance (LPSG1) to the follow-up after 3 months. Note that here LPSGs 1 and 3 flank the first training block (12× NFT or PFT) and LPSGs 5 and 7 flank the second training block (12× NFT or PFT). PFT-first participants included 10 and NFT-first 12 participants (i.e. participants with WHOQOL values for all five time points). F-values depict the interaction between group (NFTfirst, starting with NFT, PFTfirst, starting the protocol with PFT) and time [entrance, learning nights/polysomnographies (LPSG), and Follow-up]. Error bars indicate ± 1 standard error.
Figure 2
Figure 2
Absolute SMR amplitude across the neurofeedback sessions. Note that SMR amplitude (on trained electrode C3) is (i) higher for the NFT training period (‘NFT’) as compared to the baseline before (‘Baseline’) each training trial [main effect for Condition, F(1.21) = 115.98, P < 0.001]; as well as (ii) higher for the NFT training period as compared to the eyes-open resting condition before the training started on that day [‘preRest’; main effect for Condition, F(1.20) = 6.61, P = 0.018]. We here pooled all insomnia and misperception insomniacs with sufficient data points at each session (n = 22 for Baseline to NFT period; n = 21 for preRest to NFT period). To derive absolute SMR amplitude, artefact-corrected EEG data were transferred to the frequency domain by applying the FFT (fast Fourier transform) to 1-s segments. Spectral line values were calculated using one half of the spectrum and are expressed in µV/2. To reduce artefacts caused by potential signal discontinuities at the segment boarders, segments were tapered using a Hanning window (window length 10%). Finally, a periodic variance correction was applied to account for the reduction in signal strength induced by the windowing function. Error bars indicate ± 1 standard error.
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References

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