Neural and behavioral adaptations to frontal theta neurofeedback training: A proof of concept study

Abstract

Previous neurofeedback research has shown training-related frontal theta increases and performance improvements on some executive tasks in real feedback versus sham control groups. However, typical sham control groups receive false or non-contingent feedback, making it difficult to know whether observed differences between groups are associated with accurate contingent feedback or other cognitive mechanisms (motivation, control strategies, attentional engagement, fatigue, etc.). To address this question, we investigated differences between two frontal theta training groups, each receiving accurate contingent feedback, but with different top-down goals: (1) increase and (2) alternate increase/decrease. We hypothesized that the increase group would exhibit greater increases in frontal theta compared to the alternate group, which would exhibit lower frontal theta during down- versus up-modulation blocks over sessions. We also hypothesized that the alternate group would exhibit greater performance improvements on a Go-NoGo shooting task requiring alterations in behavioral activation and inhibition, as the alternate group would be trained with greater task specificity, suggesting that receiving accurate contingent feedback may be the more salient learning mechanism underlying frontal theta neurofeedback training gains. Thirty young healthy volunteers were randomly assigned to increase or alternate groups. Training consisted of an orientation session, five neurofeedback training sessions (six blocks of six 30-s trials of FCz theta modulation (4–7 Hz) separated by 10-s rest intervals), and six Go-NoGo testing sessions (four blocks of 90 trials in both Low and High time-stress conditions). Multilevel modeling revealed greater frontal theta increases in the alternate group over training sessions. Further, Go-NoGo task performance increased at a greater rate in the increase group (accuracy and reaction time, but not commission errors). Overall, these results reject our hypotheses and suggest that changes in frontal theta and performance outcomes were not explained by reinforcement learning afforded by accurate contingent feedback. We discuss our findings in terms of alternative conceptual and methodological considerations, as well as limitations of this research.

Complexity synchronization: a measure of interaction between the brain, heart and lungs

“…It is clear from the figure that the quasiperiodic behavior of the scaling indices from the 64 EEG channels are in synchrony with each other as well as with those from the ECG and respiratory ONs. This figure compares the scaling results from two individuals’ time series chosen from a neurofeedback training study (see 15 for details) based on the highest quality of raw data among all participants (i.e., the data were void of any muscle or movement artifacts) responding to two separate and distinct tasks. The upper two panels in Fig.…”

“…Two participants with the cleanest (artifact-free) raw data were selected from a neurofeedback training study 15 for analysis and the results are presented in Fig. 1 and Table 2 .…”

The Ability to Voluntarily Regulate Theta Band Activity Affects How Pharmacological Manipulation of the Catecholaminergic System Impacts Cognitive Control

“…Importantly, this does not refer to a training of theta band activity but only to the assessment of an individual ability. However, because this is an ability that can be explicitly trained, for example, by means of neurofeedback ( Corlier et al., 2016 ; Enriquez-Geppert et al., 2017 ; Kerick et al., 2023 ), and a trainable ability should—at least to a certain extent—be stable, we strongly assume that theta band modulability can be considered a stable individual ability. In the current study, we examine the relationship between the catecholaminergic system and voluntary theta band modulability using a neurophysiological data analysis approach.…”

Temporal complexity measure of reaction time series: Operational versus event time

“…In the present work, the existence of IPL scaling indices is examined using data previously recorded during a Go–NoGo shooting task under low and high time‐stress conditions as part of a neurofeedback training study (See Task and Procedures and Figure 1 below, and Kerick et al., 2023 for more details). We are not aware of previous research that has investigated the effects of time stress on scaling indices over different time scales within and across repeated experimental sessions spanning several days or weeks.…”

“…We also simulated Y(n) and X(t) time series consisting of temporal complexity consistent with our empirical observations at each time scale.It should be noted here that the number of reaction time trials for each subject, condition, and session was variable because variable numbers of errors of commission (friendly-fire errors) and omission (failure to fire at enemy targets) were inherent in the data. The mean (SD) reaction time latencies in the Low time-stress condition were 529.00 ms (164.79 ms) and 441.44 ms (121.57 ms) in the high time-stress condition collapsed across sessions and was a statistically significant main effect of condition (p < .01) (seeKerick et al, 2023 for additional analyses).…”