Targeting The Frontoparietal Network Using Bifocal Transcranial Alternating Current Stimulation During A Motor Sequence Learning Task in Healthy Older Adults Part 2
Oct 25, 2023
2.3. Motor learning task
Participants executed two different versions of the SFTT based on the SFTT task used in earlier studies [3,47]. They were asked to perform a 9-item sequence with their non-dominant left hand. The non-dominant hand was used to allow for a larger range of improvement [46]. They were orally instructed to continuously tap the same sequence as fast and as accurately as possible on a four-button keyboard (Current Designs, Philadelphia, PA, USA).
Research shows that memory is one of the important functions of our brains. It enables us to recall past experiences, acquire new knowledge and skills, and make decisions. At the same time, some research also shows that positive thinking and behavioral habits can help improve our memory.
First, exercise and a healthy diet have a huge impact on memory. Studies have found that proper exercise and a balanced diet can help maintain good health and improve brain function and memory. Therefore, maintaining good health is crucial to improving memory.
Secondly, mental health also has a great impact on improving memory. Good sleep habits, positive thoughts and emotions, and reducing stress can all help improve our memory. Research shows that emotionally stable and positive people tend to have better memory performance. At the same time, people should overcome negative emotions such as low self-esteem, anxiety, and depression, so as not to interfere with their memory processes and performance.
Finally, focus and attention can greatly improve memory. Research shows that our brains are better able to process and remember things that we spend more of our attention paying attention to and focusing on. Therefore, staying focused and concentrating on learning and memory tasks can improve our memory.
In short, a combination of good health, positive thinking and behavior habits, and attention and focus can help improve our memory. A positive attitude and good behavioral habits can help us better process and strengthen our brain functions, thereby improving our memory. It can be seen that we need to improve our memory. Cistanche deserticola can significantly improve memory, because Cistanche deserticola can also regulate the balance of neurotransmitters, such as increasing the levels of acetylcholine and growth factors. These substances are very important for memory and learning. In addition, meat can also improve blood flow and promote oxygen delivery, which can ensure that the brain receives sufficient nutrients and energy, thus improving brain vitality and endurance.
Click know ways to improve brain function
The sequences consisted of 4 digits from 2 to 5, which corresponded to the four fingers from the index (2) to the little finger (5) of the left hand. A cursor underneath the displayed sequence moved in response to every finger tap to identify the target digit, regardless of whether or not the button was pressed correctly. Different sequences were used for the baseline and the training measurements.
All sequences were matched in complexity verified with the Kolmogorov complexity test [48]. The baseline measurement consisted of one block of 90 s, and the training measurement consisted of seven blocks of 90 s, with 90 s breaks after every block which lasted 20 min in total. The task was implemented in Presentation software (Neurobehavioral Systems, Berkeley, CA, USA).
Participants performed a low and a high WM load SFTT version. The low WM load version displayed the sequence on the screen asking participants to execute the sequence without prior familiarization. This version is referred to as the “non-memorized” version. For the high WM load version, participants had to memorize the sequence before the task started.
They received the sequence on paper and were asked to learn the sequence by heart, without practicing it on the button keyboard. With the use of a distractor task, during which they had to spell random words in a reversed order sufficient memorization of the sequence was verified. More precisely, participants had to spell backward 3 words in a row and recall the sequence out loud afterward. After 3 times correct, the sequence was deemed sufficiently learned [49].
During the memorized version of the task, the participants could not see the sequence. Displayed on the screen was a sequence of 9 “X's” with the moving cursor underneath to identify the target digit. Participants performed both versions divided over day 1 and day 2 in a randomized order. This order of versions was reversed after cross-over, see Fig. 1C.
2.4. Cognitive task
Participants were asked to perform the N-back task to verify whether this stimulation paradigm enhanced WM performance. The task was implemented in Matlab (The MathWorks Inc., Natick, Massachusetts, USA) and was based on the single N-back task used by Jaeggi and colleagues [50]. The script was adapted from Quent, A.J. [51]. in terms of language (French & English), length, and difficulty level.
The task consisted of a sequence of visual stimuli that were shown on a computer screen. The participants had to respond by clicking the right “Control” button on a computer keyboard when the stimulus was the same as the stimulus presented N positions back. Participants should not respond when a different stimulus is presented. The visual stimuli consisted of 10 random shapes, eight 8-point shapes (numbers 14, 15, 17, 18, 20, 22, 23, and 27), and two 12-point shapes (numbers 20 and 24) taken from Vanderplas and Garvin [52].
The stimuli were presented for 500 ms each with a 2500 ms interstimulus interval. The participants were required to respond within the response window that starts at the onset of the stimulus until the end of the interstimulus interval (3000 ms). The task consisted of 1 to 3 back levels, in that order. The task was divided into a baseline and training session, with the baseline session consisting of 1 block per n-back level (3 blocks in total) and the training session of 3 blocks per level (9 blocks in total).
Every block consisted of 20 þ n trials, with 6 targets and 14 þ n non-targets. The reaction times, hits, misses, false alarms, and correct rejections were measured. Please see Fig. 1 B for a schematic illustration of the task. We had to exclude N ¼ 9 before cross-over N-back task data sets due to an error in the response recording. A total of N ¼ 31 N-back data sets were considered.
2.5. Transcranial alternating current stimulation
Multifocal tACS was applied to the right FPN using two neuroConn DC plus stimulators to enable bifocal stimulation (neuroConn GmbH, Ilmenau, Germany). Participants received both real (30 min) or sham (30 s) stimulation in randomized order, before or after cross-over [24,25,53]. The stimulation protocol consisted of the following parameters: in-phase (0⁰ phase lag), intensity 2 mA (peak-to-peak) was gradually ramped up/down with an interval of 8 s. The in-phase stimulation between the two stimulators was assured by a repeated trigger from stimulator A to stimulator B after every completed cycle to signal the start of a new cycle [54].
The stimulation frequency was adjusted to the personal theta peak frequency, which was recorded during an EEG recording while performing a pre-baseline N-back test of 1 block per level. Rubber concentric electrodes were used: center electrode size diameter: ca. 20 mm, area: ca. 3 cm2 and ring electrode size diameter: out 100 mm/in 70 mm, area: ca. 40 cm2.
Electrode location was defined with the use of a standard 64-channel, EEG actiCAP with a 10/20 system (Brain Products GmbH), targeting F4 corresponding to the dorsolateral prefrontal cortex (DLPFC) and P4 corresponding to the posterior parietal cortex (PPC). The paste used for conductivity with adequately low impedance was SAC2 electrode cream (Spes Medical Srl, Genova, Italy). This paste was adhesive which ensured stable electrode placements. The electrode placement and the electric field distribution were visualized with the use of a standard template in SimNIBS (Version 3.2) [55]. The script to implement bifocal stimulation with ring electrodes was adapted from the open-access Matlab script (© G. Saturnino, 2018).
A template head model was used to simulate the electrode placement and electric field distribution. For the electrode placement and electric field distribution, please see Fig. 2 A & B. At the end of the last stimulation session, we investigated whether the stimulation was well tolerated and if there was a significant difference in experienced sensations between the real and sham conditions.
Moreover, we asked the participants to indicate whether they thought they had received real or sham stimulation during the before and after crossover sessions. The stimulation sensations were described with the use of a structured interview [56]. We checked for the following sensations: itching, pain, burning, metallic/iron taste in the mouth, warmth, fatigue, and others. With the possibility to respond: “none”, “mild”, “moderate”, or “strong”.
2.6. EEG
All EEG recordings were done in a shielded Faraday cage. A customized electrode set-up with 9 electrodes was used, Frontal (Fp1, Fp2, F3, Fz, F4), parietal (Cz, P3, Pz, P4), please see Fig. 2C. Using a 64-channel ANT Neuro EEG cap with eegotmmylab software (ANT Neuro, Netherlands). EEG was recorded during the performance of the N-back task. With the use of markers, the beginning and the end of every separate N-back level were defined. Recordings were done during 3 N-back blocks resulting in approximately 3 min of recording time.
The peak frequency in the theta range (4e8 Hz) was calculated using a custom Matlab script (The MathWorks Inc., USA) adapted from the script used by SalamancaGiron and colleagues [54] and made suitable for theta frequency analysis during N-back task performance. Theta frequencies for tACS were personalized similar to previous work [54,57]. However, we did not intend to compare the efficacy of tACS with personalized frequencies to tACS with standard (non-personalized) frequencies.
Therefore, this study does not aim to demonstrate the beneficial physiological effects of tACS with personalized over tACS with standard (non-personalized) frequencies. The target electrodes F4 & P4, which are the same as the stimulation locations show a small variance in recorded theta frequency. The average theta frequency for the F4 electrode was 4.71 (range 4.12e7.77) and for the P4 electrode 4.97 (range 4.11e6.84).
2.7. Data analyses
The normality of the data was visually checked with histograms and Q-Q plots of residual values and confirmed by verification of skewness ranging between 1 and -1 [58]. P-values of < .05 indicate statistical significance. Pre-processing of the behavioral data of the SFTT was done with an in-house script implemented in Matlab. The main output measures were: correct sequences, total completed sequences, and correct sequences/completed sequences. Preprocessing of the individual N-back data was done with RStudio (version 1.4.1717, 2021) [59].
Individual data were combined in one main file using Microsoft Excel. For analysis, the data was normalized by subtraction to the baseline block related to the stimulation condition. Normalization was performed in the view of heterogenous performance levels, especially typically found in older subjects to provide better comparability between subjects.
The baseline blocks were compared in R using paired-sample t-tests. The equality of the baseline blocks was verified using Bayesian statistics by computing a Bayesian paired-samples t-test with the use of JASP software (version 0.16.0.0). All other analyses of the SFTT and the N-back data were done in Rstudio (version 1.4.1717). Data were analyzed with the use of Linear mixed-effects models that were fitted with the “largest” package. Output was a type III ANOVA table with p-values for F-tests [60].
The effect size was determined using partial eta squared with the “effect size” package. Post-hoc analysis was done by pairwise comparisons, using the estimated marginal means and Tukey correction. Analysis of the tACS stimulation sensations and blinding responses were analyzed with JASP (version 0.8.5.1) [61]. Responses to the real vs. sham stimulation estimations were analyzed using a binomial test. The stimulation sensations were analyzed using contingency tables with chi-squared analysis to control for differences between the real and sham stimulation conditions.
3. Results
3.1. Sequential finger tapping task
The two SFTTs have been analyzed separately as they differ in the amount of WM load (high and low WM load). Before the main analysis, the baseline performance between active and sham stimulation was compared and was not significantly different for both the memorized condition t(19) ¼ 0.72, p ¼ .48, d ¼ 0.16, and the non-memorized condition t(19) ¼ 0, p ¼ 1, d ¼ 0. To further analyze the null result and to confirm the equality of the groups active vs sham groups were compared in both conditions using Bayesian statistics. The analysis indicated for the memorized condition BF01 ¼ 3.41, meaning it is 3.4 times more likely that the baseline results are equal than different. The non-memorized condition indicated BF01 ¼ 4.3, therefore is 4.3 times more likely that the baseline groups are equal.
In this study, online learning is defined as a significant improvement in behavior within the training session. A significant effect of stimulation on learning is defined by a change in improvement dynamics during the training. With the use of a linear mixed effects model, the analysis of the number of correct sequences of the memorized version of the SFTT showed a significant effect for blocks F(6, 247) ¼ 18.57, p < .001, hp 2 ¼ 0.31 indicating a large effect size, as well as a significant effect for stimulation F(1, 247) ¼ 18.83, p < .001, hp 2 ¼ 0.07 with medium effect size, but no blocks stimulation interaction F(6, 247) ¼ 0.77, p ¼ .59, hp 2 ¼ 0.02.
To further define the effect of stimulation on learning, we determined the difference between the conditions at the end of the training, which showed a strong trend for a significant difference t(19) ¼ 2.07, p ¼ .052, d ¼ 0.46. The results of the nonmemorized version show a significant effect for blocks F(6, 247) ¼ 16.00, p < .001, hp 2 ¼ 0.28 (large effect), but no stimulation F(1, 247) ¼ 0.46, p ¼ .499, hp 2 ¼ 0.002 or interaction effect F(6, 247) ¼ 0.36, p ¼ .901, hp 2 ¼ 0.009. Indicating that in both conditions, participants learned significantly, but only in the memorized condition there was a significant effect of tACS stimulation on performance, see Fig. 3.
The lack of an interaction effect does not allow to conclude a significant effect of stimulation on motor learning although the trend for a difference in performance on block 7 indicates a potential for stimulation effect. To further investigate the results of the SFTT and appreciate the variance in performance the individual trajectories of the participants are indicated in the supplementary material, Supplementary Fig. 1. Analyses with nonnormalized data revealed comparable findings, for details please see the supplemental online material (SOM) and SOM Fig. 3.
3.2. Speed and accuracy
To further investigate the results of the memorized condition, the total amount of completed sequences was analyzed as a measure of speed. The results showed a significant block effect F(6, 247) ¼ 28.21, p < .001, hp 2 ¼ 0.41 (large effect) and a significant stimulation effect F(1, 247) ¼ 15.92, p < .001, hp 2 ¼ 0.06 (small effect), but no interaction effect F(6, 247) ¼ 0.23, p ¼ .968, hp 2 ¼ 0.006, see Fig. 4A. Although the active stimulation group is faster compared to the sham group, the similar pattern of improvement points towards a performance rather than a learning effect.
To see whether the increased amount of correct sequences was driven by faster sequence execution or by a simultaneous increase in accuracy, we analyzed the ratio between the total amount of sequences and the correct sequences as an accuracy measure. Upon inspection, the real stimulation group shows different dynamics in accuracy than the sham group. The real stimulation group demonstrates a steep significant increase in accuracy between the first and the second training block while the sham group's increase is more gradual t(19) ¼ 2.68, p ¼ .015. The accuracy between the groups during the 1st training block was not significantly different T(19) ¼ 0.85, p ¼ .404. Therefore, to visualize the difference in dynamics we measured the difference in accuracy about block 1.
Results showed a significant block effect F(6, 247) ¼ 3.47, p ¼ .003, hp 2 ¼ 0.08 (medium effect), and a significant stimulation effect F(1, 247) ¼ 18.31, p < .001, hp 2 ¼ 0.07 (medium effect), but no block stimulation interaction F(6, 247) ¼ 0.85, p ¼ .529, hp 2 ¼ 0.02, see Fig. 4B. In an additional analysis the comparison of behavior on block 7 shows a significant difference between verum and sham t(19) ¼ 2.31, p ¼ .032, d ¼ 0.51. Therefore, although the lack of an interaction effect does not signify significant motor learning effects, the results do indicate that accuracy significantly improved with stimulation in the early stage of training, which remained significantly different in the last block. Analyses with non-normalized data revealed comparable findings, for details please see SOM and SOM Fig. 4.
3.3. N-back task
The N-back task performance was analyzed by the following outcomes: hits, false alarms, accuracy (hits e false alarms), and reaction time for hits. All parameters were analyzed separately using linear mixed-effects models. The stimulation conditions (real vs. sham) and the three N-back difficulty levels were included as independent variables in the model. Two separate analyses were performed, one model included difficulty levels 1 and 2 to mimic the conditions comparable to the study of Violante et al. (2017), additionally, we added difficulty level 3 to the model to test for a stimulation effect on the task with higher cognitive demand [24].
For more information:1950477648nn@gmail.com
