Combining Behavior and EEG to Study the Effects of Mindfulness Meditation on Episodic Memory

Erika Nyhus; William  A. Engel; Tomas  Donatelli Pitfield; Isabella  M.W. Vakkur

doi:10.3791/61247

JoVE Journal > Neuroscience

신경과학

Combining Behavior and EEG to Study the Effects of Mindfulness Meditation on Episodic Memory

Published: May 11, 2020

doi:

10.3791/61247

Erika Nyhus^1,2, William A. Engel², Tomas Donatelli Pitfield², Isabella M.W. Vakkur²

¹Department of Psychology,Bowdoin College, ²Program in Neuroscience,Bowdoin College

Summary

Here we present a protocol for combining mindfulness meditation training, an episodic memory task, and EEG to understand the behavioral and neural effects of mindfulness meditation on episodic memory.

Abstract

Although there has been recent interest in how mindfulness meditation can affect episodic memory as well as brain structure and function, no study has examined the behavioral and neural effects of mindfulness meditation on episodic memory. Here we present a protocol that combines mindfulness meditation training, an episodic memory task, and EEG to examine how mindfulness meditation changes behavioral performance and the neural correlates of episodic memory. Subjects in a mindfulness meditation experimental group were compared to a waitlist control group. Subjects in the mindfulness meditation experimental group spent four weeks training and practicing mindfulness meditation. Mindfulness was measured before and after training using the Five Facet Mindfulness Questionnaire (FFMQ). Episodic memory was measured before and after training using a source recognition task. During the retrieval phase of the source recognition task, EEG was recorded. The results showed that mindfulness, source recognition behavioral performance, and EEG theta power in right frontal and left parietal channels increased following mindfulness meditation training. In addition, increases in mindfulness correlated with increases in theta power in right frontal channels. Therefore, results obtained from combining mindfulness meditation training, an episodic memory task, and EEG reveal the behavioral and neural effects of mindfulness meditation on episodic memory.

Introduction

There has been recent interest in mindfulness meditation to treat symptoms of mental illness and to enhance cognition, but there is still much research to be done to understand the effects of mindfulness meditation on cognitive function. Previous research has shown that mindfulness meditation can reduce symptoms of stress, depression, generalized anxiety disorder, addictions, attention deficit disorder, and pain disorders¹^,²^,³^,⁴^,⁵^,⁶^,⁷^,⁸^,⁹, as well as increase attention and executive function²^,³^,⁴^,⁵^,⁶^,⁷^,¹⁰^,¹¹^,¹²^,¹³^,¹⁴^,¹⁵^,¹⁶.

Despite interest in the effects of mindfulness meditation on cognition, little research has been done on the effects of mindfulness meditation on episodic memory¹⁷. Given the contribution of attention and executive function to episodic encoding and retrieval, mindfulness meditation should also increase episodic memory. A few recent behavioral studies have shown that mindfulness training increases recognition memory recollection¹⁸^,¹⁹ and free recall²⁰.

In addition to behavioral effects of mindfulness meditation on cognition, previous research has examined the effects of mindfulness meditation on the brain. Mindfulness meditation has been shown to change both the structure and function of the brain. Importantly, mindfulness meditation has been shown to change brain structure and function in networks related to episodic memory²¹^,²²^,²³; specifically increasing grey matter volume and activity in the prefrontal cortex¹^,²⁴^,²⁵^,²⁶^,²⁷^,²⁸^,²⁹^,³⁰^,³¹^,³² and hippocampus²⁵^,²⁷^,²⁸^,³³^,³⁴^,³⁵^,³⁶^,³⁷ as well as increasing theta (4‒8 Hz) power and coherence¹^,³⁶^,³⁸^,³⁹^,⁴⁰^,⁴¹^,⁴²^,⁴³^,⁴⁴^,⁴⁵.

Therefore, previous research has separately examined the behavioral effects of mindfulness meditation on episodic memory¹⁷^,¹⁸^,¹⁹^,²⁰ and the neural effects of mindfulness meditation¹^,²¹^,²²^,²³^,²⁴^,²⁵^,²⁶^,²⁷^,²⁸^,²⁹^,³⁰^,³¹^,³²^,³³^,³⁴^,³⁵^,³⁶^,³⁷^,³⁸^,³⁹^,⁴⁰^,⁴¹^,⁴²^,⁴³^,⁴⁴^,⁴⁵. In order to understand the effects of mindfulness meditation on episodic memory and its neural correlates it is important to measure both behavior and brain activity during episodic memory. One method for studying the neural correlates of episodic memory is with electroencephalography (EEG). Here we describe a method for combining mindfulness meditation training with an episodic memory task while measuring EEG. By combining training in mindfulness meditation with behavioral and neural measures of episodic memory we can better understand the effects of mindfulness meditation on cognitive function.

Protocol

All procedures were approved by the Institutional Review Board of Bowdoin College, in accord with federal guidelines for the protection of human subjects.

1. Subject recruitment and preparation for the experiment

Recruit 40 18-29 year old subjects who are meditation naïve, right-handed, fluent English speakers, with normal or corrected to normal vision, with no neurological conditions.
NOTE: Studying younger children and older adults would require a separate age-specific study. The development of the frontal and parietal lobes is important for performing the episodic memory task. And there is variability in the EEG across age. Studying younger children and older adults requires age specific cognitive tasks and specialized EEG recording and data analysis protocols that are not accommodated in the present protocol. Recruit only right-handed subjects to reduce variability in EEG activity.
Randomly assign 40 subjects to a mindfulness meditation experimental or waitlist control group for a total of 20 subjects in each group.
Schedule the experimental sessions and mindfulness meditation training such that the delay between pre-training and post-training experimental sessions are equal for the mindfulness meditation experimental and waitlist control groups (see Figure 1 for a visual depiction of the sessions).

Figure 1: Visual depiction of the sessions. Subjects completed the Five Facet Mindfulness Questionnaire (FFMQ)⁴⁶ and performed the episodic memory task while EEG was recorded during the pre-training and post-training experimental sessions. Subjects were randomly assigned to either train in mindfulness meditation for four weeks or remain on a waitlist to be trained in mindfulness meditation. Please click here to view a larger version of this figure.

Inform subjects about the procedures involved in the behavioral testing, the EEG recording, and the mindfulness meditation training. Ensure that subjects refrain from practicing meditation outside those conducted for the research study.

2. Mindfulness questionnaire

For each experimental session, have subjects complete the Five Facet Mindfulness Questionnaire (FFMQ)⁴⁶ (see Supplementary File 1).
Analyze the mindfulness data.
1. Measure each subject’s mindfulness by calculating scores for FFMQ Total as well as Observe, Describe, Awareness, Nonjudge, and Nonreactive scales by summing up the scores for each subscale (note that for some items the scoring must be reversed (i.e., change 1 to 5, 2 to 4, 4 to 2, and 5 to 1) according to the instructions in the FFMQ⁴⁶ (see scoring instructions in Supplementary File 1).
2. Compare subjects’ FFMQ Total as well as Observe, Describe, Awareness, Nonjudge, and Nonreactive scores for the mindfulness meditation experimental and waitlist control group across pre-training and post-training experimental sessions.

3. Episodic memory task

Prepare a list of 800 adjectives that are equated for word frequency according to the Kucera and Francis⁴⁷ word norms (see Supplementary File 2).
For each experimental session, have subjects practice the encoding phase by presenting 10 words and performing the encoding task as described below.
For each experimental session, have subjects perform the encoding phase.
1. Have subjects study a list of 200 adjectives and either create a mental image of a spatial scene described by the adjective (place task) or think about the meaning of the word and rate its pleasantness (pleasant task).
2. After presentation of each word, ask subjects to rate how well they performed the encoding task (see Figure 2 for a visual depiction of the encoding procedure).
For each experimental session, have subjects practice the source retrieval phase by presenting the 10 words that were shown at encoding practice and five new words, and perform the source retrieval task as described below.
For each experimental session, have subjects perform the source retrieval phase while recording EEG.
1. Present the 200 words that were shown at encoding randomly intermixed with 200 new words. Send time stamps that correspond to each behavioral condition to the EEG recording. Words should be presented in 20 blocks to give subjects rest breaks to rest their eyes.
2. During the presentation of each word, ask subjects to indicate if the word was new or if they recognized it as being studied in the encoding phase. For recognized words, ask subjects to indicate the source, whether the word was studied in the place task or the pleasant task (see Figure 2 for a visual depiction of the source retrieval procedure).
  NOTE: The episodic memory task can be designed using any software designed for behavioral research such as EPrime which can send time stamps to the EEG recording using Task Events (see Table of Materials). Tutorials and example experiments are available online (e.g., https://pstnet.com, https://step.talkbank.org⁴⁸).

Figure 2: Visual depiction of the experimental paradigm. During the episodic memory task, subjects studied adjectives and either imagined a scene (place task) or judged its pleasantness (pleasant task). During the source retrieval phase subjects decided which task was performed with each word (“Old Place Task” or “Old Pleasant Task”) or “New”. This figure has been modified from Nyhus et al.⁶⁰. Please click here to view a larger version of this figure.

Mark each trial based on the behavioral condition and the subject’s response (see Figure 3 and Figure 4) and analyze the episodic memory behavioral data.
1. Measure subjects’ ability to remember source information by calculating item discrimination (item d’, see Figure 3):
  Z(hit rate) – Z(false alarm rate)
2. Measure subjects’ ability to remember source information by calculating source discrimination (source d’, see Figure 4).
  Z(correct source rate) – Z(incorrect source rate)
3. Compare subjects’ item and source discrimination (item and source d’) for the mindfulness meditation experimental and waitlist control group across pre-training and post-training experimental sessions.

Figure 3: Data categories included in the measurement of word memory. Trials were marked based on the behavioral condition and the subject’s response and used to calculate item discrimination (item d’). Please click here to view a larger version of this figure.

Figure 4: Data categories included in the measurement of source memory. Trials were marked based on the behavioral condition and the subject’s response and used to calculate source discrimination (source d’). Please click here to view a larger version of this figure.

4. EEG recording and analysis

Set up the EEG cap (see Table of Materials).
NOTE: An EEG capping tutorial and other useful information is available through online resources (e.g., https://pursue.richmond.edu⁴⁹).
1. Measure the subject’s head and put all the electrodes on the correct size EEG cap according to the extended international 10-20 system.
2. Clean the subject’s forehead with an alcohol wipe.
3. Apply the EEG cap to the subject’s head by parting their hair then inserting conductive gel with a Luer-lock syringe with a blunted needle.
4. Using the EEG recording software, click on impedances and make sure they are under the resistance level recommended by the specific EEG system chosen for use by the researchers.
5. Ask the subject to remain as still as possible during the experiment. Show the subject the EEG signal when they are still and when they blink or make jaw or facial movement.
Record the EEG.
1. Set the EEG amplifier with the EEG recording software by clicking on edit workspace and set to acquire signal with a .1‒100 Hz bandpass filter and 500 Hz sampling rate for all subjects.
2. Start the EEG recording.
3. Start the source retrieval phase and ensure that the time stamps from the source retrieval task are showing up in the EEG recording.
Once the subject has completed the source retrieval task, clean the EEG cap and electrodes with deionized water and disinfectant.
Process and analyze the EEG data.
1. High-pass filter the data at 1 Hz and low-pass filter the data at 100 Hz.
2. Identify and interpolate bad channels using surrounding channels⁵⁰.
3. Re-refence the data to an average reference⁵¹.
4. Segment the data relative to the onset of each time stamp from the source retrieval task and subtract a pre-stimulus baseline period.
5. Identify and remove artifacts in the EEG data such as eye-blink and eye-movement artifacts. Detect and reject trials with large artifacts (voltage fluctuations of over 1,000 μV or data 5 standard deviations beyond the norm). Reconstruct the EEG after running independent component analysis (ICA)⁵² and identifying and removing noise components⁵³.
6. Convert the EEG data to the time-frequency domain across 100 log-spaced frequencies from 3 Hz to 125 Hz using a Morlet wavelet transformation with the wavelet increasing from 3 cycles at 3 Hz to 25 cycles at 125 Hz.
7. Compare theta power relative to pre-stimulus baseline in the mindfulness meditation experimental and waitlist control groups across pre-training and post-training experimental sessions in right frontal and left parietal channels which show effects during source retrieval⁵⁴^,⁵⁵^,⁵⁶^,⁵⁷. All analyses should control for multiple comparisons.
  NOTE: The EEG data can be processed and analyzed using publicly available software designed for signal processing such as EEGLab⁵⁸. EEGLab training workshops and tutorials are available through the Swartz Center for Computational Neuroscience (https://sccn.ucsd.edu/eeglab/index.php).

5. Mindfulness meditation training

Hire a mindfulness meditation instructor trained in the Mindfulness Based Stress Reduction (MBSR) technique⁵⁹.
NOTE: Instructors trained in the MBSR technique can be found online (e.g. https://www.brown.edu/public-health/mindfulness/programs/mbsr-teacher-recognition).
Have the 20 subjects in the mindfulness meditation experimental group meet as a group for one hour each week for four weeks with the mindfulness meditation instructor.
NOTE: The standard MBSR course is eight weeks and includes breath awareness, sitting meditation, yoga, and relaxation techniques. The mindfulness meditation training should include aspects of the standard MBSR course such as breath awareness and sitting meditation (see Supplementary File 3). These practices, which relate to focusing attention and executive function are most likely to contribute to episodic memory.
Have subjects practice mindfulness meditation for at least 20 min each day using a guided breath awareness meditation recording provided by the mindfulness meditation instructor.
Track daily mindfulness meditation practice by asking subjects how many minutes they practiced mindfulness meditation, what they did during their meditation, and how the practice was going for them through daily emailed surveys (see Table of Materials).
NOTE: Researchers should consider excluding subjects who do not spend a substantial amount of time practicing mindfulness meditation.
Schedule the post-training experimental session as soon as possible after completion of the mindfulness meditation training.
NOTE: Researchers should consider excluding subjects who are unable to complete the post-training experimental session soon after the completion of the mindfulness meditation training. Please reach out to experts in cognitive neuroscience who use the EEG technique for randomized controlled experiments for further information.

Representative Results

Representative results are reported for 40 meditation naïve, right-handed, fluent English-speaking subjects (10 male and 10 female subjects ranging from 18 to 22 years old in the mindfulness meditation experimental group and 7 male and 13 female subjects ranging from 18 to 22 years old in the waitlist control group). Behavioral and EEG data were analyzed using mixed analysis of variance (ANOVA) comparing mindfulness meditation experimental and waitlist control groups (experimental, control) across time (pre-training, post-training). All post-hoc tests corrected for multiple comparisons.

Mindfulness questionnaires
First, analysis assessed whether the mindfulness meditation training was successful. Subjects spent a substantial amount of time practicing mindfulness meditation and their mindfulness increased as measured by the FFMQ. Specifically, there was an interaction between group and time for FFMQ Total (F(1,38) = 11.15, MSE = 67.67, p <. 01) and a marginal interaction between group and time for FFMQ Describe (F(1,38) = 3.35, MSE = 12.26, p = .08) and Nonjudge (F(1,38) = 3.87, MSE = 15.37, p = .06) scales. Scores increased from pre-training to post-training for FFMQ Total (F(1,19) = 15.60, MSE = 63.34, p < .01), Describe (F(1,19) = 6.36, MSE = 8.44, p = .02), and Nonjudge (F(1,19) = 10.12, MSE = 8.60, p < .01) scales for the mindfulness meditation experimental group, whereas the waitlist control group did not change (see Table 1).

	Experimental		Control
	Pre-training	Post-training	Pre-training	Post-training
Total	128.13 (2.38)	138.07 (3.24)	123.59 (4.19)	121.25 (4.77)
Observe	26.98 (1.16)	28.70 (1.00)	23.83 (1.14)	23.70 (1.26)
Describe	29.5 (1.36)	31.82 (.99)	27.10 (1.25)	26.55 (1.26)
Awareness	25.25 (1.06)	26.95 (1.12)	25.27 (.94)	24.05 (1.28)
Nonjudge	24.65 (1.26)	27.60 (1.40)	27.50 (1.42)	27.00 (2.05)
Nonreacitve	21.75 (.99)	23.00 (1.08)	19.90 (1.09)	19.95 (1.16)

Table 1: Five Facet Mindfulness Questionnaire data. FFMQ Total as well as Observe, Describe, Awareness, Nonjudge, and Nonreactive scores for the mindfulness meditation experimental and the waitlist control group for the pre-training compared to the post-training experimental session. Means with standard errors in parentheses are shown. This table has been modified from Nyhus et al.⁶⁰.

Episodic memory
Second, analysis examined the effect of mindfulness meditation on behavioral performance of the episodic memory task. The mindfulness meditation training led to increases in source memory as measured by source discrimination (source d’). Although there was no interaction between group and time (F(1,38) = 1.16, MSE = .12, p = .29), pairwise comparisons showed that source discrimination increased from pre-training to post-training for the mindfulness meditation experimental group (F(1,19)=10.53, MSE=.12, p<.01), but not the waitlist control group (see Table 2).

		Experimental		Control
	Condition	Pre-training	Post-training	Pre-training	Post-training
Hit	Place correct source	.66 (.02)	.67 (.03)	.71 (.03)	.69 (.02)
	Pleasantness correct source	.61 (.03)	.72 (.03)	.64 (.05)	.74 (.03)
FA	Place incorrect source	.34 (.02)	.33 (.03)	.29 (.03	.31 (.02)
	Pleasantness incorrect source	.39 (.03)	.28 (.03)	.36 (.05)	.26 (.03)
Source d'		.70 (.11)	1.06 (.12)	1.04 (.17)	1.23 (.14)
Source c		-.06 (.05)	.07 (.05)	-.12 (.12)	.10 (.07)

Table 2: Source behavioral data. Hit rate, false alarm rate, source discrimination (source d’) and response bias (source c) for the mindfulness meditation experimental and the waitlist control group for the pre-training compared to the post-training experimental session. Means with standard errors in parentheses are shown. This table has been modified from Nyhus et al.⁶⁰.

EEG Results
Third, EEG analysis examined the effect of mindfulness meditation on a neural correlate of episodic memory. Specifically, theta power was examined in right frontal and left parietal channels from 1000 to 1500 ms as these effects have been found in multiple source retrieval tasks⁵⁴^,⁵⁵^,⁵⁶^,⁵⁷. For the left parietal channels, group interacted with time (F(1,37) = 9.52, MSE = .92, p < .01). Theta power increased from pre-training to post-training for the mindfulness meditation experimental group (F(1,19) = 17.37, MSE = .23, p< .01), but not the waitlist control group (see Figure 5).

Figure 5: Effect of mindfulness meditation on theta power. Theta power for the mindfulness meditation experimental and the waitlist control group for the pre-training compared to the post-training experimental session. (A) Time-frequency spectrograms across times and frequencies in a right frontal channel. (B) Time-frequency spectrograms across times and frequencies in a left parietal channel. (C) Theta power across all channels from 1000–1500 ms and differences from pre-training to post-training. (C) Black * marks analyzed channels in right frontal and left parietal regions. Color scale: decibel change from pre-stimulus baseline and p-value of pre-training to post-training differences. This figure has been modified from Nyhus et al.⁶⁰. Please click here to view a larger version of this figure.

Finally, the correlation between changes in mindfulness and changes in episodic memory behavioral performance and EEG was examined in the mindfulness meditation experimental group. There was a positive correlation between increases in FFMQ Describe scores from pre-training to post-training and EEG theta power increases from pre-training to post-training in right frontal channels (r = .72, n = 20, p < .01, two-tailed, Bonferrroni corrected; see Figure 6).

Figure 6: Correlation between changes in FFMQ and theta power. Correlation between the average difference in theta power between pre-training and post-training for hits and correct rejections in right frontal channels and the difference in FFMQ Describe scores between pre-training and post-training. This figure has been modified from Nyhus et al.⁶⁰. Please click here to view a larger version of this figure.

Supplemental File 1. Please click here to download this file.

Supplemental File 2. Please click here to download this file.

Supplemental File 3. Please click here to download this file.

Discussion

The current protocol provided the first evidence that mindfulness meditation can increase source memory and theta oscillations. By combining training in mindfulness meditation with behavioral and neural measures we are better able to understand the effects of mindfulness meditation on episodic memory and its neural correlates.

Although previous research has separately examined the behavioral effects of mindfulness meditation on episodic memory¹⁷^,¹⁸^,¹⁹^,²⁰ and the neural effects of mindfulness meditation¹^,²¹^,²²^,²³^,²⁴^,²⁵^,²⁶^,²⁷^,²⁸^,²⁹^,³⁰^,³¹^,³²^,³³^,³⁴^,³⁵^,³⁶^,³⁷^,³⁸^,³⁹^,⁴⁰^,⁴¹^,⁴²^,⁴³^,⁴⁴^,⁴⁵, no study has combined behavior and EEG during episodic memory. In addition, previous research on mindfulness meditation has often studied expert meditators¹^,¹⁷^,²¹^,²²^,²³^,²⁴^,²⁵^,²⁶^,²⁷^,²⁸^,²⁹^,³⁰^,³²^,³³^,³⁵^,³⁶^,³⁷^,³⁸^,³⁹^,⁴⁰^,⁴¹^,⁴³^,⁴⁴ and therefore risks self-selection bias. By using a longitudinal design with randomly assigned mindfulness meditation and waitlist control groups we were better able to control for group differences. Finally, previous research on mindfulness meditation has often used the full 8-week MBSR course, but the current study showed significant effects with only 4-weeks of mindfulness meditation training.

There were a number of key steps in successfully implementing these methods. First, random assignment to the mindfulness meditation experimental or waitlist control group was critical for ensuring that the groups were roughly equal. Second, it was important to focus the mindfulness meditation training on aspects of the MBSR course that relate to focusing attention and executive function (e.g., breath awareness) as these are most likely to contribute to episodic memory. Third, it was important to have subjects spend a substantial amount of time practicing mindfulness meditation and to accurately report the time they spent meditating each day. Fourth, it was important to equate the time between pre-training and post-training experimental sessions between the mindfulness meditation experimental and waitlist control groups to control for timing and to schedule the post-training experimental session as soon as possible following the mindfulness meditation training so that the effects of mindfulness meditation training did not dissipate prior to testing. Fifth, it is likely that mindfulness meditation affects episodic memory by increasing attention and executive function. Therefore, it was important to use an episodic memory task that requires executive function such as source memory. Finally, it is important to obtain high quality EEG data that is free of artifacts.

Although there were advantages to this method over existing methods, a few limitations should be noted. The effect of mindfulness meditation on source memory was weak. This could have resulted from using subjects who were healthy young adults with good memory performance or the limited amount of time that subjects spent practicing mindfulness meditation. The 4-week mindfulness meditation training was shorter than the standard 8-week MBSR course and, on average, subjects did not report spending the full 20 min practicing mindfulness meditation each day. In addition, there was no active control group, so it is unclear how mindfulness meditation compares to other treatments in boosting source memory or theta oscillations. Finally, the EEG analysis methods used here do not separate out the contribution of periodic oscillatory power from aperiodic 1/f non-oscillatory power which may affect the interpretation of the results. Therefore, future research should consider using subjects with weaker memory ability, implementing the full 8-week MBSR course, employing an active control condition, and using newly developed analysis methods that separate out oscillatory and 1/f non-oscillatory activity⁶¹.

Therefore, the current methods were successful in combining behavior and EEG to study the effects of mindfulness meditation on episodic memory. Future research should use these methods to compare mindfulness meditation with other treatments that have been shown to improve episodic memory and change the structure and function of the brain. In addition, future research should combine behavior and neural measures to examine the effects of mindfulness meditation on other facets of cognition. By combining behavioral and neural measures and comparing mindfulness meditation to alternative treatments we will be better able to determine the most promising treatments for cognitive enhancement.

Disclosures

The authors have nothing to disclose.

Acknowledgements

This work was supported by administration at Bowdoin College and the Bowdoin Life Sciences Research Fellowship, the Peter J. Grua and Mary G. O’Connell Faculty/Student Research Award, and the Kufe Family Student Research Fellowship. We thank Benjamin Tipton for leading the mindfulness meditation course and Hannah Reese for help with experiment design and analysis.

Materials

BrainVision actiCHamp	Brain Products GmbH, Gilching, Germany	BP-09020	64-channel EEG system
BrainVision Recorder	Brain Products GmbH, Gilching, Germany	BP-00020	EEG recording software for EEG data acquisition
E-Prime 2.0 Professional	Psychology Software Tools, Inc., Sharpsburg, PA	PST-100577	Software designed for behavioral research that can interface with the EEG recording
Qualtrics	Qualtrics, Provo, UT	Core XM	Survey tool

References

Cahn, B. R., Polich, J. Meditation states and traits: EEG, ERP, and neuroimaging studies. Psychological Bulletin. 132 (2), 180-211 (2006).
Creswell, J. D. Mindfulness Interventions. Annuaul Reviews of Psychology. 68, 491-516 (2017).
Eberth, J., Sedlmeier, P. The effects of mindfulness meditation: A meta-analysis. Mindfulness. 3, 174-189 (2012).
Goyal, M., et al. Meditation programs for psychological stress and well-being: a systematic review and meta-analysis. JAMA Internal Medicine. 174 (3), 357-368 (2014).
Holzel, B. K., et al. How Does Mindfulness Meditation Work? Proposing Mechanisms of Action From a Conceptual and Neural Perspective. Perspectives on Psychological Science. 6 (6), 537-559 (2011).
Sedlmeier, P., et al. The psychological effects of meditation: a meta-analysis. Psychological Bulletin. 138 (6), 1139-1171 (2012).
Tang, Y. Y., Holzel, B. K., Posner, M. I. The neuroscience of mindfulness meditation. Nature Reviews Neuroscience. 16 (4), 213-225 (2015).
Van Dam, N. T., et al. Mind the Hype: A Critical Evaluation and Prescriptive Agenda for Research on Mindfulness and Meditation. Perspectives on Psychological Science. , (2017).
MacCoon, D. G., et al. The validation of an active control intervention for Mindfulness Based Stress Reduction (MBSR). Behaviour Research and Therapy. 50 (1), 3-12 (2012).
Bailey, N. W., et al. Mindfulness meditators show enhanced working memory performance concurrent with different brain region engagement patterns during recall. bioRxiv. , (2019).
Chiesa, A., Calati, R., Serretti, A. Does mindfulness training improve cognitive abilities? A systematic review of neuropsychological findings. Clinical Psychology Review. 31 (3), 449-464 (2011).
Lutz, A., Slagter, H. A., Dunne, J. D., Davidson, R. J. Attention regulation and monitoring in meditation. Trends in Cognitive Sciences. 12 (4), 163-169 (2008).
MacCoon, D. G., MacLean, K. A., Davidson, R. J., Saron, C. D., Lutz, A. No sustained attention differences in a longitudinal randomized trial comparing mindfulness based stress reduction versus active control. PLoS One. 9 (6), 97551 (2014).
Mrazek, M. D., Franklin, M. S., Phillips, D. T., Baird, B., Schooler, J. W. Mindfulness training improves working memory capacity and GRE performance while reducing mind wandering. Psychological Science. 24 (5), 776-781 (2013).
Wang, M. Y., et al. Mindfulness meditation alters neural activity underpinning working memory during tactile distraction. bioRxiv. , (2019).
Zeidan, F., Johnson, S. K., Diamond, B. J., David, Z., Goolkasian, P. Mindfulness meditation improves cognition: evidence of brief mental training. Consciousness and Cognition. 19 (2), 597-605 (2010).
Levi, U., Rosenstreich, E. Minfulness and memory: a review of findings and a potential model. Journal of Cognitive Enhancement. , (2018).
Basso, J. C., McHale, A., Ende, V., Oberlin, D. J., Suzuki, W. A. Brief, daily meditation enhances attention, memory, mood, and emotional regulation in non-experienced meditators. Behavioral Brain Research. 356, 208-220 (2019).
Brown, K. W., Goodman, R. J., Ryan, R. M., Analayo, B. Mindfulness Enhances Episodic Memory Performance: Evidence from a Multimethod Investigation. PLoS One. 11 (4), 0153309 (2016).
Lykins, E. L. B., Baer, R. A. Performance-based tests of attentention and memory in long-term mindfulness meditators and demographically matched non-meditators. Cognitive Therapy Research. 36, 103-114 (2012).
Fox, K. C., et al. Functional neuroanatomy of meditation: A review and meta-analysis of 78 functional neuroimaging investigations. Neuroscience and Biobehavioral Reviews. 65, 208-228 (2016).
Fox, K. C., et al. Is meditation associated with altered brain structure? A systematic review and meta-analysis of morphometric neuroimaging in meditation practitioners. Neuroscience and Biobehavioral Reviews. 43, 48-73 (2014).
Tomasino, B., Fregona, S., Skrap, M., Fabbro, F. Meditation-related activations are modulated by the practices needed to obtain it and by the expertise: an ALE meta-analysis study. Frontiers in Human Neuroscience. 6, 346 (2012).
Kang, D. H., et al. The effect of meditation on brain structure: cortical thickness mapping and diffusion tensor imaging. Social Cognitive and Affective Neuroscience. 8 (1), 27-33 (2013).
Lazar, S. W., et al. Functional brain mapping of the relaxation response and meditation. Neuroreport. 11 (7), 1581-1585 (2000).
Lazar, S. W., et al. Meditation experience is associated with increased cortical thickness. Neuroreport. 16 (17), 1893-1897 (2005).
Luders, E., et al. Global and regional alterations of hippocampal anatomy in long-term meditation practitioners. Human Brain Mapping. 34 (12), 3369-3375 (2013).
Luders, E., Toga, A. W., Lepore, N., Gaser, C. The underlying anatomical correlates of long-term meditation: larger hippocampal and frontal volumes of gray matter. Neuroimage. 45 (3), 672-678 (2009).
Sperduti, M., Martinelli, P., Piolino, P. A neurocognitive model of meditation based on activation likelihood estimation (ALE) meta-analysis. Consciousness and Cognition. 21 (1), 269-276 (2012).
Tang, Y. Y., Rothbart, M. K., Posner, M. I. Neural correlates of establishing, maintaining, and switching brain states. Trends in Cognitive Sciences. 16 (6), 330-337 (2012).
Tomasino, B., Fabbro, F. Increases in the right dorsolateral prefrontal cortex and decreases the rostral prefrontal cortex activation after-8 weeks of focused attention based mindfulness meditation. Brain and Cognition. 102, 46-54 (2016).
Zeidan, F. . The Handbook of Mindfulness: Theory, Research, and Practice. The. , (2015).
Engstrom, M., Pihlsgard, J., Lundberg, P., Soderfeldt, B. Functional magnetic resonance imaging of hippocampal activation during silent mantra meditation. Journal of Alternative and Complementary Medicine. 16 (12), 1253-1258 (2010).
Holzel, B. K., et al. Mindfulness practice leads to increases in regional brain gray matter density. Psychiatry Research. 191 (1), 36-43 (2011).
Holzel, B. K., et al. Investigation of mindfulness meditation practitioners with voxel-based morphometry. Social Cognitive and Affective Neuroscience. 3 (1), 55-61 (2008).
Lou, H. C., et al. A 15O-H2O PET study of meditation and the resting state of normal consciousness. Human Brain Mapping. 7 (2), 98-105 (1999).
Luders, E., Kurth, F., Toga, A. W., Narr, K. L., Gaser, C. Meditation effects within the hippocampal complex revealed by voxel-based morphometry and cytoarchitectonic probabilistic mapping. Frontiers in Psychology. 4, 398 (2013).
Aftanas, L. I., Golosheikin, S. A. Changes in cortical activity during altered state of consciousness: study of meditation by high resolution EEG. Fiziologiia Cheloveka. 29 (2), 18-27 (2003).
Brandmeyer, T., Delorme, A. Reduced mind wandering in experienced meditators and associated EEG correlates. Experimenal Brain Research. 236 (9), 2519-2528 (2018).
Delmonte, M. M. Electrocortical activity and related phenomena associated with meditation practice: a literature review. International Journal of Neuroscience. 24 (3-4), 217-231 (1984).
Fell, J., Axmacher, N., Haupt, S. From alpha to gamma: electrophysiological correlates of meditation-related states of consciousness. Medical Hypotheses. 75 (2), 218-224 (2010).
Kubota, Y., et al. Frontal midline theta rhythm is correlated with cardiac autonomic activities during the performance of an attention demanding meditation procedure. Cognitive Brain Research. 11 (2), 281-287 (2001).
Lee, D. J., Kulubya, E., Goldin, P., Goodarzi, A., Girgis, F. Review of the Neural Oscillations Underlying Meditation. Frontiers in Neuroscience. 12, 178 (2018).
Lomas, T., Ivtzan, I., Fu, C. H. A systematic review of the neurophysiology of mindfulness on EEG oscillations. Neuroscience and Biobehavioral Reviews. 57, 401-410 (2015).
Tang, Y. Y., et al. Central and autonomic nervous system interaction is altered by short-term meditation. Proceedings of the National Academy of Sciences of the United States of America. 106 (22), 8865-8870 (2009).
Baer, R. A., Smith, G. T., Hopkins, J., Krietemeyer, J., Toney, L. Using self-report assessment methods to explore facets of mindfulness. Assessment. 13 (1), 27-45 (2006).
Kucera, H., Francis, W. N. . Computational Analysis of Present-day American English. , (1967).
MacWhinney, B., St James, J., Schunn, C., Li, P., Schneider, W. STEP–a System for Teaching Experimental Psychology using E-Prime. Behavior Research Methods, Instruments & Computers. 33 (2), 287-296 (2001).
Bukach, C. M., Stewart, K., Couperus, J. W., Reed, C. L. Using Collaborative Models to Overcome Obstacles to Undergraduate Publication in Cognitive Neuroscience. Frontiers in Psychology. 10, 549 (2019).
Srinivasan, R., Nunez, P. L., Tucker, D. M., Silberstein, R. B., Cadusch, P. J. Spatial sampling and filtering of EEG with spline laplacians to estimate cortical potentials. Brain Topography. 8 (4), 355-366 (1996).
Dien, J. Issues in the application of the average reference: Review, critiques, and recommendation. Behavior Research Methods, Instruments & Computers. 30, 34 (1998).
Bell, A. J., Sejnowski, T. J. An information-maximization approach to blind separation and blind deconvolution. Neural Computation. 7 (6), 1129-1159 (1995).
Chaumon, M., Bishop, D. V., Busch, N. A. A practical guide to the selection of independent components of the electroencephalogram for artifact correction. Journal of Neuroscience Methods. 250, 47-63 (2015).
Medrano, P., Nyhus, E., Smolen, A., Curran, T., Ross, R. S. Individual differences in EEG correlates of recognition memory due to DAT polymorphisms. Brain and Behavior. 7 (12), 1-16 (2017).
Nyhus, E., Badre, D., Addis, M., Barense, M., Duarte, A. . The Wiley Handbook on the Cognitive Neuroscience of Memory. , 131-149 (2015).
Ross, R. S., et al. Genetic variation in the serotonin transporter gene influences ERP old/new effects during recognition memory. Neuropsychologia. 78, 95-107 (2015).
Ross, R. S., Smolen, A., Curran, T., Nyhus, E. MAO-A Phenotype Effects Response Sensitivity and the Parietal Old/New Effect during Recognition Memory. Frontiers in Human Neuroscience. 12, 53 (2018).
Delorme, A., Makeig, S. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of Neuroscience Methods. 134 (1), 9-21 (2004).
Kabat-Zinn, J. . Full catastrophe living: Using the wisdom of your body and mind to face stress, pain, and illness. , (1990).
Nyhus, E., Engel, W. A., Pitfield, T. D., Vakkur, I. M. W. Increases in Theta Oscillatory Activity During Episodic Memory Retrieval Following Mindfulness Meditation Training. Frontiers in Human Neuroscience. 13, 311 (2019).
Haller, M., et al. Parameterizing neural power spectra. bioRxiv. , (2018).

Play Video

PDF

DOI

DOWNLOAD MATERIALS LIST

Cite This Article

Nyhus, E., Engel, W. A., Pitfield, T. D., Vakkur, I. M. Combining Behavior and EEG to Study the Effects of Mindfulness Meditation on Episodic Memory. J. Vis. Exp. (159), e61247, doi:10.3791/61247 (2020).