This work was supported by administration at Bowdoin College and the Bowdoin Life Sciences Research Fellowship, the Peter J. Grua and Mary G. O&#8217;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.

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
<ol>
	<li>Recruit 40 18-29 year old subjects who are meditation na&#239;ve, 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.</li>
	<li>Randomly assign 40 subjects to a mindfulness meditation experimental or waitlist control group for a total of 20 subjects in each group.</li>
	<li>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).</li>
</ol>
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/61247/61247fig1.jpg" /> 
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. <a href="https://www.jove.com/files/ftp_upload/61247/61247fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<ol start="4">
	<li>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.</li>
</ol>
2. Mindfulness questionnaire
<ol>
	<li>For each experimental session, have subjects complete the Five Facet Mindfulness Questionnaire (FFMQ)46 (see Supplementary File 1).</li>
	<li>Analyze the mindfulness data.
	<ol>
		<li>Measure each subject&#8217;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).</li>
		<li>Compare subjects&#8217; 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.</li>
	</ol>
	</li>
</ol>
3. Episodic memory task
<ol>
	<li>Prepare a list of 800 adjectives that are equated for word frequency according to the Kucera and Francis47 word norms (see Supplementary File 2).</li>
	<li>For each experimental session, have subjects practice the encoding phase by presenting 10 words and performing the encoding task as described below.</li>
	<li>For each experimental session, have subjects perform the encoding phase.
	<ol>
		<li>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).</li>
		<li>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).</li>
	</ol>
	</li>
	<li>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.</li>
	<li>For each experimental session, have subjects perform the source retrieval phase while recording EEG.
	<ol>
		<li>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.</li>
		<li>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).</li>
	</ol>
	</li>
</ol>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/61247/61247fig2.jpg" /> 
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 (&#8220;Old Place Task&#8221; or &#8220;Old Pleasant Task&#8221;) or &#8220;New&#8221;. This figure has been modified from Nyhus et al.60. <a href="https://www.jove.com/files/ftp_upload/61247/61247fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<ol start="6">
	<li>Mark each trial based on the behavioral condition and the subject&#8217;s response (see Figure 3 and Figure 4) and analyze the episodic memory behavioral data.
	<ol>
		<li>Measure subjects&#8217; ability to remember source information by&#160;calculating item discrimination (item d&#8217;, see Figure 3): 
		Z&#8203;(hit rate) &#8211; Z(false alarm rate)</li>
		<li>Measure subjects&#8217; ability to remember source information by&#160;calculating source discrimination (source d&#8217;,&#160;see Figure 4). 
		Z(correct source rate) &#8211; Z(incorrect source rate)</li>
		<li>Compare subjects&#8217; item and source discrimination (item and source d&#8217;) for the mindfulness meditation experimental and waitlist control group across pre-training and post-training experimental sessions.</li>
	</ol>
	</li>
</ol>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/61247/61247fig3.jpg" /> 
Figure 3: Data categories included in the measurement of word memory. Trials were marked based on the behavioral condition and the subject&#8217;s response and used to calculate item discrimination (item d&#8217;). <a href="https://www.jove.com/files/ftp_upload/61247/61247fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/61247/61247fig4.jpg" /> 
Figure 4: Data categories included in the measurement of source memory. Trials were marked based on the behavioral condition and the subject&#8217;s response and used to calculate source discrimination (source d&#8217;). <a href="https://www.jove.com/files/ftp_upload/61247/61247fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
4. EEG recording and analysis
<ol>
	<li>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).
	<ol>
		<li>Measure the subject&#8217;s head and put all the electrodes on the correct size EEG cap according to the extended international 10-20 system.</li>
		<li>Clean the subject&#8217;s forehead with an alcohol wipe.</li>
		<li>Apply the EEG cap to the subject&#8217;s head by parting their hair then inserting conductive gel with a Luer-lock syringe with a blunted needle.</li>
		<li>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.</li>
		<li>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.</li>
	</ol>
	</li>
	<li>Record the EEG.
	<ol>
		<li>Set the EEG amplifier with the EEG recording software by clicking on edit workspace and set to acquire signal with a .1&#8210;100 Hz bandpass filter and 500 Hz sampling rate for all subjects.</li>
		<li>Start the EEG recording.</li>
		<li>Start the source retrieval phase and ensure that the time stamps from the source retrieval task are showing up in the EEG recording.</li>
	</ol>
	</li>
	<li>Once the subject has completed the source retrieval task, clean the EEG cap and electrodes with deionized water and disinfectant.</li>
	<li>Process and analyze the EEG data.
	<ol>
		<li>High-pass filter the data at 1 Hz and low-pass filter the data at 100 Hz.</li>
		<li>Identify and interpolate bad channels using surrounding channels50.</li>
		<li>Re-refence the data to an average reference51.</li>
		<li>Segment the data relative to the onset of each time stamp from the source retrieval task and subtract a pre-stimulus baseline period.</li>
		<li>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 &#956;V or data 5 standard deviations beyond the norm). Reconstruct the EEG after running independent component analysis (ICA)52 and identifying and removing noise components53.</li>
		<li>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.</li>
		<li>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).</li>
	</ol>
	</li>
</ol>
5. Mindfulness meditation training
<ol>
	<li>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).</li>
	<li>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.</li>
	<li>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.</li>
	<li>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.</li>
	<li>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.</li>
</ol>

The authors have nothing to disclose.

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&#160;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.

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&#8210;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.

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

combining behavior and eeg to study the effects of mindfulness meditation on episodic memory

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.

Representative results are reported for 40 meditation na&#239;ve, 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 &#60;. 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 &#60; .01), Describe (F(1,19) = 6.36, MSE = 8.44, p = .02), and Nonjudge (F(1,19) = 10.12, MSE = 8.60, p &#60; .01) scales for the mindfulness meditation experimental group, whereas the waitlist control group did not change (see Table 1).
<table border="1" fo:keep-together.within-page="1">
	<tbody>
		<tr>
			<td></td>
			<td colspan="2">Experimental</td>
			<td colspan="2">Control</td>
		</tr>
		<tr>
			<td></td>
			<td>Pre-training</td>
			<td>Post-training</td>
			<td>Pre-training</td>
			<td>Post-training</td>
		</tr>
		<tr>
			<td>Total</td>
			<td>128.13 (2.38)</td>
			<td>138.07 (3.24)</td>
			<td>123.59 (4.19)</td>
			<td>121.25 (4.77)</td>
		</tr>
		<tr>
			<td>Observe</td>
			<td>26.98 (1.16)</td>
			<td>28.70 (1.00)</td>
			<td>23.83 (1.14)</td>
			<td>23.70 (1.26)</td>
		</tr>
		<tr>
			<td>Describe</td>
			<td>29.5 (1.36)</td>
			<td>31.82 (.99)</td>
			<td>27.10 (1.25)</td>
			<td>26.55 (1.26)</td>
		</tr>
		<tr>
			<td>Awareness</td>
			<td>25.25 (1.06)</td>
			<td>26.95 (1.12)</td>
			<td>25.27 (.94)</td>
			<td>24.05 (1.28)</td>
		</tr>
		<tr>
			<td>Nonjudge</td>
			<td>24.65 (1.26)</td>
			<td>27.60 (1.40)</td>
			<td>27.50 (1.42)</td>
			<td>27.00 (2.05)</td>
		</tr>
		<tr>
			<td>Nonreacitve</td>
			<td>21.75 (.99)</td>
			<td>23.00 (1.08)</td>
			<td>19.90 (1.09)</td>
			<td>19.95 (1.16)</td>
		</tr>
	</tbody>
</table>
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&#8217;). 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&#60;.01), but not the waitlist control group (see Table 2).
<table border="1" fo:keep-together.within-page="1">
	<tbody>
		<tr>
			<td></td>
			<td></td>
			<td colspan="2">Experimental</td>
			<td colspan="2">Control</td>
		</tr>
		<tr>
			<td></td>
			<td>Condition</td>
			<td>Pre-training</td>
			<td>Post-training</td>
			<td>Pre-training</td>
			<td>Post-training</td>
		</tr>
		<tr>
			<td>Hit</td>
			<td>Place correct source</td>
			<td>.66 (.02)</td>
			<td>.67 (.03)</td>
			<td>.71 (.03)</td>
			<td>.69 (.02)</td>
		</tr>
		<tr>
			<td></td>
			<td>Pleasantness correct source</td>
			<td>.61 (.03)</td>
			<td>.72 (.03)</td>
			<td>.64 (.05)</td>
			<td>.74 (.03)</td>
		</tr>
		<tr>
			<td>FA</td>
			<td>Place incorrect source</td>
			<td>.34 (.02)</td>
			<td>.33 (.03)</td>
			<td>.29 (.03</td>
			<td>.31 (.02)</td>
		</tr>
		<tr>
			<td></td>
			<td>Pleasantness incorrect source</td>
			<td>.39 (.03)</td>
			<td>.28 (.03)</td>
			<td>.36 (.05)</td>
			<td>.26 (.03)</td>
		</tr>
		<tr>
			<td>Source d&#39;</td>
			<td></td>
			<td>.70 (.11)</td>
			<td>1.06 (.12)</td>
			<td>1.04 (.17)</td>
			<td>1.23 (.14)</td>
		</tr>
		<tr>
			<td>Source c</td>
			<td></td>
			<td>-.06 (.05)</td>
			<td>.07 (.05)</td>
			<td>-.12 (.12)</td>
			<td>.10 (.07)</td>
		</tr>
	</tbody>
</table>
Table 2: Source behavioral data. Hit rate, false alarm rate, source discrimination (source d&#8217;) 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.&#160;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 &#60; .01). Theta power increased from pre-training to post-training for the mindfulness meditation experimental group (F(1,19) = 17.37, MSE = .23, p&#60; .01), but not the waitlist control group (see Figure 5).
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/61247/61247fig5.jpg" /> 
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<ins cite="mailto:Author">&#8211;</ins>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. <a href="https://www.jove.com/files/ftp_upload/61247/61247fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
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 &#60; .01, two-tailed, Bonferrroni corrected; see Figure 6).
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/61247/61247fig6.jpg" /> 
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. <a href="https://www.jove.com/files/ftp_upload/61247/61247fig6large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Supplemental File 1. <a href="https://www.jove.com/files/ftp_upload/61247/Suppl_File_1-FFMQ.zip" target="_blank">Please click here to download this file.</a>
Supplemental File 2. <a href="https://www.jove.com/files/ftp_upload/61247/Suppl_File_2_BowdoinResearchOutline.zip" target="_blank">Please click here to download this file.</a>
Supplemental File 3. <a href="https://www.jove.com/files/ftp_upload/61247/Suppl_File_3_Stims.zip" target="_blank">Please click here to download this file.</a>

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

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.

Although there has been recent interest in how mindfulness meditation can affect episodic memory as well as brain structure and function, no study has ...

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Research

JoVE Journal

Neuroscience

7.2K Views.  Bowdoin College. Hier presenteren we een protocol voor het combineren van mindfulness-meditatietraining, een episodische geheugentaak en EEG om de gedrags- en neurale effecten van mindfulness-meditatie op het episodische geheugen te begrijpen.

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

Examining the Characteristics of Episodic Memory using Event-related Potentials in Patients with Alzheimer's Disease

Our laboratory uses event-related EEG potentials (ERPs) to understand and support behavioral investigations of episodic memory in patients with amnestic mild cognitive impairment (aMCI) and Alzheimer's disease (AD). Whereas behavioral data inform us about the patients' performance, ERPs allow us to record discrete changes in brain activity. Further, ERPs can give us insight into the onset, duration, and interaction of independent cognitive processes associated with memory retrieval. In patient populations, these types of studies are used to examine which aspects of memory are impaired and which remain relatively intact compared to a control population. The methodology for collecting ERP data from a vulnerable patient population while these participants perform a recognition memory task is reviewed. This protocol includes participant preparation, quality assurance, data acquisition, and data analysis. In addition to basic setup and acquisition, we will also demonstrate localization techniques to obtain greater spatial resolution and source localization using high-density (128 channel) electrode arrays.

The methodology for collecting high-density event-related potential data while patients with Alzheimer's disease perform a recognition memory task is reviewed. This protocol will include subject preparation, quality assurance, data acquisition, and data analysis.

Our laboratory uses event-related EEG potentials (ERPs) to understand and support behavioral investigations of episodic memory in patients with amnestic ...

Medicine

Brain Imaging Investigation of the Neural Correlates of Emotional Autobiographical Recollection

Recollection of emotional autobiographical memories (AMs) is important to healthy cognitive and affective functioning 1 - remembering positive AMs is associated with increased personal well-being and self-esteem 2, whereas remembering and ruminating on negative AMs may lead to affective disorders 3. Although significant progress has been made in understanding the brain mechanisms underlying AM retrieval in general (reviewed in 4, 5), less is known about the effect of emotion on the subjective re-experience of AMs and the associated neural correlates. This is in part due to the fact that, unlike the investigations of the emotion effect on memory for laboratory-based microevents (reviewed in 6, 7-9), often times AM studies do not have a clear focus on the emotional aspects of remembering personal events (but see 10). Here, we present a protocol that allows investigation of the neural correlates of recollecting emotional AMs using functional magnetic resonance imaging (fMRI). Cues for these memories are collected prior to scanning by means of an autobiographical memory questionnaire (AMQ), therefore allowing for proper selection of emotional AMs based on their phenomenological properties (i.e., intensity, vividness, personal significance). This protocol can be used in healthy and clinical populations alike.

We present a protocol that allows investigation of the neural correlates of recollecting emotional autobiographical memories, using functional magnetic resonance imaging. This protocol can be used with both healthy and clinical participants.

Recollection of emotional autobiographical memories (AMs) is important to healthy cognitive and affective functioning 1 - remembering positive AMs is ...

Brain Imaging Investigation of the Memory-Enhancing Effect of Emotion

Emotional events tend to be better remembered than non-emotional events1,2. One goal of cognitive and affective neuroscientists is 
to understand the neural mechanisms underlying this enhancing effect of emotion on memory. A method that has proven particularly influential in the 
investigation of the memory-enhancing effect of emotion is the so-called subsequent memory paradigm (SMP). This method was originally used to investigate the 
neural correlates of non-emotional memories3, and more recently we and others also applied it successfully to studies of emotional memory (reviewed in4, 5-7). 
Here, we describe a protocol that allows investigation of the neural correlates of the memory-enhancing effect of emotion using the SMP in conjunction with 
event-related functional magnetic resonance imaging (fMRI). An important feature of the SMP is that it allows separation of brain activity specifically 
associated with memory from more general activity associated with perception. Moreover, in the context of investigating the impact of emotional stimuli, 
SMP allows identification of brain regions whose activity is susceptible to emotional modulation of both general/perceptual and memory-specific processing. 
This protocol can be used in healthy subjects8-15, as well as in clinical patients where there are alterations in the neural correlates of emotion perception 
and biases in remembering emotional events, such as those suffering from depression and post-traumatic stress disorder (PTSD)16, 17.

We present a protocol that uses functional magnetic resonance imaging to investigate the neural correlates of the memory-enhancing effect of emotion. This protocol allows identification of brain activity specifically linked to memory-related processing, contrary to more general perceptual processing, and can be used with healthy and clinical populations.

Emotional events tend to be better remembered than non-emotional events1,2. One goal of cognitive and affective neuroscientists is 
to understand the ...

Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities

In both the East and West, traditional teachings say that the mind and heart are somehow closely correlated, especially during spiritual practice. One difficulty in proving this objectively is that the natures of brain and heart activities are quite different. In this paper, we propose a methodology that uses wavelet entropy to measure the chaotic levels of both electroencephalogram (EEG) and electrocardiogram (ECG) data and show how this may be used to explore the potential coordination between the mind and heart under different experimental conditions. Furthermore, Statistical Parametric Mapping (SPM) was used to identify the brain regions in which the EEG wavelet entropy was the most affected by the experimental conditions. As an illustration, the EEG and ECG were recorded under two different conditions (normal rest and mindful breathing) at the beginning of an 8-week standard Mindfulness-based Stress Reduction (MBSR) training course (pretest) and after the course (posttest). Using the proposed method, the results consistently showed that the wavelet entropy of the brain EEG decreased during the MBSR mindful breathing state as compared to that during the closed-eye resting state. Similarly, a lower wavelet entropy of heartrate was found during MBSR mindful breathing. However, no difference in wavelet entropy during MBSR mindful breathing was found between the pretest and posttest. No correlation was observed between the entropy of brain waves and the entropy of heartrate during normal rest in all participants, whereas a significant correlation was observed during MBSR mindful breathing. Additionally, the most well-correlated brain regions were located in the central areas of the brain. This study provides a methodology for the establishment of evidence that mindfulness practice (i.e., mindful breathing) may increase the coordination between mind and heart activities.

This manuscript describes how to use the wavelet entropy index to analyze high-density electroencephalography (EEG) and electrocardiography (ECG) data. We show that the irregularity of cerebral and cardiac activities became more coordinated during mindfulness-based stress reduction practice.

In both the East and West, traditional teachings say that the mind and heart are somehow closely correlated, especially during spiritual practice. One ...

Behavior

A Real-world What-Where-When Memory Test

Episodic memory is a complex memory system which allows recall and mental re-experience of previous episodes from one&#39;s own life. Real-life episodic memories are about events in their spatiotemporal context and are typically visuospatial, rather than verbal. Yet often, tests of episodic memory use verbal material to be recalled (word lists, stories). The Real-World What-Where-When memory test requires participants to hide a total of 16 different objects in 16 different locations over two temporal occasions, 2 h apart. Another two hours later, they are then asked to recall which objects (What) they had hidden in which locations (Where) and on which of the two occasions (When). In addition to counting the number of correctly recalled complete what-where-when combinations, this task can also be used to test real-world spatial memory and object memory. This task is sensitive to normal cognitive aging, and correlates well with performance on other episodic memory tasks, while at the same time providing more ecological validity and being cheap and easy to run.

A Real-world What-Where-When Memory Test

The Real-World What-Where-When memory test is a novel episodic memory test, in which participants need to recall which objects have been hidden in which locations on which of two distinct occasions. It is easy to run and is sensitive to normal cognitive aging.

Episodic memory is a complex memory system which allows recall and mental re-experience of previous episodes from one&#39;s own life. Real-life episodic ...

Using Practice Testing, Public Speaking, and Source Monitoring to Examine the Influences of Learning Strategies and Stress on Episodic Memory

Prior research demonstrated that learning information via retrieval practice, which entails studying and taking practice tests, resulted in less memory impairment under stress than learning information via repeated studying. The present experiment combined three experimental procedures to further examine the memory mechanisms underlying the efficacy of retrieval practice in the context of stress. A list-discrimination task was implemented, in which participants learned two distinct wordlists. This was combined with a retrieval-practice manipulation, as half of the participants engaged in practice testing and half engaged in conventional studying during learning. A week later, participants underwent stress induction, using the Trier Social Stress Test. Before and after stress induction, participants completed tests of item and source memory (i.e., list discrimination). The combination of these three procedures yielded informative results: retrieval practice, in the context of stress, improved item memory but not source memory relative to conventional studying. Limitations and future directions for the use of this methodology are discussed.

The present experiment combined three experimental procedures &#8212; a retrieval-practice learning manipulation, a list-discrimination task, and a stress-induction technique &#8212; to examine the influences of different learning strategies and acute stress on multiple measures of episodic memory.

Prior research demonstrated that learning information via retrieval practice, which entails studying and taking practice tests, resulted in less memory ...

Simultaneous Monitoring of Wireless Electrophysiology and Memory Behavioral Test as a Tool to Study Hippocampal Neurogenesis

Brainwaves amplitude obtained from electroencephalography (EEG) has been well-recognized as a basis for cognitive capacity, memory, and learning on animals and humans. Adult neurogenesis mechanism is also linked to memory and learning improvement. Traditionally, researchers used to assess learning and memory parameters in rodent models by behavioral tasks. Therefore, the simultaneous monitoring of behavioral changes and EEG is particularly interesting in correlating data between brain activity and task-related behaviors. However, most of the equipment required to perform both studies are either complex, expensive, or uses a wired setup network that hinders the natural animals&#8217; movement. In this study, EEG was recorded with a wireless electrophysiology device along with the execution of a novel object recognition task (NORT). The animal&#8217;s behavior was monitored simultaneously by a video tracking system. Both recordings were analyzed offline by their timestamps which were synched to link EEG signals with the animal&#8217;s actions. Subjects consist of adult Wistar rats after medium-term environmental enrichment treatment. Six skull screw electrodes were fixed in pairs on both hemispheres over frontal, central, and parietal regions and were referenced to an electrode located posterior of the nasal bone. NORT protocol consists of exposing the animal to two identical objects for 10 min. After 2 h and 24 h, one of the objects was replaced with a novel one. Exploration time for each object was monitored by a behavioral tracking software (BTS) and EEG data recording. The analysis of the EEG synced with behavioral data consists of estimations of alpha and beta relative band power and comparisons between novel object recognition versus familiar object exploration, between three experimental stages. In this manuscript, we have discussed electrodes manufacturing process, epidural electrodes implantation surgery, environmental enrichment protocol, NORT protocol, BTS setup, EEG &#8211; BTS coupling for simultaneous monitoring in real-time, and EEG data analysis based on automatic events detection.

The protocol presented here provides information on the simultaneous electroencephalography (EEG) and behavioral assessment in real-time. We have discussed all steps involved in this protocol as an attractive solution for researchers in many fields of neuroscience, particularly in learning and memory areas.

Brainwaves amplitude obtained from electroencephalography (EEG) has been well-recognized as a basis for cognitive capacity, memory, and learning on ...

A Behavioral Task Modeling 'Everyday Memory' in an Event Arena to Foster Allocentric Representations for Rodents

The event arena provides an optimal platform to investigate learning and memory. The appetitive everyday memory task described in this paper provides a robust protocol for the investigation of episodic and spatial memory in rodents, which specifically fosters allocentric memory representation. Rats are trained to find and dig for food during the encoding phase and, after a time delay, rats are given a choice to find the reward food pellet in the correct location. There are two key elements that promote the use of an allocentric strategy in this protocol: 1) rats start from different start locations within and between sessions, 2) a stable home-base is deployed where rats have to carry their food to eat. By means of these modifications, we effectively encourage the rodents to use allocentric spatial representations to perform the task. In addition, the task provides a good paradigm for within-subject experimental design and allows experimenters to manipulate different conditions to reduce variability. Used in conjunction with behavioral and physiological techniques, the resulting rodent model provides an effective test-bed for future research into memory formation and retention.

The goal of this optimized 'everyday memory' protocol in an event arena was to employ a stable home-base that encourages the use of allocentric spatial representations. This animal model provides an effective test-bed for future research into the formation and retention of event memories using behavioral and physiological techniques.

The event arena provides an optimal platform to investigate learning and memory. The appetitive everyday memory task described in this paper provides a ...

How to Find Effects of Stimulus Processing on Event Related Brain Potentials of Close Others when Hyperscanning Partners

The partners of each pair must be able to pass the McGill Friendship Questionnaire without communicating. Each partner is then seated in front of a screen in one of two adjacent rooms. These rooms are separated by a glass window through which participants communicate to maintain feelings of togetherness while being fitted with the EEG cap. After checking for adequate EEG signals, the glass is covered by a curtain to prevent visual communication. Then, partners must be silent but are instructed to try to feel in the presence of their partner during the entire experiment. Just before it starts, participants are told that each of them will be presented with one image at a time and that these images will occur at the same time for both of them on their own screen. They are also instructed that, for each trial, the simultaneous images will always be different. However, unbeknownst to them, trials are randomized: only half of them are consistent with this instruction and actually include two different images. These trials form the DSC, that is, the different-stimuli condition. The other half of the trials are inconsistent with the instruction. They include two identical images and form the ISC (identical-stimuli condition). After the experiment, participants are sorted into two groups: those who reported that they felt in the presence of their partner during the majority of the trials and those who reported they did not. The impact of the stimulus processing of the partner is found by subtracting the mean voltages of the ERPs of the ISC (inconsistent with the instructions) from the ERPs of the DSC (consistent with the instructions) in at least two time windows (TWs): firstly, in the 75 to 150 ms TW, where the absolute values of these subtractions are greater, especially at right frontal sites, in those who felt in the presence of their partner than in those who did not; secondly, in the LPP time window (i.e., from 650 to 950 ms post onset), where ERPs are significantly less positive in the DSC than in the ISC in those in whom the raw results of the early (75-150ms) subtractions are negative.

This protocol describes key steps involved in assessing the sensitivity of the brain of one person to the stimulus processing of a close other by selecting pairs of partners, recording their electroencephalogram (EEG) simultaneously and computing their event-related brain potentials (ERPs).

The partners of each pair must be able to pass the McGill Friendship Questionnaire without communicating. Each partner is then seated in front of a screen ...

Learning and Memory: The Remember-Know Task

Source: Laboratories of Jonas T. Kaplan and Sarah I. Gimbel&#8212;University of Southern California
Our experience of memory is varied and complex. Sometimes we remember events in vivid detail, while other times we may only have a vague sense of familiarity. Memory researchers have made a distinction between memories that are recollected versus those that are familiar. A recollected item is one that is not only remembered, but carries with it details of the time at which it was learned or encoded. Like a recollected item, a familiar item is also remembered, but is void of any details about the circumstances surrounding its encoding. Many studies of recollection and familiarity have focused on the medial temporal lobe (MTL), specifically the hippocampus, since its involvement in memory encoding, consolidation, and retrieval is well-known and well-studied.1-3
This video shows how to administer the Remember-Know task4 to compare brain activation in these two types of memory retrieval. In this context, remember is another term for recollection, while know refers to memories that are familiar but not explicitly recollected. In this version of the Remember-Know task, participants are exposed to a series of color images, and asked to remember what they see. Inside an fMRI scanner, they will be exposed to both images that were studied and to novel images, and they will make a "remember," "know," or "new" judgment about each image, indicating what kind of memory they have for that item. Following the scan, whole brain and hippocampal activity will be examined to determine differential activity related to recollection and familiarity. This study is based on a study performed by Gimbel and Brewer.5

The Remember-Know Task: Memory Recall and Brain Activation

Source: Laboratories of Jonas T. Kaplan and Sarah I. Gimbel&#8212;University of Southern California
Our experience of memory is varied and complex. ...

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

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