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 border="0" cellpadding="0" cellspacing="0" style="width:572px;" width="573">
	<colgroup>
		<col />
		<col />
		<col />
		<col />
	</colgroup>
	<tbody>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">BrainVision actiCHamp</td>
			<td style="width:71px;">Brain Products GmbH, Gilching, Germany</td>
			<td style="width:119px;">BP-09020</td>
			<td style="width:167px;">64-channel EEG system</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">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 height="21">
			<td height="21" style="height:21px;width:216px;">E-Prime 2.0 Professional</td>
			<td>Psychology Software Tools, Inc., Sharpsburg, PA</td>
			<td style="width:119px;">PST-100577</td>
			<td>Software designed for behavioral research that can interface with the EEG recording</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Qualtrics</td>
			<td style="width:71px;">Qualtrics, Provo, UT</td>
			<td style="width:119px;">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>

Watch this Scientific Journal Video about Combining Behavior and EEG to Study the Effects of Mindfulness Meditation on Episodic Memory at JoVE.com

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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6.8K Views.  Bowdoin College. 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.

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

State-Dependency Effects on TMS:  A Look at Motive Phosphene Behavior

Transcranial magnetic stimulation (TMS) is a non-invasive neurostimulatory and neuromodulatory technique that can transiently or lastingly modulate cortical excitability (either increasing or decreasing it) via the application of localized magnetic field pulses.1,2 Within the field of TMS, the term state dependency refers to the initial, baseline condition of the particular neural region targeted for stimulation. As can be inferred, the effects of TMS can (and do) vary according to this primary susceptibility and responsiveness of the targeted cortical area.3,4,5

In this experiment, we will examine this concept of state dependency through the elicitation and subjective experience of motive phosphenes. Phosphenes are visually perceived flashes of small lights triggered by electromagnetic pulses to the visual cortex. These small lights can assume varied characteristics depending upon which type of visual cortex is being stimulated. In this particular study, we will be targeting motive phosphenes as elicited through the stimulation of V1/V2 and the V5/MT+ complex visual regions.6

In this article, we examine the effects of visually relevant state dependency on TMS induced motive phosphenic presentations.

Transcranial magnetic stimulation (TMS) is a non-invasive neurostimulatory and neuromodulatory technique that can transiently or lastingly modulate ...

Vibrodissociation of Neurons from Rodent Brain Slices to Study Synaptic Transmission and Image Presynaptic Terminals

Mechanical dissociation of neurons from the central nervous system has the advantage that presynaptic boutons remain attached to the isolated neuron of interest. This allows for examination of synaptic transmission under conditions where the extracellular and postsynaptic intracellular environments can be well controlled. A vibration-based technique without the use of proteases, known as vibrodissociation, is the most popular technique for mechanical isolation. A micropipette, with the tip fire-polished to the shape of a small ball, is placed into a brain slice made from a P1-P21 rodent. The micropipette is vibrated parallel to the slice surface and lowered through the slice thickness resulting in the liberation of isolated neurons. The isolated neurons are ready for study within a few minutes of vibrodissociation. This technique has advantages over the use of primary neuronal cultures, brain slices and enzymatically isolated neurons including: rapid production of viable, relatively mature neurons suitable for electrophysiological and imaging studies; superior control of the extracellular environment free from the influence of neighboring cells; suitability for well-controlled pharmacological experiments using rapid drug application and total cell superfusion; and improved space-clamp in whole-cell recordings relative to neurons in slice or cell culture preparations. This preparation can be used to examine synaptic physiology, pharmacology, modulation and plasticity. Real-time imaging of both pre- and postsynaptic elements in the living cells and boutons is also possible using vibrodissociated neurons. Characterization of the molecular constituents of pre- and postsynaptic elements can also be achieved with immunological and imaging-based approaches.

This report demonstrates a technique for mechanical isolation of individual viable neurons retaining attached presynaptic boutons. Vibrodissociated neurons have the advantages of rapid production, excellent pharmacological control and improved space-clamp without influence from neighboring cells. This method can be used for imaging of synaptic elements and patch-clamp recording.

Mechanical dissociation of neurons from the central nervous system has the advantage that presynaptic boutons remain attached to the isolated neuron of ...

Modeling Neural Immune Signaling of Episodic and Chronic Migraine Using Spreading Depression In Vitro

Migraine and its transformation to chronic migraine are healthcare burdens in need of improved treatment options. We seek to define how neural immune signaling modulates the susceptibility to migraine, modeled in vitro using spreading depression (SD), as a means to develop novel therapeutic targets for episodic and chronic migraine. SD is the likely cause of migraine aura and migraine pain. It is a paroxysmal loss of neuronal function triggered by initially increased neuronal activity, which slowly propagates within susceptible brain regions. Normal brain function is exquisitely sensitive to, and relies on, coincident low-level immune signaling. Thus, neural immune signaling likely affects electrical activity of SD, and therefore migraine. Pain perception studies of SD in whole animals are fraught with difficulties, but whole animals are well suited to examine systems biology aspects of migraine since SD activates trigeminal nociceptive pathways. However, whole animal studies alone cannot be used to decipher the cellular and neural circuit mechanisms of SD. Instead, in vitro preparations where environmental conditions can be controlled are necessary. Here, it is important to recognize limitations of acute slices and distinct advantages of hippocampal slice cultures. Acute brain slices cannot reveal subtle changes in immune signaling since preparing the slices alone triggers: pro-inflammatory changes that last days, epileptiform behavior due to high levels of oxygen tension needed to vitalize the slices, and irreversible cell injury at anoxic slice centers. 
In contrast, we examine immune signaling in mature hippocampal slice cultures since the cultures closely parallel their in vivo counterpart with mature trisynaptic function; show quiescent astrocytes, microglia, and cytokine levels; and SD is easily induced in an unanesthetized preparation. Furthermore, the slices are long-lived and SD can be induced on consecutive days without injury, making this preparation the sole means to-date capable of modeling the neuroimmune consequences of chronic SD, and thus perhaps chronic migraine. We use electrophysiological techniques and non-invasive imaging to measure neuronal cell and circuit functions coincident with SD. Neural immune gene expression variables are measured with qPCR screening, qPCR arrays, and, importantly, use of cDNA preamplification for detection of ultra-low level targets such as interferon-gamma using whole, regional, or specific cell enhanced (via laser dissection microscopy) sampling. Cytokine cascade signaling is further assessed with multiplexed phosphoprotein related targets with gene expression and phosphoprotein changes confirmed via cell-specific immunostaining. Pharmacological and siRNA strategies are used to mimic and modulate SD immune signaling.

Modeling Neural Immune Signaling of Episodic and Chronic Migraine Using Spreading Depression In Vitro

Migraine and its transformation to chronic migraine are immense healthcare burdens in need of improved treatment options. We seek to define how neural immune signaling modulates the susceptibility to migraine, modeled in vitro using spreading depression in hippocampal slice cultures, as a means to develop novel therapeutic targets.

Migraine and its transformation to chronic migraine are healthcare burdens in need of improved treatment options.  We seek to define how neural immune ...

In vivo Optogenetic Stimulation of the Rodent Central Nervous System

The ability to probe defined neural circuits in awake, freely-moving animals with cell-type specificity, spatial precision, and high temporal resolution has been a long sought tool for neuroscientists in the systems-level search for the neural circuitry governing complex behavioral states. Optogenetics is a cutting-edge tool that is revolutionizing the field of neuroscience and represents one of the first systematic approaches to enable causal testing regarding the relation between neural signaling events and behavior. By combining optical and genetic approaches, neural signaling can be bi-directionally controlled through expression of light-sensitive ion channels (opsins) in mammalian cells. The current protocol describes delivery of specific wavelengths of light to opsin-expressing cells in deep brain structures of awake, freely-moving rodents for neural circuit modulation. Theoretical principles of light transmission as an experimental consideration are discussed in the context of performing in vivo optogenetic stimulation. The protocol details the design and construction of both simple and complex laser configurations and describes tethering strategies to permit simultaneous stimulation of multiple animals for high-throughput behavioral testing.

In vivo Optogenetic Stimulation of the Rodent Central Nervous System

Optogenetics has become a powerful tool for use in behavioral neuroscience experiments. This protocol offers a step-by-step guide to the design and set-up of laser systems, and provides a full protocol for carrying out multiple and simultaneous in vivo optogenetic stimulations compatible with most rodent behavioral testing paradigms.

The ability to probe defined neural circuits in awake, freely-moving animals with cell-type specificity, spatial precision, and high temporal resolution ...

Using Linear Agarose Channels to Study Drosophila Larval Crawling Behavior

Drosophila larval crawling is emerging as a powerful model to study neural control of sensorimotor behavior. However, larval crawling behavior on flat open surfaces is complex,&#160;including: pausing, turning, and meandering. This complexity in the repertoire of movement hinders detailed analysis of the events occurring during a single crawl stride cycle. To overcome this obstacle, linear agarose channels were made that constrain larval behavior to straight, sustained, rhythmic crawling. In principle, because agarose channels and the Drosophila larval body are both optically clear, the movement of larval structures labeled by genetically-encoded fluorescent probes can be monitored in intact, freely-moving larvae. In the past, larvae were placed in linear channels and crawling at the level of whole organism, segment, and muscle were analyzed1. In the future, larvae crawling in channels can be used for calcium imaging to monitor neuronal activity. Moreover, these methods can be used with larvae of any genotype and with any researcher-designed channel. Thus the protocol presented below is widely applicable for studies using the Drosophila larva as a model to understand motor control.

Using Linear Agarose Channels to Study Drosophila Larval Crawling Behavior

The Drosophila larva is a powerful model system to study neural control of behavior. This publication describes the use of linear agarose channels to elicit sustained bouts of linear crawling and methods to quantify the dynamics of larval structures during repetitive crawling behavior.

Drosophila larval crawling is emerging as a powerful model to study neural control of sensorimotor behavior. However, larval crawling behavior on flat ...

Time-dependent Increase in the Network Response to the Stimulation of Neuronal Cell Cultures on Micro-electrode Arrays

Micro-electrode arrays (MEAs) can be used to investigate drug toxicity, design paradigms for next-generation personalized medicine, and study network dynamics in neuronal cultures. In contrast with more traditional methods, such as patch-clamping, which can only record activity from a single cell, MEAs can record simultaneously from multiple sites in a network, without requiring the arduous task of placing each electrode individually. Moreover, numerous control and stimulation configurations can be easily applied within the same experimental setup, allowing for a broad range of dynamics to be explored. One of the key dynamics of interest in these in vitro studies has been the extent to which cultured networks display properties indicative of learning. Mouse neuronal cells cultured on MEAs display an increase in response following training induced by electrical stimulation. This protocol demonstrates how to culture neuronal cells on MEAs; successfully record from over 95% of the plated dishes; establish a protocol to train the networks to respond to patterns of stimulation; and sort, plot, and interpret the results from such experiments. The use of a proprietary system for stimulating and recording neuronal cultures is demonstrated. Software packages are also used to sort neuronal units. A custom-designed graphical user interface is used to visualize post-stimulus time histograms, inter-burst intervals, and burst duration, as well as to compare the cellular response to stimulation before and after a training protocol. Finally, representative results and future directions of this research effort are discussed.

Mouse neuronal cells cultured on multi-electrode arrays display an increase in response following electrical stimulation. This protocol demonstrates how to culture neurons, how to record activity, and how to establish a protocol to train the networks to respond to patterns of stimulation.

Micro-electrode arrays (MEAs) can be used to investigate drug toxicity, design paradigms for next-generation personalized medicine, and study network ...

Combining Optogenetics with Artificial microRNAs to Characterize the Effects of Gene Knockdown on Presynaptic Function within Intact Neuronal Circuits

The purpose of this protocol is to characterize the effect of gene knockdown on presynaptic function within intact neuronal circuits. We describe a workflow on how to combine artificial microRNA (miR)-mediated RNA interference with optogenetics to achieve selective stimulation of manipulated presynaptic boutons in acute brain slices. The experimental approach involves the use of a single viral construct and a single neuron-specific promoter to drive the expression of both an optogenetic probe and artificial miR(s) against presynaptic gene(s). When stereotactically injected in the brain region of interest, the expressed construct makes it possible to stimulate with light exclusively the neurons with reduced expression of the gene(s) under investigation. This strategy does not require the development and maintenance of genetically modified mouse lines and can in principle be applied to other organisms and to any neuronal gene of choice. We have recently applied it to investigate how the knockdown of alternative splice isoforms of presynaptic P/Q-type voltage-gated calcium channels (VGCCs) regulates short-term synaptic plasticity at CA3 to CA1 excitatory synapses in acute hippocampal slices. A similar approach could also be used to manipulate and probe the neuronal circuitry in vivo.

This protocol provides a workflow on how to combine artificial microRNA-mediated RNA interference with optogenetics to stimulate specifically presynaptic boutons with reduced expression of selective gene(s) within intact neuronal circuits.

The purpose of this protocol is to characterize the effect of gene knockdown on presynaptic function within intact neuronal circuits. We describe a ...

Noninvasive EEG Recordings from Freely Moving Piglets

The method allows the recording of high-quality electroencephalograms (EEGs) from freely moving piglets directly in the pigpen. We use a one-channel telemetric electroencephalography system in combination with standard self-adhesive hydrogel electrodes. The piglets are calmed down without the use of sedatives. After their release into the pigpen, the piglets behave normally&#8212;they drink and sleep in the same cycle as their siblings. Their sleep phases are used for the EEG recordings.

Here, we present a protocol to record telemetric electroencephalograms (EEGs) from freely moving piglets directly in the pigpen without the use of a sedative, making it possible to record typical EEG patterns during non-REM sleep, like spindle bursts.

The method allows the recording of high-quality electroencephalograms (EEGs) from freely moving piglets directly in the pigpen. We use a one-channel ...

Generation of 3-D Collagen-based Hydrogels to Analyze Axonal Growth and Behavior During Nervous System Development

This protocol uses natural type I collagen to generate three-dimensional (3-D) hydrogel for monitoring and analyzing the axonal growth. The protocol is centered on culturing small pieces of embryonic or early postnatal rodent brains inside a 3-D hydrogel formed by the rat tail tendon-derived type I collagen with specific porosity. Tissue pieces are cultured inside the hydrogel and confronted to specific brain fragments or genetically-modified cell aggregates to produce and secrete molecules suitable for creating a gradient inside the porous matrix. The steps of this protocol are simple and reproducible but include critical steps to be considered carefully during its development. Moreover, the behavior of growing axons can be monitored and analyzed directly using a phase-contrast microscope or mono/multiphoton fluorescence microscope after fixation by immunocytochemical methods.

Here, we provide a method for analyzing the behavior of growing axons in 3D matrices, mimicking their natural development.

This protocol uses natural type I collagen to generate three-dimensional (3-D) hydrogel for monitoring and analyzing the axonal growth. The protocol is ...

Measuring Neural Mechanisms Underlying Sleep-Dependent Memory Consolidation During Naps in Early Childhood

Sleep is critical for daily functioning. One important function of sleep is the consolidation of memories, a process that makes them stronger and less vulnerable to interference. The neural mechanisms underlying the benefit of sleep for memory can be investigated using polysomnography (PSG). PSG is a combination of physiological recordings including signals from the brain (EEG), eyes (EOG), and muscles (EMG) that are used to classify sleep stages. In this protocol, we describe how PSG can be used in conjunction with behavioral memory assessments, actigraphy, and parent-report to examine sleep-dependent memory consolidation. The focus of this protocol is on early childhood, a period of significance as children transition from biphasic sleep (consisting of a nap and overnight sleep) to monophasic sleep (overnight sleep only). The effects of sleep on memory performance are measured using a visuospatial memory assessment across periods of sleep and wakeful-rest. A combination of actigraphy and parent report is used to assess sleep rhythms (i.e., characterizing children as habitual or non-habitual nappers). Finally, PSG is used to characterize sleep stages and qualities of those stages (such as frequencies and the presence of spindles) during naps. The advantage of using PSG is that it is the only tool currently available to assess sleep quality and sleep architecture, pointing to the relevant brain state that supports memory consolidation. The main limitations of PSG are the length of time it takes to prepare the recording montage and that recordings are typically taken over one sleep bought. These limitations can be overcome by engaging young participants in distracting tasks during application and combining PSG with actigraphy and self/parent-report measures to characterize sleep cycles. Together, this unique combination of methods allows for investigations into how naps support learning in preschool children.

This protocol describes methods used to examine neural mechanisms underlying sleep-dependent memory consolidation during naps in early childhood. It includes procedures for examining the effect of sleep on behavioral memory performance, as well as the application and recording of both polysomnography and actigraphy.

Sleep is critical for daily functioning. One important function of sleep is the consolidation of memories, a process that makes them stronger and less ...