Financial support for this study was provided by the Intramural Research Program of the National Institute of Mental Health, ZIAMH002798 (ClinicalTrial.gov Identifier: NCT00026559: Protocol ID 01-M-0185).

All participants gave written informed consent approved by the National Institute of Mental Health (NIMH) Combined Neuroscience Institutional Review Board (IRB) and were compensated for participating.
1. Set Up the Equipment
NOTE: Set up the equipment as described below (see Figure 1A)3.
<ol>
	<li>In the control room, set up two computers, one to administer the experiment, and one to record the physiological data.</li>
	<li>In the subject room, set up a standard 19 in liquid crystal display monitor and keyboard (or button box) to display stimuli to the participant and to record participant responses, respectively.</li>
	<li>To record the psychophysiology, connect the recording computer to the psychophysiology monitoring hardware using an Ethernet to USB adapter.</li>
	<li>To split the Transistor-Transistor Logic (TTL) signals between the recording and stimulus delivery hardware, connect the parallel port of the experiment computer to the breakout box using a ribbon cable.</li>
	<li>To pass the TTL signals to the psychophysiology monitoring hardware, connect the breakout box to the hardware using a ribbon cable.</li>
	<li>To pass the TTL pulses to the stimulus delivery hardware, connect the breakout box to the signal generator using a Bayonet Neill-Concelman (BNC) cable.</li>
	<li>To generate a control signal for the shock device, connect the signal generator to the shock device using a BNC cable.</li>
	<li>Set the signal generator and shock device to deliver a 100 ms, 200 Hz shock. See Figure 1B and C for all settings.</li>
</ol>
2. Program the Experiment Using Available Software
NOTE: A neurobehavioral systems software (here referred to as the experimental software; see the Table of Materials) was used. Other equivalent software can be used.
<ol>
	<li>Program four testing phases using the parameters described below and the supplementary code files provided (see the supplemental code for details).
	<ol>
		<li>For each phase, program 26 trials.</li>
		<li>Divide the trials into 4 alternating blocks of threat and safety, with 6 trials per block.</li>
		<li>At the beginning of each trial, present a cue indicating how many letters will be presented for 2,000 ms each.</li>
		<li>After the cue, present the encoding letter sequence for 2,500 &#177;1,000 ms.</li>
		<li>On low-load trials, present 5 letters sequentially, one after the other.</li>
		<li>On high-load trials, present 8 letters sequentially, one after the other.</li>
		<li>Program a maintenance period following the encoding phase for 9,000 &#177;1,000 ms.</li>
		<li>At the end of the maintenance period, present a response prompt for 2,000 ms.</li>
		<li>Program the response prompt to display a letter on the left side and a number on the right side of the monitor, with the letter representing a letter from the encoding sequence, and the number referring to a position in the sequence.</li>
		<li>Underneath the letter and number, display the words &#34;match/mismatch,&#34; referring to if the letter matched or mismatched the position number.</li>
		<li>Program the experiment so that half of the trials match and half mismatch.</li>
		<li>Use a keyboard or button box to record responses.</li>
		<li>Separate the trials by a variable-duration intertrial interval (ITI) that is dependent upon the timing of the within-trial events such that each trial is 23 s long.</li>
		<li>Vary the durations of the encoding, maintenance, and ITI periods across trials by selecting a random duration (in ms) between the ceiling and floor values for each period.</li>
		<li>Counterbalance the experiments to have half of the participants start in a safe block and half of the participants start in a threat block.</li>
		<li>In each run, present between 0 and 2 pseudorandom shocks during each of the threat blocks for a total of 3 shock presentations per run. Make sure to include an extra (dummy) trial for each shock to ensure that the same number of trials are included in the safe and threat blocks.</li>
		<li>At the beginning of each run, present five 40 ms bursts of 103 dB white noise (near-instantaneous rise/fall times) over headphones to habituate the startle response.</li>
		<li>During each run, present 3 presentations of the white noise during the following conditions to probe the startle response (see Figure 3): safe versus threat, low load versus high load, and maintenance period versus ITI.</li>
		<li>Space the probes so that they occur with a minimum interprobe interval of at least 17 s to avoid the short-term habituation of the startle response.</li>
		<li>For maintenance period trials, present probes no less than 1 s after the offset of the letter series.</li>
		<li>For ITI trials, present probes no less than 4 s after the offset of the response prompt.</li>
		<li>Set up the equipment for physiological monitoring using the associated software package, as per the manufacturer&#39;s instructions.</li>
	</ol>
	</li>
</ol>
3. Run the Experiment
<ol>
	<li>Escort the participants to the study room.</li>
	<li>Administer informed consent.</li>
	<li>Give the participants the State-Trait Anxiety Inventory Y-1 (STAI-Y1)25, the Beck Anxiety Inventory (BAI)26, the Beck Depression Inventory (BDI)27, and the Anxiety Sensitivity Index (ASI)28 to fill out prior to task instructions and set up.</li>
	<li>Inform the participants that they will see 2 types of trials and will respond to those trials based on the following details.</li>
	<li>During the low-load trials, instruct the participants to maintain a series of 5 letters in their memory in the order in which they are presented.</li>
	<li>During the high-load trials, instruct the participants to maintain a series of 8 letters in their memory in the order in which they are presented.</li>
	<li>Inform the participants that, after a delay, they will be prompted with a letter and a number that refers to the position in the sequence.</li>
	<li>Instruct the participants to indicate whether the letter and position number match or mismatch the trial sequence using the left or right arrow key, respectively.</li>
	<li>Inform the participants that trials will occur during periods of safety and periods of threat, when they are at risk of receiving unpredictable mild electrical shocks to the wrist.</li>
	<li>Inform the participants that they will hear acoustic startle probes throughout the experiment, in both the safe and threat conditions.</li>
	<li>Clean and attach electrodes to each participant, based on the diagram in Figure 2.
	<ol>
		<li>Place two disposable 11 mm silver-silver chloride (Ag-AgCl) electrodes on the palm of the left hand, about 2 cm apart, to monitor skin conductance.</li>
		<li>Place two disposable 11 mm Ag-AgCl electrodes on the inner wrist of the left hand, about 3 cm apart, to administer the electrical stimulation.</li>
		<li>Place one disposable 11 mm Ag-AgCl electrode on the inside of the left arm, just above the elbow, and one disposable electrode just under the right clavicle to monitor the heart rate.</li>
		<li>Attach two 4 mm Ag-AgCl cup electrodes to the lower side of the left orbicularis oculi muscle to measure the startle response.</li>
	</ol>
	</li>
	<li>Secure all electrodes with biomedical tape.</li>
	<li>Attach leads to the electrodes on the palm and plug them into the EDA channel of the psychophysiology monitoring hardware.</li>
	<li>Attach leads to the electrodes on the wrist and plug them into the shock device.</li>
	<li>Attach leads to the electrodes on the arm and clavicle and plug them into the ECG channel of the psychophysiology monitoring hardware.</li>
	<li>Plug the cup electrodes attached to the orbicularis oculi muscle into the electromyography (EMG) channel of the psychophysiology monitoring hardware.</li>
	<li>Shock calibration.
	<ol>
		<li>Prior to the start of the experiment, have the participants rate a series of 100-ms sample electrical stimulations to identify an intensity level that is unpleasant and uncomfortable, but not painful.
		<ol>
			<li>Administer a series presentation (~5-10) of the 100 ms shock stimulus to the wrist using the experimental software package (see supplemental code files and the Table of Materials).</li>
			<li>After each presentation, have participants verbally rate each presentation on a scale from 1 (not uncomfortable at all) to 10 (uncomfortable but not painful).</li>
			<li>Using the mA scale on the shock device, gradually increase the intensity of the shock and continue the series of stimulations the until subject rates the stimulation as a &#34;10.&#34;</li>
			<li>Record the intensity value on the participant detail packet. 
			NOTE: During the study, present the shocks at the determined intensity.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>To start the experiment, enter the participant ID number, counterbalance condition, and run number into the run box, as prompted by the experiment software. 
	NOTE: Create two counterbalanced conditions. The first counterbalance will start the experiment in a threat block, and the second counterbalance will start the experiment in a safe block. See section 2.</li>
	<li>Click &#34;start&#34; on the psychophysiology monitoring recording.</li>
	<li>Press &#34;enter&#34; on the experimental software prompt box to start the experiment.</li>
	<li>Allow the subject to complete 4 runs of the experiment. Have the participant select the left or right arrow key if the letter and position number match or mismatch the trial sequence, respectively (steps 3.7 and 3.8). 
	NOTE: Program each run length to last between 6 and 7 min. Program the shocks to be delivered pseudo-randomly between 0-2 times/run. See section 2.</li>
	<li>After each run, have the subject verbally rate their anxiety level on a scale from 0 (not anxious) - 10 (extremely anxious) during the safe and threat blocks of the run they just completed.</li>
	<li>Have the subjects verbally rate the intensity of the shocks presented during the previous run on the same 0-10 scale used in the initial calibration procedure (section 3.17).</li>
</ol>
4. Analyze the Performance
NOTE: Analyze the performance data for a single participant using the following instructions.
<ol>
	<li>Open the output file created from the experiment software.
	<ol>
		<li>To average the correct responses across the different conditions, first separate the data into safe versus threat and low load versus high load to yield 4 unique conditions of response data.</li>
		<li>Count the correct trials for each of the 4 conditions and divide this number by the total number of trials in each condition.</li>
		<li>To average the reaction time across the different conditions, separate the data as in step 4.1.1.</li>
		<li>Sum all the reaction times for each condition and divide this number by the number of trials in each condition. 
		NOTE: Omit trials that include a shock presentation, as indicated in the experimental software output.</li>
	</ol>
	</li>
	<li>At the group level, perform a 2 (safe versus threat) x 2 (low load versus high load) ANOVA across subjects to identify differences in behavioral performance and reaction times29.</li>
</ol>
5. Analyze Startle
<ol>
	<li>Prepare the raw EMG data for analysis using psychophysiology analysis software30. See Figure 4A.
	<ol>
		<li>Select &#34;Transform&#34; &#62;&#62; Digital Filters &#62;&#62; FIR &#62;&#62; Bandpass from the psychophysiology analysis software to apply a digital bandpass filter (30-300 Hz passband), smoothing the raw EMG channel (see Figure 4B).</li>
	</ol>
	</li>
	<li>Select Analysis &#62;&#62; Electromyography &#62;&#62; Derive Average Rectified EMG from the psychophysiology analysis software to rectify the smoothed EMG signal using a time-windowed mean of 20 ms (see Figure 4C).</li>
	<li>Select Analysis &#62;&#62; Stim-Response &#62;&#62; Digital Input to Stim Events from the psychophysiology analysis software to label the stimulus events that correspond to the digital inputs for each trial type. 
	NOTE: For example, trial types include safe versus threat, low load versus high load, and maintenance period versus ITI period.</li>
	<li>Extract the blink magnitude around each stimulus event30.
	<ol>
		<li>Select Analysis &#62;&#62; Stim-Response &#62;&#62; Stim-Response Analysis and specify the Mean of Channel (i.e., the channel number corresponding to the processed EMG) from the psychophysiology analysis software to extract the mean baseline activity in a fixed window of -50 to 0 ms preceding the onset of the white noise.</li>
		<li>Select Analysis &#62;&#62; Stim-Response &#62;&#62; Stim-Response Analysis and specify the Max of Channel (i.e., the channel number corresponding to the processed EMG) from the psychophysiology analysis software to identify the blink onset and peak in a fixed window of 20 to 100 ms following the onset of the white noise.</li>
	</ol>
	</li>
	<li>Exclude trials with excessive noise on the EMG channel30. 
	NOTE: The acoustic startle responses must be reliably distinguished from excessive background EMG activity or other sources of contamination (e.g., movement artifacts or voluntary and spontaneous blinks immediately preceding auditory probes; see Figure 4D).</li>
	<li>Analyze the trial-by-trial blink responses using a standard spreadsheet software.
	<ol>
		<li>Normalize the blink magnitudes into z-scores (optional).</li>
		<li>Convert the z-scores to t-scores for further analysis (t = 10x + 50; optional).</li>
		<li>Average the t-scores and/or raw scores across trials for each trial type and calculate the APS (threat versus safe) for each condition (e.g., low load versus high load and maintenance period versus ITI period).</li>
		<li>At the group level, perform a 2 (safe versus threat) x 2 (maintenance period versus ITI) ANOVA across subjects to identify the effect of WM maintenance on APS.</li>
	</ol>
	</li>
</ol>
6. Analyze the Self-report Data
<ol>
	<li>Average the anxiety ratings across runs for the safe and threat conditions.</li>
	<li>At the group level, perform a threat versus safe t-test to determine the effectiveness of the threat manipulation.</li>
</ol>

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J., Krimsky, M., Grillon, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+impact+of+induced+anxiety+on+response+inhibition.">The impact of induced anxiety on response inhibition.</a> Front. Hum. Neurosci. 7, 69 (2013).</li><li>Owen, A. M., McMillan, K. M., Laird, A. R., Bullmore, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=N-back+working+memory+paradigm:+A+meta-analysis+of+normative+functional+neuroimaging+studies.">N-back working memory paradigm: A meta-analysis of normative functional neuroimaging studies.</a> Hum. Brain Mapp. 25 (1), 46-59 (2005).</li><li>Bailey, J. E., Argyropoulos, S. V., Kendrick, A. H., Nutt, D. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Behavioral+and+cardiovascular+effects+of+7.5%+CO2+in+human+volunteers.">Behavioral and cardiovascular effects of 7.5% CO2 in human volunteers.</a> Depress. Anxiety. 21 (1), 18-25 (2005).</li><li>Thibodeau, M. A., Welch, P. G., Katz, J., Asmundson, G. J. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Pain-related+anxiety+influences+pain+perception+differently+in+men+and+women:+A+quantitative+sensory+test+across+thermal+pain+modalities.">Pain-related anxiety influences pain perception differently in men and women: A quantitative sensory test across thermal pain modalities.</a> Pain. 154 (3), 419-426 (2013).</li><li>Lamm, C., Pine, D. S., Fox, N. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Impact+of+negative+affectively+charged+stimuli+and+response+style+on+cognitive-control-related+neural+activation:+An+ERP+study.">Impact of negative affectively charged stimuli and response style on cognitive-control-related neural activation: An ERP study.</a> Brain Cogn. 83 (2), 234-243 (2013).</li></ol>

The authors report no conflicts of interest.

This paper demonstrates how to administer the Sternberg WM task during threat of shock. Using this protocol, it was possible to show that WM maintenance is sufficient to reduce anxiety, as measured by the potentiation of the acoustic startle reflex3. These results suggest that the relationship between cognition and anxiety is bidirectional3-5 and that models of anxiety (e.g., the attentional control theory)1 must explain the effect of cognition on anxiety in addition to the effect of anxiety on cognition. Although the current protocol describes the integration of the Sternberg WM task and the threat of shock paradigm, it can also serve as a framework for studying the relationship between cognition and anxiety more generally21.
By redesigning existing cognitive tasks to take place during alternating periods of safety and threat, it is possible to study the effect of anxiety on specific cognitive processes, like WM and sustained attention2,31,32. For instance, in previous work, the N-back working memory task was integrated with the threat of shock paradigm, demonstrating that anxiety interferes with WM at a low load but not a high load4,5. These results suggest that anxiety interferes with WM, but also that healthy individuals are capable of overcoming anxiety when task demands are high. The Sustained Attention to Response Task (SART) was also integrated with the threat of shock paradigm; subjects had to inhibit their responses to infrequent target stimuli. This demonstrated that the threat of shock increases the accuracy on NoGo trials during the task31,32. Together with the N-back studies, these results suggest that anxiety can both impair and facilitate performance, and that the direction of the effect depends on the specific cognitive processes engaged by the task.
Likewise, by adding precisely timed startle probes to an existing cognitive task that has been adapted to the threat of shock paradigm, it is possible to study the effect of specific cognitive tasks on anxiety. The relationship between the WM load and anxiety was initially observed during the N-back WM tasks, where increasing the number of to-be-maintained items reduced APS4,5. However, because this task requires both maintenance and manipulation, it was difficult to determine which WM components were necessary for the observed reduction in anxiety23,33. By following up these studies with the simpler Sternberg WM paradigm, it was possible to show that central executive processing was not necessary for anxiety reduction3.
This technique can be used to study both the effect of anxiety on cognition, as well as the effect of cognition on anxiety. Accordingly, it is important to manipulate both anxiety and cognitive load in this paradigm and to take reliable measures of each. When applying this method to novel cognitive paradigms, it is important to ensure that the cognitive paradigm has distinguishable levels of difficulty based on performance. If pilot testing does not show differences in performance across experimental conditions, check for ceiling/floor effects and adjust the difficulty of the task accordingly. Likewise, it is important to design the threat of shock manipulation such that it is possible to observe APS during conditions of low cognitive load. If pilot testing does not show differences in startle during conditions of low cognitive load, try checking the signal-to-noise ratio in the EMG channel.
There are 3 critical steps to ensuring the effectiveness of this protocol. First, it is important to ensure that the subject understands the cognitive task being implemented. If necessary, design a practice version of the task to ensure that the subjects understand the instructions. Second, it is important to ensure that the electrical stimulation used is of a sufficient intensity to induce anxiety in the subject. If necessary, recalibrate the intensity of the electrical stimulation after each run. Third, it is important to ensure that the signal-to-noise ratio of the EMG channel is sufficient to recover the acoustic startle response. If the channel is noisy or the impedance is too high, thoroughly clean the skin under the eye and reapply the EMG electrodes.
Although there are a number of strengths to this paradigm, there are also limitations that should be addressed. For instance, the use of aversive electrical shock can raise concern among some IRBs, especially when dealing with vulnerable populations. It should be noted that there are alternative approaches to induce anxiety besides using electric shock. These include breathing elevated levels of CO2 (7.5%) for extended periods (8-20 min)34, using the threat of an aversive thermal stimulus35, presenting negatively valenced pictures36, etc. However, it should be noted that electrical stimuli are safe (when used properly), widely used, and effective. Although this protocol recommends a standardization approach to analyze potentiated startle, raw scores may be more reliable in some cases9,10. If standardized scores are used, it is recommended to examine the raw scores as well.
The strength of this protocol is that it allows the researcher to flexibly manipulate state anxiety within-subject in a single session and to test the relationship between anxiety and specific cognitive processes. There are three potential future applications of this protocol. First, it is important to understand how cognitive and emotional systems interact at the level of neural processes. Future studies should examine the relationship between anxiety and WM maintenance-related neural activity, using this paradigm while recording BOLD activity. Second, it is important to generalize these findings to other cognitive processes, such as sustained attention and reward processing. Future studies using this protocol should manipulate these processes during periods of threat and safety. Third, it is important to understand the relationship between cognition and anxiety, in both healthy individuals and in patient populations. Future studies using this protocol should include individuals from these special populations.
In conclusion, this work presents a protocol for studying the relationship between WM load and induced anxiety. Studies using this paradigm have shown that WM maintenance is sufficient to reduce anxiety but that anxiety does not interfere with the WM load itself. Although the findings presented here are specific to the Sternberg WM paradigm, this protocol can be adapted to study the bidirectional relationship between cognition and anxiety in general.

According to the Attention Control Theory (ACT), anxiety interferes with cognitive processing by competing for access to limited Working Memory (WM) resources1. However, the ACT does not address the inverse of this relationship (i.e.&#160;the effect of cognitive processing on anxiety). By manipulating anxiety during cognitive tasks using the threat of shock paradigm, it is possible to assess both the effect of anxiety on cognition and the effect of cognition on anxiety2,3,4,5. The purpose of this protocol is to demonstrate how to administer the Sternberg WM paradigm during threat of shock paradigm to probe the bi-directional relationship between anxiety and WM maintenance.
The threat of shock paradigm is widely used in the laboratory to manipulate state anxiety6,7,8,9,10,11 and can be implemented in healthy subjects2,3,4,5 and patients12,13,14,15 alike (see Bradford et al.16 for an example). The paradigm consists of alternating blocks of threat and safety17. Subjects are at risk of receiving unpredictable electrical stimulations during the threat blocks, but not during the safe blocks. Subjects&#39; anxiety can be probed periodically using the acoustic startle reflex18,19. Subjects typically show larger startle responses during the threat blocks compared to the safe blocks, and this Anxiety-Potentiated Startle (APS) can be used as a peripheral index of change in the ongoing anxiety during the test17,18. Potentiated startle in the threat of shock paradigm is recognized by the National Institute of Mental Health (NIMH) as a physiological index of anxiety in their Research Domain Criteria matrix20. However, it is also possible to probe an individual&#39;s anxiety using a self-report Likert-type scale. Because threat of shock is a passive paradigm, other cognitive tasks can be conducted simultaneously21. By combining the threat of shock with the Sternberg WM task, it is possible to probe anxiety during WM maintenance3.
During the Sternberg WM paradigm, subjects are required to encode a series of letters in WM and respond after a brief interval3,22. Unlike more complex WM tasks (e.g., the N-back task)4,5,23, the Sternberg task does not require the manipulation of information in the WM3,22. In addition, subjects encode, maintain, and respond to items during distinct intervals. Together, these features make it possible to dissociate WM maintenance from other, more complex cognitive processes24. By probing APS during the WM maintenance interval, it is possible to determine the effect of WM maintenance on anxiety. Likewise, by comparing WM accuracy and Reaction Time (RT) between the threat and safe blocks, it is possible to determine the effect of anxiety on WM maintenance. This protocol will detail the procedural steps necessary to conduct the Sternberg WM paradigm during threat of shock, as well as the analytical steps necessary to assess APS, accuracy, and reaction time during the task.

<table><tbody><tr><td>&lt;strong&gt;Biopac System&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>System</td><td>Biopac Systems Inc.</td><td>MP150</td><td>1, Psychophysiology monitoring hardware</td></tr><tr><td>TTL integration</td><td>Biopac Systems Inc.</td><td>STP100C</td><td>1</td></tr><tr><td>EDA</td><td>Biopac Systems Inc.</td><td>EDA100C</td><td>1</td></tr><tr><td>ECG</td><td>Biopac Systems Inc.</td><td>ECG100C</td><td>1</td></tr><tr><td>EMG</td><td>Biopac Systems Inc.</td><td>EMG100C</td><td>1</td></tr><tr><td>&lt;strong&gt;Name&lt;/strong&gt;</td><td>&lt;strong&gt;Company&lt;/strong&gt;</td><td>&lt;strong&gt;Catalog Number&lt;/strong&gt;</td><td>&lt;strong&gt;Comments&lt;/strong&gt;</td></tr><tr><td>&lt;strong&gt;Other Equipment&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>Breakout box</td><td>See Alternatives</td><td>Custom</td><td>1</td></tr><tr><td>Grass Signal Generator</td><td>Grass Instruments</td><td>SD9</td><td>1</td></tr><tr><td>Shock device</td><td>Digitimer North America, LLC</td><td>DS7A</td><td>1</td></tr><tr><td>&lt;strong&gt;Name&lt;/strong&gt;</td><td>&lt;strong&gt;Company&lt;/strong&gt;</td><td>&lt;strong&gt;Catalog Number&lt;/strong&gt;</td><td>&lt;strong&gt;Comments&lt;/strong&gt;</td></tr><tr><td>&lt;strong&gt;Alternatives&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>Alternative to Breakout box</td><td>Cortech Solutions</td><td>SD-MS-TCPBNC</td><td>1</td></tr><tr><td>Alternative Grass Signal Generator</td><td>Digitimer North America, LLC</td><td>DG2A</td><td>1</td></tr><tr><td>&lt;strong&gt;Name&lt;/strong&gt;</td><td>&lt;strong&gt;Company&lt;/strong&gt;</td><td>&lt;strong&gt;Catalog Number&lt;/strong&gt;</td><td>&lt;strong&gt;Comments&lt;/strong&gt;</td></tr><tr><td>&lt;strong&gt;Audio Equipment&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>Headphones</td><td>Sennheiser Electronic GMBH &amp;amp; CO</td><td>HD-280</td><td>1</td></tr><tr><td>Headphone Amplifier</td><td>Applied Research and Technology</td><td>AMP4</td><td>1</td></tr><tr><td>Sound Pressure Level Meter</td><td>Hisgadget Inc</td><td>MS10</td><td>1</td></tr><tr><td>&lt;strong&gt;Name&lt;/strong&gt;</td><td>&lt;strong&gt;Company&lt;/strong&gt;</td><td>&lt;strong&gt;Catalog Number&lt;/strong&gt;</td><td>&lt;strong&gt;Comments&lt;/strong&gt;</td></tr><tr><td>&lt;strong&gt;Electrodes and Leads from Biopac&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>EMG</td><td>Biopac Systems Inc.</td><td>EL254S</td><td>2</td></tr><tr><td>EMG stickers</td><td>Biopac Systems Inc.</td><td>ADD204</td><td>2</td></tr><tr><td>Gel for EMG</td><td>Biopac Systems Inc.</td><td>GEL100</td><td>1</td></tr><tr><td>ECG</td><td>Biopac Systems Inc.</td><td>LEAD110</td><td>2</td></tr><tr><td>Shock</td><td>Biopac Systems Inc.</td><td>LEAD110</td><td>2</td></tr><tr><td>ECG</td><td>Biopac Systems Inc.</td><td>LEAD110S-W</td><td>1</td></tr><tr><td>ECG</td><td>Biopac Systems Inc.</td><td>LEAD110S-R</td><td>1</td></tr><tr><td>Disposable electrodes</td><td>Biopac Systems Inc.</td><td>EL508</td><td>6</td></tr><tr><td>&lt;strong&gt;Name&lt;/strong&gt;</td><td>&lt;strong&gt;Company&lt;/strong&gt;</td><td>&lt;strong&gt;Catalog Number&lt;/strong&gt;</td><td>&lt;strong&gt;Comments&lt;/strong&gt;</td></tr><tr><td>&lt;strong&gt;Software&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>Presentation</td><td>Neurobehavioral Systems</td><td>Version 18</td><td>Referred to here as experimental software</td></tr><tr><td>Acknowledge</td><td>Biopac Systems Inc.</td><td>Version 4.2</td><td>Referred to here as psychophysiology analysis software</td></tr></tbody></table>

reducing state anxiety using working memory maintenance

The purpose of this protocol is to explain how to examine the relationship between working memory processes and anxiety by combining the Sternberg Working Memory (WM) and the threat of shock paradigms. In the Sternberg WM paradigm, subjects are required to maintain a series of letters in the WM for a brief interval and respond by identifying whether the position of a given letter in the series matches a numerical prompt. In the threat of shock paradigm, subjects are exposed to alternating blocks where they are either at risk of receiving unpredictable presentations of a mild electric shock or are safe from the shock. Anxiety is probed throughout the safe and threat blocks using the acoustic startle reflex, which is potentiated under threat (Anxiety-Potentiated Startle (APS)). By conducting the Sternberg WM paradigm during the threat of shock and probing the startle response during either the WM maintenance interval or the intertrial interval, it is possible to determine the effect of WM maintenance on APS.

This protocol yields three primary data types: accuracy, RT, and APS. For accuracy and RT, this protocol involves two experimental manipulations, threat and load. For accuracy, typical results show a main effect of load but no main effect of threat and no load-by-threat interaction (trials (F(1,18) = 84.34; p &#60;0.01; see Figure 5). Subjects are typically more accurate on the low-load than the high-load trials. For RT, typical results show a main effect of both load (F(1,18) = 19.49; p &#60;0.01) and threat (F(1,18) = 8.03; p = 0.01) but no load-by-threat interaction (see Figure 6). Subjects typically show faster RTs during low-load trials than during high-load trials and faster RTs during threat blocks than during safe blocks.
This protocol also involves two experimental manipulations for APS: load and startle timing. Typical results show a load-by-timing interaction (F(1,18) = 16.63; p &#60;0.01; see Figure 7). Subjects typically show significantly larger APS during low load vs. high load trials, but only when the startle probe is delivered during the maintenance interval (MNT; maintenance period: t(18) = 3.92; p &#60;0.01; ITI: p &#62;0.05; d = 0.72). It should be noted that, because inferential statistics can vary from study to study, it is important to replicate these effects. After this experiment, a consistent decrease in APS as a function of task difficulty was found. This finding was observed in a verbal N-back task (3-back &#62; 0-back d(25) = 2.2)4, the Sternberg WM paradigm (see above, d(18) = 0.72; for replication, see Experiment 1 in Balderston et al. 20163; high load &#62; low load, d(18) = 0.44), and a complex picture recognition task (retrieval &#62; encoding, d(21) = 0.47)2. However, it should be noted that the final result may be driven in part by habituation.
Although it is difficult to determine an individual&#39;s subjective affective state during each trial, self-report data can be used to determine the effectiveness of the anxiety manipulation and as an individual difference measure. Therefore, it is important to assess the subject&#39;s affective state prior to the experiment using standardized questionnaires and to probe the subject&#39;s anxiety during the experiment. Typical results show significantly higher anxiety ratings during the threat blocks than during the safe blocks; t(18) = 8.85; p &#60;0.001.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/55727/55727fig1.jpg" /> 
Figure 1: Schematic of a Typical Equipment Setup. (A) Use separate computers to administer the task and record physiological signals from the subject. Synchronize events with the psychophysiology monitoring hardware and the shock device via the parallel port of the experiment computer. Relay the physiological signals from the psychophysiology monitoring hardware to the acquisition computer via the Ethernet cable. Deliver the shock to the subject using the shock device, which is controlled by a signal generator and triggered by the task computer. Deliver the white noise to the subject via the sound card of the task computer and record the trace using the psychophysiology monitoring hardware. (B) Required settings for the signal generator. (C) Required settings for the shock device. <a href="https://cloudflare-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/55727/55727fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/55727/55727fig2.jpg" /> 
Figure 2: Schematic of a Typical Subject Setup. Attach electrodes to deliver the shock to the subject&#39;s non-dominant wrist. Attach electrodes to measure skin conductance on the subject&#39;s non-dominant palm. Attach electrodes to measure electromyography below the right eye, over the orbicularis oculi muscle. Attach electrodes to measure electrocardiography on the subject&#39;s left bicep and right clavicle. <a href="https://cloudflare-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/55727/55727fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/55727/55727fig3.jpg" /> 
Figure 3: Schematic of Typical Experimental Design. Present the subjects with a series of letters followed by a brief maintenance period and a response prompt. During the response prompt, present the subjects with a letter (from the series) and a number. Instruct the subjects to indicate whether the number matches the position of the target letter in the previous series. Present startle probes during each trial, either during the maintenance period or the intertrial interval (ITI). <a href="https://cloudflare-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/55727/55727fig3large.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/55727/55727fig4.jpg" /> 
Figure 4: Example EMG Traces Following a White Noise Probe. (A) Raw EMG trace. (B) EMG trace bandpass filtered at 30 to 500 Hz. (C) EMG trace that has been both filtered and rectified using a 20 ms constant. (D) Raw EMG trace from a trial contaminated by baseline noise. <a href="https://cloudflare-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/55727/55727fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/55727/55727fig5.jpg" /> 
Figure 5: Typical Reaction Time (RT) Results. Subjects are typically faster during low-load trials than during high-load trials. Subjects are also typically faster under threat of shock. Bars represent the mean &#177; SEM. <a href="https://cloudflare-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/55727/55727fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/55727/55727fig6.jpg" /> 
Figure 6: Typical Anxiety-potentiated Startle (APS) Results. When startle is probed during the maintenance period (MNT), subjects typically show larger startle potentiation on low-load compared to high-load trials. However, this effect does not hold when startle is probed during the ITI. Bars represent the mean &#177;SEM. <a href="https://cloudflare-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/55727/55727fig6large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 7" class="xfigimg" src="/files/ftp_upload/55727/55727fig7.jpg" /> 
Figure 7: Typical Accuracy (percent (%) correct) Results. Subjects are typically more accurate during low load trials than high load trials; however, performance does not tend to vary as a function of threat of shock. Bars represent the mean &#177; SEM. <a href="https://cloudflare-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/55727/55727fig7large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Supplemental Code Files: Wav file for white noise presentation (40ms_wn.wav.) <a href="https://cloudflare-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/55727/40ms_wn.wav">Please click here to download this file.</a> Code necessary to setup hardware parameters for experimental software (Sternberg_threat_v5.exp.) <a href="https://cloudflare-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/55727/sternberg_threat_v5_run.exp">Please click here to download this file.</a> Code necessary to run experiment (Sternberg_threat_v5.sce.). <a href="https://cloudflare-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/55727/sternberg_threat_v5.sce">Please click here to download this file.</a>

Watch this Scientific Journal Video about Reducing State Anxiety Using Working Memory Maintenance at JoVE.com

Reducing State Anxiety Using Working Memory Maintenance

This protocol demonstrates how to measure anxiety-potentiated startle during the Sternberg Working Memory paradigm.

The purpose of this protocol is to explain how to examine the relationship between working memory processes and anxiety by combining the Sternberg Working ...

reducing-state-anxiety-using-working-memory-maintenance

Universidad San Sebastian

Research

JoVE Journal

Behavior

7.4K Views.  National Institutes of Health (NIH). This protocol demonstrates how to measure anxiety-potentiated startle during the Sternberg Working Memory paradigm. 

Video: Reducing State Anxiety Using Working Memory Maintenance

A Cognitive Paradigm to Investigate Interference in Working Memory by Distractions and Interruptions

Goal-directed behavior is often impaired by interference from the external environment, either in the form of distraction by irrelevant information that one attempts to ignore, or by interrupting information that demands attention as part of another (secondary) task goal. Both forms of external interference have been shown to detrimentally impact the ability to maintain information in working memory (WM). Emerging evidence suggests that these different types of external interference exert different effects on behavior and may be mediated by distinct neural mechanisms. Better characterizing the distinct neuro-behavioral impact of irrelevant distractions versus attended interruptions is essential for advancing an understanding of top-down attention, resolution of external interference, and how these abilities become degraded in healthy aging and in neuropsychiatric conditions. This manuscript describes a novel cognitive paradigm developed the Gazzaley lab that has now been modified into several distinct versions used to elucidate behavioral and neural correlates of interference, by to-be-ignored distractors versus to-be-attended interruptors. Details are provided on variants of this paradigm for investigating interference in visual and auditory modalities, at multiple levels of stimulus complexity, and with experimental timing optimized for electroencephalography (EEG) or functional magnetic resonance imaging (fMRI) studies. In addition, data from younger and older adult participants obtained using this paradigm is reviewed and discussed in the context of its relationship with the broader literatures on external interference and age-related neuro-behavioral changes in resolving interference in working memory.

A novel cognitive paradigm is developed to elucidate behavioral and neural correlates of interference by to-be-ignored distractors versus interference by to-be-attended interruptors during a working memory task. In this manuscript, several variants of this paradigm are detailed, and data obtained with this paradigm in younger/older adult participants is reviewed.

Goal-directed behavior is often impaired by interference from the external environment, either in the form of distraction by irrelevant information that ...

Brain Imaging Investigation of the Impairing Effect of Emotion on Cognition

Emotions can impact cognition by exerting both enhancing (e.g., better memory for emotional events) and impairing (e.g., increased emotional distractibility) effects (reviewed in 1). Complementing our recent protocol 2 describing a method that allows investigation of the neural correlates of the memory-enhancing effect of emotion (see also 1, 3-5), here we present a protocol that allows investigation of the neural correlates of the detrimental impact of emotion on cognition. The main feature of this method is that it allows identification of reciprocal modulations between activity in a ventral neural system, involved in 'hot' emotion processing (HotEmo system), and a dorsal system, involved in higher-level 'cold' cognitive/executive processing (ColdEx system), which are linked to cognitive performance and to individual variations in behavior (reviewed in 1). Since its initial introduction 6, this design has proven particularly versatile and influential in the elucidation of various aspects concerning the neural correlates of the detrimental impact of emotional distraction on cognition, with a focus on working memory (WM), and of coping with such distraction 7,11, in both healthy 8-11 and clinical participants 12-14.

We present a protocol that allows investigation of the neural mechanisms mediating the detrimental impact of emotion on cognition, using functional magnetic resonance imaging. This protocol can be used with both healthy and clinical participants.

Emotions can impact cognition by exerting both enhancing (e.g., better memory for emotional events) and impairing (e.g., increased emotional ...

Neuroscience

Assessing Working Memory in Children: The Comprehensive Assessment Battery for Children &#8211; Working Memory (CABC-WM)

The Comprehensive Assessment Battery for Children - Working Memory (CABC-WM) is a computer-based battery designed to assess different components of working memory in young school-age children. Working memory deficits have been identified in children with language-based learning disabilities, including dyslexia1,2 and language impairment3,4, but it is not clear whether these children exhibit deficits in subcomponents of working memory, such as visuospatial or phonological working memory. The CABC-WM is administered on a desktop computer with a touchscreen interface and was specifically developed to be engaging and motivating for children. Although the long-term goal of the CABC-WM is to provide individualized working memory profiles in children, the present study focuses on the initial success and utility of the CABC-WM for measuring central executive, visuospatial, phonological loop, and binding constructs in children with typical development. Immediate next steps are to administer the CABC-WM to children with specific language impairment, dyslexia, and comorbid specific language impairment and dyslexia.

Working memory predicts a significant amount of variance for a variety of cognitive tasks, including speaking, reading, and writing. However, few tools are available to assess working memory in children. We present an innovative, computer-based battery that comprehensively assesses different components of working memory in school-age children.

The Comprehensive Assessment Battery for Children - Working Memory (CABC-WM) is a computer-based battery designed to assess different components of ...

Highlighting and Reducing the Impact of Negative Aging Stereotypes During Older Adults' Cognitive Testing

As life expectancy increases, aging has become a major health challenge, resulting in a huge effort to better discriminate between normal and pathological cognitive decline. It is thus essential that cognitive tests and their administration are as fair as possible. However, an important source of bias during cognitive testing comes from negative aging stereotypes that can impair the memory performance of older adults and inflate age differences on cognitive tasks. The fear of confirming negative aging stereotypes creates an extra pressure among older adults which interferes with their intellectual functioning and leads them to perform below their true abilities. Here, we present a protocol that highlights simple but efficient interventions to alleviate this age-based stereotype threat effect. The first study showed that simply informing older participants about the presence of younger participants (threat condition) led older adults to underperform on a standardized memory test compared with younger participants, and that this performance difference was eliminated when the test was presented as age-fair (reduced-threat condition). The second study replicated these findings on short cognitive tests used to screen for predementia in clinical settings and showed that teaching older adults about stereotype threat inoculated them against its effects. These results provide useful recommendations about how to improve older adults' memory assessment both in Iab studies and in clinical settings.

Here, we present a protocol designed to show how negative aging stereotypes can impair memory performance of older adults during cognitive testing and how to reduce this deleterious effect. This method can help older people to perform at an optimal level during testing in both lab studies and clinical settings.

As life expectancy increases, aging has become a major health challenge, resulting in a huge effort to better discriminate between normal and pathological ...

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 ...

Working Memory Training for Older Participants: A Control Group Training Regimen and Initial Intellectual Functioning Assessment

The efficacy of cognitive training interventions is recently highly debated. There is no consensus on what kind of training regimen is the most effective. Also, individual characteristics as predictors of training outcome are still being investigated. In this article, we show the attempt to address this issue by examining not only the impact of working memory (WM) training on cognitive effectiveness in older adults but also the influence of the initial WM capacity (WMC) on the training&#39;s outcome. We describe in detail how to perform 5 weeks of an adaptive dual n-back training with an active control group (memory quiz). We are focusing here on technical aspects of the training as well as on the initial assessment of participants&#39; WMC. The evaluation of pre and post training performance of other cognitive dimensions was based on the results of tests of memory updating, inhibition, attention shifting, short-term memory (STM) and reasoning. We have found that the initial level of WMC predicts the efficiency of the n-back training intervention. We have also noticed the post training improvement in almost all aspects of cognitive functioning we measured, but those effects were mostly intervention independent.

A cognitive training intervention in elderly population together with the assessment of the pre training cognitive abilities is presented. We show two versions of training - experimental and active control - and demonstrate their effects on the array of cognitive tests.

The efficacy of cognitive training interventions is recently highly debated. There is no consensus on what kind of training regimen is the most effective. ...

Assessment of Stress Effects on Cognitive Flexibility using an Operant Strategy Shifting Paradigm

Stress affects cognitive function. Whether stress enhances or impairs cognitive function depends on several factors, including the 1) type, intensity, and duration of the stressor; 2) type of cognitive function under study; and 3) timing of the stressor in relation to learning or executing the cognitive task. Furthermore, sex differences among the effects of stress on cognitive function have been widely documented. Described here is an adaptation of an automated operant strategy shifting paradigm to assess how variations in stress affect cognitive flexibility in male and female Sprague Dawley rats. Specifically, restraint stress is used before or after training in this operant-based task to examine how stress affects cognitive performance in both sexes. Particular brain areas associated with each task in this automated paradigm have been well-established (i.e., the medial prefrontal cortex and orbitofrontal cortex). This allows for targeted manipulations during the experiment or the assessment of particular genes and proteins in these regions upon completion of the paradigm. This paradigm also allows for the detection of different types of performance errors that occur after stress, each of which has defined neural substrates. Also identified are distinct sex differences in perseverative errors after a repeated restraint stress paradigm. The use of these techniques in a preclinical model may reveal how stress affects the brain and impairs cognition in psychiatric disorders, such as post-traumatic stress disorder (PTSD) and major depressive disorder (MDD), which display marked sex differences in prevalence.

Stressful life events impair cognitive function, increasing the risk of psychiatric disorders. This protocol illustrates how stress affects cognitive flexibility using an automated operant strategy shifting paradigm in male and female Sprague Dawley rats. Specific brain areas underlying particular behaviors are discussed, and translational relevance of results are explored.

Stress affects cognitive function. Whether stress enhances or impairs cognitive function depends on several factors, including the 1) type, intensity, and ...

Developing Neuroimaging Phenotypes of the Default Mode Network in PTSD: Integrating the Resting State, Working Memory, and Structural Connectivity

Complementary structural and functional neuroimaging techniques used to examine the Default Mode Network (DMN) could potentially improve assessments of psychiatric illness severity and provide added validity to the clinical diagnostic process. Recent neuroimaging research suggests that DMN processes may be disrupted in a number of stress-related psychiatric illnesses, such as posttraumatic stress disorder (PTSD).
Although specific DMN functions remain under investigation, it is generally thought to be involved in introspection and self-processing. In healthy individuals it exhibits greatest activity during periods of rest, with less activity, observed as deactivation, during cognitive tasks, e.g., working memory. This network consists of the medial prefrontal cortex, posterior cingulate cortex/precuneus, lateral parietal cortices and medial temporal regions.
Multiple functional and structural imaging approaches have been developed to study the DMN. These have unprecedented potential to further the understanding of the function and dysfunction of this network. Functional approaches, such as the evaluation of resting state connectivity and task-induced deactivation, have excellent potential to identify targeted neurocognitive and neuroaffective (functional) diagnostic markers and may indicate illness severity and prognosis with increased accuracy or specificity. Structural approaches, such as evaluation of morphometry and connectivity, may provide unique markers of etiology and long-term outcomes. Combined, functional and structural methods provide strong multimodal, complementary and synergistic approaches to develop valid DMN-based imaging phenotypes in stress-related psychiatric conditions. This protocol aims to integrate these methods to investigate DMN structure and function in PTSD, relating findings to illness severity and relevant clinical factors.

This protocol describes the complementary neuroimaging techniques of resting state structural connectivity, task-induced deactivation, and structural connectivity analyses to examine the default network in post-traumatic stress disorder. The use of synergistic methods could potentially lead to improved diagnostics and assessments of severity, outcome, and other relevant clinical factors.

Complementary structural and functional neuroimaging techniques used to examine the Default Mode Network (DMN) could potentially improve assessments of ...

Medicine

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.

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 ...

The Water Maze with Platform Relocation: A Method to Assess Working Memory

Source: Hausser, N., et al. <a href="https://www-jove-com.bdigitaluss.remotexs.co/video/56044/detecting-behavioral-deficits-in-rats-after-traumatic-brain-injury">Detecting Behavioral Deficits in Rats After Traumatic Brain Injury.</a>&#160;J. Vis. Exp (2018).
In this video, we describe the Morris water maze test with platform relocation, a navigational task used to study working memory in rodents.

Wodny labirynt z przeniesieniem platformy: metoda oceny pamięci roboczej

Source: Hausser, N., et al. Detecting Behavioral Deficits in Rats After Traumatic Brain Injury.&#160;J. Vis. Exp (2018).
In this video, we describe the ...