This study was supported by the SPF-201209176152 fund at The University of Hong Kong. We thank Dr. Helen Ma for teaching the course and recruiting MBSR participants.

This research study was approved by The University of Hong Kong Institutional Review Board. All participants provided their written informed consent prior to participating in this study.
1. Experimental Design
<img alt="Table 1" src="/files/ftp_upload/55455/table01.jpg" /> 
Table 1. MBSR Course Overview. The 8-week MBSR training course taken by the participants.
<ol>
	<li>Recruit participants. 
	NOTE: Eleven healthy adults (five females) from a local MBSR course participated in this EEG study and were 28 - 52-year-old. Participant with depression based on the Beck Depression Inventory were excluded from the study. The course followed standard MBSR training (Table 1), and participants had to commit to the training structure.</li>
	<li>EEG recording system.
	<ol>
		<li>Conduct EEG recording in a quiet room using a 128-channel EEG system consisting of an EEG cap, amplifier, headbox, and desktop computer.</li>
	</ol>
	</li>
</ol>
2. EEG and ECG Data Acquisition
NOTE: EEG data collection followed a standard procedure similar to the method described previously14. Ask each participant to wash his/her hair and scalp clean before coming to the experimental site. Inform the participant about the experimental procedures, mainly that he/she will rest normally for 10 min and do mindful breathing for another 10 min while being recorded with EEG and ECG devices.
<ol>
	<li>Clean the face area and mastoid of the participant with alcohol swabs.</li>
	<li>Using a measuring tape, measure the participant&#39;s head circumference and then choose an appropriately sized cap. Take one measurement from the nasion to the inion and another measurement across the top of the ears and over the scalp. Mark the vertex (the point at mid-distance between the nasion and inion and mid-distance between the two ears) with a soft marker pen.</li>
	<li>Set the electrode positions according to the 10-5 electrode systems15. Position the cap in such a way that the Cz electrode is above the vertex, the Nz electrode is at the nasion, the Lz electrode is at the inion, the RM electrode is at the right mastoid, and the LM electrode is at the left mastoid.
	<ol>
		<li>Place the ECG electrodes at both the left and right infraclavicular fossae.</li>
		<li>Fill the electrode holders with gel using a blunt-point syringe.</li>
		<li>Keep the impedance under 20 k&#937; for each electrode. Reduce the impedance by adjusting the electrode placement to increase contact with the scalp; add more gel if necessary.</li>
		<li>Tighten the chinstrap and ask the participant to remain still.</li>
		<li>Set the sampling rate of the EEG device to 1,000 Hz. Use the left mastoid as the original reference point.</li>
	</ol>
	</li>
	<li>Record EEG data (step 1.2.1) at the beginning of the MBSR course (within 2 weeks).
	<ol>
		<li>Have the participant perform a brief body scan to relax the whole body. Have the participant pay attention to his/her breath while breathing in and breathing out. Have each participant perform 10 min of MBSR mindful breathing (MBSR mindfulness condition) and 10 min of normal rest (control condition) during EEG data collection. This generates a pre-MBSR training dataset with two conditions. 
		NOTE: Counterbalance the sequence of mindful breathing and normal rest among the participants.</li>
	</ol>
	</li>
	<li>Record EEG data again about 1 month after the participants complete the MBSR course.
	<ol>
		<li>Ask each participant to perform 10 min of mindful breathing and 10 min of normal rest during EEG data collection. This generates a post-MBSR training dataset with two conditions.</li>
	</ol>
	</li>
</ol>
3. EEG and ECG Data Analysis
NOTE: The first four steps are for data preprocessing, and the remaining steps are for wavelet entropy computation and correlation analysis. Here, an open-source software named EEGLAB16 was used for EEG analysis, but the operations should be similar across different software. In the following, the operations that are based on EEGLAB will be provided as an example. Refer to the EEGLAB manual for details (https://sccn.ucsd.edu/wiki/EEGLAB_Wiki#EEGLAB_Tutorial).
<ol>
	<li>Use the EEG software to resample the data at 250 Hz by selecting Tools &#62; Change sampling rate. Use the Finite Impulse Response (FIR) filter for band-pass filtering with a 0.5 - 100 Hz passband by selecting Tools &#62; Filter the data &#62; Basic FIR filter.
	<ol>
		<li>To reduce noise due to the mains alternating current that is country-specific, use the short, nonlinear, Infinite Impulse Response (IIR) filter for notch filtering with a 47 - 53 Hz stopband by selecting Tools &#62; Filter the data &#62; Short non-linear IIR filter. This covers all relevant frequencies in the spectrum analysis while removing unnecessary noise.</li>
	</ol>
	</li>
	<li>Use the EEG software to visually scroll through and inspect the EEG signal by selecting Plot &#62; Channel data (scroll).
	<ol>
		<li>Identify and delete EEG segments that contain obvious muscle noise and any other strange events. Left-click and drag the mouse over bad segments to highlight them and then &#34;delete&#34; or &#34;reject&#34; the segments using the EEG software. 
		NOTE: Muscle activities such as swallowing or facial expression often generate discontinuous and irregular signals. Strange events are usually caused by head or body movements, which often lead to significant shifts (at low frequencies) of the EEG signal.</li>
		<li>Determine if there is any bad channel. Reconstruct each bad channel using the spherical interpolation method by selecting Tools &#62; Interpolate channel.</li>
	</ol>
	</li>
	<li>Use the EEG software to perform Independent Component Analysis17 (ICA) on the data by selecting Tools &#62; Run ICA. Have an experienced EEG operator visually identify and discard components of eye movement and blinking, muscle movements, and components of other possible noise by selecting Tools &#62; Reject data using ICA &#62; Reject components by map; Tools &#62; Remove components. The EEG software will reconstruct the data automatically using the retained components.</li>
	<li>Use the EEG software to rereference the data to the average of all channels before further analysis by selecting Tools &#62; Re-reference.</li>
	<li>Spectrum analysis.
	<ol>
		<li>Use the EEG software command line function &#34;spectopo&#34; to compute the spectra of EEG using Welch&#39;s power spectral density estimation18 and obtain the log powers (dB) of delta (1 - 4 Hz), theta (4 - 8 Hz), alpha (8 - 12 Hz), beta (12 - 30 Hz), and gamma (30 - 80 Hz) waves.</li>
		<li>Calculate the relative power of each type of wave18 (i.e.&#160;the proportion of power in the respective frequency band relative to the total spectrum power).</li>
	</ol>
	</li>
	<li>Perform EEG source analysis using the Statistical Parametric Mapping (SPM) approach.
	<ol>
		<li>Use minimum norm estimation19 to transform the EEG signals of the scalp electrodes into 3D brain source signals. 
		NOTE: Here, the original channel number was 122, and it was transformed to 8,196 channels distributed in the three-dimensional brain model. The significance level was set at p = 0.01. Please refer to the source analysis in the SPM manual (www.fil.ion.ucl.ac.uk/spm/doc/spm8_manual.pdf).</li>
	</ol>
	</li>
	<li>Calculate the wavelet entropy for each EEG channel (as well as for the estimated three-dimensional brain source signal) using the following procedure:
	<ol>
		<li>Given a signal <img alt="Equation 1" src="/files/ftp_upload/55455/55455eq1.jpg" />, calculate the wavelet coefficients as <img alt="Equation 2" src="/files/ftp_upload/55455/image002.jpg" />, where <img alt="Equation 3" src="/files/ftp_upload/55455/image003.jpg" /> is an orthonormal basis of the Haar wavelet family and <img alt="Equation 4" src="/files/ftp_upload/55455/image004.jpg" /> represents the decomposition levels (N = 10 in this study).</li>
		<li>Define the relative energy as <img alt="Equation 5" src="/files/ftp_upload/55455/image005.jpg" />, with <img alt="Equation 6" src="/files/ftp_upload/55455/image006.jpg" />.</li>
		<li>Calculate the wavelet entropy as <img alt="Equation 7" src="/files/ftp_upload/55455/image007.jpg" />. Average the wavelet entropy for all channels to obtain the EEG wavelet entropy for each participant.</li>
	</ol>
	</li>
	<li>Use the software package to perform a peak analysis on the ECG data to obtain the heartbeat signal, which consists of the number of ECG wave peaks and the interval between the peaks. 
	NOTE: Assuming a normal heartrate of 60-100 beats/min in both the normal rest and mindful breathing states, the number of heartbeat instances N for a 10 min duration should be relatively small, satisfying 600 &#60; N &#60; 1,000.
	<ol>
		<li>To obtain reliable wavelet entropy from the heartbeat signal, use a sliding window of 500 points and a step increment of 10 points to compute a series of wavelet entropies using the same procedure as for EEG, elaborated in section 3.7. Use the mean value as the final wavelet entropy.</li>
	</ol>
	</li>
	<li>Analyze the brain and heart correlation across subjects between the wavelet entropy of ECG and that of EEG at every channel of the scalp using any statistical software that has a Pearson correlation function.
	<ol>
		<li>Use the EEG software command line function &#34;topoplot&#34; to construct a statistical scalp map for the correlation. Plot a linear correlation graph with the heart rate entropy and the average EEG entropy of only those channels that are significantly correlated with heartrate entropy (see the Representative Results section).</li>
	</ol>
	</li>
</ol>

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The authors declare that they have no competing financial interests.

This EEG study provides concrete evidence that, compared to normal rest, MBSR mindful breathing enhances the alpha waves in the frontal and occipital lobes. This is in line with the majority of reports on increased alpha waves during meditation, especially in beginners21. Alpha waves usually indicate a state of arousal and vigilance and freedom from any specific task. Such a mental state is one of the objectives of meditation; that is, keeping the mind clean and sharp, free from either flights of fancy or from falling asleep. The enhanced alpha wave synchrony during MBSR practice may also enhance the coordination between mind and body22.
The electronic activities of the vast number of neurons in the brain form a nonlinear hierarchical system, and its irregularity can be well measured by entropy8. We found that during MBSR practice, the irregularity of brain electronic activities decreased across a large part of the scalp EEG, especially in the frontal pole and bilateral occipital brain regions. Further source analysis of wavelet entropy showed that the irregularity of occipital and precuneus activities was reduced during MBSR practice. Other areas involved include the right-middle cingulate and the superior temporal lobe. This is in line with another study which indicates that gray matter increases in the precuneus after MBSR training23. Advanced meditation practitioners usually have higher parieto-occipital EEG gamma waves during non-rapid eye movement sleep1. One important feature of MBSR training is to be nonjudgmental of the mind/brain information flow. This may potentially reduce the irregularity of brain activities. Because a main information source in the brain is the visual cortex, it is plausible that the visual-related brain regions are more affected by MBSR practice than are other regions. This enables the mind to become less responsive to irrelevant information and to pay more attention to mindful breathing.
Wavelet entropy analysis showed less irregularity of heartrate during mindful breathing compared to the normal rest state, which implies a more refined balance of the autonomic nervous system due to relatively unwavering interactions between parasympathetic and sympathetic tones. Mindful breathing may also affect the heartrate because the lung sensory afferents send inhibitory projections to the cardiac vagal motor neurons in the mid-brain, which inhibit the heartrate via the vagus nerve24. The cardiorespiratory interaction is mediated by the brainstem and limbic system in the brain25. Given the connection between the central nervous system and cardiac activity, it is plausible that, by training the mind, MBSR or other meditation practices can also influence other body functions, such as resetting the baroreflex sensitivity, improving gas exchange efficiency in the lungs, and balancing the autonomic nervous system26.
The mind-body connection is emphasized in many Eastern traditions. In this respect, our study provides the first objective evidence in terms of entropy. The irregularities of brain and heart activities became more coordinated during MBSR practice, and the coordination was greatest in the bilateral somatosensory regions. This region is directly in charge of the body&#39;s sensory input and movement, and this increased coordination implies greater body awareness during MBSR practice. Mindfulness training was found to increase the activities of the viscerosomatic areas in a functional MRI study, which has better spatial resolution than EEG27. Our results suggest that the brain and the heart may become more entrained during MBSR practice, offloading unnecessary entropy in the system28. Because the heartbeat is regulated by the autonomic nervous system, wherein the autonomic tone is modulated by the interactions between the ganglion; sino-auricular node; vagus nerves; and higher-order brain structures, such as the medulla oblongata, insula, and the limbic system, the autonomic and central nervous systems may well become more coordinated during MBSR practice.
One important step in a study such as ours is to choose the right entropy index to measure the irregularity of EEG and ECG. Unlike Renu Madhavi et al., who used approximate entropy29, we have chosen to use wavelet entropy to focus on the complexity among the energies in different scales. Because the brain electronic activities originate from a vast number of neurons that have different characteristics in different scales and spectrums, wavelet entropy should be more suitable for measuring the irregularity of EEG30. Similarly, instantaneous heartrate also has different features under different time scales, so it is reasonable to apply wavelet entropy to ECG31. Furthermore, Shannon&#39;s entropy and approximate entropy are sensitive to sampling frequency, and approximate entropy requires large computation times that increase exponentially with data length, this in addition to the calculation in the source space.
Several limitations of the current study are worth noting. First, the spatial resolution of scalp EEG is relatively poor, despite its high temporal resolution. To address this, source analysis was applied in our study. EEG source reconstruction is an ill-posed problem, and an alternative approach is to have spatial priors from previous functional Magnetic Resonance Imaging (fMRI) studies, which can help to improve the reconstruction accuracy32,33. Another limitation is that the coordination between the brain and heart activities was assessed between subjects; within-subject coordination is left to future studies. Additionally, the entropy of ECG is not derived directly from the ECG signal, but rather from the heartrate interval, because the amplitude of ECG can be greatly influenced by the electrode position and skin impedance and is thus not comparable between subjects.
Previous studies usually performed a spectrum analysis of the EEG signal, dismissing irregularity in EEG as noise. In contrast, we propose to use wavelet entropy as a more sensitive index to measure the chaotic electronic activities of the brain as a reflection of the mind state. The proposed methodology provides objective evidence of the traditional assumption that the mind and heart are more coordinated during meditation.
Because wavelet entropy can monitor meditation state quite sensitively, it may be used in the mental training field, just as entropy is used in clinical practice to measure the level of anesthesia during surgery. Using an index similar to wavelet entropy, wireless EEG devices can be used to conveniently monitor an individual&#39;s daily meditation practice or other mental training for educational purposes. Nonetheless, because a wireless EEG can usually only stably measure the channels in the forehead, we need to explore whether these channels are sensitive enough to monitor state of mind.
The critical steps of this procedure include keeping the skin impedance for each electrode low for better signal quality and less noise; appropriately and properly conducting MBSR training; ensuring a good, non-fatigued mental state before the experiment, as meditation requires great energy; choosing the proper entropy index (wavelet entropy); and being familiar with SPM source analysis of EEG data.

With advances in neuroscience and medicine1, we are in a much better position to understand the non-pharmacological benefits of mindfulness and meditation on mental and physical diseases. Numerous studies have shown that Mindfulness-based Stress Reduction (MBSR) training as a kind of mental exercise can actively reduce unnecessary stress in the mind and the body2. During the practice of meditation, the body and mind may become more entrained. These issues have attracted the interest of both the academic community and the general public and have motivated us to design a methodology to explore potential coordination between mind and body (or more specifically, between the brain and the heart) under different conditions to assess the effect of MBSR training and practice.
The irregularity of electroencephalograms (EEG) is usually regarded as noise in the conventional EEG spectrum analysis, but it may contain useful information and can be measured by signal entropy. EEG entropy has been used as an index of clinical conditions such as epilepsy, Alzheimer&#39;s disease, and anesthesia, and some evidence suggests that the entropy of EEG may be a more sensitive measure for monitoring brain states than the traditional EEG spectrum analysis3,4. Entropy has long been used in thermodynamics as an index to describe the chaotic state of a system. The brain waves measured by EEG are known for their dynamic activity due to brain networks operating between order and chaos5. Among the various kinds of entropies, wavelet entropy offers a method to calculate the entropy of energies under different observation scales, which is well-suited for analyzing EEG signals with highly varied features in different scales. For a completely random signal, the relative energies at different resolution levels will be the same, and the entropy will reach its maximum. Hence, the wavelet entropy serves as a measure of chaotic activities in EEG and can be used as an index for differentiating different mind states6.
Previous electrocardiogram (ECG) studies provided consistent evidence of meditation-related changes in heartrate variability when compared to pre-meditation states7. Heartbeat is regulated by the autonomic (including the sympathetic and parasympathetic) nervous system. The autonomic tone is modulated by interactions between the ganglion; sino-auricular node; vagus nerves; and higher-order brain structures, such as the medulla oblongata, the insula, and the limbic system. These structures form a hierarchical system, and its nonlinear activity can be measured through entropy8. For example, the neural bursts in higher-order brain structures can influence the heartrate rhythm9. In clinical conditions, brain disorders such as epileptic seizures disrupt the heart rhythm10. Neurocardiology research also suggests a close interplay between the heart and the brain11.
One difficulty in examining the association between brain and heart signals is that they are quite different in signal content and time scale. Hence, a major challenge is to identify a common measure that is not only applicable to both brain and heart signals, but also meaningful for interpretation in the context of mind-body coordination. In this paper, we propose the use of wavelet entropy to detect possible coordination between irregularities in brain and heart activities by correlating the entropies of EEG and heartbeat signals, which can then be evaluated by way of an MBSR experiment.
Because MBSR involves mindfulness practice in both body and mind, we hypothesize that MBSR mindful breathing practice may affect not only brain activity, but also the heartbeat. Previous studies mainly investigated the effect of mindfulness training and/or practice on the brain or the heart separately and based on different aspects; the simultaneous changes in these two closely related systems remain largely unexplored. Only a few studies reported better coordination of the body and mind after mindfulness training12. Using the proposed methodology, the representative results and a previous study13 showed that MBSR mindfulness training may increase the coordination of the body and mind in terms of their chaotic activities, which may offer new insight into the effects of mindfulness training on the central and peripheral nervous systems.

<table border="0" cellpadding="0" cellspacing="0" width="818">
	<tbody>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">Synamps 2/RT 128-channel EEG/EP/ERP</td>
			<td style="width:284px;">Compumedics NeuroScan</td>
			<td style="width:119px;">Model 8050</td>
			<td style="width:199px;">Amplifiers and 
			EEG acquisition</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Quik-CapEEG 128 electrodes</td>
			<td style="width:284px;">Compumedics NeuroScan</td>
			<td style="width:119px;"></td>
			<td>EEG caps</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Quik-Gel</td>
			<td style="width:284px;">Compumedics NeuroScan</td>
			<td style="width:119px;"></td>
			<td>conductive electrode gel</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">EEGLAB</td>
			<td style="width:284px;">Swartz Center for Computational Neuroscience</td>
			<td style="width:119px;"></td>
			<td>EEG analysis software</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Matlab R2011a</td>
			<td style="width:284px;">MathWorks</td>
			<td style="width:119px;"></td>
			<td>EEGLAB is based on Matlab</td>
		</tr>
		<tr height="22">
			<td height="22" style="height:23px;width:216px;">SPM</td>
			<td style="width:284px;">Wellcome Trust Centre for Neuroimaging</td>
			<td style="width:119px;"></td>
			<td>EEG source analysis software</td>
		</tr>
	</tbody>
</table>

using wavelet entropy to demonstrate how mindfulness practice increases coordination between irregular cerebral and cardiac activities

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

Spectrum Analysis
In the spectrum analysis of EEG data, compared to normal rest, there were enhanced alpha (8 - 12 Hz) and beta (12 - 30 Hz) and reduced delta (1 - 4 Hz) waves during MBSR mindful breathing. The increment of alpha waves was globally significant, especially in the frontal and occipital lobes, whereas the increment of beta waves was mainly in the frontal lobe. Decreased delta waves were noted in the central-parietal areas (Figure 1). However, we did not find significant change between pre- and post-MBSR training.
<img alt="Figure 1" src="/files/ftp_upload/55455/55455fig1.jpg" /> 
Figure 1. Spectrum Analysis of MBSR Mindful Breathing and Normal Rest Conditions. Spectrum analysis shows that MBSR practice can change brain activities, as indicated by the different spectrums of EEG. Original source: Reference 13. <a href="http://cloudflare.jove.com/files/ftp_upload/55455/55455fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Figure 2 illustrates the defined region of interest: the occipital lobe (channels N19 - 21, N41 - 46, N67 - 72, N96 - 100, and N119), the middle frontal lobe (channels N53 - 61 and N79 - 83), and the middle parietal lobe (channels N48 - 50, N64 - 66, and N74 - 76). Table 2 shows the power of each spectrum and the entropy under MBSR mindful breathing and normal rest conditions, along with the p-value of the t-test of the difference between the two conditions for each region of interest. We combined the pre- and post-MBSR training EEG data since there was no difference between the two stages.
<img alt="Figure 2" src="/files/ftp_upload/55455/55455fig2.jpg" /> 
Figure 2. Defined Region of Interest. The defined areas of EEG channels that represent the middle frontal lobe, middle parietal lobe, and occipital lobe. <a href="http://cloudflare.jove.com/files/ftp_upload/55455/55455fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td></td>
			<td></td>
			<td>Delta</td>
			<td>Theta</td>
			<td>Alpha</td>
			<td>Beta</td>
			<td>Gamma</td>
			<td>Entropy</td>
		</tr>
		<tr>
			<td>Middle Frontal Lobe</td>
			<td>MBSR</td>
			<td>18.96 &#177; 3.10</td>
			<td>22.29 &#177; 5.86</td>
			<td>30.90 &#177; 7.76</td>
			<td>0.12 &#177; 43.77</td>
			<td>-467.25 &#177; 79.48</td>
			<td>0.753 &#177; 0.060</td>
		</tr>
		<tr>
			<td></td>
			<td>Rest</td>
			<td>22.40 &#177; 6.56</td>
			<td>22.76 &#177; 5.98</td>
			<td>26.91 &#177; 7.14</td>
			<td>-7.11 &#177; 42.27</td>
			<td>-449.76 &#177; 102.92</td>
			<td>0.785 &#177; 0.066</td>
		</tr>
		<tr>
			<td></td>
			<td>p-value</td>
			<td>0.0243</td>
			<td>0.6555</td>
			<td>0.0085</td>
			<td>0.0114</td>
			<td>0.4419</td>
			<td>0.0084</td>
		</tr>
		<tr>
			<td>Occipital Lobe</td>
			<td>MBSR</td>
			<td>15.39 &#177; 3.30</td>
			<td>17.91 &#177; 6.53</td>
			<td>33.38 &#177; 6.50</td>
			<td>-8.03 &#177; 40.72</td>
			<td>-466.23 &#177; 61.46</td>
			<td>0.719 &#177; 0.048</td>
		</tr>
		<tr>
			<td></td>
			<td>Rest</td>
			<td>19.82 &#177; 6.32</td>
			<td>20.38 &#177; 8.11</td>
			<td>29.73 &#177; 5.30</td>
			<td>-11.62 &#177; 40.22</td>
			<td>-439.03 &#177; 102.81</td>
			<td>0.763 &#177; 0.055</td>
		</tr>
		<tr>
			<td></td>
			<td>p-value</td>
			<td>0.0134</td>
			<td>0.1213</td>
			<td>0.0125</td>
			<td>0.0796</td>
			<td>0.8936</td>
			<td>0.0098</td>
		</tr>
		<tr>
			<td>Parietal Lobe</td>
			<td>MBSR</td>
			<td>17.95 &#177; 3.60</td>
			<td>19.46 &#177; 6.54</td>
			<td>32.84 &#177; 6.68</td>
			<td>2.68 &#177; 38.23</td>
			<td>-487.72 &#177; 104.13</td>
			<td>0.738 &#177; 0.072</td>
		</tr>
		<tr>
			<td></td>
			<td>Rest</td>
			<td>21.24 &#177; 6.37</td>
			<td>21.16 &#177; 8.00</td>
			<td>29.79 &#177; 6.60</td>
			<td>-1.27 &#177; 34.42</td>
			<td>-490.01 &#177; 123.83</td>
			<td>0.764 &#177; 0.075</td>
		</tr>
		<tr>
			<td></td>
			<td>p-value</td>
			<td>0.0157</td>
			<td>0.0963</td>
			<td>0.0177</td>
			<td>0.1507</td>
			<td>0.2878</td>
			<td>0.0368</td>
		</tr>
	</tbody>
</table>
Table 2. Spectrum and Entropy across Brain Areas. MBSR mindful breathing and normal rest conditions are compared among the three predefined Regions of Interest (ROI).
Wavelet Entropy Analysis
Analysis of the wavelet entropy of EEG showed decreased EEG entropy during MBSR mindful breathing compared to normal rest, both for pre- and post-MBSR training. Because there was no significant difference between the two stages, they were merged to produce an averaged map (shown in the third row of Figure 3). The main areas with decreased entropy were at the frontal lobe and the parietal-occipital lobe.
<img alt="Figure 3" src="/files/ftp_upload/55455/55455fig3.jpg" /> 
Figure 3. Wavelet Entropy Analysis. Rest 1 and MBSR 1 denote pre-MBSR training, whereas Rest 2 and MBSR 2 denote post-MBSR training. Wavelet entropy analysis shows that MBSR practice can reduce the irregularity of brain electronic activities. Original source: Reference13. <a href="http://cloudflare.jove.com/files/ftp_upload/55455/55455fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Source Analysis of Wavelet Entropy
Source analysis of the EEG signals can improve the spatial resolution by deconvolving the scalp EEG into electrical activities over the cortical surface20. The analysis shows that the major brain regions affected by the MBSR mindfulness training were in the left-middle occipital lobe, precuneus, superior temporal lobe, and left fusiform (Figure 4). In Table 3, for the four anatomical labels obtained from the source analysis, we provide t-test results for the difference in the entropies of two different states, MBSR mindfulness state versus normal rest.
<img alt="Figure 4" src="/files/ftp_upload/55455/55455fig4.jpg" /> 
Figure 4. Source Analysis. The source analysis shows that the entropies of various brain regions (highlighted in red) decrease during the MBSR mindful breathing state. <a href="http://cloudflare.jove.com/files/ftp_upload/55455/55455fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Anatomical label</td>
			<td>voxels</td>
			<td>x, y, z</td>
			<td>t-value</td>
		</tr>
		<tr>
			<td>middle temporal gyrus L</td>
			<td>1,728</td>
			<td>-44 -60 16</td>
			<td>3.77</td>
		</tr>
		<tr>
			<td>precuneus R</td>
			<td>1,324</td>
			<td>&#160; 2 -56 28</td>
			<td>3.63</td>
		</tr>
		<tr>
			<td>occipital L</td>
			<td>749</td>
			<td>&#160;-2 -102 -12</td>
			<td>3.61</td>
		</tr>
		<tr>
			<td>fusiform L</td>
			<td>142</td>
			<td>-102</td>
			<td>3.51</td>
		</tr>
	</tbody>
</table>
Table 3. Source Analysis Report. Anatomical brain regions with significant differences in entropy between MBSR mindful breathing and normal rest conditions. L, the left side of brain. R, the right side of the brain (p &#60; 0.001, t-test, uncorrected).
Heart Rate Wavelet Entropy
The analysis revealed no significant difference in average heartrate, but the heartrate entropy was lower during MBSR mindful breathing, both pre- and post-MBSR training (Table 4).
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td></td>
			<td>Rest1</td>
			<td>MBSR1</td>
			<td>Rest2</td>
			<td>MBSR2</td>
		</tr>
		<tr>
			<td>Heart Rate (beats per minute)&#160;</td>
			<td>68.2 &#177; 9.5</td>
			<td>67.7 &#177; 9.3</td>
			<td>71.8 &#177; 8.1</td>
			<td>70.7 &#177; 8.4</td>
		</tr>
		<tr>
			<td>Heart Rate Wavelet Entropy</td>
			<td>0.89 &#177; 0.05</td>
			<td>0.79 &#177; 0.11*</td>
			<td>0.89 &#177; 0.07</td>
			<td>0.80 &#177; 0.12#</td>
		</tr>
	</tbody>
</table>
Table 4. Heartrate and Heartrate Wavelet Entropy of MBSR Mindful Breathing and Normal Rest Conditions. The two conditions are compared at both pre- and post-MBSR training. *Significant difference (p &#60;0.05) between Rest 1 and MBSR 1. #Significant difference (p &#60;0.05) between Rest 2 and MBSR 2.
Correlation between Heart and Brain Activities
Given the potential connection between the brain and the heart, we analyzed the correlation between the whole-brain EEG entropy (i.e.&#160;the average of EEG entropies over all EEG channels for a single subject) and the heartrate entropy during MBSR mindful breathing and normal resting states. The entropies of the brain and heart were significantly correlated during MBSR mindful breathing but not during normal rest. As shown in the upper part of Figure 5, the significance of the correlation between the entropy of the EEG in each channel and the heartrate entropy is most prominent in the central part of the brain. The lower plot shows the correlation between the EEG entropy in the central regions (average of those dotted channels that are significantly correlated with heartrate entropy) and heartrate entropy.
<img alt="Figure 5" src="/files/ftp_upload/55455/55455fig5.jpg" /> 
Figure 5. Correlation between the Wavelet Entropies of Electronic Activities of the Heart and Brain. The wavelet entropy of brain activity is the average of the significant channels in the central region, as shown in the upper-right figure. The wavelet entropy of the heart is based on the heartrate interval. The correlation (lower panel) is calculated across all subjects for both pre- and post-MBSR training results. Original source: Reference 13. <a href="http://cloudflare.jove.com/files/ftp_upload/55455/55455fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities at JoVE.com

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

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

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

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14.5K Views.  The University of Hong Kong. This manuscript describes how to use the wavelet entropy index to analyze high-density electroencephalography (EEG) and electrocardiography (ECG) data. We show that the irregularity of cerebral and cardiac activities became more coordinated during mindfulness-based stress reduction practice.

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

How to Detect Amygdala Activity with Magnetoencephalography using Source Imaging

In trace fear conditioning a conditional stimulus (CS) predicts the occurrence of the unconditional stimulus (UCS), which is presented after a brief stimulus free period (trace interval)1. Because the CS and UCS do not co-occur temporally, the subject must maintain a representation of that CS during the trace interval. In humans, this type of learning requires awareness of the stimulus contingencies in order to bridge the trace interval2-4. However when a face is used as a CS, subjects can implicitly learn to fear the face even in the absence of explicit awareness*. This suggests that there may be additional neural mechanisms capable of maintaining certain types of "biologically-relevant" stimuli during a brief trace interval. Given that the amygdala is involved in trace conditioning, and is sensitive to faces, it is possible that this structure can maintain a representation of a face CS during a brief trace interval.
It is challenging to understand how the brain can associate an unperceived face with an aversive outcome, even though the two stimuli are separated in time. Furthermore investigations of this phenomenon are made difficult by two specific challenges. First, it is difficult to manipulate the subject's awareness of the visual stimuli. One common way to manipulate visual awareness is to use backward masking. In backward masking, a target stimulus is briefly presented (&lt; 30 msec) and immediately followed by a presentation of an overlapping masking stimulus5. The presentation of the mask renders the target invisible6-8. Second, masking requires very rapid and precise timing making it difficult to investigate neural responses evoked by masked stimuli using many common approaches. Blood-oxygenation level dependent (BOLD) responses resolve at a timescale too slow for this type of methodology, and real time recording techniques like electroencephalography (EEG) and magnetoencephalography (MEG) have difficulties recovering signal from deep sources.
However, there have been recent advances in the methods used to localize the neural sources of the MEG signal9-11. By collecting high-resolution MRI images of the subject's brain, it is possible to create a source model based on individual neural anatomy. Using this model to "image" the sources of the MEG signal, it is possible to recover signal from deep subcortical structures, like the amygdala and the hippocampus*.

This article describes how to record amygdala activity with magnetoencephalography (MEG). In addition this article will describe how to conduct trace fear conditioning without awareness, a task that activates the amygdala. It will cover 3 topics: 1) Designing a trace conditioning paradigm using backward masking to manipulate awareness. 2) Recording brain activity during the task using magnetoencephalography. 3) Using source imaging to recover signal from subcortical structures.

In trace fear conditioning a conditional  stimulus (CS) predicts the occurrence of the unconditional stimulus (UCS),  which is presented after a brief ...

Best Current Practice for Obtaining High Quality EEG Data During Simultaneous fMRI

Simultaneous EEG-fMRI allows the excellent temporal resolution of EEG to be combined with the high spatial accuracy of fMRI. The data from these two modalities can be combined in a number of ways, but all rely on the acquisition of high quality EEG and fMRI data. EEG data acquired during simultaneous fMRI are affected by several artifacts, including the gradient artefact (due to the changing magnetic field gradients required for fMRI), the pulse artefact (linked to the cardiac cycle) and movement artifacts (resulting from movements in the strong magnetic field of the scanner, and muscle activity). Post-processing methods for successfully correcting the gradient and pulse artifacts require a number of criteria to be satisfied during data acquisition. Minimizing head motion during EEG-fMRI is also imperative for limiting the generation of artifacts.
Interactions between the radio frequency (RF) pulses required for MRI and the EEG hardware may occur and can cause heating. This is only a significant risk if safety guidelines are not satisfied. Hardware design and set-up, as well as careful selection of which MR sequences are run with the EEG hardware present must therefore be considered.
The above issues highlight the importance of the choice of the experimental protocol employed when performing a simultaneous EEG-fMRI experiment. Based on previous research we describe an optimal experimental set-up. This provides high quality EEG data during simultaneous fMRI when using commercial EEG and fMRI systems, with safety risks to the subject minimized. We demonstrate this set-up in an EEG-fMRI experiment using a simple visual stimulus. However, much more complex stimuli can be used. Here we show the EEG-fMRI set-up using a Brain Products GmbH (Gilching, Germany) MRplus, 32 channel EEG system in conjunction with a Philips Achieva (Best, Netherlands) 3T MR scanner, although many of the techniques are transferable to other systems.

Simultaneous electroencephalography (EEG) and functional Magnetic Resonance imaging (fMRI) is a powerful neuroimaging tool. However, the inside of an MRI scanner forms a difficult environment for EEG data recording and safety must be considered whenever operating EEG equipment inside a scanner. Here, we present an optimised EEG-fMRI data acquisition protocol.

Simultaneous  EEG-fMRI allows the excellent temporal resolution of EEG to be combined with the  high spatial accuracy of fMRI. The data  from these two ...

Mindfulness in Motion (MIM): An Onsite Mindfulness Based Intervention (MBI) for Chronically High Stress Work Environments to Increase Resiliency and Work Engagement

A pragmatic mindfulness intervention to benefit personnel working in chronically high-stress environments, delivered onsite during the workday, is timely and valuable to employee and employer alike. Mindfulness in Motion (MIM) is a Mindfulness Based Intervention (MBI) offered as a modified, less time intensive method (compared to Mindfulness-Based Stress Reduction), delivered onsite, during work, and intends to enable busy working adults to experience the benefits of mindfulness. It teaches mindful awareness principles, rehearses mindfulness as a group, emphasizes the use of gentle yoga stretches, and utilizes relaxing music in the background of both the group sessions and individual mindfulness practice. MIM is delivered in a group format, for 1 hr/week/8 weeks. CDs and a DVD are provided to facilitate individual practice. The yoga movement is emphasized in the protocol to facilitate a quieting of the mind. The music is included for participants to associate the relaxed state experienced in the group session with their individual practice. To determine the intervention feasibility/efficacy we conducted a randomized wait-list control group in Intensive Care Units (ICUs). ICUs represent a high-stress work environment where personnel experience chronic exposure to catastrophic situations as they care for seriously injured/ill patients. Despite high levels of work-related stress, few interventions have been developed and delivered onsite for such environments. The intervention is delivered on site in the ICU, during work hours, with participants receiving time release to attend sessions. The intervention is well received with 97% retention rate. Work engagement and resiliency increase significantly in the intervention group, compared to the wait-list control group, while participant respiration rates decrease significantly pre-post in 6/8 of the weekly sessions. Participants value institutional support, relaxing music, and the instructor as pivotal to program success. This provides evidence that MIM is feasible, well accepted, and can be effectively implemented in a chronically high-stress work environment.

Mindfulness in Motion MIM: An Onsite Mindfulness Based Intervention MBI for Chronically High Stress Work Environments to Increase Resiliency and Work Engagement

The Mindfulness in Motion (MIM) protocol offers a pragmatic Mindfulness Based Intervention (MBI) on-site, for persons working in chronically high-stress work environments that significantly increases resiliency and work engagement. The protocol has proven feasible, beneficial, and is easily adaptable to other high-stress workplaces.

A pragmatic mindfulness intervention to benefit personnel working in chronically high-stress environments, delivered onsite during the workday, is timely ...

How to Study Placebo Responses in Motion Sickness with a Rotation Chair Paradigm in Healthy Participants

Placebo responses occur in every medical intervention when patients or participants expect to receive an effective treatment to relieve symptoms. However, underlying mechanisms of placebo responses are not fully understood. It has repeatedly been shown that placebo responses are associated with changes in neural activity but for many conditions it is unclear whether they also affect the target organ, such as the stomach in motion sickness. Therefore, we present a methodology for the multivariate assessment of placebo responses by subjective, behavioral and objective measures in motion sickness with a rotation chair paradigm. The physiological correlate of motion sickness is a shift in gastric myoelectrical activity towards tachygastria that can be recorded with electrogastrography. The presented study applied the so-called balanced placebo design (BPD) to investigate the effects of ginger compared to placebo and the effects of expectations by verbal information. However, the study revealed no significant main or interactional effects of ginger (as a drug) or information on outcome measures but showed interactions when sex of participants and experimenters are taken into considerations. We discuss limitations of the presented study and report modifications that were used in subsequent studies demonstrating placebo responses when rotation speed was lowered. In general, future placebo studies have to identify the appropriate target organ for the studied placebo responses and to apply the specific methods to assess the physiological correlates.

An experimental rotation chair paradigm is used to investigate whether a placebo intervention on motion sickness affects subjective outcome measures only or also affects behavioral and objective measures in a balanced placebo design.

Placebo responses occur in every medical intervention when patients or participants expect to receive an effective treatment to relieve symptoms. However, ...

A Mouse Model of Subchronic and Mild Social Defeat Stress for Understanding Stress-induced Behavioral and Physiological Deficits

Stressful life events often increase the incidence of depression in humans. To study the mechanisms of depression, the development of animal models of depression is essential. Because there are several types of depression, various animal models are needed for a deeper understanding of the disorder. Previously, a mouse model of subchronic and mild social defeat stress (sCSDS) using a modified chronic social defeat stress (CSDS) paradigm was established. In the paradigm, to reduce physical injuries from aggressors, the duration of physical contact between the aggressor and a subordinate was reduced compared to in the original CSDS paradigm. sCSDS mice showed increased body weight gain, food intake, and water intake during the stress period, and their social behaviors were suppressed after the stress period. In terms of the face validity of the stress-induced overeating and overdrinking following the increased body weight gain, the sCSDS mice may show some features related to atypical depression in humans. Thus, a mouse model of sCSDS may be useful for studying the pathogenic mechanisms underlying depression. This protocol will help establish the sCSDS mouse model, especially for studying the mechanisms underlying stress-induced weight gain and polydipsia- and hyperphagia-like symptoms.

Here, methods for developing a mouse model of subchronic and mild social defeat stress are described and used to investigate the pathogenic features of depression including hyperphagia- and polydipsia-like symptoms following increased body weight.

Stressful life events often increase the incidence of depression in humans. To study the mechanisms of depression, the development of animal models of ...

Battery of Behavioral Tests Assessing General Locomotion, Muscular Strength, and Coordination in Mice

Behavioral testing is used in pre-clinical trials to assess the phenotypic effects and outcomes that a particular disease or treatment has on the animal&#39;s wellbeing and health. There are numerous behavioral tests that may be applied. We selected a test for general locomotion, the open field test (OFT); a test for muscular strength, the mesh test (MT); and a test for coordination, the rotarod test (RR). Testing can be accomplished on a weekly or monthly basis. As a test for general locomotion, the OFT works by objectively monitoring movement parameters while the mouse is in an open field apparatus. The field is generally a 2&#39; x 2&#39; box, and the movements are recorded through laser sensing or through video capture. The mouse is placed in the center of the open field and allowed to move freely for the test. The MT uses the latency for a mouse to fall off an inverted screen as a measure of muscular strength. A mouse is placed on a screen, which is inverted over a clear box, and is timed for their latency to fall. Three trials are performed, with the best of the three trials scored for that day. A score of 60 s is the maximum time a mouse is left inverted. Mice are given a 5-min rest period between mesh test trials. Lastly, an accelerated protocol on the RR assesses motor coordination and endurance. During a trial, a mouse walks on a rotating rod as it increases in speed from 4 rpm to 40 rpm over 5 min. The trial ends when the mouse touches the magnetized pressure sensor upon falling. Each mouse undergoes three trials, and the best trial is scored for that day. This combined behavioral data allows for the global assessment of mobility, coordination, strength, and movement of the test animals. At least two out of the three behavioral testing measures must show improvement for an animal to qualify as having overall improved motor function.

Behavioral testing is used in pre-clinical trials to assess the phenotypic effects of a disease or treatment on an animal's wellbeing. In order to globally assess motor functioning, we selected tests for general locomotion, muscular strength, and coordination: the open field test, the mesh test, and the rotarod test, respectively.

Behavioral testing is used in pre-clinical trials to assess the phenotypic effects and outcomes that a particular disease or treatment has on the ...

How to Build a Dichoptic Presentation System That Includes an Eye Tracker

The presentation of different stimuli to the two eyes, dichoptic presentation, is essential for studies involving 3D vision and interocular suppression. There is a growing literature on the unique experimental value of pupillary and oculomotor measures, especially for research on interocular suppression. Although obtaining eye-tracking measures would thus benefit studies that use dichoptic presentation, the hardware essential for dichoptic presentation (e.g. mirrors) often interferes with high-quality eye tracking, especially when using a video-based eye tracker. We recently described an experimental setup that combines a standard dichoptic presentation system with an infrared eye tracker by using infrared-transparent mirrors1. The setup is compatible with standard monitors and eye trackers, easy to implement, and affordable (on the order of US$1,000). Relative to existing methods it has the benefits of not requiring special equipment and posing few limits on the nature and quality of the visual stimulus. Here we provide a visual guide to the construction and use of our setup.

We recently proposed a method that allows dichoptic visual stimulus presentation and binocular eye tracking simultaneously1. The key is the combination of an infrared eye tracker and the corresponding infrared-transparent mirrors. This manuscript provides an in depth protocol for initial setup and everyday operation.

The presentation of different stimuli to the two eyes, dichoptic presentation, is essential for studies involving 3D vision and interocular suppression. ...

Reversible Cooling-induced Deactivations to Study Cortical Contributions to Obstacle Memory in the Walking Cat

On complex, naturalistic terrain, sensory information about an environmental obstacle can be used to rapidly adjust locomotor movements for avoidance. For example, in the cat, visual information about an impending obstacle can modulate stepping for avoidance. Locomotor adaptation can also occur independent of vision, as sudden tactile inputs to the leg by an expected obstacle can modify the stepping of all four legs for avoidance. Such complex locomotor coordination involves supraspinal structures, such as the parietal cortex. This protocol describes the use of reversible, cooling-induced cortical deactivation to assess parietal cortex contributions to memory-guided obstacle locomotion in the cat. Small cooling loops, known as cryoloops, are specially shaped to deactivate discrete regions of interest to assess their contributions to an overt behavior. Such methods have been used to elucidate the role of parietal area 5 in memory-guided obstacle avoidance in the cat.

Complex locomotion in naturalistic environments requiring careful coordination of the limbs involves regions of the parietal cortex. The following protocol describes the use of reversible cooling-induced deactivation to demonstrate the role of parietal area 5 in memory-guided obstacle avoidance in the walking cat.

On complex, naturalistic terrain, sensory information about an environmental obstacle can be used to rapidly adjust locomotor movements for avoidance. For ...

Utilizing the Modified T-Maze to Assess Functional Memory Outcomes After Cardiac Arrest

Background: Evaluating mild to moderate cognitive impairment in a global cerebral ischemia (i.e. cardiac arrest) model can be difficult due to poor locomotion after surgery. For example, rats who undergo surgical procedures and are subjected to the Morris water maze may not be able to swim, thus voiding the experiment.
New Method: We established a modified behavioral spontaneous alternation T-maze test. The major advantage of the modified T-maze protocol is its relatively simple design that is powerful enough to assess functional learning/memory after ischemia. Additionally, the data analysis is simple and straightforward. We used the T-maze to determine the rats&#39; learning/memory deficits both in the presence or absence of mild to moderate (6 min) asphyxial cardiac arrest (ACA). Rats have a natural tendency for exploration and will explore the alternate arms in the T-maze, whereas hippocampal-lesioned rats tend to adopt a side-preference resulting in decreased spontaneous alternation ratios, revealing the hippocampal-related functional learning/memory in the presence or absence of ACA.
Results: ACA groups have higher side-preference ratios and lower alternations as compared to control.
Comparison with Existing Method(s): The Morris water and Barnes maze are more prominent for assessing learning/memory function. However, the Morris water maze is more stressful than other mazes. The Barnes maze is widely used to measure reference (long-term) memory, while ACA-induced neurocognitive deficits are more closely related to working (short-term) memory.
Conclusions: We have developed a simple, yet effective strategy to delineate working (short-term) memory via the T-maze in our global cerebral ischemia model (ACA).

This protocol describes the use of a modified T-maze to evaluate functional learning/memory in asphyxia cardiac arrest-induced cerebral ischemia.

Background: Evaluating mild to moderate cognitive impairment in a global cerebral ischemia (i.e. cardiac arrest) model can be difficult due to poor ...

Continuous Noninvasive Measuring of Crayfish Cardiac and Behavioral Activities

A crayfish is a pivotal aquatic organism that serves both as a practical biological model for behavioral and physiological studies of invertebrates and as a useful biological indicator of water quality. Even though crayfish cannot directly specify the substances that cause water quality deterioration, they can immediately (within a few seconds) warn humans of water quality deterioration via acute changes in their cardiac and behavioral activities.
In this study, we present a noninvasive method that is simple enough to be implemented under various conditions due to a combination of simplicity and reliability in one model.
This approach, in which the biological organisms are implemented into environmental evaluation processes, provides a reliable and timely alarm for warning of and preventing acute water deterioration in an ambient environment. Therefore, this noninvasive system based on crayfish physiological and ethological parameter recordings was investigated for the detection of changes in an aquatic environment. This system is now applied at a local brewery for controlling&#160;quality of the water used for beverage production, but it can be used at any water treatment and supply facility for continuous, real-time water quality evaluation and for regular laboratory investigations of crayfish cardiac physiology and behavior.

This article presents a noninvasive biomonitoring system for the continuous recording and analyses of crayfish cardiac and locomotor activities. This system consists of a near-infrared optical sensor, a video-tracking module, and software for evaluating crayfish heartbeats that reflects its physiological condition and characterizes crayfish behavior during heartbeat fluctuations.

A crayfish is a pivotal aquatic organism that serves both as a practical biological model for behavioral and physiological studies of invertebrates and as ...