This study was funded by the National Institutes of Health grant UH3-NS115631 to PS. Funding sources approved the study design but had no role in study execution, data analysis, or manuscript preparation. We also thank Dr. Edward F. Chang, Dr. Philip A. Starr, and the participants in our study.

This PBD protocol was implemented in a parent clinical trial protocol (NCT03029884), approved by UCSF Human Research Protection Program and FDA. Each participant (3 female and 2 male, age range: 51-67 years) signed written informed consent; they were recruited from the UCSF pain management center or referred by physicians in the United States.
1. Pain body diagram setup
<ol>
	<li>Patient inclusion criteria: Include participants with the following pain diagnoses: several neuropathic pain etiologies, including central post-stroke pain (2 patients) and neurodegenerative spine disease with radicular pain (1 patient), complex regional pain syndrome (1 patient), and spinal cord injury (1 patient). All participants have completed post-high school education.</li>
	<li>Import a gender-appropriate PBD template (Supplementary Figure 1), displaying both front and back body surfaces, into an illustration application that contains a pressure-sensitive drawing tool on a touch-sensitive digital tablet. Download the PBD template to the tablet&#39;s photo library, then click the Import button.</li>
	<li>Create a new layer on top of the PBD template by clicking the Layers icon followed by the + button for the participant to draw on. This results in two layers, one with the PBD and one to be drawn in with colors indicating pain.</li>
	<li>Create a new brush with a x=y pressure-to-hue transformation curve by first clicking the Brush Library icon, then + to open the brush studio.</li>
	<li>Click on the button labeled Color Dynamics, then scroll down to the color pressure section. For the hue slider, click the Numeric Percentage to ensure the pressure transformation graph visualizes a straight 45&#176; line. 
	NOTE: Double-clicking the graph will provide the option to reset the graph to the straight x=y graph.</li>
	<li>To define the hue gradient range from green to blue to red, adjust the hue slider under the color pressure section by clicking the Percentage Number Listed and inputting a numeric value of 81%. 
	&#8203;NOTE: Another way of doing this is to enter hexadecimal limits of #008000 to #FF0000 if the application allows for manual entry of hexacodes.</li>
	<li>Select a pen size that accommodates the needs of study participants by adjusting the pen tool slider. A pen size of 30% is a good starting size for most participants.</li>
</ol>
2. Instructions for the participants
<ol>
	<li>Describe the PBD anatomy and orientation of the body templates in portrait mode, the drawing and erasing tools, the tactile pinch-to-zoom and the panning functionality to the patient.</li>
	<li>Explain the pressure-to-hue linear transformation to participants in the following manner: inform participants that increased pressure applied to the stylus will result in hues that shift from green to blue to red which should be colored into the diagram to represent mild to moderate to severe pain intensity at any given location, respectively.</li>
	<li>Using the teach-back method24, confirm the participant&#39;s understanding of the PBD task by asking them to explain how to fill in the diagram using their own words.</li>
	<li>Allow participants at least 15 min of practice time to draw multiple PBDs on a flat surface to ensure accurate representation of pain location and intensity. Allow for any adjustments to be made to maximize usability. Review the PBDs immediately afterwards with each participant to ensure consistency and that colors are drawn as intended.</li>
</ol>
3. Data collection and pre-processing
<ol>
	<li>Ask participants to complete PBDs during baseline or at various time points after some treatment or intervention. Allow for an open-ended amount of time for the completion of each PBD so that each map can be completed to the participant&#39;s satisfaction.</li>
	<li>Save completed PBDs with a standardized filename containing patient ID as well as date and time when PBD was completed. 
	NOTE: These files are saved temporarily on the tablet device.</li>
	<li>Bulk export completed PBDs in either portable document format (.PDF) or photoshop document (.PSD) file format that retains image layers for pre-processing. To bulk export, first click the Select icon to choose the desired images, then click Share to open a menu of image formats for export. Click the format file of choice. 
	NOTE: Exported files are uploaded to a secure research server.</li>
	<li>Download PBDs and open in a raster-based image editor.</li>
	<li>Isolate the colored pixels of interest from the top layer of the PBD file by adding two mask layers: one completely black layer below the colored-in layer and one black mask layer to exclude pixels outside the template body outline above the colored-in layer. This will result in processed PBDs that only contain the colored pixels within the body outline on a black background (Figure 1).</li>
	<li>Export the processed PBDs as portable network graphics (.png) files by clicking and selecting the following sequence of buttons: File &#62; Export &#62; Export As &#62; PNG &#62; Export.</li>
</ol>
4. PBD Quantification
<ol>
	<li>Convert each pixel value in the PBD from RGB (red, green, blue) color space into HSV (hue, saturation, value) color space using OpenCV225, a publicly available Python package. Extract the hue value for each pixel by running the python scripts titled rgba2hsv(filename) (Supplementary Coding File 1) and measure_SAnoblur(filename, sigma-1.0) (Figure 1). 
	NOTE: These scripts quantify and adjust hue values to create a continuous hue scale from 0-139.5. The HSV values on OpenCV2 range from 0-179. The lightest green, which represents the least pain intensity, corresponds to the hue value of 39.5. Some red hues correspond to values between 0-10. Yellow and orange colors which correspond to hue values between 10-39.5 are not used by the pen tool. The red hue values from 0-10 are reassigned to 179 to correctly represent the most pain intensity. The hue scale ranges from 39.5-179 after this adjustment. Then, 39.5 is subtracted from each hue value so that the final scale ranges from 0-139.5.</li>
	<li>Calculate and normalize the three PBD metrics by running the Python script titled quantifypain(filename) (Supplementary Coding File 1).
	<ol>
		<li>The script calculates each metric as described below.
		<ol>
			<li>PBD coverage: Divide the number of colored pixels by the total number of pixels available within the body diagram. The range for the number of colored pixels for females is 0&#160;to 820,452 pixels (total pixels) and the range for males is 0&#160;to 724,608 pixels (total pixels).</li>
			<li>PBD sum intensity: Add the hue values for all pixels in the body diagram. The range for the sum of hue values for females is 0 to 114,453,054 and the range for males is 0 to 101,082,816.</li>
			<li>PBD mean intensity: Divide the sum of all hue values by the total number of colored pixels.</li>
		</ol>
		</li>
		<li>Use the script to normalize all PBD measures on a 0 to 100 scale by following calculations described below.
		<ol>
			<li>PBD coverage: Multiply PBD coverage by 100.</li>
			<li>PBD sum: Divide PBD sum intensity by the maximum PBD sum intensity and multiply by 100. The maximum PBD sum intensity equals the total number of pixels in the body diagram multiplied by 139.5 (i.e., for females, it is 820,452 pixels multiplied by 139.5 which equals 114,453,054; for males, it is 724,608 pixels multiplied by 139.5 which equals 101,082,816).</li>
			<li>PBD mean: Divide PBD mean intensity by the maximum hue value of 139.5 and multiply by 100.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Repeat steps 4.1 and 4.2 to process each PBD (with extension .png) file. Compile the outputs in a spreadsheet to run further analyses.</li>
</ol>

A Novel Approach for Pain Assessment from Pain Body Diagrams

<ol>
	<li>Yong, R. J., Mullins, P. M., Bhattacharyya, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Prevalence+of+chronic+pain+among+adults+in+the+United+States.">Prevalence of chronic pain among adults in the United States.</a> Pain. 163 (2), e328-e332 (2022).</li><li>Shirvalkar, P., Veuthey, T. L., Dawes, H. E., Chang, E. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Closed-Loop+Deep+Brain+Stimulation+for+Refractory+Chronic+Pain.">Closed-Loop Deep Brain Stimulation for Refractory Chronic Pain.</a> Frontiers in Computational Neuroscience. 12, 18 (2018).</li><li>Bittar, R. G., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Deep+brain+stimulation+for+pain+relief:+A+meta-analysis.">Deep brain stimulation for pain relief: A meta-analysis.</a> Journal of Clinical Neuroscience. 12 (5), 515-519 (2005).</li><li>Wewers, M. E., Lowe, N. K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+critical+review+of+visual+analogue+scales+in+the+measurement+of+clinical+phenomena.">A critical review of visual analogue scales in the measurement of clinical phenomena.</a> Research in Nursing &amp; Health. 13 (4), 227-236 (1990).</li><li>Price, D. D., Bush, F. M., Long, S., Harkins, S. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+comparison+of+pain+measurement+characteristics+of+mechanical+visual+analogue+and+simple+numerical+rating+scales.">A comparison of pain measurement characteristics of mechanical visual analogue and simple numerical rating scales.</a> Pain. 56 (2), 217-226 (1994).</li><li>Shafshak, T. S., Elnemr, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+Visual+Analogue+Scale+Versus+Numerical+Rating+Scale+in+Measuring+Pain+Severity+and+Predicting+Disability+in+Low+Back+Pain.">The Visual Analogue Scale Versus Numerical Rating Scale in Measuring Pain Severity and Predicting Disability in Low Back Pain.</a> JCR: Journal of Clinical Rheumatology. 27 (7), 282-285 (2021).</li><li>Strong, J., Ashton, R., Chant, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Pain+Intensity+Measurement+in+Chronic+Low+Back+Pain.">Pain Intensity Measurement in Chronic Low Back Pain.</a> The Clinical Journal of Pain. 7 (3), 209 (1991).</li><li>Smith, S. M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Interpretation+of+chronic+pain+clinical+trial+outcomes:+IMMPACT+recommended+considerations.">Interpretation of chronic pain clinical trial outcomes: IMMPACT recommended considerations.</a> Pain. 161 (11), 2446 (2020).</li><li>Dworkin, R. H., Kerns, R. D., McDermott, M. P., Turk, D. C., Veasley, 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+ACTTION+guide+to+clinical+trials+of+pain+treatments,+part+II:+mitigating+bias,+maximizing+value.">The ACTTION guide to clinical trials of pain treatments, part II: mitigating bias, maximizing value.</a> Pain Reports. 6 (1), e886 (2021).</li><li>Melzack, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+McGill+Pain+Questionnaire:+Major+properties+and+scoring+methods.">The McGill Pain Questionnaire: Major properties and scoring methods.</a> Pain. 1 (3), 277-299 (1975).</li><li>Gracely, R. H., McGrath, P., Dubner, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Validity+and+sensitivity+of+ratio+scales+of+sensory+and+affective+verbal+pain+descriptors:+Manipulation+of+affect+by+diazepam.">Validity and sensitivity of ratio scales of sensory and affective verbal pain descriptors: Manipulation of affect by diazepam.</a> Pain. 5 (1), 19-29 (1978).</li><li>Williamson, A., Hoggart, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Pain:+a+review+of+three+commonly+used+pain+rating+scales.">Pain: a review of three commonly used pain rating scales.</a> Journal of Clinical Nursing. 14 (7), 798-804 (2005).</li><li>Main, C. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Pain+assessment+in+context:+a+state+of+the+science+review+of+the+McGill+pain+questionnaire+40+years+on.">Pain assessment in context: a state of the science review of the McGill pain questionnaire 40 years on.</a> Pain. 157 (7), 1387-1399 (2016).</li><li>Schott, G. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+cartography+of+pain:+The+evolving+contribution+of+pain+maps.">The cartography of pain: The evolving contribution of pain maps.</a> European Journal of Pain. 14 (8), 784-791 (2010).</li><li>Palmer, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Pain+charts;+a+description+of+a+technique+whereby+functional+pain+may+be+diagnosed+from+organic+pain.">Pain charts; a description of a technique whereby functional pain may be diagnosed from organic pain.</a> The New Zealand Medical Journal. 48 (264), 187-213 (1949).</li><li>Villa, M. G., Palsson, T. S., Royo, A. C., Bjarkam, C. R., Boudreau, S. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Digital+Pain+Mapping+and+Tracking+in+Patients+With+Chronic+Pain:+Longitudinal+Study.">Digital Pain Mapping and Tracking in Patients With Chronic Pain: Longitudinal Study.</a> Journal of Medical Internet Research. 22 (10), e21475 (2020).</li><li>Egsgaard, L. L., Christensen, T. S., Petersen, I. M., Br&#248;nnum, D. S., Boudreau, S. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Do+Gender-Specific+and+High-Resolution+Three+Dimensional+Body+Charts+Facilitate+the+Communication+of+Pain+for+Women?+A+Quantitative+and+Qualitative+Study.">Do Gender-Specific and High-Resolution Three Dimensional Body Charts Facilitate the Communication of Pain for Women? A Quantitative and Qualitative Study.</a> JMIR Human Factors. 3 (2), e19 (2016).</li><li>Lor, M., Rabago, D., Backonja, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Evaluation+of+the+Use+of+Colors+and+Drawings+for+Pain+Communication+for+Hmong+Patients.">Evaluation of the Use of Colors and Drawings for Pain Communication for Hmong Patients.</a> Pain Management Nursing. 22 (6), 811-819 (2021).</li><li>Sehgal, N., Gordon, D. B., Hetzel, S., Backonja, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=34;Misha&amp;#34;+Colored+pain+drawing+as+a+clinical+tool+in+differentiating+neuropathic+pain+from+non-neuropathic+pain.">34;Misha&amp;#34; Colored pain drawing as a clinical tool in differentiating neuropathic pain from non-neuropathic pain.</a> Pain Medicine. 22 (3), 596-605 (2021).</li><li>Takata, K., Hirotani, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Pain+drawing+in+the+evaluation+of+low+back+pain.">Pain drawing in the evaluation of low back pain.</a> International Orthopaedics. 19 (6), 361-366 (1995).</li><li>Barbero, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Test-retest+reliability+of+pain+extent+and+pain+location+using+a+novel+method+for+pain+drawing+analysis.">Test-retest reliability of pain extent and pain location using a novel method for pain drawing analysis.</a> European Journal of Pain. 19 (8), 1129-1138 (2015).</li><li>Dixit, A., Lee, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quantification+of+Digital+Body+Maps+for+Pain:+Development+and+Application+of+an+Algorithm+for+Generating+Pain+Frequency+Maps.">Quantification of Digital Body Maps for Pain: Development and Application of an Algorithm for Generating Pain Frequency Maps.</a> JMIR Formative Research. 6 (6), e36687 (2022).</li><li>Borsook, D., Upadhyay, J., Hargreaves, R., Wager, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Enhancing+choice+and+outcomes+for+therapeutic+trials+in+chronic+pain:+N-of-1++imaging+(++i).">Enhancing choice and outcomes for therapeutic trials in chronic pain: N-of-1+ imaging (+ i).</a> Trends in Pharmacological Sciences. 41 (2), 85-98 (2020).</li><li>Weiss, B. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Help+patients+understand.">Help patients understand.</a> Manual for Clinicians. AMA Foundation. , (2007).</li><li>Bradski, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+OpenCV+Library.">The OpenCV Library.</a> Dr. Dobb's Journal of Software Tools. (120), 122-125 (2000).</li><li>. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Male+And+Female+Body+Front+And+Back+View.+Blank+Human+Body+Template+For+Medical+Infographic.+Isolated+Vector+Illustration.">Male And Female Body Front And Back View. Blank Human Body Template For Medical Infographic. Isolated Vector Illustration.</a> Royalty Free SVG, Cliparts, Vectors, And Stock Illustration. Image 95218807. 123RF. , (2023).</li><li>Van Rossum, G., Drake, F. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Python 3 Reference Manual. , (2009).</li><li>Shannon, C. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+mathematical+theory+of+communication.">A mathematical theory of communication.</a> The Bell System Technical Journal. 27 (3), 379-423 (1948).</li><li>Timme, N. M., Lapish, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+Tutorial+for+Information+Theory+in+Neuroscience.">A Tutorial for Information Theory in Neuroscience.</a> eNeuro. 5 (3), 0052-0018 (2018).</li><li>Cohen, M. X. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Analyzing Neural Time Series Data: Theory and Practice. , (2014).</li><li>Freedman, D., Diaconis, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=On+the+histogram+as+a+density+estimator:L+2+theory.">On the histogram as a density estimator:L 2 theory.</a> Zeitschrift for Wahrscheinlichkeitstheorie und Verwandte Gebiete. 57 (4), 453-476 (1981).</li></ol>

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Critical steps within the protocol 
The key steps include: PBD setup, patient instruction, and pre-processing. For PBD setup, each gender specific PBD should visualize a front and back view26, and be overlaid with an empty layer on an illustration application to isolate hue values. Furthermore, pen size must meet patients&#39; illustration needs and hue gradients must be defined to quantitatively analyze PBDs. Patient instruction and understanding of the tool are fundamental for reliable data. Sufficient time should be allotted for participants to practice implementing the tool on the PBD. Use the teach-back method to confirm participant&#39;s understanding of tasks and surveys periodically during testing, approximately once every 10 PBDs. In order to keep track of individual PBDs, it is also a good idea to name each file with a unique title and timestamp after completion. Following data collection, each PBD metric could be extracted using Python27 scripts (see Supplementary Coding File 1). The measures of PBD coverage, sum intensity, and mean intensity can be repeated before and after any treatment or intervention to track pain responses within patient. To extract these metrics, one researcher not directly involved in data collection, should overlay black mask layers to isolate only colors drawn inside the body outline, then compute HSV pixel values using custom software code provided as Supplementary File 1.
Modifications and troubleshooting in the technique 
Methodological steps were refined during the data collection of patient 1. These include allowing more time for patients to familiarize themselves with controlling the pressure sensitivity of the pen, correctly setting up body diagram layers for later masking and analysis, limiting the use of symbols or words on PBDs, and adjusting absolute pen pressure sensitivity according to each participant&#39;s strength and dexterity (though the transformation between relative pressure and hue remained constant). Patients were allowed to select their pen size to best represent their pain; however, selecting one fixed size may allow for better future inter-patient comparisons. In future iterations, prototyping a method that uses one color channel (e.g., red, green, or blue) and varying the lightness of the color based on pen pressure can minimize possible loss of precision when converting from RGB to HSV color space.
Limitations of the technique 
PBDs require patients to have sufficient baseline motor strength and dexterity in at least one upper extremity with good fine motor ability in the fingers at a minimum to complete diagrams independently and to accurately translate their pain experience via pressure. While standard pain metrics such as NRS and MPQ can be entered on paper or a keyboard by an assistant through verbal communication, this modification with PBDs is not yet validated. PBDs also lack depth as a two-dimensional illustration. The level of detail in a three-dimensional body diagram has been qualitatively demonstrated to expand the communication of pain information17. Further characterizing the depth of pain can capture novel pain information not examined in scales such as the NRS, VAS, and MPQ. Body diagrams are not currently designed to capture more abstract somatization or deeper forms of pain. For instance, patient 4 self-reported that the pain location and pain intensity were not well characterized by the body diagram in the usability survey, as he felt they did not capture his internal neuropathic pain. Patient 5 often drew dotted lines in body diagrams to indicate heaviness within their body, which can confound metric calculations. Future PBDs iterations could be expanded to represent the somatization of pain or visceral pain in a quantifiable method. Finally, PBDs were analyzed in an N-of-1 framework, where nearly 100 separate PBDs were generated for each participant. Group-level analyses were not possible due to the small number of overall participants. Therefore, test-retest reliability could not be determined in this study since the responses to NRS scales face anchoring bias, suggesting the same NRS scores may not be equivalent to the same PBDs tested after the trial. Future research will be needed to evaluate PBD metrics in a group-wise analysis setting and the method&#39;s test-retest reliability in a larger sample.
Significance of the method with respect to existing methods 
PBDs have been widely used in clinical and research settings to demonstrate a participant&#39;s pain intensity across their body14, 15, yet this tool remained largely limited by its qualitative nature. While digital pain mapping has been used to longitudinally track chronic pain16, patients lacked the ability to represent pain intensity and location in a combined, precise technique. This novel pressure-hue transformation incorporated with PBDs provides composite spatial and quantitative pain metrics that can be repeatedly measured and tracked across time to capture a participant&#39;s pain experience.&#160;Here, three extracted PBD metrics that differentially reflected pain intensity and location within a patient, i.e., PBD coverage, sum intensity, and mean intensity, were demonstrated to carry high validity and concordance with standardized pain measures such as NRS intensity, VAS intensity, VAS unpleasantness, and MPQ. All PBD measures were correlated to the VAS and NRS scores in four out of five patients and significantly correlated to the MPQ in three out of five. Additionally, the information theory approach28,29,30,31 revealed non-linear relationships that were not detected with more common statistical methods. In the study, four of five patients had significant MI between PBD measures and NRS, VAS intensity, VAS unpleasantness, and MPQ, demonstrating significant, but not total, overlap in information content. Thus, the PBD measures were highly concordant with standardized pain measures, yet PBD mean appeared to reflect a combination of intensity and location information that was not present in conventional pain metrics.
Future applications of this technique 
The present results demonstrate that PBDs may be especially appropriate for patients who experience and quantify their pain on a non-linear scale. Similar to how verbal descriptors can provide another dimension for participants to evaluate pain, the PBDs provide a unique graphical and pressure-based interpretation of their pain. By implementing a novel pressure-hue transformation, body diagrams provide information on the location, spread, and regional variation in the intensity of pain, which to our knowledge, has not been demonstrated before. Together with neural data collected during any DBS trial, PBD metrics can be a powerful tool in localizing pain in different body regions to different brain regions and help inform mechanistic studies on pain signaling pathways. The pressure-hue transformation implemented in PBDs can be used in many clinical and research settings to analyze pain relief in response to treatment or compare pain over time. This method not only produces unique, visually intuitive diagrams to assess pain but also accurately captures a patient&#39;s experience beyond a singular numerical score.

Chronic pain is a debilitating neuropsychiatric condition affecting over 50 million adults in the United States1. However, common clinical tools to track subjective pain intensity (such as the numeric rating scale [NRS] or visual analog scale [VAS]) are reductionistic and fail to communicate the complex nature of pain symptom intensity spanning somatosensory, cognitive, or affective domains2,3. Accurately tracking an individual&#39;s pain intensity is critical to the diagnosis of pain syndromes, monitoring disease progression, and assessing the potential efficacy of therapies such as medications or brain stimulation.
The widely used NRS pain intensity tool requires the subject to rate pain intensity as an integer value from 0-10, representing no pain to the worst possible pain. While easy to administer and understand, the NRS is limited by respondent anchoring bias, expectation bias, and variable interpretation of individual values4,5; these also limit between-participant comparisons. The VAS, a continuous scale from 0-100, may reduce the impact of anchoring but can still face similar limitations as those of NRS4. Several studies have demonstrated a high degree of agreement between the NRS and VAS for chronic lower back pain severity6,7 and clinical practice5, but consensus guidelines highlight the many shortcomings of relying on similar scales in clinical pain trial design or interpretation8,9. The short-form McGill pain questionnaire 2 (MPQ) further dissects the somatosensory and affective dimensions of pain using ratings of verbal descriptors10, to aid in distinguishing between sensory and affective pain dimension11. Although these pain rating scales are commonly used to track pain intensity12,13, they fail to capture detailed topographic information such as pain location, or intensity variation across body regions.
Pain body diagrams (PBDs) are an open-ended, free-form pain assessment tool allowing respondents to illustrate a visual representation of pain location and intensity on a schematic human body outline14,15. PBDs are an effective communication tool between participants and medical providers which help track pain symptoms longitudinally16. PBD&#39;s free-form graphical format may decrease anchoring bias. Recent modifications to PBDs, such as the introduction of sex-specific body diagrams, have increased their effectiveness as a communication tool by aligning the visually represented body form with the respondent&#39;s anatomy, thereby increasing self-identification and response accuracy17. Furthermore, the use of color to signify intensity has been shown to allow effective communication of pain symptoms overcoming cultural and language barriers. For instance, the colors white and red were most commonly selected to indicate no pain and severe pain, respectively, in a Hmong patient population18. While PBDs are an effective tool19,20, they have been limited by their qualitative nature.
The use of PBDs on digital tablets has substantially expanded the tools available for quantifying pain location and intensity. Barbero et al. quantified the pain extent or the number of pixels drawn within a PBD of patients with chronic lower back and neck pain and showed good test-retest reliability and significant correlation with VAS measures21. Body diagrams have also been analyzed to create pain frequency maps to show the most to least frequently painful areas of the body21,22. While these methods quantify spatial pain information, so far, no method has incorporated both pain intensity and location into composite metrics.
The following protocol demonstrates a method for obtaining novel, visually intuitive, colored PBDs and extracting three quantitative metrics that together reflect a composite of pain intensity and location information. To do this, five participants undergoing a research trial of deep brain stimulation (DBS) for refractory chronic neuropathic pain were selected to test the current approach, using a N-of-1 study design23. Participants were instructed to report the intensity of their momentary pain symptoms by applying varying levels of pen pressure on a tablet illustration application to produce color hues that corresponded to varying pain intensities at different body locations. PBD-derived metrics of coverage, sum intensity, and mean intensity were compared to more common validated pain metrics (i.e., NRS, VAS and MPQ) using statistical and mutual information (MI) analyses.
Over a 10-day inpatient hospital stay, patients undergoing evaluation completed PBDs (mean &#177; standard deviation (SD) = 121.8 &#177; 34.3 PBDs per patient; range 84-177; 609 PBDs total) in addition to validated pain scales such as the NRS, VAS, and MPQ multiple times daily. PBDs were collected via a tablet application and uploaded as time-stamped files to secured research servers when completed. Pain intensity NRS, VAS and MPQ were acquired using REDCap survey tools, a secure web application. Both surveys and PBDs were administered in person by research assistants to ensure patients received the needed assistance to complete their evaluations accurately. The following steps detail PBD setup, participant instruction, data collection, and PBD analysis used to reliably quantify pain (Figure 1).

<table><tbody><tr><td>Adobe Photoshop v.21.2.1</td><td>Adobe Inc.</td><td>N/A</td><td>Photo editor application to pre-process pain body diagrams</td></tr><tr><td>Apple Pencil 2nd generation</td><td>Apple Inc.</td><td>A2051</td><td>Digital pen for pressure-hue transformation</td></tr><tr><td>iPad Pro (11-inch, 2nd generation)</td><td>Apple Inc.</td><td>MY332LL/A</td><td>Touch-sensitive digital tablet</td></tr><tr><td>Pain Body Diagram Template</td><td>123RF</td><td>95218807</td><td>Copyright Use</td></tr><tr><td>Procreate v5.3.1</td><td>Procreate</td><td>N/A</td><td>Commercially available illustration applicataion</td></tr><tr><td>REDCap v13.2.1</td><td>Vanderbilt University</td><td>N/A</td><td>Secure web survey and database application</td></tr></tbody></table>

quantifying pain location and intensity with multimodal pain body diagrams

To quantify an individual&#39;s subjective pain severity, standardized pain rating scales such as the numeric rating scale (NRS), visual analog scale (VAS), or McGill pain questionnaire (MPQ) are commonly used to assess pain on a numerical scale. However, these scales are often biased and fail to capture the complexity of pain experiences. In contrast, clinical practice often requires patients to report areas of pain by drawing on a body diagram, which is an effective but qualitative tool. The method presented here extracts quantifiable metrics from pain body diagrams (PBDs) which are validated against the NRS, VAS, and MPQ pain scales. By using a novel pressure-hue transformation on a digital tablet, different drawing pressures applied with a digital stylus can be represented as different hues on a PBD. This produces a visually intuitive diagram of hues ranging from green to blue to red, representing mild to moderate to most painful regions, respectively. To quantify each PBD, novel pain metrics were defined: (1) PBD mean intensity, which equals the sum of each pixel&#39;s hue value divided by the number of colored pixels, (2) PBD coverage, which equals the number of colored pixels divided by the total number of pixels on the body, and (3) PBD sum intensity, which equals the sum of all pixels&#39; hue values. Using correlation and information theory analyses, these PBD metrics were shown to have high concordance with standardized pain metrics, including NRS, VAS and MPQ. In conclusion, PBDs can provide novel spatial and quantitative information that can be repeatedly measured and tracked over time to comprehensively characterize a participant&#39;s pain experience.

The PBD mean, sum, and coverage uniquely provide information about pain responses not captured in other standardized pain scales. Between the two PBDs (Figure 2A,B), the mean pain intensity is identical (PBD mean = 79.6). An increased coverage and sum, however, reveals the greater spatial spread of pain and total pain intensity, respectively, that differentiate the two PBDs (Figure 2B). To accurately quantify pain using these metrics, researchers should avoid the following common PBD setup mistakes (Figure 2C). Excessively large pen thickness and extraneous elements outside the body outline, such as circling body regions or written descriptors will not be captured in the PBD processing. Similarly, a white pen used to remove color rather than the eraser tool will skew PBD metrics. Practice and reinforced instruction will empower patients to create accurate and quantifiable PBDs that reveal variability in pain intensity and distribution.
The PBD metrics were validated against the NRS, VAS, and MPQ (Figure 3B; Supplementary Figure 2) and scored high in usability (Supplementary Figure 1 and Supplementary Figure 2).
PBD metrics correlated to standard pain metrics 
The PBD metrics were correlated with the NRS, VAS, and MPQ for most patients (Figure 3A, Supplementary Figure 1A,B). In four of five patients, the PBD sum, coverage, and mean were correlated to their VAS and NRS (Spearman&#39;s correlation, rs = 0.33-0.72, p &#60; 0.004, Supplementary Table 1). For three out of five participants, PBD metrics were also significantly correlated with MPQ scores (Spearman&#39;s correlation, rs&#160;= 0.38-0.53, p &#60; 0.004, Supplementary Table 1). However, patient 4 did not show significant correlations between the PBD metrics and standard pain scores. We further characterized non-linear relationships between&#160;PBD and standard metrics using information theory analyses (Supplementary Figure 2).
PBD metrics avoid response anchoring and share mutual information with standard pain metrics 
PBD metrics contained more information (i.e., entropy) than the NRS. Across patients, NRS contained less information (2.32 &#177; 0.37 bits) compared to VAS intensity, VAS unpleasantness, MPQ total, PBD sum, PBD coverage, and PBD mean (3.21 &#177; 0.49 bits, 3.20 &#177; 0.31 bits, 3.16 &#177; 0.23 bits, 3.06 &#177; 0.32 bits, 3.34 &#177; 0.16 bits, 3.22 &#177; 0.39 bits, respectively; Supplementary Figure 2). This was confirmed with a one-way repeated measures ANOVA (F(4,1) = 12.10, p &#60; 0.05) and a Tukey&#39;s t-test for individual comparisons (all p &#60; 0.05). This shows PBD metrics had less response anchoring than the NRS.
The PBD was further validated against established metrics by mutual information analyses (permutation testing, &#945;=0.05). In four of five patients, PBD metrics significantly shared MI with the NRS, VAS intensity, VAS unpleasantness, and the MPQ (p &#60; 0.05, Figure 3B). In contrast, patient 4&#39;s PBD metrics did not significantly share MI with established metrics. Since their NRS contained the least information across patients&#39; (Supplementary Figure 2), this suggests the NRS failed to capture nuances in pain experience that were captured by the PBD. In all patients, the NRS shared significant MI with VAS intensity, VAS unpleasantness, and MPQ while the PBD sum shared MI with PBD coverage and PBD mean (p &#60; 0.05, Figure 3B). Altogether, for most patients, the PBD metrics shared MI with established pain metrics.
PBDs were easy to use for most participants 
In the study, four of the five patients found the PBD easy to use and to accurately reflect their pain (Supplementary Table 2). However, patient 4 reported that the PBD was difficult to use (5 on a 5-point Likert Scale). This is primarily because they have deep, visceral pain-which is not well-captured in a 2-dimensional (2D) PBD. While patients varied in their familiarity with PBDs (2.8 &#177; 1.2, range 1-4, 5-point Likert Scale), they all used comparable electronics daily (5.0 &#177; 0.0, 5-point Likert Scale) and found the PBD to be user-friendly (5.2 &#177; 0.4, range 5-6, 6-point Likert Scale).
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/65334/65334fig1v2.jpg" /> 
Figure 1. Pain body diagram (PBD) analysis workflow. Patients drew on blank PBD templates to represent the pain&#39;s location and intensity. Completed PBDs contained hues that ranged from green to blue to red, representing mild to moderate to severe pain regions, respectively. PBDs were masked to include only pixels within the body outline and then the template was removed to isolate only pixels containing hues. From the PBDs, PBD coverage (%), sum intensity (normalized to 0-100), and mean intensity (normalized to 0-100) were calculated. For PBD coverage, the number of colored pixels were first divided by the total number of pixels within the diagram (820,452 pixels for females, 724,608 pixels for males), then multiplied by 100. For PBD sum intensity, the hue values for all pixels in the body diagram were first summed (female range: 0-114,453,054; male range: 0-101,082,816). The sum was then divided by the maximum PBD sum intensity (females: 820,452 pixels multiplied by maximum hue value 139.5, males: 724,608 pixels by 139.5) and multiplied by 100. For PBD mean intensity, the sum of all hue values was divided by the total number of colored pixels, then normalized by dividing by the maximum hue value of 139.5. <a href="https://www.jove.com/files/ftp_upload/65334/65334fig1v2large.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/65334/65334fig02.jpg" /> 
Figure 2. Representative PBDs showing examples of good and bad PBDs. (A,B) Good PBDs show the utility of calculating 3 pain metrics. (C) Bad PBD examples include excessively thick pen size, extraneous elements outside the body diagram, and inaccurate erasing. <a href="https://www.jove.com/files/ftp_upload/65334/65334fig02large.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/65334/65334fig03.jpg" /> 
Figure 3.&#160;PBD metrics were validated against standard pain metrics via Spearman&#39;s correlation and mutual information analyses. (A) VAS intensity and PBD sum plotted with linear best-fit lines drawn for each patient. (B) Group-level data showing the mean mutual information (MI) between each pain metric, with MI indicated by color bar on the right. The text in each box represents the number of patients with statistically significant MI for a given pairwise comparison (e.g., 3/5 indicates 3 patients with significant values). MI is presented by the observed MI divided by the theoretical max MI. Abbreviations: NRS=numeric rating scale; VAS intensity = visual analog scale intensity; VAS unpl. = visual analog scale pain unpleasantness, MPQ=short form McGill pain questionnaire 2; PBD=pain body diagram; PBD cov. = PBD coverage, MI = mutual information, sig. = significant. <a href="https://www.jove.com/files/ftp_upload/65334/65334fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Supplementary Figure 1. PBD mean (A) and PBD coverage (B) plotted against VAS intensity with linear best-fit lines drawn for each patient. Abbreviations: VAS=visual analog scale; PBD=pain body diagram. <a href="https://www.jove.com/files/ftp_upload/65334/JoVE_Figure_S1.ai" target="_blank">Please click here to download this File.</a>
Supplementary Figure 2. Entropy per pain metric across patients. On the group-level, NRS intensity had lower entropy than every other pain metric as shown by a repeated measures one-way ANOVA followed by Tukey&#39;s test post-hoc for specific comparisons * = p &#60; 0.05, ** = p&#160;&#60; 0.001. Abbreviations: NRS=numeric rating scale; VAS=visual analog scale; MPQ=McGill pain questionnaire; PBD=pain body diagram. <a href="https://www.jove.com/files/ftp_upload/65334/JoVE_Figure_S2.ai" target="_blank">Please click here to download this File.</a>
Supplementary Table 1. Spearman&#39;s correlations between PBD metrics and self-reported standard pain measures. Spearman&#39;s correlation coefficients (rho) for three extracted PBD metrics against NRS, VAS, and MPQ pain measures. Abbreviations: NRS=numeric rating scale; VAS=visual analog scale; MPQ=McGill pain questionnaire; PBD=pain body diagram. <a href="https://www.jove.com/files/ftp_upload/65334/JoVE_Table_S1.xlsx" target="_blank">Please click here to download this File.</a>
Supplementary Table 2. Patient impressions of completing a PBD were revealed through PBD-specific and system usability scale-modified questions. The modified usability scale questions alternated in positive and negative statements and were ranked on a 5-point scale (1=strongly agree, 5=strongly disagree). Abbreviation: PBD=pain body diagram. <a href="https://www.jove.com/files/ftp_upload/65334/JoVE_Table_S2.xlsx" target="_blank">Please click here to download this File.</a>
Supplementary Coding File 1:&#160;Python script for PBD metrics. The annotated python code processes a pain body diagram PNG file and outputs PBD mean, coverage, and sum values for each file. The script also includes import statements to download the required packages for the program to run. <a href="https://www.jove.com/files/ftp_upload/65334/PBD processing final.zip" target="_blank">Please click here to download this File.</a>
Supplementary File 1: Supplementary file for methodological details. <a href="https://www.jove.com/files/ftp_upload/65334/Supplementary Materials_RE.zip" target="_blank">Please click here to download this File.</a>

Watch this Scientific Journal Video about Quantifying Pain Experience – An Illustrative Approach Using the Pain Body Diagram at JoVE.com

Author Spotlight: Quantifying Pain Experience &#8211; An Illustrative Approach Using the Pain Body Diagram 

Current pain scales used to quantify pain severity, such as visual analog scales, fail to capture the complexity of subjective pain experiences. Pain body diagrams are qualitative but may be more informative. The goal of this method is to extract quantitative metrics from pain body diagrams using novel pressure-hue transformation.

Quantifying Pain Location and Intensity with Multimodal Pain Body Diagrams

To quantify an individual&#39;s subjective pain severity, standardized pain rating scales such as the numeric rating scale (NRS), visual analog scale ...

quantifying-pain-location-intensity-with-multimodal-pain-body

Research

Education

JoVE Core

JoVE Journal

Introduction to Psychology

Neuroscience

Unit 1

Sensation and Perception

SCIENTISTS IN ACTION

3.2K Views.  University of California, San Francisco. Pijn is een subjectieve, veelzijdige ervaring die moeilijk te beoordelen kan zijn met behulp van numerieke beoordelingsschalen of enquêtes met descriptoren. Hier laten we zien hoe de locatie en ernst van pijn kwantitatief kunnen worden vastgelegd in een gecombineerd illustratief hulpmiddel, het pijnlichaamsdiagram. De numerieke beoordelingsschaal, waarbij patiënten wordt gevraagd pijn van 0 tot 10 te beoordelen, is het meest gebruikte hulpmiddel voor het beoordelen van pijn.De visuel...

Video: Auteur in de schijnwerpers: kwantificering van pijnervaring - een illustratieve benadering met behulp van het pijnlichaamsdiagram 

Physiological, Morphological and Neurochemical Characterization of Neurons Modulated by Movement

The role of individual neurons and their function in neuronal circuits is fundamental to understanding the neuronal mechanisms of sensory and motor functions. Most investigations of sensorimotor mechanisms rely on either examination of neurons while an animal is static1,2 or record extracellular neuronal activity during a movement.3,4 While these studies have provided the fundamental background for sensorimotor function, they either do not evaluate functional information which occurs during a movement or are limited in their ability to fully characterize the anatomy, physiology and neurochemical phenotype of the neuron. A technique is shown here which allows extensive characterization of individual neurons during an in vivo movement. This technique can be used not only to study primary afferent neurons but also to characterize motoneurons and sensorimotor interneurons. Initially the response of a single neuron is recorded using electrophysiological methods during various movements of the mandible followed by determination of the receptive field for the neuron. A neuronal tracer is then intracellularly injected into the neuron and the brain is processed so that the neuron can be visualized with light, electron or confocal microscopy (Fig. 1). The detailed morphology of the characterized neuron is then reconstructed so that neuronal morphology can be correlated with the physiological response of the neuron (Figs. 2,3). In this communication important key details and tips for successful implementation of this technique are provided. Valuable additional information can be determined for the neuron under study by combining this method with other techniques. Retrograde neuronal labeling can be used to determine neurons with which the labeled neuron synapses; thus allowing detailed determination of neuronal circuitry. Immunocytochemistry can be combined with this method to examine neurotransmitters within the labeled neuron and to determine the chemical phenotypes of neurons with which the labeled neuron synapses. The labeled neuron can also be processed for electron microscopy to determine the ultrastructural features and microcircuitry of the labeled neuron. Overall this technique is a powerful method to thoroughly characterize neurons during in vivo movement thus allowing substantial insight into the role of the neuron in sensorimotor function.

A technique is described to quantify the in vivo physiological response of mammalian neurons during movement and correlate the physiology of the neuron with neuronal morphology, neurochemical phenotype and synaptic microcircuitry.

The role of individual neurons and their function in neuronal circuits is fundamental to understanding the neuronal mechanisms of sensory and motor ...

Determining Pain Detection and Tolerance Thresholds Using an Integrated, Multi-Modal Pain Task Battery

Human pain models are useful in the assessing the analgesic effect of drugs, providing information about a drug&#39;s pharmacology and identify potentially suitable therapeutic populations. The need to use a comprehensive battery of pain models is highlighted by studies whereby only a single pain model, thought to relate to the clinical situation, demonstrates lack of efficacy. No single experimental model can mimic the complex nature of clinical pain. The integrated, multi-modal pain task&#160;battery presented here encompasses the electrical stimulation task, pressure stimulation task, cold pressor task, the UVB inflammatory model which includes a thermal task and a paradigm for inhibitory conditioned pain modulation. These human pain models have been tested for predicative validity and reliability both in their own right and in combination, and can be used repeatedly, quickly, in short succession, with minimum burden for the subject and with a modest quantity of equipment. This allows a drug to be fully characterized and profiled for analgesic effect which is especially useful for drugs with a novel or untested mechanism of action.

Human pain models are valuable tools used to assess the analgesic potential of novel compounds and predict their clinical efficacy, especially when used in an integrated manner. Although implementation of these models is complex, with proper execution, the pain models described in this protocol can provide predictive and reliable results.

Human pain models are useful in the assessing the analgesic effect of drugs, providing information about a drug&#39;s pharmacology and identify ...

Behavior

A Protocol of Manual Tests to Measure Sensation and Pain in Humans

Numerous qualitative and quantitative techniques can be used to test sensory nerves and pain in both research and clinical settings. The current study demonstrates a quantitative sensory testing protocol using techniques to measure tactile sensation and pain threshold for pressure and heat using portable and easily accessed equipment. These techniques and equipment are ideal for new laboratories and clinics where cost is a concern or a limiting factor. We demonstrate measurement techniques for the following: cutaneous mechanical sensitivity on the arms and legs (von-Frey filaments), radiant and contact heat sensitivity (with both threshold and qualitative assessments using the Visual Analog Scale (VAS)), and mechanical pressure sensitivity (algometer, with both threshold and the VAS). The techniques and equipment described and demonstrated here can be easily purchased, stored, and transported by most clinics and research laboratories around the world. A limitation of this approach is a lack of automation or computer control. Thus, these processes can be more labor intensive in terms of personnel training and data recording than the more sophisticated equipment. We provide a set of reliability data for the demonstrated techniques. From our description, a new laboratory should be able to set up and run these tests and to develop their own internal reliability data.

The goal of this procedure is to demonstrate a battery of quantitative techniques for sensory and pain measurement in humans. The equipment and techniques described are commonly found in pain clinics or are easy to obtain.

Numerous qualitative and quantitative techniques can be used to test sensory nerves and pain in both research and clinical settings. The current study ...

Psychophysically-anchored, Robust Thresholding in Studying Pain-related Lateralization of Oscillatory Prestimulus Activity

In perceptual studies, it is often important to objectively assess the equality of delivered stimulation across participants or to quantify the intra-individual sensation magnitude that is evoked by stimulation over multiple trials. This requires a robust mapping of stimulus magnitude to perceived intensity and is commonly achieved by psychophysical estimation methods such as the staircase procedure. Newer, more efficient procedures like the QUEST algorithm fit a psychophysical function to the data in real time while at the same time maximizing the efficiency of data collection. A robust estimate of the threshold intensity between painful and nonpainful perceptions can then be used to reduce the influence of variations in sensory input in subsequent analyses of oscillatory brain activity. By stimulating at a constant threshold intensity determined by an adaptive estimation procedure, the variance in the ratings can be directly attributed to perceptual processes. Oscillatory activity can then be contrasted between &#34;pain&#34; and &#34;no-pain&#34; trials directly, yielding activity that closely relates to perceptual classification processes in nociception.

Psychophysical methods such as the QUEST estimation procedure can efficiently yield robust estimates of the stimulation intensity at which nonpainful sensations transition into painful sensations. By stimulating repeatedly at the threshold intensity, the variability in rating responses can directly be attributed to perceptual classifications in subsequent analyses.

In perceptual studies, it is often important to objectively assess the equality of delivered stimulation across participants or to quantify the ...

Imaging Odor-Evoked Activities in the Mouse Olfactory Bulb using Optical Reflectance and Autofluorescence Signals

In the brain, sensory stimulation activates distributed populations of neurons among functional modules which participate to the coding of the stimulus. Functional optical imaging techniques are advantageous to visualize the activation of these modules in sensory cortices with high spatial resolution. In this context, endogenous optical signals that arise from molecular mechanisms linked to neuroenergetics are valuable sources of contrast to record spatial maps of sensory stimuli over wide fields in the rodent brain.
Here, we present two techniques based on changes of endogenous optical properties of the brain tissue during activation. First the intrinsic optical signals (IOS) are produced by a local alteration in red light reflectance due to: (i) absorption by changes in blood oxygenation level and blood volume (ii) photon scattering. The use of in vivo IOS to record spatial maps started in the mid 1980's with the observation of optical maps of whisker barrels in the rat and the orientation columns in the cat visual cortex1. IOS imaging of the surface of the rodent main olfactory bulb (OB) in response to odorants was later demonstrated by Larry Katz's group2. The second approach relies on flavoprotein autofluorescence signals (FAS) due to changes in the redox state of these mitochondrial metabolic intermediates. More precisely, the technique is based on the green fluorescence due to oxidized state of flavoproteins when the tissue is excited with blue light. Although such signals were probably among the first fluorescent molecules recorded for the study of brain activity by the pioneer studies of Britton Chances and colleagues3, it was not until recently that they have been used for mapping of brain activation in vivo. FAS imaging was first applied to the somatosensory cortex in rodents in response to hindpaw stimulation by Katsuei Shibuki's group4.
The olfactory system is of central importance for the survival of the vast majority of living species because it allows efficient detection and identification of chemical substances in the environment (food, predators). The OB is the first relay of olfactory information processing in the brain. It receives afferent projections from the olfactory primary sensory neurons that detect volatile odorant molecules. Each sensory neuron expresses only one type of odorant receptor and neurons carrying the same type of receptor send their nerve processes to the same well-defined microregions of &tilde;100&mu;m3 constituted of discrete neuropil, the olfactory glomerulus (Fig. 1). In the last decade, IOS imaging has fostered the functional exploration of the OB5, 6, 7 which has become one of the most studied sensory structures. The mapping of OB activity with FAS imaging has not been performed yet.
Here, we show the successive steps of an efficient protocol for IOS and FAS imaging to map odor-evoked activities in the mouse OB.

This article presents the protocols of intrinsic optical signals and flavoproteins autofluorescence signals imaging to map odor-evoked activities at the surface of the olfactory bulb in mice.

In the brain, sensory stimulation activates distributed populations of neurons among functional modules which participate to the coding of the stimulus. ...

Multi-Modal Signals for Analyzing Pain Responses to Thermal and Electrical Stimuli

The assessment of pain relies mostly on methods that require a person to communicate. However, for people with cognitive and verbal impairments, existing methods are not sufficient as they lack reliability and validity. To approach this problem, recent research focuses on an objective pain assessment facilitated by parameters of responses derived from physiology, and video and audio signals. To develop reliable automated pain recognition systems, efforts have been made in creating multimodal databases in order to analyze pain and detect valid pain patterns. While the results are promising, they only focus on discriminating pain or pain intensities versus no pain. In order to advance this, research should also consider the quality and duration of pain as they provide additional valuable information for more advanced pain management. To complement existing databases and the analysis of pain regarding quality and length, this paper proposes a psychophysiological experiment to elicit, measure, and collect valid pain reactions. Participants are subjected to painful stimuli that differ in intensity (low, medium, and high), duration (5 s / 1 min), and modality (heat / electric pain) while audio, video (e.g., facial expressions, body gestures, facial skin temperature), and physiological signals (e.g., electrocardiogram [ECG], skin conductance level [SCL], facial electromyography [EMG], and EMG of M. trapezius) are being recorded. The study consists of a calibration phase to determine a subject&#8217;s individual pain range (from low to intolerable pain) and a stimulation phase in which pain stimuli, depending on the calibrated range, are applied. The obtained data may allow refining, improving, and evaluating automated recognition systems in terms of an objective pain assessment. For further development of such systems and to investigate pain reactions in more detail, additional pain modalities such as pressure, chemical, or cold pain should be included in future studies. Recorded data of this study will be released as the &#8220;X-ITE Pain Database&#8221;.

This article focuses on the experimental elicitation of pain through heat (thermal) and electrical stimulation while recording physiological, visual, and paralinguistic responses. It aims at collecting valid multimodal data for analyzing pain based on its intensity, quality, and duration.&#160;

The assessment of pain relies mostly on methods that require a person to communicate. However, for people with cognitive and verbal impairments, existing ...

Dynamic Quantitative Sensory Testing to Characterize Central Pain Processing

Central facilitation and modulation of incoming nociceptive signals play an important role in the perception of pain. Disruption in central pain processing is present in many chronic pain conditions and can influence responses to specific therapies. Thus, the ability to precisely describe the state of central pain processing has profound clinical significance in both prognosis and prediction. Because it is not practical to record neuronal firings directly in the human spinal cord, surrogate behavior tests become an important tool to assess the state of central pain processing. Dynamic QST is one such test, and can probe both the ascending facilitation and descending modulation of incoming nociceptive signals via TS and CPM, respectively. Due to the large between-individual variability in the sensitivity to noxious signals, standardized TS and CPM tests may not yield any meaningful data in up to 50% of the population due to floor or ceiling effects. We present methodologies to individualize TS and CPM so we can capture these measures in a broader range of individuals than previously possible. We have used these methods successfully in several studies at the lab, and data from one ongoing study will be presented to demonstrate feasibility and potential applications of the methods.

By assessing pain in response to repetitive or different types of standardized stimuli, dynamic quantitative sensory testing (QST) can reveal changes in the central processing of pain. We present methods to optimize and individualize two dynamic QST measures: temporal summation (TS) and conditioned pain modulation (CPM).

Central facilitation and modulation of incoming nociceptive signals play an important role in the perception of pain. Disruption in central pain ...

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography

Microstate and omega complexity are two reference-free electroencephalography (EEG) measures that can represent the temporal and spatial complexities of EEG data and have been widely used to investigate the neural mechanisms in some brain disorders. The goal of this article is to describe the protocol underlying EEG microstate and omega complexity analyses step by step. The main advantage of these two measures is that they could eliminate the reference-dependent problem inherent to traditional spectrum analysis. In addition, microstate analysis makes good use of high time resolution of resting-state EEG, and the four obtained microstate classes could match the corresponding resting-state networks respectively. The omega complexity characterizes the spatial complexity of the whole brain or specific brain regions, which has obvious advantage compared with traditional complexity measures focusing on the signal complexity in a single channel. These two EEG measures could complement each other to investigate the brain complexity from the temporal and spatial domain respectively.

This article describes the protocol underlying electroencephalography (EEG) microstate analysis and omega complexity analysis, which are two reference-free EEG measures and highly valuable to explore the neural mechanisms of brain disorders.

Microstate and omega complexity are two reference-free electroencephalography (EEG) measures that can represent the temporal and spatial complexities of ...

Intracerebroventricular Treatment with Resiniferatoxin and Pain Tests in Mice

The transient receptor potential vanilloid type 1 (TRPV1), a thermosensitive cation channel, is known to trigger pain in the peripheral nerves. In addition to its peripheral function, its involvement in brain functions has also been suggested. Resiniferatoxin (RTX), an ultrapotent TRPV1 agonist, has been known to induce long-term desensitization of TRPV1, and this desensitization has been an alternative approach for investigating the physiological relevance of TRPV1-expressing cells. Here we describe a protocol for intracerebroventricular (i.c.v.) treatment with RTX in mice. Procedures are described for testing nociception to peripheral TRPV1 stimulation (RTX test) and mechanical stimulation (tail pressure test) then follow. Although the nociceptive responses of mice that had been administered RTX i.c.v. were comparable to those of the control groups, RTX-i.c.v.-administered mice were insensitive to the analgesic effect of acetaminophen, suggesting that i.c.v. RTX treatment can induce supraspinal-selective TRPV1 desensitization. This mouse model can be used as a convenient experimental system for studying the role of TRPV1 in brain/supraspinal function. These techniques can also be applied to studies of the central actions of other drugs.

The transient receptor potential vanilloid type 1 (TRPV1) in the supraspinal region has been suggested to play some roles in the brain function. Described here is a protocol for intracerebroventricular injection of resiniferatoxin for supraspinal TRPV1 desensitization in mice. Procedures for some pain tests are also presented.

The transient receptor potential vanilloid type 1 (TRPV1), a thermosensitive cation channel, is known to trigger pain in the peripheral nerves. In ...

A Quantitative Sensory Testing Paradigm to Obtain Measures of Pain Processing in Patients Undergoing Breast Cancer Surgery

Chronic pain following surgery, persistent postsurgical pain, is an important highly prevalent condition contributing to significant symptom burden and lower quality of life. Persistent postsurgical pain is relatively refractory to treatment hence generating a high need for preventive strategies and treatments. Therefore, the identification of patients at risk of developing persistent pain is an area of active ongoing research. Recently it was demonstrated that peri-operative disruptions in central pain processing may be able to predict persistent postsurgical pain at long term follow-up in breast cancer patients. The aim of the current report is to present a short protocol to obtain pain thresholds to different stimuli at multiple sites and a measure of endogenous analgesia in breast cancer patients. We have used this method successfully in a clinical context and detail some representative results from a clinical study.

Persistent postsurgical pain may be related to changes in pain processing. By assessing pain in response to standardized stimuli, changes in pain processing can be elucidated. We present methods to obtain pain thresholds to different stimuli and a measure of endogenous analgesia in patients undergoing breast cancer surgery.

Chronic pain following surgery, persistent postsurgical pain, is an important highly prevalent condition contributing to significant symptom burden and ...

Quantifying Pain Location and Intensity with Multimodal Pain Body Diagrams

Samenvatting

Meer video's verkennen

Hoofdstukken in deze video