This work was supported by the University Research Fund (URF) from the University of Pennsylvania, and the American Heart Association&#39;s Student Scholarships in Cerebrovascular Disease &#38; Stroke. Special thanks to the researchers, clinicians and staff of the Laboratory for Cognition and Neural Stimulation for their ongoing support.

This study was approved by the local Institutional Review Board and meets all criteria set forth by Good Clinical Practice Guidelines. All participants provided informed consent before any study procedures began. Study participants were expected to participate in three separate sessions (outlined in Table 1). The elements of the experiment are described in stepwise fashion below. Session order was randomized.
<table border="1" fo:keep-together.within-page="always" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td rowspan="3">Session A</td>
			<td>Pre-rTMA VR Task</td>
			<td>Resting Motor Threshhold*&#160;</td>
			<td>rTMR at STG or SMG</td>
			<td>Post-rTMS VR Behavioral Task</td>
		</tr>
		<tr>
			<td></td>
			<td>5/10 pulses elicit MEP o finger twitch (*First session only)</td>
			<td>110% of RMT for 20 min at 1 Hz (1200 pulses total)</td>
			<td></td>
		</tr>
		<tr>
			<td>15 min</td>
			<td>60 min</td>
			<td>20 min</td>
			<td>15 min</td>
		</tr>
		<tr>
			<td rowspan="3">Session B</td>
			<td>Pre-rTMA VR Task</td>
			<td>Resting Motor Threshhold*&#160;</td>
			<td>rTMR at Vertex</td>
			<td>Post-rTMS VR Behavioral Task</td>
		</tr>
		<tr>
			<td></td>
			<td>5/10 pulses elicit MEP o finger twitch (*First session only)</td>
			<td>110% of RMT for 20 min at 1 Hz (1200 pulses total)</td>
			<td></td>
		</tr>
		<tr>
			<td>15 min</td>
			<td>60 min</td>
			<td>20 min</td>
			<td>15 min</td>
		</tr>
		<tr>
			<td rowspan="3">Session C</td>
			<td>Pre-rTMS paper &#38; Pencil Behavioral Task</td>
			<td>Resting Motor Threshhold*&#160;</td>
			<td>rTMR at STG or SMG</td>
			<td>Post-rTMS paper &#38; Pencil Behavioral Task</td>
		</tr>
		<tr>
			<td>Bell&#39;s test; Ota&#39;s circle cancellation; stay cancellation; line bisection task</td>
			<td>5/10 pulses elicit MEP o finger twitch (*First session only)</td>
			<td>110% of RMT for 20 min at 1 Hz (1200 pulses total)</td>
			<td>Bell&#39;s test; Ota&#39;s circle cancellation; stay cancellation; line bisection task</td>
		</tr>
		<tr>
			<td>10 min</td>
			<td>60 min</td>
			<td>20 min</td>
			<td>10 min</td>
		</tr>
	</tbody>
</table>
Table 1.&#160;Structure for each study session.&#160;Session order was randomized. Estimated time for each item in italics. MEP=motor evoked potential; rTMS=Repetitive Transcranial Magnetic Stimulation; P&#38;P=Paper and Pencil Stroke Diagnostic Tests; RMT=Resting Motor Threshold
1. Paper &#38; pencil behavioral tasks
<ol>
	<li>Have the subject complete the line bisection task (LBT).
	<ol>
		<li>Have the subject sit at a table directly across from the tester. Provide the subject with a writing utensil. Provide the subject with the stimulus sheet (Figure 1), ensuring it is placed directly in front of the subject. 
		&#8203;NOTE: Although not performed in this experiment, it would be ideal to present each line to be bisected individually on separate sheets of paper to avoid biasing subject with additional context (See Ricci and Chatterjee, 200142).</li>
		<li>Instruct the subject to bisect (divide into halves) each line printed on the stimulus sheet and get as close to the middle as possible.</li>
		<li>Tell the subject to keep their head and shoulders centered as best as possible, to complete the task as quickly and accurately as possible, and to notify the tester when they are finished. Monitor the subject to ensure they are not leaning or tilting their head excessively.</li>
		<li>Collect the sheet from the subject when the subjects say they are done.</li>
	</ol>
	</li>
	<li>Have the subject complete the Bell&#39;s Test.
	<ol>
		<li>Provide the subject with the Bell&#39;s test stimuli sheet (Figure 2).</li>
		<li>Instruct the subject to circle or cross out all of the bells on the stimulus sheet, to do so as quickly and accurately as possible, to keep their head and shoulders as centered as possible, and to notify the tester when they are finished.</li>
		<li>Monitor the subject to ensure they are not leaning or tilting their head excessively. When the subject says they are finished, ask the subject if they are sure, and allow them to double check their work.</li>
		<li>Collect the sheet from the subject when the subjects say they are done a second time.</li>
	</ol>
	</li>
	<li>Have the subject complete the star cancellation task.
	<ol>
		<li>Present the subject with the stimulus sheet (Figure 3), ensuring it is directly in front of them.</li>
		<li>Instruct the subject to circle or cross out all of the stars on the stimulus sheet, to do so as quickly and accurately as possible, to keep their head and shoulders as centered as possible, and to notify the tester when they are finished.</li>
		<li>Monitor the subject to ensure they are not leaning or tilting their head excessively.</li>
		<li>Collect the sheet from the subject when the subjects say they are done.</li>
	</ol>
	</li>
	<li>Have the subject complete the Ota&#39;s circle cancellation task.
	<ol>
		<li>Provide the subject with the Ota&#39;s circle cancellation stimulus sheet (Figure 4), ensuring it is placed directly in front of the subject.</li>
		<li>Instruct the subject to cross out or circle all of the open/incomplete circles, to do so as quickly and accurately as possible, to keep their shoulders as centered as possible, and to notify the tester when they are finished.</li>
		<li>Monitor the subject to ensure they are not leaning or tilting their head excessively.</li>
		<li>Collect the sheet from the subject when the subjects say they are done.</li>
		<li>Repeat this task (steps 1.4.1 through 1.4.4) with another copy of the stimulus sheet, but this time the stimulus sheet should be rotated 180 degrees from the orientation it was originally presented.</li>
	</ol>
	</li>
</ol>
2. TMS procedures
<ol>
	<li>Create a model for neuronavigation prior to the first session.
	<ol>
		<li>Obtain the subject&#39;s 3T T1 MRI scan in a NIFTI or dicom file type.</li>
		<li>Upload that MRI scan into the neuronavigational software to create a 3D representation of the subject&#39;s brain.
		<ol>
			<li>Select New Empty Project within the software. Drag the subject&#39;s MRI scan onto the field labeled &#34;File:&#34;.</li>
			<li>Go to the Reconstructions tab.</li>
			<li>Select New Skin and on the next screen, drag the green boundary lines to encompass the entire image of the brain. Select compute skin. Adjust the Skin/Air Threshold accordingly to get an optimal reconstruction.</li>
			<li>Go back to the Reconstructions tab and select New Full Brain Curvilinear and drag the green boundary lines to encompass the entire image of the brain. Set slice spacing to 1 mm and set end depth to 18 mm. Select Compute Curvilinear.</li>
			<li>Go to Landmarks tab and select Configure Landmarks. Select New to create a landmark on the reconstruction. Place landmarks on the tip of the nose, bridge of the nose, left tragus, and right tragus.</li>
			<li>Go to the Targets tab and select Configure Targets. Select the Curvilinear Brain &#38; Targets view. Using the inspector, peel to a depth of 5-7 mm.</li>
			<li>Follow guidelines of Shah-Basak et al. (2018)14, Neggers et al. (2006)11 and Oliveri and Vallar (2009)39 to locate the superior temporal gyrus or the supramarginal gyrus, and place a marker at those sites.</li>
			<li>Place a marker where the two central sulci meet along the median longitudinal fissure for sham stimulation at the vertex.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>During the first session, find the subject&#39;s Resting Motor Threshold (may be completed before or after behavioral task).
	<ol>
		<li>Have the subject seated in front of an optical tracking camera and place a tracker on the subject using a headband or glasses.</li>
		<li>Attach three disposable electrodes on the subject&#39;s right hand and wrist.
		<ol>
			<li>Attach one disk electrode to the subject&#39;s first dorsal interosseous. Attach a second disk electrode to the subject&#39;s second knuckle on their right pointer finger. Attach a ground electrode to the subject&#39;s right wrist.</li>
		</ol>
		</li>
		<li>Plug these electrodes into an electrode adaptor, which inputs into an MEP tracking software.</li>
		<li>Open the subject&#39;s project within the neuronavigational software by selecting New Online Session.</li>
		<li>Select the targets to be stimulated in this session (Vertex, SMG, STG).</li>
		<li>Go to the Polaris tab and ensure the subject tracker is within view of the camera.</li>
		<li>Go to Registration tab.</li>
		<li>Using a pointer registered to the neuronavigational software, touch the subjects&#39; face in the same locations that the landmarks were placed in step 2.1.2.5.
		<ol>
			<li>Click Sample and go to Next Landmark when the pointer is positioned properly on the subject&#39;s head for each landmark.</li>
		</ol>
		</li>
		<li>Go to Validation tab.</li>
		<li>Using the pointer, touch the subject in various spots on their head and ensure the crosshairs on the screen line up with the spot being pointed to on the subject.
		<ol>
			<li>If they do not line up, redo step 2.2.8 and make sure the pointer is as precisely placed on the landmarks as possible.</li>
		</ol>
		</li>
		<li>Go to Perform tab and ensure the Full Brain Curvilinear View is selected so the experimenter can precisely locate brain regions to target.</li>
		<li>Set driver to be the TMS coil that will be used.</li>
		<li>Plug handheld TMS coil into TMS machine.</li>
		<li>Turn on the TMS Machine and set to single pulse. Set stimulation intensity appropriately; in this experiment, 65% of machine output was used as a starting point.</li>
		<li>Place the handheld TMS coil on the left side of the subject&#39;s head and stimulate within the motor cortex using single pulses of TMS to identify the location that stimulates the FDI. It may be helpful to have an assistant to watch the subject&#39;s finger to identify when the FDI muscle twitches due to stimulation.</li>
		<li>Alter the stimulation intensity until stimulation elicits MEP of at least 50 mV exactly 5/10 times, and this will be the resting motor threshold (rMT).</li>
	</ol>
	</li>
	<li>Stimulation in between tasks
	<ol>
		<li>Repeat steps 2.2.1 through 2.2.13, substituting an air-cooled TMS coil for the handheld coil.</li>
		<li>Set stimulation parameters to repetitive TMS at a rate of 1 Hz for 20 minutes (1200 pulses total) with an intensity of 110% of rMT in accordance with parameters set by Shah-Basak et al. (2018)15.</li>
		<li>Place an air-cooled TMS coil with a built-in cooling system on the subject&#39;s head targeting the SMG or STG for active sessions or the Vertex for sham sessions (Figure 5).</li>
		<li>Proceed with stimulation.</li>
	</ol>
	</li>
</ol>
3. VR behavioral task
<ol>
	<li>Install supporting software.
	<ol>
		<li>Download and install Pupil core software from the Pupil Labs website.</li>
		<li>Download and install Unity 3D 2018.3 from the Unity website.</li>
		<li>Download and install OpenVR tool through Unity Asset Store or through Steam.</li>
	</ol>
	</li>
	<li>Set up the VR hardware (e.g., HTC Vive Pro).
	<ol>
		<li>Place base stations on opposite sides of the room, ensuring a clear line of sight, and plug them in.</li>
		<li>Press the Channel/Mode button on the back of each sensor to cycle through channels until one of them is set to channel &#34; b&#34; and one is set to &#34; c.&#34; Both status LEDs should be white.</li>
		<li>Install Pupil Labs Binocular insert into HTC Vive Pro. Connect the Link Box to the computer (Power, USB-A, and HDMI or Mini DisplayPort).</li>
		<li>Connect the headset to the Link Box. Adjust top and side straps on headset. Adjust the lens distance.</li>
	</ol>
	</li>
	<li>Launch SteamVR.
	<ol>
		<li>Launch SteamVR by clicking on the VR icon in the top right corner of Steam.
		<ol>
			<li>Turn on controllers with the power button.</li>
			<li>On SteamVR, click Settings | Pair New Device to pair each controller by following on-screen instructions.</li>
			<li>Click Room Setup from the SteamVR menu and follow on-screen instructions.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Launch Pupil Core Software.</li>
	<li>Place headset on the seated subject&#39;s head and give them both controllers. Ensure the straps are tight but comfortable. Ensure both eyes are visible by visually confirming they are centered in the Pupil Core Software&#39;s camera feeds.</li>
	<li>Open the VR task in the Unity Editor and hit the Play button.</li>
	<li>Run the experiment.
	<ol>
		<li>Ask the subject to look straight ahead and click the Tare Camera button on the screen.</li>
		<li>Click the Begin Tutorial button and wait for the subject to complete the tutorial. The tutorial consists of audio instruction about the operation of the VR system controller, descriptions and examples of symmetrical (decoy) and asymmetrical (target) flowers, and a 1-minute practice session with a small number of decoy and target flowers. The tutorial lasts 75-100 seconds and tutorial performance data is not collected.</li>
		<li>When subject is finished, click the Calibrate Eye Tracking button.
		<ol>
			<li>If the calibration is successful, the subject will automatically begin the task. Otherwise, repeat step 3.7.3.</li>
		</ol>
		</li>
		<li>Begin the first trial by clicking the Next Trial button. 
		NOTE: During the VR task, subjects are placed in a virtual forest (Figure 6). Three curved box hedges formed a semi circle within reaching distance in front of the subject. Each trial consisted of a varying number of flowers, each with 16 petals, distributed among the hedges at a direct line of sight (Figure 7). Subjects were instructed to &#34;pick&#34; (hold their controller over a flower so that the flower would highlight, then depress the trigger button with their index finger) all asymmetrical &#34;target&#34; flowers and leave alone all symmetrical &#34;decoy&#34; flowers. Each trial would end when the subject successfully picks all of the asymmetrical target flowers, but also would end if the subject ran out of time (2-minute time limit) or if the subject inadvertently picked all of the symmetrical decoy flower. In all of these cases the remaining flowers on the bushes would be cleared, and the experimenter would be prompted to begin the next trial.</li>
		<li>Wait until the subject is no longer actively completing a trial and then repeat step 3.7.4 unless at least 12 trials have been completed.</li>
		<li>Click the Play button again to end the task.</li>
	</ol>
	</li>
</ol>

The authors have nothing to disclose.

We successfully induced and measured USN symptoms with TMS and VR, respectively. While we did not have significant results when compared to sham trials, we were able to compare multiple metrics of egocentric neglect (average head angle, time spent looking at flowers in either hemispace) and allocentric neglect (performance in selecting flowers with asymmetric petals on the left vs. the right side) between the different experimental groups, and found significant differences in average head angle between subjects stimulated at the STG and those stimulated at the SMG and marginally significant effects in the average visual axis. Of interest, there is still debate concerning the proportional contribution of temporal (STG) and parietal (PPC) contribution to USN-relevant spatial processing12,43, and the increased rightward head angle we detected in the SMG-stimulated group may provide some support for the implication of PPC of the egocentric variety of USN.

There were multiple critical steps in this protocol. This method is limited by the subtle clinical effects achieved with rTMS, so proper stimulation parameters and cortical region targeting is critical - TMS stimulation intensity should always be based on the rMT and TMS coil targeting should always be precisely determined with high-resolution MRI images and proper targeting software like Brainsight. The method is also limited by the relatively short duration of the inhibitory effect created by rTMS stimulation (~20 minutes, or roughly the duration of stimulation26), so rapid transition from rTMS stimulation back to the VR or paper &#38; pencil tasks is of paramount importance to detect this effect. Assuring that the VR equipment is set up and the software is properly calibrated during the pre-TMS VR sessions helps maximize the proportion of post-stimulation time spent collecting data.
As enumerated in the introduction, a number of groups have developed novel VR-based tools for the assessment of USN. Many of these systems also utilize the distinct measurement advantages of computerized tasks, and some groups have attempted to differentiate the various subtypes of USN including extrapersonal vs. peripersonal neglect symptoms and egocentric vs. allocentric symptoms37,40. We believe that the method adds two novel contributions to this existing work. First, we provide a broader array of datasets (head position, eye tracking, etc.) that can be analyzed to detect and characterize even subtle cases of USN. Second, we induced USN symptoms in healthy volunteers using TMS, helping assure that the VR-based diagnostic tool was isolating induced USN symptoms and avoiding the possible confounding effects of visual, motor, and cognitive comorbidities seen in acquired brain injury patients. In addition, the task contrasts with a trend in recent studies that focuses on navigation tasks. We contend that a task that demands interaction with a number of distributed objects across both left and right hemispaces is potentially more demanding and may increase the sensitivity of the VR task as a diagnostic tool. In addition, this format allows for more of a game-like task with multiple trials, which in turn allows for titration of the task&#39;s difficulty level from round to round. This type of titration helps the task avoid ceiling and floor effects (i.e., the task being too hard for those with significant deficits or too easy for those with subtle deficits).
There are many possible future applications of the method. With regard to the study of USN, we believe that the addition of eye-tracking data will enable VR tasks to differentiate between attentional and intentional symptoms by separating data measuring asymmetry of search pattern from data measuring asymmetry of motor action. Furthermore, TMS can be used to isolate specific neurologic deficits beyond USN, creating a means by which investigators can design and validate a wide variety of novel VR tools to help diagnose and characterize these deficits in patients who suffer from acquired brain injury. Although the technique involves healthy participants and artificial neurologic deficits in an effort to reliably isolate and characterize USN specifically, we believe that VR tools that are validated by the method can then be applied in populations of patients with mixed neurologic deficits (motor, visual, etc.) by way of user interface innovations such as EEG- or EMG-based brain-computer interfaces44,45. In addition, VR-based tasks like the one we present here can also be modified to serve as cognitive rehabilitation tools, a growing area of research and development31,46.
We faced a number of frustrating issues in testing. The eye tracking became uncalibrated upon small shifts in the HMD&#39;s position and the software sometimes failed. The application needed more development and suffered from correctable issues like subject starting position and range of flower placement (some flowers were placed outside of the subject&#39;s field of view and invalidated some trials). We had too few subjects. Nevertheless, we were still able to detect the subtle perturbations of two neural networks associated with USN with the novel VR tool. While the ambitious experiment yielded marginal results, we believe many of the challenges it faced will be ameliorated as the technology continues to improve. We argue that the promise of the results, in combination with other encouraging trends within the field, support the idea that VR systems are an excellent substrate for the development of novel diagnostic tools for USN.

Unilateral spatial neglect (USN) is a syndrome characterized by inattention to or inaction in one side of space that affects between 23-46% of acute stroke survivors, most commonly involving injury to the right cerebral hemisphere and resulting in a tendency to ignore the left side of space and/or the survivor&#39;s body1,2. Although the majority of patients with USN experience significant recovery in the short term, subtle USN symptoms often persist3. USN can increase patient risk for falls and impede activities of daily living2,4 It has also been shown to negatively impact both motor and global functional outcomes5,6.
Deficits in USN can be conceptualized as existing across multiple dimensions, such as whether a person ignores one side of space with respect to their own body (egocentric) or with respect to an external stimulus (allocentric)7,8,9, or whether a person is unable to direct their attention (attentional) or actions (intentional) toward one side of space10. Patients often exhibit a complex constellation of symptoms that can be characterized along more than one of these dimensions. This variability of USN syndromes is thought to result from varying degrees of injury to specific neuroanatomical structures and neuronal networks, which are complex11. Allocentric neglect has been associated with lesions of the angular gyrus (AG) and superior temporal gyrus (STG), while the posterior parietal cortex (PPC) including the supramarginal gyrus (SMG) has been implicated in egocentric processing12,13,14,15. Attentional neglect is thought to involve lesions in the right IPL16, while intentional neglect is thought to be secondary to damage of the right frontal lobe17 or basal ganglia18.
Clinical assessment of USN currently relies on pen-and-paper neuropsychological instruments. These conventional assessment tools may be less sensitive than more technologically sophisticated tools, resulting in misdiagnosing or under-diagnosing some patients with USN19. Better characterization of residual deficits could facilitate the delivery of therapy to patients with milder USN and potentially improve their overall recovery, but such characterization would require very sensitive diagnostic tools. USN poses similar challenges in the laboratory setting, where it can be difficult to isolate from the motor and visual impairments that commonly accompany USN among stroke patients.
Virtual reality (VR) presents a unique opportunity to develop new tools for the diagnosis and characterization of USN. VR is a multisensory 3D environment presented in the first person with real time interactions in which individuals are able to perform tasks involving ecologically valid objects20. It is a promising tool for assessing USN; the ability to precisely control what the user sees and hears allows developers to present a wide variety of virtual tasks to the user. In addition, the sophisticated hardware and software packages currently available allow for real-time collection of a wealth of data about the user&#39;s actions, including eye, head, and limb movements, far exceeding the metrics offered by traditional diagnostic tests21. These data streams are instantaneously available, opening up the possibility for real-time adjustment of diagnostic tasks based on user performance (e.g. targeting the ideal difficulty level for a given task). This feature can facilitate task adaptation to the wide range of severity seen in USN, which is regarded as a priority in the development of new diagnostic tools for USN22. In addition, immersive VR tasks may impose an increased burden on the patients&#39; attentional resources23,24, resulting in increased errors which can facilitate the detection of neglect symptoms; indeed, some VR tasks have been shown to have increased sensitivity when compared to conventional paper-and-pencil measures of USN24,25.
In this study, the goal was to create an assessment tool that requires no expertise in neurology to operate and that can reliably detect and characterize even subtle cases of USN. We built a virtual reality-based, game-like task. We then induced a USN-like syndrome in healthy subjects with transcranial magnetic stimulation (TMS), a noninvasive brain stimulation technique that utilizes electromagnetic pulses emitted from a handheld stimulation coil, which pass through the scalp and skull of the subject and induce electric currents in the subject&#39;s brain that stimulate neurons26,27. This technique has been utilized in the study of USN by others13,17,28,29,30, though to our knowledge never in conjunction with a VR-based assessment tool.
Many researchers are already working on diagnostic and therapeutic applications of VR systems. Recent reviews31,32 explored a number of projects aimed at the assessment of USN with VR-based techniques, and a number of other studies with this aim have been published33,34,35,36,37,38,39,40,41. The majority of these studies do not utilize the full complement of VR technology that is currently available to the consumer market (e.g., a head-mounted display (HMD) and eye-tracking inserts), limiting their datasets to a smaller number of easily-quantifiable metrics. In addition, all of these studies were performed on patients with acquired brain injury leading to USN, requiring screening methods to assure that patients could at least participate in the assessment tasks (e.g., excluding patients with large visual field deficits or cognitive impairment). It is possible that more subtle cognitive, motor, or visual deficits passed under the threshold of these screening methods, possibly confounding the results of these studies. It is also possible that such screening biased the samples of participants in these studies toward a particular subtype of USN.
To avoid the screening biases of prior studies, we recruited healthy subjects and artificially simulated USN symptoms with a standard TMS protocol that is well-described in a recent manuscript15, with the goal of inducing allocentric USN-like symptoms by targeting the STG and egocentric USN-like symptoms by targeting the SMG. We designed the task to actively adjust its difficulty trial to trial and to differentiate between different subtypes of USN, specifically allocentric vs. egocentric symptoms. We also used standard paper &#38; pencil assessments of USN to formally demonstrate that the deficits we induced with rTMS are USN-like. We believe the method will be useful to other researchers who want to test novel VR tools for the assessment and rehabilitation of USN.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>AirFilm Coil (AFC) Rapid Version</td>
			<td>Magstim</td>
			<td>N/A</td>
			<td>Air-cooled TMS coil</td>
		</tr>
		<tr>
			<td>Alienware 17 R4 Laptop</td>
			<td>Dell</td>
			<td>N/A</td>
			<td>NVIDIA GeForce GTX 1060 (full specs at https://topics-cdn.dell.com/pdf/alienware-17-laptop_users-guide_en-us.pdf)</td>
		</tr>
		<tr>
			<td>BrainSight 2.0 TMS Neuronavigation Software</td>
			<td>Rogue Research Inc</td>
			<td>N/A</td>
			<td>TMS neural targeting software</td>
		</tr>
		<tr>
			<td>CED 1902 Isolated pre-amplifier</td>
			<td>Cambridge Electronic Design Limted</td>
			<td>N/A</td>
			<td>EMG pre-amplifier</td>
		</tr>
		<tr>
			<td>CED Micro 401 mkII</td>
			<td>Cambridge Electronic Design Limted</td>
			<td>N/A</td>
			<td>Multi-channel waveform data acquisition unit</td>
		</tr>
		<tr>
			<td>CED Signal 5</td>
			<td>Cambridge Electronic Design Limted</td>
			<td>N/A</td>
			<td>Sweep-based data acquisition and analysis software. Used to measure TMS evoked motor responses.</td>
		</tr>
		<tr>
			<td>HTC Vive Binocular Add-on</td>
			<td>Pupil Labs</td>
			<td>N/A</td>
			<td>HTC Vive, Vive Pro, or Vive Cosmos eye tracking add-on with 2 x 200Hz eye cameras.</td>
		</tr>
		<tr>
			<td>Magstim D70 Remote Coil</td>
			<td>Magstim</td>
			<td>N/A</td>
			<td>Hand-held TMS coil</td>
		</tr>
		<tr>
			<td>Magstim Super Rapid 2 plus 1</td>
			<td>Magstim</td>
			<td>N/A</td>
			<td>Transcranial Magnetic Stimulation Unit</td>
		</tr>
		<tr>
			<td>Unity 2018</td>
			<td>Unity</td>
			<td>N/A</td>
			<td>cross-platform VR game engine</td>
		</tr>
		<tr>
			<td>Vive Pro</td>
			<td>HTC Vive</td>
			<td>N/A</td>
			<td>VR hardware system with external motion sensors; 1440x1600 pixels per eye, 90 Hz refresh rate, 110&#176; FoV</td>
		</tr>
	</tbody>
</table>

virtual reality tools for assessing unilateral spatial neglect: a novel opportunity for data collection

Unilateral spatial neglect (USN) is a syndrome characterized by inattention to or inaction in one side of space and affects between 23-46% of acute stroke survivors. The diagnosis and characterization of these symptoms in individual patients can be challenging and often requires skilled clinical staff. Virtual reality (VR) presents an opportunity to develop novel assessment tools for patients with USN.
We aimed to design and build a VR tool to detect and characterize subtle USN symptoms, and to test the tool on subjects treated with inhibitory repetitive transcranial magnetic stimulation (TMS) of cortical regions associated with USN.
We created three experimental conditions by applying TMS to two distinct regions of cortex associated with visuospatial processing- the superior temporal gyrus (STG) and the supramarginal gyrus (SMG) - and applied sham TMS as a control. We then placed subjects in a virtual reality environment in which they were asked to identify the flowers with lateral asymmetries of flowers distributed across bushes in both hemispaces, with dynamic difficulty adjustment based on each subject's performance. 
We found significant differences in average head yaw between subjects stimulated at the STG and those stimulated at the SMG and marginally significant effects in the average visual axis.
VR technology is becoming more accessible, affordable, and robust, presenting an exciting opportunity to create useful and novel game-like tools. In conjunction with TMS, these tools could be used to study specific, isolated, artificial neurological deficits in healthy subjects, informing the creation of VR-based diagnostic tools for patients with deficits due to acquired brain injury. This study is the first to our knowledge in which artificially generated USN symptoms have been evaluated with a VR task.

Data were collected from healthy individuals using the protocol outlined above to demonstrate how the different variables that can be extracted from the virtual reality task can be analyzed to detect subtle differences between groups.
In this study, 7 individuals (2 male) with an average age of 25.6 and an average of 16.8 years of education each underwent three separate sessions of TMS. These subjects were broken into two groups: four participants received repetitive TMS at the supramarginal gyrus (SMG), while three other participants received TMS stimulation at the superior temporal gyrus (STG). All participants received sham TMS during a separate session, which was used as a covariate in analyses to account for individual variability in response to TMS. During each session, participants were administered the virtual reality task before and after TMS stimulation to examine change in performance.
First, the average head angle (Figure 8) was examined to determine whether the virtual reality task was sensitive enough to identify a difference between the SMG and STG groups. Head angle change scores were calculated by subtracting pre-TMS scores from the post-TMS scores. An ANCOVA was run to determine whether there was a difference between groups in head angle following TMS stimulation. Sham TMS head angle change scores were used as a covariate to account for individual differences. While keeping in mind that the analyses were conducted using a small pilot sample, a significant difference was found in head angle change scores between the two groups, F(1,4) = 10.25, p = 0.03, where the SMG group had an average change score directed more towards the right side of space compared to the STG group (Figure 9).
A similar pattern was found using the line bisection test, in which the SMG group placed the line significantly more towards the right in the post-TMS administration compared to pre-TMS, t(4) = 2.78, p = 0.04. This finding was not found in the STG group, t(3) = 3.18, p = 0.56. While there was no significant difference in head angle before and after TMS in the virtual reality task in either the SMG or STG groups, the finding that the SMG group had an average head angle change score directed significantly more to the right compared to the STG group demonstrates a similar finding. This finding from the virtual reality task is consistent with the results of the traditional paper-and-pencil task, as both demonstrated a pattern in which the SMG group may have had a subtle neglect and looked more towards the right compared to the STG group. Data gathered from the virtual reality task can be visualized on an individual participant level to examine performance before and after the TMS stimulation, as can be seen in Figure 9.
Next, flowers were separated by which side of the flower contained the defective flower petal (i.e., right petal vs. left petal, see Figure 10) to specifically assess for signs of allocentric neglect on an individual target level. While there was no difference in head angle change scores between the two groups for flowers with shorter petals on the left side, F(1,4) = 0.09, p = 0.78, there was a significant difference in head angle change scores between the two groups for flowers with a smaller petal on the right side, F(1,4) = 9.52, p = 0.04. Specifically, participants in the SMG group had a tendency to look further to the right (higher flower-to-head angle, see Figure 11) when searching for the short petal on the right side of the flower. The angle of the subject&#39;s head with respect to the bush (bush angle, see Figure 12) is also available for analysis, allowing for the detection of allocentric neglect with respect to the bush. These analyses demonstrate how variables can be made more specific to capture subtle, specific aspects of neglect.
There are a number of other ways the data may be analyzed. We examined the average number of seconds that participants looked at each flower to determine whether one group had more difficulty identifying defective flowers (as characterized by more seconds spent looking at the flower). In this example, data were extracted from flowers that had a defective petal that was 95% the size of the rest of the petals, as this scale was hypothesized to be the most sensitive. A mixed ANCOVA was run to compare group (SMG vs. STG) and flower visual field (right vs. left). Pre- and post-TMS change scores were calculated and used as the outcome variable to examine whether either group showed an increase in time spent looking at flowers following TMS. The sham TMS condition for both left and right flowers were once again used as covariates to account for individual variability. While there was no significant difference between groups, F(1,3) = 0.12, p = 0.76, there was a marginally significant difference in flower visual field, F(1,3) = 5.62, p = 0.098 (Figure 13). The effect does not reach statistical significance; and more subjects should be assessed moving forward. Despite this, these data serve as an example of how data can be limited to specific flower types and visual field within the virtual reality environment. As these analyses demonstrate, comparing participants&#39; performance can provide researchers with a sensitive and dynamic way to measure the effects of TMS or neglect more generally depending on the examiners&#39; specific research question.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/61951/61951fig01.jpg" /> 
Figure 1: Line bisection task stimulus sheet <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/61951/61951fig01large.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/61951/61951fig02.jpg" /> 
Figure 2: Bell&#39;s test stimulus sheet <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/61951/61951fig02large.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/61951/61951fig03.jpg" /> 
Figure 3: Star cancellation test stimulus sheet <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/61951/61951fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/61951/61951fig04.jpg" /> 
Figure 4: Ota circle cancellation stimulus sheet <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/61951/61951fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/61951/61951fig05.jpg" /> 
Figure 5: Repetitive TMS stimulation; neuronavigational software (left), magnetic stimulation unit (center), and air-cooled coil in position over author CH (right). <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/61951/61951fig05large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/61951/61951fig06.jpg" /> 
Figure 6: Virtual forest environment seen by the subject during the VR task <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/61951/61951fig06large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 7" class="xfigimg" src="/files/ftp_upload/61951/61951fig07.jpg" /> 
Figure 7: Layout of three curvedbox hedges with target and decoy flowers distributed across <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/61951/61951fig07large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 8" class="xfigimg" src="/files/ftp_upload/61951/61951fig08.jpg" /> 
Figure 8: Head angle - angle between anterior axis of the head and torso <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/61951/61951fig08large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 9" class="xfigimg" src="/files/ftp_upload/61951/61951fig09.jpg" /> 
Figure 9. This figure demonstrates two analyses using head angle during task performance: 
(Left) SMG vs. STG group head angle change scores. On this scale, a score of 0 indicates that they looked at the center of each flower, while positive scores indicate that they looked towards the right, and negative scores indicate that they looked towards the left. The SMG group had positive scores, indicating that they looked more to the right on average following stimulation, whereas the STG group had negative scores, indicating that they looked more to the left following stimulation. SMG and STG group had significantly different head angle change scores. (Right). Mean head angle plotted for each participant pre-TMS and post-TMS. The STG group did not show strong differences before and after TMS stimulation, unlike the SMG participants who appeared to look more towards the right visual field following stimulation (as represented by positive numbers).&#160;<a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/61951/61951fig09large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 10" class="xfigimg" src="/files/ftp_upload/61951/61951fig10.jpg" /> 
Figure 10: Asymmetric target flowers, with smaller petals on the left (left) and smaller petals on the right (right). <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/61951/61951fig10large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 11" class="xfigimg" src="/files/ftp_upload/61951/61951fig11.jpg" /> 
Figure 11: Flower to head angle - angle subtended by the head&#39;s anterior axis and the flower from the head at the instant in which the flower was picked/identified <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/61951/61951fig11large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 12" class="xfigimg" src="/files/ftp_upload/61951/61951fig12.jpg" /> 
Figure 12: Bush angle - angle subtended by the flower and the center of the flower&#39;s bush from the head at the instant in which the flower was picked/identified <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/61951/61951fig12large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 13" class="xfigimg" src="/files/ftp_upload/61951/61951fig13.jpg" /> 
Figure 13. Mean change score for seconds spent looking at each flower before and after TMS. Negative scores indicate that participants spent less time looking at flowers in the post-TMS administration compared to the pre-TMS administration, whereas positive numbers indicate more time spent looking at flowers post-TMS. Data are separated by whether flowers were located in the left vs. right visual field within the virtual environment. Data were also separated by group (SMG vs. STG). Flowers were restricted to those with a defective petal at a scale of 0.95. Though not statistically significant, there was a marginal effect of flower visual field. Qualitatively, there appears to be greater variability for flowers in the left visual field compared to the right.&#160;<a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/61951/61951fig13large.jpg" style="font-size: 14px;" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about Virtual Reality Tools for Assessing Unilateral Spatial Neglect: A Novel Opportunity for Data Collection at JoVE.com

Virtual Reality Tools for Assessing Unilateral Spatial Neglect: A Novel Opportunity for Data Collection

The goal was to design, build, and pilot a novel virtual reality task to detect and characterize unilateral spatial neglect, a syndrome affecting 23-46% of acute stroke survivors, expanding the role of virtual reality in the study and management of neurologic disease.

Unilateral spatial neglect (USN) is a syndrome characterized by inattention to or inaction in one side of space and affects between 23-46% of acute stroke ...

virtual-reality-tools-for-assessing-unilateral-spatial-neglect-novel

Nanyang Technological University

Research

JoVE Journal

Medicine

3.8K Views.  University of Pennsylvania. The goal was to design, build, and pilot a novel virtual reality task to detect and characterize unilateral spatial neglect, a syndrome affecting 23-46% of acute stroke survivors, expanding the role of virtual reality in the study and management of neurologic disease.

Video: Virtual Reality Tools for Assessing Unilateral Spatial Neglect: A Novel Opportunity for Data Collection

A Novel Rescue Technique for Difficult Intubation and Difficult Ventilation

We describe a novel non surgical technique to maintain oxygenation and ventilation in a case of difficult intubation and difficult ventilation, which works especially well with poor mask fit.
Can not intubate, can not ventilate" (CICV) is a potentially life threatening situation. In this video we present a simulation of the technique we used in a case of CICV where oxygenation and ventilation were maintained by inserting an endotracheal tube (ETT) nasally down to the level of the naso-pharynx while sealing the mouth and nares for successful positive pressure ventilation.
A 13 year old patient was taken to the operating room for incision and drainage of a neck abcess and direct laryngobronchoscopy. After preoxygenation, anesthesia was induced intravenously. Mask ventilation was found to be extremely difficult because of the swelling of the soft tissue. The face mask could not fit properly on the face due to significant facial swelling as well. A direct laryngoscopy was attempted with no visualization of the larynx. Oxygen saturation was difficult to maintain, with saturations falling to 80%. In order to oxygenate and ventilate the patient, an endotracheal tube was then inserted nasally after nasal spray with nasal decongestant and lubricant. The tube was pushed gently and blindly into the hypopharynx. The mouth and nose of the patient were sealed by hand and positive pressure ventilation was possible with 100% O2 with good oxygen saturation during that period of time. Once the patient was stable and well sedated, a rigid bronchoscope was introduced by the otolaryngologist showing extensive subglottic and epiglottic edema, and a mass effect from the abscess, contributing to the airway compromise. The airway was secured with an ETT tube by the otolaryngologist.This video will show a simulation of the technique on a patient undergoing general anesthesia for dental restorations.

We describe a technique to maintain oxygenation and ventilation using an endotracheal tube inserted nasally to the level of the naso-pharynx while sealing the mouth and nares for successful positive pressure ventilation.

We describe a novel non surgical technique to maintain oxygenation and ventilation in a case of difficult intubation and difficult ventilation, which ...

Collection Protocol for Human Pancreas

This dissection and sampling procedure was developed for the Network for Pancreatic Organ Donors with Diabetes (nPOD) program to standardize preparation of pancreas recovered from cadaveric organ donors. The pancreas is divided into 3 main regions (head, body, tail) followed by serial transverse sections throughout the medial to lateral axis. Alternating sections are used for fixed paraffin and fresh frozen blocks and remnant samples are minced for snap frozen sample preparations, either with or without RNAse inhibitors, for DNA, RNA, or protein isolation. The overall goal of the pancreas dissection procedure is to sample the entire pancreas while maintaining anatomical orientation.
Endocrine cell heterogeneity in terms of islet composition, size, and numbers is reported for human islets compared to rodent islets 1. The majority of human islets from the pancreas head, body and tail regions are composed of insulin-containing &beta;-cells followed by lower proportions of glucagon-containing &alpha;-cells and somatostatin-containing &delta;-cells. Pancreatic polypeptide-containing PP cells and ghrelin-containing epsilon cells are also present but in small numbers. In contrast, the uncinate region contains islets that are primarily composed of pancreatic polypeptide-containing PP cells 2. These regional islet variations arise from developmental differences. The pancreas develops from the ventral and dorsal pancreatic buds in the foregut and after rotation of the stomach and duodenum, the ventral lobe moves and fuses with the dorsal 3. The ventral lobe forms the posterior portion of the head including the uncinate process while the dorsal lobe gives rise to the rest of the organ. Regional pancreatic variation is also reported with the tail region having higher islet density compared to other regions and the dorsal lobe-derived components undergoing selective atrophy in type 1 diabetes4,5.
Additional organs and tissues are often recovered from the organ donors and include pancreatic lymph nodes, spleen and non-pancreatic lymph nodes. These samples are recovered with similar formats as for the pancreas with the addition of isolation of cryopreserved cells. When the proximal duodenum is included with the pancreas, duodenal mucosa may be collected for paraffin and frozen blocks and minced snap frozen preparations.

This video demonstrates a dissection procedure for processing human pancreas into multiple storage formats. Anatomical orientation is maintained throughout the pancreatic regions to allow definition of regional islet composition and density.

This dissection and sampling procedure was developed for the Network for Pancreatic Organ Donors with Diabetes (nPOD) program to standardize preparation ...

Development of an Audio-based Virtual Gaming Environment to Assist with Navigation Skills in the Blind

Audio-based Environment Simulator (AbES) is virtual environment software designed to improve real world navigation skills in the blind. Using only audio based cues and set within the context of a video game metaphor, users gather relevant spatial information regarding a building's layout. This allows the user to develop an accurate spatial cognitive map of a large-scale three-dimensional space that can be manipulated for the purposes of a real indoor navigation task. After game play, participants are then assessed on their ability to navigate within the target physical building represented in the game. Preliminary results suggest that early blind users were able to acquire relevant information regarding the spatial layout of a previously unfamiliar building as indexed by their performance on a series of navigation tasks. These tasks included path finding through the virtual and physical building, as well as a series of drop off tasks. We find that the immersive and highly interactive nature of the AbES software appears to greatly engage the blind user to actively explore the virtual environment. Applications of this approach may extend to larger populations of visually impaired individuals.

Audio-based Environment Simulator (AbES) is virtual environment software designed to improve real world navigation skills in the blind.

Audio-based Environment Simulator (AbES) is virtual environment software designed to improve real world navigation skills in the blind. Using only audio ...

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

The scaled subprofile model (SSM)1-4 is a multivariate PCA-based algorithm that identifies major sources of variation in patient and control group brain image data while rejecting lesser components (Figure 1). Applied directly to voxel-by-voxel covariance data of steady-state multimodality images, an entire group image set can be reduced to a few significant linearly independent covariance patterns and corresponding subject scores. Each pattern, termed a group invariant subprofile (GIS), is an orthogonal principal component that represents a spatially distributed network of functionally interrelated brain regions. Large global mean scalar effects that can obscure smaller network-specific contributions are removed by the inherent logarithmic conversion and mean centering of the data2,5,6. Subjects express each of these patterns to a variable degree represented by a simple scalar score that can correlate with independent clinical or psychometric descriptors7,8. Using logistic regression analysis of subject scores (i.e. pattern expression values), linear coefficients can be derived to combine multiple principal components into single disease-related spatial covariance patterns, i.e. composite networks with improved discrimination of patients from healthy control subjects5,6. Cross-validation within the derivation set can be performed using bootstrap resampling techniques9. Forward validation is easily confirmed by direct score evaluation of the derived patterns in prospective datasets10. Once validated, disease-related patterns can be used to score individual patients with respect to a fixed reference sample, often the set of healthy subjects that was used (with the disease group) in the original pattern derivation11. These standardized values can in turn be used to assist in differential diagnosis12,13 and to assess disease progression and treatment effects at the network level7,14-16. We present an example of the application of this methodology to FDG PET data of Parkinson's Disease patients and normal controls using our in-house software to derive a characteristic covariance pattern biomarker of disease.

Multivariate techniques including principal component analysis (PCA) have been used to identify signature patterns of regional change in functional brain images. We have developed an algorithm to identify reproducible network biomarkers for the diagnosis of neurodegenerative disorders, assessment of disease progression, and objective evaluation of treatment effects in patient populations.

The scaled subprofile model (SSM)1-4 is a multivariate PCA-based algorithm that identifies major sources of variation in patient and control group brain ...

A Novel Model of Mild Traumatic Brain Injury for Juvenile Rats

Despite growing evidence that childhood represents a major risk period for mild traumatic brain injury (mTBI) from sports-related concussions, motor vehicle accidents, and falls, a reliable animal model of mTBI had previously not been developed for this important aspect of development. The modified weight-drop technique employs a glancing impact to the head of a freely moving rodent transmitting acceleration, deceleration, and rotational forces upon the brain. When applied to juvenile rats, this modified weight-drop technique induced clinically relevant behavioural outcomes that were representative of post-concussion symptomology. The technique is a rapidly applied procedure with an extremely low mortality rate, rendering it ideal for high-throughput studies of therapeutics. In addition, because the procedure involves a mild injury to a closed head, it can easily be used for studies of repetitive brain injury. Owing to the simplistic nature of this technique, and the clinically relevant biomechanics of the injury pathophysiology, the modified weight-drop technique provides researchers with a reliable model of mTBI that can be used in a wide variety of behavioural, molecular, and genetic studies.

The modified weight-drop technique is an easy, cost-effective procedure used for the induction of mild traumatic brain injury in juvenile rats. This novel technique produces clinically relevant symptomology that will advance the study of mild traumatic brain injury (mTBI) and concussion.

Despite growing evidence that childhood represents a major risk period for mild traumatic brain injury (mTBI) from sports-related concussions, motor ...

A Murine Model of Irreversible and Reversible Unilateral Ureteric Obstruction

Obstruction of the kidney may affect native or transplanted kidneys and results in kidney injury and scarring. Presented here is a model of obstructive nephropathy induced by unilateral ureteric obstruction (UUO), which can either be irreversible (UUO) or reversible (R-UUO). In the irreversible UUO model, the ureter may be obstructed for variable periods of time in order to induce increasingly severe renal inflammation and interstitial fibrotic scarring. In the reversible R-UUO model the ureter is obstructed to induce hydronephrosis, tubular dilation and inflammation. After a suitable period of time the ureteric obstruction is then surgically reversed by anastomosis of the severed previously obstructed ureter to the bladder in order to allow complete decompression of the kidney and restoration of urinary flow to the bladder. The irreversible UUO model has been used to investigate various aspects of renal inflammation and scarring including the pathogenesis of disease and the testing of potential anti-inflammatory or anti-fibrotic therapies. The more challenging model of R-UUO has been used by some investigators and does offer significant research potential as it allows the study of inflammatory and immune processes and tissue remodeling in an injured and scarred kidney following the removal of the injurious stimulus. As a result, the R-UUO model offers investigators the opportunity to explore the resolution of kidney inflammation together with key aspects of tissue repair. These experimental models are of relevance to human disease as patients often present with obstruction of the renal tract that requires decompression and are commonly left with significant residual kidney impairment that has no current treatment options and may lead to eventual end stage kidney failure.

The murine model of irreversible unilateral ureteric obstruction (UUO) is presented together with the model of reversible UUO in which the ureteric obstruction is relieved by anastomosis of the severed ureter into the bladder. These models enable the study of renal inflammation and scarring as well as tissue remodeling.

Obstruction of the kidney may affect native or transplanted kidneys and results in kidney injury and scarring. Presented here is a model of obstructive ...

A Detailed Protocol for Perspiration Monitoring Using a Novel, Small, Wireless Device

Perspiration monitoring can be utilized for the detection of certain diseases, such as thermoregulation and mental disorders, particularly when the patients are unaware of such disorders or are having difficulty expressing their symptoms. Until now, several devices for perspiration monitoring have been developed; however, such devices tend to have a relatively large exterior, considerable power consumption, and/or less sensitivity.
Recently, we developed a small, wireless device for perspiration monitoring. The device consists of a temperature/relative humidity (T/RH) sensor, battery-driven small data logger, and silica gel as a desiccant in a small cylindrical exterior. The T/RH sensor is placed between the detection windows (through which the water vapor from the skin enters) and the silica gel. The underlying principle of the perspiration monitoring device is based on Fick&#39;s law of diffusion, which means that water vapor flux from the skin to the silica gel (i.e.&#160;transepidermal water loss and perspiration) can be captured by change in humidity at the T/RH sensor. In addition, a baseline subtraction method was adopted to distinguish perspiration and transepidermal water loss.
As shown in the previous report, the developed device can monitor the perspiration at any sites of the body in an easy, wireless manner. However, detailed methods of how to use the device have not been disclosed yet. In this article, therefore, we would like to show the point-by-point tutorials of how to use the device for perspiration monitoring, by showing the sympathetic activity test with the sympathetic skin response monitoring as an example.

Recently, we developed a small wireless device for perspiration monitoring. In this article, we present detailed protocols on how to use the device for perspiration monitoring with an example of the sympathetic activity test.

Perspiration monitoring can be utilized for the detection of certain diseases, such as thermoregulation and mental disorders, particularly when the ...

A Novel Clinical Grade Isolation Method for Human Kidney Perivascular Stromal Cells

Mesenchymal Stromal Cells (MSCs) are tissue homeostatic and immune modulatory cells that have shown beneficial effects in kidney diseases and transplantation. Perivascular Stromal Cells (PSCs) share characteristics with bone marrow MSCs (bmMSCs). However, they also possess, most likely due to local imprinting, tissue-specific properties and play a role in local tissue homeostasis. This tissue specificity may result in tissue specific repair, also within the human kidney. We previously showed that human kidney PSCs (kPSCs) have enhanced kidney epithelial wound healing whereas bmMSCs did not have this potential. Moreover, kPSCs can ameliorate kidney injury in vivo. Therefore, kPSCs constitute an interesting source for cell therapy, particularly for kidney diseases and renal transplantation. Here we show the detailed isolation and culture method for kPSCs from transplant-grade human kidneys based on whole-organ perfusion of digestive enzymes via the renal artery and enrichment for the perivascular marker NG2. In this way, large cell quantities can be obtained that are suitable for cellular therapy.

Here we present a novel clinical grade isolation and culture method for kidney Perivascular Stromal Cells (kPSCs) based on whole organ perfusion with digestive enzymes and NG2-cell enrichment. With this method, it is possible to acquire sufficient cell numbers for cellular therapy.

Mesenchymal Stromal Cells (MSCs) are tissue homeostatic and immune modulatory cells that have shown beneficial effects in kidney diseases and ...

High-Throughput Analysis of Optical Mapping Data Using ElectroMap

Optical mapping is an established technique for high spatio-temporal resolution study of cardiac electrophysiology in multi-cellular preparations. Here we present, in a step-by-step guide, the use of ElectroMap for analysis, quantification, and mapping of high-resolution voltage and calcium datasets acquired by optical mapping. ElectroMap analysis options cover a wide variety of key electrophysiological parameters, and the graphical user interface allows straightforward modification of pre-processing and parameter definitions, making ElectroMap applicable to a wide range of experimental models. We show how built-in pacing frequency detection and signal segmentation allows high-throughput analysis of entire experimental recordings, acute responses, and single beat-to-beat variability. Additionally, ElectroMap incorporates automated multi-beat averaging to improve signal quality of noisy datasets, and here we demonstrate how this feature can help elucidate electrophysiological changes that might otherwise go undetected when using single beat analysis. Custom modules are included within the software for detailed investigation of conduction, single file analysis, and alternans, as demonstrated here. This software platform can be used to enable and accelerate the processing, analysis, and mapping of complex cardiac electrophysiology.

This protocol describes the setup and use of ElectroMap, a MATLAB-based open-source software platform for analysis of cardiac optical mapping data. ElectroMap provides a versatile high-throughput tool for analysis of optical mapping voltage and calcium datasets across a wide range of cardiac experimental models.

Optical mapping is an established technique for high spatio-temporal resolution study of cardiac electrophysiology in multi-cellular preparations. Here we ...

Integrating Augmented Reality Tools in Breast Cancer Related Lymphedema Prognostication and Diagnosis

Breast cancer related lymphedema (BCRL) is a detrimental condition characterized by fluid accumulation in the upper limb in breast cancer patients subjected to axillary surgery and/or radiations. Its etiology is multifactorial and include also tumor-specific pathological features, such as lymphovascular invasion (LVI) and extranodal extension (ENE). To date, no widely employed guidelines for the early diagnosis of BCRL are available. Here, we illustrate a protocol for a digitally assisted BCRL assessment using a 3D laser scanner (3DLS) and a tablet computer. It has been specifically optimized in a discovery cohort of high-risk breast cancer patients. This study provides a proof-of-principle that augmented reality tools, such as 3DLS, can be incorporated into the clinical workup of BCRL to allow for a precise, reproducible, reliable, and cheap diagnosis.

Breast cancer related lymphedema is frequent in breast cancer survivors, but there are not widely employed guidelines for its diagnosis and quantification. Here, we introduce a reliable and cost-effective protocol to define, quantify, and compare upper limb volume in breast cancer patients.