Special thanks to Bo Johnels, Nasser Hosseini, Roy Tranberg and Patrik Almstr&#246;m for help with the initiation of this project. The research data presented in this protocol was gathered at the Sahlgrenska University Hospital.

All methods described here have been part of the studies approved by the Regional Ethical Review Board in Gothenburg, Sweden (318-04, 225-08).
1. Setting up the Motion Capture System
<ol>
	<li>Mount 4 cameras on the wall approximately 1.5 - 3 m away from the measurement area at the height of 1.5 - 2.5 m facing the measurement area. Mount one camera on ceiling just above the measurement area (Figure 1). Start the camera system.</li>
	<li>Place the L-shape calibration frame on the table with the short axis in line with the edge of the table and the long axis pointing forward. 
	NOTE: The coordinate system is defined with X-axes directed forward (anteriorly in the sagittal plane), Y-axis directed laterally (in the frontal plane), and Z-axis directed upward (superiorly, perpendicular to the transversal plane).</li>
	<li>Open the 3D tracking and data acquisition software (Track Manager), start calibration by selecting Capture | Calibrate, enter the calibration time of 30 s and click OK.</li>
	<li>Move the wand in all directions throughout the entire measurement area (75 &#215; 75 &#215; 65 cm) above the chair and table to ensure that all 5 cameras capture the wand in as many orientations as possible16,17. After the calibration, the results show on the screen. Accept calibration residuals below 0.5 mm.</li>
	<li>Have the subject, wearing a sleeveless top, sit in a in a height adjustable chair with their back against the chair&#180;s back, the upper arm in neutral adducted position, the palm of the hand resting at the table and the wrist aligned to the edge of the table. Check that the knee, hip, and elbow angles are approximately 90&#176;.</li>
	<li>Place the retroreflective passive markers with double-adhesive tape on the skeletal landmarks18 on the tested hand (third metacarpophalangeal joint), wrist (styloid process of ulna), elbow (lateral epicondyle), right and left shoulder (middle part of acromion), thorax (upper part of sternum), and forehead (notch between eyebrows).</li>
	<li>Place two markers on the cup (upper and lower edge).</li>
</ol>
2. Procedures for Motion Capture of the Drinking Task
<ol>
	<li>Place the hard-plastic cup (diameter of 7 cm, height of 9.5 cm) with 100 mL of water 30 cm from the table edge, in the midline of the body. The position of cup on the table is selected deliberately to keep the task performance natural and close to real-life situation.</li>
	<li>Ask the subject to perform the drinking task in a comfortable self-paced speed by i) reaching and grasping the cup, ii) lifting the cup from the table towards mouth, iii) taking a drink (one sip), iv) placing the cup back on the table behind a marked line (30 cm from the table edge), and v) returning to the initial position with the hand on the edge of the table.</li>
	<li>Ensure that subject understands the instructions and can reach the cup comfortably with the less-affected arm without leaning forward.</li>
	<li>Prior each recording, ensure that the start position (initial position) is correct, ask the subject to be ready, start the capture manually and give verbal instruction &#34;you can start now.&#34;</li>
	<li>When the subject finishes the task, stop the recording manually.</li>
	<li>Record five trials with short pause between each trial (approximately 30 s), starting with the less-affected arm.</li>
	<li>Check that the data acquisition has been successful (95 - 100% data for each identified marker). 
	NOTE: Marker data is automatically transferred in real-time to the data acquisition software (Track Manager). A pre-defined Automatic Marker Identification (AIM) model is used for automatic identification of the markers.</li>
	<li>When incomplete data are detected, perform extra trials after identifying the problem and adjusting the sitting or marker positions to ensure full visibility of the markers in order to obtain at least 3 successful trials. 
	NOTE: Possible problems that may occur are that markers may fall off or they are occluded from the cameras viewing angle, which results in incomplete data. However, the camera and marker set-up, as used in this protocol, produces data loss due to gaps only in very rare occasions. In total, the motion capture session takes approximately 10 - 15 min to complete.</li>
</ol>
3. Data Analysis
<ol>
	<li>Transfer the recorded data from Track Manager directly into Matlab by clicking File | Export | Directly into Matlab.</li>
	<li>Use the Matlab command at the command prompt: (&#62;&#62; workspace) to see the set of Matlab variables. 
	NOTE: Key Matlab variables containing data to be used in the instructions and in creating the analysis are: 
	QTMmeasurements.Frames - the number of frames captured 
	QTMmeasurements.FrameRate - the number of frames captured per second (240) 
	QTMmeasurements.Trajectories.Labeled.Count - number of labels (10) 
	QTMmeasurements.Trajectories.Labeled.Labels - labels as defined in the Track Manager 
	QTMmeasurements.Trajectories.Labeled.Data - measurement data in a 3D array of 10 x 3 x number&#160;of frames, where for each frame and each label the 3 coordinates are recorded</li>
	<li>In Matlab, filter the x, y, z values using the (butter) and (filtfilt) instructions with a 6Hz second-order Butterworth filter in in both forward and reverse directions, giving a zero-phase distortion and forth-order filtering. 
	NOTE: Example 
	[b, a] = butter(2, 6/240/2); % Cutoff frequency 6Hz and with respect to &#189; sampling frequency 
	xfiltered = filtfilt(b, a, QTMmeasurements.Trajectories.Labeled(1,1,:));</li>
	<li>In Matlab, create a program to use the x, y, z values for each frame sample and each label to calculate the kinematic variables such as tangential velocity of the hand, and joint angles. The kinematic variables are shown in Table 2.</li>
	<li>In Matlab, create a program to break the sequence of samples into 5 logical phases: reaching, forward transport, drinking, back transport, and returning the hand to the initial position (Figure 2). Definitions for start and end of each phase are showed in detail in Table 1.</li>
	<li>In Matlab, use the (plot) instruction to create plots of positions, velocities, joints angles, and angle-angle diagrams.</li>
</ol>

<ol>
	<li>Alt Murphy, M., H&#228;ger, C. K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Kinematic+analysis+of+the+upper+extremity+after+stroke+-+how+far+have+we+reached+and+what+have+we+grasped?.">Kinematic analysis of the upper extremity after stroke - how far have we reached and what have we grasped?.</a> Physical Therapy Reviews. 20 (3), 137-155 (2015).</li><li>Bustren, E. L., Sunnerhagen, K. S., Alt Murphy, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Movement+Kinematics+of+the+Ipsilesional+Upper+Extremity+in+Persons+With+Moderate+or+Mild+Stroke.">Movement Kinematics of the Ipsilesional Upper Extremity in Persons With Moderate or Mild Stroke.</a> Neurorehab Neural Re. 31 (4), 376-386 (2017).</li><li>Sivan, M., O'Connor, R. J., Makower, S., Levesley, M., Bhakta, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Systematic+review+of+outcome+measures+used+in+the+evaluation+of+robot-assisted+upper+limb+exercise+in+stroke.">Systematic review of outcome measures used in the evaluation of robot-assisted upper limb exercise in stroke.</a> J Rehabil Med. 43 (3), 181-189 (2011).</li><li>Demers, M., Levin, M. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Do+Activity+Level+Outcome+Measures+Commonly+Used+in+Neurological+Practice+Assess+Upper-Limb+Movement+Quality?.">Do Activity Level Outcome Measures Commonly Used in Neurological Practice Assess Upper-Limb Movement Quality?.</a> Neurorehab Neural Re. 31 (7), 623-637 (2017).</li><li>Levin, M. F., Kleim, J. A., Wolf, S. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=What+do+motor+"recovery"+and+"compensation"+mean+in+patients+following+stroke?.">What do motor "recovery" and "compensation" mean in patients following stroke?.</a> Neurorehab Neural Re. 23 (4), 313-319 (2009).</li><li>Alt Murphy, M., Willen, C., Sunnerhagen, K. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Responsiveness+of+Upper+Extremity+Kinematic+Measures+and+Clinical+Improvement+During+the+First+Three+Months+After+Stroke.">Responsiveness of Upper Extremity Kinematic Measures and Clinical Improvement During the First Three Months After Stroke.</a> Neurorehab Neural Re. 27 (9), 844-853 (2013).</li><li>van Dokkum, L., 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=The+contribution+of+kinematics+in+the+assessment+of+upper+limb+motor+recovery+early+after+stroke.">The contribution of kinematics in the assessment of upper limb motor recovery early after stroke.</a> Neurorehab Neural Re. 28 (1), 4-12 (2014).</li><li>Alt Murphy, M., Willen, C., Sunnerhagen, K. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Kinematic+variables+quantifying+upper-extremity+performance+after+stroke+during+reaching+and+drinking+from+a+glass.">Kinematic variables quantifying upper-extremity performance after stroke during reaching and drinking from a glass.</a> Neurorehab Neural Re. 25 (1), 71-80 (2011).</li><li>Subramanian, S. K., Yamanaka, J., Chilingaryan, G., Levin, M. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Validity+of+movement+pattern+kinematics+as+measures+of+arm+motor+impairment+poststroke.">Validity of movement pattern kinematics as measures of arm motor impairment poststroke.</a> Stroke. 41 (10), 2303-2308 (2010).</li><li>Michaelsen, S. M., Dannenbaum, R., Levin, M. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Task-specific+training+with+trunk+restraint+on+arm+recovery+in+stroke:+randomized+control+trial.">Task-specific training with trunk restraint on arm recovery in stroke: randomized control trial.</a> Stroke. 37 (1), 186-192 (2006).</li><li>Kwakkel, 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=Standardized+measurement+of+sensorimotor+recovery+in+stroke+trials:+Consensus-based+core+recommendations+from+the+Stroke+Recovery+and+Rehabilitation+Roundtable.">Standardized measurement of sensorimotor recovery in stroke trials: Consensus-based core recommendations from the Stroke Recovery and Rehabilitation Roundtable.</a> Int J Stroke. 12 (5), 451-461 (2017).</li><li>Wagner, J. M., Lang, C. E., Sahrmann, S. A., Edwards, D. F., Dromerick, A. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Sensorimotor+impairments+and+reaching+performance+in+subjects+with+poststroke+hemiparesis+during+the+first+few+months+of+recovery.">Sensorimotor impairments and reaching performance in subjects with poststroke hemiparesis during the first few months of recovery.</a> Phys Ther. 87 (6), 751-765 (2007).</li><li>van Kordelaar, J., van Wegen, E., Kwakkel, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Impact+of+time+on+quality+of+motor+control+of+the+paretic+upper+limb+after+stroke.">Impact of time on quality of motor control of the paretic upper limb after stroke.</a> Arch Phys Med Rehab. 95 (2), 338-344 (2014).</li><li>Thielman, G., Kaminski, T., Gentile, A. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Rehabilitation+of+reaching+after+stroke:+comparing+2+training+protocols+utilizing+trunk+restraint.">Rehabilitation of reaching after stroke: comparing 2 training protocols utilizing trunk restraint.</a> Neurorehab Neural Re. 22 (6), 697-705 (2008).</li><li>Armbruster, C., Spijkers, W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Movement+planning+in+prehension:+do+intended+actions+influence+the+initial+reach+and+grasp+movement?.">Movement planning in prehension: do intended actions influence the initial reach and grasp movement?.</a> Motor Control. 10 (4), 311-329 (2006).</li><li>Qualisys. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Qualisys Track Manager user manual. , (2008).</li><li>Alt Murphy, M., Banina, M. C., Levin, M. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Perceptuo-motor+planning+during+functional+reaching+after+stroke.">Perceptuo-motor planning during functional reaching after stroke.</a> Exp Brain Res. , (2017).</li><li>Sint Jan, S. V. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Color atlas of skeletal landmark definitions : guidelines for reproducible manual and virtual palpations. , (2007).</li><li>Alt Murphy, M., Sunnerhagen, K. S., Johnels, B., Willen, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Three-dimensional+kinematic+motion+analysis+of+a+daily+activity+drinking+from+a+glass:+a+pilot+study.">Three-dimensional kinematic motion analysis of a daily activity drinking from a glass: a pilot study.</a> J Neuroeng Rehabil. 3, 18 (2006).</li><li>Alt Murphy, M., Willen, C., Sunnerhagen, K. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Movement+kinematics+during+a+drinking+task+are+associated+with+the+activity+capacity+level+after+stroke.">Movement kinematics during a drinking task are associated with the activity capacity level after stroke.</a> Neurorehab Neural Re. 26 (9), 1106-1115 (2012).</li><li>Alt Murphy, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Development and validation of upper extremity kinematic movement analysis for people with stroke. Reaching and drinking from a glass. , (2013).</li><li>Persson, H. C., Alt Murphy, M., Danielsson, A., Lundgren-Nilsson, A., Sunnerhagen, K. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+cohort+study+investigating+a+simple,+early+assessment+to+predict+upper+extremity+function+after+stroke+-+a+part+of+the+SALGOT+study.">A cohort study investigating a simple, early assessment to predict upper extremity function after stroke - a part of the SALGOT study.</a> BMC Neurol. 15, 92 (2015).</li><li>Hoonhorst, M. H., 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=How+Do+Fugl-Meyer+Arm+Motor+Scores+Relate+to+Dexterity+According+to+the+Action+Research+Arm+Test+at+6+Months+Poststroke?.">How Do Fugl-Meyer Arm Motor Scores Relate to Dexterity According to the Action Research Arm Test at 6 Months Poststroke?.</a> Arch Phys Med Rehab. 96 (10), 1845-1849 (2015).</li><li>Pang, M. Y., Harris, J. E., Eng, J. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+community-based+upper-extremity+group+exercise+program+improves+motor+function+and+performance+of+functional+activities+in+chronic+stroke:+a+randomized+controlled+trial.">A community-based upper-extremity group exercise program improves motor function and performance of functional activities in chronic stroke: a randomized controlled trial.</a> Arch Phys Med Rehab. 87 (1), 1-9 (2006).</li><li>Alt Murphy, 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=SALGOT+-+Stroke+Arm+Longitudinal+study+at+the+University+of+Gothenburg,+prospective+cohort+study+protocol.">SALGOT - Stroke Arm Longitudinal study at the University of Gothenburg, prospective cohort study protocol.</a> BMC Neurol. 11, 56 (2011).</li></ol>

The authors have nothing to disclose.

The protocol can successfully be used to quantify the movement performance and quality in individuals with moderate and mild upper extremity sensorimotor impairments at all stages after stroke. The feasibility of this protocol has been proved in a clinical setting as early as 3 days post stroke, and showed that the system can be used by trained health professional without specific technical qualifications. Technical expertise is, however, needed to create and develop a program for data analysis. From this aspect, the upper extremity motion capture differs from gait analysis, in which ready-made analysis programs are generally directly provided by the manufacturers. In everyday life, arms and hands can be used in many different tasks involving manipulation and interaction with different object in various sizes, locations, and affordances. This makes each set-up unique. Further, different goals and constraints of the task will also affect the kinematic outcome, since the kinematics are highly task-specific. In the future, more efforts should be made to create a standardized protocol for kinematic analysis of basic tasks, such as, drinking, eating, taking hand to the mouth, and bimanual object manipulation, which would allow a better comparison of the results between different studies.
Based on our early experiences, with a 3-camera capture system, in which the problem with segmentations and gaps was observed, it can be suggested that a 5-camera system that allows different positions for cameras (and one above the measurement area) is optimal for the upper extremity analysis. For a clinically feasible measurement set-up, a simple set-up with limited number of markers and simplified analysis, as described in this protocol can be advocated. When the assessment of movement performance and quality aims to follow patients&#39; recovery, make prediction of future outcomes, select optimal treatment options, or evaluate the effectiveness of treatment and rehabilitation interventions, a simple, easy to use method would be enough. On the other hand, a more comprehensive biomechanical analysis using cluster-based markers would be required for more detailed modeling, particularly when the axial joint rotations and shoulder complex are of interest.
Increased clinical use of kinematic analysis is advocated by many researches in the area of neurology and stroke rehabilitation. Objective and valid methods for evaluation of motor function during natural activities and tasks are of high interest among clinicians and researchers. A recent consensus paper recommends adding kinematic measures in future stroke trials alongside with clinical assessments to distinguish between true recovery and compensation11. Challenges remain though, to determine a core set of kinematic outcomes and tasks for inclusion in trials, and to encourage broader collaboration between investigators to reach consensus11. The current 3D motion capture protocol together with published validation studies of this protocol can be one step on that direction.

Kinematic analysis describes the movements of the body through space and time, including linear and angular displacements, velocities, and accelerations. The optoelectronic motion capture systems use multiple high-speed cameras that either send out infra-red light signals to capture the reflections from passive markers placed on the body or transmit the movement data from active markers containing infrared emitting diodes. These systems are considered as &#39;gold standard&#39; for the acquisition of kinematic data1. These systems are valued for their high accuracy and flexibility in measurements of diverse tasks. Kinematic measures have shown to be effective in capturing smaller changes in movement performance and quality that may be undetected with traditional clinical scales2,3. It has been suggested that kinematics should be used for distinction between true recovery (restoration of premorbid movement characteristics) and the use of compensatory (alternative) movement patterns during the accomplishment of a task4,5.
Upper extremity movements can be quantified using end-point kinematics, generally obtained from a hand marker, and angular kinematics from joints and segments (i.e., trunk). End-point kinematics provide information about trajectories, speed, temporal movement strategies, precision, straightness, and smoothness, while angular kinematics characterize movement patterns in terms of temporal and spatial joint and segment angles, angular velocities, and interjoint coordination. End-point kinematics, such as, movement time, speed, and smoothness are effective to capture the deficits and improvements in movement performance after stroke6,7,8 and angular kinematics show whether the movements of joints and body segments are optimal for a specific task. Kinematics from people with impairments are often compared with movement performance in individuals without impairments8,9. End-point and angular kinematics are correlated in a way that a movement performed with effective speed, smoothness, and precision will require good movement control, coordination, and use of effective and optimal movement patterns. For example, a patient with stroke who moves slowly usually also shows decreased smoothness (increased number of movement units), lower maximum velocity, and increased trunk displacement8. On the other hand, improvements in endpoint kinematics, such as movement speed and smoothness might occur independently from the changes of compensatory movement strategies of trunk and arm10. It has been established that kinematic analysis may provide additional and more precise information about how the task is accomplished after an injury or disease, which in turn is essential for individualized effective treatment to reach optimal motor recovery11. Kinematic analysis is increasingly used in clinical studies to describe the movements in people with upper extremity impairments after stroke8,9, to evaluate motor recovery7,12,13 or to determine the effectiveness of therapeutic interventions10,14.
Movement tasks often studied in stroke are pointing and reaching, although the use of functional tasks that incorporate manipulation of real everyday objects is increasing1. Since kinematics of reaching depend on the experimental constraints such as the selection of objects and the goal of the task15, it is essential to assess movements during purposeful and functional tasks in which the real difficulties in individual&#39;s daily life will be reflected more closely.
Thus, the aim of this paper is to provide a detailed description of a simple standardized protocol used for kinematic analysis of a purposeful and functional task, drinking task, applied to individuals with upper extremity impairments in acute and chronic stages after stroke. Results from the validation of this protocol for individuals with moderate and mild stroke impairment will be summarized.

<table border="0" cellpadding="0" cellspacing="0" width="665">
	<tbody>
		<tr height="63">
			<td height="63" style="height:63px;width:195px;">5 camera optoelectronic ProReflex Motion capture system (MCU 240 Hz)</td>
			<td style="width:153px;">Qualisys AB, Gthenburg, Sweden</td>
			<td style="width:68px;">N/A</td>
			<td style="width:251px;">Movement analysis system with passive retroreflective markers</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:195px;">Markers&#160;</td>
			<td style="width:153px;">Qualisys AB, Gthenburg, Sweden</td>
			<td style="width:68px;">N/A</td>
			<td style="width:251px;">Retroleflective passive circular markers, diameter of 12 mm</td>
		</tr>
		<tr height="84">
			<td height="84" style="height:84px;width:195px;">Calibration frame and wand</td>
			<td style="width:153px;">Qualisys AB, Gthenburg, Sweden</td>
			<td style="width:68px;">N/A</td>
			<td style="width:251px;">L-shape calibration frame (defines the origin and orientation of the coordinate system); T-shape wand (300 mm)</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:195px;">Qualisys Track Manager</td>
			<td style="width:153px;">Qualisys AB, Gthenburg, Sweden</td>
			<td style="width:68px;">N/A</td>
			<td style="width:251px;">3D Tracking software</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:195px;">Matlab</td>
			<td style="width:153px;">Mathworks, Inc, Natick, Ca</td>
			<td style="width:68px;">N/A</td>
			<td style="width:251px;">Data analysis software</td>
		</tr>
	</tbody>
</table>

kinematic analysis using 3d motion capture of drinking task in people with and without upper-extremity impairments

Kinematic analysis is a powerful method for objective assessment of upper extremity movements in a three-dimensional (3D) space. Three-dimensional motion capture with an optoelectronic camera system is considered as golden standard for kinematic movement analysis and is increasingly used as outcome measure to evaluate the movement performance and quality after an injury or disease involving upper extremity movements. This article describes a standardized protocol for kinematic analysis of drinking task applied in individuals with upper extremity impairments after stroke. The drinking task incorporates reaching, grasping and lifting a cup from a table to take a drink, placing the cup back, and moving the hand back to the edge of the table. The sitting position is standardized to the individual&#39;s body size and the task is performed in a comfortable self-paced speed and compensatory movements are not constrained. The intention is to keep the task natural and close to a real-life situation to improve the ecological validity of the protocol. A 5-camera motion capture system is used to gather 3D coordinate positions from 9 retroreflective markers positioned on anatomical landmarks of the arm, trunk, and face. A simple single marker placement is used to ensure the feasibility of the protocol in clinical settings. Custom-made Matlab software provides automated and fast analyses of movement data. Temporal kinematics of movement time, velocity, peak velocity, time of peak velocity, and smoothness (number of movement units) along with spatial angular kinematics of shoulder and elbow joint as well as trunk movements are calculated. The drinking task is a valid assessment for individuals with moderate and mild upper extremity impairment. The construct, discriminative and concurrent validity along with responsiveness (sensitivity to change) of the kinematic variables obtained from the drinking task have been established.

The protocol described in this article has been applied to individuals with stroke and healthy controls2,6,8,19,20,21. In total, kinematic data from 111 individuals with stroke and 55 healthy controls have been analyzed in different studies. The upper extremity impairment after stroke was defined as moderate (FMA-UE score 32-57) or mild (FMA-UE score 58-66)8,22,23,24. In healthy controls, no significant differences were found between the dominant and non-dominant arm except for the peak velocity and therefore the non-dominant arm was chosen for comparison 2,8. The majority of data was collected within a large longitudinal cohort study, the Stroke Arm Longitudinal study at University of Gothenburg (SALGOT), which includes a non-selected sample of 122 individuals with stroke and involves the assessments at 3 days post stroke and follow up at 10 days, 4 weeks, 3, 6, and 12 months25.
In summary, our results show that the protocol is feasible in clinical settings since a large number of patients were tested as early as 3 days post-stroke at the acute hospitals stroke unit. Feasibility was also proven by the fact that two experienced physiotherapists managed to calibrate and use the motion capture system on daily basis without any larger technical problems (no support from the system providers was needed during the 3 years of data collection). The data quality was good and the pre-programmed automatic procedures for analyses could generally be applied. Only in few recordings, the phases were not detected correctly, often due to the extra movements in the beginning/end of the movement or when the movement speed was extremely low in patients with more severe impairments. In these cases, the extra trials were often used after a manual inspection of the plotted data. The test protocol demonstrated a good consistency in test-retest in healthy individuals and provided clear and accurate results19.
The movements in every phase of the drinking task and for the entire task are slower (Table 3) in people with stroke, although the relative time spend in each phase is similar to controls21. Similarly, both tangential and angular velocities are lower in people with stroke compared to healthy controls (Table 3). The peak velocity occurred approximately at 38% of the total reaching time in stroke and a 46% in controls, which means that the deceleration phase was prolonged in stroke. This indicates that individuals with stroke need to rely more on the feedback driven movement control during the second half of reaching.
The velocity profiles in people with stroke are segmented and show multiple peaks, which is reflected in the high number of the movement units (NMU). The mean value for the NMU is significantly larger in individuals with stroke compared to controls. Individuals with stroke reach the cup with a more flexed elbow (less elbow extension) and with the shoulder more abducted while drinking compared to the healthy participants, which reflects the compensatory movement pattern in stroke. Even though the glass was positioned within the arm reach, individuals with stroke did lean forward (trunk displacement) approximately 8 cm compared to 3 cm in controls while performing the drinking task. Decreased interjoint coordination between shoulder and elbow joint in reaching was only observed in individuals with higher degree of impairment (moderate stroke) compared to controls. The exact values for kinematics and the magnitude of effect sizes for all groups are shown in Table 3.
The analysis of construct validity of kinematic variables from drinking task showed that movements after stroke can be described with two major factors, the end-point kinematics and kinematics describing angular movement patterns8. Altogether, five measures (movement time, peak velocity, number of movement units, peak angular velocity of elbow joint, and trunk displacement) explained 86% of the variance in kinematic data8. These results are in line with concurrent validity analyses, in which three kinematic variables, movement time (MT), movement smoothness (NMU) and trunk displacement (TD), together explained 67% of the total variance in scores of clinical assessments as assessed with Action Research Arm Test20. The discriminative validity between groups with mild and moderate arm impairment after stroke and controls was good for majority of kinematics, but the largest effect sizes were noted for the smoothness, total movement time, peak angular velocity of elbow (PAVE) and trunk displacement (Table 3)8. Shoulder abduction during drinking is also discriminative between moderate and mild stroke groups. In addition, the same four kinematic variables: MT, NMU, PAVE, and TD demonstrated to be effective in detecting real clinical improvement during the first 3 months after stroke6. Thus, it can be concluded that these four kinematic variables (MT, NMU, PAVE, TD) are reliable, valid and sensitive to changes (responsive) for assessment of upper extremity function and activity after stroke.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/57228/57228fig1.jpg" /> 
Figure 1: The 5-camera motion capture system set-up for drinking task. From each camera, infra-red light flashes reach retro-reflective markers and reproduce the 2D position of the marker in the cameras image sensor with high spatial resolution and accuracy in real-time. The 3D coordinates of the marker are created when two cameras are viewing the same marker from two different angles. Four cameras are mounted on the walls around the testing area facing slightly downward at approximately 2 m distance and one camera is mounted facing down from the ceiling above the measurement area. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/57228/57228fig1large.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/57228/57228fig2.jpg" /> 
Figure 2: Representative velocity profiles for a healthy control (A) and an individual with moderate stroke impairment (B). The phases of the drinking task are shown. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/57228/57228fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Phase name</td>
			<td>Start</td>
			<td>Detected by</td>
			<td>End</td>
			<td>Detected by</td>
		</tr>
		<tr>
			<td>Reaching 
			(includes grasping)</td>
			<td>Hand movement begins</td>
			<td>The hand marker velocity exceeds 2% of the peak velocity (searched backward from the peak velocity); if this value is higher than 20mm/s the start is tracked backward to a point where the velocity is&#160; not less or equal to 20 mm/s&#160;</td>
			<td>Hand begins to move towards the&#160; mouth with the glass&#160;</td>
			<td>Velocity of the glass exceeds 15 mm/s</td>
		</tr>
		<tr>
			<td>Forward transport 
			(glass to mouth)</td>
			<td>Hand begins to move towards the mouth with the glass</td>
			<td>Velocity of the glass exceeds 15 mm/s</td>
			<td>Drinking begins</td>
			<td>Distance between the face and glass marker is below 15% of steady state* during drinking</td>
		</tr>
		<tr>
			<td>Drinking</td>
			<td>Drinking begins</td>
			<td>Distance between the face and glass marker is below 15% of steady state during drinking</td>
			<td>Drinking ends</td>
			<td>Distance between the face and glass marker exceeds 15% of steady state during drinking</td>
		</tr>
		<tr>
			<td>Back transport (glass to table, includes release of grasp)</td>
			<td>Hand begins to move to put the glass back to table</td>
			<td>Distance between the face and glass marker exceeds 15% of steady state during drinking</td>
			<td>Hand releases the glass and begins to move back to initial position</td>
			<td>Velocity of the glass below 10 mm/s</td>
		</tr>
		<tr>
			<td>Returning 
			(hand back to initial position)</td>
			<td>Hand releases the glass and begins to move back to initial position</td>
			<td>Velocity of the glass below 10 mm/s</td>
			<td>Hand is resting in initial position</td>
			<td>Hand marker velocity returned to 2% of the peak velocity&#160;</td>
		</tr>
		<tr>
			<td colspan="5">*Steady state in the drinking phase indicates an averaged value of the 100 frames around the shortest distance between the face and glass marker</td>
		</tr>
	</tbody>
</table>
Table 1: Phase definitions for the start and end of each phase of the drinking task.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Variable</td>
			<td>Specification</td>
		</tr>
		<tr>
			<td>End-point kinematics</td>
			<td>Calculated from the hand marker</td>
		</tr>
		<tr>
			<td>Movement time, s</td>
			<td>Calculated for each phase and as total movement time for the entire task; definitions for start and stop are provided in Table 1</td>
		</tr>
		<tr>
			<td>Peak tangential velocity, mm/s</td>
			<td>Calculated for reaching phase, combines both arm and trunk movement</td>
		</tr>
		<tr>
			<td>Time to peak hand velocity, s, %</td>
			<td>Absolute and relative values for reaching, characteraizes movement strategy (acceleration and deceleration time)</td>
		</tr>
		<tr>
			<td>Time to first velocity peak, s, %</td>
			<td>Absolute and relative values for reaching, characteraizes the initial movement effort</td>
		</tr>
		<tr>
			<td>Number of movement units, n</td>
			<td>Calculated for reaching, forward transport, back transport, and returning phase. One movement unit is defined as a difference between a local minimum and next maximum velocity value that exceeds the amplitude limit of 20 mm/s, and the time between two subsequent peaks has to be at least 150 ms. The minimum value for drinking task is 4, at least one unit per movement phase. Those peaks reflect repetitive acceleration and deceleration during reaching and correspond to movement smoothness and efficiency.</td>
		</tr>
		<tr>
			<td>Angular kinematics, degrees</td>
			<td>Calculated for shoulder and elbow joint</td>
		</tr>
		<tr>
			<td>Elbow extension</td>
			<td>Minimum angle of elbow flexion detected in the reaching phase, determined by the angle between the vectors joining the elbow and wrist markers and the elbow and shoulder markers</td>
		</tr>
		<tr>
			<td>Shoulder abduction</td>
			<td>Maximum angle in frontal plane detected during reaching and drinking phase, respectively; determined by the angle between the vectors joining the shoulder and elbow markers and the vertical vector from the shoulder marker toward the hip</td>
		</tr>
		<tr>
			<td>Shoulder flexion</td>
			<td>Maximum angle in sagittal plane detected during reaching and drinking, respectively; determined by the angle between the vectors joining the shoulder and elbow markers and the vertical vector from the shoulder marker toward the hip</td>
		</tr>
		<tr>
			<td>Peak angular velocity of the elbow joint, degrees/s</td>
			<td>Peak velocity of the elbow extension detected during the reaching phase</td>
		</tr>
		<tr>
			<td>Interjoint coordination, r</td>
			<td>Temporal cross-correlation of zero time lag between the shoulder flexion and elbow extension during the reaching phase. A Pearson&#39;s correlation coefficient closer to 1 indicates stronger correlation and indicates that joint motion of the two joints is tightly coupled.</td>
		</tr>
		<tr>
			<td>Trunk displacement, mm</td>
			<td>Maximum displacement of the thorax marker from the initial position during the entire drinking task</td>
		</tr>
	</tbody>
</table>
Table 2: Definitions of kinematic variables used in studies presented in the representative results.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Kinematic variables, mean (SD)&#160;</td>
			<td>Healthy</td>
			<td>Stroke</td>
			<td>Effect size (healthy vs stroke)</td>
			<td>Mild 
			stroke</td>
			<td>Effect size 
			(healthy vs&#160; mild stroke)</td>
			<td>Moderate stroke&#160;</td>
			<td>Effect size 
			(mild stroke vs&#160; moderate stroke)</td>
		</tr>
		<tr>
			<td>End-point kinematics</td>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Total movement time, s</td>
			<td>6.49 (0.83)</td>
			<td>11.4 (3.1)</td>
			<td>0.54*</td>
			<td>9.30 (1.68)</td>
			<td>0.46*</td>
			<td>13.3 (2.9)</td>
			<td>0.44*</td>
		</tr>
		<tr>
			<td>Number Movement Units, (smoothness), n</td>
			<td>2.3 (0.3)</td>
			<td>8.4 (4.2)</td>
			<td>0.54*</td>
			<td>5.4 (2.1)</td>
			<td>0.42*</td>
			<td>11.1 (3.6)</td>
			<td>0.50*</td>
		</tr>
		<tr>
			<td>Peak velocity in reach, mm/s</td>
			<td>616 (93.8)</td>
			<td>431 (82.7)&#160;</td>
			<td>0.54*</td>
			<td>471 (87.7)</td>
			<td>0.37*</td>
			<td>395 (62.0)</td>
			<td>0.22*</td>
		</tr>
		<tr>
			<td>Peak angular velocity elbow in reach, &#176;/s</td>
			<td>121.8 (25.3)</td>
			<td>64.9 (20.5)</td>
			<td>0.62*</td>
			<td>78.0 (19.3)</td>
			<td>0.57*</td>
			<td>53.3 (13.6)</td>
			<td>0.38*</td>
		</tr>
		<tr>
			<td>Time to peak velocity in reach, %</td>
			<td>46.0 (6.9)</td>
			<td>38.4 (8.6)</td>
			<td>0.20*</td>
			<td>39.5 (8.7)</td>
			<td>0.15*</td>
			<td>37.5 (8.8)</td>
			<td>0.01</td>
		</tr>
		<tr>
			<td>Time to first peak in reach, %</td>
			<td>42.5 (6.9)</td>
			<td>27.1 (12.2)</td>
			<td>0.39*</td>
			<td>33.0 (9.9)</td>
			<td>0.25*</td>
			<td>21.8 (11.9)</td>
			<td>0.22*</td>
		</tr>
		<tr>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Angular joint kinematics</td>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Elbow extension in reach-to-grasp, degree</td>
			<td>53.5 (7.8)</td>
			<td>64.1 (11.5)</td>
			<td>0.24*</td>
			<td>60.5 (10.4)</td>
			<td>0.13</td>
			<td>67.2 (11.9)</td>
			<td>0.09</td>
		</tr>
		<tr>
			<td>Shoulder abduction in drinking, degree</td>
			<td>30.1 (10.1)</td>
			<td>47.6 (14.9)</td>
			<td>0.33*</td>
			<td>37.2 (5.3)</td>
			<td>0.07</td>
			<td>57.1 (14.5)</td>
			<td>0.47*</td>
		</tr>
		<tr>
			<td>Trunk Displacement, mm</td>
			<td>26.7 (16.8)</td>
			<td>77.2 (48.6)</td>
			<td>0.34*</td>
			<td>50.1 (22.9)</td>
			<td>0.26*</td>
			<td>101.7 (53.4)</td>
			<td>0.30*</td>
		</tr>
		<tr>
			<td>Interjoint coordination, Pearson r</td>
			<td>0.96 (0.02)</td>
			<td>0.82 (0.35)</td>
			<td>0.08</td>
			<td>0.95 (0.02)</td>
			<td>0.03</td>
			<td>0.69 (0.46)</td>
			<td>0.14</td>
		</tr>
		<tr>
			<td colspan="8">* p&#60;0.05; Effect size statistics are calculated as eta squared, &#951;2</td>
		</tr>
	</tbody>
</table>
Table 3: Kinematic variables for individuals with stroke, for subgroups of moderate&#160;and mild upper extremity impairment&#160;along with healthy controls. Effect sizes for discrimination between groups above 0.4 (very large effect) are marked bold.

Watch this Scientific Journal Video about Kinematic Analysis Using 3D Motion Capture of Drinking Task in People With and Without Upper-extremity Impairments at JoVE.com

Kinematic Analysis Using 3D Motion Capture of Drinking Task in People With and Without Upper-extremity Impairments

This protocol describes an objective method to evaluate the movement performance and sensorimotor function of the upper extremity applied to individuals with stroke and healthy controls. A standardized test procedure, kinematic analysis and outcome variables for three-dimensional motion capture of drinking task are provided.

Kinematic analysis is a powerful method for objective assessment of upper extremity movements in a three-dimensional (3D) space. Three-dimensional motion ...

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10.5K Views.  University of Gothenburg. This protocol describes an objective method to evaluate the movement performance and sensorimotor function of the upper extremity applied to individuals with stroke and healthy controls. A standardized test procedure, kinematic analysis and outcome variables for three-dimensional motion capture of drinking task are provided.

Video: Kinematic Analysis Using 3D Motion Capture of Drinking Task in People With and Without Upper-extremity Impairments

Correlating Behavioral Responses to fMRI Signals from Human Prefrontal Cortex: Examining Cognitive Processes Using Task Analysis

The aim of this methods paper is to describe how to implement a neuroimaging technique to examine complementary brain processes engaged by two similar tasks. Participants' behavior during task performance in an fMRI scanner can then be correlated to the brain activity using the blood-oxygen-level-dependent signal. We measure behavior to be able to sort correct trials, where the subject performed the task correctly and then be able to examine the brain signals related to correct performance. Conversely, if subjects do not perform the task correctly, and these trials are included in the same analysis with the correct trials we would introduce trials that were not only for correct performance. Thus, in many cases these errors can be used themselves to then correlate brain activity to them. We describe two complementary tasks that are used in our lab to examine the brain during suppression of an automatic responses: the stroop1 and anti-saccade tasks. The emotional stroop paradigm instructs participants to either report the superimposed emotional 'word' across the affective faces or the facial 'expressions' of the face stimuli1,2. When the word and the facial expression refer to different emotions, a conflict between what must be said and what is automatically read occurs. The participant has to resolve the conflict between two simultaneously competing processes of word reading and facial expression. Our urge to read out a word leads to strong 'stimulus-response (SR)' associations; hence inhibiting these strong SR's is difficult and participants are prone to making errors. Overcoming this conflict and directing attention away from the face or the word requires the subject to inhibit bottom up processes which typically directs attention to the more salient stimulus. Similarly, in the anti-saccade task3,4,5,6, where an instruction cue is used to direct only attention to a peripheral stimulus location but then the eye movement is made to the mirror opposite position. Yet again we measure behavior by recording the eye movements of participants which allows for the sorting of the behavioral responses into correct and error trials7 which then can be correlated to brain activity. Neuroimaging now allows researchers to measure different behaviors of correct and error trials that are indicative of different cognitive processes and pinpoint the different neural networks involved.

The goal of our research is to correlate behavior to brain activity. Accurate behavioral measures and imaging techniques allow us to elucidate brain-behavior relationships.

The aim of this methods paper is to describe how to implement a neuroimaging technique to examine complementary brain processes engaged by two similar ...

Visualization and Genetic Manipulation of Dendrites and Spines in the Mouse Cerebral Cortex and Hippocampus using In utero Electroporation

In utero electroporation (IUE) has become a powerful technique to study the development of different regions of the embryonic nervous system 1-5. To date this tool has been widely used to study the regulation of cellular proliferation, differentiation and neuronal migration especially in the developing cerebral cortex 6-8. Here we detail our protocol to electroporate in utero the cerebral cortex and the hippocampus and provide evidence that this approach can be used to study dendrites and spines in these two cerebral regions.
Visualization and manipulation of neurons in primary cultures have contributed to a better understanding of the processes involved in dendrite, spine and synapse development. However neurons growing in vitro are not exposed to all the physiological cues that can affect dendrite and/or spine formation and maintenance during normal development. Our knowledge of dendrite and spine structures in vivo in wild-type or mutant mice comes mostly from observations using the Golgi-Cox method 9. However, Golgi staining is considered to be unpredictable. Indeed, groups of nerve cells and fiber tracts are labeled randomly, with particular areas often appearing completely stained while adjacent areas are devoid of staining. Recent studies have shown that IUE of fluorescent constructs represents an attractive alternative method to study dendrites, spines as well as synapses in mutant / wild-type mice 10-11 (Figure 1A). Moreover in comparison to the generation of mouse knockouts, IUE represents a rapid approach to perform gain and loss of function studies in specific population of cells during a specific time window. In addition, IUE has been successfully used with inducible gene expression or inducible RNAi approaches to refine the temporal control over the expression of a gene or shRNA 12. These advantages of IUE have thus opened new dimensions to study the effect of gene expression/suppression on dendrites and spines not only in specific cerebral structures (Figure 1B) but also at a specific time point of development (Figure 1C).
Finally, IUE provides a useful tool to identify functional interactions between genes involved in dendrite, spine and/or synapse development. Indeed, in contrast to other gene transfer methods such as virus, it is straightforward to combine multiple RNAi or transgenes in the same population of cells. 
In summary, IUE is a powerful method that has already contributed to the characterization of molecular mechanisms underlying brain function and disease and it should also be useful in the study of dendrites and spines.

Visualization and Genetic Manipulation of Dendrites and Spines in the Mouse Cerebral Cortex and Hippocampus using In utero Electroporation

This article describes in detail a protocol to electroporate in utero the cerebral cortex and the hippocampus at E14.5 in mice. We also show that this is a valuable method to study dendrites and spines in these two cerebral regions.

In utero electroporation (IUE) has become a powerful technique to study the development of different regions of the embryonic nervous system 1-5. To date ...

Optogenetic Perturbation of Neural Activity with Laser Illumination in Semi-intact Drosophila Larvae in Motion

Drosophila larval locomotion is a splendid model system in developmental and physiological neuroscience, by virtue of the genetic accessibility of the underlying neuronal components in the circuits1-6. Application of optogenetics7,8 in the larval neural circuit allows us to manipulate neuronal activity in spatially and temporally patterned ways9-13. Typically, specimens are broadly illuminated with a mercury lamp or LED, so specificity of the target neurons is controlled by binary gene expression systems such as the Gal4-UAS system14,15. In this work, to improve the spatial resolution to "sub-genetic resolution", we locally illuminated a subset of neurons in the ventral nerve cord using lasers implemented in a conventional confocal microscope. While monitoring the motion of the body wall of the semi-intact larvae, we interactively activated or inhibited neural activity with channelrhodopsin16,17 or halorhodopsin18-20, respectively. By spatially and temporally restricted illumination of the neural tissue, we can manipulate the activity of specific neurons in the circuit at a specific phase of behavior. This method is useful for studying the relationship between the activities of a local neural assembly in the ventral nerve cord and the spatiotemporal pattern of motor output.

Optogenetic Perturbation of Neural Activity with Laser Illumination in Semi-intact Drosophila Larvae in Motion

Here we describe a protocol for optogenetic manipulation of motoneuronal activity while monitoring changes in motor output (muscle contraction) in semi-intact Drosophila larvae using lasers within a conventional confocal microscope. This technique enables researchers to achieve local perturbation of neural activity in a few neuromeres to elucidate the dynamics of motor circuits.

Drosophila larval locomotion is a splendid model system in developmental and physiological neuroscience, by virtue of the genetic accessibility of the ...

Investigation of Synaptic Tagging/Capture and Cross-capture using Acute Hippocampal Slices from Rodents

Synaptic tagging and capture (STC) and cross-tagging are two important mechanisms at cellular level that explain how synapse-specificity and associativity is achieved in neurons within a specific time frame. These long-term plasticity-related processes are the leading candidate models to study the basis of memory formation and persistence at the cellular level. Both STC and cross-tagging involve two serial processes: (1) setting of the synaptic tag as triggered by a specific pattern of stimulation, and (2) synaptic capture, whereby the synaptic tag interacts with newly synthesized plasticity-related proteins (PRPs). Much of the understanding about the concepts of STC and cross-tagging arises from the studies done in CA1 region of the hippocampus and because of the technical complexity many of the laboratories are still unable to study these processes. Experimental conditions for the preparation of hippocampal slices and the recording of stable late-LTP/LTD are extremely important to study synaptic tagging/cross-tagging. This video article describes the experimental procedures to study long-term plasticity processes such as STC and cross-tagging in the CA1 pyramidal neurons using stable, long-term field-potential recordings from acute hippocampal slices of rats.

This video article describes experimental procedures to study long-term plasticity and its associative processes such as synaptic tagging, capture and cross-tagging in the CA1 pyramidal neurons using acute hippocampal slices from rodents.

Synaptic tagging and capture (STC) and cross-tagging are two important mechanisms at cellular level that explain how synapse-specificity and associativity ...

Assessment and Communication for People with Disorders of Consciousness

In this experiment, we demonstrate a suite of hybrid Brain-Computer Interface (BCI)-based paradigms that are designed for two applications: assessing the level of consciousness of people unable to provide motor response and, in a second stage, establishing a communication channel for these people that enables them to answer questions with either &#39;yes&#39; or &#39;no&#39;. The suite of paradigms is designed to test basic responses in the first step and to continue to more comprehensive tasks if the first tests are successful. The latter tasks require more cognitive functions, but they could provide communication, which is not possible with the basic tests. All assessment tests produce accuracy plots that show whether the algorithms were able to detect the patient&#39;s brain&#39;s response to the given tasks. If the accuracy level is beyond the significance level, we assume that the subject understood the task and was able to follow the sequence of commands presented via earphones to the subject. The tasks require users to concentrate on certain stimuli or to imagine moving either the left or right hand. All tasks are designed around the assumption that the user is unable to use the visual modality, and thus, all stimuli presented to the user (including instructions, cues, and feedback) are auditory or tactile.

With this experiment, one might be able to detect consciousness in people with disorders of consciousness. Furthermore, the approach can create a simple communication channel that enables people to give simple YES/NO answers to questions.

In this experiment, we demonstrate a suite of hybrid Brain-Computer Interface (BCI)-based paradigms that are designed for two applications: assessing the ...

Peering at Brain Polysomes with Atomic Force Microscopy

The translational machinery, i.e., the polysome or polyribosome, is one of the biggest and most complex cytoplasmic machineries in cells. Polysomes, formed by ribosomes, mRNAs, several proteins and non-coding RNAs, represent integrated platforms where translational controls take place. However, while the ribosome has been widely studied, the organization of polysomes is still lacking comprehensive understanding. Thus much effort is required in order to elucidate polysome organization and any novel mechanism of translational control that may be embedded. Atomic force microscopy (AFM) is a type of scanning probe microscopy that allows the acquisition of 3D images at nanoscale resolution. Compared to electron microscopy (EM) techniques, one of the main advantages of AFM is that it can acquire thousands of images both in air and in solution, enabling the sample to be maintained under near physiological conditions without any need for staining and fixing procedures. Here, a detailed protocol for the accurate purification of polysomes from mouse brain and their deposition on mica substrates is described. This protocol enables polysome imaging in air and liquid with AFM and their reconstruction as three-dimensional objects. Complementary to cryo-electron microscopy (cryo-EM), the proposed method can be conveniently used for systematically analyzing polysomes and studying their organization.

While ribosome structure has been extensively characterized, the organization of polysomes is still understudied. To overcome this lack of knowledge, we present here a detailed preparation protocol for accurate imaging of mammalian polysomes by atomic force microscopy (AFM) in air and liquid.

The translational machinery, i.e., the polysome or polyribosome, is one of the biggest and most complex cytoplasmic machineries in cells. Polysomes, ...

Sagittal Plane Kinematic Gait Analysis in C57BL/6 Mice Subjected to MOG35-55 Induced Experimental Autoimmune Encephalomyelitis

Kinematic gait analysis in the sagittal plane has frequently been used to characterize motor deficits in multiple sclerosis (MS). We describe the application of these techniques to identify gait deficits in a mouse model of MS, known as experimental autoimmune encephalomyelitis (EAE). Paralysis and motor deficits in mice subjected to EAE are typically assessed using a clinical scoring scale. However, this scale yields only ordinal data that provides little information about the precise nature of the motor deficits. EAE disease severity has also been assessed by rotarod performance, which provides a measure of general motor coordination. By contrast, kinematic gait analysis of the hind limb in the sagittal plane generates highly precise information about how movement is impaired. To perform this procedure, reflective markers are placed on a hind limb to detect joint movement while a mouse is walking on a treadmill. Motion analysis software is used to measure movement of the markers during walking. Kinematic gait parameters are then derived from the resultant data. We show how these gait parameters can be used to quantify impaired movements of the hip, knee, and ankle joints in EAE. These techniques may be used to better understand disease mechanisms and identify potential treatments for MS and other neurodegenerative disorders that impair mobility.

Kinematic gait analysis in the sagittal plane yields highly precise information about how movement is executed. We describe the application of these techniques to identify gait deficits for mice subjected to autoimmune-mediated demyelination. These methods may also be used to characterize gait deficits for other mouse models featuring impaired locomotion.

Kinematic gait analysis in the sagittal plane has frequently been used to characterize motor deficits in multiple sclerosis (MS). We describe the ...

3D Kinematic Analysis for the Functional Evaluation in the Rat Model of Sciatic Nerve Crush Injury

Compared to the Sciatic Functional Index (SFI), kinematic analysis is a more reliable and sensitive method for performing functional evaluations of sciatic nerve injury rodent models. In this protocol, we describe a novel kinematic analysis method that uses a three-dimensional (3D) motion capture apparatus for functional evaluations using a rat sciatic nerve crush injury model. First, the rat is familiarized with treadmill walking. Markers are then attached to the designated bone landmarks and the rat is made to walk on the treadmill at the desired speed. Meanwhile, the posterior limb movements of the rat are recorded using four cameras. Depending on the software used, marker tracings are created using both automatic and manual modes and the desired data are produced after subtle adjustments. This method of kinematic analysis, which uses a 3D motion capture apparatus, offers numerous advantages, including superior precision and accuracy. Many more parameters can be investigated during the comprehensive functional evaluations. This method has several shortcomings that require consideration: The system is expensive, can be complicated to operate, and may produce data deviations due to skin shifting. Nevertheless, kinematic analysis using a 3D motion capture apparatus is useful for performing functional anterior and posterior limb evaluations. In the future, this method may become increasingly useful for generating accurate assessments of various traumas and diseases.

We introduce a kinematic analysis method that uses a three-dimensional motion capture apparatus containing four cameras and data processing software for performing functional evaluations during fundamental research involving rodent models.

Compared to the Sciatic Functional Index (SFI), kinematic analysis is a more reliable and sensitive method for performing functional evaluations of ...

Conducting Concurrent Electroencephalography and Functional Near-Infrared Spectroscopy Recordings with a Flanker Task

Concurrent EEG and fNIRS recordings offer an excellent opportunity to gain a full understanding of the neural mechanism of cognitive processing by inspecting the relationship between the neural and hemodynamic signals. EEG is an electrophysiological technology that can measure the rapid neuronal activity of the cortex, whereas fNIRS relies on the hemodynamic responses to infer brain activation. The combination of EEG and fNIRS neuroimaging techniques can identify more features and reveal more information associated with the functioning of the brain. In this protocol, fused EEG-fNIRS measurements were performed for concurrent recordings of evoked-electrical potentials and hemodynamic responses during a Flanker task. In addition, the critical steps for setting up the hardware and software system as well as the procedures for data acquisition and analysis were provided and discussed in detail. It is expected that the present protocol can pave a new avenue for improving the understanding of the neural mechanisms underlying various cognitive processes by using the EEG and fNIRS signals.

The present protocol describes how to perform concurrent EEG and fNIRS recordings and how to inspect the relationship between the EEG and fNIRS data.&#160;

Concurrent EEG and fNIRS recordings offer an excellent opportunity to gain a full understanding of the neural mechanism of cognitive processing by ...

Brain Morphology of Cannabis Users With or Without Psychosis: A Pilot MRI Study

Cannabis is the illicit drug most commonly used worldwide, and its consumption can both induce psychiatric symptoms in otherwise healthy subjects and unmask a florid psychotic picture in patients with a prior psychotic risk. Previous studies suggest that chronic and long-term cannabis exposure may exert significant negative effects in brain areas enriched with cannabinoid receptors. However, whether brain alterations determined by cannabis dependency will lead to a clinically significant phenotype or to a psychotic outbreak at some point of an abuser&#8217;s life remains unclear. The aim of this study was to investigate morphological brain differences between chronic cannabis users with cannabis-induced psychosis (CIP) and non-psychotic cannabis users (NPCU) without any psychiatric conditions and correlate brain deficits with selective socio-demographic, clinical and psychosocial variables.
3T magnetic resonance imaging (MRI) scans of 10 CIP patients and 12 NPCU were acquired. The type of drug, the frequency, and the duration, as well socio-demographic, clinical and psychosocial parameters of dependency were measured. CIP patients had extensive grey matter (GM) decreases in right superior frontal gyrus, right precentral, right superior temporal gyrus, insula bilaterally, right precuneus, right medial occipital gyrus, right fusiform gyrus, and left hippocampus in comparison to chronic cannabis users without psychosis. Finally, in CIP patients, the results showed a negative correlation between a domain of the Brief Psychiatric Rating Scale (BPRS), BPRS-Activity, and selective GM volumes. Overall, the results suggest that cannabis-induced psychosis is characterized by selective brain reductions that are not present in NPCU. Therefore, neuroimaging studies may provide a potential ground for identifying putative biomarkers associated with the risk of developing psychosis in cannabis users.

This is a 3T magnetic resonance imaging study aiming to investigate grey matter volume differences between cannabis-induced psychosis patients and non-psychotic chronic cannabis users.

Cannabis is the illicit drug most commonly used worldwide, and its consumption can both induce psychiatric symptoms in otherwise healthy subjects and ...

Kinematic Analysis Using 3D Motion Capture of Drinking Task in People With and Without Upper-extremity Impairments

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Sagittal Plane Kinematic Gait Analysis in C57BL/6 Mice Subjected to MOG35-55 Induced Experimental Autoimmune Encephalomyelitis

3D Kinematic Analysis for the Functional Evaluation in the Rat Model of Sciatic Nerve Crush Injury

Conducting Concurrent Electroencephalography and Functional Near-Infrared Spectroscopy Recordings with a Flanker Task

Brain Morphology of Cannabis Users With or Without Psychosis: A Pilot MRI Study