This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2022R1A2C1006046), by the Original Technology Research Program for Brain Science through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (2019M3C7A1031995), by National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2022R1A6A3A13053491), and by the MSIT(Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2023-RS-2023-00258971) supervised by the IITP (Institute for Information &#38; Communications Technology Planning &#38; Evaluation).

All experimental procedures were reviewed and approved by the Institutional Review Board of Seoul National University Bundang Hospital. For the experiments in this study, 34 participants with stroke were recruited. Signed informed consent was obtained from all participants. A signed informed consent was obtained from a legal representative if a participant met the criteria but could not sign the consent form because of disability.
1. Experimental setup
<ol>
	<li>Patient recruitment
	<ol>
		<li>Perform the screening process using the following inclusion criteria: 
		Aged 18 to 85 years with the presence of impaired upper limb functions; 
		First-ever ischemic or hemorrhagic stroke confirmed by brain computed tomography or magnetic resonance imaging; 
		Participant&#39;s ability to follow the instructions for clinical assessment and the EEG study; 
		Absence of a history of any other psychiatric or neurological diseases except stroke.</li>
		<li>Exclude patients based on the following: 
		Previous disease involving the central nervous system (e.g., traumatic brain injury, brain tumor, Parkinson&#39;s disease); 
		Inability to wear the EEG cap; and 
		Inability to follow the instructions for the clinical assessment and the EEG study. 
		NOTE: The inclusion and exclusion criteria were chosen to select the participants capable of participating in the experiment and to regulate the demographic factors that could influence the results.</li>
		<li>Provide all recruited participants with information about the details of the experimental procedure.</li>
	</ol>
	</li>
	<li>Experimental system: EEG
	<ol>
		<li>Use an EEG system consisting of 32 Ag/AgCl scalp electrodes, a textile EEG cap, and EEG recording software for data recording.</li>
		<li>Use a personal computer (PC) with EEG recording software installed and connect the PC to the EEG device via Bluetooth.</li>
		<li>Use another PC with a numerical analysis and programming software application for engineering (see Table of Materials).</li>
		<li>For stimuli presentation, connect the PC to a dedicated trigger box (Figure 1). 
		NOTE: The detailed specifications of the two PCs are provided in Table of Materials.</li>
	</ol>
	</li>
	<li>Experimental paradigm based on programming software 
	&#8203;NOTE: The participants performed a hand extension task using the affected and unaffected hands, during which EEG data were measured. Figure 2 shows the experimental paradigm of this study.
	<ol>
		<li>Present two visual stimuli, CLOSE and OPEN, for 30 s each, on the center of a monitor to measure baseline resting-state EEG data, during which the participant closes and opens the eyes. 
		NOTE: Because resting-state EEG data are relatively less contaminated by unwanted physiological artifacts, they are useful for verifying the quality of EEG data and identifying individual EEG characteristics with respect to the resting state.</li>
		<li>Present a hand motion image for 3 s to instruct the participant to make a hand extension movement, followed by a fixation mark for 5 s for resting. 
		&#8203;NOTE: This procedure was regarded as a trial and was repeated 10 times in a single session. Each participant underwent 4 sessions for each hand. The participant had a break whenever he/she wanted after performing each session to prevent excessive fatigue.</li>
	</ol>
	</li>
</ol>
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/64753/64753fig01.jpg" /> 
Figure 1: Schematic of the equipment setup. A PC (PC1) presenting experimental stimuli was connected to a trigger box, and another PC (PC2) was connected to an EEG amplifier. Stimulation events generated in PC1 were delivered to the EEG amplifier via the trigger box connected to PC1. <a href="https://www.jove.com/files/ftp_upload/64753/64753fig01large.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/64753/64753fig02.jpg" /> 
Figure 2: Experimental paradigm used in this study. A single trial consisted of a hand extension movement of 3 s followed by a relaxation of 5 s. This pattern was repeated 10 times in a single session. A total of eight sessions were performed; four sessions involved affected hand movement, while the other four involved unaffected hand movement.This figure was adapted from Shim et al.17 with permission from Mary Ann Liebert, Inc. <a href="https://www.jove.com/files/ftp_upload/64753/64753fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
2. Recording movement-related EEG data
<ol>
	<li>EEG setup
	<ol>
		<li>Seat the participant in a comfortable armchair in front of a monitor. 
		NOTE: The distance between the participant and the monitor should be at least 60 cm to prevent eye fatigue. However, an excessive distance should be avoided (e.g., &#62;150 cm) because it could distract the participant&#39;s concentration.</li>
		<li>To accurately wear the cap for EEG measurement, define the Cz location based on the international 10-20 system using the intersection of the longitudinal line connecting the nasion and inion and the transverse line connecting the upper part of both auricles. 
		NOTE: An EEG cap may not be required depending on the EEG measurement equipment. In such case, EEG electrodes are directly attached to the scalp according to the international 10-20 system15.</li>
		<li>For an accurate EEG measurement, use an appropriately sized EEG cap according to the head size of the participant and place it so that the Cz electrode position is placed on the individual Cz location.</li>
		<li>Fix the chin strap with appropriate tightness; this will prevent the participant from being uncomfortable during swallowing and blinking in the experiment. After that, confirm that the T9 and T10 electrode positions of the EEG cap are in the temporal region above both auricles, and the Fpz electrode position of the EEG cap is located in the middle of the forehead. 
		NOTE: If those electrodes are out of the designated location, consider changing the cap. Three EEG cap sizes were used in our study (54 cm: small, 56 cm: medium, 58 cm: large).</li>
		<li>After correctly placing the EEG cap, attach 32 Ag/AgCl scalp electrodes on the scalp according to the extended international 10-10 system, with the ground and reference electrodes at Fpz and FCz, respectively16. 
		NOTE: The location of the reference electrode (FCz) is relatively less influenced by various physiological artifacts, such as those from electrooculography, electromyography, and electrocardiography, because it is located around the central area (Cz) of the scalp.</li>
		<li>Adjust the impedance level between the EEG electrodes and the scalp using conductive gel, and fix the hair with the gel to prevent any obstruction between the EEG electrodes and the scalp. 
		&#8203;NOTE: It is important to confirm whether any bridge between adjacent EEG electrodes is created due to gel leakage.</li>
		<li>Use the software for EEG recording.</li>
		<li>Turn on the EEG system and execute Configuration &#62; Select Amplifier. Choose Liveamp &#62; &#62; amplifier &#62; connect. Search for Liveamp function for the wireless connection (Figure 3).</li>
		<li>Execute the Impedance check function to monitor the impedance level for each electrode. 
		NOTE: Conducting the experiment with an impedance level of &#60;20 K&#937; is recommended (Figure 4).</li>
		<li>Execute the monitoring function to confirm whether the EEGs of all electrodes have similar amplitude levels through real-time EEG signal monitoring (Figure 5). 
		NOTE: The amplitude of the EEG signal is generally between 10 &#181;V and 100 &#181;V, and the alpha (8-12 Hz) power increases around the occipital area when the eyes are closed. Therefore, the quality of EEG data can be qualitatively confirmed by monitoring the amplitude level and alpha oscillations on the channels around the occipital area while the eyes are closed.</li>
	</ol>
	</li>
	<li>Paradigm setup
	<ol>
		<li>For stable EEG data acquisition, use two separate PCs for presenting external stimuli and recording EEG data (see Figure 1).</li>
		<li>To present experimental stimuli to the participants, create a stimulation program based on the experimental paradigm using programming software (introduced in step 1.3). 
		NOTE: A software-based stimulation program was created in this study, but other software can be used depending on their compatibility with the EEG equipment used for the experiment as well as user convenience. The programming software-based stimulus script is provided in Supplementary File 1 (Experimental_stimulus.m). The event information, indicating the onset point of stimuli, is generated by the in-house programming software, transmitted to the EEG amplifier via the trigger box, and ultimately to the EEG recording software (Figure 1).</li>
		<li>Execute the program presenting the experimental stimuli in monitoring mode (refer to sub step 2.1.10). Subsequently, confirm that the event information is correctly marked in a timely manner at the bottom of the EEG recording software each time a stimulus is presented, as shown in Figure 6. 
		NOTE: Information on the time point is recorded whenever a new stimulus is presented and is subsequently used for data segmentation. Therefore, it is important to obtain the exact time points of experimental events as much as possible to prevent inaccurate data segmentation, which would lead to unreliable results in the analysis.</li>
		<li>Initiate the EEG recording software, then independently run the stimulus presentation program developed based on the experimental paradigm using programming software to prevent the data omission. 
		&#8203;NOTE: For convenience of data analysis, creating file names with a consistent rule for data storage (e.g., Sub1_Session1) is recommended.</li>
	</ol>
	</li>
	<li>EEG recording
	<ol>
		<li>Measure EEG at a sampling rate of 1,000 Hz following the experimental paradigm introduced in step 1.3. 
		NOTE: The sampling rate can be changed depending on the range of EEG frequencies the researcher wants to investigate. It is generally recommended to use a sampling rate of &#62;200 Hz where EEG information can be investigated at &#8804;100 Hz based on the Nyquist theorem. This is because most EEG information exists below 100 Hz.</li>
		<li>Maintain the same experimental environments (e.g., experimental place, equipment, room temperature, etc.) as much as possible between the participants and instruct them to minimize unnecessary movements during the EEG measurement.</li>
	</ol>
	</li>
</ol>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/64753/64753fig03.jpg" /> 
Figure 3: Wireless connection procedure between an EEG amplifier and PC with the EEG recording software. Follow the steps in order: (A) select amplifier, (B) connect amplifier, (C) search for the connected amplifier for wireless connection, (D) connection complete. <a href="https://www.jove.com/files/ftp_upload/64753/64753fig03large.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/64753/64753fig04.jpg" /> 
Figure 4:&#160;Impedance check procedure for each channel. All channels should be adjusted to green color for stable EEG measurement. It is recommended to conduct the experiment with an impedance of less than 20 K&#937;. <a href="https://www.jove.com/files/ftp_upload/64753/64753fig04large.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/64753/64753fig05.jpg" /> 
Figure 5: Real-time data monitoring procedure for each channel. Signals from all channels being measured can be monitored in real-time and can be zoomed in/out using the option (red box) on the top bar. <a href="https://www.jove.com/files/ftp_upload/64753/64753fig05large.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/64753/64753fig06.jpg" /> 
Figure 6: Screenshot for monitoring event information. The red bars indicate event makers that are presented each time a stimulus is provided by PC1. <a href="https://www.jove.com/files/ftp_upload/64753/64753fig06large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
3. EEG data analysis
NOTE: This study provides precise guidelines for replicating the research concept. Therefore, it provides a brief outline of the analysis process and representative results. The detailed processes and the associated results can be found in a previous study17. This serves as an indication that Mary Ann Liebert, Inc. has granted permission for the use of copyrighted material.
<ol>
	<li>Preprocessing
	<ol>
		<li>Remove eye-related artifacts from the raw EEG data using mathematical procedures based on principal component analysis implemented in EEG data preprocessing software18,19 (see Table of Materials). 
		NOTE: If any epoch displayed prominent artifacts (&#177; 100 &#181;V), even after preprocessing in any of the electrodes, it was excluded from further analysis. The average number of rejected epochs, including their standard deviation, was 3.69 &#177; 7.15 for the affected hand-movement task and 1.62 &#177; 3.95 for the unaffected hand-movement task.</li>
		<li>Apply a bandpass filter between 0.1 Hz and 55 Hz. Segment the preprocessed EEG data from -1 s to 3.5 s for each trial based on the task onset to contain the baseline period used for event-related spectral perturbation (ERSP) and functional network analyses.</li>
	</ol>
	</li>
	<li>ERSP analysis 
	&#8203;NOTE: The measured EEG data were validated via ERSP analysis for a low-beta frequency band (12-20 Hz) associated with voluntary movements.
	<ol>
		<li>Conduct a short-time Fourier transform for each trial to calculate EEG spectral powers, for which the newtimef function of the EEGLAB toolbox in the programming software was used20 (a non-overlapping Hanning window, 250 ms window size).</li>
		<li>Normalize the power spectrum of each trial by subtracting the average power of the baseline period (-1 to 0 s) to investigate the changes in spectral powers between the hand movement task and the baseline period.</li>
		<li>Estimate baseline-normalized ERSP maps for each patient by averaging the normalized power spectra across trials.</li>
	</ol>
	</li>
	<li>Functional network analysis 
	&#8203;NOTE: A functional network analysis was conducted to investigate EEG changes from a brain network perspective. To compute weighted whole-brain network indices based on graph theory, brain connectivity between different regions was computed first using the phase locking value (PLV). A functional connectivity matrix was then computed using the results of the PLV-based connectivity analysis, which was subsequently used to compute whole-brain network indices17. All functional network analyses were performed using the programming software.
	<ol>
		<li>Calculate the Hilbert transform-based phase locking value (PLV) for a low-beta frequency band (12-20 Hz) using an in-house function21,22. The in-house function for computing the Hilbert Transform-based PLV is provided in Supplementary File 2 (myPLV.m).</li>
		<li>Assess the PLVs between all possible pairs of the 32 EEG electrodes at each time point during the task periods (0-3.5 s) and create a symmetric adjacency matrix (32 x 32, number of electrodes = 32) by averaging the PLVs over the task period. Use the PLV matrix as input data for network analysis17,23.</li>
		<li>Evaluate four weighted global-level network indices based on graph theory using the Brain Connectivity Toolbox (https://sites.google.com/site/bctnet): (1) strength, (2) clustering coefficient, (3) path length, and (4) small-worldness17,24,25.</li>
	</ol>
	</li>
</ol>

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K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Relationship+of+motor+and+cognitive+abilities+to+functional+performance+in+stroke+rehabilitation.">Relationship of motor and cognitive abilities to functional performance in stroke rehabilitation.</a> Brain Injury. 15 (5), 443-453 (2001).</li><li>Vecchio, F., 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=Acute+cerebellar+stroke+and+middle+cerebral+artery+stroke+exert+distinctive+modifications+on+functional+cortical+connectivity:+A+comparative+study+via+EEG+graph+theory.">Acute cerebellar stroke and middle cerebral artery stroke exert distinctive modifications on functional cortical connectivity: A comparative study via EEG graph theory.</a> Clinical Neurophysiology. 130 (6), 997-1007 (2019).</li><li>Hwang, H. J., Kwon, K., Im, C. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Neurofeedback-based+motor+imagery+training+for+brain-computer+interface+(BCI).">Neurofeedback-based motor imagery training for brain-computer interface (BCI).</a> Journal of Neuroscience Methods. 179 (1), 150-156 (2009).</li><li>Park, S. A., 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=Evaluation+of+feature+extraction+methods+for+EEG-based+brain-computer+interfaces+in+terms+of+robustness+to+slight+changes+in+electrode+locations.">Evaluation of feature extraction methods for EEG-based brain-computer interfaces in terms of robustness to slight changes in electrode locations.</a> Medical &amp; Biological Engineering &amp; Computing. 51 (5), 571-579 (2013).</li></ol>

MS, NJP, WSK, and HJH have a patent-pending entitled &#34;Method for providing information related to motor impairment and devices used therefor&#34;, Number 10-2022-0007841.

This study has introduced an EEG experiment for measuring upper limb movement-related neuronal activities in individuals with stroke. The experimental paradigm and methods of acquisition and analysis of EEG were applied to determine the ERD patterns in the ipsilesional and contralesional motor cortex. 
The results of the ERSP maps (Figure 7) demonstrated the difference in the degree of neuronal activation when moving the impaired and unaffected hands. The results ...

Upper limb motor impairment is one of the most common consequences of stroke and is related to limitations in activities of daily living1,2. Alpha (8-13 Hz) and beta (13-30 Hz) band rhythms are known to be closely associated with movements. In particular, studies have shown that altered neural activity in the alpha and lower beta (12-20 Hz) frequency bands during movement of an impaired limb is correlated with the degree of motor impairment in individuals with stroke3,4,5. Based on these findings, electroencephalography (EEG) has emerged as a potential biomarker that reflects both severity of motor impairment and possibility of motor recovery6,7. However, previously developed EEG-based biomarkers have proved inadequate for investigating the characteristics of motor impairment in individuals with stroke, largely due to their reliance on resting-state EEG data rather than task-induced EEG data8,9,10. Complex information processing related to motor impairments, such as the interaction between ipsilesional and contralesional hemispheres, can be revealed only through task-induced EEG data, not resting-state EEG. Therefore, further studies are not only required to explore the relationship between neuronal activities and motor impairment characteristics and to clarify the usefulness of EEG generated during movement of the impaired body part as a potential biomarker for motor impairment in individuals with stroke11.
Implementing EEG for assessing behavioral effects requires task-specific paradigms and protocols. To date, various EEG protocols have been suggested12, where individuals with stroke performed imagined or actual movements to induce movement-related brain activities11,13. In the case of imagined movements, about 53.7% of participants could not definitely imagine a corresponding movement (called &#34;illiteracy&#34;) and thus failed to induce movement-related brain activities14. Moreover, it is difficult for individuals with severe stroke to move the entire upper extremity, and there is a possibility of unnecessary artifacts during data acquisition due to unstable movements. Therefore, guidance based on expert know-how is required to acquire task-related high-quality EEG data and neurophysiologically interpretable results. In this study, we comprehensively designed an experimental paradigm for individuals with stroke to perform a relatively simple hand movement task and provided an experimental procedure with detailed guidance.
By outlining the visualized experimental protocol in this article, we aimed to illustrate the specific concepts and methods used for the acquisition and analysis of neuronal activities related to the movement of the upper limb using an EEG system. In demonstrating the difference in neuronal activities via EEG between the paretic and nonparetic upper limbs in participants with hemiplegic stroke, this study aimed to present the feasibility of EEG using the described protocol as a potential biomarker for the severity of motor impairment in individuals with stroke in a cross-sectional context.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>actiCAP</td>
			<td>Easycap, GmbH Ltd., Herrsching, Germany</td>
			<td>CAC-32-SAMW-56</td>
			<td>Textile EEG cap platform to accommodate EEG electrodes</td>
		</tr>
		<tr>
			<td>Brain Vision Recorder (Software)</td>
			<td>Brain Products GmBH Ltd., Munich, Germany</td>
			<td>-</td>
			<td>Software used to record EEG signal</td>
		</tr>
		<tr>
			<td>Curry 7 (Software)</td>
			<td>Compumedics, Australia</td>
			<td>-</td>
			<td>Software used in preprocessing of EEG data</td>
		</tr>
		<tr>
			<td>LiveAmp</td>
			<td>Brain Products, GmbH Ltd., Gilching, Germany</td>
			<td>LA-055606-0348</td>
			<td>EEG system (amplifier) used for the measurement</td>
		</tr>
		<tr>
			<td>MATLAB R2019a (Software)</td>
			<td>MathWorks Inc., Natick, MA, USA</td>
			<td>-</td>
			<td>Software used to run the experimental stimulus and analyze the EEG data</td>
		</tr>
		<tr>
			<td>Recording PC</td>
			<td>Lenovo Group Limited, Hong Kong, China</td>
			<td>Model: X58K</td>
			<td> 
			Intel Core i7-7700HQ CPU@2.80 GHz, RAM 8 GB 
			/EEG data recording using Brain Vision Recorder</td>
		</tr>
		<tr>
			<td>Sensor&#38;Trigger Extension(STE)</td>
			<td>Brain Products GmBH Ltd., Munich, Germany</td>
			<td>STE-055604-0162</td>
			<td>Adds physioloigcal signals to the EEG amplifier</td>
		</tr>
		<tr>
			<td>Splitter box</td>
			<td>Brain Products GmBH Ltd., Munich, Germany</td>
			<td>BP-135-1600</td>
			<td>Connects Ag/AgCl electrodes to the EEG amplifier</td>
		</tr>
		<tr>
			<td>Stimulation PC</td>
			<td>Hansung Corporation, Seoul, Korea</td>
			<td>Model: ThinkPad P71</td>
			<td> 
			Intel Core i7-8750H CPU@2.20 GHz, RAM 8 GB 
			Presenting stimulation screen using MATLAB</td>
		</tr>
		<tr>
			<td>TriggerBox</td>
			<td>Brain Products GmBH Ltd., Munich, Germany</td>
			<td>BP-245-1010</td>
			<td>Receives trigger signal from PC and relay it to EEG recording system</td>
		</tr>
	</tbody>
</table>

electroencephalography network indices as biomarkers of upper limb impairment in chronic stroke

Alteration of electroencephalography (EEG) signals during task-specific movement of the impaired limb has been reported as a potential biomarker for the severity of motor impairment and for the prediction of motor recovery in individuals with stroke. When implementing EEG experiments, detailed paradigms and well-organized experiment protocols are required to obtain robust and interpretable results. In this protocol, we illustrate a task-specific paradigm with upper limb movement and methods and techniques needed for the acquisition and analysis of EEG data. The paradigm consists of 1 min of rest followed by 10 trials comprising alternating 5 s and 3 s of resting and task (hand extension)-states, respectively, over 4 sessions. EEG signals were acquired using 32 Ag/AgCl scalp electrodes at a sampling rate of 1,000 Hz. Event-related spectral perturbation analysis associated with limb movement and functional network analyses at the global level in the low-beta (12-20 Hz) frequency band were performed. Representative results showed an alteration of the functional network of low-beta EEG frequency bands during movement of the impaired upper limb, and the altered functional network was associated with the degree of motor impairment in chronic stroke patients. The results demonstrate the feasibility of the experimental paradigm in EEG measurements during upper limb movement in individuals with stroke. Further research using this paradigm is needed to determine the potential value of EEG signals as biomarkers of motor impairment and recovery.

Figure 7&#160;presents the topographical low-beta ERD maps of each hand-movement task. A significantly strong low-beta ERD was observed in the contralesional hemisphere compared with the ipsilesional hemisphere for both the affected and unaffected hand-movement tasks.
<img alt="Figure 7" class="xfigimg" src="/files/ftp_upload/64753/64753fig07.jpg" /> 
Figure 7:&#160;Mean topograp...

Watch this Scientific Journal Video about Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke at JoVE.com

Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke

The experimental protocol demonstrates the paradigm for acquiring and analyzing electroencephalography (EEG) signals during upper limb movement in individuals with stroke. The alteration of the functional network of low-beta EEG frequency bands was observed during the movement of the impaired upper limb and was associated with the degree of motor impairment.

Alteration of electroencephalography (EEG) signals during task-specific movement of the impaired limb has been reported as a potential biomarker for the ...

electroencephalography-network-indices-as-biomarkers-upper-limb

Research

JoVE Journal

Neuroscience

771 Views.  Korea University. The experimental protocol demonstrates the paradigm for acquiring and analyzing electroencephalography (EEG) signals during upper limb movement in individuals with stroke. The alteration of the functional network of low-beta EEG frequency bands was observed during the movement of the impaired upper limb and was associated with the degree of motor impairment.

Video: Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke

Lateral Chronic Cranial Window Preparation Enables In Vivo Observation Following Distal Middle Cerebral Artery Occlusion in Mice

Focal cerebral ischemia (i.e., ischemic stroke) may cause major brain injury, leading to a severe loss of neuronal function and consequently to a host of motor and cognitive disabilities. Its high prevalence poses a serious health burden, as stroke is among the principal causes of long-term disability and death worldwide1. Recovery of neuronal function is, in most cases, only partial. So far, treatment options are very limited, in particular due to the narrow time window for thrombolysis2,3. Determining methods to accelerate recovery from stroke remains a prime medical goal; however, this has been hampered by insufficient mechanistic insights into the recovery process. Experimental stroke researchers frequently employ rodent models of focal cerebral ischemia. Beyond the acute phase, stroke research is increasingly focused on the sub-acute and chronic phase following cerebral ischemia. Most stroke researchers apply permanent or transient occlusion of the MCA in mice or rats. In patients, occlusions of the MCA are among the most frequent causes of ischemic stroke4. Besides proximal occlusion of the MCA using the filament model, surgical occlusion of the distal MCA is probably the most frequently used model in experimental stroke research5. Occlusion of a distal (to the branching of the lenticulo-striate arteries) MCA branch typically spares the striatum and primarily affects the neocortex. Vessel occlusion can be permanent or transient. High reproducibility of lesion volume and very low mortality rates with respect to the long-term outcome are the main advantages of this model. Here, we demonstrate how to perform a chronic cranial window (CW) preparation lateral to the sagittal sinus, and afterwards how to surgically induce a distal stroke underneath the window using a craniotomy approach. This approach can be applied for sequential imaging of acute and chronic changes following ischemia via epi-illuminating, confocal laser scanning, and two-photon intravital microscopy.

Lateral Chronic Cranial Window Preparation Enables In Vivo Observation Following Distal Middle Cerebral Artery Occlusion in Mice

Surgical occlusion of a distal middle cerebral artery branch (MCAo) is a frequently used model in experimental stroke research. This manuscript describes the basic technique of permanent MCAo, combined with the insertion of a lateral cranial window, which offers the opportunity for longitudinal intravital microscopy in mice.

Focal cerebral ischemia (i.e., ischemic stroke) may cause major brain injury, leading to a severe loss of neuronal function and consequently to a host of ...

Evaluation of Bioenergetic Function in Cerebral Vascular Endothelial Cells

The integrity of the blood-brain-barrier (BBB) is critical to prevent brain injury. Cerebral vascular endothelial (CVE) cells are one of the cell types that comprise the BBB; these cells have a very high-energy demand, which requires optimal mitochondrial function. In the case of disease or injury, the mitochondrial function in these cells can be altered, resulting in disease or&#160;the opening of the BBB. In this manuscript, we introduce a method to measure mitochondrial function in CVE cells by using whole, intact cells and a bioanalyzer. A mito-stress assay is used to challenge the cells that have been perturbed, either physically or chemically, and evaluate their bioenergetic function. Additionally, this method also provides a useful way to screen new therapeutics that have direct effects on mitochondrial function. We have optimized the cell density necessary to yield oxygen consumption rates that allow for the calculation of a variety of mitochondrial parameters, including ATP production, maximal respiration, and spare capacity. We also show the sensitivity of the assay by demonstrating that the introduction of the&#160;microRNA, miR-34a, leads to a pronounced and detectable decrease in mitochondrial activity. While the data shown in this paper is optimized for the bEnd.3 cell line, we have also optimized the protocol for primary CVE cells, further suggesting the utility in preclinical and clinical models.

Endothelial cell mitochondria are critical to maintain blood-brain-barrier integrity. We introduce a protocol to measure bioenergetic function in cerebral vascular endothelial cells.

The integrity of the blood-brain-barrier (BBB) is critical to prevent brain injury. Cerebral vascular endothelial (CVE) cells are one of the cell types ...

An In Ovo Model for Testing Insulin-mimetic Compounds

Elevated blood glucose levels in type 2 diabetes mellitus (T2DM), a complex and multifactorial metabolic disease, are caused by insulin resistance and &#946;-cell failure. Various strategies, including the injection of insulin or the usage of insulin-sensitizing drugs, were pursued to treat T2DM or at least reduce the symptoms. In addition, the application of herbal compounds has attracted increasing attention. Thus, it is necessary to find efficient test systems to identify and characterize insulin-mimetic compounds. Here we developed a modified chick embryo model, which enables testing of synthetic compounds and herbal extracts with insulin-mimetic properties. Using a fluorescence microscopy-based primary screen, which quantifies the translocation of Glucose transporter 4 (Glut4) to the plasma membrane, we were able to identify compounds, mainly herbal extracts, which lead to an increase of intracellular glucose concentrations in adipocytes. However, the efficacy of these substances requires further verification in a living organism. Thus, we used an in-ovo approach to identify their blood glucose-reducing properties. The approval by an ethics committee is not needed since the use of chicken embryos during the first two-thirds of embryonic development is not considered an animal experiment. Here, the application of this model is described in detail.

An In Ovo Model for Testing Insulin-mimetic Compounds

In this study, we describe an in-ovo system as a promising tool to test insulin-mimetic compounds in a living organism. The main advantages include adequate throughput rates and acceptable costs, which enable the identification of phytochemicals with insulin-mimetic properties.

Elevated blood glucose levels in type 2 diabetes mellitus (T2DM), a complex and multifactorial metabolic disease, are caused by insulin resistance and ...

The Combined Use of Transcranial Direct Current Stimulation and Robotic Therapy for the Upper Limb

Neurologic disorders such as stroke and cerebral palsy are leading causes of long-term disability and can lead to severe incapacity and restriction of daily activities due to lower and upper limb impairments. Intensive physical and occupational therapy are still considered main treatments, but new adjunct therapies to standard rehabilitation that may optimize functional outcomes are being studied.
Transcranial direct current stimulation (tDCS) is a noninvasive brain stimulation technique that polarizes underlying brain regions through the application of weak direct currents through electrodes on the scalp, modulating cortical excitability. Increased interest in this technique can be attributed to its low cost, ease of use, and effects on human neural plasticity. Recent research has been performed to determine the clinical potential of tDCS in diverse conditions such as depression, Parkinson&#39;s disease, and motor rehabilitation after stroke. tDCS helps enhance brain plasticity and seems to be a promising technique in rehabilitation programs.
A number of robotic devices have been developed to assist in the rehabilitation of upper limb function after stroke. The rehabilitation of motor deficits is often a long process requiring multidisciplinary approaches for a patient to achieve maximum independence. These devices do not intend to replace manual rehabilitation therapy; instead, they were designed as an additional tool to rehabilitation programs, allowing immediate perception of results and tracking of improvements, thus helping patients to stay motivated.
Both tDSC and robot-assisted therapy are promising add-ons to stroke rehabilitation and target the modulation of brain plasticity, with several reports describing their use to be associated with conventional therapy and the improvement of therapeutic outcomes. However, more recently, some small clinical trials have been developed that describe the associated use of tDCS and robot-assisted therapy in stroke rehabilitation. In this article, we describe the combined methods used in our institute for improving motor performance after stroke.

The combined use of transcranial direct current stimulation and robotic therapy as an add-on for conventional rehabilitation therapy may result in improved therapeutic outcomes due to modulation of brain plasticity. In this article, we describe the combined methods used in our institute for improving motor performance after stroke.

Neurologic disorders such as stroke and cerebral palsy are leading causes of long-term disability and can lead to severe incapacity and restriction of ...

An Emerging Target Paradigm to Evoke Fast Visuomotor Responses on Human Upper Limb Muscles

To reach towards a seen object, visual information has to be transformed into motor commands. Visual information such as the object&#8217;s color, shape, and size are processed and integrated within numerous brain areas, then ultimately relayed to the motor periphery. In some instances, a reaction is needed as fast as possible. These fast visuomotor transformations, and their underlying neurological substrates, are poorly understood in humans as they have lacked a reliable biomarker. Stimulus-locked responses (SLRs) are short latency (&#60;100 ms) bursts of electromyographic (EMG) activity representing the first wave of muscle recruitment influenced by visual stimulus presentation. SLRs provide a quantifiable output of rapid visuomotor transformations, but SLRs have not been consistently observed in all subjects in past studies. Here we describe a new, behavioral paradigm featuring the sudden emergence of a moving target below an obstacle that consistently evokes robust SLRs. Human participants generated visually guided reaches toward or away from the emerging target using a robotic manipulandum while surface electrodes recorded EMG activity from the pectoralis major muscle. In comparison to previous studies that investigated SLRs using static stimuli, the SLRs evoked with this emerging target paradigm were larger, evolved earlier, and were present in all participants. Reach reaction times (RTs) were also expedited in the emerging target paradigm. This paradigm affords numerous opportunities for modification that could permit systematic study of the impact of various sensory, cognitive, and motor manipulations on fast visuomotor responses. Overall, our results demonstrate that an emerging target paradigm is capable of consistently and robustly evoking activity within a fast visuomotor system.

Presented here is a behavioral paradigm that elicits robust fast visuomotor responses on human upper limb muscles during visually guided reaches.

To reach towards a seen object, visual information has to be transformed into motor commands. Visual information such as the object&#8217;s color, shape, ...

The Impact of Motor Task Conditions on Goal-Directed Arm Reaching Kinematics and Trunk Compensation in Chronic Stroke Survivors

Trunk compensation is the most common movement strategy to substitute for upper extremity (UE) motor deficits in chronic stroke survivors. There is a lack of evidence examining how task conditions impact trunk compensation and goal-directed arm reaching kinematics. This protocol aims to investigate the impact of task conditions, including task difficulty and complexity, on goal-directed arm reaching kinematics. Two non-disabled young adults and two chronic stroke survivors with mild UE motor impairment were recruited for testing the protocol. Each participant performed goal-directed arm reaches with four different task conditions (2 task difficulties [large vs. small targets] X 2 task complexities [pointing vs. picking up]). The task goal was to reach and point at a target or pick up an object located 20 cm in front of the home position as quickly as possible with a stylus or a pair of chopsticks, respectively, in response to an auditory cue. Participants performed ten reaches per task condition. A 3-dimensional motion capture camera system was used to record trunk and arm kinematics. Representative results showed that there was a significant increase in movement duration, movement jerkiness, and trunk compensation as a function of task complexity, but not task difficulty in all participants. Chronic stroke survivors showed significantly slower, jerkier, and more feedback-dependent arm reaches and significantly more compensatory trunk movements than non-disabled adults. Our representative results support that this protocol can be used to investigate the impact of task conditions on motor control strategies in chronic stroke survivors with mild UE motor impairment.

This protocol is intended to investigate the impact of task conditions on movement strategies in chronic stroke survivors. Further, this protocol can be used to examine if a restriction in elbow extension induced by neuromuscular electrical stimulation causes trunk compensation during goal-directed arm reaches in non-disabled adults.

Trunk compensation is the most common movement strategy to substitute for upper extremity (UE) motor deficits in chronic stroke survivors. There is a lack ...

Reliable Acquisition of Electroencephalography Data during Simultaneous Electroencephalography and Functional MRI

Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), EEG-fMRI, combines the complementary properties of scalp EEG (good temporal resolution) and fMRI (good spatial resolution) to measure neuronal activity during an electrographic event, through hemodynamic responses known as blood-oxygen-level-dependent (BOLD) changes. It is a non-invasive research tool that is utilized in neuroscience research and is highly beneficial to the clinical community, especially for the management of neurological diseases, provided that proper equipment and protocols are administered during data acquisition. Although recording EEG-fMRI is apparently straightforward, the correct preparation, especially in placing and securing the electrodes, is not only important for safety but is also critical in ensuring the reliability and analyzability of the EEG data obtained. This is also the most experience-demanding part of the preparation. To address these issues, a straightforward protocol that ensures data quality was developed. This article provides a step-by-step guide for acquiring reliable EEG data during EEG-fMRI using this protocol that utilizes readily available medical products. The presented protocol can be adapted to different applications of EEG-fMRI in research and clinical settings, and may be beneficial to both inexperienced and expert operators.

This article provides a straightforward protocol for acquiring good quality electroencephalography (EEG) data during simultaneous EEG and functional magnetic resonance imaging by utilizing readily available medical products.

Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), EEG-fMRI, combines the complementary properties of scalp EEG ...

Evaluation of Motor Impairment in C. elegans Models of Amyotrophic Lateral Sclerosis

The neurodegenerative disease amyotrophic lateral sclerosis (ALS) features progressive loss of motor neurons accompanied by muscle weakness and motor impairment that worsens with time. While considerable advances have been made in determining genetic drivers of ALS for a subset of patients, the majority of cases have an unknown etiology. Further, the mechanisms underlying motor neuron dysfunction and degeneration are not well understood; therefore, there is an ongoing need to develop and characterize representative models to study these processes. Caenorhabditis elegans can adapt their movement to the physical constraints of their surroundings, with two primary movement paradigms studied in a laboratory environment- crawling on a solid surface and swimming in liquid. These represent a complex interplay between sensation, motor neurons, and muscles. C. elegans models of ALS can exhibit impairment in one or both of these movement paradigms. This protocol describes two sensitive assays for evaluating motility in C. elegans: an optimized radial locomotion assay measuring crawling on a solid surface and an automated method for tracking and analyzing swimming in liquid (thrashing). In addition to the characterization of baseline motor impairment of ALS models, these assays can detect suppression or enhancement of the phenotypes from genetic or small molecule interventions. Thus, these methods have utility for studying ALS&#160;models and any C. elegans strain that exhibits altered motility.

Evaluation of Motor Impairment in C. elegans Models of Amyotrophic Lateral Sclerosis

This protocol describes two sensitive assays for discriminating among mild, moderate, and severe motor impairment in C. elegans models of amyotrophic lateral sclerosis, with general utility for C. elegans strains, with altered motility.

The neurodegenerative disease amyotrophic lateral sclerosis (ALS) features progressive loss of motor neurons accompanied by muscle weakness and motor ...

Construction and Implementation of Carbon Fiber Microelectrode Arrays for Chronic and Acute In Vivo Recordings

Multichannel electrode arrays offer insight into the working brain and serve to elucidate neural processes at the single-cell and circuit levels. Development of these tools is crucial for understanding complex behaviors and cognition and for advancing clinical applications. However, it remains a challenge to densely record from cell populations stably and continuously over long time periods. Many popular electrodes, such as tetrodes and silicon arrays, feature large cross-diameters that produce damage upon insertion and elicit chronic reactive tissue responses associated with neuronal death, hindering the recording of stable, continuous neural activity. In addition, most wire bundles exhibit broad spacing between channels, precluding simultaneous recording from a large number of cells clustered in a small area. The carbon fiber microelectrode arrays described in this protocol offer an accessible solution to these concerns. The study provides a detailed method for fabricating carbon fiber microelectrode arrays that can be used for both acute and chronic recordings in vivo. The physical properties of these electrodes make them ideal for stable and continuous long-term recordings at high cell densities, enabling the researcher to make robust, unambiguous recordings from single units across months.

Construction and Implementation of Carbon Fiber Microelectrode Arrays for Chronic and Acute In Vivo Recordings

This protocol describes a procedure for constructing carbon fiber microelectrode arrays for chronic and acute in vivo electrophysiological recordings in mouse (Mus musculus) and ferret (Mustela putorius furo) from multiple brain regions. Each step, following the purchase of raw carbon fibers to microelectrode array implantation, is described in detail, with emphasis on microelectrode array construction.

Multichannel electrode arrays offer insight into the working brain and serve to elucidate neural processes at the single-cell and circuit levels. ...

Confocal Microscopy to Measure Three Modes of Fusion Pore Dynamics in Adrenal Chromaffin Cells

Dynamic fusion pore opening and closure mediate exocytosis and endocytosis and determine their kinetics. Here, it is demonstrated in detail how confocal microscopy was used in combination with patch-clamp recording to detect three fusion modes in primary culture bovine adrenal chromaffin cells. The three fusion modes include 1) close-fusion (also called kiss-and-run), involving fusion pore opening and closure, 2) stay-fusion, involving fusion pore opening and maintaining the opened pore, and 3) shrink-fusion, involving shrinkage of the fusion-generated &#937;-shape profile until it merges completely at the plasma membrane.
To detect these fusion modes, the plasma membrane was labeled by overexpressing mNeonGreen attached with the PH domain of phospholipase C &#948; (PH-mNG), which binds to phosphatidylinositol-4,5-bisphosphate (PtdIns(4,5)P2) at the cytosol-facing leaflet of the plasma membrane; vesicles were loaded with the fluorescent false neurotransmitter FFN511 to detect vesicular content release; and Atto 655 was included in the bath solution to detect fusion pore closure. These three fluorescent probes were imaged simultaneously at ~20-90 ms per frame in live chromaffin cells to detect fusion pore opening, content release, fusion pore closure, and fusing vesicle size changes. The analysis method is described to distinguish three fusion modes from these fluorescence measurements. The method described here can, in principle, apply to many secretory cells beyond chromaffin cells.

This protocol describes a confocal imaging technique to detect three fusion modes in bovine adrenal chromaffin cells. These fusion modes include 1) close-fusion (also called kiss-and-run), involving fusion pore opening and closure, 2) stay-fusion, involving fusion pore opening and maintaining the opened pore, and 3) shrink-fusion, involving fused vesicle shrinkage.

Dynamic fusion pore opening and closure mediate exocytosis and endocytosis and determine their kinetics. Here, it is demonstrated in detail how confocal ...