We thank Professor Hans-Leo Teulings from Neuroscript LLD for the digital technology support.
This study was supported by the Wu Jieping Foundation (Grant No.: 320.6750.18456).

Our study was approved by the Academic Ethics Committee of the Biological Sciences Division of the Chinese PLA General Hospital in Beijing, China.
1. General aspects of method development
<ol>
	<li>Use a USB digitizer (e.g., Wacom Cintiq Pro 16) and a handheld stylus pen for the handwriting movements. The detailed specifications of the digitizer are as follows: external dimensions (width x depth x height) 410 x 265 x 17.5 mm, spatial resolution 3840 x 2160 dots, pixel size 0.090 x 0.090 mm, temporal resolution 30 ms, and a pressure level of 8,192.</li>
	<li>Connect a laptop PC to the digitizer to collect and exhibit the handwriting traces.</li>
	<li>Use a software (e.g., Neuroscript MovAlyzeR) for data recording, processing, and analyzing.</li>
	<li>Patient inclusion/exclusion criteria
	<ol>
		<li>Recruit MCI participants who present with a memory complaint, an objectively impaired memory function, intact activities of daily living, and the absence of dementia21. Besides, they should have the educational level of more than 2 years of preliminary school in mainland China, otherwise, they might have difficulty in writing Chinese characters.</li>
		<li>Exclude participants who have obvious visual and upper limb disability.</li>
	</ol>
	</li>
</ol>
2. Handwriting task
<ol>
	<li>Run the software and a non-inking stylus pen.</li>
	<li>Create an example of Chinese characters on the writing area of the digitizer (see Figure 1).</li>
	<li>Allow the subjects to position the writing area into a comfortable position.</li>
	<li>Allow the subjects to write on the writing area and accommodate the pen and the surface of the writing area.</li>
	<li>Set the sampling rate in the software at 200 Hz.</li>
	<li>Instruct the subjects to write his/her name in Chinese with the dominant hand. 
	NOTE: A signature in either cursive or a printed version is acceptable, as the subject wished.</li>
	<li>Instruct the subjects to write the Chinese character &#8220;<img alt="Equation 6" src="/files/ftp_upload/61841/61841equ06.jpg" />&#8221; (pronounced &#8220;Zheng&#8221;) with the dominant hand. 
	NOTE: The Chinese character &#8220;<img alt="Equation 6" src="/files/ftp_upload/61841/61841equ06.jpg" />&#8221; in a printed version is acceptable.
	<ol>
		<li>Remind the subjects to write in a printed version prior to beginning handwriting. 
		NOTE: Ensure that the subject sits and writes in an upright position.</li>
	</ol>
	</li>
	<li>Keep instructions visible during each trial.
	<ol>
		<li>Repeat the handwriting trial three times.</li>
		<li>If the character &#8220;<img alt="Equation 6" src="/files/ftp_upload/61841/61841equ06.jpg" />&#8221; was in written in the wrong stroke order, stop the trial and trace and show the subject how to write the character in the correct stroke order.</li>
		<li>If any hesitation was derived from a lack of knowledge, stop the trial and show the subject how to write the character correctly.</li>
	</ol>
	</li>
</ol>
3. Data analysis
<ol>
	<li>Run the software; right click on Experiment and select Properties.</li>
	<li>Select Processing, and then select Segmentation.</li>
	<li>Click on Add first segmentation at any rate, Add last segmentation at any rate, and Move segmentation point to nearest pendown if on a penlift in Segmentation Flags.</li>
	<li>Click on At pendown trajectories in Segmentation Methods. 
	NOTE: All of these adjustments for the default mode were done to improve the analysis of the Chinese handwriting.</li>
</ol>
4. Parameter calculation
<ol>
	<li>Run the software, select the subjects in &#8220;<img alt="Equation 6" src="/files/ftp_upload/61841/61841equ06.jpg" />&#8221;, and click on Handwriting Trials.</li>
	<li>Use the tracing system and trace the handwriting process and stroke order of &#8220;<img alt="Equation 6" src="/files/ftp_upload/61841/61841equ06.jpg" />&#8221; step by step.</li>
	<li>Find the segmentation of stroke #3 of &#8220;<img alt="Equation 6" src="/files/ftp_upload/61841/61841equ06.jpg" />&#8221; and read out the &#8220;Average Absolute Velocity&#8221; in &#8220;extracted data&#8221;. 
	NOTE: The handwriting analysis software will automatically calculate the &#8220;Average Absolute Velocity&#8221; of each segmentation. 
	CAUTION: Stroke #3 of &#8220;<img alt="Equation 6" src="/files/ftp_upload/61841/61841equ06.jpg" />&#8221; is a horizonal movement (from left to right) of the pen tip that is shorter than Character 1 and Character 5 (Figure 1A).</li>
	<li>Find the segmentation of stroke #4 of &#8220;<img alt="Equation 6" src="/files/ftp_upload/61841/61841equ06.jpg" />&#8221; and read out of the &#8220;Average Absolute Velocity&#8221; in the &#8220;extracted data&#8221;. 
	CAUTION: Stroke #4 of &#8220;<img alt="Equation 6" src="/files/ftp_upload/61841/61841equ06.jpg" />&#8221; is a vertical movement (from up to down) of the pen tip that is shorter than Character 2 (Figure 1).</li>
	<li>Read out the &#8220;Pen Pressure&#8221; of each segmentation in the &#8220;extracted data&#8221; and obtain an &#8220;Average Pen Pressure&#8221; of &#8220;<img alt="Equation 6" src="/files/ftp_upload/61841/61841equ06.jpg" />&#8221;. 
	NOTE: The handwriting analysis software will automatically calculate the &#8220;Average Pen Pressure&#8221; of each segmentation.</li>
	<li>Run the software, select the subjects in &#8220;<img alt="Equation 6" src="/files/ftp_upload/61841/61841equ06.jpg" />&#8221;, and click on Handwriting Trials.</li>
	<li>Using the tracing system, trace the handwriting process and stroke order of the signature step by step.</li>
	<li>Find the segmentation of the stroke between the characters and read out the &#8220;Absolute Size&#8221; and &#8220;Road Length&#8221; in the &#8220;extracted data&#8221;.</li>
	<li>Obtain the in-air length tortuosity in the segmentation between characters according to the equation. 
	NOTE: The segmentation of the stroke between characters was an in-air segmentation (Figure 2).
	<ol>
		<li>Calculate the in-air length tortuosity: 1-Absolute Size/Road Length %. 
		NOTE: Tortuosity, defined by the ratio of the arc length to the Euclidean distance between end points, is a measure of curvature, and therefore indexes the smoothness of a specific writing output20. A highly tortuous curve has several bends or curves, whereas a low tortuous curve is one with relatively wide loops/curves and more straightness. 
		CAUTION: Most Chinese names are composed of two or three characters. If the signature has two characters, there is only one segmentation of stroke between characters. If the signature has three characters, there are two stroke segmentations between characters. In-air length tortuosity in the segmentation between characters would be an average value.</li>
	</ol>
	</li>
</ol>
5. Statistical analysis
<ol>
	<li>Evaluate group differences using a Student&#8217;s t-test. A P value less than 0.05 was considered statistically significant. Conduct all statistical analyses using the SPSS 22.0 statistical software package.</li>
</ol>

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<li>Johnson, B. P., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Understanding+macrographia+in+children+with+autism+spectrum+disorders%5BTitle%5D)+AND+%22Research+in+Developmental+Disabilities%22%5BJournal%5D)">Understanding macrographia in children with autism spectrum disorders.</a> Research in Developmental Disabilities. 34 (9), 2917-2926 (2013).</li>
<li>Afonso, O., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Writing+difficulties+in+Alzheimer%27s+disease+and+Mild+Cognitive+Impairment%5BTitle%5D)+AND+%22Reading+and+Writing%22%5BJournal%5D)">Writing difficulties in Alzheimer's disease and Mild Cognitive Impairment.</a> Reading and Writing. 32 (1), 217-233 (2019).</li>
<li>Drotár, P., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Evaluation+of+handwriting+kinematics+and+pressure+for+differential+diagnosis+of+Parkinson%27s+disease%5BTitle%5D)+AND+%22Artificial+Intelligence+in+Medicine%22%5BJournal%5D)">Evaluation of handwriting kinematics and pressure for differential diagnosis of Parkinson's disease.</a> Artificial Intelligence in Medicine. 67, 39-46 (2016).</li>
<li>Garre-Olmo, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Kinematic+and+pressure+features+of+handwriting+and+drawing%3A+preliminary+results+between+patients+with+mild+cognitive+impairment%2C+Alzheimer+disease+and+healthy+controls%5BTitle%5D)+AND+%22Current+Medicinal+Chemistry%22%5BJournal%5D)">Kinematic and pressure features of handwriting and drawing: preliminary results between patients with mild cognitive impairment, Alzheimer disease and healthy controls.</a> Current Medicinal Chemistry. 14, 960-968 (2017).</li>
<li>Cohen, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Digital+clock+drawing%3A+differentiating+%22thinking%22+versus+%22doing%22+in+younger+and+older+adults+with+depression%5BTitle%5D)+AND+%22Journal+of+the+International+Neuropsychological+Society%22%5BJournal%5D)">Digital clock drawing: differentiating "thinking" versus "doing" in younger and older adults with depression.</a> Journal of the International Neuropsychological Society. 20 (9), 920-928 (2014).</li>
<li>Rosenblum, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Handwriting+as+an+objective+tool+for+Parkinson%27s+disease+diagnosis%5BTitle%5D)+AND+%22Journal+of+Neurology%22%5BJournal%5D)">Handwriting as an objective tool for Parkinson's disease diagnosis.</a> Journal of Neurology. 260 (9), 2357-2361 (2013).</li>
<li>Grace, N., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Do+handwriting+difficulties+correlate+with+core+symptomology%2C+motor+proficiency+and+attentional+behaviours%5BTitle%5D)+AND+%22Journal+of+Autism+and+Developmental+Disorders%22%5BJournal%5D)">Do handwriting difficulties correlate with core symptomology, motor proficiency and attentional behaviours.</a> Journal of Autism and Developmental Disorders. 47 (4), 1-12 (2017).</li>
<li>Petersen, R. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Mild+cognitive+impairment+as+a+diagnostic+entity%5BTitle%5D)+AND+%22Journal+of+Interactive+Marketing%22%5BJournal%5D)">Mild cognitive impairment as a diagnostic entity.</a> Journal of Interactive Marketing. 256 (3), 183-194 (2004).</li>
<li>Ishikawa, T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Handwriting+features+of+multiple+drawing+tests+for+early+detection+of+Alzheimer%27s+Disease%3A+A+preliminary+result%5BTitle%5D)+AND+%22Studies+in+Health+Technology+and+Informatics%22%5BJournal%5D)">Handwriting features of multiple drawing tests for early detection of Alzheimer's Disease: A preliminary result.</a> Studies in Health Technology and Informatics. 264, 168-172 (2019).</li>
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The authors have nothing to disclose.

The critical steps in the protocol confirm the legibility of &#8220;<img alt="Equation 6" src="/files/ftp_upload/61841/61841equ06.jpg" />&#8221;. In detail, within an entire character, the #3 stroke needs to be shorter than the other horizonal strokes, and the #4 stroke needs to be shorter than the #2 stroke. More specifically, more attentional resources are needed during the writing of the #3 stroke and #4 stroke6,12, and both strokes have a similar length limit. An inappropriate stroke length might have given rise to a bias in the detection of velocity.
The software with the digitizer screen is an on-line data collecting software without a pencil&#8211;paper-like shape. To start and stop writing, subjects needed to follow the instructions of the researchers or the prompting instruments. These explicit conditions could be stressors for subjects, distracting their attentional resources and affecting the performance of the handwriting. The troubleshooting of this method is even more severe in patients from rural areas who are not familiar with electronic devices. Enough warm-up time could be helpful. Another way to minimize the effects of this limitation is to place a paper sheet on top of the digitizer. In addition, devices that can collect off-line data with pencil&#8211;paper-like shapes, such as Smartpen plus Livescribe notebook, could be another modification. As far as we know, data from MovAlyzeR software and Smartpen will be compatible in the near future.
First, because the primary focus was on the dynamic analysis of handwriting, static parameters, such as character width and height, were not included and analyzed in the current study. Actually, &#8220;<img alt="Equation 6" src="/files/ftp_upload/61841/61841equ06.jpg" />&#8221; has been confirmed as a good writing task for detecting micrographia in PD12. Second, some researchers chose to limit the size of the character during the handwriting task (e.g., 1 cm, 2 cm, and 4 cm in amplitude)19. As has been found, subjects require more time to write in a smaller amplitude relative to a larger one. A definite amplitude was not set in this study while the shape of Chinese characters might be an implicit limit for a particular stroke.
To our knowledge, this is the first study of a digital handwriting analysis for simplified Chinese character users. More handwriting tasks regarding simplified Chinese characters can be used to find cognitive deficits and motor dysfunctions in patients with neuropsychiatric disorders.
A digital handwriting analysis can complement traditional pencil&#8211;paper cognitive tests, such as Trail-Making Test, MMSE, Montreal Cognitive Assessment, and others17,22. Analyzing handwriting features during a cognitive test is a new paradigm for motor-cognitive dual tasks23. This method might be of help for diagnosing motor cognitive risk syndrome and cerebral small vessel disease.

Mild cognitive impairment (MCI) is considered as a transitional but progressively degenerative cognitive phase that precedes the onset of Alzheimer&#8217;s disease (AD)1. It has been reported that the AD progression rate per year is 15% while nearly 75% of moderate and severe MCI cases might remain undiagnosed2. Recent studies have reported that patients with MCI have difficulty in some aspects of fine motor tasks3, and those patients who showed motor disorders, such as slow gait, had a high risk of dementia4.
Handwriting is a complex human activity that entails an intricate blend of cognitive, kinesthetic, and perceptual-motor components including visual and kinesthetic perception, motor planning, eye-hand coordination, visual-motor integration, dexterity, and manual skills1. Handwriting analysis has been utilized to detect cognitive and motor dysfunctions in many types of neurodegenerative diseases, such as AD and Parkinson&#8217;s disease (PD)5. In addition, some aspects of handwriting problems have been reported to be an indicator for MCI and related to disease progression6. As a majority of the population uses language, studies that investigate handwriting analysis in Chinese speakers (especially simplified Chinese characters) are still lacking.
There have been several articles that have investigated handwriting abnormalities or &#8220;agraphia&#8221; in individuals with MCI. For example, by utilizing the traditional pencil&#8211;paper methods, Zhou and colleagues tried to unveil the distinct writing abilities between patients with MCI and individuals without MCI. The differences between the groups were not obvious, with the exception of writing errors7. Kawa et al. found handwriting features in patients with MCI using a smart pen, which could dynamically analyze the stroke and pen speed during writing2. WACOM hardware and MovAlyzeR software can detect more real-time information compared with traditional pencil and paper methods and smartpen paper methods. Therefore, dynamic handwriting data, such as pen-down pressure, velocity, acceleration, and jerk, have been found to be a new focus of handwriting analysis relative to static data, such as letter size and space between words2.
However, another phenomenon that cannot be overlooked is the cross-cultural effect of handwriting. The writing systems of different nations are not always the same (e.g., English letters are written from left to right while Hebrew letters are written from right to left)8. In this issue, even reviews have confirmed the effectiveness of handwriting analysis9,10 in alphabetic languages, and the wide gap between Chinese characters and western letters has hindered the interchange capability of handwriting analysis in the methods and results11 of these studies.
There exist several major dissimilarities between western languages (e.g., English) and Chinese. First, there are many more horizonal movements of the pen tip during Chinese character writing compared with letter writing12. Second, unlike the alphabet language, which is associated with phonemes, Chinese is considered to be logographic7. As a result, most Chinese characters have their own unique stroke order, and the width and height of strokes need to be strictly limited. Otherwise, unlimited widths and heights might cause increased illegibility11 (&#8220;<img alt="Equation 1" src="/files/ftp_upload/61841/61841equ01.jpg" />&#8221; and &#8220;<img alt="Equation 2" src="/files/ftp_upload/61841/61841equ02.jpg" />&#8221; are completely different Chinese characters. Also, &#8220;<img alt="Equation 3" src="/files/ftp_upload/61841/61841equ03.jpg" />&#8221;, &#8220;<img alt="Equation 4" src="/files/ftp_upload/61841/61841equ04.jpg" />&#8221;, and &#8220;<img alt="Equation 5" src="/files/ftp_upload/61841/61841equ05.jpg" />&#8221; are different Chinese characters).
&#8220;<img alt="Equation 6" src="/files/ftp_upload/61841/61841equ06.jpg" />&#8221; (pronounced &#8220;Zheng&#8221;) is a typical, simple, and commonly used Chinese character that nearly every Chinese speaker with a two-year educational level can read and write. It has been selected as a writing task in previous Chinese handwriting analysis studies6,12. Researchers decided to use &#8220;<img alt="Equation 6" src="/files/ftp_upload/61841/61841equ06.jpg" />&#8221; as the writing task because it is &#8220;square-like&#8221; and composed of five strokes, all of which are horizonal (#1, #3, #5 stroke, from left to right) or vertical (#2, #4 stroke, from up to down) (Figure 1). According to many fine motor studies, fulfilling the #3 stroke (horizonal) and the #4 stroke (vertical) requires pure wrist and finger movements, respectively6,12,13. As a result, the stroke velocity of both strokes could be a proper manifestation14.
In addition, pen pressure during handwriting is a handwriting feature that has been shown to outperform other kinematic features in reflecting motor control5,15. However, there are no relevant studies in Chinese patients, though positive results have been confirmed by research groups from the Czech Republic, Spain, Israel, and other countries8,16,17.
The signature has been commonly used as a handwriting task in numerous studies5. In general, a signature requires little thinking or in-air time18. &#8220;In-air&#8221; is defined as when the pressure of the pen tip to the screen is 0 during the handwriting, and &#8220;in-air time&#8221; is the sum of the time &#8220;in-air&#8221; during the handwriting. Individuals who suffer from many neuropsychiatric disorders might have deficits in psychomotor control, and thus they exhibit increased in-air time of the signature. For example, Rosenblum et al. found that Israeli patients with depression and Parkinson&#8217;s disease showed longer in-air time compared with healthy controls during writing their own name in Hebrew8,19. As Chinese characters have their own shape, in this study, it was decided to use the in-air length tortuosity in segmentation between characters during writing the name as a potential indicative parameter. Tortuosity, defined by the ratio of the arc length to the Euclidean distance between end points, is a measure of curvature, and therefore indexes the smoothness of a specific writing output20.

<table><tbody><tr><td></td><td>Microsoft</td><td>Surface Pro 2</td><td>computer</td></tr><tr><td></td><td>MovAlyeR</td><td>3.4</td><td>software</td></tr><tr><td></td><td>WACOM</td><td>Cintiq</td><td>digitizer</td></tr></tbody></table>

digital handwriting analysis of characters in chinese patients with mild cognitive impairment

An increasing amount of evidence shows that cognitive deficits and movement dysfunctions are not separated. Patients with mild cognitive impairment (MCI) can manifest fine motor disorders of the upper extremities. Handwriting is a complex and unique human activity involving both motor and cognitive coordination. Researchers from western countries have discovered that patients with MCI have abnormal handwriting features. However, no relevant studies have been conducted in the Chinese population. Owing to the cross-culture phenomenon of handwriting, the aim of this study is to find new handwriting tasks to demonstrate the differences in handwriting features between elderly patients with MCI and age-matched healthy individuals.

The demographic data of the subjects demonstrated that all the groups matched well in age, gender, educational level, dominant hand, and other parameters.
As shown in Table 1, during the writing of the Chinese character &#8220;<img alt="Equation 6" src="/files/ftp_upload/61841/61841equ06.jpg" />&#8221;, elderly subjects with MCI exhibited a lower average absolute velocity of the #3 (2.46 &#177; 0.40 vs 1.82 &#177; 0.55, P = 0.001) and #4 stroke (2.61 &#177; 0.46 vs 1.93 &#177; 0.50, P &#60; 0.001) and a higher average pen pressure (237.43 &#177; 39.77 vs 281.99 &#177; 37.70, P = 0.001) compared with healthy elderly subjects. Additionally, during the signing of Chinese names, the elderly subjects with MCI exhibited a higher in-air length tortuosity in segmentations between the characters compared with the healthy elderly subjects (12.57 &#177; 6.96 vs 31.66 &#177; 7.53, P &#60; 0.001).
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/61841/61841fig01.jpg" /> 
Figure 1: Chinese character &#8220;<img alt="Equation 6" src="/files/ftp_upload/61841/61841equ06.jpg" />&#8221; written in the software. (A). The Chinese character &#8220;<img alt="Equation 6" src="/files/ftp_upload/61841/61841equ06.jpg" />&#8221; with only the in-screen segmentations exhibited. The red circles are the beginnings and ends of the segmentations. The blue lines are the in-screen segmentation traces. (B). The Chinese character &#8220;<img alt="Equation 6" src="/files/ftp_upload/61841/61841equ06.jpg" />&#8221; with both the in-air and in-screen segmentations exhibited. The blue lines are the in-screen segmentation traces. The grey lines are the in-air segmentation traces. <a href="https://www.jove.com/files/ftp_upload/61841/61841fig01large.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/61841/61841fig02.jpg" /> 
Figure 2: Chinese signatures of healthy elderly subjects and elderly subjects with MCI. (A). The Chinese signature &#8220;<img alt="Equation 7" src="/files/ftp_upload/61841/61841equ07.jpg" />&#8221; (a subject of the healthy elderly group) with only the in-screen segmentations exhibited. The red circles are the beginnings and ends of segmentations. The blue lines are the in-screen segmentation traces. (B). The Chinese signature &#8220;<img alt="Equation 7" src="/files/ftp_upload/61841/61841equ07.jpg" />&#8221; (a subject of the healthy elderly group) with both in-air and in-screen segmentations exhibited. The blue lines are the in-screen segmentation traces. The grey lines are the in-air segmentation traces. The red areas emphasize the in-air length segmentations between characters (tortuosity = 5.34%). The left area is the segmentation between &#8220;<img alt="Equation 8" src="/files/ftp_upload/61841/61841equ08.jpg" />&#8221; and &#8220;<img alt="Equation 9" src="/files/ftp_upload/61841/61841equ09.jpg" />&#34; (absolute size = 2.2226; road length = 2.4658; tortuosity = 9.98%). The right area is the segmentation between &#8220;<img alt="Equation 9" src="/files/ftp_upload/61841/61841equ09.jpg" />&#8221; and &#8220;<img alt="Equation 10" src="/files/ftp_upload/61841/61841equ10.jpg" />&#8221; (absolute size = 2.9607; road length = 2.9821; tortuosity = 0.71%). (C). The Chinese signature &#8220;<img alt="Equation 11" src="/files/ftp_upload/61841/61841equ11.jpg" />&#8221; (a subject of the elderly with MCI group) with only the in-screen segmentations exhibited. The red circles are the beginnings and ends of the segmentations. The blue lines are the in-screen segmentation traces. (D). The Chinese signature &#8220;<img alt="Equation 11" src="/files/ftp_upload/61841/61841equ11.jpg" />&#8221; (a subject of the elderly with MCI group) with both in-air and in-screen segmentations exhibited. The blue lines are the in-screen segmentation traces. The grey lines are the in-air segmentation traces. The red area emphasizes the in-air length segmentations between characters (absolute size = 1.2100; road length = 1.7072; tortuosity = 29.12%). <a href="https://www.jove.com/files/ftp_upload/61841/61841fig02large.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 rowspan="2"></td>
			<td>Healthy Elderly</td>
			<td>Elderly with MCI</td>
			<td rowspan="2">P Value</td>
		</tr>
		<tr>
			<td>N=20</td>
			<td>N=20</td>
		</tr>
		<tr>
			<td>Gender (male/female)</td>
			<td>10/10</td>
			<td>8/12</td>
			<td>0.74</td>
		</tr>
		<tr>
			<td>Age (years)</td>
			<td>69.70&#177;4.51</td>
			<td>70.39&#177;3.42</td>
			<td>0.602</td>
		</tr>
		<tr>
			<td>Dominant hand (Right%)</td>
			<td>100</td>
			<td>100</td>
			<td></td>
		</tr>
		<tr>
			<td>Educational (years)</td>
			<td>9.60&#177;3.72</td>
			<td>8.22&#177;3.30</td>
			<td>0.237</td>
		</tr>
		<tr>
			<td>MMSE (score)</td>
			<td>28.90&#177;0.79</td>
			<td>26.33&#177;0.77</td>
			<td>&#60;0.001</td>
		</tr>
		<tr>
			<td>Average absolute velocity of 3# stroke of &#8220;<img alt="Equation 6" src="/files/ftp_upload/61841/61841equ06.jpg" />&#8221;</td>
			<td>2.46&#177;0.40</td>
			<td>1.82&#177;0.55</td>
			<td>0.001</td>
		</tr>
		<tr>
			<td>Average absolute velocity of 4# stroke of &#8220;<img alt="Equation 6" src="/files/ftp_upload/61841/61841equ06.jpg" />&#8221;</td>
			<td>2.61&#177;0.46</td>
			<td>1.93&#177;0.50</td>
			<td>&#60;0.001</td>
		</tr>
		<tr>
			<td>Average pen pressure of &#8220;<img alt="Equation 6" src="/files/ftp_upload/61841/61841equ06.jpg" />&#8221;</td>
			<td>237.43&#177;39.77</td>
			<td>281.99&#177;37.70</td>
			<td>0.001</td>
		</tr>
		<tr>
			<td>Tortuosity of in air length &#8220;Signature&#8221; (%)</td>
			<td>12.57&#177;6.96</td>
			<td>31.66&#177;7.53</td>
			<td>&#60;0.001</td>
		</tr>
		<tr>
			<td colspan="4">MCI: Mild Cognitive Impairment</td>
		</tr>
		<tr>
			<td colspan="4">Character 3 &#8221;<img alt="Equation 6" src="/files/ftp_upload/61841/61841equ06.jpg" />&#8221; is a horizonal movement.</td>
		</tr>
		<tr>
			<td colspan="4">Character 4 &#8221;<img alt="Equation 6" src="/files/ftp_upload/61841/61841equ06.jpg" />&#8221; is a vertical movement.</td>
		</tr>
	</tbody>
</table>
Table 1: Demographic and handwriting analysis data of subjects.

Watch this Scientific Journal Video about Digital Handwriting Analysis of Characters in Chinese Patients with Mild Cognitive Impairment at JoVE.com

Digital Handwriting Analysis of Characters in Chinese Patients with Mild Cognitive Impairment

This study proposed a digital handwriting analysis of characters in individuals with mild cognitive impairment to find more information than is revealed by traditional pencil&#8211;paper handwriting analysis.

An increasing amount of evidence shows that cognitive deficits and movement dysfunctions are not separated. Patients with mild cognitive impairment (MCI) ...

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Universidad Científica del Sur

Research

JoVE Journal

Medicine

4.5K Views.  Chinese PLA General Hospital. This study proposed a digital handwriting analysis of characters in individuals with mild cognitive impairment to find more information than is revealed by traditional pencil–paper handwriting analysis.

Video: Digital Handwriting Analysis of Characters in Chinese Patients with Mild Cognitive Impairment

Handwriting Analysis Indicates Spontaneous Dyskinesias in Neuroleptic Na&#239;ve Adolescents at High Risk for Psychosis

Growing evidence suggests that movement abnormalities are a core feature of psychosis. One marker of movement abnormality, dyskinesia, is a result of impaired neuromodulation of dopamine in fronto-striatal pathways. The traditional methods for identifying movement abnormalities include observer-based reports and force stability gauges. The drawbacks of these methods are long training times for raters, experimenter bias, large site differences in instrumental apparatus, and suboptimal reliability. Taking these drawbacks into account has guided the development of better&#160;standardized and more efficient procedures to examine movement abnormalities through handwriting analysis software and tablet. Individuals at&#160;risk for psychosis showed significantly more dysfluent pen movements (a proximal measure for dyskinesia) in a handwriting task. Handwriting kinematics offers a great advance over previous methods of assessing dyskinesia, which could clearly be beneficial for understanding the etiology of psychosis.

Handwriting analysis software significantly improves upon existing instruments measuring movement disorders. Individuals at&#160;risk for psychosis and healthy controls completed handwriting tasks to test for dyskinesia. Results suggest that youth at&#160;risk for psychosis exhibit dyskinesia and that handwriting analysis could significantly contribute to wider dissemination of early identification efforts

Growing evidence suggests that movement abnormalities are a core feature of psychosis. One marker of movement abnormality, dyskinesia, is a result of ...

Behavior

Comparing the Frequency Effect Between the Lexical Decision and Naming Tasks in Chinese

In psycholinguistic research, the frequency effect can be one of the indicators for eligible experimental tasks that examine the nature of lexical access. Usually, only one of those tasks is chosen to examine lexical access in a study. Using two exemplar experiments, this paper introduces an approach to include both the lexical decision task and the naming task in a study. In the first experiment, the stimuli were Chinese characters with frequency and regularity manipulated. In the second experiment, the stimuli were switched to Chinese two-character words, in which the word frequency and the regularity of the leading character were manipulated. The logic of these two exemplar experiments was to explore some important issues such as the role of phonology on recognition by comparing the frequency effect between both the tasks. The results revealed different patterns of lexical access from those reported in the alphabetic systems. The results of Experiment 1 manifested a larger frequency effect in the naming task as compared to the LDT, when the stimuli were Chinese characters. And it is noteworthy that, in Experiment 1, when the stimuli were regular Chinese characters, the frequency effect observed in the naming task was roughly equivalent to that in the LDT. However, a smaller frequency effect was shown in the naming task as compared to the LDT, when the stimuli were switched to Chinese two-character words in Experiment 2. Taking advantage of the respective demands and characteristics in both tasks, researchers can obtain a more complete and precise picture of character/word recognition.

Researchers adopt both the lexical decision task and the naming task to investigate some important topics such as character/word recognition by comparing the frequency effect between these two tasks. This article introduces this approach through two exemplar experiments and elaborates on the underlying logic.

In psycholinguistic research, the frequency effect can be one of the indicators for eligible experimental tasks that examine the nature of lexical access. ...

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Mild cognitive impairment (MCI) is the first sign of dementia among elderly populations and its early detection is crucial in our aging societies. Common MCI tests are time-consuming such that indiscriminate massive screening would not be cost-effective. Here, we describe a protocol that uses machine learning techniques to rapidly select candidates for further screening via a question-based MCI test. This minimizes the number of resources required for screening because only patients who are potentially MCI positive are tested further.
This methodology was applied in an initial MCI research study that formed the starting point for the design of a selective screening decision tree. The initial study collected many demographic and lifestyle variables as well as details about patient medications. The Short Portable Mental Status Questionnaire (SPMSQ) and the Mini-Mental State Examination (MMSE) were used to detect possible cases of MCI. Finally, we used this method to design an efficient process for classifying individuals at risk of MCI. This work also provides insights into lifestyle-related factors associated with MCI that could be leveraged in the prevention and early detection of MCI among elderly populations.

This methodology produces decision trees that target population groups more prone to suffering from mild cognitive impairment and are useful for cost-effective selective screening of the disease.

Mild cognitive impairment (MCI) is the first sign of dementia among elderly populations and its early detection is crucial in our aging societies. Common ...

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

Older adults are slower at recognizing visual objects than younger adults. The same is true for recognizing that a letter string is a real word. People with Alzheimer&#39;s disease (AD) or Mild Cognitive Impairment (MCI) demonstrate even longer responses in written word recognition than elderly controls. Despite the general tendency towards slower recognition in aging and neurocognitive disorders, certain characteristics of words influence word recognition speed regardless of age or neuropathology (e.g., a word&#8217;s frequency of use). We present here a protocol for examining the influence of lexical characteristics on word recognition response times in a simple lexical decision experiment administered to younger and older adults and people with MCI or AD. In this experiment, participants are asked to decide as quickly and accurately as possible whether a given letter string is an actual word or not. We also describe mixed-effects models and principal components analysis that can be used to detect the influence of different types of lexical variables or individual characteristics of participants on word recognition speed.

This article describes how to implement a simple lexical decision experiment to assess written word recognition in neurologically healthy participants and in individuals with dementia and cognitive decline. We also provide a detailed description of reaction time analysis using principal components analysis (PCA) and mixed-effects modeling.

Older adults are slower at recognizing visual objects than younger adults. The same is true for recognizing that a letter string is a real word. People ...

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

As Parkinson&#8217;s disease (PD) is a heterogeneous disorder, personalized medicine is truly required to optimize care. In their current form, standard scores from paper and pencil symptom- measures traditionally used to track disease progression are too coarse (discrete) to capture the granularity of the clinical phenomena under consideration, in the face of tremendous symptom diversity. For this reason, sensors, wearables, and mobile devices are increasingly incorporated into PD research and routine care. These digital measures, while more precise, yield data that are less standardized and interpretable than traditional measures, and consequently, the two types of data remain largely siloed. Both of these issues present barriers to the broad clinical application of the field&#8217;s most precise assessment tools. This protocol addresses both problems. Using traditional tasks to measure cognition and motor control, we test the participant, while co-registering biophysical signals unobtrusively using wearables. We then integrate the scores from traditional paper-and-pencil methods with the digital data that we continuously register. We offer a new standardized data type and unifying statistical platform that enables the dynamic tracking of change in the person&#8217;s stochastic signatures under different conditions that probe different functional levels of neuromotor control, ranging from voluntary to autonomic. The protocol and standardized statistical framework offer dynamic digital biomarkers of physical and cognitive function in PD that correspond to validated clinical scales, while significantly improving their precision.&#160;

This protocol offers a digitization of portions of traditional clinical tasks commonly used to measure cognition and motor control in Parkinson&#8217;s disease. Clinical tasks are digitized while biophysical rhythms are co-registered from different functional levels of the nervous systems, ranging from voluntary, spontaneous, automatic to autonomic.

As Parkinson&#8217;s disease (PD) is a heterogeneous disorder, personalized medicine is truly required to optimize care. In their current form, standard ...

Analysis of Learning and Memory Ability in an Alzheimer's Disease Mouse Model using the Morris Water Maze

A Morris water maze (MWM) experiment forces experimental animals to swim and learn to find a platform hidden in the water. It is widely used in scientific research to assess the learning and memory of animals. Due to the extensive use of the MWM test, visual experimental protocols are essential for researchers. This manuscript uses the latest studies to introduce the protocol of the MWM test. Alzheimer&#8217; Disease (AD) is characterized by a progressive loss of memory and cognitive function. An alternative and complementary treatment used for AD is Manual Acupuncture (MA). To assess the learning and memory ability of AD model mice, the MWM test was conducted. The visible platform trial, hidden platform trial, probe trial, and reversal trial of MWM were used to evaluate spatial learning and memory ability. In the visible platform trial, the swimming speed and escape latency of mice in different groups was not significantly different. In the hidden platform and reversal trials, the AD group showed a long escape latency. The escape latency decreased significantly after the MA treatment. Low platform crossover number and the proportion of time in the SW quadrant in the probe trial increased after the MA treatment (p &#60; 0.05 or p &#60; 0.01). The results of the MWM tests suggest that MA can effectively improve the spatial learning and memory abilities of AD model mice. Rigorous experimental operations provided assurance of the reliability of the results.

Herein, a protocol to conduct the Morris water maze tests to evaluate the ability of learning and memory of Alzheimer&#8217;s Disease model mice and to assess the effect of manual acupuncture for treating them is described.

A Morris water maze (MWM) experiment forces experimental animals to swim and learn to find a platform hidden in the water. It is widely used in scientific ...

Evaluation of the Cognitive Performance of Hypertensive Patients with Silent Cerebrovascular Lesions

Evidence accumulated from the last decade has proven that silent cerebrovascular lesions (SCLs) and their underlying pathogenic processes contribute to cognitive decline in the elderly. However, the distinct effects of each type of the lesions on cognitive performance remain unclear. Moreover, research data from Chinese elderly with SCLs is scarce. In this study, 398 otherwise healthy hypertensive elderly subjects (median age 72 years) were included and assessed. All participates were required to complete a battery of structured neuropsychological assessment, including forward and backward digit span tests, symbol digit modalities test, Stroop test, verbal fluency test and Montreal Cognitive Assessment. These tests were used to assess attention, executive function, information processing speed, language, memory and visuospatial function. A multi-sequence 3T MRI scanning was arranged within one month of the neuropsychological assessment to evaluate the burden of SCLs. SCLs were rated visually. Cerebral microbleeds (CMBs) and silent lacunes (SLs) were identified as strictly lobar CMBs and SLs or deep CMBs and SLs according to their locations, respectively. Similarly, white matter hyperintensities (WMHs) were separated into periventricular WMHs (PVHs) and deep WMHs (DWMHs). A series of linear regression models were used to assess the correlation between each type of SCLs and individual cognitive function domain. The results showed that CMBs tend to impair language-related cognition. Deep SLs affect executive function, but this association disappeared after controlling for other types of SCLs. PVHs, rather than DWMHs, are associated with cognitive decline, especially in executive function and processing speed. It is concluded that different aspects of SCLs have differential impact on cognitive performance in hypertensive elderly Chinese.

Here, we present a protocol to assess whether various types of silent cerebrovascular lesions are differentially associated with deficits in certain cognitive domains in a cohort of 398 hypertensive elderly Chinese, using a combination of neuropsychological tests and multi-sequence 3T MRI scanning.

Evidence accumulated from the last decade has proven that silent cerebrovascular lesions (SCLs) and their underlying pathogenic processes contribute to ...

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

Impaired functional connectivity in the Default Mode Network (DMN) may be involved in the progression of Alzheimer&#39;s Disease (AD). The Posterior Cingulate Cortex (PCC) is a potential imaging marker for monitoring the progression of AD. Previous studies did not focus on the functional connectivity between the PCC and nodes in regions outside the DMN, but our study is an effort to explore these overlooked functional connections. For collecting data, we used functional Magnetic Resonance Imaging (fMRI) and Granger Causality Analysis (GCA). fMRI provides a non-invasive method for studying the dynamic interactions between the different brain regions. GCA is a statistical hypothesis test for determining whether one-time series is useful in forecasting another. In simple terms, it is judged by comparing the &#34;Known all the information on the last moment, the distribution of the probability of X at this time&#34; and the &#34;Known all the information on the last moment except Y, the distribution of the probability of X at this time&#34;, to determine whether there is a causal relationship between Y and X. This definition is based on the complete information source and stationary chronological sequence. The main step of this analysis is to use X and Y to establish the regression equation and draw a causal relationship by a hypothetical test. Since GCA can measure causal effects, we used it to investigate the anisotropy of the functional connectivity and explore the hub function of the PCC. Here, we screened 116 participants for MRI scanning, and after preprocessing the data obtained from neuroimaging, we used GCA to derive the causal relationship of each node. Finally, we concluded that the directed connection is significantly different between the Mild Cognitive Impairment (MCI) and AD groups, both from the PCC to the whole brain and from the whole brain to the PCC.

Based on resting-state functional magnetic resonance imaging with Granger causality analysis, we investigated the alterations in the directed functional connectivity between the posterior cingulate cortex and whole brain in patients with Alzheimer&#39;s Disease (AD), patients with Mild Cognitive Impairment (MCI), and healthy controls.

Impaired functional connectivity in the Default Mode Network (DMN) may be involved in the progression of Alzheimer&#39;s Disease (AD). The Posterior ...

Characterizing the Relationship Between Eye Movement Parameters and Cognitive Functions in Non-demented Parkinson's Disease Patients with Eye Tracking

Cognitive impairment is a common phenomenon in Parkinson&#8217;s disease that has implications on the prognosis. A simple, noninvasive and objective proxy measurement of cognitive function in Parkinson&#8217;s disease will be helpful in detecting early cognitive decline. As a physiological metric, eye movement parameter is not confounded by the subject&#39;s attributes and intelligence and can function as a proxy marker if it correlates with cognitive functions. To this end, this study explored the relationship between the eye movement parameters and performance in cognitive tests in multiple domains. In the experiment, a visual search task with eye tracking was set up, where subjects were asked to look for a number embedded in an array of alphabets scattered randomly on a computer screen. The differentiation between the number and the alphabet is an overlearned task such that the confounding effect of cognitive ability on the eye movement parameters is minimized. The average saccadic amplitude and fixation duration were captured and calculated during the visual search task. The cognitive assessment battery covered domains of frontal-executive functions, attention, verbal and visual memory. It was found that prolonged fixation duration was associated with poorer performance in verbal fluency, visual and verbal memory, allowing further exploration on the use of eye movement parameters as proxy markers for cognitive function in Parkinson&#8217;s disease patients. The experimental paradigm has been found to be highly tolerable in our group of Parkinson&#39;s disease patients and could be applied transdiagnostically to other disease entities for similar research questions.

Here, we present a protocol to study the relationship between the eye movement parameters and cognitive functions in non-demented Parkinson&#39;s disease patients. The experiment used an eye tracker to measure the saccadic amplitude and fixation duration in a visual search task. The correlation with performance in multi-domain cognitive tasks was subsequently measured.

Cognitive impairment is a common phenomenon in Parkinson&#8217;s disease that has implications on the prognosis. A simple, noninvasive and objective proxy ...

Neuroscience

Dual-Task Stroop Paradigm for Detecting Cognitive Deficits in High-Functioning Stroke Patients

General clinical cognitive assessment scales are not sensitive enough to cognitive impairment in high-functioning stroke patients. The dual-task assessment has advantages for identifying cognitive deficits in high-functioning stroke patients and has been gradually applied in clinical assessment and cognitive training. Moreover, the Stroop paradigm has higher sensitivity and specificity for attentional assessment than conventional clinical cognitive assessment scales. Therefore, this study presents the dual-task assessment based on the Stroop paradigm to identify cognitive deficits in high-functioning stroke patients. This study demonstrates a single- and dual-task evaluation based on the Stroop paradigm and confirms its feasibility through case experiments and synchronized functional near-infrared spectroscopy evaluation. The Stroop reaction time and correct rate are used as the main indicators to evaluate the cognitive level of the subjects. This study protocol aims to provide new ideas to figure out the ceiling effect in general clinical assessment failure for high-functioning stroke patients.

Clinical assessment scales are notsensitive enough to cognitive dysfunction in high-functioning stroke patients. The dual-task paradigm presents advantages and potential in the assessment and cognitive training of cognitive dysfunction. The study here proposes a dual-task Stroop paradigm to identify cognitive dysfunction in high-functioning stroke patients.