A Neurotechnological Assessment Tool to Understand How Cognitive Deficits Influence Upper

Extremity Motor Recovery After Stroke

Shirin Tooloee Sc.B. Biomedical Engineering, Brown University, 2018

Thesis Submitted in partial fulfillment of the requirements for the Degree of Master of Science in the

Department of Engineering at Brown University

PROVIDENCE, RHODE ISLAND MAY 2019

This thesis by Shirin Tooloee is accepted in its present form by the Department of Engineering as satisfying the thesis requirements for the degree of Master of Science

Date________________ Signature: _______________________________ Dr. John Simeral, Advisor

Date________________ Signature: _______________________________ Dr. David Lin, Reader

Date________________ Signature: _______________________________ Dr. Joseph J. (Trey) Crisco, Reader

Approved by the Graduate Council

Date________________ Signature: __________________________________ Dr. Andrew G. Campbell, Dean of the Graduate School

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Acknowledgements First and foremost, I’d like to thank Dr. David Lin for brainstorming this project with me and allowing me to be a part of the work he is doing with SMaHRT at MGH. You’ve been an incredibly helpful, responsive, and thoughtful mentor throughout this design process, and have provided me a space to learn so much. I’d also like to thank Dr. John Simeral for being my advisor and aiding me in finding a meaningful project for my thesis. I appreciate your time and support throughout this year. Thank you to Dr. Joseph Crisco for not only agreeing to be on my thesis committee, but also for giving me my first research position as a sophomore. Thanks to the BrainGate lab for supporting me in my research the past two years. It has been an incredible experience to be a part of such ground-breaking work with remarkably talented individuals. A special thank you to Dr. Leigh Hochberg who not only provided me an undergraduate research position, but also for continuing to support me into my graduate studies. Thank you to my friends at Brown for the best possible experience I could’ve asked for. You all made it impossible for me to leave, giving me even more of a reason to stay for my 5th year. Last, but definitely not least, I’d like to thank my family for the unconditional love and support. Thank you for always believing in me and motivating me. Everything I do is with you and for you. None of this would be possible without you guys.

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Table of Contents

Acknowledgements iTable of Contents iiIntroduction 1

1.1 Stroke Overview and Motivation 11.2 SMaHRT 1

1.2.1 Overview 11.2.2 Current Research Assessments 2

1.2.2.1 Motor Assessments and EEG 21.2.2.2 InMotion Robot 3

Cognition 52.1 Why is cognition important to track? 52.2 Defining Cognition 52.3 Cognitive and Motor Interaction 62.4 State-of-the-Art Cognitive Assessment 8

Game Design 113.1 Uses 13

3.1.1 As Assessment 133.1.2 As Therapy 13

3.4 GUI 143.5 Outcome Measures 15

Conclusion 16Appendix 17References 25

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Introduction

1.1 Stroke Overview and Motivation

Stroke is one of the most common causes of adult disability, resulting in both physical

and cognitive impairments. Physical impairments caused by stroke include the loss or limitation

of mobility and functional muscle control (Langhorne, Coupar, and Pollock 2009) while

cognitive impairments include deficits in speech and attention control, and can affect memory,

thinking, and mood (Rinne et al. 2018). Despite cognitive impairment being found in up to 90%

of stroke survivors, cognitive impairments are poorly phenotyped, meaning poorly categorized

and utilized to inform rehabilitation and stroke recovery. This is because contemporary stroke

practice has often focused on physical manifestations of stroke rather than cognitive

impairments. Therefore, it is important to develop a quantitative cognitive assessment to

understand how physical recovery is interrelated with cognitive impairment. This will facilitate a

better understanding of the neuroanatomical basis of cognitive impairment and the development

of neurorehabilitation treatments targeting specific cognitive deficits.

1.2 SMaHRT

1.2.1 Overview

Work on this thesis was performed as part of a study of stroke patients at Massachusetts

General Hospital (MGH) called SMaHRT, which stands for the Stroke Motor Rehabilitation and

Recovery Study. That study, led by Dr. David Lin, started in June of 2017 as a natural history

research study of arm motor recovery after stroke. At the point of time this thesis is being

written, the study has recruited one hundred patients. The study enrolls patients aged 18-80 who

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exhibit upper extremity motor weakness after ischemic stroke during their acute hospitalization.

The patients must have no prior neurological, psychiatric, or developmental disorders and

consent to receiving follow-up care at MGH in order to complete research visits during acute

hospitalization, 6 week, 3 month, 6 month, and 12 month time points. During these research

visits a set of outcome measures from motor assessments, EEG, and the BIONIK InMotion ARM

are gathered in order to track motor recovery over time. The aim of the study is to better

understand motor recovery in order to deliver improved patient outcomes through personalized

strategies and neurotechnologies to aid in rehabilitation (“SMaHRT – CNTR” n.d.).

1.2.2 Current Research Assessments

Once an eligible patient has agreed to join the study, occupational and physical therapists

help research staff with inpatient testing. This includes motor assessments, EEG, and robot

assessments, if possible, with the patient. For a couple of reasons access to the robot can

sometimes be difficult, and in these cases, it is forgone. At this point in time, five of the one

hundred patients enrolled in the study have used the robot for evaluation purposes. At each time

point, patients complete the following set of assessments in order to track the course of their

motor recovery.

1.2.2.1 Motor Assessments and EEG

First, the patients complete a set of standard post-stroke motor assessments to evaluate

their upper extremity motor function. These include the Fugl-Meyer, box and blocks (Image A),

nine-hole peg (Image B), and other standard stroke motor assessments. Scoring is based on the

patient’s ability to successfully complete the motor task. Then, EEG data is gathered while the

patient wears an EEG cap and is instructed to do various tasks such as sitting with eyes closed

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and then eyes open. EEG data is used to better understand how certain tasks map to specific

brain areas. Finally, a set of evaluations are completed with the InMotion Robot ARM.

1.2.2.2 InMotion ARM

The BIONIK InMotion ARM is an FDA-approved rehabilitation device that assists

clinicians in evaluating and treating patients with upper extremity impairments or injuries. The

robot comes with built-in games that assess and rehabilitate function in the upper extremity and

can generate patient evaluations and progress reports. The InMotion ARM offers task-specific

training for patients of varying impairment severities. Robot-assisted therapy modes include

stabilization, resistance, and active-assisted in which no patient-initiated movement is required.

Though the ARM is used in the study as a means of motor recovery assessment, it has a dual

function as a therapy device in which patients can work with physical therapists on regaining

upper extremity function (BIONIK Inc n.d.).

To use the device, the patient sits at the table shown in Image C, facing the computer

screen. Their affected arm rests on the forearm pad while their hand grasps the arm handle. Thus,

the patient controls movement of the manipulandum in the x-y plane over the table. The

manipulandum movements are mapped to the screen such that the patient can control the

computer cursor. Currently, the InMotion ARM is used to assess the patient’s movement and

strength capabilities.

Patients complete the following assessments: passive radial 8 (robot-assisted), active

radial 8 (patient-driven), clockwise/counterclockwise circle drawing, stabilization and resistance.

A radial 8 task is a standard game used to test a patient’s ability to move the screen cursor from

the center of a circle to 8 points along the circle’s circumference. In relation to this task, passive

means that the robot controls the manipulandum and active means that the patient controls the

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manipulandum. The strength tasks included with the robot are stabilization and resistance.

During the stabilization task, the patient is instructed to hold the joystick in the center,

preventing the machine from moving the manipulandum outwards. The resistance task requires

that the patient push out against the robot, and like a spring, the resistance becomes stronger the

further it is moved outwards. The kinematic data from these tasks is gathered as a functional

outcome measure to track motor recovery.

While cognitive impairment is seen in many stroke patients, there are no cognitive

assessments included in the standard physical rehabilitation tests described. Additionally, though

the InMotion ARM comes pre-loaded with several assessments and therapies, none exist to

challenge both cognitive and motor impairments. Thus, cognition has yet to be explored in the

context of motor recovery in this study.

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Cognition

2.1 Why is cognition important to track?

It is known that cognition is important for patient outcome. That is, patients with poor

cognition tend to not do well in their recovery. There is also increasing evidence that cognition is

important for motor recovery and the assessment of motor recovery. Taken together, assessing

cognitive recovery quantitatively will ultimately allow us to develop better treatments for stroke

recovery. At this point in the study, the research team will reflect on the course of the study thus

far and decide, going forward, which measures have been useful, and which have not. Thus,

cognition has been recognized as a potentially confounding variable when looking strictly at

motor recovery outcome measures and can be useful in determining the best trajectory for stroke

recovery. Thus, clinical practice could be improved by better understanding how cognitive

changes are related to motor recovery outcomes. This thesis work aims to enable a cognitive

assessment in the context of this motor recovery study that will allow for the development of

better treatments for stroke recovery.

2.2 Defining Cognition

Cognitive function involves a slew of mental processes that allow a person to carry out

tasks. The DSM-5 defines six key neurocognitive domains involved in cognitive function as

follows: language, learning and memory, social cognition, complex attention, executive function,

and perceptual-motor function (“Figure 2: Neurocognitive Domains. The DSM-5 Defines Six

Key Domains Of...” n.d.). Language encompasses word finding, object recognition, fluency,

grammar and syntax, and receptive language. Of these, word finding and object recognition are

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easily tested in cognitive assessments to determine aphasia severity. Learning and memory

subdomains include free recall, cued recall, recognition memory, semantic and autobiographical

long-term memory, and implicit learning. Social cognition is the recognition of emotions, theory

of mind, and insight. Attention is an important aspect of cognitive function, and as will be

described later, a key component in successfully execution of tasks. Complex attention refers to

sustained attention, the ability to focus on a task for an extended period of time, divided

attention, the ability to focus on multiple tasks at once, selective attention, the ability to

preferentially process important information over less-relevant information (e.g. between task

and distraction), and processing speed, a measure of the efficiency of cognitive function (Cohen

2011). Executive function is very important in motor function as it involves planning, decision-

making, working memory, responding to feedback, inhibition, and flexibility. Finally,

perceptual-motor function includes visual perception, visuoconstructional reasoning, and

perceptual-motor coordination. Visuoconstructional reasoning describes an individual’s ability to

reproduce geometric figures and how well planned out that process is. Perceptual-motor

coordination encompasses tasks such as hand-eye coordination and tracking (Ruffolo 2011).

These neurocognitive domains are important for motor planning and execution as well as

attention to the assigned task. It is then intuitive that with impaired cognition, motor planning

and execution would be difficult, even with the unaffected side.

2.3 Cognitive and Motor Interaction

Along with cognition itself, there is an aspect of cognition and motor interaction that

warrants investigation, both anatomically and physically. It is well known that there are specific

areas of the brain affected by stroke and those that are important in motor recovery. Similarly,

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there are brain areas responsible for cognition. Tracking both cognitive and motor outcomes in

stroke patients allows for the in-depth analysis of how these areas relate to one another and to the

impact of the stroke.

In 2017 Rinne et al. published a study on cognitive and motor interaction in the

Proceedings of the National Academy of Sciences. In this study, controls and hemiparetic stroke

patients were instructed to complete a set of tracking tasks with both arms to determine whether

influences of attention control are seen in lower level motor functions of dexterity and strength.

The study defined attention control (or executive control) as a higher cognitive function that

closely interacts with the motor system by determining response selection and inhibition. Thus,

attention control is required in order to maintain performance as difficulty or the number of

distractions increases. It was shown that attention is vital to successful motor function in both

paretic and non-paretic arms. Image D shows that with an increasing number of distractors,

accuracy of the tracking task decreased for both subject groups and in both arms. These results

suggest that the salience network, a large-scale brain network responsible for recruitment of

relevant functional networks, and its attention-control function are necessary for all volitional

motor acts while its damage contributes significantly to the cardinal motor deficits of stroke. The

study notes that severe motor impairment coexisted with normal attention control whereas

normal motor performance was never associated with impaired attention control, meaning that

dexterity and force generation require intact attention control (Rinne et al. 2018). Thus, cognition

is important for successful completion of all motor tasks and should be considered when

measuring patient performance.

As demonstrated by Rinne et al., execution is a challenge for the unaffected side and even

in healthy individuals with impaired cognition. In the context of SMaHRT, a patient with

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cognitive deficits might receive a low score on a motor task due to a lack of understanding the

task or the inability to plan the action. Because patients often experience both cognitive and

motor impairments after stroke, it is important to include cognition as a factor in recovery when

attempting to understand how patients regain motor function. The first step in achieving this is a

quantitative assessment of both cognitive and motor stroke recovery.

2.4 State-of-the-Art Cognitive Assessment

The current state-of-the-art cognitive assessment is the Montreal Cognitive Assessment

or MoCA. The MoCA, shown in Image E, evaluates various cognitive domains including

visuospatial/executive, naming, memory, attention, language, abstraction, delayed recall, and

orientation, making it a valuable tool in assessing cognition as a whole. Several publications

have demonstrated the utility of the MoCA in assessing cognitive function and long-term

outcome for stroke patients.

Quinn et al. studied the value of the Montreal Cognitive Assessment (MoCA) in

predicting functional outcome in stroke patients. Bedside tests such as the MoCA are designed to

be quick, easy to administer, and have a high sensitivity to cognitive impairment while

minimizing the bias from aphasia. This assessment takes less than five minutes to complete and

incorporates several important aspects of cognition including apraxia and neglect. The study

maintains that cognitive assessments help to target the rehabilitation approach, provide useful

prognostic info, and highlight the emergence of complications (Quinn, Elliott, and Langhorne

2018).

Similarly, Zietemann et al. found that the administration of the MoCA within 7 days of

hospitalization for stroke predicts long-term cognitive outcome, functional outcome, and

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mortality after stroke. The measure taken at this stage in stroke recovery was used as a baseline

to compare with future testing which took place at 6, 12 and 36 months. The study determined

that patients with a baseline MoCA score of greater than or equal to 26 remained stable between

time points whereas those below that threshold exhibited increased functional impairment. It is

noted that this decline may be in part due to the known influence of cognitive deficits on

measures of disability and performance of daily activities. The NIH Stroke Scale, an established

predictor of functional outcome after stroke, lacks a cognitive component. The study found that

adding the MoCA score to this led to an increase in the C-statistic for all outcomes including

functional outcome, thus solidifying the clinical utility of cognitive screening. Since patients

with cognitive impairment are more likely to have poor adherence to treatment guidelines and

have restricted access to rehabilitation programs despite evidence for considerable functional

gains, Zietemann et al. concludes that cognitive testing in the first few days following

hospitalization helps identify high risk patients and determine long-term patient outcome

(Zietemann et al. 2018).

The evaluations currently administered in SMaHRT lack a cognitive component despite

literature demonstrating the benefits of utilizing cognitive assessments with stroke patients. If the

patient fails to complete a task, the score reflects a lack of motor ability whereas in some cases

cognitive impairment could be preventing the patient from understanding the task or from proper

motor planning to successfully execute the task. Given the diversity of patients evaluated in the

study, researchers identified a cognitive load in doing motor tasks, especially when interfacing

with the robot as patients must look at the screen and process it. Thus, it would be valuable to

introduce cognitive-motor assessments that actively challenge both aspects of a patient’s ability

together to the list of evaluations. The MoCA has shown its value in assessing cognitive function

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but has yet to be used in the context of stroke motor recovery. From this came the idea to utilize

the first element of the assessment, the visuospatial/executive task, and introduce it to the

assessments and physical rehabilitation activities already performed with the InMotion ARM in

order to collect kinematic data reflecting both cognitive and motor functionality.

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Game Design

Modeled after the Trails Making task from the state-of-the-art MoCA assessment came

the design for the digital assessment to be used with the InMotion ARM. The Trails Making

Test, shown in Image F, was chosen because it is easy to replicate and challenges cognitive and

motor function jointly. The test is a widely used and accepted cognitive assessment for stroke

patients and is typically done with pen and paper. The patient is instructed to draw one

continuous line through all of the targets in a sequential order, and the outcome measure gathered

is time for completion. Transitioning this game to the robot allows for the extraction of more

robust kinematic data that will more wholly illustrate the patient’s progress.

Again, the patient sits at the table and uses their affected arm to move the manipulandum

in the x-y plane directly over the table. The arm movements map to and control the computer

cursor on the screen in front of the patient. The object of the digital Trails Making Test is for the

patient to move their affected arm in physical space to move the cursor between targets on the

screen and acquire them in the correct order, which requires cognitive reasoning and planning.

Thus, the patient must move their impaired limb to move the cursor to each target in sequence,

making it a physical task which can be assessed by metrics such as smoothness of movement.

Being adopted from the MoCA where it is a cognitive assessment tool, cognitive effects are

measured by latency of response, time to recognize the next target, and time for completion.

Creating this task on the robot to be administered at each research visit, both physical and

cognitive data is gathered, which can then be analyzed to better understand the interaction

between cognitive and motor stroke recovery.

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The games are written in the Tcl/Tk language, which the InMotion ARM uses as its

primary gaming language. When the assessment is initialized, 12 targets are displayed on the

screen. There are two distinct versions of the Trails Making Test, A and B. Trails A displays

targets numbered from 1-12 as illustrated in Image G. The patient is instructed to acquire the

targets in ascending numerical order. Trails B is slightly more difficult in that it displays both

numbers 1-6 and letters A-F, as shown in Image H. The patient is instructed to acquire the targets

in alternating numerical and alphabetical order (ie. 1, a, 2, b…). When the patient moves the

cursor to acquire the correct target, the target changes color to indicate to the patient that they

have successfully reached the target of interest. If the patient scrolls over the wrong target, the

target will not change colors.

An important consideration in developing the game was eliminating the practice bias.

This can be an issue with the pen and paper version of the test, as there is only one version of

Trails A and B. Assessing the patient multiple times with the same version of the test, it becomes

unclear whether the patient is actively thinking about the sequence of targets or if they have

learned the positions of the targets over their many trials. Thus, the proposed solution to

circumvent the practice bias for the digital game was to randomize target positions each trial.

However, this became tricky as sequential targets must have straight, uninterrupted paths

between them, as seen in the paper version (Image I). It is important for the targets to be situated

in this manner so that it is clear that the patient is actively trying to move towards the correct

target, which would be difficult to determine if another target lies between targets 1 and 2 for

example. Additionally, once development of the game began, the utility of the Trails Making

Test as both an assessment and a form of therapy became apparent. Thus, a distinction was made

between the two uses of the test to maximize its efficacy.

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3.1 Uses

3.1.1 As Assessment

To develop the Trails A and B tasks as assessments, it was important to ensure that

sequential targets had uninterrupted, straight line paths between them and that patients received a

new, unique assessment at each time point to eliminate the practice bias. To achieve this, five

unique Trails A and Trails B assessments were created by hard coding the positions of the

targets. This allowed for the circumvention of complex coding algorithms to create randomized

target locations that also accounted for straight line paths between targets. With five versions of

each Trails Making Test, a different assessment will be administered at each time point. Like the

paper version of the assessment, a trail is left behind to track cursor movements so that

smoothness and movement efficiency can be analyzed.

3.1.2 As Therapy

The Trails Making Test can be a great therapeutic activity for the rehabilitation of

cognitive and motor function. In this version of the test, the targets are displayed on the screen

using a randomization function. Unlike in the assessments, straight, uninterrupted paths between

targets are unnecessary as it is being used as a rehabilitation tool, not as a means of determining

a patient’s recovery progress. Thus, randomization of the target locations is an appropriate

method of displaying targets for this version of the digital Trails Making Test. When all targets

have been acquired, the targets will disappear and 12 new, random targets will appear after a two

second delay. The game will continue to regenerate targets until the clinician exits the gaming

interface. Patients passing over other targets to get to the target of interest is not of concern

because the task is meant for rehabilitation and practice rather than assessing their recovery

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outcome. However, due to the possibility that a patient might have to pass over other targets in

order to acquire all of the targets, there is no trail left behind the cursor so as to not occlude the

rest of the targets. Repetition of the game on the InMotion Robot will allow for rehabilitation of

both cognitive and motor functions.

3.4 GUI

The InMotion system features its own graphical user interface (GUI) for selection of its

pre-loaded games. In order to make the use of the Trails assessments and therapy games easy for

the user, a graphical user interface (GUI) was created to include all of the options available for

use. When prompted through the computer’s Terminal, the selection window pops up as shown

in Image O. The instructions at the top of the page explain that the user will be prompted to make

selections one variable at a time, and when all the selections have been made a start button will

appear at the bottom of the selection window. The first prompt is to choose input type, either

mouse or robot, by checking the desired box. All of the Trails Making assessments and therapy

games can be played on a computer or the InMotion ARM. If played on a computer, the cursor is

controlled by mouse movement. Once that selection has been made, the prompt will change to

ask the user to choose game style: therapy or assessment. The next prompt asks the user to

choose trails A or B. At this point, if therapy was chosen the start button will appear at the

bottom of the selection window. If assessment was chosen, then the user is prompted to select

which of the five versions of the test they want, after which the start button will appear as shown

in Image P. The GUI allows for all of the assessments and therapy games to be run from one

master file, making any future changes to the games much easier as everything is located in one

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place. It also presents an easy-to-use interface for the therapist or clinician to choose their desired

mode of play.

3.5 Outcome Measures

By capturing arm movement information, robust kinematic data can be gathered from the

assessment and analyzed as performance measures to track the patient’s progress. Additionally,

extraction of motor features such as velocity of cursor movement as well as cognitive features

such as the time to recognize the next target makes it a powerful tool for understanding cognitive

and motor interaction in the context of stroke recovery. This tool can be used as a smarter

assessment of cognitive and motor recovery and as a better therapy tool for rehabilitation.

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Conclusion

This thesis presents a novel assessment tool and therapy game for the purpose of better

understanding how cognitive deficits influence motor recovery in stroke patients. Modeled after

a state-of-the-art cognitive assessment and implemented on the gold-standard InMotion

Rehabilitation Robot for assessment and therapy of upper extremity impairments, the

neurotechnological tool can be used to study how cognitive impairment impacts physical

recovery. Now that game development has concluded, the future steps in this project include

implementation of the assessments in clinical practice and analysis of the kinematic data. With

use of the digital Trails Making assessments in SMaHRT, robust kinematic data reflecting both

the patients cognitive and motor stroke recovery can be analyzed to better understand their

interaction. A better understanding of how cognition influences motor recovery will allow for the

development of neurorehabilitation treatments targeting specific cognitive deficits and provide

insight into the neuroanatomical basis of cognitive impairment.

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Appendix

Image A: Box and blocks task. Patient must use one hand to move one block at a time from one side of the box, over the divider, to the other side. The task is timed and the number of blocks successfully transferred is the measurement

recorded. (“Orthopartner.Ch” n.d.)

Image B: Nine hole peg task. Patient instructed to place a peg one at a time into each hole in the block, and once all

pegs are placed in the holes, to remove them one at a time. The task is timed and done with one hand. (“Amazon.Com: 9 Hole Pegboard | Wooden Box Peg Board for Finger Dexterity, Physical Therapy, Fine Motor

Coordination, Sensory Rehabilitation: Home & Kitchen” n.d.)

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Image C: BIONIK InMotion Robot. Patient sits at table (1) facing the screen. Their affected arm is used to control the manipulandum (2) in the x-y plane over the table which controls the on-screen cursor. Their elbow rests on the

padded arm rest (3) while their hand grasps the arm handle (4). (BIONIK Inc n.d.)

Image D: Graph from Rinne et al. showing that accuracy of a tracking task decreases with an increasing number of distractors in both control and patient groups in both arms suggesting that attention control is important in low level

motor tasks. (Rinne et al. 2018)

2

3

4

1

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Image E: Montreal Cognitive Assessment (MoCA). State-of-the-art cognitive assessment that tests several of the

cognitive domains outlined in the DSM-5.

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Image F: Visuospatial/exeutive task from the MoCA from which the digital assessment is modeled.

Image I: Trails Making Test with straight, uninterrupted paths between sequential targets.

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Image J: Trails A from start. The green circle is the cursor and is to be used in acquiring the white targets.

Image K: Trails A finished. Dark targets have been acquired. Targets only change color when the correct target in

numerical order is reached with the green cursor ball.

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Image L: Trails B from start. The green circle is the cursor to be used in acquiring the white targets.

ImageM: Trails B finished. Dark targets have been acquired. Targets only change color when the correct target is

acquired with the green cursor ball - in this case alternating numerical and alphabetical order.

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Image N: Therapy game

Image O: First prompt of the GUI built for easy selection of which Trails Making Test the clinician wants to run.

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Image P: After each selection, the instruction prompt will change until the user has made all the necessary

selections. At this point, the start button will appear at the bottom of the selection window.

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References “Amazon.Com: 9 Hole Pegboard | Wooden Box Peg Board for Finger Dexterity, Physical

Therapy, Fine Motor Coordination, Sensory Rehabilitation: Home & Kitchen.” n.d. Accessed April 30, 2019. https://www.amazon.com/Healthstar-Pegboard-Dexterity-Coordination-Rehabilitation/dp/B071HLH94K.

BIONIK Inc. n.d. “InMotion ARMTM :: Bionik Laboratories Corp. (BNKL).” Accessed April 30, 2019. https://www.bioniklabs.com/products/inmotion-arm.

Cohen, Ronald A. 2011. “Sustained Attention.” In Encyclopedia of Clinical Neuropsychology, edited by Jeffrey S. Kreutzer, John DeLuca, and Bruce Caplan, 2440–43. New York, NY: Springer New York. https://doi.org/10.1007/978-0-387-79948-3_1334.

“Figure 2: Neurocognitive Domains. The DSM-5 Defines Six Key Domains Of...” n.d. ResearchGate. Accessed April 29, 2019. https://www.researchgate.net/figure/Neurocognitive-domains-The-DSM-5-defines-six-key-domains-of-cognitive-function-and-each_fig1_266325299.

Langhorne, Peter, Fiona Coupar, and Alex Pollock. 2009. “Motor Recovery after Stroke: A Systematic Review.” The Lancet Neurology 8 (8): 741–54. https://doi.org/10.1016/S1474-4422(09)70150-4.

“Orthopartner.Ch.” n.d. Accessed April 30, 2019. https://www.orthopartner.ch/site/index.cfm?id_art=111691&step=126d69&vsprache=de&c=441079&parentNode=116d5c:12465a:197f20&t=detail&prod=924883.

Quinn, Terence J., Emma Elliott, and Peter Langhorne. 2018. “Cognitive and Mood Assessment Tools for Use in Stroke.” Stroke 49 (2): 483–90. https://doi.org/10.1161/STROKEAHA.117.016994.

Rinne, Paul, Mursyida Hassan, Cristina Fernandes, Erika Han, Emma Hennessy, Adam Waldman, Pankaj Sharma, et al. 2018. “Motor Dexterity and Strength Depend upon Integrity of the Attention-Control System.” Proceedings of the National Academy of Sciences 115 (3): E536–45. https://doi.org/10.1073/pnas.1715617115.

Ruffolo, Jessica Somerville. 2011. “Visual-Motor Function.” In Encyclopedia of Clinical Neuropsychology, edited by Jeffrey S. Kreutzer, John DeLuca, and Bruce Caplan, 2647–52. New York, NY: Springer New York. https://doi.org/10.1007/978-0-387-79948-3_1417.

“SMaHRT – CNTR.” n.d. Accessed April 30, 2019. http://cntr.mgh.harvard.edu/smahrt/. Zietemann, Vera, Marios K. Georgakis, Thibaut Dondaine, Claudia Müller, Anne-Marie

Mendyk, Anna Kopczak, Hilde Hénon, et al. 2018. “Early MoCA Predicts Long-Term Cognitive and Functional Outcome and Mortality after Stroke.” Neurology 91 (20): e1838–50. https://doi.org/10.1212/WNL.0000000000006506.