nformation and communication...

NoTremor Deliverable D6.2 .2 -PU- Grant Agreement No. 610391

December, 31, 2016 -1- UNITN

SEVENTH FRAMEWORK PROGRAMME

INFORMATION AND COMMUNICATION TECHNOLOGIES

Project:

NoTremor‐ Virtual, Physiological and Computational Neuromus‐cular Models for the Predictive Treatment of Parkinson’s disease

(NoTremor, Grant Agreement No. 610391)

Deliverable number and title:

D6.2.2 NoTremor framework evaluation (final version)

Lead beneficiary: UNITN

WP. no, title and activity type

WP6 – Application scenarios development, validation and evaluation

Contributing Task (s) T6.1 – Correlation of objective and clinical scales and moni‐toring of a cohort evolution

T6.2 – Simulation of established medication

T6.3 – Assessment and evaluation

Dissemination level PU ‐ Public

Delivery date December 2016

Status



Authors List

Editor: Università degli Studi di Trento (UNITN)

Name / Surname Beneficiary Name (Short Name)

Contact email

Mauro Da Lio UNITN [email protected]

Mariolino De Cecco UNITN [email protected]

Paolo Bosetti UNITN [email protected]

Daniele Bortoluzzi UNITN [email protected]

Konstantinos Moustakas UPAT [email protected]

Tasos Dimas UPAT [email protected]

Giannis Lakoumentas UPAT [email protected]

Dimitar Stanev UPAT [email protected]

Otilia Kocsis UPAT [email protected]

Elisavet Xroni UPAT [email protected]

Veltista Dimitra UPAT [email protected]

Dimitrios Tzovaras CERTH [email protected]

Konstantinos Votis CERTH [email protected]

Panagiotis Moschonas CERTH [email protected]

Alex Blenkinsop USFD [email protected]

Seb James USFD [email protected]

Peter Brown UOX [email protected]

Chrystalina Antoniades UOX [email protected]

Michele Hu UOX [email protected]

Jill Gallagher PUK [email protected]



Executive Summary

This deliverable (D6.2.2) is the focal point of the project. It deals with the evaluation of the NoTremor system, which is carried with a twofold approach: a) with a variety of technical validation assessment tasks concerning the system and its building modules, and b) using the system in selected use cases. The patients and control cohorts for the use cases are 4 (section 2.1): 1) the Oxford Dis‐covery cohort, which is a subset of the UK Discovery study with a rich set of clinical as‐sessments; 2) the Oxford Surgery cohort, which is a small cohort of subjects with surgi‐cal electrode implantation and recording of LFP; 3) the Santa Chiara cohort which is a longitudinal study simulating the use of the NoTremor system for monitoring; 4) the Thessaloniki cohort, which is a rich cohort combining internal model parameter estima‐tion with objective metrics. The main achievement of the NoTremor system is the combination of a mathematical model of the Parkinsonian basal ganglia and motor system with a newly developed mo‐tor test (the line test). Using algorithms that allow fitting the model onto the recorded executions of the test by individual patients, the system allows estimating the individual patient’s connection strengths in the basal ganglia (sections 2.2 and 2.3). The Parkinsonian basal ganglia are described by 3 parameters (“Da”, modelling the ef‐fect of dopamine, “dis” and “mpy” modelling variations of the connection strengths in response to loss of dopamine and “T” modelling the adaptation of the motor system to the noisy basal ganglia – which has the meaning of intended movement time). As such the NoTremor system describes the PD as variations of the above 4 parame‐ters compared to healthy subjects (3 of the 4 parameters describing modifications in the basal ganglia and the 4th a consequence in the motor planning stage). Hence the disease is described with a point {Da, dis, mpy, T} in a 4‐dimensional space, rather than being described with a single scalar, such as e.g., UPDRS or any other sca‐lar metrics that can be derived by testing. Another fundamental difference with metrics and indicators of various kinds that are emerging in the literature, is that {Da, dis, mpy, T} maps onto variations of coupling strengths (the product of the density and potency of synaptic innervation) that actually happen in the basal ganglia and motor system as the disease progresses. Section 2.2 summarizes the mathematical model of the Parinsonian basal and motor system and describes the final parameterization used to model PD. Section 2.3 illustrates the final (quite complex) strategy that is used for estimating the parameters of individual sub‐jects. One first finding of the NoTremor system is the discovery of two clusters in the 4‐dimensional parameter space that have been named the “dis” cluster and “mpy” clus‐ters. Of the two, the “mpy” cluster looks to be implausible for the description of PD (as pointed out by spectral analysis and other considerations). It has been found (section 2.4) that this cluster is correlated with lesser probability of PD and might be a marker indicating that the subject might not have classical PD, but rather a mimicking disease such as Progressive Supranuclear Palsy or Multiple System Atrophy. The “dis” cluster, instead, has an elongated shape that is compatible with the notion of disease evolution.



In particular, a cross section of the parameter space in the Da‐dis plane shows that dis (the most important variation of connection strengths) is related to Da in what looks like a causal relationship: when Da decreases dis increases as expected if it has to be a compensatory reaction of the brain to loss of dopamine (sections 2.4, section 2.6.1 and animation related to Fig.13). The correlation between clinical scales and the model parameters {Da, dis, mpy, T} has been carried out in section 2.4 (corresponding to task T6.1). Several regression models (linear, but also nonlinear models such as Neural Network, Gaussian processes and Random Forest predictors) have been tested. In the end, the simple linear regression has been selected (and is reported in this deliverable). We have found a regression model between the 4 parameters {Da, dis, mpy, T} and the UPDRS‐3 total score that is significant for all the 4 parameters (section 2.4, Fig.12; and similar to Kinetigraph benchmark except the latter requires a much longer observation time). This means that every point in the 4‐dimensional parameter space maps onto one “predicted” value of the UPDRS score. The fact that this mapping is meaningful implies: a) that the overall model‐measurement‐fitting‐parameter estimation chain output does have some useful information content. In other words, that the estimated parameters, albeit affected by various forms of noise, still have a significant amount of signal (con‐siderations about the level of noise in the estimated parameters can be found in section 2.3.5, while section 2.6 clearly shows that using more tests for computing the average trajectory – the basic for parameter estimation – significantly improves the accuracy of the estimated parameters). b) that the estimated parameters can be used to predict an “equivalent” UPDRS score. In combination with remote monitoring tools such as described in section 2.6, this gives NoTremor the ability to better monitor the evolution of the disease, also revealing short term fluctuations and longer terms trends (that could be used, for example, to precisely assess the subject’s response to different medications). Moreover, as shown in section 2.6, evolution of the disease tends to activate compensatory mechanisms (such as e.g., reduction of Da induces increase of dis). Hence, at the macroscopic level, the same UPDRS score occurs for different points in the disease space, which however can be discriminated by the NoTremor system, allowing in principle more resolution in tracking changes that have not yet to affect the UPDRS score. This attribute may prove particularly important in the characterisation of early and ‘pre‐symptomatic’ PD, where internal compensation can effectively mask clinical deficit or its progression for several years. To be able to track the hidden changes related to the disease process would be of major importance in the development and testing of disease modifying treatments. A linear regression model based on behavioural parameters derived from the same line test (Peak Speed, Standard Deviation of Peak Speed and Standard deviation of Time of Peak Speed) was also found to be able to predict the UPDRS score, but not able to bet‐ter the performance of the prediction model based on the mathematical model param‐eters. From this we conclude that the mathematical model was highly efficient in cap‐turing the ability of performance in the line task to predict total UPDRS, and could achieve this using a biologically informed set of hidden parameters.



Monitoring of the disease evolution, based on the discovery cohort (section 2.4.1) did not show significant variations when cohort 1 and cohort 2 where considered in their entirety. However, a significant variation in Da was observed when the comparison was restricted to the subjects that carried out both the baseline and follow up test (hence comparing exactly the same cohort 18 months apart). Concerning the Levodopa use case (section 2.4.2) no significant variations were ob‐served in the 9 subjects of the Discovery cohort but significant variations were observed for Da and dis parameters between ON and OFF state for the surgical cohort. This may be related to the fact that the second cohort is more advanced (hence potentially big‐ger difference between ON and OFF states) and that the Levodopa challenge for the Discovery cohort was carried out with a less severe protocol (attenuated levodopa chal‐lenge, see D2.3). Further use cases are described in described in section 2.5 (correlation of force and line test with many objective and clinical indicators) and in section 2.6 (use of the system for longitudinal monitoring, including the assessment of several interesting behavioural parameters over time, such as, error rates, peak speed, distractor and fatigue effects). Technical validation activities are described in section 3. In particular section 3.1 studies the question whether 3 model parameters (Da, dis and mpy) are really necessary for the basal ganglia modelling. A model without the mpy parameter has been studied showing that with only Da and dis, the model would fail to correlate with UPDRS score. Technical assessment of the line test and of the Lumped model was carried out in D6.2.1 (section 3) and not repeated here. Previous technical validation tasks (completed in D6.2.1) for the Lumped Model and for the Line Task computer/tablet implementations are referenced in section 3.2. An alternative basal ganglia model (which might be the core of future extensions) is evaluated in section 3.3. An explanation for the stepped trajectories (often observed in advanced PD) is given in section 3.4. The central argument in this explanation is related to the 4th model param‐eter “T”. This should be the movement time that adapts to the basal ganglia noisy out‐put (by reducing movement speed). This adaptation is likely to be related to the for‐ward models of movement within the cerebellum. As the disease progresses and basal ganglia function degrades, it may be the case that the forward model encoded in the cerebellum can no longer learn how long the movement should take. If the movement takes longer than it should relative to the cerebellar forward model, then “T” is too small and the action tremor is the result (Fig. 27 and Fig. 28). The validation of the oculomotor model is given in section 3.5. Section 3.6 deals with the modelling of antisaccades. Section 4 finally deals with some studies of the impact and usability of the NoTremor system. In particular section 4.1 reports a focus group study of the use of the line test tool and section 4.2 related human factors.



Table of contents

Authors List......................................................................................................................... 2

Executive Summary ............................................................................................................ 3

List of figures ...................................................................................................................... 7

List of tables ..................................................................................................................... 10

List of abbreviations and acronyms ................................................................................. 11

1 Introduction .................................................................................................................. 12

2 Application and use of the NoTremor system .............................................................. 12 2.1 Cohorts summary ................................................................................................... 12 2.2 Final parameterized mathematical model for the line test .................................. 14 2.2.1 Parameterization of Parkinson’s Disease ........................................................ 15 2.2.2 Simulation of target reach movement ............................................................ 16

2.3 Updated model fitting strategy and first findings ................................................. 18 2.3.1 Exploration of the parameter space ............................................................... 19 2.3.2 First findings: different types of fits. ............................................................... 20 2.3.3 Selection of the most plausible local minimum .............................................. 23 2.3.4 Computational issues ...................................................................................... 24 2.3.5 Accuracy of parameter fits .............................................................................. 24

2.4 Correlation and Levodopa study (Oxford Discovery and Surgery cohorts) ........... 26 2.4.1 Changes over time ........................................................................................... 31 2.4.2 Acute Changes with levodopa ......................................................................... 33

2.5 Statistical and correlation analysis for force and line tests (Oxford and Thessaloniki cohort) ..................................................................................................... 38 2.5.1 Force tests analysis ......................................................................................... 38 2.5.2 Correlation of force test features with the Hoehn & Yahr scale .................... 38 2.5.3 Line test correlation analytics ......................................................................... 42

2.6 Longitudinal monitoring (Santa Chiara cohort) ..................................................... 45 2.6.1 Monitoring hidden brain states ...................................................................... 45 2.6.2 Use of the NoTremor platform for monitoring objective metrics. ................. 48

3 Other technical validation activities ............................................................................. 54 3.1 Why three model parameters (investigation on the number of model parameters) .................................................................................................................. 54 3.2 Technical validation of the line test and of the lumped model............................. 55 3.3 Multiscale modelling: alternative basal ganglia model ......................................... 55 3.4 Stepped trajectories as an emergent phenomenon.............................................. 57 3.5 Oculomotor model validation ................................................................................ 59 3.6 Modelling anti‐saccades in Parkinson’s Disease ................................................... 62

4 Evaluation of the NoTremor platform (line test) .......................................................... 66 4.1 Focus group study .................................................................................................. 66 4.2 Human factors and impact assessment ................................................................. 68

5 Conclusion ..................................................................................................................... 69

6 Appendix 1 .................................................................................................................... 69



List of figures Fig. 1 The mathematical model for the line task. Green boxes: Nuclei of the basal ganglia modelled using second order delay differential equations. Connections between them are labelled with their associated axonal transmission delays. Dark blue boxes: Cortical nuclei. Salience input is defined in FRin1 and FRin2. Yellow and white boxes: Minimum jerk optimal control model. Light blue box: Limb plant model. ................................................................................................................................................................. 14

Fig. 2 Simulation of one target reach movement for a healthy subject, compared to control jr154 (simulated trajectory solid line vs. actual recorded trajectory in dotted line). .......................................... 17

Fig. 3 Simulation of the same subject (jr154) with settings that correspond to a fully Parinsonian basal ganglia ...................................................................................................................................................... 18

Fig. 4 Simulation of the same subject (jr154) with health settings and no distractor. The reaction anticipates of about 20 ms in this example (the actual anticipation depends on the phase of the motor cortex at the time the salience is raised at the level of the movement. .................................................... 18

Fig. 5 Location of good fits (blue dots) in the parameter space (see text for detailed explanations of sub charts). ..................................................................................................................................................... 20

Fig. 6 Examples of good and ill conditioned fits (Top/Bottom). ................................................................. 21

Fig. 7 Different types of fits. Each point represents a good fit for the Discovery cohort 1. The “dis” cluster is elongated parallel to the dis axis and shows clear correlation of mpy and Da with dis. The “mpy” cluster is a rounded cluster located at about mpy=0.6 and with very similar values of Da and dis. ...................... 22

Fig. 8 Power spectra of the motor cortex P channel as predicted by the model for the centre of the dis cluster (left) and for the centre of the mpy cluster (right). Beta oscillations (and second harmonics) occur in the dis cluster. In the mpy cluster only the second harmonics is present. ............................................. 23

Fig. 9 Fitting basal ganglia parameters for the Discovery cohort 1 (labels indicate the three digits of each patient ID). Subjects in red colour are related to lesser good fits. ............................................................. 24

Fig. 10 Accuracy of parameter estimation. Arrows show the displacement of the fitting parameters when omitting on trial in the recorded tests (see text). ...................................................................................... 25

Fig. 11 Comparison of the fitting charts of a subject with large variation in the estimated parameters reveal that the removal of one borderline trial may affect the mean trajectory enough to significantly alter the fits (here the mean trajectory was computed from 17 trials). .................................................... 26

Fig. 12 A multiple linear regression model performed with all four model parameters and total UPDRS III score as the dependant variable was able to predict almost 40% of the variance in UPDRS. See Table 2 for further details of regression model (point labels identify the last three digits of the subjects IDs). .......... 28

Fig. 13 Linear regression model performed with mpy and beta band LFP power as the dependant variable was able to predict two thirds of the variance in beta band power. ......................................................... 36

Fig. 14 Boxplots of the force test results. Each row shows the differentiation between HY scale of each dataset and one of the four features extracted from the time series analysis: mean, stdev, skewness, kurtosis. .................................................................................................................................................... 41

Fig. 15 Monitoring of patient’s SC665 model parameters over time. Top: initial parameters on week 1. Bottom: final parameters on week 82 (grey dots represent the parameters of previous weeks). ............. 46

Fig. 16 Evolution of Da (top) and dis (bottom) over 1,5 years for one subject. .......................................... 47

Fig. 17 Evolution of error rates, peak speed and reaction time of one subject. ......................................... 48

Fig. 18 Distribution of reaction time for correct and incorrect movement for 4 PD subjects (top to bottom), without distractor (left) and with distractor (right). ................................................................... 49

Fig. 19 Distribution of reaction time for correct and incorrect movement for 4 healthy subjects (top to bottom), without distractor (left) and with distractor (right). ................................................................... 50

Fig. 20 Error rate vs. reaction time trade‐off for the PD subjects. Each dot represents the average reaction time and peak speed of the tests carried out over one week (together the dots represent the whole



interval of observations lumped per weeks). Distinction between tests carried out with and without distractor is made. The progression over time cannot be appreciated in this type of chart but can be inferred from charts like Fig.16. ................................................................................................................ 51

Fig. 21 Error rate vs. reaction time trade‐off for the healthy subjects. This chart com pares to the previous one. Healthy controls performed the monitoring tasks for are fewer observation weeks (because they were less motivated). Hence there were fewer experimental points. ....................................................... 52

Fig. 22 Effect of the distractor for the Parinsonians subjects. ................................................................... 53

Fig. 23 Effect of the distractor for the Parinsonians subjects. ................................................................... 54

Fig. 24 A model with only two BG parameters (dis and Da) does not fit the UPDRS score. ........................ 55

Fig. 25 The NoTremor model incorporating the spiking basal ganglia. The components of the model to the right of the grey dashed line are identical to those in the rate‐coded line task model shown in Fig. 1. Here, the two channels, P and S are not explicitly shown. Interface populations exist to convert the rate codes which are present in the sensory cortex (Sctx) and motor cortex (Mctx) populations and to covert spikes in GPi into a rate code to input to Mctx. Within the 5 populations of the basal ganglia (coloured cyan), 60 Izhikevich neurons are arranged into a P channel and an S channel.The model was created in SpineCreator, with a SpineML component called minjerk implementing the minimum jerk model described in section 2.2. A version of the rate‐coded line task model was implemented in SpineCreator to verify that the common part of the model (to the right of the grey dashed line) produced the same results given by the Mathematica and MATLAB implementations. ........................................................... 56

Fig. 26 Simulated peri‐stimulus time histograms of the responses in STN, two GPe populations and GPi for the spiking basal ganglia model. The graphs give mean firing rate based on the result of recording spikes from 30 neurons (i.e. one channel) over 100 simulations. The stimulus was an impulse delivered to Striatum, replicating the impulse used when training the rate‐coded basal ganglia model....................... 57

Fig. 27 Hypothesised mechanism for the generation of action tremor. (Deterministic rate model) Left: Healthy trajectory (T=0.295). Middle: Reducing the value of the parameter that governs the planned movement duration (T=0.245) induces instability in the trajectory. Right: Rendering the model Parinsonian but leaving T at its healthy value (da=0, dis=2, mpy=1, T=0.295) increases movement duration beyond the healthy value of T, resulting in a similar instability. ................................................. 58

Fig. 28 Stepped trajectories in the spiking model. Column 1: Effect of reducing the planned duration of movement “T” (Healthy, T=0.35). Reducing T (T=0.25) gives rise to instability in the trajectory. Column 2: GPi normalised firing rate activity in healthy model (top) and Parkinsonian model (bottom). Column 3: Effect of reducing dopamine parameter on the movement trajectory with T=0.35. In PD (bottom) insufficient disinhibition of motor cortex from GPi yields slowed movements. Noise from the highly active GPi gives rise to fluctuations in the trajectory. ............................................................................... 59

Fig. 29 Latency to saccade onset for a horizontal saccade of 12°. Results for 6 different target luminance values are shown; in each case the fixation luminance was 0.2 on the same, arbitrary scale. The horizontal axis denotes the gap between fixation offset and target onset; negative values indicate an overlap rather than a gap. A gap of 0 indicates a step paradigm. .............................................................. 60

Fig. 30 Simulated saccade trajectory obtained by using EOM activations of the strand oculomotor model of Qi Wei as input to our model. ............................................................................................................... 61

Fig. 31 Mean error rate. Each bar shows average error rate of 20 groups of eye movements. Error bars show average standard deviation. Each group contains 50 individual saccades. For an anti‐saccade, subject is required to move away from the presented stimulus. An error is recorded if the subject saccades towards the stimulus. Y‐axis shows proportion of saccades that were recorded as errors (normalised). See text for x‐axis label definitions. .................................................................................... 63

Fig. 32 Mean reaction times. Error bars show average standard deviation. Top: Pro‐saccade. Bottom: Anti‐saccade. 1000 eye movements, binned into groups of 50. See text for x‐axis label definitions. Y‐axis is reaction time in milliseconds. ................................................................................................................ 64

Fig. 33 Comparison between model (top) and data (bottom). Data from Kitagawa et al. (1994).............. 65

Fig. 34 Analysis of the cause of errors. Top: Prefrontal cortex error. Bottom: Pro‐saccade being initiated too quickly. ............................................................................................................................................... 65



List of tables Tab. 1 Group hidden parameters for the 29 patients in the first test campaign recruited from the Discovery cohort. SEM is standard error of the mean. .............................................................................. 27

Tab. 2 A multiple linear regression model performed with all four model parameters and total UPDRS III score as the dependant variable was able to predict almost 40% of the variance in UPDRS (r2 = 0.376; F[4,24] = 3.619, p = 0.019). Data from 29 patients in the first test campaign. ........................................... 28

Tab. 3 A multiple linear regression model performed on behavioural parameters extracted from the line task performance and total UPDRS III score as the dependant variable. This was able to predict almost 40% of the variance in UPDRS (r2 = 0.367; F[5,23] = 2.663, p = 0.049). Data from 29 patients in the first test campaign. .......................................................................................................................................... 29

Tab. 4 A multiple linear regression model performed with all four model parameters and total UPDRS III score as the dependant variable was able to predict almost 50% of the variance in UPDRS (r2 = 0.476; F[4,19] = 4.319, p = 0.012) when the five values in the mpy cluster were excluded (giving data from 24 patients from the first test campaign)....................................................................................................... 31

Tab. 5 Group hidden parameters for the 12 patients in the second test campaign recruited from the Discovery cohort about 18 months following the first test campaign. ...................................................... 31

Tab. 6 Group hidden parameters for the seven patients who performed the line task at enrolment in to the Discovery cohort and again ~18 months later (mk229, pdsjr432, jr 434, jr 441, jr 464, jr 436 and jr 469). Embolded estimates are significantly different. ............................................................................... 32

Tab. 7 Group hidden parameters for the nine patients who performed the line task in the second test campaign both off and on levodopa. Da was marginally lower off levodopa, but none of the hidden parameters differed significantly between the treatment conditions. (jr018, jr030, jr434, jr441, jr457, jr459, jr460, jr464, jr469) .......................................................................................................................... 33

Tab. 8 Group hidden parameters for nine post‐surgical patients who performed the line task both off and on levodopa. Da was lower off levodopa. Da was significantly increased and dis decreased on medication. ................................................................................................................................................................. 34

Tab. 9 A multiple linear regression model performed with all four model parameters and average beta band (15‐35 Hz) power at rest OFF medication as the dependant variable was able to predict 90% of the variance in UPDRS (r2 = 0.933; F[4,8] = 14.002, p = 0.013). ....................................................................... 35

Tab. 10 Datasets performed force tracking test. ....................................................................................... 38

Tab. 11 Hypothesis testing results of Thessaloniki Cohort 1. ..................................................................... 39

Tab. 12 Hypothesis testing results of Thessaloniki Cohort 2 ...................................................................... 39

Tab. 13 Hypothesis testing results of Oxford Cohort ................................................................................. 40

Tab. 14 Description of some clinical parameters used for correlation analysis of experimental data. ....... 44



List of abbreviations and acronyms

(in alphabetic order)

BG Basal ganglia CPU Central Processing UnitDa, dis, mpy, T, Parameters of the mathematical model DAE Differential algebraic equationDE Differential Evolution minimization algorithm DOF Degree of freedomDoW Description of Work EMG ElectromyographyEOM Extra ocular muscle

EU Europena Union GUI Graphical User Interface GPi, GPe Globus Pallidus (interior/exterior)HMI Human Machine Interaction LFP Local Field Potentials (measurements of electric activity with

electrodes implanted in the basal ganglia) NM Nelder‐Mead minimization algorthmON Levodopa challenge. Normal state of medicament. OFF Levodopa challenge. State with deprivation of medicament. PD Parkinson’s Disease SA Simulated Annealing minimization algorithmUI User InterfaceUPDRS (3, III) Unified Parkinson’s Disease Rating Scale (third scale)



1 Introduction This deliverable (D6.2.2) deals with the validation of the NoTremor system under sever‐al complementary aspects, and is thus the focal point of the project. The NoTremor system is essentially made of the following (main) components:

a) A parametric mathematical model capable of linking internal (altered) brain

connections (in particular in the Parkinsonian basal ganglia) to observable motor

behaviour.

b) Computerized measurement tools (in particular a tablet based «line tracking»

test) to measure the motor behaviour of human subjects.

c) A set of algorithms that estimate the mathematical model parameters (e.g., the

internal altered connections in the BG) that fit the measured behaviours.

d) Additional algorithms that derive metrics from the recorded tests (e.g., error

rates, peak movement speed, reaction times, etc.) that complement the internal

states estimated by steps a‐b‐c.

e) A visual analytics platform that allows collecting, organizing, presenting and ex‐

ploring measurements and correlations, for use by clinicians.

The validation of NoTremor has been carried out with the following tasks: T6.3, which deals with the individual study and assessment of the above NoTremor components. T6.1, which deals with the actual use of the system on 4 different cohorts of patients and control subjects, in order to assess the functionality of the measurement –parameters estimation workflow and to correlate the estimated internal parameters with objective metrics and clinical data. T6.2, which deals with using the system to estimate the variations that likely occur in‐side the Parkinsonian BG, between ON and OFF state (the Levodopa Challenge), to un‐derstand which model parameters better describe the effect of dopamine (dopaminer‐gic parameters) and which describe longer‐term synaptic modifications. This document is structured in a way to emphasize the novel and most valuable achievements (thus, aspects that have been sufficiently described in previous delivera‐bles, including assessments completed in D6.2.1, are not described again). The following sections present the three WP tasks, and, while doing this, the main novel achievements (not presented in previous deliverables) are explained.

2 Application and use of the NoTremor system

2.1 Cohorts summary

The NoTremor system has been used with 4 different cohorts, each focused on the evaluation of different aspects and potential uses.



Here the 4 cohorts are summarized. We will refer to these cohorts later, when present‐ing various aspects of the evaluation.

1) The “Discovery” cohort is a subset of a long‐term clinical study of early Parkin‐

son’s disease subjects, on‐going in the UK. This cohort is divided in two cam‐

paigns.

a) The Discovery cohort 1, comprises 29 PD subjects ON, i.e. taking their usual

dopaminergic medication (originally 31 subjects, but one opted out of the No‐

Tremor study and one was later diagnosed as having a different disease ‐

Multiple System Atrophy) and 3 healthy subjects of similar age (another subject,

that was used to calibrate the parameters of the control model is included for

technical verification). A few of these subjects were tested again in cohort 2 (see

next).

b) The Discovery cohort 2, comprises a total of 21 PD subjects (after exclusion of

3), of whom 9 subjects carried out the Levodopa challenge test performing as‐

sessments following overnight withdrawal of levodopa medication (OFF) and 1

hour following their usual medication (ON; 4 of which are follow up) and 12

tested only in the ON medication state (of which 3 are follow up).

In summary, the Discovery cohort comprises 50 subjects (after 3 omissions), of

whom 7 were seen at baseline and 18 months later and 9 carried out assess‐

ments in the OFF and ON state (During Levodopa challenge).

The Discovery cohort inherits from the Discovery UK study a rich set of clinical

assessments, which make these subjects well suited to carry out correlation

studies between the clinical parameters, the model parameters and the objec‐

tive metrics that can be also derived from the tests.

2) The “Surgery” cohort is a cohort of people that had electrode implantation dur‐

ing Deep Brain Stimulation (DBS) for their complex phase Parkinson’s disease.

The electrode was used to record LFP signals synchronized with the execution of

the line and force test. There are 13 subjects that carried out the line test of

which 10 carried out the test also in the OFF state. There are 8 more subjects

that carried out only the force test.

3) The “Thessaloniki” cohort is a cohort that carried out a battery of computerized

tests (line, force, Fitts, rest and postural tremor, gait, etc.). As for clinical data,

only the Hoehn and Yahr grade is available in this case. The cohort is divided in

two campaigns.

a) Thessaloniki cohort 1 with 43 subjects and 10 controls.

b) Thessaloniki cohort 2, with 6 controls and 37 subjects (of which 24 follow

up).



4) The “Santa Chiara” cohort is a longitudinal study, using the line test for frequent

(daily) monitoring at home. The study involved 4 PD subjects, 4 healthy age

matched controls, 2 controls with a different disease and some other short‐

term technical controls. The observation periods span from a few months (for

controls) to nearly a couple of years for two of the PD subjects.

2.2 Final parameterized mathematical model for the line test

The mathematical model for the line test is the most important of the models devel‐oped by NoTremor. This model has been described (with the others) in previous deliv‐erables (mainly D3.2 and D4.1) and was illustrated in project review 2. The model provides a multi‐scale description of the perception‐action loop in the line task. For the readers’ convenience, its block diagram summarized in Fig. 1 (note the de‐tailed modelling of the basal ganglia nuclei).

Fig. 1 The mathematical model for the line task. Green boxes: Nuclei of the basal ganglia modelled us‐ing second order delay differential equations. Connections between them are labelled with their asso‐ciated axonal transmission delays. Dark blue boxes: Cortical nuclei. Salience input is defined in FRin1 and FRin2. Yellow and white boxes: Minimum jerk optimal control model. Light blue box: Limb plant

model.

In particular, the mathematical model describes the competition between two af‐fordances (moving onto the target vs. moving onto the distractor) by means of two channels (move left vs. move right, labelled as P and S in the block diagram). These two channels compete in the BG, which (should) select one and ultimately (should) gate the output of the motor cortex of the target and suppress the output related to moving to‐wards the distractor (left channel and right channel output of Pmctx and Smctx in Fig.1).



2.2.1 Parameterization of Parkinson’s Disease

The definition of the model parameters that describe individual patients has been car‐ried out with several iterations. The main goal was to define a minimum set of adjusta‐ble parameters that can: a) span the set of observed motor behaviours in both healthy and Parkinsonian basal ganglia and b) replicate the oscillatory activity that has been ob‐served in healthy basal ganglia, and the changes that occur to those patterns of oscilla‐tions with the advance of Parkinson’s Disease. In particular, the work carried out at USFD ended up with the definition of a generic PD model that replicates the following features of PD:

1) TA and TI GPe populations oscillate in anti‐phase with each other at beta fre‐

quencies.

2) Different channels oscillate in phase, reflecting pathological synchrony, giving

rise to the increased beta power seen in the LFP of PDs.

3) The motor cortical activity of the primary channel is reduced and delayed (in‐

creased latency to movement and slowed movement). The motor cortical activi‐

ty of the secondary channel is suppressed later (and may even not be sup‐

pressed completely).

4) Beta power at rest is increased (presumably contributing to rigidity).

5) Beta power during preparation phase is reduced (presumably contributing to

postural instability).

The model has been finalized for WP6 with the specification of a number of its parame‐ters that are allowed to vary, for fitting individual patient’s observations. The model has the following parameters that can be varied to represent the progression of Parkinson’s Disease:

a) Three parameters for the Basal Ganglia:

‐ Da, which models the effect of dopamine as a modulation of the input to the

two striatal populations (str1 increases and str2 decreases). Da ranges from 0.3

(health) to 0.05 (“complete” PD).

‐ dis, which models the compensatory increase of several connections strengths

within the basal ganglia, which is known to happen in PD.

‐ mpy, which models a differentiation of the input connection strengths of Ti

and Ta neurons.

Overall dis and mpy are expected to model some longer‐term variations in the

connections strengths of the basal ganglia, whereas Da should describe the ef‐

fect of dopamine (including short terms effects). It is highly probable that the

many parameters that are changed by both dis and mpy in fact vary inde‐

pendently of each other. However, allowing all parameters to be fitted inde‐

pendently would lead to over‐fitting the model due to the relative scarcity of da‐

ta with which to constrain these parameters. It should therefore be noted that



these lumped parameters are approximations of what we believe are likely

changes that occur in the Parkinsonian brain.

b) One parameter for the motor planning:

‐ T, is the intended (at planning time) duration of the movement (i.e., the

movement time of the motor primitive). Initially we attempted not to include T

in the set of tuneable parameters, but the model was not at all able to fit the

observed trajectories, which show large variations in movement time and speed

(in other words Da, dis and mpy alone were not able to produce all the move‐

ment speeds observed). The parameter T was then included in the model. As

this document will show below for the Santa Chiara cohort, people can voluntar‐

ily change the duration of the movement; however forcing shorter movements

results in an increase of error rates. We can therefore postulate that T (which

strictly speaking is not a BG parameter and is not expected to be directly influ‐

enced by dopamine loss) changes because the cerebellum and brainstem adapts

the movement time to obtain some preferred (possibly personal) trade‐off be‐

tween movement time and error rate. That means that T is adapted to the level

of noise that results from the altered BG in PD. As a matter of fact, when T was

included in the tuneable parameters, fits of good quality were possible.

c) One parameter describing sensorimotor delay:

‐ , the reaction time (target jump to movement onset) has been included in

the tuneable parameters, not because the purpose was to estimate it (it can be

measured in the experimental trajectories) but because of the need to allow fi‐

ne temporal alignment between the simulated and recorded trajectory.

2.2.2 Simulation of target reach movement

Before introducing the fitting strategy, it is worth analysing how the model can be used for simulation and what kind of predictions the model gives. The model is used to simulate the initial rest phase, the following preparatory phase (where the salience for both possible target locations is raised to alert levels) and the actual movement phase where the salience for the target is set to the maximum level and that for the distractor (if present) is set to a somewhat lower, but still high, level (or set to the rest level if there is no distractor). Fig. 2 compares the simulation with the basal ganglia parameters of healthy subjects ( 0.3, 1, 0) to the recorded execution of one healthy control (jr154). The simulated trajectory is shown in solid green and the recorded one in dotted green. For this comparison, the movement time (parameter “T”) in the control block of the model has been adjusted to match the subject actual movement time. However, the connection strengths in the BG have the baseline values for the healthy instantiation of the model. Comparing the two curves, it is clear that for a healthy subject it is sufficient to adapt T to already get a fairly good fit.



In the simulation of Fig. 2 the rest phase takes place before t=0.5s. The preparatory phase between 0.5 and 1 s and the movement phase after t=1s (the target step oc‐curred before t=1s: at t=1 s the reaction time is elapsed). The figure also plots the motor cortex oscillations (P channel and S channel). At rest, there is no oscillation (in the healthy subject); in the preparatory phase, anti‐phase os‐cillations build up at beta frequency (the purpose of which is to prepare the motor sys‐tem for a quicker movement when a decision is finally taken). Finally, the movement is obtained by suppressing the distractor channel and releasing the target channel (t>1s).

Fig. 2 Simulation of one target reach movement for a healthy subject, compared to control jr154 (simu‐lated trajectory solid line vs. actual recorded trajectory in dotted line).

In a healthy subject the preparatory phase is efficient and the release/suppression of the target/distractor channel is quite fast and complete. Hence the movement is fast. However, following the modelling assumptions that have been discussed above, we can imagine how the same subject would behave in case of PD. For this virtual experiment, we set the basal ganglia parameters to the values that describe a Parkinsonian patient ( 0.05, 2, 1). As shown in Fig. 3 the resulting movement is slower and delayed. The reason for increased delay is that the gating of the P channel occurs later (compare the P channel curves in the two figures). The reason for reduced speed (even without changing T) is because the gating of the P channel is incomplete. The an‐tagonistic channel (S channel) is fully suppressed in this example. However, there may be cases where the S channel is not completely suppressed. If this happens the move‐ment stops before the target (we argued that this might be the reason for extremely stepped trajectories that are observed in more advanced PD). In the simulation below it has also to be noted that oscillations appear in the rest phase (where there are no oscillations in the healthy subject) and no oscillations occur in the preparatory phase (where there is beta in the healthy subject). However, Fig. 3 is an ex‐treme example. For less extreme settings of Da, dis and mpy, reduced and in phase os‐cillations still occur at the preparatory phase (and at rest).



Fig. 3 Simulation of the same subject (jr154) with settings that correspond to a fully Parinsonian basal ganglia

Another virtual experiment of interest is simulating the effect of the distractor. Experi‐mental data (see below) show that the reaction time increases about 40 ms with the distractor. To simulate the response of a healthy subject without distractor, the level of salience of the secondary channel is set to the rest level (no stimulus) in the movement phase. The following Fig. 4 shows the results of such a virtual experiment. In this exam‐ple the movement anticipation is about 20 ms (the exact anticipation depends on the phase of the P channel cortex when the salience steps and may vary for the same sub‐ject). Other subjects may show larger simulated anticipation closer to the average ex‐perimental value of 40 ms.

Fig. 4 Simulation of the same subject (jr154) with health settings and no distractor. The reaction antici‐pates of about 20 ms in this example (the actual anticipation depends on the phase of the motor cortex

at the time the salience is raised at the level of the movement.

2.3 Updated model fitting strategy and first findings

The fitting strategy that was developed with the prototypical Lumped model, had to be substantially updated for use with the parameterized mathematical model of the line task. In fact, the global optimization algorithms that worked with the Lumped model did not work satisfactorily with the parameterized model of the line task for various rea‐sons:

a) The parameters dis and mpy tend to have similar and opposite effect to Da (this

is because dis and mpy vary to compensate Da), and Da and T have similar ef‐

fects; therefore the objective function that represents the distance between the



experimental and simulated trajectory tends to form wide and quite flat valleys,

at least for some subjects.

b) During the optimizations search, the variation of the parameters affect the

phase of the motor cortex channels at the time salience steps up. Hence, small

variations of the model parameter during search can result in locally slightly bet‐

ter or worse fits and the objective function close to the optimum tend to be un‐

dulated with many local minima that are almost equal. It is unclear whether

there actually are many local minima or the bottom of the objective function is

meander‐like. The net effect anyway is that any gradient descent algorithm does

not work and non‐gradient local minimization algorithms (such as e.g. the

Nelder‐Mead) tend to stick on one of these local minima.

c) The computation time for one simulation such as shown in Fig. 3 above is about

30 s for a fast CPU (an i7 processor at 3 GHz) for standard integration accuracy.

The accuracy goal for the integration of the system of equations has thus been

reduced to lesser digits, which allowed the integrator to take larger steps, re‐

ducing the time needed for one simulation to about 8 s CPU. However the re‐

duced accuracy causes some numerical noise that contributed to the irregularity

of the objective function close to the minimum.

We initially tested two global optimization approaches Simulated Annealing (SA) and Differential Evolution (DE) but neither of the two worked satisfactorily. SA required a very large number of iterations and tended to stick anyway in one of the local pseudo minima. DE ran 48 hours for one fit, after which it was stopped and was still far from the convergence.

2.3.1 Exploration of the parameter space

The solution to the above issues was thus to use a robust local minimization algorithm (Nelder‐Mead, NM) to explore the parameter space starting from a regular grid of initial points (an idea somewhat inspired by the Random Search methods where local minima are found starting from random guess points to finally select the best; but in our case, we opted for a regular grid of starting points). One example of this strategy is shown in Fig. 5. The three top panes show projections of the parameter space on the dis‐mpy, dis‐Da and dis‐T parameters. The light grey cross‐es mark the grid of guess starting points: a small simplex was built around each of them to start the NM algorithm which then moved down hill to the nearest local minimum. The blue dots represent very good minima and, in particular, the point circled by red is the best of them and the point circled by brown is the second best. The green dots and the yellow dots are other points where the NM algorithm stopped, but they are related to local minima that are not as good as the blue ones. Overall the charts give an indication of the shape of the objective function valleys (an information that would be lost with SA and DE methods). Also, the spread of the blue dots, or the distance between the best and second best point, indicate whether the fit‐ting problem is more or less ill conditioned. In this example, some uncertainty exists



among the blue dots and, in particular, the best solution (labelled 1131) is somewhat apart from the second best (labelled 100). The rest of the panes in Fig. 5 show the value of the objective function for the best point on a linear scale (bottom left), the experimental and fitting trajectory with anno‐tated fitting parameters and position (x) and velocity (v) residuals, and the individual trajectories that were averaged. The inspection of the individual trajectories gives some insight concerning variations in execution, and whether stepped trajectories happened. The subject UPDRS 3 score is annotated in the top left chart. In some cases, the fit is better conditioned than above, for example, in Fig. 6 top. In other cases, the fit is however very ill conditioned, such as, e.g., in Fig. 6, bottom.

Fig. 5 Location of good fits (blue dots) in the parameter space (see text for detailed explanations of sub charts).

2.3.2 First findings: different types of fits.

The latter example (Fig. 6, bottom) shows the existence of two clusters of local minima, which are two separate valleys in the objective function. One cluster runs somewhat elongated parallel to the dis axis, with small values for the mpy parameter. The other cluster, somewhat round, is located near dis=1.2 and mpy=0.6. In the following, the former cluster (the one elongated parallel to the dis axis) will be referenced as the “dis” cluster. The latter cluster, the one centred at about dis=1.2 and mpy=0.6 will be referenced as the “mpy” cluster.

1 Labels ijk indicate the guess point that originated the solution with the following coding: Da=0.1i, dis=1+0.1j, mpy=0.1k.



Fig. 6 Examples of good and ill conditioned fits (Top/Bottom).

Considering the meaning of the dis and mpy parameters (section 2.2.1) and in particular that they represent two different types of alteration of the connection strengths in the basal ganglia, one can conclude that the two clusters represent two different arrange‐ments of the internal connections that “might” deal with disease compensation. However, the fact that the model predicts two different ways in which connection strengths can be altered to fit the observed trajectories does not necessarily mean that both represent real situations or configurations that are really realized or that both are valid for PD. Fig. 7 shows all the good local minima of the Discovery cohort 1 (all the blue dots of charts like Fig. 6). The two clusters are now shown with filled and empty circle symbols. For the dis cluster a clear correlation is observed between dis and Da (Fig. 7, bottom left), which means that the increase in connection strength (dis) and the decrease of dopamine (Da) are correlated, which might be somewhat expected during the progres‐sion of the disease. With this interpretation, the disease moves from dis=1, mpy=0,



Da=0.3 along the main axis of the dis cluster. Also, as dis increases, mpy increases too, but only slightly.

Fig. 7 Different types of fits. Each point represents a good fit for the Discovery cohort 1. The “dis” clus‐ter is elongated parallel to the dis axis and shows clear correlation of mpy and Da with dis. The “mpy”

cluster is a rounded cluster located at about mpy=0.6 and with very similar values of Da and dis.

The interpretation of the mpy cluster is more problematic. First because there is no clear correlation between the three basal ganglia parameters (the cluster is rounded) which is against the idea that the disease evolves; and second because the cluster is not connected to the healthy state (dis=1, Da=0.3, mpy=0) so that one should assume that this cluster is reached very quickly in the evolution of the disease which then stops there. It might be that this cluster is an artefact of the way the lumped parameters, dis and mpy, have been defined. Alternatively, it may represent a state other than PD. As yet we have no definite answer to this question. On‐going experimental work on the di‐chotomous organisation of the GPe may help to address this question in the near fu‐ture. It would be necessary to map the trajectory of the parameters of many patients for sufficiently long time in order to better understand how the disease evolves and whether mpy is part of Parkinsonian trajectories (while this is not possible for the No‐Tremor cohorts, the NoTremor projects have provided tools for doing this). A contribution to this question may be given in Fig. 8, where the power spectra of the motor cortex oscillations (P channel) is compared for the centre of the dis cluster (left) and the centre of the mpy cluster (right). In the mpy cluster there is no power in the be‐ta band, which also suggests that the mpy cluster is not related to PD.



Fig. 8 Power spectra of the motor cortex P channel as predicted by the model for the centre of the dis cluster (left) and for the centre of the mpy cluster (right). Beta oscillations (and second harmonics) oc‐

cur in the dis cluster. In the mpy cluster only the second harmonics is present.

2.3.3 Selection of the most plausible local minimum

Given a collection of local minima, resulting from the exploration of the parameter space, e.g., such as Fig. 5, they can be considered as candidates for the model parame‐ters of the subject. It is then necessary to select the most plausible point. The obvious choice of taking the global minimum might not be the best choice though. That is because the minimized objective function does not include terms related to oth‐er (not observable) internal variables. For example, the internal potentials will not be accessible in the intended use of the NoTremor system. Hence, by fitting only the tra‐jectory, terms that could further restrain the solution are missing, and we are left with a number of almost equivalent trajectory fits. The method for the selection of the most plausible fit is described below (which is the final result of several trials and errors).

1) First, the points that correspond to good fits are clustered. For example, in the

case of Fig. 6 bottom, two clusters would be obtained. These clusters aim at col‐

lecting all the points that belong to a same objective function basin.

2) The centroid of each cluster is computed.

3) The centroid that lies closer to the dis cluster (Fig. 7) is selected. By doing this

we exploit the a priori expectation that fits will fall in the dis cluster and, also,

we tend to reject points of the mpy cluster, which are unlikely given the above

considerations. Note however that if no candidate point falls in the dis cluster,

points of the mpy cluster are still considered as a last resource (this might be the

case of patients that cannot be fitted in the dis cluster, because, e.g., they might

have a different disease).

4) The candidate point closer to the selected centroid is then selected as the most

plausible fit. This exploits the expectation that a good fit is close to the centre of

the basin it belongs and, also, by taking a point near the centre of the basin we

are likely to reduce errors as this point minimizes its distance to all other points

of the same basin.



Fig. 9 Fitting basal ganglia parameters for the Discovery cohort 1 (labels indicate the three digits of each patient ID). Subjects in red colour are related to lesser good fits.

2.3.4 Computational issues

The NM algorithm requires approximately 100‐300 iterations to find the local minimum nearest to one of the guess points of the grid, which translates to approximately 15‐30 minutes of CPU. After some attempt the grid of initial search points has been located at

1.1, 1.3, 1.5, 1.7, 1.9, 0.1, 0.3, 0.5, 0.7, 0.9, 0.2, 0.1 , which makes about 50 search points. Combinations with 2.2 were often omitted be‐cause they were not necessary in the cohorts under study. This reduced the number of grid points to a typical value of ~40. The CPU time for fitting one subject was, on aver‐age, about 12‐14 hours. By using parallel computation on a 4 cores CPU the time required to fit for example the whole Discovery cohort 1 is about 8 full days (or two week overnights). Multiple computers were used to further parallelize the work but anyway, the limita‐tion was that any modification (for example to the model or to data pre‐processing or to the objective function to be optimized) that required refitting would have given an answer in a couple of weeks. Hence the number of variations that could be tested con‐cerning the mathematical model or the objective function or any regularization term in the objective function was necessarily limited.

2.3.5 Accuracy of parameter fits

In order to have an indicative estimation of the errors that might occur in the parame‐ter estimation procedure a second fitting procedure has been run on the Discovery co‐hort 2 while randomly dropping one of the recorded steps per test. That simulates the possibility that one of the many‐recorded executions forming the mean trajectory might have been not observed (this is a simplification to the random pooling of a given number of events from a population of events2). The advantage of this approach is that the whole chain, measurement to fitting param‐eters, is tested, so that a holistic comprehensive estimate of the errors can be obtained.

2 Developing a complete random pooling accuracy estimation would have required repeating the fitting procedure many times, which was not affordable given the computation times described above.

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The result of this analysis reveals that there are ill conditioned cases (but also cases that are better conditioned). Overall the level of noise is not small. The main reason for the nosy is that the number of averaged trials was necessarily limited. Typically subjects carried out two‐line test (each lasting 2 minutes) as part of a much longer clinical as‐sessment. In a different scenario, such as remote monitoring at home, a much larger number of trials can be averaged (see section 2.6 for an assessment of how much the noise can be reduced in continuous monitoring scenarios).

Fig. 10 Accuracy of parameter estimation. Arrows show the displacement of the fitting parameters when omitting on trial in the recorded tests (see text).

Fig. 1 shows the displacement of the fitting parameters when omitting one trial in the two‐recorded test. The subject with the largest variation in the estimated parameters is mk188. It is interesting to look at the fitting charts of this subject (Fig. 11) because they can help understanding why the parameter estimation vary. In particular, the compari‐son shows that one borderline trajectory happened to be dropped between the two fits. That had a large impact on the mean trajectory (that was computed by averaging only 17 trials). As a general conclusion, a larger number of trials (such as when using the system for monitoring) should be used, and may help reducing the fitting noise.

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Fig. 11 Comparison of the fitting charts of a subject with large variation in the estimated parameters reveal that the removal of one borderline trial may affect the mean trajectory enough to significantly

alter the fits (here the mean trajectory was computed from 17 trials).

2.4 Correlation and Levodopa study (Oxford Discovery and Surgery co‐horts)

The data presented here were those from the Discovery cohort that were available for analysis. The parameters of the mathematical model derived from fitting the line test data are summarised in the table 1.



Hidden Parameter

Mean SEM

Da 0.190 0.018

dis 1.151 0.033

mpy 0.156 0.039

T 0.679 0.050

Tab. 1 Group hidden parameters for the 29 patients in the first test campaign recruited from the Dis‐covery cohort. SEM is standard error of the mean.

These parameters were tested for their ability to predict the total motor UPDRS III scores within the same (first test campaign) cohort, by way of validating the model and its utility. The total motor UPDRS III score was selected as (statistical) prediction of this based on manual performance in a single, simple task would be a challenging test of our approach. The UPDRS III is the most regularly used instrument for the assessment of PD, and is regularly used in the clinic and in studies to evaluate the presence, severity, progression and treatment of PD symptoms. However, the UPDRS takes time to per‐form, involves trained clinical staff, is not objective and is difficult to repeat with any frequency due to its investigator and location dependency. Thus, should analysis of a simple test that can be easily and regularly performed at home, provide reliable esti‐mates of UPDRS scores this would be of considerable value to the clinical and research communities in the field. Strikingly, a multiple linear regression model performed with all four model parameters and total UPDRS III score as the dependant variable was able to predict almost 40% of the variance in UPDRS (r2 = 0.376; F[4,24] = 3.619, p = 0.019; Fig 12). As a benchmark, this is similar to the value of the FDA and EU approved Parkin‐son’s Kinetigraph (PKG; GlobalKinetics Corporation) in predicting UPDRS scores, except such accurate prediction with the latter device requires 10 days of continuous record‐ing.3

3 Griffiths RI, Kotschet K, Arfon S, Xu ZM, Johnson W, Drago J, Evans A, Kempster P, Raghav S, Horne MK.

Automated assessment of bradykinesia and dyskinesia in Parkinson's disease. J Parkinsons Dis. 2012;2:47‐55.



Fig. 12 A multiple linear regression model performed with all four model parameters and total UPDRS III score as the dependant variable was able to predict almost 40% of the variance in UPDRS. See Table 2 for further details of regression model (point labels identify the last three digits of the subjects IDs).

Tab. 2 A multiple linear regression model performed with all four model parameters and total UPDRS III score as the dependant variable was able to predict almost 40% of the variance in UPDRS (r2 = 0.376;

F[4,24] = 3.619, p = 0.019). Data from 29 patients in the first test campaign.

As can be seen from table 2, Da, dis and mpy made significant contributions to the line‐ar regression model and T trended in this direction. Note that Da and dis had the big‐gest effect and that increases in both were associated with decreases in UPDRS. A re‐gression model without T was also significant, but captured less of the variance in UP‐DRS (r2 = 0.284; F[3,25] = 3.202, p = 0.037). The linear regression performed with all four model parameters and total UPDRS III as the dependant variable was not bettered by a linear regression model based on multi‐

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UPDRS III

PredictedUPDRSIII

Model

Unstandardized Coeffi‐

cients

Standardized

Coefficients

t Sig. B Std. Error Beta

1 (Constant) 86.809 18.842 4.607 .000

Da ‐78.045 26.857 ‐.849 ‐2.906 .008

dis ‐53.037 16.236 ‐1.035 ‐3.267 .003

mpy 27.671 8.595 .653 3.220 .004

T 14.803 7.848 .441 1.886 .071



ple behavioural parameters extracted from the same line task performance (r2 = 0.367; F[5,23] = 2.663, p = 0.049; table 3).

Coefficients

Model


cients

Standardized

Coefficients


1 (Constant) 8.899 20.353 .437 .666

RT SD ‐67.956 52.932 ‐.500 ‐1.284 .212

PK Speed 10.237 4.485 .728 2.283 .032

SD of PK

Speed ‐29.467 12.966 ‐.610 ‐2.273 .033

Time of PK

Speed ‐13.152 19.867 ‐.206 ‐.662 .515

SD of

Time of PK

Speed

148.495 54.867 1.322 2.706 .013

Tab. 3 A multiple linear regression model performed on behavioural parameters extracted from the line task performance and total UPDRS III score as the dependant variable. This was able to predict al‐most 40% of the variance in UPDRS (r2 = 0.367; F[5,23] = 2.663, p = 0.049). Data from 29 patients in the

first test campaign.

Linear regression with a reduced set of behavioural parameters (Peak Speed, Standard Deviation of Peak Speed and Standard deviation of Time of Peak Speed) also led to sig‐nificant prediction of total UPDRS III (r2 = 0.302; F[3,25] = 3.608, p = 0.027). From this we conclude that the mathematical model was highly efficient in capturing the ability of performance in the line task to predict total UPDRS, and could achieve this using a bio‐logically informed set of hidden parameters. However, given that behavioural performance in the line task offered comparable pre‐dictive value to the hidden parameters of the lumped model, the question arises is there any additional value to be had from the hidden parameters? The nature of the small mpy cluster may be informative in this regard. Previously (see section 2.3.2) it was noted that there is no correlation between this cluster and the other three hidden pa‐rameters, which infers that there is no systematic progression in mpy values in this clus‐ter as other parameters change with disease progression. Moreover, the power spectra of the motor cortex oscillations (P channel, Fig 1) for the mpy cluster failed to show a spectral peak in the beta frequency band, something that is expected in Parkinson’s disease. In contrast, mpy values in the dis cluster correlated with other hidden parame‐ters and were associated with a beta frequency band peak in the P channel. This sug‐gests the hypothesis that the small mpy cluster might be due to patients in whom per‐formance of the line test was compromised by a different pathophysiological process. We know that perhaps 10‐20 % of the patients diagnosed with PD will turn out to have a different pathophysiology. Modestly in favour of the latter is that if we take the inde‐



pendently determined ‘probability of PD’ score across the patients then 40% of those in the dis cluster have 90% probability (instead of 95%), whereas this rises to 60% for those in the mpy cluster. Patients with a misdiagnosis also tend to have a shorter dis‐ease duration. Disease duration in those in the dis cluster was 3.5 years, whereas for those in the mpy cluster this was 3.0 years. Similarly disease duration from diagnosis was 1.9 years for those in the dis cluster, but only 1.4 years for those in the mpy cluster. However, probably due to the small number of cases (n = 5) in the mpy cluster, none of these differences were significant. Given the above suggestive findings, we performed an exploratory analysis in which we repeated the multiple linear regression model with all four model parameters and total UPDRS III as the dependant variable, but with the five values in the mpy cluster exclud‐ed. The same multiple linear regression as before was now able to predict almost 50% of the variance in UPDRS (r2 = 0.476; F[4,19] = 4.319, p = 0.012). All four parameters of the model contributed significantly to this relationship (table 4).



Coefficients

Model


cients

Standardized

Coefficients


1 (Constant) 86.040 21.399 4.021 .001

Da ‐70.907 31.401 ‐.631 ‐2.258 .036

dis ‐58.546 17.435 ‐1.103 ‐3.358 .003

mpy 61.486 18.380 .614 3.345 .003

T 20.795 8.590 .531 2.421 .026

Tab. 4 A multiple linear regression model performed with all four model parameters and total UPDRS III score as the dependant variable was able to predict almost 50% of the variance in UP‐DRS (r2 = 0.476; F[4,19] = 4.319, p = 0.012) when the five values in the mpy cluster were excluded

(giving data from 24 patients from the first test campaign).

Together the above findings raise the possibility that the distribution of hidden parame‐ters in the mathematical model can identify those patients who may potentially turn out not to have Parkinson’s disease. This is particularly exciting as the patients in this cohort had relatively short disease histories, where misdiagnosis is more common. Whether model parameters can help identify those patients who turn out not to have Parkinson’s disease, but rather a mimic such as multiple system atrophy or progressive supranuclear gaze palsy, will require long‐term follow‐up and post‐mortem studies.

2.4.1 Changes over time

Hidden Parameter

Mean SEM

Da 0.197 0.097

dis 1.154 0.082

mpy 0.201 0.086

T 0.646 0.083

Tab. 5 Group hidden parameters for the 12 patients in the second test campaign recruited from the Discovery cohort about 18 months following the first test campaign.

To explore whether the hidden parameters might be expected to change over time we derived these in a second cohort of 12 patients drawn from the Discovery cohort 18 months after those included in the first test campaign. When these were compared to the 29 patients in the first campaign, there was no significant difference in the hidden parameters (compare table 5 with table 1). In addition, there was no significant predic‐tion of disease duration in a multiple linear regression model performed with all four model parameters and disease duration as the dependant variable, probably because of the small sample size.



The lack of change in the hidden parameters over time in the two cross‐sectional sam‐ples may be because of the considerable variability between subjects and the fact that the bulk of striatal dopaminergic denervation and loss of dopaminergic neurons in the ventrolateral tier of the substantia nigra compacta has already occurred within 1‐2 years of diagnosis.4 Thus the effect size to be detected is likely to have been small. We can help overcome the above by either contrasting hidden parameter estimates from samples in which we expect a bigger effect size, or by controlling for inter‐individual variance by looking for prospective differences in paired samples. One possi‐ble way to approach the former is to contrast the results of the 29 patients in the first test campaign with those from the 13 patients in the surgery cohort who also per‐formed the line task on medication. Mean disease durations and median Hoehn and Yahr scores were 3 years and II in the first group, and 13 years and III in the second group, and yet the model parameters did not differ significantly between the two groups. However, the effect size to be detected in this contrast between first test cam‐paign and surgical cohorts may have been less than anticipated. First, the rate of striatal dopaminergic denervation and loss of dopaminergic neurons in the ventrolateral tier of the substantia nigra compacta has already become very slow by about 5 years following diagnosis,4 and second, patients studied following implantation of deep brain stimula‐tion electrodes have temporarily altered basal ganglia circuits through the stun effect of surgery.5 The latter is evidenced by the sometimes substantial temporary improvement in parkinsonism, and may have compromised our modelling. Importantly, seven subjects prospectively performed the line task both in the first test campaign and ~18 months later (both times on medication). Their data are summarised in table 6. Within this small‐paired subgroup Da fell in every subject over the course of the 18 months (Related samples Wilcoxon signed‐rank test for Da p = 0.018, rest N.S.).

Hidden Parameter

First Campaign 18 months later

Mean SEM Mean SEM

Da 0.210 0.025 0.125 0.022

dis 1.156 0.076 1.232 0.027

mpy 0.137 0.093 0.068 0.058

T 0.711 0.134 0.610 0.061

Tab. 6 Group hidden parameters for the seven patients who performed the line task at enrolment in to the Discovery cohort and again ~18 months later (mk229, pdsjr432, jr 434, jr 441, jr 464, jr 436 and jr

469). Embolded estimates are significantly different.

4 Kordower JH, Olanow CW, Dodiya HB, Chu Y, Beach TG, Adler CH, Halliday GM, Bartus RT (2013) Disease duration and the integrity of the nigrostriatal system in Parkinson's disease. Brain 136:2419–2431. 5 Mestre TA, Lang AE, Okun MS. (2016) Factors influencing the outcome of deep brain stimulation: Place‐bo, nocebo, lessebo, and lesion effects. Mov Disord.31:290‐296.



This result helps validate the hidden parameter Da which models the effect of dopa‐mine as a modulation of the input to the two striatal neuronal populations. Although Da cannot directly be equated with the effect of dopaminergic denervation nor therapy, because these processes impact on more than just these two neuronal populations, it is reasonable to predict Da will fall with decreasing dopaminergic activity over the course of PD. However, this result needs be validated in the future in a larger group of patients who are prospectively studied each time following acute withdrawal of medication for PD.

2.4.2 Acute Changes with levodopa

The above first test campaign and 18 months cohorts were studied on medication for PD, when this was prescribed. What happens to the hidden parameters of the model when patients are recorded both on and off drugs? A small pilot cohort of nine patients performed the line task while they were off medication and then repeated the test af‐ter taking their usual medication. This was performed during the same morning in an abbreviated levodopa test. We use the term abbreviated here, as a supramaximal does of levodopa was not given as is often the case in standard levodopa tests. Accordingly, the change in UPDRS III on medication (30 ± [SEM] 5 % improvement) was also rather less than seen when supramaximal doses of levodopa are given after overnight with‐drawal of medication (40‐60 % improvement) in standard levodopa tests. Perhaps for this reason (small effect size in a small sample) the change in hidden parameters did not significantly predict the change in UPDRS following levodopa, whether absolute or per‐centage changes were considered in a multiple linear regression model with all four hidden parameters and total UPDRS III as the dependant variable. Moreover, none of the hidden parameters changed significantly between states, as shown in the Table.

Hidden Parameter

OFF ON

Mean SEM Mean SEM

Da 0.161 0.011 0.168 0.025

dis 1.204 0.056 1.195 0.094

mpy 0.162 0.057 0.080 0.058

T 0.683 0.062 0.588 0.058

Tab. 7 Group hidden parameters for the nine patients who performed the line task in the second test campaign both off and on levodopa. Da was marginally lower off levodopa, but none of the hidden pa‐rameters differed significantly between the treatment conditions. (jr018, jr030, jr434, jr441, jr457,

jr459, jr460, jr464, jr469)

In addition, ten post‐surgical patients performed the line task both after overnight withdrawal of levodopa and again the same morning after a supramaximal challenge of levodopa (100mg more than their standard morning dose). Thus, these subjects under‐went a standard levodopa test, but as previously noted these patients also differed from those in the Discovery cohort in so far as they had much longer disease histories (10±[SEM]2.2 years), more severe impairment prior to surgery (mean UPDRS motor



score OFF medication 42±4.6), bigger reduction in motor UPDRS on treatment prior to surgery (61±6.1%) and confounding stun effects after surgery. One of the 10 patients was excluded due to poor fitting in the off medication condition (RMSE four times greater than the mean RMSE of the remaining nine patients off medication).

Hidden Parameter

OFF ON

Mean SEM Mean SEM

Da 0.117 0.034 0.173 0.042

dis 1.363 0.075 1.154 0.063

mpy 0.224 0.038 0.163 0.074

T 0.577 0.080 0.530 0.049

Tab. 8 Group hidden parameters for nine post‐surgical patients who performed the line task both off and on levodopa. Da was lower off levodopa. Da was significantly increased and dis decreased on med‐

ication.

Despite the stun effect Da rose on medication in seven out of the nine remaining sub‐jects (table 8; Friedman’s test on change in parameters, p = 0.05; post‐hoc related sam‐ples Wilcoxon signed‐rank test for Da p = 0.038). In addition, the parameter dis de‐creased on medication (post‐hoc related samples Wilcoxon signed‐rank test for dis p = 0.038; mpy and T p>0.3). This last finding is interesting as it suggests that the model can pick up dopaminergic effects other than those parameterised through Da, which cap‐tures the classical effects of dopaminergic input to the striatum. As these additional dopaminergic effects are expressed in the parameter dis they must necessarily relate to relatively proportionate and acute weakening of cortex‐striatum, STN‐GPe, GPe‐STN, GPe‐GPi, and STN‐GPi connection strengths on levodopa. The predominant dopamine receptors in STN and GPe are D2 receptors. Thus, one would expect that the activity in their efferent target nuclei would be changed less in the presence of Levodopa, since their firing rates would have been reduced. This is in agreement with the fitted values of dis, which are smaller when the model is fitted to patients on Levodopa medication. The direction of change in Da and mpy with levodopa was also similar to that in the non‐surgical, Discovery patients who underwent the abbreviated levodopa test, alt‐hough in the latter case the changes were smaller and non‐significant. Note that failure to assess the motor UPDRS during the levodopa test in the post‐surgery patients pre‐vented us from exploring whether the change in hidden parameters predicted the change in UPDRS following levodopa in this group. The same nine patients also had recordings of local field potentials in the GPi (n=7) or STN (n=2) during the levodopa test. These, like recordings from the same sites in other patients with PD, had spectral peaks within the beta band (15‐35 Hz) OFF medication. Such activity is thought to be exaggerated in PD and is suppressed by dopaminergic therapy and deep brain stimulation. Moreover, the change in its power with treatments correlates with clinical improvement. The questions therefore arise do any of the hid‐den parameters in the OFF medication state predict the change in local beta activity



with medication, and, does any change in these parameters with medication correlate with the change in local beta power? To address the first question we performed a multiple linear regression model with all four hidden parameters estimated in the OFF medication state and the % change in the peak in the beta band (15‐35 Hz) power at rest with treatment in the nucleus (STN or GPi) contralateral to performance of the line task as the dependant variable.6 Strikingly, this model was able to predict 85% of the variance in the relative change in beta power (r2 = 0.854; F[4,8] = 5.831, p = 0.058; Tab 9). However, as only mpy made a significant contribution to the linear regression model, we next performed a simple regression be‐tween mpy and beta band power off medication. This was significant (r2 = 0.657; F[1,7] = 13.423, p = 0.008; Fig 13).

Model

Unstandardized Coefficients

Standardized

Coefficients


1 (Constant) -1.180 .708 -1.666 .171

Da .100 1.516 .029 .066 .951

dis .489 .385 .458 1.272 .272

mpy 1.422 .409 .971 3.473 .026

T -.168 .249 -.137 -.674 .537

Tab. 9 A multiple linear regression model performed with all four model parameters and average beta band (15‐35 Hz) power at rest OFF medication as the dependant variable was able to predict 90% of

the variance in UPDRS (r2 = 0.933; F[4,8] = 14.002, p = 0.013).

Next we explored the second question posed above; does any change in the hidden pa‐rameters with medication correlate with the change in the peak in local beta power? We therefore performed a multiple linear regression model with the change in the four hidden parameters induced by medication and the % change in the peak7 in beta band power at rest in the nucleus with treatment contralateral to performance of the line task as the dependant variable. This model was able to predict 71% of the variance in the relative change in beta band power, but this did not reach significance given the 9 data sets and the four factors (r2 = 0.688; F[4,8] = 2.210, p = 0.231). Nevertheless, mpy demonstrated a trend to significance in this model (p=0.068), and so we performed a simple linear regression with change in mpy alone. This demonstrated a significant cor‐relation with the change in beta (r2 = 0.451; F[1,7] = 5.753, p = 0.048). All things consid‐ered, the weaker correlation when considering the change in mpy induced by medica‐tion raises the possibility that our parameter estimates were less secure in the ON drug

6 Note that LFP power is best represented as relative, either as % total power or as % change on medica‐tion, otherwise correlations are acutely confounded by the proximity of the electrode to the LFP genera‐tor. 7 Mean of peak frequency and two 1Hz bins above and two 1 Hz bins below this, with peak frequency de‐fined in the OFF state. % change derived as (ON beta‐OFF beta)/OFF beta



state. One reason why this might be in this particular cohort of patients may be the presence of dyskinesias during the line test performance on medication, as the exist‐ence of prominent dyskinesias are often an indication for PD surgery.

Fig. 13 Linear regression model performed with mpy and beta band LFP power as the dependant varia‐ble was able to predict two thirds of the variance in beta band power.

An aspect of these findings that deserves more comment is the paradox that, although the hidden parameters Da and dis rose and fell upon treatment, respectively, it was on‐ly mpy that correlated with the change in beta activity in the basal ganglia upon dopa‐minergic therapy. The parameter mpy models the imbalance between input connection strengths to Ti (prototypic) and Ta (arkypallidal) neurons in the globus pallidus externa (GPe), and our findings imply that beta oscillations may be more sensitive to this imbal‐ance than to the combined direct effects of Da and dis. This underscores the value of considering several key parameters in the same model; some, like Da and dis, may work in opposite directions, so that the emergent effects on other features may not neces‐sarily be straightforward. Summary of Discovery Cohort Findings We can conclude from the first and second test campaigns drawn from the Discovery cohort that:

1) Group hidden parameter estimates remain broadly similar across different pa‐

tient samples (contrast tables 1, 5 and 7).

2) Hidden parameter estimates correlate with total motor UPDRS scores (Fig 12

and table 2). Similar correlations are seen with the behavioural parameters de‐

rived from the line task (table 3).



3) The mathematical model has potential advantages over simple behavioural pa‐

rameters from the line task, in

a. the suggestion that the mpy cluster might indicate a different patho‐

physiology and hence misdiagnosis of PD (table 4 and section 2.3.2),

b. the ability of a second hidden parameter, Da, to demonstrate disease

progression in prospectively followed patients (table 6).

c. the increase in Da and decrease in dis after a supramaximal challenge of

levodopa (table 8).

4) Overall, the UPDRS can be predicted from a 4‐dimensional parameter space

map onto one “predicted” value of the UPDRS score. This means that the same

UPDRS can actually be predicted from different combinations of hidden param‐

eters. This in itself is not implausible. The evolution of PD tends to activate com‐

pensatory mechanisms (such as e.g., reduction of Da induces increase of dis).

Hence, at the macroscopic level, the same UPDRS score occurs for different

points in the disease space, which however can be discriminated by the No‐

Tremor system, allowing in principle more resolution in tracking changes that

have not yet to affect the UPDRS score. This attribute may prove particularly

important in the characterisation of early and ‘pre‐symptomatic’ PD, where in‐

ternal compensation can effectively mask clinical deficit or its progression for

several years. To be able to track the hidden changes related to the disease

process would be of major importance in the development and testing of dis‐

ease modifying treatments.

5) However, the first and second test campaigns and the surgical cohort were un‐

der‐powered for definitive conclusions to be made, given the inter‐subject vari‐

ability in the hidden parameters of the lumped model. They do, though, serve to

motivate further exploration of the lumped model and its applications in more

extensive patient cohorts. Key here, will be further

a. Prospective study of the sensitivity of the model parameters to change

over time in more patients

b. Follow‐up of patients with parameter estimates in the mpy cluster to de‐

termine whether these are mis‐diagnosed as PD

c. Sufficiently powered study of on and off medication effects in patients

that are not post‐operative, in whom the contrast between states is

more marked (full overnight withdrawal of medication and challenge

with a supramaximal dose of levodopa).

d. refinements to the line‐task, particularly exploration as to whether re‐

peated testing can improve the prediction of UPDRS scores (see also sec‐

tion 2.6).

e. Investigation of the finer structure of the association of different hidden

parameters with different elements of the PD phenotype as inferred

from UPDRS sub‐scores in a sufficiently powered sample.



2.5 Statistical and correlation analysis for force and line tests (Oxford and Thessaloniki cohort)

In addition to the correlation analysis presented in section 2.3.1, supplementary statis‐tical analysis was conducted for the Oxford and Thessaloniki cohorts. The correlation study considered model parameters, clinical data and several features extracted from the force and line tests. Feature extraction methodology has been described in details in Deliverable 4.2.1 and metric variables of time series analysis have been presented in Deliverable 4.2.2, thus these topics are only referred to in this section.

2.5.1 Force tests analysis

Force tracking tests were performed on the Oxford and the Thessaloniki Cohorts, with test data from both patients and controls. Three subsets of these cohorts are consid‐ered here, excluding patients that did not perform all the tests. Details with respect to number of participants, the existence of controls in the dataset and type of scale are presented in Tab. 10.

cohort / dataset location # participants # experiments controls included Hoehn & Yahr values

COHORT 1 / DATASET 1 Thessaloniki 52 203 yes (10) 0, 1, 2, 3

COHORT 2 / DATASET 2 Thessaloniki 36 139 no 1, 2, 3

COHORT 3 / DATASET 3 Discovery 20 80 yes (3) 0, 1, 2

Tab. 10 Datasets performed force tracking test.

During the execution of the force test the following variables are measured: target stimulus, distractor and the patient’s force response. The distractor is only used to differentiate the experiments according to its presence or absence. The target stimulus and response signals are used to define the “deviation” signal (d), which represents the absolute value of the error of between the target and produced force, as better described in Deliverable 4.2.2.

| | Many types of features can be extracted from d. According to Deliverable 4.2.189, four features are used here: mean (d), standard deviation (d), skewness(d) and kurtosis(d).

2.5.2 Correlation of force test features with the Hoehn & Yahr scale

The Shapiro‐Wilk method was used to assess normality of the 4 features and the Hoehn & Yahr scale (HY). The variables do not follow the normal distribution, and thus the

8 Wang, Xiaozhe, Kate Smith, and Rob Hyndman. "Characteristic‐based clustering for time series data." Data mining and knowledge Discovery 13.3 (2006): 335‐364. 9 Wang, Xiaozhe, Kate Smith‐Miles, and Rob Hyndman. "Rule induction for forecasting method selection: Meta‐learning the characteristics of univariate time series." Neurocomputing 72.10 (2009): 2581‐2594.



Kruskal‐Wallis test was selected to analyse possible correlations10. The Kruskal‐Wallis test is a non‐parametric counterpart of the 1‐way ANOVA, and it is used to compare medians of multiple (more than two) non‐parametric distributions. By comparing the medians of the distributions of our experimental data, low p‐values from Kruskal‐Wallis test indicated the existence of differences in the medians between each feature grouped by the H&Y scale. Furthermore, show the results with variable significance (no‐tation: * p<0.05 significant, ** p<0.01 more significant, *** p<0.001 very significant). Tab. 11, Tab. 12 and Tab. 13 show the results of the Kruskal‐Wallis test. Three different experiments were carried‐out on three different cohorts:

Any: the whole dataset is used

No: experiments without the distractor

Yes: experiments with the distractor

Tab. 11 Hypothesis testing results of Thessaloniki Cohort 1.

Tab. 12 Hypothesis testing results of Thessaloniki Cohort 2

10 http://www.statisticssolutions.com/kruskal‐wallis‐test/

Dataset 1

distracter presence feature p‐value (Kruskal‐Wallis test) significance

any

mean 0.00003161 ***

standard deviation 0.02066000 *

skewness 0.00003464 ***

kurtosis 0.00008195 ***

no

mean 0.00118600 **

standard deviation 0.05191000

skewness 0.00163000 **

kurtosis 0.00215200 **

yes

mean 0.03682000 *


skewness 0.03012000 *

kurtosis 0.05369000

Dataset 2


any

mean 0.00181600 **


skewness 0.02132000 *

kurtosis 0.14750000

no

mean 0.06334000


skewness 0.02409000 *

kurtosis 0.07203000

yes

mean 0.02047000 *


skewness 0.05605000

kurtosis 0.06422000



Tab. 13 Hypothesis testing results of Oxford Cohort

significance symbols: * p<0.05, ** p<0.01, *** p<0.001

In the vast majority of the cases presented above the results show statistical significance, espe‐

cially in the first case where all the test data are analysed together. The boxplots in Fig. 14 show the difference between the medians of participants’ groups, relatively to their H&Y score. The results are identical and suggest the following conclusions:

The mean of the deviation increases as PD severity increases as measured by H&Y.

The stdev of the deviation increases as PD severity increases.

The skewness of the deviation decreases as PD severity increases. The kurtosis of the deviation decreases as PD severity increases.

Dataset 3


any

mean 0.00002210 ***

standard deviation 0.00129400 **

skewness 0.00290600 **

kurtosis 0.00814700 **

no

mean 0.00183800 **


skewness 0.02549000 *

kurtosis 0.05586000

yes

mean 0.00919300 **


skewness 0.06957000

kurtosis 0.13450000



Fig. 14 Boxplots of the force test results. Each row shows the differentiation between HY scale of each dataset and one of the four features extracted from the time series analysis: mean, stdev, skewness,

kurtosis.

In conclusion, the analysis shows that the force tracking test can successfully contribute to Parkinson’s Disease evaluation. Time series features extracted from deviation can be used to distinguish PD patients from controls, as well as to monitor the severity of the disease in terms of the Hoehn & Yahr scale.



2.5.3 Line test correlation analytics

The line test correlation analysis was performed on the same cohorts described in sec‐tion 2.5.1, namely the Thessaloniki and Oxford Cohorts. In this case a large number of clinical parameters are considered for potential correlations. They are listed in Tab. 14. The diagrams presented in Appendix 1 (Fig. A1.1 to A1.12), have been used for the discovery of potential correlations between the considered variables (parameters, clinical scales and met‐rics). These table‐like figures show the correlations between each couple of variables, indicating how variables how variables are correlated and if the correlation is statistically significant. Namely, a green ellipsis show good and significant correlation, a blue ellipsis show a lower cor‐relation but remaining statistically significant, a cyan ellipsis show some correlation which how‐ever lacks statistical significance, while the red ellipses indicate the absence of any significant correlation. 1) For the Discovery cohort 1. UPDRS III correlates well with Purdue total, age, H&Y, as well as model’s mpy parameter (individually taken11). H&Y shows good correlation with age, disease duration onset/diag, Purdue total, getgo average and getgo best (Fig. A1.1). Correlation between model parameters is found between Da‐ , Da‐dis and dis‐T. Other signifi‐cant correlations are found between ‐disease duration onset, Da‐Purdue assembly and T‐getgo‐best (Fig. A1.1). 2) For the Discovery cohort 2. UPDRS III correlates well with age, as well as model’s dis parame‐ter. H&Y does not show any significant correlation12. Correlation between model parameters is shown between Da‐dis and mpy‐T. Other significant correlations are found between Purdue to‐tal‐flamingo time/EQ5D index, Purdue assembly‐flamingo time and flamingo time‐EQ5D index (Fig. A1.2). 3) For the Discovery cohort 2, Levodopa OFF (Fig.A1.3) and Levodopa ON (Fig. A1.4) the correla‐tions found are controversial (However for the Levodopa Discovery cohort considerations made in section 2.4 and in D6.1 may provide an explanation for lack of correlation). 4) For the Surgery cohort, Levodopa OFF (Fig.A1.6) and Levodopa ON (Fig. A1.7) reveal correla‐tions between model parameters (considerations made in section 2.4 and in D6.1 hold). 5) For the Thessaloniki cohort 1 (Fig. A1.8, Fig. A1.9). H&Y does not show any significant correla‐tion. Correlation between model parameters is shown between Da‐dis, Da‐mpy and less corre‐lation between Da‐ , dis‐mpy. 5) For the Thessaloniki cohort 2 (Fig. A1.10, Fig. A1.11). H&Y does not show any significant cor‐relation. Correlation between model parameters is shown between Da‐dis. Summarizing these findings we can draw the following conclusions:

1. UPDRS III shows correlation with model’s dis and mpy (individually) and other clinically measured data.

2. There is a correlation between model parameters, meaning that the evolution, in the parameter space follows some rules.

11 One must note that lack of correlation with individual model parameters does not necessarily indicate

model deficiencies. In fact, the expected correlation is between the vector of model parameters together {Da, dis, mpy, T} – which represents the disease point in the parameter space – and the other observable metrics and scales. 12 There are three main reasons why findings might differ between the two Discovery campaigns/cohorts. First the number of patients differs so that the same relationship may be significant and insignificant in the bigger (cohort 1) and smaller (cohort 2) samples, respectively. Second a large number of correlations were per‐formed without correction for multiple testing, thereby increasing the risk of Type I and Type II errors. Third, cohort 2 was recorded when disease was more advanced (by ~ 18 months).



3. H&Y does not seem to correlate well with model parameters. 4. There is a correlation of model parameters with other clinically measured variables.

These results demonstrate that the proposed analysis approach is useful to investigate possible relations between a large number of different variables, and it can be used for fast visual in‐spection of correlations on large data sets.



Variable Name Data Type Variable description

SubjID String Unique patient identifier

Visit_date Date Date of visit clinical assessment

Gender binary: {Male, Female} Gender

age continuous Age at assesment in years

disease_duration_onset continuous Disease duration since onset in years

disease_duration_diag continuous Disease duration since diagnosis in years

purdue_total ordinal: 0, no upper bound

Total of the first three Purdue pegboard test sub‐tests. Test to measure manual dexterity. A higher score corresponds with better dexterity

purdue_assembly ordinal: 0, no upper bound

Total for the assembly part of the Purdue pegboard testTest to measure manual dexterity. A higher score corresponds with better dexterity

getgo_average continuous Get up and Go Test. Time (minutes) taken for an in‐dividual to get up from a chair, walk three meters, turn around, walk back to the chair and sit down. Average of three attempts. A higher score corre‐sponds with worse motor function.

getgo_best continuous Best time from the three get up and go tests, see above.

flamingo_time continuous: 0 to 30 secs Flamingo test. Time (seconds) that a person can stand on one leg. If the patient can do this for 30 seconds the test is stopped. A lower score corre‐sponds with worse motor function.

EQ5D_index discrete EuroQol health states. Quality of life index. 5 ques‐tions rated 1‐3 and then transformed into index us‐ing an equation

EQ5D_vas_score continuous, bounded 0 to 100

Health quality visual analogue scale, 0 is worst health, 100 is perfect health

UPDRS_III ordinal: 0 to 132 MDS UPDRS part III "Motor Examination". 33 ques‐tions rated 0‐4. A higher score corresponds with worse motor problems. Clinician assessed.

Hoehn_and_yahr ordinal: 0 to 5 Hoehn and Yahr stage. Parkinson's disease severity. 0 is no disease; 5 is bedridden

Schwab_england ordinal: 0 to 100 Modified Schwab & England Activities of Daily Living. Variable is measured from 0% to 100% in 10% in‐crements. 0% is bedridden, 100% completely inde‐pendent

Freezing_gait_total ordinal: 0 to Freezing of Gait Questionnaire. 6 Questions rated 0‐4. A higher score corresponds with worse freezing of Gait.

probability_pd Probability: 0 to 100 % Probability that the individual has idiopathic Parkin‐son's Disease as rated by a clinician at this visit.

LEDD_total continuous, mg per day Levodopa equivalent daily dose, a quantitative measure of the amount of PD medication a person is taking.

Tab. 14 Description of some clinical parameters used for correlation analysis of experimental data.



2.6 Longitudinal monitoring (Santa Chiara cohort)

The longitudinal study was carried out in Trento with 4 PD subjects, 2 control subjects with a different disease (Essential Tremor and Cerebellar Ataxia) and 4 age matched healthy controls (the Santa Chiara cohort). Concerning the 4 PD subjects, this study simulates what might be the use of the No‐Tremor system in practice: i.e., subjects carry out tests at home daily (the testing ses‐sions may last one week and be interleaved with several longer periods). The test data of the subjects is collected remotely and analysed using both objective metrics derived from the test logs (in particular reaction time, error rates and peak speed) and fitting the model on the test data to obtained personalized parameters.

2.6.1 Monitoring hidden brain states

Fig. 15 demonstrates the use of the NoTremor system for the monitoring of the brain connection strengths over time. For this, tests are grouped on weekly basis and the av‐erage trajectory of each week is fitted. Over a range of 82 weeks an evolution can be observed (an animation is provided sepa‐rately), where Da slightly increases (from 0.2 to 0,25), dis slightly decreases (from 1.2 to 1.1) and T decreases too (from 0.64 to 0.52 s). In other words, the performance of this subject appears to improve somewhat in 1.5 years. This is confirmed by inspection of the mean and individual trajectories. On week 82 the movement speed is faster and the landing on the target is sharper (the trajectory is more rounded on week 1 – which is a sign of lower Da). Also, trajectories on week 1 show irregularities (hints of stepwise be‐haviour) that are not visible on week 82.



Fig. 15 Monitoring of patient’s SC665 model parameters over time. Top: initial parameters on week 1. Bottom: final parameters on week 82 (grey dots represent the parameters of previous weeks).

Hence, overall, this subject improved his performance (might be due to better treat‐ment). The subject UPDRS was rated 7 on March 19 2015 and 14 on July 22 2015, H&Y is 1. One should also note that the level of noise is reduced compared to section 2.3.5 (that can be better appreciated in the video). The reason is that here we average one week of tests per fit: that is 10 times more tests that those carried out in the Discovery cross sectional study. Fig. 16 plots the evolution of Da and dis parameters for the subject in the 82 weeks of monitoring. There are some intervals (one in particular) with missing data, which how‐ever do not affect the conclusions. Da increases from 0.2 to about 0.25 while dis de‐creases from 1.2 to about 1.1 (both variations are in the direction of improved behav‐iour). Most of improvement occurs in the earlier months.



Fig. 16 Evolution of Da (top) and dis (bottom) over 1,5 years for one subject.

To confirm the findings of the previous figures –and as a first example of dense tem‐poral objective monitoring– objective metrics derived from the tests are plotted in Fig. 17. From top to bottom, the figure shows: a) the error rate, which decreases in the 1.5 years of monitoring (which agrees with the improvement detected by the model pa‐rameters; b) the peak speed, which improves with time; c) the reaction time (which in‐creases with time).



Fig. 17 Evolution of error rates, peak speed and reaction time of one subject.

The latter is worth a comment: as shown in the model (Fig. 3) the basal ganglia affect only a small part of the reaction time. Most of the reaction time is cognitive and it might be that the subject has adopted a more conservative reaction time in order to deal with the disease and reduce the errors. A widening between the reaction time with and without distractor is also observed, which might be an adaptation strategy contrib‐uting to the reduction of the error rate shown on top.

2.6.2 Use of the NoTremor platform for monitoring objective metrics.

The line test tool and the visual analytics tools can be useful for monitoring the state of a patient in relation to objective indicators that can be derived from the tests (even



without using the models). These indicators also cast some light into the internal pro‐cesses of the Parkinsonian patients. We have chosen four types of charts that, we believe, have particular relevance. These charts can be obtained if enough test data are available (e.g., when subjects carry out test with the portable tablet at home for some week). They can be periodically re as‐sessed at distance of years.

2.6.2.0 Error rates and distribution of correct and incorrect reaction times

Provided enough tests are collected, the individual reactions recorded can be used to estimate the distribution of reaction times for the correct and for the incorrect re‐sponses.

Fig. 18 Distribution of reaction time for correct and incorrect movement for 4 PD subjects (top to bot‐tom), without distractor (left) and with distractor (right).



The above chart provides an overview of the distribution of reaction time with and without distractor for the 4 PD subjects.

Fig. 19 Distribution of reaction time for correct and incorrect movement for 4 healthy subjects (top to bottom), without distractor (left) and with distractor (right).

Considering Fig. 18, and comparing left to right (i.e., comparing with/without distractor) one can see that while there is virtually no error without distractor (which is also seen in Fig. 17, top), with the distractor many movements occur in the wrong direction, indi‐cating an incorrect selection of action in the basal ganglia. One can also notice how, for the fastest reactions, in the order of 0.3, 0.35 s, the probability of incorrect movement is almost 50% (meaning that action is selected almost randomly in these short times). When reaction time happens to be above 0.4 s, errors virtually disappears (because enough time is given to the action selection mechanism). It is worth noticing that sub‐ject SC854 makes virtually no error with the distractor. However, that is because he/she adopts a very conservative reaction time with all reactions occurring later than0.4 s.



Another aspect that is also evident is that, with the distractor, and adaptation occurs with the increase of the mean reaction time (in fact the mean reaction time on the right is greater than on the left). All these observations agree with the conceptual architec‐ture of the model (albeit the current model is deterministic and cannot predict errors). Overall, charts like the above show with great precision the difference in reaction times and error rates of subjects. For example, while SC854 is slow (and makes no error for this), SC183 is faster than SC665, yet he/she makes fewer errors. A comparison with healthy subjects is given in the chart of Fig. 19. Errors tend to be less and of more random nature (distributed over a larger reaction time interval). The worst performing healthy subject is SC186, who makes significant errors for reaction time be‐low 0.4 s and is still better than 3 of 4 subjects in Fig. 18, while the best performing PD subject, is only marginally better in terms of error distribution and slower in terms of correct reaction time distribution.

2.6.2.1 Error‐speed trade‐off

People may adopt different trade‐off between movement time and error rate (but they cannot achieve faster movement without affecting errors).

Fig. 20 Error rate vs. reaction time trade‐off for the PD subjects. Each dot represents the average reac‐tion time and peak speed of the tests carried out over one week (together the dots represent the whole interval of observations lumped per weeks). Distinction between tests carried out with and

without distractor is made. The progression over time cannot be appreciated in this type of chart but can be inferred from charts like Fig.16.



Fig. 20 shows the 4 Parknsonian patients and Fig. 21 the 4 healthy subjects. In general, the PD patients have higher error rates, both with and without distractor. Subject SC665 shows a gradual decrease of the error rate in the 1.5 years of testing, which was commented with Fig. 17, top. Subject SC183 used different trade‐off points during the testing period: peak speed varied between 2.5 (s‐1) to 6 (s‐1). The corresponding error rate is clearly correlated, and increased with higher speeds (shorter movement times). Subject SC854 used very low movement speed (as pointed in Fig. 18 he/she also used long reaction times) obtaining very low error rates, but these are perfectly in line with the trend of SC183. Subjects SC665 and SC562, while using low speed, still have very large error rates, both with and without distractor.

Fig. 21 Error rate vs. reaction time trade‐off for the healthy subjects. This chart com pares to the previ‐ous one. Healthy controls performed the monitoring tasks for are fewer observation weeks (because

they were less motivated). Hence there were fewer experimental points.

2.6.2.2 Distractor effect

The distractor affects the time for action selection, as already pointed in Fig. 4. The fol‐lowing figures show the speed profile with and without distractor for the PD patients. Consistent with the mathematical model and the model conceptual framework, the ef‐fect of the distractor is an almost a pure additional lag, which is about 50 ms.



Fig. 22 Effect of the distractor for the Parinsonians subjects.

2.6.2.3 Fatigue effect

The fatigue effect is evaluated by splitting each test in three parts and averaging the tri‐als in the first part (the “fresh” part, which corresponds to first 20 seconds of testing) and the trials of the last part (the fatigued part corresponding to the last 20 seconds of testing). Comparison with the distractor highlights a fundamental difference: while the distrac‐tor acts as an additional lag, fatigue (which corresponds to loss of dopamine during the test) affects the shape of the trajectory, with reduced and delayed peak speed.



Fig. 23 Effect of the distractor for the Parinsonians subjects.

3 Other technical validation activities

3.1 Why three model parameters (investigation on the number of model parameters)

So far the mathematical model used had 3 parameters for the basal ganglia (Da, dis and mpy). This section deals with the question of the appropriate number of parameters. Initially, the possibility of using even more than 3 parameters was considered. More pa‐rameters mean describing the changes of connection strengths in a higher dimensional space, and hence maybe the ability to better model the different aspects of the disease. However, using more parameters imply a greater risk of overfitting and less robustness to measurement noise. On the other hand, using fewer parameters, e.g., 2, might result in a model that cannot describe sufficiently well the changes of connections. We have thus carried out a test with a model with reduced dimensionality. Of the three parameters mpy looks the one which varies less (and is also associated to the undesira‐ble mpy cluster). So we have forced the model to mpy=0, hence remaining only two pa‐rameters for the basal ganglia: Da and dis. Fitting this two‐parameters model to the data have yielded new (best fits) where the behaviour has been approximated under the assumption that no change id mpy occurs.



A linear regression model, has been fitted on this reduced dimensionality model. The results are shown in Fig. 24 (which directly compares to Fig. 12). The conclusion is that with two parameters the model loses the ability to describe the disease.

Fig. 24 A model with only two BG parameters (dis and Da) does not fit the UPDRS score.

3.2 Technical validation of the line test and of the lumped model.

This was carried out in D6.2.1 section 3.

3.3 Multiscale modelling: alternative basal ganglia model

The methodology of multiscale modelling is an important aspect of the NoTremor pro‐ject. The line task model described in section 2.2 is a multi‐scale model; it incorporates a rate‐coded basal ganglia model which gates the cortical activity that drives a control‐theoretic trajectory generation model. In this section, we describe the integration of a spiking model developed from the one described in deliverable D3.2.2 into the line task model, where it replaces the rate coded model used in the work described in section 2.2. The spiking basal ganglia consist of the same populations as those in the rate coded BG model (inside the dashed box of Fig. 1). Each population consists of 60 Izhikevich neu‐ron models, arranged in two channels (P and S). Connectivity between the populations follows the same pattern as in the rate‐coded model. Where one‐to‐one connectivity is specified in the rate coded model, one‐to‐one connectivity is implemented for the two spiking neuron channels; the thirty neurons in the P channel of an efferent population will connect with random probability to any of the thirty neurons in the P channel of the afferent population. None of the neurons in the efferent P channel will connect to a neuron in the afferent S channel. The scheme is similar for “cross‐channel” connections, such as the connection from GPeTA to GPi, in this case there is random connectivity

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from efferent P channel neurons to afferent S channel neurons and from efferent S channel neurons to afferent P channel neurons. The model’s network diagram is shown in Fig. 24.

Fig. 25 The NoTremor model incorporating the spiking basal ganglia. The components of the model to the right of the grey dashed line are identical to those in the rate‐coded line task model shown in Fig. 1. Here, the two channels, P and S are not explicitly shown. Interface populations exist to convert the rate codes which are present in the sensory cortex (Sctx) and motor cortex (Mctx) populations and to covert spikes in GPi into a rate code to input to Mctx. Within the 5 populations of the basal ganglia

(coloured cyan), 60 Izhikevich neurons are arranged into a P channel and an S channel.The model was created in SpineCreator, with a SpineML component called minjerk implementing the minimum jerk model described in section 2.2. A version of the rate‐coded line task model was implemented in

SpineCreator to verify that the common part of the model (to the right of the grey dashed line) pro‐duced the same results given by the Mathematica and MATLAB implementations.

Weights in the spiking BG model were tuned to give realistic firing rates with no striatal input and to match the dynamics of the same experimental data used to tune the rate‐coded model. While an extensive search of the parameter space for the spiking model



has not been carried out; it has been possible to reproduce the key result of a tri‐phasic response in STN (Fig. 25 a). Some example trajectories resulting from embedding the spiking BG model in the line task are shown in section 3.4.

Fig. 26 Simulated peri‐stimulus time histograms of the responses in STN, two GPe populations and GPi for the spiking basal ganglia model. The graphs give mean firing rate based on the result of recording spikes from 30 neurons (i.e. one channel) over 100 simulations. The stimulus was an impulse delivered

to Striatum, replicating the impulse used when training the rate‐coded basal ganglia model.

3.4 Stepped trajectories as an emergent phenomenon

Many of the more advanced Parkinson’s patients show movements that do not have a smooth velocity profile. That is to say that the movement of the hand towards the tar‐get line fluctuates in speed, with some instances of the hand actually coming to a com‐plete halt. This phenomena has been noted in previous studies of Parkinson’s patients and is often referred to as “action tremor” and has a frequency of roughly 5‐10Hz. This is distinct from the “resting tremor” that is more commonly associated with Parkinson’s disease. Fluctuations in the speed of movement are seen in the NoTremor computational mod‐el, as well as in the spiking model. They have two distinct causes.



Firstly, in both the spiking and the rate coded models, ~6‐7Hz oscillations during movement are induced by rendering the model Parinsonian without changing the planned movement time described by parameter “T”. Since the Parinsonian model makes slower movements, the planned movement time, T, becomes an insufficient time to complete the movement to the target. This induces instability in the “sensory feedback loop” of the model that manifests as 6‐7Hz oscillations in the speed profile. The biophysical substrate of “T” is likely to be related to the forward models of move‐ment within the cerebellum. As the disease progresses and basal ganglia function de‐grades, it may be the case that the forward model encoded in the cerebellum can no longer learn how long the movement should take. If the movement takes longer than it should relative to the cerebellar forward model then “T” is too small and the action tremor is the result.

Fig. 27 Hypothesised mechanism for the generation of action tremor. (Deterministic rate model) Left: Healthy trajectory (T=0.295). Middle: Reducing the value of the parameter that governs the planned movement duration (T=0.245) induces instability in the trajectory. Right: Rendering the model Pa‐

rinsonian but leaving T at its healthy value (da=0, dis=2, mpy=1, T=0.295) increases movement duration beyond the healthy value of T, resulting in a similar instability.

This formulation is very interesting, as it implies that action tremor arises when both basal ganglia parameters are reset by PD AND there is failure of the forward model en‐coded in the cerebellar system (cerebellar‐thalamo‐cortical loop) to adjust to these changes. In other words, the model predicts deranged functioning of the cerebellar sys‐tem in the presence of action tremor. This sits well with recent lines of evidence that PD rest tremor is associated with cerebellar system dysfunction and that action tremor, through its association with rest tremor, may similarly involve cerebellar system dys‐function.13 The key evidence here is that lesioning of high frequency stimulation of the cerebellar receiving area of the thalamus is able to abolish tremor. The second cause of action tremor is shown by the spiking model. Rendering the spik‐ing model Parinsonian results in basal ganglia output being non‐zero for the selected channel. Thus, the basal ganglia is still slightly inhibiting the motor cortical channel that 13 Wu T1, Hallett M. The cerebellum in Parkinson's disease. Brain. 2013 Mar;136(Pt 3):696‐709. Louis ED, Levy G, Côte LJ, Mejia H, Fahn S, Marder K. Clinical correlates of action tremor in Parkinson dis‐ease. Arch Neurol. 2001;58(10):1630–4.



is attempting to initiate the movement. The effect of this imperfect gating of the motor command is that the level of activity in motor cortex is below what it should be, and therefore the movement is slower than intended. Furthermore, since the spiking mod‐el is stochastic, the firing rate in the GPi is noisy. Thus, in the Parkinsonian spiking model a noisy signal is sent back to the motor cortex. This noise is propagated through the optimal control model and appears as fluctuating speed of movement. The predic‐tion of this mechanism is that the fluctuations in the speed profile should be stochastic. It therefore seems plausible that both processes are present.

Fig. 28 Stepped trajectories in the spiking model. Column 1: Effect of reducing the planned duration of movement “T” (Healthy, T=0.35). Reducing T (T=0.25) gives rise to instability in the trajectory. Column 2: GPi normalised firing rate activity in healthy model (top) and Parkinsonian model (bottom). Column 3: Effect of reducing dopamine parameter on the movement trajectory with T=0.35. In PD (bottom) in‐sufficient disinhibition of motor cortex from GPi yields slowed movements. Noise from the highly ac‐

tive GPi gives rise to fluctuations in the trajectory.

It is possible that these two processes are linked. It may be that the noisy basal ganglia output causes the likely duration of the movement to become “unlearnable” by the cerebellar forward model. As the disease progresses, movements become slower but also noisier. There may come a point when the cerebellum can no longer learn the in‐creased value of “T”. T then becomes too small relative to the real duration of the movement, leading to the 6‐7Hz action tremor described above.

3.5 Oculomotor model validation

The oculomotor model, constrained by known anatomy and neurophysiological data, was validated by its emergent behaviour. The model was tuned to produce saccades to a single luminance following fixation on a central, fixation luminance. Experiments were then run with the model to establish its behaviour under the well‐known gap, step and overlap paradigms. A gap trial introduces a short gap between the fixation luminance being extinguished and the target luminance onset. An overlap trial has the fixation on‐set occurring before the fixation offset. Step trials are those for which fixation offset and target onset are concurrent.



Figure 28 shows the result of varying the gap for different target luminances presented at an angle of 12° to the left, producing horizontal saccades. The results indicate that where there is an overlap between fixation and target (for negative values of the gap variable on the x‐axis), movement onset latency is increased, in line with the experi‐mental data of Reulen (1984). The mechanism behind this phenomenon is the diffuse excitation of SNr by the STN population during the overlap period, which works against the target luminance activity in striatum, which inhibits SNr in the target region. Mini‐mal latencies are obtained for gaps exceeding 0.1 s, which is sufficient time for the ac‐tivity in STN associated with the fixation to have dropped to background levels.

Fig. 29 Latency to saccade onset for a horizontal saccade of 12°. Results for 6 different target luminance values are shown; in each case the fixation luminance was 0.2 on the same, arbitrary scale. The hori‐zontal axis denotes the gap between fixation offset and target onset; negative values indicate an over‐

lap rather than a gap. A gap of 0 indicates a step paradigm.

To validate the correctness of the biomechanical part of the oculomotor model we con‐sidered the comparative study of the results it generates, to those obtained using other similar models of the respective literature. Such data however are not easy to be found. We were able to get the EOM activations for one horizontal simulated saccade of known amplitude as it has been simulated buy the strand oculomotor model of Qi Wei 14. Next, we used those activations as input to our model and performed a forward sac‐cade simulation. The generated trajectory was finally evaluated on its physiological cor‐rectness and on its plausibility. The provided activations were fed to our model and the obtained simulated saccade trajectory is presented in Fig. 30.

14Wei, Q., Sueda, S., & Pai, D. K. (2010). Physically based modelling and simulation of extra ocular mus‐

cles. Progress in Biophysics and Molecular Biology, 103(2–3), 273–283. http://doi.org/10.1016/j.pbiomolbio.2010.09.002



Fig. 30 Simulated saccade trajectory obtained by using EOM activations of the strand oculomotor mod‐el of Qi Wei as input to our model.

The horizontal component curve of the 25deg simulated abduction matches in all the features of a saccade that are available in the respective literature. More specifically:

The duration of the saccade from the movement onset to the eyeball stabiliza‐

tion is in accordance with values from literature. In 15 Robinson states that the

duration of a 25deg abduction saccade (temporal in Robinson’s terminology) is

90msec, while our model executes the same saccade in 105 msec.

15 ROBINSON, D. A. (1964). THE MECHANICS OF HUMAN SACCADIC EYE MOVEMENT. The Journal of Phys‐iology, 174, 245–264. http://doi.org/VL ‐ 174

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The above is an indicative example of the forward saccade simulations that were exe‐cuted in the context of evaluating the biomechanical part of the oculomotor model. The model’s correct functionality has been tested in several gaze positions both secondary and tertiary.

3.6 Modelling anti‐saccades in Parkinson’s Disease

In this section, we give a brief report of modelling results we have obtained very recent‐ly in order to develop an account of anti‐saccade phenomena. They are predicated on extensions of the basic oculomotor model that incorporate prefrontal cortical loops through basal ganglia, and their modulation of the lower (motoric level) level loop con‐taining frontal eye fields. Interpreting these results requires a few key observations: 1 ‐ in PD, the number of antisaccade (AS) errors increases, the reaction time (RT) of cor‐rect AS increases, and the RT of prosaccades (PS) stays roughly constant. 2 ‐ there is suggestion in the literature that this is a result of PD affecting the ability of fixation neurons in the superior colliculus (SC) to inhibit saccade neurons. i.e., inhibi‐tion of saccade neurons is reduced, so pro‐saccade (PS) RTs don't decrease. 3 ‐ We've tested this idea and it didn't look promising (see following), and also tested our own idea, that there is an increase in the responsiveness to phasic stimuli in the PD brain, to compensate for the slowing effects of a loss of DA, again meaning that PS RTs are maintained. We've implemented this by increasing the weights in the phasic, reti‐na‐SC‐thalamus pathway (although the mechanism is speculative) 4 ‐ AS errors can be driven by (at least) two things. A failure of working memory/top down processes, and a visual system that is too responsive to visual stimuli (this has been discussed in the literature) 5 ‐ Both the fixation‐inhibition‐loss idea, and the increased‐response‐to‐phasic‐stimuli idea predict that there would be an increase in the number of the second type of error, but not the first. Simulation results are in the graphs below. The conditions are the same in each figure, abbreviations as follows: PDe: Early PD, with our fast‐phasic‐path compensation PDl: Late PD, with our fast‐phasic‐path compensation



PDe DA: Early PD, with only DA reduction and no compensation PDl DA: Late PD, with only DA reduction and no compensation Fix Inhib: Healthy model (no DA loss), including a 'hack' to simulate the inhibitory ef‐fects of fixation neurons, because these weren't present in the base model FI PDe: Early PD, with the fixation‐inhibition‐loss compensation FI PDl: Late PD, with the fixation‐inhibition‐loss compensation Graphs show results from 5 simulations of 200 trials each, so 1000 trials total which have been randomly divided into 'bins' of 50 trials each. A mean and SD for each 'bin' was calculated. Bars show grand means, and error bars show the 'mean standard devi‐ation' to indicate the general variability of the results (as opposed to the SD of the means, which would just show how 'accurate' our grand means were, which is not par‐ticularly interesting to us).

Fig. 31 Mean error rate. Each bar shows average error rate of 20 groups of eye movements. Error bars show average standard deviation. Each group contains 50 individual saccades. For an anti‐saccade,

subject is required to move away from the presented stimulus. An error is recorded if the subject sac‐cades towards the stimulus. Y‐axis shows proportion of saccades that were recorded as errors (normal‐

ised). See text for x‐axis label definitions.



Fig. 32 Mean reaction times. Error bars show average standard deviation. Top: Pro‐saccade. Bottom: Anti‐saccade. 1000 eye movements, binned into groups of 50. See text for x‐axis label definitions. Y‐

axis is reaction time in milliseconds.



Fig. 33 Comparison between model (top) and data (bottom). Data from Kitagawa et al. (1994).

Fig. 34 Analysis of the cause of errors. Top: Prefrontal cortex error. Bottom: Pro‐saccade being initiat‐ed too quickly.

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Note that only our hypothesis (first three bars; fast‐phasic‐path compensation) can ac‐count for the pattern of results seen in PD across all of AS RTs, AS error rates and PS RTs. Fig 33 shows a breakdown of the origin of the errors in each simulation ‐ i.e., the mean number of errors that were due to a failure of PFC, and those that were due to a pro‐saccade kicking in too fast. Remember point 5 above suggests that in PD, there should be an increase in the second type, where the first type should stay roughly con‐sistent. Again, our hypothesis is the only one that accounts for this prediction. Without compensation, or with the fixation‐inhibition‐loss, the loss of DA slows things too much, so pro‐saccades always slow, meaning that they do not kick in fast enough to produce this second type of error.

4 Evaluation of the NoTremor platform (line test) User needs were assessed at the start of the project to guide the design and develop‐ment of the NoTremor framework. Parkinson’s UK led a ‘Priority Setting Partnership’ to identify the top 10 unmet needs/research priorities in everyday management of the condition for people living with Parkinson’s, their carers and health professionals. This process was carried out according to an internationally recognised process and involved over 1,000 people with Parkinson’s, their carers and health and social care profession‐als and provided a hugely rich dataset for NoTremor to define its user requirements ef‐fectively. The results of this undertaking have been previously reported (deliverable 1.2). The top 10 research priorities identified reflect the complex and diverse range of chal‐lenges that are faced by people with the condition. These include motor symptoms (balance and falls, and fine motor control), non‐motor symptoms (sleep and urinary dysfunction), mental health issues (stress and anxiety, dementia, and mild cognitive im‐pairments), side effects of medications (dyskinesia) and the need to develop more per‐sonalised interventions specific to the different phenotypes of Parkinson’s and better monitoring methods. It has been previously stated that of the top 10 priorities identi‐fied by this comprehensive methodology four have particular relevance to the No‐Tremor project. These are: treating balance and falls, reducing dyskinesias, identifying different types of Parkinson’s e.g. tremor dominant to help tailor treatments and im‐proving dexterity. The evaluation of the NoTremor integrated platform should be considered in the con‐text of these unmet needs of people affected by Parkinson’s, their carer’s and health and social care professionals

4.1 Focus group study The Parkinson’s UK research support team has internationally acknowledged expertise in involving the voice of people affected by Parkinson’s in all aspects of research. They



assessed the current status of the line test and advised on the appropriate assessment. As the most likely application of the line test is not fully established it was deemed to be too early to ask for assessment by healthcare professionals, as this would need to be within a defined context of use. Similarly, the utility in addressing specific unmet needs has yet to be elucidated. An initial assessment of usability for the target population to inform future development was therefore recommended as the most appropriate ap‐proach. To achieve this, two focus groups were held. The subjects were self‐selected from a widely circulated request for volunteers and had received basic information about the line task before deciding to attend. The groups were structured with a set agenda, structured feedback questionnaire and open questioning. There were 5 or 6 partici‐pants in each focus group. They ranged in time since diagnosis from 3 months to 11 years. All had slowness of movement and 91% suffered from tremor. The participants used the test on both the laptop PC and an I‐pad. For each version, the test was used with both the dominant and non‐dominant hand and also with and without distractor line. The subjects generally found the test easy and comfortable to use, particularly with the dominant hand. The majority of participants thought that the red line distractor did not make it more difficult to use the test16 (dominant hand 100%, other hand 80%). As no test data were collected during the focus groups we cannot confirm whether there was an unperceived impact on reaction time, the NoTremor data (section 2.6.2.2) indicate that an effect is expected. There were some practical problems experienced by the users with the iPAD version – the iPAD slid around when the test was in progress and also the stylus generally had to be held at the bottom of the screen otherwise the lines were obscured. There were no comments regarding similar difficulties with the lap top version. The participants would all be happy to use the test on their routine clinic visit or at home once a week. There was less support for using the test at home once a day. The discussion included a lot of questions about the exact purpose of the test. Partici‐pants had a lot of questions about how it related to their particular symptoms (for ex‐ample would the assessment be equally valid for participants who had tremor in domi‐nant hand or non‐dominant hand only, or for those who did not have hand tremor)? The impact of non‐motor symptoms was also raised. They also wanted more infor‐mation about what they were aiming for – e.g. fastest possible response, most accurate response even if slower etc. (This is not considered to be a discriminating factor based on the NoTremor line test data analyses, but users indicated that an objective would be good to have). Improvement in response and/or accuracy over time was thought to be likely and participants’ impression was that the test was more like a tool to improve motor skills (by trying to beat your own previous results). All wanted to know how

16 Interestingly, the objective measurements (see section 2.6 and Figs. 16‐21) reveal that there actually is a very important effect of the distractor, both in terms of reaction times and error rates. The subjective impressions of the persons that carried out the tests is incorrect.



feedback would be given – to their neurologist and to themselves17. Other comments were: the stress of using a device and what happens if something goes wrong, the time/frequency required for the test needs to be realistic due to other commitments, the boredom factor also needs to be thought about. There were several queries about whether an I‐phone version was a possibility (for the line test this would not be an op‐tion due to the small screen size). The use by cohort study participants in Oxford, Santa Chiara and Thessaloniki, although not formally assessed, did not reveal any systematic problem. In particular, the subjects of the S, Chiara Cohort, which were involved for a long time, had all opportunities to ask clarifications on the use of the system (also they participated to two dissemination events and had much clearer explanation of the whole system). Maybe because of these, no issue such as those of the focus group were raised. A full report on the user assessment is available.

4.2 Human factors and impact assessment

The unmet needs assessment carried out as part of this project gives an indication of the potential opportunities to impact the management of Parkinson’s and this could be built on in the future by rigorous assessment of the utility of the line test together with the other Notremor outputs in the clinical setting. Further development of the No‐Tremor outputs needs to take into account the level of variability in the symptom pro‐file and experience of people affected by Parkinson’s – this is of key importance to both those with the condition and to the healthcare professionals involved in their manage‐ment. This issue was a key one raised by focus group participants. Consideration of the influence of non‐motor symptoms on performance of line test (e.g. cognitive im‐pairment) should be considered. Further development should also take into account the regulatory landscape around devices to be used in a healthcare setting and also how this device compares with oth‐ers which are available/in development for use in Parkinson’s disease. Involvement of people affected by Parkinson’s is recognised by researchers in academia, industry and regulatory authorities, amongst others, as a key requirement at all stages in developing tools to aid diagnosis, monitoring and treatment of disease and should be included in any future development of the NoTremor platform. Establishment of how the NoTremor outputs can be integrated into the day to day management of people affected by Parkinson’s will allow a full assessment of the po‐tential impact.

17 A general lesson that can be drawn from these questions is that subjects have great interest in what the new system actually is and how it works. The instructions and explanations given to the subject will have to be improved in actual development of the system for exploitation.



5 Conclusion The main project achievement is the development of a mathematical model for action‐selection and motor control that, by means of 4 parameters, models the (main) internal changes that occur in Parkinson’s disease (see discussion in section 2.2). One of these parameters (Da) represents the effects of loss of dopamine, two other pa‐rameters (dis and mpy) represent compensatory changes in connection strengths of the basal ganglia and the last parameter (T) represents cerebellar adaptation in motor planning. The model describes intentional motor control in finger aiming task. A one‐dimensional tablet‐based finger tracking task has been developed to measure the real behaviour of human (patients and control) and algorithms have been developed to use this observed behaviour to estimate the parameters of the subject. We have shown the whole measurement‐estimation chain produces estimates of the model parameters that, besides being plausible (in particular the parameters respond according to accepted conceptual frameworks), also correlate with the UPDRS clinical. Hence NoTremor has developed a new tool to understand what happens in PD and, in particular, to estimate the internal states of the Parkinsonian brain (the model parame‐ters) with a simple motor test. The “line test” is indeed simple enough to allow monitor‐ing of patients with high frequency and accuracy. The application of NoTremor to a few use cases has provided very valuable insights into what happens in the basal ganglia while the disease progresses. With longitudinal stud‐ies, it will be possible to study the evolution of the disease in the 4 parameters disease space and, for a long‐term goal, to assess the effectiveness of treatments in shorter time and with greater confidence (the positive statistic correlations is an indication that the overall approach shows promise, even if the lumping of the control parameters might benefit from further exploration). A certain amount of noise is present and affects the system (noise in the clinical scales used for comparison, noise in measured trajectories, in the fitting process and in the model). However, it has been shown that by averaging several trials (e.g., like in the longitudinal monitoring use case) the amount of noise can be considerably reduced. In addition, future extensions and improvements are possible, but maybe the most im‐portant of these is the potential formulation of a mechanism explaining stepped trajec‐tories and action‐tremor (as opposed to resting tremor) as a failure of the cerebellum (caused by too much basal ganglia output noise) to estimate the necessary movement time.

6 Appendix 1 Charts representing correlations between clinical scales, model parameters (individually considered) and other metrics for the line test. See section 2.5.3. A green ellipsis show good and significant correlation, a blue ellipsis show a lower correlation but remaining statistically significant, a cyan ellipsis show some correlation which however lacks sta‐tistical significance, while the red ellipses indicate the absence of any significant corre‐lation



Fig. A1.1 ‐ Summary of correlation analysis for Oxford Discovery Cohort performed on subjects. UPDRS III correlates well with Purdue total, age, H&Y, as well as model’s mpy parameter. H&Y shows good

correlation with age, disease duration onset/diag, Purdue total, getgo average and getgo best. Correla‐tion between model parameters is shown between Da‐taud, Da‐dis and dis‐T. Other significant correla‐

tions are found between taud‐disease duration onset, Da‐purdue assembly and T‐getgo‐best.



Fig. A1.2 ‐ Summary of correlation analysis for Oxford Discovery Cohort performed on subjects follow up 18 months. UPDRS III correlates well with age, as well as model’s dis parameter. H&Y does not show any significant correlation. Correlation between model parameters is shown between Da‐dis and mpy‐T. Other significant correlations are found between Purdue total‐flamingo time/EQ5D index, purdue

assembly‐flamingo time and flamingo time‐EQ5D index.



Fig. A1.3 ‐ Summary of correlation analysis for Oxford Discovery Cohort performed on subjects follow up Levodopa OFF. There seem to be good correlation between different variables unfortunately, it is

not statistically significant due to the low number of subjects.



Fig. A1.4 ‐ Summary of correlation analysis for Oxford Discovery Cohort performed on subjects follow up Levodopa ON. There seem to be good correlation between different variables unfortunately, it is

not statistically significant due to the low number of subjects.



Fig. A1.5 ‐ Summary of correlation analysis for Oxford Discovery Cohort performed on controls. There seem to be good correlation between different variables unfortunately; it is not statistically significant

due to the low number of subjects.



Fig.A1.6 ‐ Summary of correlation analysis for Oxford Surgery Cohort performed on OFF. Correlation between model parameters is shown between Da‐dis, Da‐mpy and dis‐mpy.



Fig. A1.7 ‐ Summary of correlation analysis for Oxford Surgery Cohort performed on ON. Correlation between model parameters is shown between Da‐dis.



Fig. A1.8 ‐ Summary of correlation analysis for Thessaloniki Cohort 1 performed on patients. H&Y does not show any significant correlation. Correlation between model parameters is shown between Da‐dis,

Da‐mpy and less correlation between Da‐taud, dis‐mpy.



Fig. A1.9 ‐ Summary of correlation analysis for Thessaloniki Cohort 1 performed on controls. Correla‐tion between model parameters is shown between Da‐dis.



Fig. A1.10 ‐ Summary of correlation analysis for Thessaloniki Cohort 2 performed on patients. H&Y does not show any significant correlation. Correlation between model parameters is shown between

Da‐dis.



Fig. A1.11 ‐ Summary of correlation analysis for Thessaloniki Cohort 2 performed on controls. Correla‐tion between model parameters is shown between Da‐dis.

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