Electrophysiological responses in ... - essay.utwente.nl

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Faculty of Science and Technology,Biomedical Engineering

Electrophysiological responses in cortex,related to performing repetitive movements

following different types of external cues

Silvana Huertas Penen

M.Sc. ThesisOctober 7, 2020

Supervisor:Dr. ir. T. Heida

Examination Committee:Prof.dr.R.J.A van WezelDr.E.H.F. van Asseldonk

J.J.A Heijs

Biomedical Signals and Systems groupFaculty of Electrical Engineering,

Mathematics and Computer ScienceUniversity of Twente

P.O. Box 2177500 AE Enschede

The Netherlands

Summary

Parkinson’s disease affects around 1% of the population over 60 years and is gener-ated due to the degeneration of nigrostriatal neurons and pathways.The most com-mon motor symptoms presented in Parkinson’s disease are tremor at rest, freezingof gait and bradykinesia. New neurorehabilitation methods are being used to im-prove the symptoms for these patients, mainly based on the use of external cues toguide movements. It has been recognised that when using external cues that haveappearances rates above 2Hz, the rehabilitation of repetitive movement is not asefficient as when using cues with appearances rates below this value. Furthermore,it has been noticed that the use of cues that follow multiple patterns have better re-sults in rehabilitation than when cues that follow only one pattern are used.

This thesis studies the cortex connectivity and activity associated to the use of cueswith frequencies above and below 2Hz and isorhythmic and polyrhythmic cues. It ishypothesised that the differences in the neurorehabilitation process using differenttypes of cues is related to the synchronisation of different neuronal networks, theBasal Ganglia-thalamo-cortical network and the Cerebellum-thalamo-cortical net-work. Furthermore, the differences in activity and connectivity between visual andauditory cues were also studied. To prove the hypotheses, an experimental studywith healthy participants was developed, in which multiple types of cues were pre-sented to the participants, and the data obtained from the study was analysed, con-cerning the activity and connectivity of cortex regions.

The results of the study showed that the activity of the cortex regions associatedto the Cerebellum-thalamo-cortical network is related with synchronisation of theregions when using frequencies below 2Hz, and the activity of the Basal Ganglia-thalamo-cortical network related to the frequencies above 2Hz. These differenceswere also found when using isorhythmic and polyrhythmic cues. For the visual andauditory cues, no clear differences were found. Finally, the hypotheses related to theconnectivity of the Basal Ganglia-thalamo-cortical and Cerebellum-thalamo-corticalnetworks were not validated using the phase-locking value measure for connectivity.

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IV SUMMARY

Contents

Summary iii

List of acronyms vii

1 Introduction 11.1 Parkinson’s Disease . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Cueing as therapy for Parkinson’s disease patients . . . . . . . . . . . 21.3 Basal Ganglia circuits vs Cerebellar circuits . . . . . . . . . . . . . . . 21.4 Activity and Connectivity . . . . . . . . . . . . . . . . . . . . . . . . . . 5

1.4.1 Task-related Activity . . . . . . . . . . . . . . . . . . . . . . . . 51.4.2 Functional Connectivity . . . . . . . . . . . . . . . . . . . . . . 6

1.5 Types of cues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61.5.1 Continuous vs Discrete cues . . . . . . . . . . . . . . . . . . . 61.5.2 Polyrhythmic vs Isorhythmic cues . . . . . . . . . . . . . . . . . 71.5.3 Visual vs Auditory Cues . . . . . . . . . . . . . . . . . . . . . . 81.5.4 EEG Frequency Bands . . . . . . . . . . . . . . . . . . . . . . 8

1.6 Goals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91.7 Report organisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

2 Material and Methods 112.1 Participants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112.2 Measurement Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

2.2.1 EEG . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112.2.2 Acceleration data . . . . . . . . . . . . . . . . . . . . . . . . . . 12

2.3 Data acquisition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132.3.1 Experimental setup . . . . . . . . . . . . . . . . . . . . . . . . 13

2.4 Storage of data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142.5 Data Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

2.5.1 Data pre-processing . . . . . . . . . . . . . . . . . . . . . . . . 162.5.2 Data Processing . . . . . . . . . . . . . . . . . . . . . . . . . . 172.5.3 Statistical Analysis . . . . . . . . . . . . . . . . . . . . . . . . . 19

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VI CONTENTS

3 Results 233.1 ERD/ERS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

3.1.1 Statistical Analysis . . . . . . . . . . . . . . . . . . . . . . . . . 233.1.2 Cueing Isorhythmic Frequency Comparison . . . . . . . . . . . 253.1.3 Cueing Polyrhytmic Frequency Comparison . . . . . . . . . . . 293.1.4 Cueing Rhythmicity Comparison . . . . . . . . . . . . . . . . . 303.1.5 Cueing Visual and Auditory Comparison . . . . . . . . . . . . . 31

3.2 Connectivity PLV . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 323.2.1 Connectivity values . . . . . . . . . . . . . . . . . . . . . . . . 323.2.2 Number of Connectivities . . . . . . . . . . . . . . . . . . . . . 32

4 Discussion 354.1 ERD/ERS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

4.1.1 Cueing Frequency Comparison . . . . . . . . . . . . . . . . . . 354.1.2 Cueing Rhythmicity Comparison . . . . . . . . . . . . . . . . . 364.1.3 Cueing Visual and Auditory Comparison . . . . . . . . . . . . . 37

4.2 Connectivity PLV . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 374.2.1 Connectivity Values . . . . . . . . . . . . . . . . . . . . . . . . 384.2.2 Number of Connectivities . . . . . . . . . . . . . . . . . . . . . 38

4.3 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 394.3.1 Activity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 394.3.2 Connectivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

5 Conclusions and Recommendations 41

References 43

Appendices

A Synchronisation/Desynchronization topoplots 51A.1 Auditory Beta Band . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51A.2 Visual and Auditory alpha band . . . . . . . . . . . . . . . . . . . . . . 51A.3 Visual and Auditory gamma band . . . . . . . . . . . . . . . . . . . . . 51

B Connectivity values Topoplot 63

List of acronyms

PD Parkinson’s disease

FOG freezing of gait

FOUL freezing of upper limbs

BG Basal Ganglia

DA Dopamine

GPi Internal Globus Pallidus

STN Subthalamic Nucleus

SMS Sensorimotor Synchronization

BGTC Basal Ganglia-thalamo-cortical network

CTC Cerebello-thalamo-cortical

SNr Substantia Nigra

STN Subthalamic nucleus

PLV Phase-Locking value

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VIII LIST OF ACRONYMS

Chapter 1

Introduction

1.1 Parkinson’s Disease

Parkinson’s disease (PD) is the second most common worldwide progressive neu-rodegenerative disease, and the first most common movement disorder [1] [2]. Theprevalence of PD remains at 1% of the total worldwide population over 60 years ofage [1] [2]. PD is caused by the death of dopaminergic nigrostriatal neurons [1] [3] [4]and damages to the nigrostriatal dopaminergic pathway [4] [5] generated by thedecrease in Dopamine (DA) levels, due to the degeneration of the DA producingcells [1] [3] [4]. The consequences of these damages are predominantly motorsymptoms and cognitive alterations. Some of the motor symptoms present aretremor at rest, freezing of gait (FOG), bradykinesia, postural instability, absenceof voluntary movements, freezing of upper limbs (FOUL) and rigidity [1] [6] [7].

The main treatments for PD patients are pharmacotherapies aiming to increasethe level of DA. Levodopa is the most used medicine [4]. However, this pharma-cotherapy presents serious side effects. 40% of patients who are treated with thismedicine suffer from impulse control disorders and need to discontinue its use [4].Additionally, around 15% to 20% of the patients who take Ledovopa and need todiscontinue its use, show withdrawal symptoms [4]. Furthermore, gait deficit is nor-mally resistant to this medicine [8]. Because of the secondary effects of Levodopaand the impossibility to improve gait using it, it is necessary to develop new andmore efficient treatments. Currently, multiple types of neurorehabilitation are beingstudied, mainly consisting on the use of external cues to guide movements, therebyimproving the motor symptoms of PD patients [4] [9] [10].

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2 CHAPTER 1. INTRODUCTION

1.2 Cueing as therapy for Parkinson’s disease patients

The rehabilitation method of using external cues as guides to perform repetitivemovements, better known as Sensorimotor Synchronization (SMS), has demon-strated promising results for some patients [11] [12] [13] [14]. This type of therapyconsists of presenting auditory or visual cues, at a certain frequency, to the patients.The auditory cues are normally generated by a metronome, while the visual cues aregenerated with a blinking light [15]. The patients need to perform natural repetitivemovements, such as walking or moving their lower/upper extremities, in response tothese cues [11]. As a result, the PD patients’ walking speed, cadence and the stridelength improve [11] [12] [13] [14]. Comparatively, the quantity of FOG and FOULdecreases [11] [12] [13] [14].

Arias et al., del Olmo et al. and Nombela et al. [8] [16] [17] have proven that thepattern at which the cues appear (rhythmicity) is an important determinant when itcomes to the efficacy of the PD therapy [8] [16] [17]. There are two main types ofrhythms: isorhythm and polyrhythm. An isorhythmic rhythm is one that follows onlyone pattern, while a polyrhythmic one that two or more different patterns. Whenfollowing two patterns, one is call the main pattern, the one the patients follow, andthe other the secondary pattern. In the context of cues, it can be defined as twoconflicting cues that appear at the same time but follow two different patterns [18].Furthermore, not only the rhythmicity of the cues is important but also the regularityat which the cues are presented, also known as frequency [8] [16] [17]. Multipleclinical studies have proven that when presenting cues with frequencies above 2Hz,the quantity of FOG and FOUL increases [8] [19] [20] [21] [22]. In contrast, whenthe cues are presented at frequencies below 2Hz, the FOG and FOUL episodesdecrease and the gait pattern improves [8] [19] [20] [21] [22]. One of the reasonsbehind these differences is that the cues following frequencies below 2Hz, betterknow as discrete, are clearly defined by a beginning and end [19] [21] [23], whilethose following a frequency above the 2Hz does not present a clear beginning orend, better known as continuous cues [19] [21] [23]. To better understand thesephenomena, the neurophysiology behind the generation of repetitive movementsfollowing external cues, with different frequency and rhythmicity, will be described.

1.3 Basal Ganglia circuits vs Cerebellar circuits

Repetitive movements are influenced by the internal motor timing of the brain. Thisis the mechanism with which the brain controls the rate at which the movementsare performed [24]. For example, the internal motor timing is the one in charge of

1.3. BASAL GANGLIA CIRCUITS VS CEREBELLAR CIRCUITS 3

controlling the walking and writing of a person. If this mechanism is damaged, themotor tasks will be affected, making it difficult to walk properly or perhaps tap thefingers; as seen in patients with PD. It has been proposed that the recognition andtemporal encoding of motor tasks are controlled by the Basal Ganglia (BG) and theCerebellum [25] [26] [27] [28] [29] [30] [31] [32]. The BG has been hypothesised asthe internal timekeeper, as the region in charge of detecting the regularity of externalcues, follow them [25] [26] [27] [28] [29] [30] and as the regulator of the continuousmovements [21] [22]. On the contrary, the cerebellum is involved in the decodifi-cation of more complex motor timing tasks, like the decomposition of polyrhythmicstimuli, the correction of timing errors created by the BG [27] [28] [30] [31] [32] andthe regulation of discrete movements [21] [22].

To understand the role of both the BG and the Cerebellum in the control of mo-tor timing, it is important to understand the pathways and regions associated withmotor timing. It is conjectured that the BG pathway associated with the motor tim-ing is the Basal Ganglia-thalamo-cortical network (BGTC), believed to be composedby the Internal Globus Pallidus (GPi), the Substantia Nigra (SNr), the Subthalamicnucleus (STN), the post-commissural putamen and the primary motor (red in Figure1.1), supplementary motor(orange colour), sensorimotor (yellow colour) and pre-frontal/frontal (green colour) cortex [33] [34]. For the motor timing, the BGTC path-way is connected to the thalamus .Nevertheless, recent studies have also identifiedthe possibility that the BG is directly connected to the cortex, bypassing the thalamusand the putamen [32]. Figure 1.2 shows an illustration of the possible newly identi-fied connections between the BG and the cerebral cortex. It is hypothesised that theCerebellar pathway involved in motor timing is the Cerebello-thalamo-cortical (CTC),composed by the cerebellar cortex, the dentate nucleus, the thalamus and the pre-motor(blue in Figure 1.1) and parietal cortex(purple colour) [33] [35]. Recent studiesdemonstrated that the CTC and BGTC networks are also interconnected, throughthe dentate nucleus and the Pontine nuclei (Figure 1.2).

Based on the neurophysiology of the BGTC and the CTC, it can be explained thatthe degeneration of the nigrostriatal dopaminergic pathway in PD patients, results ina disturbed control of the internal motor timing. Reducing the capability of decodingcontinuous cues [8] [25] [36] [37].


Figure 1.1: In this figure the cortex regions that are hypothesised to be part of theBGTC and CTC networks are presented. The premotor and parietalcortex (red squares/lines) are hypothesised to be part of the CTC net-work and to be connected to the Cerebellum, as part of the motor timingmechanism. The supplementary motor, the primary motor, the primarysomatosensory, the frontal and prefrontal cortex (blue squares/lines)are hypothesised to be part of the BGTC network and to be connectedto the Basal Ganglia, as part of the motor timing mechanisms. Thefigure taken from [38] and edited.

1.4. ACTIVITY AND CONNECTIVITY 5

Figure 1.2: This Figure shows the hypothesised anatomical connections betweenthe BG, the Cerebellum and the brain cortex. The BG is represented byits parts: STN, GPi and SNr. It is possible to see that the Cerebellumreceives information from the BG through the pontine nuclei, and sendsinformation to the BG through the dentate nucleus. Abbreviations: STN,subthalamic nucleus; GPi, Internal Globus Pallidus; SNr, Substantia Ni-gra. Obtained from [32]

1.4 Activity and Connectivity

1.4.1 Task-related Activity

To analyse the activity of the brain cortex related to the different timing tasks net-works (CTC and BGTC), the event-related synchronisation and desynchronisation(ERS/ERD)measure, of electrodes/ brain regions, have been calculated.The ERD/ERS phe-nomena can be related to changes in the strength of the connections between thecomponents of a specific neuronal network [39]. This measure gives informationabout the power of different regions of the brain cortex, at an event in time, com-pared to a resting state [39]. If the power of a signal during the event is lower thanthe signal’s power during the resting state, there is a desynchronisation over theneurons of that brain region. If the power is higher during the event, there is a syn-chronisation. These changes in power are specific to a frequency band [39].

In the EEG study by Stavrinou et al. [40], with healthy participants, the ERD/ERSwas used to quantify the activity of the brain cortex concerning finger tapping, with afrequency below 2Hz. In it, a synchronisation in the beta band, of the premotor elec-trodes (CTC network) and a desynchronisation of the sensorimotor cortex (BGTC


network) were found [40], in relation to a resting state. Maganotti et al. [41] evaluatedthe ERD/ERS in healthy participants when performing finger tapping tasks at 2Hz.The results showed a desynchronisation, in relation to the resting state, in the alphaand beta band, in the central parietal and lateral parietal cortex (CTC network) andthe central cortex (BGTC network). On the contrary, a synchronisation was foundfor the prefrontal and occipital regions [41].

1.4.2 Functional Connectivity

The brain connectivity refers to the oscillatory synchronisation between differentbrain regions, that happens when the brain transmits information [42]. Two brainfunctional connectivity analyses exist; phase-based connectivity and power-basedconnectivity [42]. The phase synchronisation analysis has been used in studiesobserving connectivity in the hypothesised CTC and BGTC networks, in healthyparticipants and PD patients. With this type of analysis, the strength of the phasecoupling between two brain signals is calculated [42] [43]. One of the measuresto calculate phase synchronisation is the Phase-Locking value (PLV). It can detectweak nonlinear interactions in oscillatory systems [40] [44]. This is important be-cause the dynamics of the CTC, BGTC and Cerebellum-BG networks are primarilynon-linear [45]. When the two signals are completely phase synchronised, the PLVvalue is 1 and when they are not phase synchronised the PLV is equal to zero [42].In the study by Stavrinou et al. [40] the PLV was used to analyse the connectivitydifferences, in healthy participants, when performing imaginary and real rhythmicfinger tapping. In this EEG (60 electrodes) study, isorhythmic cues that appearedat a frequency of 0.7Hz was presented to the participants. The results obtainedwere that there was a high connectivity, in the beta band, between the frontal central(premotor) and parietal central areas, in imaginary and real finger tapping.

1.5 Types of cues

1.5.1 Continuous vs Discrete cues

It is believed, that the use of external discrete cues to guide repetitive movementswould be translated into the use of the hypothesised CTC timing pathway and theavoidance of the hypothesised BGTC timing pathway [19] [26] [30] [31]. Based onthe brain physiology, different regions of the brain should activate and connect whendecoding continuous and discrete cues [19] [26] [30]. However, it is not completelyelucidated how the Cerebellum-Cortex and the BG-cortex pathways are activatedand connected when stimulated by discrete and continuous external cues, while

1.5. TYPES OF CUES 7

performing repetitive movements [9] [32]. Understating these differences could givea better comprehension of PD and better results in the treatment of PD patients us-ing SMS.

The majority of studies that try to identify the differences between finger tappingat continuous and discrete cues, have been behavioural studies, focusing on theaccuracy and variability of the finger tapping [31]. Specifically, in PD patients, it hasbeen found that the variability of the finger tapping is higher when the patients fol-low cues with frequencies above 2Hz, than when following cues with frequenciesbelow this value [20]. This difference is associated with the damage of the BG dueto the depletion of DA, and the fact that the BG controls only part of the motor timingmechanism, as explained in Section 1.3.

To understand this phenomenon in more detail, some studies have tried to eluci-date differences in brain activation, when performing repetitive movements followingcontinuous or discrete external cues. An fMRI study [46] identified a higher activity,measured with BOLD, of the cerebellum when performing finger tapping at frequen-cies above 2Hz. Nevertheless, fMRI does not allow to have information related tofrequency bands [46], reducing the possibility to better understand the activity of thebrain. This is why other studies have tried to identify the cerebellar activity, in timingmechanisms, through the brain cortex regions, using EEG. Torres E. et al. [47], inan EEG study with healthy participants, found specific activity patterns in the motorcortex, related to the activation of the cerebellum due to the onset of motor activity.

1.5.2 Polyrhythmic vs Isorhythmic cues

The results of the SMS therapy for PD patients are dependent on the type of usedcues. The rhythmicity of the cues is one of the main characteristics to be anal-ysed in SMS therapy [33]. Multiple clinical studies have proven that polyrhyth-mic cues (music) have a better performance in PD therapy than isorhythmic cues(metronome) [31] [48]. Nevertheless, the physiological mechanisms behind thishave not yet been completely understood [8] [33], neither how this will be trans-lated into activity and connectivity of different brain regions. It is hypothesised thatthe Cerebellum can encode complex timing processes, with multiple beats, while theBG encodes simple timing processes based on one beat [30]. This could be trans-lated into the activation and connection of the Cerebellum-Cortex pathway when us-ing polyrhythmic cues [30] and the BG when using isorhythmic cues [30]. In the fMRIstudy of Thaut et al. [27] the brain networks associated with the use of polyrhythmicand isorhythmic cues were studied. The use of polyrhythmic cues leads to more


overall activation of the brain than the isorhythmic cues, mainly in the parietal cortex(CTC), cerebellum, primary motor and primary somatosensory cortex (BGCT). Nev-ertheless, not significant differences were found between these two types of tappingtasks. It is necessary to mention that in this study the participants tapped followingthe main and secondary pattern/beat for the polyrhythmic task, characteristic thatcould affect the result of the study since only musically trained people can correctlydo so, as explained in the [27].

1.5.3 Visual vs Auditory Cues

Two different types of cues have been used for SMS therapies for PD patients: vi-sual and auditory [31]. Some studies have determined that the use of auditory cuescan be more useful than visual ones since PD patients have shown more tappingaccuracy concerning the auditory external cue. This has been attributed to the dif-ferences in the frequency limit at which the human body can synchronise [31]. Theupper-frequency limit at which humans can synchronise their movements when us-ing visual cues is believed to be around 2.17 Hz while for auditory cues around 8Hz. [31]. Notwithstanding, there has not been a clear difference between auditoryand visual stimuli concerning the activity and connectivity of brain cortex. In themeta-analysis study by Witt et al. [15], multiple fMRI and PET studies were used torecognise the different regions of the brain cortex that activate when using visual andauditory cues. For both tasks, the dorsal premotor cortices (CTC) were activated.For the auditory tasks, the right dorsolateral prefrontal cortex (BGTC) was activatedand the Brodmann’s area 44. For the visual tasks, the bilateral occipital lobe and theleft posterior cerebellum were activated [15].

1.5.4 EEG Frequency Bands

The study of activity and connectivity of brain regions is based on the selection of fre-quency bands. Related to changes in connectivity and activity when performing fin-ger tapping tasks at different frequencies and rhythms, the beta band (13-3-Hz) hasshown to be the band that provides more information. Previous studies showed anincreased connectivity in the beta band between the basal ganglia and the brain cor-tex, and between the cerebellar pathway and the cortex [32]. Furthermore, changesin the gamma band (30-70Hz) were shown to be related to the performance of amotor task [49] (E.g. finger tapping) and the perception of auditory stimuli [50]. Fi-nally, the alpha band (8-13Hz) has shown to present changes in PD patients [51]and changes in synchronisation in healthy participants when performing repetitivemotor tasks [52].

1.6. GOALS 9

1.6 Goals

The purpose of this EEG study is to analyse the differences in brain activity andconnectivity when a person performs repetitive movements led by continuous anddiscrete, visual and auditory, polyrhythmic and isorhythmic cues. To our knowledge,no other study has investigated the influences of all these types of cues on cortexactivity and connectivity. The knowledge associated with this study will help eluci-date if the cortex regions believed to be connected to the BG and Cerebellum areconnected/activated. This will possibly create a basic knowledge about the connec-tivity and activity of the cortex when using external cues and with it, open the door tostudies with PD patients, in which the use of external cues will be studied to createrehabilitation therapies that are better able to bypass the BGTC pathway, damagedin PD patients, and use the CTC pathway instead. With it, decreasing the motorsymptoms of PD patients and maximising the capability of using external cues forrehabilitation.

The hypotheses of this study are:

• Discrete cues could generate high synchronisation values in the premotor andparietal cortex, mainly in the beta band, compared to a resting state and con-tinuous cues. Additionally, the synchronisation of these regions could increase,compared to the continuous tasks. This based on the fact that premotor andparietal cortex are thought to be part of the CTC network and that this networkis synchronised when presented with discrete cues [27] [30].

• Continuous cues will result in high synchronisation values in the frontal, pre-frontal and sensorimotor cortex, mainly in the beta band, compared to theresting state and discrete cues. Furthermore, the synchronisation of theseregions will increase, compared to discrete cues. This because these cortexregions are believed to be part of the BGTC network, that it is synchronisedwhen performing tasks with continuous cues [33] [35] [39].

• Polyrhythmic cues will generate high synchronisation values of the premotorand parietal cortex, compared to the resting state and isorhythmic tasks. More-over, the synchronisation of these cortex regions should increase compared tothe isorhythmic tasks. This because the CTC network is believed to be syn-chronised when using polyrhythmic cues. [33] [35] [39].

• Isorhythmic cues will generate a synchronisation of the frontal, prefrontal andsensorimotor cortex, compared to the resting state and polyrhythmic cues.


This moreover the BGTC network is thought to be synchronised when isorhyth-mic cues are used. Because of this, also the synchronisation between thefrontal, prefrontal and sensorimotor cortex will increase compared to the polyrhyth-mic cues [19] [53].

Additionally, a comparison in activity and connectivity between auditory and visualtasks will be performed. This will be done to investigate if there are significant differ-ences between these two types of cues, concerning the activation and/or connectionof the BTC or CTC networks in the brain cortex [16] [31].

1.7 Report organisation

The remainder of this report is organised as follows. In Chapter 2, the setup of thestudy and the analysis of the data is described. The subsections of this chapterare Participants, Measurement Setup, Data Acquisition, Storage of the data, DataAnalysis and Statistical Analysis. Then, in Chapter 3 the results of the study areshown and in Chapter 4 a discussion is made. Finally, in Chapter 5, conclusionsand recommendations are given.

Chapter 2

Material and Methods

To test the hypotheses described in Section 1.6, an experimental study was devel-oped, in which healthy subjects were asked to performed discrete and continuousfinger tapping tasks using polyrhythmic and isorhythmic, visual and auditory cues.

2.1 Participants

The experiments were performed with 9 healthy subjects aged between 21 and 28years. All the participants were recruited from the Radboud University in Nijmegen,the Netherlands. The first inclusion criterion was the size of the head of the subjects,due to the availability of the EEG caps. The participants had Small head circumfer-ence (47-51cm) or Medium head circumference (51-56 cm).

Subjects with skin diseases in the head, with psychiatric/ neurological disorderspresented in the last year or with epilepsy, were excluded from the study as well asthose who underwent brain/head surgery, those with a record of substance abuseand those that were taking EEG-affecting drugs, like antidepressant/anti-psychoticor others [54] [55]. The participants signed an informed consent before starting thestudy.

2.2 Measurement Setup

2.2.1 EEG

To detect the brain activity in the cortex, a waveguardTM original EEG cap, devel-oped by ANT Neuro, was used. The layout of the cap is composed of 32 chan-nels, positioned using the 10-10 International system (Figure 2.1) with a samplingfrequency of 1024Hz. This cap was used together with the eegosportTM hard-

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12 CHAPTER 2. MATERIAL AND METHODS

ware/software and amplifier.

Figure 2.1: This figure shows the 32 electrodes montage used in this study. Thismontage is based on the 10-10 EEG International system. This figureis a made modification of the figure obtained from [56].

2.2.2 Acceleration data

To determine the exact moment at which the participants performed a finger tap,the accelerometer of the waveguardTM system was positioned in the index fin-ger of the dominant hand of each participant, sampling acceleration data at a fre-

2.3. DATA ACQUISITION 13

quency of 1024Hz. This accelerometer was connected to one of the channels of theeegosportTM .

2.3 Data acquisition

2.3.1 Experimental setup

For the experimental sessions, each participant was asked to wear the accelerome-ter sensor at the knuckle of the index finger, of the dominant hand. At the beginningof the study, the experimenter positioned a cap of superficial electrodes on the headof the participant. To correctly position the cap, the experimenter used four anatom-ical determinants of the head: nasion, inion and left and right pre-auricular points.Afterwards, the subject sat in a chair without an armrest in front of a table with acomputer monitor. Each participant positioned the dominant hand on the table. Thesubjects were instructed to relax and sit as still as possible and only move the indexfinger, so to reduce possible noise in the acquisition of data.

Since the goal of the study is to identify the differences in brain connectivity andactivity when subjects perform repetitive movements led by different external cues,a 2x2x2 dimensions experimental design was used. The first dimension refers to thetype of cue (visual or auditory), the second to the type of rhythm followed by thecue (isorhythmic or polyrhythmic) and the third to the frequency of the cue (contin-uous or discrete). The experiment was composed of eight different configurations,called tasks. In Figure 2.2 is possible to see the characteristics, name and abbrevia-tions of each task. The order of the tasks was randomised between subjects. Eachtask was subdivided into 5 trials of 16 seconds, with a 5 seconds stop between trialsand 15 seconds pause. The average over the pauses was used as a resting state.With a total experiment time of around 20 minutes.

Cues with a frequency of 1Hz and 3.2Hz were selected to represent discrete andcontinuous cues, respectively. The polyrhythmic cues were presented at a rhythmicrelation of [2:3] [27], between the main and secondary beat/image. The participantswere instructed to tap with the main beat/image. The tones of the auditory cues wereselected based on the musical scale. For the isorhythmic cues and the main beat ofthe polyrhythmic cues, the musical note used was D3, represented by a frequencyof 146Hz [57]. For the secondary beat of the polyrhythmic cue, the frequency of thenote was 880Hz, representing the A5 note [57]. These tones were selected sincethey are easily discernible by the human ear and do not generate a high level ofdiscomfort when hearing them repetitively.


Figure 2.2: This figure illustrates the three characteristics of the tasks that are usedin the experiment: type of cue, type of rhythm and frequency. Thereis a total of 8 tasks: Visual Isorhythmic 1Hz, VI1; Visual polyrhythmic1Hz,VP1; Visual isorhythmic 3.2Hz, VI3; Visual polyrhythmic 3.2Hz,VP3; Auditory isorhythmic 1Hz, AI1; Auditory polyrhythmic 1Hz, AP1;Auditory isorhythmic 3.2Hz, AI3; Auditory polyrhythmic 3.2Hz, AP3.

The isorhythmic visual cues are represented by one white circle positioned in themiddle of the computer screen. The background around the white circle is gray. Thecircle appears and disappears following the cueing frequency, 1 or 3.2Hz. For thepolyrhythmic visual cues, two white circles and one red dot were presented in thecomputer screen. The red dot was positioned in the middle of the screen, whilethe white circles were positioned one in the left side of the screen and the other inthe right side. The circle at which the participants needed to tap was the left cir-cle, meaning that this one was following the mean beat, while the circle in the rightside was appearing and disappearing following the secondary beat. The participantneeds to focus the gazed on the red dot. Subfigures (a) and (b) in Figure 2.3 showthe isorhythmic and polyrhythmic cues montages.

2.4 Storage of data

The acquired and processed data were saved in an open-source relational databasemanagement system, called PostgreSQL. This system was mainly used to organisethe data systematically, making the access and structure of it clear and easy. Theschema of the database was developed using DBeaver. Using this database, the

2.4. STORAGE OF DATA 15

(a) Figure 1

(b) Figure 2

Figure 2.3: The subfigure (a) shows the isorhythmic visual task. The white circle inthe center of the gray background is going to be flickering following thefrequency of the task and the participant will tap every time the circleappears. The subfigure (b) shows the polyrhythmic visual task. The leftwhite circle will flicker following the main pattern/rhythm, while the rightcircle will flicker following the secondary pattern. The participant will tapevery time the left circle appears. The participant should focused thegazed, all the time, in the red dot in the middle of the figure.


relations between the data tables were created.

2.5 Data Analysis

2.5.1 Data pre-processing

Accelerometer data

First, the accelerometer data was divided into 8 different parts, each one specific toa task (See Figure 2.2 for the tasks). Second, the norm of the 3D accelerometersignals was calculated for each of the 8 tasks. Third, the acceleration signal wasfiltered using a zero-phase, second-order, non-casual Butterworth filter with cut-offfrequencies of 0.5 and 20Hz. Finally, the identification of the finger tapping wasperformed. To achieve this, the maximum local values (peaks) of the accelerationwere calculated in each task. A maximum value was marked as a peak only if thedistance between two maximum values was at least 0.935 seconds when tapping at1Hz and 0.2475 seconds when tapping at 3.2Hz. These times were calculated as1/tapping frequency minus 0.065seconds.

EEG data

To obtain the EEG activity and connectivity of the brain cortex regions, an analysisof the acquired data was performed off-line, using Matlab [58] and EEGLab [59] tool-boxes. The CPz electrode was used as a reference for all the electrodes. First, theEEG data was filtered using a zero-phase, fourth-order bandpass Butterworth filterwith cutoff frequencies of 1Hz and 70Hz and a Notch Filter of 50Hz. Second, a blindsource separation method called Independent Component Analysis (ICA) [60] wasimplemented to eliminate the eye blinks from the EEG data. Once all the indepen-dent components were obtained, a visual inspection of them was performed, usingmultiple scalp maps (one for each component) and time-signals. The maps gaveinformation about the level of projection of each component around the brain, whilethe time signals showed the presence of the eye blinks in time. The componentsshowing only strong far-frontal projections or only strong frontal-lateral projectionswere marked as eye blink components and provisionally eliminated from the data.If the time signals after the provisional elimination of the ICA component, markedas eye blink, did not present anymore the eye blinks, this ICA component was per-manently eliminated, if not, the ICA component was not eliminated from the data.Third, the EEG data was split into eight different types of epochs, each one asso-ciated with a specific task (See Figure 2.2). Finally, the data was filtered in threedifferent bands: alpha(8-13Hz), beta (13-30Hz) and gamma (30-70Hz).

2.5. DATA ANALYSIS 17

2.5.2 Data Processing

Task-related Activity

To obtain the cortex activity, the event-related (de)synchronisation (ERD/ERS) mea-sure was selected. To calculate it, it was necessary to epoch the EEG data. Theepochs were centred on the acceleration peaks and had a different length depend-ing on the tapping frequency, as implemented by Toma el. (2002), Pfurtscheller etal. (2003), Chung et al. (2018) and Androulidakis et al. (2007) [23] [61] [62] [63].For 1Hz, the epoch’s length was of ± 1 second and for 3.2Hz of ±0.3125 sec-onds, centre around the peak. This type of epoching was implemented to calculatethe ERD/ERS based on the tapping task activity. The epochs with high levels ofnoise were eliminated. The level of noise was calculated using a power threshold.All epochs above this threshold were marked as noisy and therefor removed. Thethreshold was calculated using Equation 2.1, for each channel. The power signal isthe power of the total epoch signal of each channel, calculated as the square valueof the amplitude of each sample.

Threshold = mean(|Powersignal|) + 3 ∗ std(|Powersignal|) (2.1)

The ERD/ERS was computed for each of the 32 electrodes minues the twomastoid electrodes, for each epoch, each participant and each frequency band (al-pha,beta and gamma). It was calculated as the proportional decrease or increase,respectively, of the power compared to the resting state data [39]. It was computedusing equation 2.2, where Ptask refers to the average power of each epoch of a spe-cific tapping task and the Presting to the mean power of the resting state. Topoplotsfigures of the ERD/ERS were created to visualise the results.

ERD/ERS =Ptask − Presting

Presting(2.2)

Connectivity

The PLV measure was chosen to analyse the functional phase connectivity. To in-crease the accuracy of the PLV calculation, it was decided to analyse in-depth thefrequency bands to see if it was possible to reduce them, increasing the accuracy ofthe calculations. To do so, a visual analysis of the power spectra of each frequency


band, for each participant and each task was done. With this, it was possible to de-termine that for the alpha band, there were clear peaks only between 8 and 11Hz,for beta between 15 and 25Hz and gamma between 30 and 45Hz. This for all theparticipants and all the tasks. Another characteristic necessary to correctly calculatethe PLV is the length of the epochs. This length is related to the frequency band inwhich the PLV will be calculated and the signal-to-noise ratio [42]. Cohen et al. [42]affirms that the length of the epochs should include, at least, 3 cycles of the signal .To determine the correct length of the epochs for the PLV calculation, a model withtwo sinusoidal signals was created. The frequency of the sinusoidal signals wasset at the highest frequency within the specific frequency band: 11Hz for the alphaband, 25Hz for the beta and 45Hz for the gamma. The difference between these twosignals was that one was phase-shifted in time, intending the phase of one of thesignals to change over time. Since the shift in phase between these two signals wasknown, it was possible to identify if the PLV was correctly calculated for the epochs.If the epochs were too long, abrupt changes in PLV were seen in between epochs;if the epochs were too short, multiple consecutive epochs had the same PLV valueand computational time was unnecessary increased. Based on visual inspection ofthe sinusoidal tests, the length of the epochs was determined. For the alpha band,the selected length was 0.41s, for beta 0.18 and gamma 0.10. With these lengths,the number of cycles for each frequency band was around 4.5. The division of thedata into epochs was done without using the peaks of the tapping tasks as the cen-tre of the epochs, but linearly, to analyse the connectivity over time. Additionally, theepochs had a 50% overlap between them.

Once the data was divided into epochs,a 1 by 1 one (frequency: frequency) fre-quency PLV for two electrodes was calculated for each participant, each new fre-quency band, each task, each trial and each epoch. A total of 496 possible compar-isons were done. Nevertheless, those related to the electrodes in the mastoids werenot taken into account. The first step to obtain the PLV was to calculate the instan-taneous phase angle of the epochs in time. To do so, the signals were bandpass-filtered for each of the bands, alpha (8-11Hz), beta(15-25Hz) and gamma (30-45Hz),and then a Hilbert transformation was implemented; obtaining the instantaneousphase angles for each epoch [43] [44]. Once the instantaneous phase angles wereacquired, Equation 2.3 was used to calculate the PLV between two electrodes, xand y. Where N is the total samples of each epoch, φx and φy are the instantaneousphase angles of the electrodes over the freqency band and ∆t is the period of each


epoch [44].

PLV =

∣∣∣∣∣ 1

N

N−1∑j=0

ei(φx(j∆t)−φy(j∆t)

∣∣∣∣∣ (2.3)

2.5.3 Statistical Analysis

Activity

To find if there was a significant difference between the ERD/ERS in time, a statisti-cal analysis was performed over all the epochs, for each band, each task and eachparticipant. If the data was normally distributed, an ANOVA test was conducted, or ifnot, a Friedman’s test was implemented. The normality of the data was tested usinga Shapiro-Wilk test. Afterwards, to statistically analyse the ERD/ERS data, and re-duce the statistical error produced by comparing too many electrodes, the electrodeswere grouped in 16 regions, as shown in Figure 2.4. The 16 regions are Frontal right(FR),Frontal left (FL), Frontal centre(FC), Frontal Central right(FCR), Frontal Centralleft(FCL), Central right(CR), Central left(CL), Central centre(CC),Temporal right(TR),Temporal left (TF), Parietal right(PR), Parietal left(PL), Parietal centre(PC), Occipitalright(OR), Occipital left(OL) and Occipital centre(OC). These regions were selectedbased on the cortex regions hypothesised to be associated with the BGTC (frontal,prefrontal, supplementary motor, primary motor and primary and somatosensorycortex) and the CTC (premotor and parietal cortex) networks.

To obtain the ERD/ERS values over the brain regions, a mean value over all theelectrodes that were part of a region was determined; this for all the participants,bands, epochs and tasks. Additionally, to be able to compare the ERD/ERS resultsin between tasks, without obtaining false-positive due to the high differences in ab-

Table 2.1: This table shows 12 pairs of tasks that are used for the statistical taskscomparisons. Each value of these pairs are the abbreviations of the task.The comparisons were always done as Task1-Task2.

TASK 1 TASK 2 TASK 1 TASK 2VI1 VI3 AP1 AI1VP1 VP3 AP3 AI3AI1 AI3 AI1 VI1AP1 AP3 AI3 VI3VP1 VI1 AP1 VP1VP3 VI3 AP3 VP3


solute values between the tasks, proportions between different cortex regions werecalculated, and subsequently, statistical comparisons between the tasks, shown inTable 2.1, were performed. The proportions to calculated were selected based onthe topoplot figures of ERD/ERS for each of the tasks and bands and the hypothesesof the study; the Cerebellar regions where always put as the dividend and the BG asthe divisor of the proportions. Before the proportions were calculated, a normalisa-tion of the ERD/ERS values was carried out, this to avoid false statistical results dueto the fact that the ERD/ERS can be negative or positive and the proportions do notrepresent properly this relation. The normalisation consisted of changing the datato values between 1 and 2, avoiding having NaNs or infinite proportion values. Thenormalisation was done independently for each participant, each band, each taskand each epoch.

If there was not a significant difference between the ERD/ERS values over time, theproportions of ERD/ERS of different cortex regions were calculated as a mean overall epochs, for each task. On the contrary, if the data was significantly different overtime, the proportions of ERD/ERS over different cortex regions, were calculate foreach epoch. To compare the proportions of ERD/ERS between two different tasks(comparisons shown in Table 2.1), if the data was normally distributed, a paired t-test was used. If the data was not normally distributed, a Wilcoxon’s matched pairssigned rank test was used (See Table 2.1 for the comparison of tasks). To test thenormality of the data, a Shapiro-Wilks test was implemented.

Connectivity

To check if there was a significant difference in connectivity vales over time, for aspecific task for each participant, a statistical analysis was done. With this, it waspossible to know if there were specific changes in connectivity over time. If the datawas normally distributed, an ANOVA test was conducted, or if not, then a Friedman’stest was implemented. To check the normality of the data, a Shapiro-Wilk test wasused. If there were no significant differences over the epochs/time, the data wastreated as static, for each task. Subsequently, two different statistical analyses werecarried out: one to compare the values of connectivity between tasks and another tocompare the number of connectivities between tasks. As with the ERD/ERS data,the PLV electrodes connectivity data was grouped in the 16 cortex regions, as shownin Figure 2.4.

To calculate the connectivity between a pair of brain regions, the PLV value betweentwo electrodes was investigated and each electrode was determined as belonging


to one of the 16 cortex regions. Once the values of connectivity between the dif-ferent brain regions were obtained, a statistical analysis for each pair of tasks(SeeTable 2.1), for each pair of brain regions, was executed. If the data was normallydistributed, a paired t-test was carried out, or if not, a Wilcoxon’s matched pairssigned rank test was implemented. For the statistical analysis comparing the num-ber of connectivities in each of the 16 cortex regions, two steps were staged. Thefirst one was to select the top 10% connectivity values for each task, each bandand each participant. The second step was to quantify, for each of the 16 cortex re-gions, the number of connectivities, obtained in the step before. Once the numbersof connectivities for each region were quantified, a Shapiro-Wilks normality test wasperformed. If the data was normally distributed, paired t-tests were carried out, tocompare these numbers of each brain region in between two tasks (See Table 2.1for the tasks). If the data was not normally distributed, a Wilcoxon’s matched pairssigned rank test was performed.


Figure 2.4: Figure of the electrodes positions and division of the brain cortex in 16regions. Each region has the abbreviation of the name of it, the elec-trodes that are part of the region and the abbreviation of the pathwayto which it is hypothesised to be are part of. The division between re-gions is made with the black lines, the abbreviations of the regions withthe blue colour, the regions that are hypothesis to be part of the BGTChave this abbreviation in red and those in the CTC network have thisabbreviation in green. Abbreviations: CTC, Cerebello-thalamo-corticalnetwork; BGTC, Basal Ganglia-thalamo-cortical netowrk

Chapter 3

Results

3.1 ERD/ERS

3.1.1 Statistical Analysis

The ERD/ERS data over the epochs was not-normally distributed. This implies thatthe Friedman’s test was used to check if there was a difference in time for theERD/ERS values. Non of the epochs were significant different. Because of this,the ERD/ERS was calculated as static over time, using a mean over the epochsfor each task and each cortex region. As mention in the Section 2.5.3, the calcu-lated proportions were selected based on the topoplots for each one of the tasks.From the statistical analyses made with the proportions it was possible to identifythe proportions that were significantly different for different comparisons of tasks.The significant results can be seen in Figure 3.1. If the values are below 1, it meansthat the divisor (the cortex region hypothesised to be part of the BGTC network) hasa higher value of synchronisation than the dividend (the cortex region hypothesisedto be part of the CTC network). On the contrary, if the proportion value is higherthan 1, the dividend is higher than the divisor. The proportion between the Parietalcenter and Central left cortex for the VI1 task and for the VI3 task is above 1, mean-ing that the Parietal center region, hypothesised to be part of the CTC network, hashigher ERD/ERS value than the Central left region, hypothesised to be part of theBGTC network. Additionally, the VI1 proportion in this case is higher than the VI3proportion, meaning that the synchronisation of the Parietal center regions is higherin the VI1 task, than in the VI3 task. This is in agreement with the hypothesis thatthe CTC network could be more synchronised during tapping activities following dis-crete cues than when tapping with continues cues.

This is also the case for the proportion between Parietal center and Central left,for the tasks AI1 and AI3, for the proportion Parietal center and Central right, for the

23

24 CHAPTER 3. RESULTS

Figure 3.1: This BarGraph shows the significantly different ERD/ERS proportionsbetween two tasks, for the analysis of synchronisation. The y axis rep-resents the values of the proportions and the x axis the cortex regionsused for each proportion. The bars are grouped in sets of two bars.These sets are conformed by the proportion value related to each oneof the two tasks that are being compared. Above each bar, the abbrevi-ations of the tasks can be found. Additionally, each bar comes with theerror bar calculated using the data from the 9 participants.

tasks VI1 and VI3 and for the proportion between Parietal left and Central right forthe tasks AP1 and AI1. On the contrary, for the proportion between Parietal left andCentral center, the value for tasks VI1 and VI3 is below 1, meaning that the synchro-nisation of the Central center region is higher than the one for the Parietal left one.The proportion value for the task VI3 is higher than for the task VI1, meaning thatthis result is in agreement with the hypothesis that the BGTC networks should havea higher synchronisation when performing repetitive movements following continuescues than discrete ones. This is the same case for the proportion between FrontalCentral left and Central center for the tasks VI1-VI3, AI1-AI3 and AP1-AP3 and forthe proportion Frontal Central right and Central center, for the tasks VI1-VI3 andAP1-AP3. In the next sections, these results will be correlated to the synchronisa-tion and desynchronisation patterns of the topoplot graphs, and will be explained inmore detail.

3.1. ERD/ERS 25

3.1.2 Cueing Isorhythmic Frequency Comparison

Beta Band

The subfigure (a) in Figure 3.2 shows the ERD/ERS results in a topoplot for the betaband, in the VI1 task. The regions with higher synchronisation(ERS), compared tothe resting state, are the Parietal center, Occipital center and Frontal right cortex.Additionally, the Parietal bilateral, Frontal Central left and Central center cortex alsopresent a high synchronisation (ERS), compared to the resting state. On the con-trary, the Temporal right cortex presents a desynchronisation(ERD). The subfigure(b) of Figure 3.2 shows the ERD/ERS topoplot, for the beta band, for the VI3 task.Here, the highest values of synchronisation are in the Parietal center, Occipital cen-ter, Central center, Frontal Central left and Frontal Center cortex. The only regionsthat are desynchronised are the Temporal right and the Parietal right cortex. Com-paring the ERD/ERS patterns for both tasks, VI1 and VI3, it is possible to identifythat for the VI1 task the synchronisation pattern around the Central center region isless synchronised than the pattern around the Central center region in the VI3 task.This is in agreement with the hypothesis that the Central region of the brain has ahigher synchronisation during repetitive movements made following continues cues(3.2Hz) compared to discrete cues (1Hz). Furthermore, if we compare the patternsaround the Parietal right and center cortex and Central bilateral cortex, it is possibleto see that for the VI3 task the proportion of these two regions should be lower thanfor the VI1 task. This is supported by the results obtained in the statistical analysis,as shown in Figure 3.1. This is the same case with the proportion between the Pari-etal center and the Central left cortex.

For the AI1 task (See Appendix A.1, Figure A.1 for the topoplot), the regions withhigher synchronisation are the Parietal center, Central center, Frontal center andFrontal right cortex. For the AI3 task (See Appendix A.1, Figure A.2 ), the regionswith highest synchronisation are the Parietal center, the Central center, the FrontalCentral left and the Central center cortex. Followed by the Parietal bilateral, Centralbilateral and Frontal right cortex. Analysing the patterns, in the case of the AI1 task,the Parietal center region is more activated than the Central center and Central bi-lateral regions. On the contrary, in the AI3 task, the synchronisation of the Parietalcenter cortex is similar to the synchronisation of the Central center cortex. This canbe supported by the statistical analysis, in which the proportion between Parietalcenter and Central left is higher in the AI1 task than in the AI3 task, with valuesabove 1. This is in accordance with the hypothesis that the Parietal regions are partof the hypothesised CTC network and should be more synchronised when perform-ing finger tapping following discrete cues compared to continues cues; and with the


hypothesis that the Central cortex is part of the hypothesised BGTC network andthis network should be more synchronised when pursuing repetitive movements fol-lowing continues cues compared to discrete cues. Furthermore, the Frontal Centralbilateral cortex presents less synchronisation for both tasks, AI1 and AI3, comparedto the Central center cortex. This does not support the hypothesis that the FrontalCentral cortex is part of the CTC network and should be more synchronised than theCentral center cortex (BGTC network) when using discrete cues. However, whencomparing the proportion between Frontal Central left and Central center, in bothtasks, the proportion has a higher value, below 1, for the AI3 task than for the AI1task. This is compatible with the hypothesis that the Central center cortex shouldbe more synchronised in the AI3 task than in the AI1 task, due to the fact that itis hypothesis that the Central center cortex is part of the hypothesised BGTC net-work and this should have higher synchronisation when performing continues cues,compared to discrete ones.

Alpha Band

In Appendix A.2 the topoplots for the visual tasks, in alpha band, are shown. Forthe VI1 task (See Figure A.5), the highest synchronisation values are in the Frontal,Central center and Right parietal cortex. For the VI3 task (See Figure A.6, theFrontal and Central center cortex have the highest synchronisation values. Theseresults are similar for the auditory tasks ( See Appendix A.2, Figures A.9 and A.10).The only main difference is that for the AI1 task, the Parietal right region has a lowersynchronisation than in the VI1 task.

Gamma Band

In Appendix A.3, Figure A.13 shows the topoplot for the VI1 task, in the gammaband. The highest synchronisation values are presented in the Central right, Frontalbilateral, pars opercularis (FC5) and Parietal center and right cortex. With desyn-chronisation only in the right temporal cortex. For the VI3 task, the highest syn-chronisation is around the Frontal bilateral regions and Central regions. With thehighest desynchronisation in Temporal bilateral. The rest of the regions presentedare desynchronised. For the auditory tasks, the patterns are somehow different thanthe visual tasks. For the AI1 task, the regions with higher synchronisation are theFrontal cortex, mainly Frontal center region, while for the AI3 tasks, the patterns aresimilar to those of the VI3 task in the gamma band. The only difference is that in theAI3, the synchronisation in the Central region is higher than for the VI3.

3.1. ERD/ERS 27

(a) Figure 1

(b) Figure 2

Figure 3.2: The subfigure (a) shows the ERD/ERS results in a topoplot for the taskVI1, in the beta band. The colours of the image are related to the mag-nitude of synchronisation. In this case, the blue tones are associatedwith lower values of synchronisation while the red colours are associ-ated with the highest values of synchronisation. The maximum valueis 7.5 and the minimum 0. The changes in synchronisation are relatedwith a specific task (VI1) compared to a resting state. The subfigure (b)shows the ERD/ERS results in a topoplot for the task VI3, in the betaband. The colours of the image are related to the magnitude of syn-chronisation. In this case, the blue tones are associated with the lowestvalues of desynchronisation while the red colours are associated withthe highest values of synchronisation. The maximum value is 2 and theminimum -1. The changes in synchronisation are related with a spe-cific task (VI3) compared to a resting state. Abbreviations:VI1, visualisorhythmic 1Hz; VI3, visual isorhythmic 3.2Hz.


(a) Figure 1

(b) Figure 2

Figure 3.3: The subfigure (a) shows the ERD/ERS results in a topoplot for the taskVP1, in the beta band. The colours of the image are related to themagnitude of synchronisation. In this case, the blue tones are associ-ated with the lowest values of synchronisation while the red colours areassociated with the highest values of synchronisation. The maximumvalue of synchronisation is 6 and the minimum 0. The changes in syn-chronisation are related with a specific task (VP1) compared to a restingstate. The subfigure (b) shows the ERD/ERS results in a topoplot forthe task VP3, in the beta band. The colours of the image are relatedto the magnitude of synchronisation. In this case, the blue tones areassociated with the lowest values of desynchronisation while the redcolours are associated with the highest values of synchronisation. Themaximum value is 2 and the minimum -1. The changes in synchronisa-tion are related with a specific task (VP3) compared to a resting state.Abbreviations: VP1, visual polyrhythmic 1Hz; VP3, visual polyrhythmic3.2Hz.

3.1. ERD/ERS 29

3.1.3 Cueing Polyrhytmic Frequency Comparison

Beta Band

For the VP1 (See subfigure (a) in Figure 3.3), in the beta band, the regions withhigher synchronisation are the Parietal center, Central center and Frontal right cor-tex. Followed by the Parietal left, Frontal central left and Frontal center and leftcortex. In this case, non of the regions presented desynchronisation. For VP3 task(See subfigure (b) in Figure 3.3), the cortex regions with the highest synchronisationwere the Parietal center, Central center, Frontal right and Frontal center cortex. Theregions with higher desynchronised were the Temporal bilateral cortices.Comparingthe patterns of both tasks, is it possible to identify that the proportion between theParietal center and Central center cortex is higher in the VP1 task than in the VP3.This is in agreement with the hypothesis that the hypothesised regions that are partof the BGTC network synchronise more when using polyrhythmic continues cues,compared to polyrhythmic discrete cues. However, this difference was not foundsignificant during the statistical tests. Furthermore, the Parietal left cortex in thetask VP1 presents higher activation than the same region in the VP3 task. This is inagreement with the conjecture that the Parietal regions are part of the hypothesisedCTC network and that this network should have a higher synchronisation when us-ing polyrhythmic discrete cues than when using polyrhythmic continues cues.

For the AP1 task (See Appendix A, Figure A.3) the patterns in the topoplot aresimilar to those in the VP1 task. However the synchronisation around the Parietalleft cortex is higher for the AP1 task than for the VP1 task. For the AP3 task, thepatterns are also similar to those in the VP3 task. Nevertheless, in the AP3 task thesynchronisation around the Central left and the Frontal Central left cortex is higherthan in the VP3 task.

Alpha Band

For the VP1 task (See Appendix A, Figure A.7), in the alpha band, the regions withhigher synchronisation are the Frontal bilateral and center, the Frontal Central centerand the Central center and left cortex. For the VP3 task (Figure A.8), the highestsynchronisation values are found in the Parietal right, Central center, Central leftand Frontal center regions. For the AP3 task ( Figures A.12), the regions with highersynchronisation are the Central center, Frontal Central right and left and the Frontalcenter. These synchronisation patterns are the same for the AP1 and AP3. So,no clear differences are seen when changing the frequency of stimulation in theauditory polyrhythmic tasks.


Gamma Band

The topoplots of the visual and auditory polyrhythmic tasks, in which the frequencyvaries, can be found in Appendix A. The VP1 (Figure A.15) shows that the highersynchronisation values for this task are in the Frontal left cortex and pars opercularis(FC5), followed by the Central right cortex. For the VP3 task (Figure A.16), thehighest synchronisation values are in the Frontal left and right regions. For thesame task but with auditory cues, the patterns differ. For the AP1 task (Figure A.19)the main synchronisation is in the Frontal bilateral cortex. For the AP3 (Figure A.20)all the regions were desynchronised, with lower desynchronisation values aroundthe Frontal left and right cortex, pars opercularis (FC5), Central left and center, andParietal center cortex.

3.1.4 Cueing Rhythmicity Comparison

Beta Band

Comparing the topoplots of the VI1 and VP1 (See subfigure (a) of Figure 3.2 andsubfigure (a) of Figure 3.3), the patterns of synchronisation are similar in betweenboth tasks. The only two differences that can be seen, is that in the VI1 task, theCentral center cortex has higher synchronisation compared to the VP1 task and thatfor the VP1 task, the patterns are more narrowed. The first difference is in agree-ment with the statistical results in which the proportion between the Frontal Centralleft and Central center is higher (below 1) for the VP1 task than the VI1 task, mean-ing that the Central center is higher in the VI1 task. For the VI3 and VP3 task, thepatterns are again similar, having more narrow patterns in the VP3 task. Not seeingclear differences between these two tasks, was also supported by the fact that notsignificant differences where found between these to tasks, in any of the proportions.

On the contrary, AI1 and AP1 tasks (See Appendix A, Figures A.1 and A.3), somedifferences were found. For example, the Parietal left and Frontal Central left cortexare more synchronised in the polyrhythmic task than in the isorhythmic one. Thedifference in the Parietal synchronisation is in consensus with the statistical analysisresults, in which the proportions between the Parietal left and Central right and Pari-etal right and Central right are higher for the AP1 task than for the AI1 task. However,for the proportions including the Frontal Central left cortex, there was not significantdifference between these tasks. For the AI3 and AP3 tasks (See Appendix A, Fig-ures A.2 and A.4), the main difference is that the AP3 task has narrow patterns thanthe AI3, however, the patterns are similar for both tasks.

3.1. ERD/ERS 31

Alpha Band

For the VI1 and VP1 tasks (See Appendix A, Figure A.5 and A.7), the synchronisa-tion patterns are quite different. In the isorhythmic task, the main synchronisation isaround the Frontal center, Frontal Central lateral, Central center and Parietal rightcortex. For the polyrhythmic task, the main synchronisation is around the Frontalcenter, Frontal Central right, Central right and left cortex. For the VI3 and VP3tasks (See Appendix A in Figure A.6 and A.8), the patterns are also different. Forthe isorhythmic case the Central center, Frontal Central bilateral and Frontal centercortex are highly synchronised. While for the polyrhythmic case, the Parietal right,Central bilateral and Central center are highly synchronised. For the AI1 and AP1tasks (See Appendix A, Figures A.9 and A.11) the patterns are similar in betweenthem, with not obvious differences. This is the same case for the AI3 and AP3 tasks(See Appendix A, Figures A.10 and A.12).

Gamma Band

The synchronisation patterns between VI1 and VP1 tasks (See Appendix A, FiguresA.13 and A.15) are similar. Both of them have a high synchronisation in the Frontalleft cortex and pars opercularis (FC5). However, the VI1 task presents higher syn-chronisation in the Central right and Frontal right regions compared to the VP1 task.Furthermore, in the VI1, there is a pattern that goes from the Frontal center and leftcortex to the Parietal right cortex that is not presented in the VP1 task. For the samecomparison of tasks, but with a frequency of 3.2Hz, the higher synchronisation ispresented in the Frontal bilateral cortex and the Central right cortex. The main dif-ference in patterns between VI3 and VP3 is that there is a background patter for theVI3 task that is not presented in the VP3 task.

3.1.5 Cueing Visual and Auditory Comparison

Generally, for all the the bands, the main differences between auditory and visualtasks are related with the synchronisation of the Central and Frontal Central regionsand the shape of the patterns. For the visual tasks, the patterns are more narrowedaround these regions, while for the auditory the synchronisation patterns are morewide. Additionally, in the auditory tasks, there is generally a higher synchronisationof the Central and Frontal Central regions, compared to the visual tasks. With thestatistical results, it is possible to see that not significant differences in proportionsvalues, between different regions, were found between auditory and visual tasks.


3.2 Connectivity PLV

3.2.1 Connectivity values

The statistical analysis to evaluate if the data of connectivity was static over time,led to the result that the data was indeed static over time. Because of this, it waspossible to calculate the PLV value over a task as the mean value over the multipleepochs, for each electrode. The statistical analyses over the connectivity values be-tween different regions, comparing two tasks, resulted in some significant differentconnectivities. Some of these connectivities can be found in Appendix B, wherethe results of connectivity are shown in topographic plots. If the line representinga connectivity is yellow, it means that the Task 1 of the comparison, had a higherconnectivity. If the connectivity is blue, the Task 2 has a higher mean value of PLV.When analysing these results in relation to the tasks, it was not possible to relatethese significant connectivities with the specific tasks. For example, when com-paring all the tasks in which the tapping frequency varies, there are not commonconnectivities in between these tasks. These behaviour is similar for all the tasksand all the frequency bands.

3.2.2 Number of Connectivities

The statistic analyses made for the number of connectivities, in the regions hypoth-esised to belong to the BGTC and CTC networks (See Figure 2.4), led to the resultof only two significant differences: one in the beta band, for the frontal right regionbetween the VI1 and VI3 tasks and another, in the gamma band, for the frontal cen-tral region between the AI1 and AI3 tasks. Nonetheless, if the connectivity plotsfor all the electrodes are visually analysed (E.g. Figure 3.5), it is possible to seethat there are different number of connectivities between multiple dissimilar regions,when comparing two tasks (E.g. Parietal region Figure 3.5). This incoherence willbe discussed in the methodology discussion section.

3.2. CONNECTIVITY PLV 33

Figure 3.4: Bar graph showing the number of connectivities in the Frontal right re-gion, in the beta band. If there was a significant different between thetasks, an asterix was used to marked it. Each one of the bars repre-sente the connectivity values of one of the tasks that are being com-pared, in the Frontal right cortex. The bars are grouped in sets of two,where each bar represents one of the tasks that are being compared.Additionally, each bar contains a bar error , calculated using the numberof connectivities for each one of the 9 participants. Abbreviations: FR,Frontal right region

Figure 3.5: Left side of the figure are the top 5% connectivity values for the task VI1,in the beta band. Right side of the figure are the top 5% connectivityvalues for the task VI3, in the beta band. Abbreviations: VI1, visualisorhythmic 1Hz; VI3, visual isorhythmic 3.2Hz

Chapter 4

Discussion

4.1 ERD/ERS

4.1.1 Cueing Frequency Comparison

The hypotheses described in Section 1.6 were that the cortex regions hypothesisedto be involved in the CTC network would have a higher activation when finger tap-ping following polyrhythmic cues, compared to finger tapping following isorhythmiccues. The second hypothesis was that the regions hypothesised to be involved inthe CTC network would have a higher activation when the participants tap followingdiscrete (1Hz) stimuli, compared to following continues (3.2Hz) cues. On the con-trary, the cortex regions, hypothesised to be part of the BGTC network, will have ahigher activation when using continues cues (3.2Hz).

As mentioned in Section 3.1, the Parietal center, Occipital center and Frontal rightcortex had the highest synchronisation values during the isorhythmic visual and au-ditory finger-tapping task following an external cue with a frequency of 1Hz, com-pared to the resting state (See subfigure (a) in Figure 3.2). Additionally, the FrontalCentral left, Central center and Parietal bilateral were also synchronised. The resultsof synchronisation of the Frontal right and the Center cortex are similar to those ob-tained in the finger-tapping EEG study by Luft et al. [64], in which activation of theseregions was seen, when finger tapping at 2Hz, in healthy participants. These syn-chronisations, in the beta band, are related to the decision to initiate or finalised amotor activity [65] and regulate it activity [64]. Furthermore, the synchronisation ofthe Frontal Central left was also found in the study by Stavrinou et al. [40] whenperforming finger tapping at a frequency below 2Hz. However, in the same studyby Stavrinou et al, a desynchronisation of the Central regions has found; result thatwas not found in this study.

35

36 CHAPTER 4. DISCUSSION

The synchronisation of the Occipital cortices has been related to visual activity per-formed during a visual task, as shown in the fMRI study of Yang et al. [66]. Further-more, the synchronisation of the Parietal center and Frontal Central left (premotor)cortex were also identified in the PET study by Samuel.M et al. [35] as a result ofthe activation of the cerebellum-cortex pathway. This finding is related to the factthat the cerebellum receives input from the Parietal and Frontal Central (premotor)regions of the brain, as shown in the diffusion-weighted echo-planar study of Ram-nani N. et al., in which fibres between the cerebellum network and the parietal andpremotor cortex were found for Macaque Monkeys and humans [67]. In Samuel.M et al. study [35] the activation of the Parietal and premotor cortex happened onthe right side of these cortices. However, in the obtained results, the Parietal andpremotor activation is more clear in the left side, being partially incongruent with thestudy of Samuel. M et al [35]. Notwithstanding, the activation of the Parietal left andFrontal Central left cortex can still be related to the activation of the cerebellum, notto the timing of the task, but the attention to the motor task, as explained in the studyby Rushworth M et al [68]. To summarise, in this study was possible to confirm thehypothesis that some of the regions that are hypothesised to be part of the CTCnetwork, are synchronised when performing discrete finger tapping. Specifically, theParietal bilateral cortex and the Frontal Central cortex (premotor cortex).

Conversely, the Parietal right cortex is desynchronised, compared to the restingstate, when tapping at 3Hz in visual and auditory tasks. This outcome is in agree-ment with the results presented by Manganotti et al. [41] and the hypothesis thatthe parietal cortex is desynchronised when the tapping frequencies are above 2Hz.This because this cortex region is hypothesised to be connected to the cerebellum,that in turn it is hypothesised to be in charge of decoding discrete and not continu-ous stimuli [21] [22]. The synchronisation of the Frontal center and Occipital cortexis also congruent with the results obtained in the study by Manganotti et al. [41].Additionally, the synchronisation of the Frontal center region and the Central center(sensorimotor) cortex is also in agreement with the hypothesis that the regions thatare part of the BG are synchronised when performing finger tapping at frequenciesabove 2Hz [33] [34]. To summarise, the hypothesis stating that the cortex regionshypothesised to be part of the BGTC network would be synchronised when usingcontinues cues, was confirmed; specifically for the Frontal and Central cortex.

4.1.2 Cueing Rhythmicity Comparison

Concerning the rhythmicity of the tasks, it is possible to see in subfigure (a) inFigures 3.2 and 3.3, that the general synchronisation patterns between the visual

4.2. CONNECTIVITY PLV 37

isorhythmic and polyrhythmic tasks are similar. Nevertheless, for the 1Hz case,the isorhythmic task presents a higher synchronisation in the Central center regioncompared to the polyrhythmic task. This could be linked to the hypothesis that theisorhythmic cues generate a higher activation in the regions hypothesised to be partof the BGTC network, than the polyrhythmic task. For the AP1 task, the increasein the Parietal and Frontal right cortex could identify the ability of polyrhythms toactivate more the Cerebellum, in contrast to isorhythmic cues [30]. However, theseresults should be considered carefully and further investigation should be carriedout because non significant differences were found for the Frontal central regions,for poly and isorhythmic tasks. Moreover, one of the main reasons why the differ-ences between the polyrhythmic and isorhythmic cues are not always significantcould be that the polyrhythmic cues implemented in this study where not ”complex”enough to be processed differently by the CTC network [53].

4.1.3 Cueing Visual and Auditory Comparison

When comparing the auditory and the visual topoplots it is possible to see thatthe general synchronisation and desynchronisation patterns are common for bothcases. Notwithstanding, when using the auditory tasks, the synchronisation of theCentral and Frontal Central cortex is higher than in the visual tasks. These dif-ferences in synchronisation could be related to the fact that it has been shown, inmultiple fMRI and PET studies, that the regions of the cerebellum ( the hemisphericlobule VI in both sides and the right-hemispheric lobules VIIB and VIIA [69]) that areactivated during sensory auditory processing [70] are different from those activatedduring visual stimulation (right-side of lobule X and vermal lobule VI [69]).

4.2 Connectivity PLV

The hypotheses related to the connectivity are that the Frontal Center and parietalcortex, hypothesised to be part of the CTC network, would have a higher connectivitymagnitude and/or number of connectivities, when using tasks that follow polyrhyth-mic external cues and/or 1Hz frequency cues, respectively compared to tasks thatwould increase the connection in Central and Frontal areas, hypothesised to be partof the BGTC network, like isorhythmic and 3.2Hz frequency cues.


4.2.1 Connectivity Values

Analysing the statistical results related to the values of connectivity, it is possible tosay that there are not clear patterns of connection for any of the tasks. As mention inSection 3.2.1, if the different tasks in which the frequency, the rhythmicity or the typeof cues are visualised together (See Appendix B), it is not possible to find commonconnectivities in between the tasks. The non coherent results can be due to multiplefactors. One of these factors could be that it is not possible to detect the connectivityof the Cerebellum and BG pathways in the brain cortex [49]. This because theintricate folding of the Cerebellum and the deep brain position of the BG , relativeto the cortex, cause a cancellation of the signals coming from these regions, beforebeing able to detect them in the brain cortex [49]. Another factor could be thatthe hypothesised CTC and BGTC networks do not display oscillatory modulationsin a phase-locked manner. This would imply, that the PLV measure is not usefulto identify the connectivities of the CTC and BGTC networks in the brain cortex,and that other non-phase-locked connectivity measures should be implemented tosee if this is the reason why, in this studym not clear patterns of connectivity wereidentified [49].

4.2.2 Number of Connectivities

The incoherence between the statistical results and the graphic results in relationto the number of connectivities, could be a consequence of the low power of thestatistical tests [71]. In this case, the low power of the statistical tests could berelated to the low quantity of EEG electrodes (32) and/or the fact that the regionsselected to do the statistical tests were formed by a low number of electrodes (1, 2or maximum three electrodes). To evaluate if indeed these two are the causes of theincoherence between graphic and statistical results, three different new approachescould be implemented. The first one would be to perform a study in which an EEGof at least 64 electrodes would be used, increasing the number of electrodes byregion. The second approach will be to decrease the number of regions, subdividingthe cortex in only 4 main regions: Frontal, Frontal Central, Central and Parietal.With this division, the number of electrodes by region will also increase. The lastapproach would be one in which the number of electrodes increases and the numberof regions decreases, in other words, a fusion of the two approaches explainedbefore. Additionally, it will be interesting to perform a graph analysis of the numberof connectivities, to see if there are interactions that are not been taken into accountwhen only counting the number of connectivities by regions [72].

4.3. METHODOLOGY 39

4.3 Methodology

4.3.1 Activity

The method used to generated the epochs for the analysis of the calculation of theERD/ERS should be studied in more detail. In this case, the epochs were generatedbased on the finger tapping moment, based on the accelerometer data. However,another way to epoch the data would be not based on the finger tapping but asregular epochs, made in time, with overlap between them. With this, the analysismade would be related to the type of task and not with the moment of finger tapping.Additionally, it would be interesting to analyse the precision of the finger tappingsince it could be that the latency is affected by the type of cue used.

4.3.2 Connectivity

During the processing steps to calculate the connectivity values, the frequencybands of interest were analysed and narrowed down, based on the peaks foundin the power spectra. The selection of these peaks was made with a visual inspec-tion but without an in-depth analysis of it. This is why, for future work, it will beinteresting to use methods that automatically find the peaks, increasing the preci-sion of the method and improving the selection of the frequency bands. This couldbe translated into a more precise results in the connectivity values and number ofconnectivities. For the connectivity methodology, the length of the epochs is alsoan important characteristic and can influence the results. As explained in Section2.5.2, the length of the epochs was determined independently for each band, us-ing a model of two sinusoidal signals. Moreover, this method could be improvedby analysing the speed at which the frequencies of two real EEG signals changes,basing the epoching processing in real EEG data. Furthermore, based on Cohen’sbook [42], the number of cycles that should be presented in an epoch should in-crease when the frequency band increases. This means, that for example, if thereare 4 cycles, in one epoch, for the beta band there should be 7 cycles, for the gammaband.

Another reason why the results for connectivity values and number of connectivi-ties are negative, could be related to the calculation process of the PLV. In this case,the selected epochs were non-related to the onset of the finger tapping or the pre-sentation of the stimulus. Nevertheless, in the fMRI study of Wu.X et al [73] theactivation of the cerebellum when performing repetitive movements was time-lockedto the stimulus. Another method to improve the results of connectivity would beto use high-density EEG, increasing the number of electrodes in each brain region


and with it, increasing the data for the statistic analysis.Furthermore, future studiesshould include at least 15 participants to obtain more robust results. Finally, to bet-ter understand when the premotor and parietal cortex are activated, concerning theactivation of the Cerebellum and BG for the timing mechanisms, it would be wiseto perform a study in which the EEG signals of the brain cortex are acquired at thesame time as fMRI signals of the Cerebellum and BG are being acquired.

Chapter 5

Conclusions and Recommendations

The hypothesis of finding higher activation in the parietal and premotor cortex net-work in the brain cortex, when performing tapping tasks at 1Hz compared to tappingtask at 3.2Hz, was validated in this study by the visual inspection of the synchroni-sation/desynchronisation topoplots and the statistical analysis. On the contrary, thehypothesis of higher synchronisation of the parietal and premotor cortex when pre-senting polyrhythmic cues, compared to isorhythmic, was not completely validatedby the statistical results. However, a visual inspection of the topoplots of synchroni-sation, lead to believe that there is indeed a higher activation of the Frontal Centerregion when using polyrhythmic cues. To further evaluate this characteristic, fu-ture work should include multiple polyrhythmic cues with higher complexity. This toevaluate if indeed the complexity of the rhythm generates a higher activation of theparietal and premotor cortex, related to higher activation in the cerebellum. Aboutthe differences between auditory and visual cues, future studies should include be-havioural analysis of accuracy and variability of the finger tapping, to have moreclarity about the relation between physiological movement, brain activity and type ofcue (visual/auditory).

The hypotheses of finding higher connectivity values and/or connectivity numbersbetween/in the parietal and premotor cortex when performing discrete tapping tasksor polyrhythmic cues, and finding higher connectivity values and/or connectivitynumbers between/in the frontal and central cortex when performing continues tap-ping tasks or isorhythmic cues were not completly validated by this study. Never-theless, with these results, it is not possible to reject completely the hypotheses.Instead, future work should be done in which non-phase-locking measures for con-nectivity should be evaluated. Moreover, a stimuli-locked analysis should also bedone to identify if the connectivity between the parietal and premotor cortex is strictlyrelated to the onset of the finger tap and/or appearance of the stimuli. Additionally,studies using higher density EEG and/or regions with higher number of electrodes

41

42 CHAPTER 5. CONCLUSIONS AND RECOMMENDATIONS

should be performed, to increase the accuracy of the data and the power of the sta-tistical analyses.

Finally, based on the results of this study, the rehabilitation processes using externalcues for Parkinson’s disease patients should be centre around the use of discretecues since with this cue, it seems that the Cerebellum is more active than whenusing continues cues, enabling with it, a bypass of the BG, region that is damagedin Parkinson’s disease patients. For the rhythmicity and types of cues, further studyshould be performed in Parkinson’s disease patients to further valiate the differencesin brain synchronisation and in rehabilitation.

Bibliography

[1] S. Sveinbjornsdottir, “The clinical symptoms of parkinson’s disease,” Journal ofneurochemistry, vol. 139, pp. 318–324, 2016.

[2] O.-B. Tysnes and A. Storstein, “Epidemiology of parkinson’s disease,” Journalof Neural Transmission, vol. 124, no. 8, pp. 901–905, 2017.

[3] C. A. Davie, “A review of parkinson’s disease,” British medical bulletin, vol. 86,no. 1, pp. 109–127, 2008.

[4] M. J. Armstrong and M. S. Okun, “Diagnosis and treatment of parkinson dis-ease: A review,” Jama, vol. 323, no. 6, pp. 548–560, 2020.

[5] C. Raza, R. Anjum et al., “Parkinson’s disease: Mechanisms, translational mod-els and management strategies,” Life sciences, 2019.

[6] A. Nieuwboer, S. Vercruysse, P. Feys, O. Levin, J. Spildooren, and S. Swin-nen, “Upper limb movement interruptions are correlated to freezing of gait inparkinson’s disease,” European Journal of Neuroscience, vol. 29, no. 7, p.1422–1430, 2009.

[7] V. Cabreira, P. Soares-da Silva, and J. Massano, “Contemporary options for themanagement of motor complications in parkinson’s disease: Updated clinicalreview,” Drugs, vol. 79, no. 6, pp. 593–608, 2019.

[8] C. Nombela, L. E. Hughes, A. M. Owen, and J. A. Grahn, “Into the groove:can rhythm influence parkinson’s disease?” Neuroscience & Biobehavioral Re-views, vol. 37, no. 10, pp. 2564–2570, 2013.

[9] M. W. Rodger and C. M. Craig, “Beyond the metronome: auditory events andmusic may afford more than just interval durations as gait cues in parkinson’sdisease,” Frontiers in neuroscience, vol. 10, p. 272, 2016.

[10] S. Dalla Bella, C.-E. Benoit, N. Farrugia, P. E. Keller, H. Obrig, S. Mainka, andS. A. Kotz, “Gait improvement via rhythmic stimulation in parkinson’s disease islinked to rhythmic skills,” Scientific reports, vol. 7, p. 42005, 2017.

43

44 BIBLIOGRAPHY

[11] I. Lim, E. van Wegen, C. de Goede, M. Deutekom, A. Nieuwboer, A. Willems,D. Jones, L. Rochester, and G. Kwakkel, “Effects of external rhythmical cue-ing on gait in patients with parkinson’s disease: a systematic review,” Clinicalrehabilitation, vol. 19, no. 7, pp. 695–713, 2005.

[12] S. J. Spaulding, B. Barber, M. Colby, B. Cormack, T. Mick, and M. E. Jenkins,“Cueing and gait improvement among people with parkinson’s disease: a meta-analysis,” Archives of physical medicine and rehabilitation, vol. 94, no. 3, pp.562–570, 2013.

[13] T. C. Rubinstein, N. Giladi, and J. M. Hausdorff, “The power of cueing to cir-cumvent dopamine deficits: a review of physical therapy treatment of gait dis-turbances in parkinson’s disease,” Movement disorders: official journal of theMovement Disorder Society, vol. 17, no. 6, pp. 1148–1160, 2002.

[14] J. Spildooren, S. Vercruysse, P. Meyns, J. Vandenbossche, E. Heremans,K. Desloovere, W. Vandenberghe, and A. Nieuwboer, “Turning and unilateralcueing in parkinson’s disease patients with and without freezing of gait,” Neu-roscience, vol. 207, pp. 298–306, 2012.

[15] S. T. Witt, A. R. Laird, and M. E. Meyerand, “Functional neuroimaging correlatesof finger-tapping task variations: an ale meta-analysis,” Neuroimage, vol. 42,no. 1, pp. 343–356, 2008.

[16] P. Arias and J. Cudeiro, “Effects of rhythmic sensory stimulation (auditory, vi-sual) on gait in parkinson’s disease patients,” Experimental brain research, vol.186, no. 4, pp. 589–601, 2008.

[17] M. F. del Olmo and J. Cudeiro, “Temporal variability of gait in parkinson dis-ease: Effectsof a rehabilitation programme based on rhythmic sound cues,”Parkinsonism & related disorders, vol. 11, no. 1, pp. 25–33, 2005.

[18] S. Handel and J. S. Oshinsky, “The meter of syncopated auditory polyrhythms,”Perception & Psychophysics, vol. 30, no. 1, pp. 1–9, 1981.

[19] R. Huys, B. E. Studenka, N. L. Rheaume, H. N. Zelaznik, and V. K. Jirsa, “Dis-tinct timing mechanisms produce discrete and continuous movements,” PLoScomputational biology, vol. 4, no. 4, 2008.

[20] F. Luft, S. Sharifi, W. Mugge, A. C. Schouten, L. J. Bour, A.-F. van Rootselaar,P. H. Veltink, and T. Heida, “Deficits in tapping accuracy and variability in tremorpatients,” Journal of neuroengineering and rehabilitation, vol. 16, no. 1, p. 54,2019.

BIBLIOGRAPHY 45

[21] E. L. Stegemoller, D. P. Allen, T. Simuni, and C. D. MacKinnon, “Motor corticaloscillations are abnormally suppressed during repetitive movement in patientswith parkinson’s disease,” Clinical Neurophysiology, vol. 127, no. 1, pp. 664–674, 2016.

[22] E. L. Stegemoller, T. Simuni, and C. MacKinnon, “Effect of movement frequencyon repetitive finger movements in patients with parkinson’s disease,” MovementDisorders, vol. 24, no. 8, pp. 1162–1169, 2009.

[23] K. Toma, T. Mima, T. Matsuoka, C. Gerloff, T. Ohnishi, B. Koshy, F. Andres, andM. Hallett, “Movement rate effect on activation and functional coupling of motorcortical areas,” Journal of neurophysiology, vol. 88, no. 6, pp. 3377–3385, 2002.

[24] E. Thelen, “Timing in motor development as emergent process and product,” inAdvances in psychology. Elsevier, 1991, vol. 81, pp. 201–211.

[25] J. M. Mayville, K. J. Jantzen, A. Fuchs, F. L. Steinberg, and J. S. Kelso, “Corticaland subcortical networks underlying syncopated and synchronized coordina-tion revealed using fmri,” Human Brain Mapping, vol. 17, no. 4, pp. 214–229,2002.

[26] R. B. Ivry and R. M. Spencer, “The neural representation of time,” Current opin-ion in neurobiology, vol. 14, no. 2, pp. 225–232, 2004.

[27] M. H. Thaut, M. Demartin, and J. N. Sanes, “Brain networks for integrativerhythm formation,” PloS one, vol. 3, no. 5, 2008.

[28] J. Coull and A. Nobre, “Dissociating explicit timing from temporal expectationwith fmri,” Current opinion in neurobiology, vol. 18, no. 2, pp. 137–144, 2008.

[29] M. J. Hove, M. T. Fairhurst, S. A. Kotz, and P. E. Keller, “Synchronizing withauditory and visual rhythms: an fmri assessment of modality differences andmodality appropriateness,” Neuroimage, vol. 67, pp. 313–321, 2013.

[30] L. A. Chauvigne, K. M. Gitau, and S. Brown, “The neural basis of audiomotorentrainment: an ale meta-analysis,” Frontiers in human neuroscience, vol. 8, p.776, 2014.

[31] B. H. Repp, “Sensorimotor synchronization: a review of the tapping literature,”Psychonomic bulletin & review, vol. 12, no. 6, pp. 969–992, 2005.

[32] D. Milardi, A. Quartarone, A. Bramanti, G. Anastasi, S. Bertino, G. A. Basile,P. Buonasera, G. Pilone, G. Celeste, G. Rizzo et al., “The cortico-basal ganglia-cerebellar network: past, present and future perspectives,” Frontiers in systemsneuroscience, vol. 13, p. 61, 2019.

46 BIBLIOGRAPHY

[33] Y. Koshimori and M. H. Thaut, “Future perspectives on neural mechanismsunderlying rhythm and music based neurorehabilitation in parkinson’s disease,”Ageing research reviews, vol. 47, pp. 133–139, 2018.

[34] A. Galvan, A. Devergnas, and T. Wichmann, “Alterations in neuronal activityin basal ganglia-thalamocortical circuits in the parkinsonian state,” Frontiers inneuroanatomy, vol. 9, p. 5, 2015.

[35] M. Samuel, A. Ceballos-Baumann, J. Blin, T. Uema, H. Boecker, R. Passing-ham, and D. Brooks, “Evidence for lateral premotor and parietal overactivity inparkinson’s disease during sequential and bimanual movements. a pet study.”Brain: a journal of neurology, vol. 120, no. 6, pp. 963–976, 1997.

[36] K. Braunlich, C. A. Seger, K. G. Jentink, I. Buard, B. M. Kluger, and M. H.Thaut, “Rhythmic auditory cues shape neural network recruitment in parkin-son’s disease during repetitive motor behavior,” European Journal of Neuro-science, vol. 49, no. 6, pp. 849–858, 2019.

[37] M. H. Thaut, G. C. McIntosh, R. R. Rice, R. A. Miller, J. Rathbun, and J. Brault,“Rhythmic auditory stimulation in gait training for parkinson’s disease patients,”Movement disorders: official journal of the Movement Disorder Society, vol. 11,no. 2, pp. 193–200, 1996.

[38] M. J. Fu, J. J. Daly, and M. C. Cavusoglu, “Assessment of eeg event-relateddesynchronization in stroke survivors performing shoulder-elbow movements,”in Proceedings 2006 IEEE International Conference on Robotics and Automa-tion, 2006. ICRA 2006. IEEE, 2006, pp. 3158–3164.

[39] G. Pfurtscheller and F. L. Da Silva, “Event-related eeg/meg synchronization anddesynchronization: basic principles,” Clinical neurophysiology, vol. 110, no. 11,pp. 1842–1857, 1999.

[40] M. L. Stavrinou, L. Moraru, L. Cimponeriu, S. Della Penna, and A. Bezerianos,“Evaluation of cortical connectivity during real and imagined rhythmic fingertapping,” Brain topography, vol. 19, no. 3, pp. 137–145, 2007.

[41] P. Manganotti, C. Gerloff, C. Toro, H. Katsuta, N. Sadato, P. a. Zhuang, L. Leo-cani, and M. Hallett, “Task-related coherence and task-related spectral powerchanges during sequential finger movements,” Electroencephalography andClinical Neurophysiology/Electromyography and Motor Control, vol. 109, no. 1,pp. 50–62, 1998.

[42] M. X. Cohen, Analyzing neural time series data: theory and practice. MITpress, 2014.

BIBLIOGRAPHY 47

[43] S. Lee, A. Liu, Z. J. Wang, and M. J. McKeown, “Abnormal phase coupling inparkinson’s disease and normalization effects of subthreshold vestibular stim-ulation,” Frontiers in human neuroscience, vol. 13, p. 118, 2019.

[44] V. Sakkalis, “Review of advanced techniques for the estimation of brain con-nectivity measured with eeg/meg,” Computers in biology and medicine, vol. 41,no. 12, pp. 1110–1117, 2011.

[45] J. Wang, G. Dam, S. Yildirim, W. Rand, U. Wilensky, and J. C. Houk, “Reci-procity between the cerebellum and the cerebral cortex: Nonlinear dynamics inmicroscopic modules for generating voluntary motor commands,” Complexity,vol. 14, no. 2, pp. 29–45, 2008.

[46] C. D. Wurster, H. Graf, H. Ackermann, K. Groth, J. Kassubek, and A. Riecker,“Neural correlates of rate-dependent finger-tapping in parkinson’s disease,”Brain Structure and Function, vol. 220, no. 3, pp. 1637–1648, 2015.

[47] E. Torres and S. Beardsley, “Cerebellar source localization using event-relatedpotentials in a simple motor task,” in 2019 9th International IEEE/EMBS Con-ference on Neural Engineering (NER). IEEE, 2019, pp. 1076–1079.

[48] D. Rose, Y. Delevoye-Turrell, L. Ott, L. E. Annett, and P. J. Lovatt, “Musicand metronomes differentially impact motor timing in people with and withoutparkinson’s disease: Effects of slow, medium, and fast tempi on entrainmentand synchronization performances in finger tapping, toe tapping, and steppingon the spot tasks,” Parkinson’s Disease, vol. 2019, 2019.

[49] L. M. Andersen, K. Jerbi, and S. S. Dalal, “Can eeg and meg detect signalsfrom the human cerebellum?” NeuroImage, p. 116817, 2020.

[50] T. Boonstra, A. Daffertshofer, C. Peper, and P. Beek, “Amplitude and phasedynamics associated with acoustically paced finger tapping,” Brain research,vol. 1109, no. 1, pp. 60–69, 2006.

[51] D. Stoffers, J. Bosboom, J. Deijen, E. C. Wolters, C. J. Stam, and H. W.Berendse, “Increased cortico-cortical functional connectivity in early-stageparkinson’s disease: an meg study,” Neuroimage, vol. 41, no. 2, pp. 212–222,2008.

[52] G. Pfurtscheller and A. Aranibar, “Evaluation of event-related desynchroniza-tion (erd) preceding and following voluntary self-paced movement,” Electroen-cephalography and clinical neurophysiology, vol. 46, no. 2, pp. 138–146, 1979.

48 BIBLIOGRAPHY

[53] B. M. Adhikari, C. M. Epstein, and M. Dhamala, “Enhanced brain network ac-tivity in complex movement rhythms: a simultaneous functional magnetic res-onance imaging and electroencephalography study,” Brain connectivity, vol. 8,no. 2, pp. 68–81, 2018.

[54] M. Bewernitz and H. Derendorf, “Electroencephalogram-based pharmacody-namic measures: a review,” International journal of clinical pharmacology andtherapeutics, vol. 50, no. 3, p. 162, 2012.

[55] D. L. Timmermann, A. W. Zimmerman, and R. A. Muenchen, “Methylphenidateeffects on eeg, behavior, and performance in boys with adhd,” Pediatric Neurol-ogy, vol. 18, no. 3, pp. 244–250, 1998.

[56] waveguard touch - 32 channel dry electrode EEG cap, eegmagine MedicalImaging Solutions, 3 2018.

[57] N. R. Council et al., Hearing loss: Determining eligibility for social security ben-efits. National Academies Press, 2004.

[58] MATLAB, version 9.10.0.1296695 (R2019b). Natick, Massachusetts: TheMathWorks Inc., 2010.

[59] A. Delorme and S. Makeig, “Eeglab: an open source toolbox for analysis ofsingle-trial eeg dynamics including independent component analysis,” Journalof neuroscience methods, vol. 134, no. 1, pp. 9–21, 2004.

[60] T.-P. Jung, S. Makeig, C. Humphries, T.-W. Lee, M. J. Mckeown, V. Iragui, andT. J. Sejnowski, “Removing electroencephalographic artifacts by blind sourceseparation,” Psychophysiology, vol. 37, no. 2, pp. 163–178, 2000.

[61] A. G. Androulidakis, A. A. Kuhn, C. Chu Chen, P. Blomstedt, F. Kempf,A. Kupsch, G.-H. Schneider, L. Doyle, P. Dowsey-Limousin, M. I. Hariz et al.,“Dopaminergic therapy promotes lateralized motor activity in the subthalamicarea in parkinson’s disease,” Brain, vol. 130, no. 2, pp. 457–468, 2007.

[62] G. Pfurtscheller, B. Graimann, J. E. Huggins, S. P. Levine, and L. A. Schuh,“Spatiotemporal patterns of beta desynchronization and gamma synchroniza-tion in corticographic data during self-paced movement,” Clinical neurophysiol-ogy, vol. 114, no. 7, pp. 1226–1236, 2003.

[63] J. W. Chung, R. G. Burciu, E. Ofori, S. A. Coombes, E. A. Christou, M. S. Okun,C. W. Hess, and D. E. Vaillancourt, “Beta-band oscillations in the supplemen-tary motor cortex are modulated by levodopa and associated with functionalactivity in the basal ganglia,” NeuroImage: clinical, vol. 19, pp. 559–571, 2018.

BIBLIOGRAPHY 49

[64] F. Luft, S. Sharifi, W. Mugge, A. C. Schouten, L. J. Bour, A. F. Van Rootse-laar, P. H. Veltink, and T. Heida, “Distinct cortical activity patterns in parkinson’sdisease and essential tremor during a bimanual tapping task,” Journal of neu-roengineering and rehabilitation, vol. 17, no. 1, pp. 1–10, 2020.

[65] J. Tanji and E. Hoshi, “Behavioral planning in the prefrontal cortex,” Currentopinion in neurobiology, vol. 11, no. 2, pp. 164–170, 2001.

[66] Y.-l. Yang, H.-x. Deng, G.-y. Xing, X.-l. Xia, and H.-f. Li, “Brain functional net-work connectivity based on a visual task: visual information processing-relatedbrain regions are significantly activated in the task state,” Neural regenerationresearch, vol. 10, no. 2, p. 298, 2015.

[67] N. Ramnani, T. E. Behrens, H. Johansen-Berg, M. C. Richter, M. A. Pinsk, J. L.Andersson, P. Rudebeck, O. Ciccarelli, W. Richter, A. J. Thompson et al., “Theevolution of prefrontal inputs to the cortico-pontine system: diffusion imagingevidence from macaque monkeys and humans,” Cerebral Cortex, vol. 16, no. 6,pp. 811–818, 2006.

[68] M. Rushworth, H. Johansen-Berg, S. M. Gobel, and J. Devlin, “The left parietaland premotor cortices: motor attention and selection,” Neuroimage, vol. 20, pp.S89–S100, 2003.

[69] O. Baumann and J. B. Mattingley, “Scaling of neural responses to visual andauditory motion in the human cerebellum,” Journal of Neuroscience, vol. 30,no. 12, pp. 4489–4495, 2010.

[70] A. Petacchi, A. R. Laird, P. T. Fox, and J. M. Bower, “Cerebellum and audi-tory function: An ale meta-analysis of functional neuroimaging studies,” Humanbrain mapping, vol. 25, no. 1, pp. 118–128, 2005.

[71] E. Dumas-Mallet, K. S. Button, T. Boraud, F. Gonon, and M. R. Munafo, “Lowstatistical power in biomedical science: a review of three human research do-mains,” Royal Society open science, vol. 4, no. 2, p. 160254, 2017.

[72] L. J. de Schipper, A. Hafkemeijer, J. van der Grond, J. Marinus, J. M. Hensel-mans, and J. J. van Hilten, “Altered whole-brain and network-based functionalconnectivity in parkinson’s disease,” Frontiers in neurology, vol. 9, p. 419, 2018.

[73] X. Wu, J. Ashe, and K. O. Bushara, “Role of olivocerebellar system in timingwithout awareness,” Proceedings of the National Academy of Sciences, vol.108, no. 33, pp. 13 818–13 822, 2011.

50 BIBLIOGRAPHY

Appendix A

Synchronisation/Desynchronizationtopoplots

A.1 Auditory Beta Band

The multiple topoplots for the results of ERD/ERS, in beta band, for the auditorytasks are found in this Appendix.

A.2 Visual and Auditory alpha band

In this section the topoplots of the ERD/ERS values, in the alpha band, for auditoryand visual tasks , are shown.

A.3 Visual and Auditory gamma band

In this section the topoplots of the ERD/ERS values, in the gamma band, for auditoryand visual tasks , are shown.

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52 APPENDIX A. SYNCHRONISATION/DESYNCHRONIZATION TOPOPLOTS

Figure A.1: Topoplot of the ERD/ERS values, for the beta band, of the task in whichauditory isorhythmic stimuli where presented at a frequency of 1Hz.Abbreviations: AI1, auditory isorhythmic 1Hz.

Figure A.2: Topoplot of the ERD/ERS values, for the beta band, of the task in whichauditory isorhythmic stimuli where presented at a frequency of 3.2 Hz.Abbreviations:AI3, auditory isorhythmic 3.2Hz.

A.3. VISUAL AND AUDITORY GAMMA BAND 53

Figure A.3: Topoplot of the ERD/ERS values, for the beta band, of the task in whichauditory polyrhythmic stimuli where presented at a frequency of 1Hz.Abbreviations:AP1, auditory polyrhythmic 1Hz.

Figure A.4: Topoplot of the ERD/ERS values, for the beta band, of the task in whichauditory polyrhythmic stimuli where presented at a frequency of 3.2 Hz.Abbreviations:AP3, auditory polyrhythmic 3.2Hz.


Figure A.5: Topoplot of the ERD/ERS values, for the alpha band, of the task inwhich visual isorhythmic stimuli where presented at a frequency of 1Hz.Abbreviations:VI1, visual isorhythmic 1Hz.

Figure A.6: Topoplot of the ERD/ERS values, for the alpha band, of the task inwhich visual isorhythmic stimuli where presented at a frequency of 3.2Hz. Abbreviations:VI3, visual isorhythmic 3.2Hz.


Figure A.7: Topoplot of the ERD/ERS values, for the alpha band, of the task inwhich visual polyrhythmic stimuli where presented at a frequency of1Hz. Abbreviations:VP1, visual polyrhythmic 1Hz.

Figure A.8: Topoplot of the ERD/ERS values, for the alpha band, of the task inwhich visual polyrhythmic stimuli where presented at a frequency of 3.2Hz. Abbreviations:VP3, visual polyrhythmic 3.2Hz.


Figure A.9: Topoplot of the ERD/ERS values, for the alpha band, of the task inwhich auditory isorhythmic stimuli where presented at a frequency of1Hz. Abbreviations: AI1, auditory isorhythmic 1Hz.

Figure A.10: Topoplot of the ERD/ERS values, for the alpha band, of the task inwhich auditory isorhythmic stimuli where presented at a frequency of3.2 Hz. Abbreviations:AI3, auditory isorhythmic 3.2Hz.


Figure A.11: Topoplot of the ERD/ERS values, for the alpha band, of the task inwhich auditory polyrhythmic stimuli where presented at a frequency of1Hz. Abbreviations:AP1, auditory polyrhythmic 1Hz.

Figure A.12: Topoplot of the ERD/ERS values, for the alpha band, of the task inwhich auditory polyrhythmic stimuli where presented at a frequency of3.2 Hz. Abbreviations:AP3, auditory polyrhythmic 3.2Hz.


Figure A.13: Topoplot of the ERD/ERS values, for the gamma band, of the taskin which visual isorhythmic stimuli where presented at a frequency of1Hz. Abbreviations:VI1, visual isorhythmic 1Hz.

Figure A.14: Topoplot of the ERD/ERS values, for the gamma band, of the task inwhich visual isorhythmic stimuli where presented at a frequency of 3.2Hz. Abbreviations:VI3, visual isorhythmic 3.2Hz.


Figure A.15: Topoplot of the ERD/ERS values, for the gammaband, of the task inwhich visual polyrhythmic stimuli where presented at a frequency of1Hz. Abbreviations:VP1, visual polyrhythmic 1Hz.

Figure A.16: Topoplot of the ERD/ERS values, for the gamma band, of the task inwhich visual polyrhythmic stimuli where presented at a frequency of3.2 Hz. Abbreviations:VP3, visual polyrhythmic 3.2Hz.


Figure A.17: Topoplot of the ERD/ERS values, for the gamma band, of the task inwhich auditory isorhythmic stimuli where presented at a frequency of1Hz. Abbreviations: AI1, auditory isorhythmic 1Hz.

Figure A.18: Topoplot of the ERD/ERS values, for the gamma band, of the task inwhich auditory isorhythmic stimuli where presented at a frequency of3.2 Hz. Abbreviations:AI3, auditory isorhythmic 3.2Hz.


Figure A.19: Topoplot of the ERD/ERS values, for the gamma band, of the task inwhich auditory polyrhythmic stimuli where presented at a frequency of1Hz. Abbreviations:AP1, auditory polyrhythmic 1Hz.

Figure A.20: Topoplot of the ERD/ERS values, for the gamma band, of the task inwhich auditory polyrhythmic stimuli where presented at a frequency of3.2 Hz. Abbreviations:AP3, auditory polyrhythmic 3.2Hz.

Appendix B

Connectivity values Topoplot

In this appendix, the connectivity values, for the beta band, that are significantlydifferent between the tasks are shown in topoplots. In each Figure 4 topoplots arepresent, one for each comparison of tasks in which one of the characteristics of thetask is changed.

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64 APPENDIX B. CONNECTIVITY VALUES TOPOPLOT

Figure B.1: This figure presents 4 topoplots that show the significantly different con-nectivity values for all the tasks in which the frequency is changed. Thetop left topoplot shows the connectivity differences between the taskVI1 and VI3, the bottom left topoplot shows the connectivity differencesbetween the tasks AI1 and AI3. The top right topoplot shows the con-nectivity differences between the VP1 and VP3 tasks. The bottom righttopoplot shows the connectivity differences between the AP1 and AP3tasks. The yellow lines represent a higher mean value of connectivityin the first Task compared to the second. The blue lines represent ahigher mean value of connectivity in the second Task compared to thefirst.

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Figure B.2: This figure presents 4 topoplots that show the significantly different con-nectivity values for all the tasks in which the rhythmicity is changed. Thetop left topoplot shows the connectivity differences between the tasksVP1 and VI1, the bottom left topoplot shows the connectivity differencesbetween the tasks AP1 and AI1. The top right topoplot shows the con-nectivity differences between the VP3 and VI3 tasks. The bottom righttopoplot shows the connectivity differences between the AP3 and AI3tasks. The yellow lines represent a higher mean value of connectivityin the first Task compared to the second. The blue lines represent ahigher mean value of connectivity in the second Task compared to thefirst.

66 APPENDIX B. CONNECTIVITY VALUES TOPOPLOT

Figure B.3: This figure presents 4 topoplots that show the significantly different con-nectivity values for all the tasks in which the type of the cue is changed.The top left topoplot shows the connectivity differences between thetasks AI1 and VI1, the bottom left topoplot shows the connectivity dif-ferences between the tasks AP1 and VP1. The top right topoplot showsthe connectivity differences between the AI3 and VI3 tasks. The bottomright topoplot shows the connectivity differences between the AP3 andVP3 tasks. The yellow lines represent a higher mean value of connec-tivity in the first Task compared to the second. The blue lines representa higher mean value of connectivity in the second Task compared to thefirst. If not lines are presented, this means that there are not significantvalues between the tasks.

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