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MASTER RECHERCHE EN SCIENCES COGNITIVES - COGMASTER -

ENS - EHESS - UNIVERSITE PARIS V FRANCE

THE DEFAULT NETWORK IN ALTERED STATES OF CONSCIOUSNESS:

A STUDY BY EEG

Realisé par:

Zulay Lugo

2009-2010

Directeur de stage : Pr Steven Laureys MD, PhD Encadrant: Noirhomme Quentin Ir, PhD

Laboratoire: "Coma Science Group" Cyclotron Research Center

Université de Liège Belgique

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ABSTRACT

The default-mode network (DMN) is a group of cerebrals areas which include posterior

cingulate cortex / precuneus, anterior cingulate cortex, medial prefrontal cortex, and

temporo-parietal junctions which are co-activated in a dominant way during the activity

of the brain at rest. The functional signification of this network remains unclear and it

was proposed as the network which would support the basic functions which maintains

the consciousness of the human being. Nevertheless, the DMN remains poorly studied

and characterized by electroencephalography. To identify the pattern of EEG activity

associated with the default network at rest and possible changes in that pattern in the

altered state of consciousness, a study was conducted simultaneously recording EEG /

fMRI in healthy subjects in the waking state and under light and deep sedation using

propofol. We calculated the correlation coefficient between the electrical signal at

different locations of the EEG and the BOLD signal from fMRI representative of the

default network. The results did not confirm the presence of EEG patterns associated

with the default network in the resting state. We conclude on the need to improve

certain technical aspects of simultaneous record EEG / fMRI in order to obtain optimal

results from their use.

Key Words: default mode network, EEG, altered states of consciousness

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TABLE OF CONTENTS

I.- INTRODUCTION..................................................................................................................... 5

1. The default mode network.................................................................................................... 5

1.1 Anatomy of the default network ............................................................................. 5

1.2 Functions of the default network .............................................................................. 7

1.2.1 The sentinel hypothesis..................................................................................... 8

1.2.2 The mentation hypothesis................................................................................ 8

2. Consciousness........................................................................................................................ 10

2.1 Consciousness as a multidimensional concept.......................................................... 10

2.2 Altered states of consciousness................................................................................... 11

3. The default mode network and consciousness...................................................................... 12

3.1 fMRI and PET studies ............................................................................................... 12

3.2 Electrophysiological studies..................................................................................... 13

II.- OBJECTIVES........................................................................................................................... 15

III.- MATERIALS AND METHODS.......................................................................................... 16

1. Data acquisition.......................................................................................................... 16

2. EEG/fMRI analysis................................................................................................... 18

2.1 EEG signal processing............................................................................................ 18

2.2 Identification of the default network by fMRI................................................... 22

2.3 EEG/fMRI signals analysis.................................................................................. 24

IV.- RESULTS................................................................................................................................ 25

1. General results.............................................................................................................. 25

2. EEG patterns of the Default Network........................................................................ 27

V.- DISCUSSION........................................................................................................................... 30

1. Possible explanatory factors ...................................................................................... 30 2. The evaluation of the default network in altered states of consciousness............... 31

3. Considerations about the method used...................................................................... 32

VI.- CONCLUSIONS AND PERSPECTIVES............................................................................ 34

VI.- BIBLIOGRAPHY................................................................................................................. 35

VII.- APPENDIX........................................................................................................................... 39

ACKNOWLEDGMENTS........................................................................................................... 52

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I.- INTRODUCTION

Over the last ten years, increasing attention has been paid to the study of spontaneous

brain activity and its significance for cognition and behavior. The concept of a “default

mode of brain function” was introduced by Raichle et al. (2001) after observing that a

number of areas, including the precuneus, bilateral temporo-parietal junctions and the

medial prefrontal cortex, were more active at rest than when the subjects were involved

in an attention-demanding task.

Nevertheless, up to date, the functional significance of the default network remains a

matter of debate. It has been implicated in cognitive processes like, stimulus-

independents thoughts (Mason et al., 2007) or self-related thoughts (Buckner, Andrews-

Hannas & Schacter, 2008). It has also been involved in processes of memory (Mazoyer,

et al., 2001; Greicius, Srivastava, Reiss & Menon, 2004), anticipation (Spreng & Grady,

2006) and consideration of the thoughts and perspectives of other persons (Buckner et al.

2008; Otti et al., 2010).

The default network has been also suggested as a candidate network sub-serving basic

functions related to consciousness (Boly et al. 2008). Studies of regional metabolism in

vegetative state patients (VS, i.e., state of arousal without awareness, Zeman, 1997)

have identified a systematic metabolic dysfunction in a wide frontoparietal network

which includes brain areas that have been described as part of the default mode network

(lateral and medial frontal regions bilaterally, parieto-temporal and posterior parietal

areas bilaterally, posterior cingulated and precuneus cortices (Boly et al., 2009). This

and other studies that we will review later, have given evidence pointing to the default

network as part of the functional network supporting the function of human

consciousness.

It has been proposed that altered states of consciousness, such as sleep, anesthesia,

coma, vegetative state, epileptic loss of consciousness and somnambulism, are a

privileged way to investigate the links between spontaneous brain activity and behavior

(Cavanna & Trimble, 2006; Boly et al., 2008) because these states are characterized by

the alteration of intrinsic brain activity, which induces dramatic changes in the contents

of consciousness and responses to promising not only in regard to research into the

possible neural correlates of consciousness, but also with regard to its use for diagnostic

purpose in the differentiation of altered states of consciousness very difficult to separate

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such as vegetative and minimally conscious state.

However, a previous approach to the study of the default network in pathological states

of consciousness can be done by studying the physiological altered states of

consciousness, such as sleep or deep sedation anesthesia in healthy subjects (Greicius et

al., 2008; Horovitz et al., 2008). This would allow the determination of what we might

call the "normal" pattern give these states of consciousness. Then, by applying the same

techniques to patients with altered states of consciousness, we may establish network

disturbances that could contribute evidences in favor of the hypothesis of the default

network as support of the human consciousness.

We hence think that would be useful to deepen in the study and characterization of

possible alterations in the default network under such non-pathological conditions, with

the aim to establish good anatomical and functional characteristics of this network

which can be later evaluated in patients with altered states of consciousness. Differences

found between the network in healthy subjects and in the patients, may help to better

identify the anatomical and functional correlates of human consciousness.

1. The default mode network

A "default mode network" (DMN) has been highlighted in functional neuroimaging

studies as a set of brain regions showing synchronized activity at rest or in task-

independent cognitive state (Raichle et al., 2001). This concept comes from the

observation of a number of brain regions whose activity decreased during the

performance of cognitive task. By means of positron-emission tomography (PET), it

was made possible to estimate the brain oxygen extraction fraction (OEF), which s

defined as the ratio of oxygen used to oxygen delivered by flowing blood. This OEF is

remarkably uniform in the awake but resting state in normal subjects, Raichle et al

(2001) found that the changes in the OEF was strongly correlated in a group of areas,

indicating a small but systematic modulation of these areas at rest, areas he called for

the first time the ‘default network’.

1.1) Anatomy of the default network

The areas initially described by Raichle et al (2001) as belonging to the default network

were mainly in the midline, such as the posterior cingulated cortex, the precuneus and

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the medial prefrontal cortex and also (although less noticeable in this study) by the

lateral parietal cortex. In later studies, other areas have been also described as part of

this network such as the temporo-parietal junction (Boly et al., 2008; Vanhaudenhuyse

et al., 2010), the bilateral inferior parietal cortex (Mantini, Perrucci, Del Gratta, Romani,

& Corbetta, 2007; Greicius et al., 2008), the hippocampus (Grecius et al., 2004) and the

medial temporal cortex (Buckner et al., 2008). Table 1 presents the main regions

described as belonging to the default network.

_____________________________________________________________________________________ REGION ABREV INCLUDED BRAIN AREAS _____________________________________________________________________________________ Ventral medial prefrontal cortex vMPFC 24, 10 m/10 r/10 p, 32ac Posterior cingulate/retosplenial cortex PCC/Rsp 29/30, 23/31 Inferior parietal lobule IPL 39, 40 Lateral temporal cortex† LTC 21 Dorsal medial prefrontal cortex dMPFC 24, 32ac, 10p, 9 Hippocampal formation†† HF+ Hippocampus proper, EC, PH _____________________________________________________________________________________ TABLE 1. Core regions associated with the brain’s default network. Labels correspond to those originally used by Brodmann for humans with updates by Petrides and Pandya (1994), Vogt et al. (1995), Morris et al. (2000), and Ongür et al. (2003). †LTC is particularly poorly characterized in humans and is therefore the most tentative estimate. ††HF+includes entorhinal cortex (EC) and surrounding cortex (e.g., parahippocampal cortex; PH). Adapted from Buckner et al. (2008)

But, as its name suggests, the default network is a set of areas whose main feature is the

activation together in the resting state and deactivation, also jointly, during the

realization of a variety of cognitive tasks. Consequently, their anatomy could be better

understood as multiple interacting subsystems (Buckner et al., 2008). Figure 1 shows a

schematic representation of the interrelation of theses sub-systems.

• The subsystem of the medial temporal lobe, which provides information from

past experiences in the form of memories and associations that are the building

blocks of mental simulation.

• The medial prefrontal system, which facilitates the flexible use of this

information during the building of self-relevant mental simulations.

• Finally, these two subsystems converge on important nodes of integration,

including the posterior cingulated cortex

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Figure 1. Functional correlation strengths for multiple regions within the default network. The structure of the default network has a core set of regions (red) that are all correlated with each other. Lateral temporal cortex (LTC) is distant because of its weaker correlation with the other structures. The medial temporal lobe subsystem (blue) includes both the hippocampal formation (HF) and parahippocampal cortex (PHC). This subsystem is correlated with key hubs of the default network including posterior cingulate cortex/retrosplenial cortex (PCC/Rsp), ventral medial prefrontal cortex (vMPFC), and inferior parietal lobe (IPL). The dorsomedial prefrontal cortex (dMPFC) is negatively correlated with the medial temporal lobe subsystem suggesting functional dissociation. Adapted from Buckner et al. (2008)

1.2 Functions of the default network

The best evidence for understanding the function of the default network, comes from the

context in which it is activated, when individuals are at rest or engaged in internally

focused task: retrieval of autobiographical memory, imagining the future and

anticipation (taking) of other's perspectives.

However, the anatomical correlations of the network, mainly at the retrosplenial area

and the precuneus, suggests another possible role for the default network, that of

monitoring the external environment when focused attention is relaxed. In this way,

there have been two main hypotheses of its function,. So, we would have the hypothesis

of the default network as a watchdog of our external environment -the sentinel

hypothesis- and the other, the default network acting as support for internal cognitive

functions or self-referenced -the mentation hypothesis- (Buckner et al., 2008).

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1.2.1 The sentinel hypothesis

In 2001 Raichle et al., had already proposed that the deactivation of the areas located

mainly in the midline within the posterior cingulated, precuneus ant the medial

prefrontal cortex, suggested that specific brain functions, unique to resting state, were

being temporarily suspended during the performance of cognitive activities. In this

hypothesis, they suggested that, the posterior cingulate cortex and the precuneus would

form a tonically active brain region that continually meet the information about the

external environment (and possibly on our internal environment);this would have an

important evolutionary function of warning against possible predators and that must be

maintained at all times while awake and would be disabled only when other mental

tasks require attentional resource allocation. In this hypothesis, the medial prefrontal

cortex would be implied in the integration of emotional and cognitive processes by

incorporating emotional biasing signals or markers into decisional processes. Hence,

these three regions together, would act as the sentinels of our brain at rest, by collecting

and evaluating the internal and external information. The sentinel hypothesis, however,

does not account for the presence in the network of brain structures closely linked to

memory processes, such as the hippocampus.

1.2.2 The mentation hypothesis

In the context of the mentation hypothesis, the default network would have mostly

cognitive role, forming part of several mental processes such as autobiographical

memory, anticipation, self-referential and, finally, while spontaneous cognition

supporting, also a role in supporting human consciousness. Figure 2 shows the

activation of default network's areas during the execution of different cognitive tasks,

supporting its possible role in all this mental functions.

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Figure 2. The default network is activated by diverse forms of tasks that require mental simulation of alternative perspectives or imagined scenes. Adapted from Buckner et al. (2008)

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2) Consciousness

2.1 Consciousness as a multidimensional concept

Conscious has two major components: awareness (i.e., the content of consciousness)

and arousal (i.e., the level of consciousness). The level of arousal depends on the tonic

excitatory influence of cholinergic, noradrenergic, histaminergic, serotonergic, and

dopaminergic activating structures of the brain stem and diencephalon, formerly known

as the reticulo-thalamic activating system (Zeman, 2001). Awareness is in turn

composed by 2extra components: perception of the environment or the external world

(external awareness) and of our inner world and self-awareness encompassing inner

speech, mental imagery, and thoughts (internal awareness) (Laureys, 2005). Fig 3

shows the distribution of the altered states of consciousness in the two dimensions of it.

Fig 3. Simplified illustration of the two main components of consciousness: arousal (or wakefulness) and the content of consciousness (i.e. awareness). In normal physiological states, arousal and awareness are positively correlated (with the exception of dream activity during REM-sleep). Patients in coma or under general anesthesia are unconscious because they cannot be awakened. Patients in the vegetative or the minimally conscious state are awake but have no or low awareness. Locked-in patients are awake and aware but are nearly completely paralyzed and may be mistaken for coma or vegetative state. Adapted from Laureys et al., (2005)

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2.2 Altered states of consciousness

Three main types of altered states of consciousness can be defined: physiologically,

pharmacologically, and pathologically altered conscious states (Boveroux, et al., soumis)

• Normal sleep, encompassing light (stage II) and deep (stage III and IV) slow

wave sleep stages, and dreaming [mostly occurring during rapid eye movement

(REM) sleep] can be considered as the main physiologically altered conscious

states.

• Pharmacologically induced unconscious states are commonly named deep

sedation or general anesthesia. Sedative anesthetic agents can be separated into 3

main categories: the classic anesthetic agents, the dissociative anesthetic agents,

and the minimally sedative agents. Examples of classic anesthetic agents are

benzodiazepines, barbiturates, propofol, and halogenated compounds such as

desflurane or sevoflurane. They are able to induce graded states of sedation and

combine alterations of wakefulness and awareness functions.

• The last category of altered states of consciousness refers to pathologic altered

conscious states, which are the result of severe brain insults of various origins,

and encompass comatose, vegetative, and minimally conscious states.

- Coma: coma is a state of unarousable unresponsiveness characterized

by the deficiency of the arousal systems and thus also by absence of awareness. The

comatose patient lacks the sleep–wake cycles that can be observed in the VS (Laureys,

Boly, Moonen & Maquet, 2009).

- The vegetative state (VS): it was defined by Jennet et Plum in 1972 to

describe those patients who ‘awaken’ from their coma (meaning they open their eyes

spontaneously or on stimulation) but remain unaware of self or environment (meaning

they show only reflex motor responses) (Zeman, 1997; Laureys et al., 2004)

- The minimally conscious state (MCS): describes the condition of

patients who show, on a reproducible or sustained basis limited but clearly discernible

evidence of awareness of self or environment but are unable to functionally

communicate their thoughts and feelings (Giacino et al., 2002).

- The locked-in syndrome: describes patients who are awake and conscious but

selectively deefferentes, i.e., have no means of producing speech, limb or facial

movements. (Laureys et al., 2005). Its interest in the study of the altered states of

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consciousness is that, it is almost the mirror image of the vegetative state (patient able

to move but completely unconscious), an entity with which is very often confused.

3) Default mode network and consciousness

As described, the default network, while supporting the spontaneous cognitive activity,

could be regarded as one of the pillars of the state or the function of human

consciousness (Boly et al., 2008). This hypothesis has recently been supported by

several studies that show a clear association between altered states of consciousness and

the (dys)function of the default network.

3.1 fMRI and PET studies.

PET and fMRI have been widely used for the identification and evaluation of the

default network both in healthy subjects and in subjects with pathological cerebral

conditions, having shown a good ability to identify structural pattern of functional

connectivity of this system. (Rachle et al., 2001; Damoiseaux et al., 2006). We will

focus on this section with respect to the studies that have allowed evidence of a possible

relationship between the function or the operation of the default network and awareness.

An early study done in subjects under light sedation, has been found a reduced

functional connectivity in the posterior cingulated cortex suggesting that, these

reductions in default network's connectivity may represent a stable correlate of reduced

consciousness (Grecius et al., 2008 ).

As we mentioned previously, Boly et al. (2009) had already demonstrated the alteration

of functional connectivity in the default network in a vegetative state patient compared

with a normal subject, suggesting a possible relationship between the functioning of the

default network and the consciousness alteration in the patient. But also, the same study

evidenced the absence of network connectivity in a patient with a diagnosis of brain

death, further support the hypothesis that the functioning of the default network may be

involved in maintaining the state of consciousness.

A more recent study (Vanhaudeynhese et al, 2010) evaluated the default network's

connectivity in patients with different altered states of consciousness: coma, vegetative

state (VS), minimal state of consciousness (MSC) and a patient with locked-in

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syndrome (LIS), comparing it with that of healthy control subjects. The results showed

a decrease of the default network connectivity in severely injured patients, in proportion

to the degree of impairment of consciousness; whereas, the LIS patient, showed a

connectivity similar to the controls; strongly supporting this finds the idea of the default

network as a functional network whose activities largely depend on the presence of

consciousness in human beings.

3.2 Electrophysiological studies

Electrophysiological methods, such as the electroencephalogram (EEG) have been also

used in the evaluation of the default network. Although the EEG assessment of brain

activity at rest is frequently used after so many years, only recently, and through

technique combined EEG / fMRI, has begun to characterize the rhythms associated with

certain cognitive functions and many more recently has begun to try characterization of

the default network by this method.

A recent study of the spatial distribution of EEG energy fields in the default network,

found an alpha activity of bilateral posterior distribution that decreased significantly

with eye opening, a prefrontal theta activity also decreasing with eyes opening, a

prefrontal delta activity that increases with eye opening, a Beta-1 activity of similar

distribution to alpha and also decreased with eyes open and finally, a prefrontal

distribution for the beta-2 and gamma rhythms without changes to the eye opening

(Chen, Feng, Zhao, Yin & Wang, 2008).

With the technique combining EEG/fMRI, in 2002 a study was conducted to identify

brain regions whose BOLD signal varied with fluctuations in alpha rhythm modulation

at rest (Goldman, Stern, Engel & Cohen, 2002). They found that the increased alpha

power was correlated with decreased MRI signal in multiple regions of occipital,

superior temporal, inferior frontal and cingulated cortex, and with increased signal in

the thalamus and the insula; which they interpreted as confirmation of the alpha rhythm

as an index of cortical inactivity and the thalamus as the possible generator of this

rhythm. Moosmann et al., (2003) using the combined EEG/fMRI and near infrared

spectroscopy, found un inverse relationship between alpha activity and the BOLD

signal in the occipital cortex.

Lauf et al., 2003a, also conducted a study to identify the BOLD signal variations

associated with alpha fluctuations, finding a strong negative correlation of parietal and

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frontal activity with alpha power and only sparse and nonsystematic positive correlation.

They conducted another study (Lauf et al., 2003b) to establish the neuroanatomical

patterns of resting state fluctuations in the brain and they found a little positive

correlation with alpha power that was sparse and restricted to two foci in the cingulated

gyrus and occipital cortex, and widespread negative correlation with alpha power in a

bilateral fronto-parietal network. Also they found that the power in a 17–23 Hz range of

beta activity was positively correlated with the activity in retrosplenial, temporo-parietal,

and dorsomedial prefrontal cortices and finally, for the theta band they found no

correlation. This was the first study to attempt to establish an EEG pattern for the

resting state, evaluating other frequency bands besides the alpha band.

In this same line of identifying the EEG patterns of the default network, a more recent

study (Mantini et al., 2007), has established the presence of six resting states networks,

the first corresponding to the default mode network, and in which they found a

coalescence of EEG rhythms but only a positive correlation for the alpha and beta

rhythm.

The default network has been studied little by EEG in conditions of altered states of

consciousness. Only one study showed the persistence of DMN connectivity during

light sleep (Horovitz et al.,2008), but in this work, the EEG record was used only as a

marker of sleep stages. So, it has not been established so far the variation of the default

network in altered states of consciousness, using simultaneous EEG / fMRI recording.

We, therefore, propose the realization of this study with assessment EEG/fMRI of the

default mode network following the protocol used by Mantini et al., (2007) and

extending to the study of the correlation in altered states of consciousness induced by

anesthesia with the aim to confirm the presence of a specific EEG pattern of the default

network and, by comparing this pattern with the activities obtained during the recording

in conditions of light and deep sedation, to establish network's alterations associated

with these altered states of consciousness, getting more indirect evidence about the role

of the default mode network in the emergence and maintenance of human consciousness.

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II.- OBJECTIVES

1) General objective: to establish the EEG patterns of the DMN in resting state in

normal subjects.

2) Specific objectives:

• To determine the correlation of the different bands of frequency of the EEG in

healthy subjects at rest.

• To determine the variations of this pattern in the same subjects under light and

deep sedation.

• By comparing the electric patterns of the subjects in the wakefulness condition

with those in the conditions of light and deep sedation: to determine the

alterations in the activity of the default mode network that could be related to

the altered states of consciousness.

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III.- MATERIALS AND METHODS

1. Data acquisition

1.1 Type of study: we designed an experimental transverse study . The study was

approved by the Ethics Committee of the Faculty of Medicine of the University of

Liège. Written informed consent was obtained from all subjects.

1.2 Subjects: 19 healthy right-handed volunteers, 3 male, 16 female, aged from 19 to

31 years (mean: 23 years, SD: 3) participated to the study. Subjects were recruited

among medical, paramedical or students of health sciences, who knew or were able to

understand the risks inherent in the use of sedation with propofol. All were

compensated for their participation in the study.

1.3 Sedation protocol

- Anesthesia was achieved with a computer-controlled intravenous infusion of propofol

to obtain constant effect-site concentrations. Propofol (2,6 di-isopropylphenol) is a very

short acting non-opioid sedative–hypnotic agent, structurally unrelated to other hypnotic

compounds, It is thought to work by potentiating the binding of gamma-amino butyric

acid to receptor sites in the central nervous system (Peter et al 2005). It induces sedation

rapidly and reliably, and has additive or synergistic hypnotic effects with many other

agents (barbiturates, benzodiazepines, opioids and ketamine) used in anaesthesia

(Bryson, Fulton and Faulds 1995). Its rapid onset of action and amnesic properties,

coupled with smooth and rapid recovery, make propofol an appealing agent for

procedural sedation and analgesia (Peter et al., 2005) and the choice of this agent for

this study is relied to these pharmacological properties and the relative safety of its

use. Arterial blood samples were also taken immediately before and after the scan in

each clinical state for subsequent determination of the concentration of propofol and for

blood gas analysis .

The level of consciousness was evaluated clinically throughout the study with the

Ramsay scale (RS) (Ramsay et al., 1974). The RS scores sedation at six different levels,

according to how arousable the patient is. To assess the level of consciousness, the

subject was asked to strongly squeeze the hand of the investigator. She/he was

considered fully awake or having recovered consciousness if the response to verbal

command ("squeeze may hand") was clear and Strong (Ramsay 2), in mild sedation if

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the response to verbal command was clear but slow (Ramsay 3), and in deep sedation if

there was no response to verbal command (Ramsay 5-6). So, we defined four test

conditions:

• Wakefulnes ( W1): state of consciousness at baseline (RS= 2)

• Sedation 1 (S1): state of mild sedation (RS= 3)

• Sedation 2 (S2): state of deep sedation (RS= 5-6)

• Recovery of consciousness (W2): waking state of anesthesia (R= 2)

Before and after each scanning session, a reaction time task was also performed to

provide additional information on the clinical state of the subjects. Lying in the scanner,

subjects were instructed to press a keypad as fast as possible each time they heard a

beep through the headphones. At each test, a block of 20 beeps was presented.

To ensure safety, the following physiological parameters were monitored: 4-lead

electrocardiogram, non invasive blood pressure on the left arm, pulse oxymetry (SpO2)

and end tidal carbon dioxide partial pressure (ETCO2). This last measure was not

expected to be precise but merely to inform us about the subjects breathing.

1.4 Functional MRI acquisition: Functional images (200 images in each condition)

were acquired on a 3 Tesla Siemens Allegra scanner (32 slices, repetition time = 2460

ms, echo time = 40 ms, field of view = 220mm, voxel size = 3.45x3.45x3 mm, matrix

size = 64x64x32). A high resolution T1 image was also acquired in each volunteer at

the end of the whole experiment, for co-registration to the functional data.

1.5 EEG acquisition:

EEG was recorded utilizing two MR-compatible 32-channels amplifiers (Brain Amp

MR plus, Brain Products GmbH, Gilching, Germany) and an MR-compatible EEG cap

(Braincap MR, Falk Minow Services, Herrsching Breitbrunn, Germany) with 64 ring-

type electrodes. EEG caps included 60 scalp electrodes, four electrodes for referencing

(two on the nose and one on each mastoid) as well as two electrooculogram (EOG) one

electromyogram (EMG) and three electrocardiogram (ECG) channel which were online

referenced to FCz. The electrodes were placed following the 10-20 International

System.(Appendix 1). Electrode-skin impedance was kept below 5 kΩ in addition to

the 5 kΩ resistor built into the electrodes. EEG was digitized at 5000 Hz sampling rate

with a 500 nV resolution. Data were analog-filtered by a band limiter low pass filter at

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250Hz (30 dB/Octave) and a high pass filter with 10 s time constant corresponding to a

high pass frequency of 0.0159 Hz. Data were transferred outside the scanner room

through fibre optic cables to a personal computer where the EEG system running Brain

Recorder Software v1.03 (Brain Products GmbH, Gilching, Germany) was

synchronized to the scanner clock. For analysis, EEG data were low-pass filtered (FIR

filter, −36 dB at 70 Hz), and down-sampled to 250 Hz. The duration of the registration

was 8 minutes (12 subjects) and 14 minutes (9 subjects) in each condition.

2. EEG/fRMI analysis

To determine the correlation between the EEG signal and the default network as

identified by of MRIthe following steps were followed (Appendix 2 shows an outline of

these steps)

• Processing of raw EEG records in order to eliminate artifacts.

• Selection of EEG electrodes to include in the spectral analysis.

• Spectral analysis of the EEG.

• Identification of the default network by fMRI.

• Convolution of the EEG waveform with the HRF

• Calculating the correlation coefficient

These tests were the focus of our work for the stage and the choice of each parameter

was widely discussed with the stage manager and other team members with extensive

experience in EEG / fRMI.

2.1 EEG signal processing

2.1.1 Removal of artifacts

As is well known, a major problem of EEG–fMRI simultaneous, consists of the

contamination of EEG recordings by gradient switching -the gradient related artifact -

(GRA) and the pulse related artefacts (PRA) (Fig. 3 y 4) The former is related to the

echo planar sequence used in fMRI. The latter is thought to have multiple causes related

to the interaction between the static magnetic field and the heart beat, such as pulsatile

motion of recording electrodes, cardio-ballistic head movements and Hall effects in

blood vessels (Allen, Polizzi, Krakow, Fish, & Lemieux,1998). Therefore, it is

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necessary to treat the raw signals to eliminate these artifacts to make usable the obtained

signals. Several methods have been proposed and used for this treatment

Fig 4. Gradient related artifact (GRA) Fig 5. Pulse related artifact (PRA)

a) For GRA rejection:

- The Averaged Artifact Subtraction (AAS) (Allem et al 1998): Compute a GRA

template using the mean of the n last scan and after subtract the GRA template from the

current EEG. Because the gradient artifact is very reproducible across consecutive

volume acquisitions, it is satisfactorily removed from raw data with this method

followed by adaptive noise cancellation to reduce any residual artifact ( Leclerq et al.

2009)

b) For PRA rejection:

- A new method based on ICA has been recently developed to remove the PRA:

the constraint independent corposant analysis (cICA, Leclerq et al 2009). The

application of this method is developed in five steps: 1) computing a PRA template for

each of the N° channels of the EEG signal; 2) computing the artefact related

components based on these constraints using the cICA algorithm; 3) clustering the

estimated artifact components and computing the average of each cluster to produce

more robust estimates of the artifact sources; 4) finding the subspace of neural sources

within the demixing matrix, i.e. the complementary space of the artifact sub-space,

using a Gram–Schmidt procedure and 5) replacing artifact sources by void sources and

reprojecting all sources in signal space. This algorithm potentially leads to a more

robust estimation than other ICAs of the PRA sources without increasing the

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computational cost. In addition, the identification of sources to suppress is automated,

data driven and not observer-dependent.

In previous studies of joint analysis EEG/MRI, have been used AAS methods for the

elimination of artifacts both gradient-related as pulse- related (Lauf et al. 2003), and a

combination of the AAS method for the GRA and the ICA for PRA (Mantini et al.,

2007). In the current study, has been chosen to use the method of cICA because its

efficient in rejecting PRA for EEG data recorded during wakefulness and NREM sleep,

this last characteristic being important to an study under anesthesia. This method has

been validate by comparing with the others methods of artifacts rejects (Leclerq et al.,

2009), showing, in the frequency domain (where all the correction methods globally

decrease signal power), that is the method which removes the most power in all

frequency bands and pleasingly provides the spectrum closest to the recording obtained

outside the scanner. (Fig 5)

Fig. 6. Power spectrum of the EEG recorded at electrode F8 after correction by the different PRA rejection methods. The power spectrum of the original signal and signal recorded with the head outside the MR scanner are given as references. Adapted from Leclerq et al., 2009

The application of the cICA algorithm, was followed by the visual inspection of the

records in order to manually remove those devices not addressed by the mainly those

21

related to subject movements. Those EEG with a percentage of artifacts more than 10%

of the total recording time were discarded.

2.1.2 Selection of EEG electrodes to include in the spectral analysis

Different groups of EEG electrodes have been analyzed in previous studies of the

default network: averaged signals from the occipital electrodes O1-O2 with the

reference electrode located at FCz (Lauf et al., 2003a) or at the ear (Moosmann et al.,

2003), averaged signals from all electrodes (Moosmann et al., 2003; Mantini et al.

2007), from groups of electrodes across different regions (O1,O2, C3, C4, F3, F4,

Moosmann et al., 2003), with non clear consensus on which electrodes could provide

better information for the activity of our interest. In this regard, some have found no

differences in measurements of the power of the frequency bands between different

locations, (Lauf et al., 2003), while others point to this factor as influential in obtaining

dissimilar results for the same frequency among different studies (Nunez et al., 2001;

Sheeringa et al., 2008).

We decided to do the analysis of three groups of signals with the objective of covering

more of electrodes but restricted to the locations corresponding macroscopically to the

network by default: 1) the signals coming from three groups of electrodes in frontal

(F3-F4-FC3-FC4), centro-parietal (CP3-CP4-P3-P4) and occipital (O1, O2) location,

each group referenced to the central electrode (Fz, Cz and Oz respectively); 2) the

signals coming from the midline electrodes (Fz-Cz-Pz-Oz-FPz- AFz -CPz -POz )

referenced to the mean of the mastoid electrodes (M1, M2), 3) and for control and

comparative purposes, the signals coming from the average of all electrodes (60) also

referenced to the mean of the mastoid electrodes.

2.1.3 Spectral analysis of EEG

To determine the spectral characteristics of the artifact-free EEG data, the power

spectrum was calculated by using the method of the Morlet wavelet of ordre 7, this

transformation being broadly equivalent to other non-linear transformations (such as

short-term Fourier transform or the Hilbert transform), so, all three could be used for

this calculation (Kiebel, Tallon-Baudry & Friston, 2005). This approach enable us to

remove small artifacts like eye blink whiwh were discarded before averaging. Figure 7

shows the waveform of the Morlet wavelet.

22

Figure 7. Morlet wavelet

The resulting spectrogram was divided into six sub-bands, corresponding to delta (1–4

Hz), theta (4–8 Hz), alpha (8–12 Hz), beta1 (13–16 Hz), beta 2 (16-24 ) and gamma

(25–45 Hz) rhythms. We calculated one wavelet for ech sub-band, centred on 2.5, 6, 10,

14 and 20 Hz respectively with a width of 1.5, 2, 2, 2 and 4.

2.2 Identification of the default network by fMRI

2.2.1 Choice of the method

There are two main ways to analyze resting–state functional connectivity MRI (rs-

fcMRI): (1) hypothesis-driven seed-voxel (Fox et al., 2005) and (2) data driven

Independent Component Analysis (ICA) approaches (McKeown et al., 1998), each

offering their own advantages and limitations.

The seed-voxel approach consists in extracting the BOLD time course from a

region of interest and determines the temporal correlation between this signal (seed) and

the time course from all other brain voxels (Fox et al., 2005). This method, which is

quite straightforward and gives very intuitive results has been widely adopted and

seems to give very consistent results (Fox and Raichle, 2007), but it has raised some

controversial issues mostly related to the pre-processing procedure, especially

concerning the regressing out of the global activity from the BOLD signal which might

induce some spurious anti-correlations. (Murphy et al., 2009)

23

Contrary to the previous approach, ICA-based analysis (McKeown et al., 1998)

does not require an a priori definition of seed regions. It analyses the entire BOLD

dataset and decomposes it into components that are maximally statistically independent

(Hyvarinen et al., 2001). A number of studies have shown that ICA is a powerful tool

which can simultaneously extract a variety of different coherent neuronal networks (De

Luca et al., 2006; Esposito et al., 2008; Greicius et al., 2003; Greicius et al., 2004;

McKeown et al., 1998) and separate them from other signal modulations such as those

induced by head motion or physiological confounds (e.g., cardiac pulsation, respiratory

cycle and slow changes in the depth and rate of breathing), (Birn et al., 2008, Perlbarg et

al., 2007). ICA decomposes the signal in spatial patterns and their associated time

course maximally independent for each other. However, ICA does not provide any

classification or ordering of the independent components (ICs), and it is left to the user

to decide which IC corresponds to the DMN. Automatic approaches have been proposed

to remove user-bias in selecting the component. (Greicius et al., 2004, Esposito et al.,

2005).

In this study was used the ICA approach for the identification of the default network on

account of the expertise of the person carrying out this analysis, however, a

simultaneous analysis (results not shown in this work) done on the same group of

subjects using the seed-voxel approach, obtained similar results regarding the

identification of the default network

2.2.2 Data preprocessing and analysis

fMRI data were preprocessed using the "BrainVoyager"s sotware package (R. Goebel,

Brain Innovation, Masstricht, The Netherlands). Preprocessing of functional scans

included 3D motion correction, linear trend removal, slice scan time correction and

filtering out of low frequencies of up to 0.005 Hz. The data were spatially smoothed

with a Gaussian filter of full width of half maximum value of 8 mm. ICA (Formisano et

al., 2004) was performed with the ‘‘BrainVoyager’’ software package using thirty

components (Ylipaavalniemi and Vigario, 2008).

We performed, as implemented in Brain Voyager (self-organizing ICA Esposito et al.,

2005) a spatial similarity test on single subjects ICs and a previously created template of

24

the Default Mode based on 20 controls to select the DMN component. For each subject

we obtained one spatial pattern and its associated time course.

2.3 EEG/fMRI signals analysis.

2.3.1 Convolution with hemodynamic response function

For each scan and frequency band, the mean power spectrum was computed. Only data

which were not artefacted were included in the computation. Each power time series of

EEG bands was convoluted with five different hemodynamic response function (HRF).

The thirty signals were normalized by subtracting the minimum value and dividing by

the difference of maximum and minimum values.

HRF was modeled as a mixture of 2 Gamma functions as implemented in SPM. The

canonical hemodynamic response function has a delay of response of 6 sec, a delay of

undershoot of 16 sec and a length of 32 sec. The four other HRF had a delayed onset of

-2,-1, 2, 1 sec from the canonical HRF. Figure 8 shows the five HRF calculated.

Figure 8. Hemodynamic response functions

2.3.2 Correlation

For each subject and frequency band, five correlations coefficients were

computed between the five HRF and the IC waveform ( it was compute with matlab,

Mathworks Inc., USA). The correlation coefficient with maximum value was selected as

most representative of the link between the EEG time course and the IC waveform.

25

IV. RESULTS

A total of nineteen simultaneous records EEE/fMRI were examined, of which 11 were

discarded for the analysis: nine due to the presence of artifacts (mainly related to

movement) and two for lack of analysis of the default network in fMRI. Therefore, we

finally analyzed data from eight subjects: 2 men, 6 women, aged between 20 and 24

years (mean = 21.4, SD = 1.2).

We obtained the correlation coefficient between EEG frequency bands and the BOLD

signal in each condition for three groups of electrodes:

- Midline electrodes group: Fz-Cz-Pz-Oz-FPz- AFz -CPz -POz, referenced to the

averaged mastoid electrodes

- Paramedian electrode groups: anterior (frontal): F3-F4-FC3-FC4 (referenced to

Fz), middle (centro-parietal) : CP3-CP4-P3-P4 referenced to Pz, and back

(occipital): O1-O2 referenced to Oz. These electrodes were chosen on account of

its superficial location very close to the anatomical areas associated with the

default network.

- Finally, we did the analyses for the set of all electrodes (60 electrodes).

1. General results

• Wakefulness and recovery conditions: We found a large inter-subject variability

of correlation coefficients, which were generally rather low , considering the

highest values for the theta band at the central-parietal electrodes (mean=

0.1532 Std= 0.2028) and the lowest for the gamma band at the occipital

electrodes (mean=0.0121, Std: 0.0942) (all coefficients for all the electrodes

groups and the four conditions are showed in the Appendix 4). In general, the

highest correlations were obtained by the electrodes in the centro-parietal

location and the lowest in the occipital and midline locations (this one with the

lowest values for the alpha band). However, it is not possible to determine a

homogeneous pattern of correlation of the frequency bands in this condition

(table 2)

26

Electrode Groups

Delta Theta Alpha Beta1 Beta2 Gamma

Occipital

0.1389 0.0981 0.0299 0.0247 0.0993 0.0121

Centro-Parietal

0.1034 0.1532 0.0961 0.1471 0.1525 0.0802

Frontal

0.0915 0.0856 0.0726 0.0739 0.0711 0.0249

All

0.1249 0.1021 0.036 0.1221 0.0834 0.0339

Midline 0.0888 0.0833 0.0224 0.0774 0.0902 0.0554

Table 2. Correlation coefficients (mean) in the wakefulness for all the locations studied

With respect to the alpha band, the feature rhythm of wakefulness at rest, we found low

levels of correlation in all locations, including in the occipital where there is a

predominance of delta band. This distribution, however, was completely different in the

normal wakefulness condition, in which alpha band was predominant in all locations,

with the greatest correlations weightings to the electrodes in centro-parietal location (r=

0.1199.

For the beta-1 and beta-2 bands, the largest coefficients in the wakefulness condition

were located also in the central-parietal electrodes (beta1, r= 0.1471; beta2, r= 0.1525).

In the awakening condition we found negatives coefficients for these bands in all

locations.

• Sedation conditions: As in the wakefulness condition, the coefficients showed

low values and high inter-subject variability. In these conditions, it can be seen

a net predominance of negative correlations for almost all bands in all locations,

the alpha band showing a negative correlation in both stages of sedation for all

locations. It may also be noted a tendency for positive correlations of the delta

band in the stage of deep sedation, which although low, may be observed at all

locations except in the centro-parietal regions Figure 9 shows the variation of

the six frequency bands in the centro-parietal location in the four conditions.

27

Figure 9. Variations of the correlations coefficients for each frequency band across the four conditions in the centro-parietal region

• Finally, in the recovering condition, besides the predominance of correlation for

the alpha rhythm, the persistence of some negatives correlations, especially in

the frontal regions can be observed.

2. EEG patterns of the default network

Our first objective was to determine the pattern of EEG rhythms of the default network

in the awaking condition, our hypothesis being so, the existence of such a pattern

characteristic for this state.

To answer this, we analyzed the correlation coefficients of alpha, beta1 and beta2,

which had been found significantly correlated with the default network in a previous

works (Mantini et al., 2007). We chose the data from midline electrodes and we ordered

a one-sample t-test, which showed no significant correlation for the analyzed

frequency bands (alpha band t= 0.6794; beta1 band t = 0.1777; beta2 band t= 0.0506;

for a statistical significance level set at p< 0.0167 after Bonferroni correction).

As a secondary analysis, the same test was conducted to analyze the signal of these

three bands coming from all electrodes and found no significant correlation (alpha band

t= 0.4198; beta1 band t= 0.0440; beta2 t= 0.0346). The same analysis for these bands

and for the theta band was made in the frontal group, central group and occipital

electrode groups, also finding no significant association of EEG rhythms with the

default network to these locations (data included in the Appendix 5).

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29

30

V. DISCUSSION

The aim of this work was to determine the relationship between the brain electrical

activity and areas that make up the brain's default mode network, using simultaneous

recording of EEG activity with fMRI identification of brain areas belonging to that

network. To accomplish this, we obtained the time course of the spectral power for each

EEG band, which was then convolved with the fMRI hemodynamic response to

calculate the correlation coefficient between the two signals. Analyses performed did

not support our hypothesis of a relationship between a particular type of brain electrical

activity and the default mode network at rest.

1.- Possible explanatory factors

Three factors could partially explain these results:

1.1 The depth of the source signal that is intended to measure. As noted, the structures

that make up the default network (mainly precuneus, posterior cingulated cortex and

dorsomedial prefrontal cortex) are deep structures of the midline whose electric fields

are largely tangential to the recording electrodes, which affects the registration of its

electrical signal, much weaker than for other cortical structures.

In this study, we try to improve the spatial resolution and reduce the number of

electrical signals and also noise from other areas, taking groups of electrodes in the

scalp surface, roughly corresponding to the location in depth of the structures of the

default network. It should be noted that higher correlations were obtained in just one of

these groups of electrodes (centro-parietal) and the lowest in general corresponded to

those recorded in the midline electrodes.

1.2 Another factor could be that, in this study, was carried out the correlation with the

default network as a whole and not in separate areas. In other studies which have found

significant associations of a rhythm, mainly alpha, in the resting state with some

especific brain areas , (Moonann et al., 2003; Goldman et al.2002) these associations

have not been made with respect specifically to the default mode network (as has been

done in this study) but with any area in the brain showing an activation correlated to

that particular rhythm. We have followed the work of Mantini (Mantini et al.(2007),

who have searched the correlation directly with the default network, but they have also

obtained very low correlation values (between 0.03 and 0.28 for the highest).

31

1.3 Inter-subjects variability: the results also show a high variability of inter and intra-

subject correlations coefficients. In 2006, Gonçalvez et al. have described this large

inter-subject variability in brain rhythms having found, in seven subjects studied,

negative and positive correlations for the same rhythm (alpha) in different brain areas

and even in the same area they found different correlations among the patients. In our

results, we observed a large inter and intra-subject variability, with correlation

coefficients of opposite sign among different subjects for the same frequency band in

the same area, which might partially explain the lack of identification of a specific

pattern.

As noted by Lauf et al., (2008), the neural and hemodynamic oscillations recorded at

rest via surface EEG and BOLD fMRI are often only indirectly linked, because both

signals are generated differently and exhibit different temporo-spatial properties, so , in

an stable experimental context, , their overlap is further influenced by the brain state, or

context, in which they are acquired.

In this regard, the conditions for acquisition of data in this study were very different

from other similar studies (Mantini et al., 2007); in which subjects are generally asked

simply to remain relaxed in the scan, with eyes closed with any other auditory or visual

stimulation. The subjects in the present study were evaluated in the context of

preparation for anesthesia, and the total duration of their participation was extended for

several hours. From this point of view, this may account also the different results.

2. The evaluation of the default network in altered states of consciousness

The results in the four conditions tested (wakefulness, light sedation, deep sedation and

recovery), showed: in the wakefulness condition, a predominance of slow rhythms

(theta and delta) in all locations and low alpha activity (which is the marker rhythm of

wakefulness) was observed. As theta activity is prominent in the earliest stages of sleep,

but has also been associated with cognitive processes that require the use of attentional

or short-term memory (Sheringe et al., 2008) , it could be hypothesized that subjects in

this first phase, were not in the resting state as defined for the default mode network, but

rather engaged in tasks of attention or just sleepy (the latter also supported by the

presence of delta activity. On the other hand, during the recovering phase after sedation,

a clear prominence of alpha activity in all locations and very few slow activity can be

seen, which is in line with normal brain activity in awake.

32

In the analysis of rhythms variation during the stages of sedation, the most interesting

observation is the prominence of negative correlations starting from the stadium of light

sedation for all frequency bands in almost all locations (except for beta1 in central –

parietal and midline; beta2 in frontal and delta in the midline location), which are

maintained in the stage of deep sedation (unconsciousness), but noting further the

presence of positive correlations for the delta band in all locations (except central-

parietal) and a slight variation, significant from the statistical point of view, of beta2

band through the four conditions.

Previous studies show the persistence of default network connectivity in stages of light

sedation (Greicius et al., 2008; Horovitz et al., 2008) but decreased focal points at the

posterior cingulate cortex in this condition (Greicius et al., 2008). However, there are

no studies of the association of EEG rhythms and the default network during sedation.

In the present study, low values of correlation coefficients do not allow statements

about trends (for the variations are slightly different from zero). In this case, we

consider that a larger number of subjects might have been possible to ascertain whether

this variability of rhythms in the default network through the four levels of

consciousness studied is significant. This variability is, indeed, a normal finding in

normal EEG records, however, in this study, the relationship or the association of this

variation with the default network has shown be practically nonexistent.

3. Considerations about the method used

Among the technical aspects to be considered, and that may have influenced the

whole results, we analyze the conditions identified by Lauf as necessaries for success

in this type of study (Lauf et al 2008): 1) equal length of the sessions: in our case, 12

subjects had records during 8 minutes and 9 subjects during 14 minutes in each state,

and in the final analysis, seven of the eight records were during eight minutes and only

one had a duration of fourteen minutes; 2) consistent spatial coverage: we use a 32-

channel EEG with 60 electrodes on the scalp); 3) a proper reduction of GRA and PRA

artifacts: in this regard, it is noteworthy that in discarded records, about half had a

significant persistence of artifacts GRA, especially in sedation, which was significantly

influenced by the movements of the patients during these conditions (mainly snoring)

since the presence of motion artifacts also affect the execution of the algorithm for the

elimination of other types of artifacts; 4) enough smoothering of fMRI date; 5) a

33

consistent approach to derive the regressors based on EEG: in this regard, various

methods existent, without finding a clear advantage of one over the other. For example,

for the calculation of the regressor, the EEG signal can be used as the independent

variable and the BOLD signal as the dependent variable, but it can be reversed or also it

is possible the fusion of data using all as the dependent and independent variable at the

same time (Lauf, 2008). 6) Finally, the choice of montages and references, which can

determine the collection of more local or global properties of the EEG. We have

mentioned that, we tried to improve this choosing a greater amount of electrodes and

closer to the regions of interest .

We may finally wonder, given the amount of different cognitive functions in which

have been implicated brain areas which belongs to the default network, if we can set a

pattern "fixed" or "stable" of electrical activity in this network, which has, indeed, a

dynamic behavior. The presence of excessive theta activity in waking initial registration,

makes us wonder if, despite being physically at rest, the subjects were in a state

fluctuating between the rest and the mental activity. As mentioned, the conditions of the

study required a close and careful cooperation of the patient, which may simply have

turned off the default network at this early stage to other areas devoted to attention. In

this regard, Lauf et al., (2006), have proposed a theory of the brain function from the

resting state, based on a study in which they found two patterns of brain activity to

which the brain can move from a basal alpha activity: an occipito-parietal pattern

associated to an increase in theta activity and a fronto-parietal pattern associated with

increased beta activity. It should be noted that the regions identified in this study not

showed the characteristics of areas of the default mode network, however, is an

illustration of the presence of mental processes that may be normal fluctuations of

activity in a brain that we say is "at rest".

34

VI. CONCLUSIONS AND PERSPECTIVES

The work did not allow the identification of a particular EEG pattern associated with the

default network in the resting state using the simultaneous EEG/fMRI recording.

Nevertheless, we believe that improvements are possible in the protocol that would

achieve this goal in a second phase of the project.

In general terms, we must try to improve the factors cited by Lauf et al.(2006) to

increase the chances of obtaining conclusive results. It is necessary, in principle, to

increase the number of available subjects for analysis (this is especially critical for the

realization of the comparison between the four conditions of wakefulness and sedation).

In this regard, the EEG tracings retreat to eliminate as many artifacts as possible is the

first step to follow. This will be done with a new version -in preparation- of the

algorithm for the cICA method.

We also need a better model for the correlation between the two signals (EEG / BOLD).

Having found no variations for the frequency bands separately, we propose to assess the

whole spectrum variation, to see if the whole EEG activity correlates with the activity of

the default network. We think that it could also help circumvent the inter-subject

variability.

Finally, despite the many technical aspects to care, the study of electroencephalographic

activity in combination with functional magnetic resonance imaging, offers the

advantage of complementary evaluation of different aspects of brain function allowing

real time assessment of cognitive events (EEG) and spatial location (fMRI). The study

of the foundations of consciousness, both cognitive and neurobiological level, has

benefited greatly from this practice and we are confident we can correct the factors that

allow optimal application of the neurophysiologic techniques such as EEG to his best

knowledge.

35

BIBLIOGRAPHY

Allen, P.J., Polizzi, G., Krakow, K., Fish, D.R., & Lemieux, L.,1998. Identification of EEG events in the MR scanner: the problem of pulse artifact and a method for its subtraction. Neuroimage, 8 (3), 229–239.

Allen, P.J., Josephs, O., Turner, R., 2000. A method for removing imaging artifact from continuous EEG recorded during functional MRI. Neuroimage, 12 (2), 230–239

Boly M, Philips C, Tshibanda L, Vanhaudenhuyse A, Schabus M, Dang-Vu T.T., Moonen G, Hustinix R, Maquet P and Laureys S. (2008). Intrinsic Brain Activity in Altered States of Consciousness: How Conscious Is the Default Mode of Brain Function? Ann. N.Y. Acad. Sci, 1129, 119-129.

Boly M, Tshibanda L, Noirhomme Q, Vanhaudenhuyse, A., Schnakers C, Ledoux D, Boveroux P, Garweg C, Lambermont B, Philips C, Luxen A, Bassetti C, Maquet P, Laureys S. (2009). Functional connectivity in the default network during resting state is preserved in a vegetative but not in a brain dead patient. Hum Brain Mapp, 30, 2393-2400 .

Boveroux, P., Vanhaudenhuyse, A., Lauwick, S., Bruno, M-A., Noihomme, Q., Faymonville, M-E., Lamy, M., Moonen, G., Luxen, A., Deguldre, C., Plenevaux, A;, Schnakers, C., Phillips, C., Brichant, J-F., Bonhomme, V., Maquet, P., Greicius, M., Laureys, S., & Boly, M. (soumis) Within- and between-networks resting state fMRI connectivity reflects the level of consciousness during anesthesia.

Birn, R.M., Murphy, K., & Bandettini, P.A. (2008). The effect of respiration variations on independent component analysis results of resting state functional connectivity. Hum Brain Mapp, 29, 740-750

Bryson, H.M., Fulton, B.R., & Faulds, D. (1995). Propofol: an Update of its Use in Anaesthesia and Conscious Sedation. Drugs, 50(3),513-559.

Buckner, R.L, Andrews-Hanna, J.R, Schacter, D.L. (2008) The brain's default network: anatomy, function and relevance to disease. Ann N Y Acad Sci, 11, 1-38.

Cavanna A.E., Trimble, M.R. (2006) The precuneus: a review of its functional anatomy and behavioural correlates. Brain, 129, 564-583.

Chen, A., Feng, W., Zhao, H., Yin, Y., & Wang, P. (2008). EEG default mode network in the humain brain: Spectral regional field powers. Neuroimage, 41, 561- 574.

Damoiseaux J.S., Rombouts, S.A., Barkohof F., Scheltemns P, Stam C, Smith SM, Beckman C.F. (2006) Consistent resting-state networks across healthy subjets. Proc Natl Acad Sci U S A, 103(7), 13848-13853.

De Luca, M., Beckman, C.F., De Stefano, N., Matthews, P.M., & Smith, S.M. (2006). fMRI resting state networks define distinct modes of long-distance interactions in the human brain. Neuroimage, 29, 1359-1367.

Esposito, F., Aragri, A., Pesaresi, I., Cirillo, S., Tedeschi, G., Marciano, E., Goebel, R.,& Di Salle, F. (2008). Independent component model of the default-mode brain function: combining individual-level and population-level analyses in resting state fMRI. Magn Reson Imaging, 26, 905-913.

36

Formisano, E., Esposito, F., Di Salle, F.,& Goebel, R. (2004). Cortex-based independent component analysis of fMRI time series. Magn Reson Imaging, 22, 1493-1504.

Fox, M.D., Raichle, M.E. (2007). Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat Rev Neurosci, 8, 700-711

Fox, MD., Snyder, A., Vincent, J.L., Corbetta, M., Van Essen, D.C., & Raichle, M.E. (2005). The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc Acad Sci USA, 102, 9673-9678.

Giacino, J.T., Ashwal, S., Childs, N., Cranford, R., Jennett, B., Katz, D.L., Kelly, J.P., Rosenberg, J.H., Whyte, J., Zafonte, R.D., & Zasler, N.D. (2002). The minimally conscious state: definition and diagnostic criteria. Neurology, 58, 349–353.

Goldman, R.I., Stern, J.M., Engel, J., & Cohen, M.S. (2002). Simultaneous EEG and fMRI of the alpha rhythm. NeuroReport, 13(1820), 2487-2492.

Gonçalves, S.I., de Munck, J.C., Pouwels P.J.W., Schoonhoven, R., Kuijer, J.P.A., Maurits, N.M., Hoogduin, J.M., Van Someren, E.J.W., Heethaar R.M., & Lopes da Silva F.H. (2006). Correlating the alpha rhythm to BOLD using simultaneous EEG/fMRI: Inter-subject variability. NeuroImage,30,203-13.

Greicius M.D., Krasnow, B., Reiss, A.L., & Menon, V. (2003). Functional connectivity in the resting brain: a network analysis of the default mode hpothesis. Proc Natl Acad Sci U S A, 100, 253-258.

Greicius, M.D., Srivastava, G Reiss, A.L., and Menon, V. (2004). Default-mode network activity distiguishes Alzheimer's disease from healthy aging: Evidence from funcional MRI. PNAS, 101(13), 4637-42.

Greicius, M.D., Kiviniemi, V., Tervonen O, Vainionpaa V, Alahuhta S, Reiss A.L., and Menon, V. (2008). Persistent default-mode network connectivity during light sedation. Hum Brain Mapp, 29(7), 839-847 .

Hyvarinen, A., Karhunen, J., & Oja, E. (2001) Independent Component Analysis.

Horovitz, S.G., Fukunaga, M., de Zwart, J.A., van Gelderen, P., Fulton, S., Balkin, T., & Duyn, J.H. (2008). Low Frequency BOLD Fluctuations During Wakefulness and Light Sleep: A Simultaneous EEG-fMRI Study. Hum Brain Mapp, 29, 671-682.

Kiebel, S.J., Tallon-Baudry, C., ans Friston, K.J. (2005). Parametric Analysus of Osicllatory Activity as Measured With EEG/MEG. Hum Brain Mapp, 26, 170-177.

Laufs, H., Kleinschmidt, A., Beyerle, A., Eger, E., Salek-Haddadi, A., Preibisch, C., & Krokowa, K. (2003a). EEG-correalted fMRI of human alpha activity. NeuroImage, 19, 1463-76.

Lauf, H., Krakow, K., Sterzer, P., Eger, E., Beyerle, A., Salek-Haddadi, A., and Kleinschmidt, A. (2003b). Electroencephalographic signatures of attentional and cognitive default modes in spontaneous brain activity fluctuations at rest. PNAS, 100(19), 11053-11058.

Lauf, H., Holt, J.L., Elfont, R., Krams, M., Paul, J.S., Krakow, K., & Kleinschmidt, A. (2006). Where the BOLD signal goes when alpha EEG leaves. NeuroImage, 31, 1408-18. Lauf, H. Endogenous Brain Oscillations and Related Networks

37

Detected by Surface EEG-Combined fMRI. (2008). Hum Brain Mapp, 29, 762-69.

Laureys, S., Famoville, M.E., De Tiège, X., Peigneux, P., Berré, J., Moonen, G., Goldman, S., and Maquet, P. (2004). Brain function in the vegetative state. Advances in Experimental Medicine and Biology, 550, 229-38.

Laureys S. (2005). The neural correlates of (un)awareness: lessons from the vegetative state. Trends Cogn Sci, 9, 556-559.

Laureys, S., Pellas, F., Van Eeckhout, P., Ghorbel, S., Schnarkers, C., Perrin, F., Berré, J., Faymonville, M.E., Pantke, K-H., Damas, F., Lamy, M., Moonen, G., and Goldman, S. (2005). The locked-in syndrome: what is it like to be conscious but paralysed and voiceless?. Progress in Brain Research, 150, 495-511.

Laureys, S., Boly, M., Moonen, G., and Maquet, P. (2009). Coma. Encyclopedia of Neurosciences, 2, 1133-1142.

Leclerq, I., Balteau, E., Dang-VU, T., Schabus, M., Luxen, A., Maquet, P., & Phillips, C.(2009). Rejection of pulse related artefact (PRA) from continous electroencephalographic (EEG) time series recorded during functional magnetic resonance imaging (fMRI) using constraint independent component analysis (cICA). NeuroImage, 44, 679-691.

McKeown, M.J., Makeig, S., Brown, G.G., Jung, T.P., Kindermann, S.S., Bell, A.J., & Sejnowski, T.J. (1998). Analysis of fMRI data by blind separation into independent spatial components. Hum Brain Mapp, 6, 160-188.

Mantini D, Perrucci M.G., Del Gratta C., Romani G.L. and Corbetta M. (2007). Electrophysiological signatures of resting state networks in the human brain. PNAS, 104(32), 13170-13175 .

Mason, M.F., Norton, M.I., Van Horn, J.D., Wegner, D.M., Grafton, S.T. and Macrae, C.N.(2007). Wandering Minds: The Default Network and Stimulus- Independent Thought. Science, 315, 393-95.

Mazoyer, B., Zago, L., Mellet, E., Bricogne, S., Etard, O., Houdé, O., Crivello, F., Joliot, M., Petit, L. and Tzourio-Mazoyer, N. (2001). Cortical networks for working memory and executive functions sustain the conscious resting state in man. Brain Research Bulletin, 54(3), 287-298.

Moosmann, M., Ritter, P., Krastel, I., Brink, A., Thees, S., Blankenburg, F., Taskin, B., Obrig, H., & Villringer, A. (2003). Correlates of alpha rhythm in functional magnetic esonance imaging and near infrared spectroscopy. NeuroImage, 20, 145-158.

Murphy, K., Birn, R.M., Handwerker, D.A., Jones, T.B., & Bandettini, P.A.(2009). The impact of global signal regression on resting state correlations: are anti- correlated networks introduced? Neuroimage, 44, 893-905.

Nunez, P.L., Wingeier, B.M., & Silberstein, R.B. (2001). Spatial-Temporal Structures of Human Alpha Rhythms: Theory, Microcurrent Sources, Multiscale Measurements, and Global Binding of Local Networks. Human Brain Mapp, 13, 125-164.

38

Otti, A., Guendel, H., Läer, L., Wohlschlaeger, A.M., Lane, R.D., Decety, J., Zimmer, C., Henningsen, P. and Noll-Hussong, M. (in press). I Know the pain you feel – how the human brain’s default mode predicts our resonance to another’s suffering. Neuroscience (2010), doi: 10.1016/j.neuroscience.2010.04.072.

Perlbarg, V., Bellec, P., Anton, J.L., Pelegrini-Issac, M., Doyon, J.,& Benali, H. (2007). CORSICA: correction of structured noise in fMRI by automatic identification of ICA components. Magn Reson Imaging, 25, 35-46.

Raichle, M.E., MacLeod, A.M., Snyder, A.Z., Powers, W.J, Gusnar, D.A. and Shulman, G.L. (2001). A default mode of brain function. PNAS, 98(2), 576-682 (*)(*)

Ramsay, M.A., Savege, T.M., Simpson, B.R., & Goodwin, R. (1974). Controlled sedation with alphaxalone-alphadolone. Br Med J, 2( 5920), 656-9.

Spreng, N.R., & Grady, C.L. (2009). Patterns of Brain Activity Supporting Autobiographical Memory, Prospection, and Theory of Mind, and Their Relationship to the Default Mode Network. Journal of Cognitive Neuroscience, 22(6), 1112-1123.

Scheeringa, R., Bastiaansen, M., Peterson, K.M., Oostenveld, R., Norris, D.G., & Hagoort, P. (2008). Frontel theta activity correlates negatively with the default mode network in resting state. International Journal of Psychophysiology, 67, 242-251 .

Vanhaudenhuyse, A., Noirhomme, Q., Tshibandaa L, Bruno MA, Boveroux, P., Schnakers C, Soddu A, Papa M, Perlbarg V, Ledoux D, Brichant JF, Moonen G, Phillips C, Maquet P, Greicius M, Laureys S, Boly M (2010). Default Network connectivity reflects the level of consciousness in non-communicative brain- damaged patients. Brain, 133 (Pt1), 161-71.

Ylipaavalniemi, J., & Vilgario, R. (2008). Analyzing consistency of independent components: an fMTI illustration. Neuroimage, 39, 168-180.

Zed, P.J., Abu-Laban, R.B., Chan, W.W.Y., and Harrison, D.V. (2005). Efficacy, safety and patient satisfaction of propofol for procedural sedation and analgesia in the emergency department: a prospective study. CJEM-JCMU, 9(6), 421-427.

Zeman A. (1997). Persistent vegetative state. Lancet, 350, 795-799.

Zeman, A. (2001) Consciousness. Brain, 124, 1263-1289.

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APPENDIX

APPENDIX 1: International System 10-20 for EEG electrodes and structures of the default network............................................................................................................................40

APPENDIX 2:

Outline of steps in order to obtain the correlation between the EEG signal and BOLD signal...............................................................................................................................41

APPENDIX 3:

Default Mode Network images as identified by ICA in the four states of consciousness evaluated ........................................................................................................................42 APPENDIX 4:

Correlation Coefficients .................................................................................................43

Correlation Coefficients for all the subjects in the:

Wakefulness Condition....................................................................................................xx

Correlation coefficients for all the subjects in the

sedation 1 condition.........................................................................................................xx

Correlation coefficients for all the subjects in the

sedation 2 condition........................................................................................................xx

Correlation coefficients for all the subjects in the

recovery condition ..........................................................................................................xx

APPENDIX 5:

T-test results for theta, alpha, beta1 and beta2 bands in all electrode locations during the wakefulness condition (Bonferroni correction applied).................................................51

40

APPENDIX 1: International System 10-20 for EEG electrodes and structures of

the default network

C

Schematic view of the distribution of electrodes in the international system l 10-20. A:

lateral view, B: top view. In this basic scheme may be added additional recording

electrodes, so, in our study were used FC3 yFC4 (located between F3-C3 yF4-4

respectively, CP3 and CP4, located between C3-C4-P3 and P4 respectively and added

also the midline electrodes Oz-FPz- AFz -CPz -POz. C: schematic view two of the

most important structures of the default network, the precuneus (and directly below the

posterior cingulate cortex), and its location with respect to the recording electrodes.

Left Right

41

APPENDIX 2: Outline of steps in order to obtain the correlation between the EEG signal and BOLD signal

42

APPENDIX 3: Default Mode Network images as identified by ICA in the four states of consciousness evaluated

(Soddu et al., in preparation)

43

APPENDIX 4: CORRELATION COEFFICIENTS

CORRELATION COEFFICIENTS FOR ALL THE SUBJECTS IN THE WAKEFULNESS CONDITION

CENT-PARIET LOCATION

Delta Theta Alpha Beta1 Beta2 Gamma

Subject 1 0.0840 0.1285 0.0801 0.0684 0.1621 0.1639 Subject 2 0.2154 0.3758 0.1312 0.1010 0.0951 0.0647 Subject 3 -0.0788 -0.0696 0.0293 0.0472 -0.0805 -0.0588 Subject 4 0.1930 0.2898 0.2219 0.0610 0.1807 -0.0511 Subject 5 0.1449 0.1623 0.0828 0.2931 0.3154 0.0921 Subject 6 -0.0272 -0.0214 -0.0301 0.1456 0.0975 0.1292 Subject 7 0.2463 0.4422 0.2896 0.3737 0.2760 0.2195 Subject 8 0.0500 -0.0818 -0.0358 0.0870 0.1744 0.0825

FRONTAL LOCATION

Delta Theta Alpha Beta1 Beta2 Gamma

Subject 1 0.0682 0.0795 0.0574 60.0439 0.0971 0.0598 Subject 2 0.2321 0.1610 0.2060 0.0796 0.0750 0.0841 Subject 3 -0.0596 0.0644 -0.1255 0.0595 -0.0303 0.0976 Subject 4 0.0641 0.1965 0.2570 0.0515 0.1670 0.0835 Subject 5 0.1115 0.0799 -0.1087 0.1501 0.0544 -0.1040 Subject 6 0.0952 -0.0643 -0.0535 0.1874 0.1088 0.1455 Subject 7 0.1620 0.1500 0.3192 0.1495 0.0427 -0.0328 Subject 8 0.0593 0.0185 0.0294 -0.0424 0.0541 -0.0979

OCCIPITAL LOCATION

Delta Theta Alpha Beta1 Beta2 Gamma

Subject 1 0.0451 -0.0622 -0.0719 -0.1849 -0.0307 -0.0906 Subject 2 0.0538 -0.0357 -0.0299 -0.0554 -0.1355 -0.0742 Subject 3 0.0949 0.0305 -0.0286 0.0564 0.0144 -0.0592 Subject 4 0.1086 0.0567 -0.0134 0.1385 0.1361 -0.0348 Subject 5 0.1365 0.0710 0.0325 0.1781 0.0821 0.0322 Subject 6 0.1739 0.1933 0.0370 -0.0696 0.0133 0.0444 Subject 7 0.2261 0.2178 0.1436 0.1917 -0.0502 0.1019 Subject 8 0.2726 0.3139 0.1702 -0.0567 0.0712 0.1774

44

ALL ELECTRODES Delta Theta Alpha Beta1 Beta2 Gamma

Subject 1 0.0744 0.1105 60.0255 60.0927 60.0450 0.0618 Subject 2 0.3572 0.3429 0.0779 0.1217 0.1201 0.1320 Subject 3 -0.1149 -0.1291 -0.0348 0.0411 -0.0640 -0.0624 Subject 4 0.0881 0.1692 0.1717 0.0720 0.0988 0.0211 Subject 5 0.1807 0.1280 -0.0409 0.2968 0.1689 -0.0603 Subject 6 0.1791 -0.0351 -0.0292 0.1091 0.1241 0.1659 Subject 7 0.2172 0.3037 0.2503 0.3487 0.1736 0.0742 Subject 8 0.0178 -0.0730 -0.0814 0.0802 0.0909 -0.0610

MIDLINE ELECTRODES

Delta Theta Alpha Beta1 Beta2 Gamma

Subject 1 0.0767 0.137 0.0405 -0.0228 0.0494 0.0991 Subject 2 0.2319 0.2294 0.093 0.0806 0.1075 0.071 Subject 3 -0.1249 -0.1993 -0.1148 -0.2078 -0.1213 -0.0348 Subject 4 0.1113 0.1578 0.2223 0.1234 0.0856 0.0534 Subject 5 0.1782 0.0976 -0.0803 0.2169 0.2084 0.0429 Subject 6 0.047 0.0578 -0.1341 0.0614 0.1081 0.1949 Subject 7 0.228 0.2484 0.2284 0.2657 0.2304 0.0781 Subject 8 -0.0379 -0.0621 -0.0758 0.1015 0.0534 -0.0615

45

CORRELATION COEFFICIENTS FOR ALL THE SUBJECTS IN THE

SEDATION 1 CONDITION

OCCIPITAL ELECTRODES

Delta Theta Alpha Beta1 Beta2 Gamma

Subject 1 -0.0893 0.1130 -0.2047 -0.1404 -0.1784 -0.1048 Subject 2 0.1399 0.0918 -0.0062 0.0319 0.0952 0.1389 Subject 3 -0.0731 0.0332 0.0541 -0.0419 0.0513 -0.0338 Subject 4 0.1396 0.0949 0.0633 -0.0766 -0.0137 0.0386 Subject 5 -0.1607 -0.1757 -0.0459 -0.1035 -0.1002 -0.1851 Subject 6 -0.1509 -0.1079 -0.1216 -0.0724 -0.0563 -0.0851 Subject 7 -0.1037 -0.1603 -0.0729 -0.1776 -0.1427 0.0174 Subject 8 -0.0413 -0.0580 -0.0516 0.0522 -0.1550 -0.0421

CENT-PARIET ELECTRODES

Delta Theta Alpha Beta1 Beta2 Gamma

Subject 1 -0.1245 0.1691 -0.1324 -0.1202 -0.0979 0.0572 Subject 2 0.1677 0.0127 0.0387 -0.0204 0.0655 0.0589 Subject 3 -0.2153 -0.0173 0.0595 0.2362 0.1942 0.0821 Subject 4 0.1069 0.0808 0.0359 -0.0417 -0.1030 0.0132 Subject 5 -0.1623 -0.1486 0.1019 0.0854 -0.1075 -0.2323 Subject 6 -0.1798 -0.1474 -0.1466 -0.1196 -0.0162 -0.0509 Subject 7 -0.1860 -0.1731 -0.1093 0.1012 -0.1077 -0.655 Subject 8 0.0161 0.0310 0.1331 0.1407 -0.0858 -0.0343

FRONTAL ELECTRODES

Delta Theta Alpha Beta1 Beta2 Gamma

Subject 1 -0.1236 0.1098 -0.0849 -0.0756 0.1076 0.1203 Subject 2 0.1711 -0.0132 -0.0198 -0.0329 0.0596 0.0604 Subject 3 -0.2439 -0.0390 -0.1310 0.2233 0.2488 0.1612 Subject 4 0.0929 0.0793 0.0239 0.0250 -0.0886 -0.132 Subject 5 -0.2136 -0.2189 -0.1152 -0.0978 0.1623 -0.2999 Subject 6 0.1818 0.1760 0.1243 0.1647 0.335 0.0804 Subject 7 0.1612 0.2064 0.2415 0.1716 0.1240 0.1383 Subject 8 0.0679 0.0796 0.1035 0.1025 0.0854 0.0313

46

ALL ELECTRODES

Delta Theta Alpha Beta1 Beta2 Gamma

Subject 1 -0.0793 -0.0988 -0.1276 -0.0808 -0.1271 -0.0801 Subject 2 0.1390 0.0837 0.0904 0.0737 0.0562 0.0576 Subject 3 -0.3013 -0.0077 0.0881 0.1536 0.0498 -0.0221 Subject 4 0.0774 0.0584 0.0572 -0.0496 -0.1050 -0.0129 Subject 5 -0.1841 -0.1642 0.0153 0.0839 0.0746 -0.2781 Subject 6 -0.1936 -0.1539 -0.0949 -0.1310 0.0075 -0.0765 Subject 7 -0.2643 -0.2360 -0.1856 -0.1430 -0.1161 -0.1703 Subject 8 0.0771 -0.1007 0.0664 -0.0704 -0.0866 -0.0484

MIDLINE ELECTRODES

Delta Theta Alpha Beta1 Beta2 Gamma

Subject 1 -0.1031 -0.0833 -0.1598 -0.0687 -0.1389 -0.1238 Subject 2 0.0723 0.091 0.0945 0.0693 0.0368 0.0467 Subject 3 -0.0270 -0.0158 0.0751 0.0722 -0.0025 -0.0315 Subject 4 0.1172 0.1346 0.1622 0.0893 0.0499 0.0327 Subject 5 -0.1403 -0.1029 0.0802 0.1214 -0.0703 -0.232 Subject 6 -0.2112 -0.152 -0.086 -0.0779 0.0237 -0.0364 Subject 7 0.2665 -0.235 -0.2099 -0.1328 -0.1128 -0.165 Subject 8 0.1091 -0.0572 0.0342 -0.0134 -0.0826 -0.0614

47

CORRELATION COEFFICIENTS FOR ALL THE SUBJECTS IN THE SEDATION 2 CONDITION

OCCIPITAL ELECTRODES

Delta Theta Alpha Beta1 Beta2 Gamma

Subject 1 0.1229 -0.1561 -0.1510 -0.2431 -0.1247 -0.0583 Subject 2 0.1842 0.1330 0.0144 -0.1937 -0.0514 0.2128 Subject 3 0.0878 0.0444 0.0417 0.0599 -0.656 0.0539 Subject 4 -0.1469 -0.1624 -0.0390 -0.1528 -0.3553 -0.2395 Subject 5 -0.1541 -0.0813 -0.1419 0.0400 0.0788 -0.0689 Subject 6 -0.1004 -0.1763 -0.0864 -0.0239 0.0560 -0.0481 Subject 7 0.1465 -0.0150 -0.1349 -0.2073 -0.1346 0.3095 Subject 8 0.1426 0.0788 -0.1048 -0.0272 0.0841 0.0159

CENT-PARIET ELECTRODES

Delta Theta Alpha Beta1 Beta2 Gamma

Subject 1 -0.1132 -0.1598 -0.2831 -0.3856 -0.2259 -0.0632 Subject 2 -0.1177 -0.0186 -0.0659 -0.0620 -0.0899 -0.0883 Subject 3 0.0778 0.0651 0.0746 0.1034 -0.1388 0.0696 Subject 4 0.1695 -0.1074 0.0602 -0.1029 -0.2897 -0.1958 Subject 5 -0.1484 -0.0808 -0.2404 0.0873 0.1683 0.0514 Subject 6 -0.1838 -0.1766 -0.0610 -0.1287 -0.0041 0.0257 Subject 7 0.0835 0.0721 -0.1130 -0.3403 -0.2299 0.2340 Subject 8 0.1965 -0.0458 -0.1233 0.0389 -0.0613 0.0827

FRONTAL ELECTRODES

Delta Theta Alpha Beta1 Beta2 Gamma

Subject 1 0.1134 -0.0813 -0.1904 -0.3091 -0.3138 -0.1036 Subject 2 0.1451 0.0619 0.0495 0.0932 0.1883 0.1695 Subject 3 0.0877 -0.0332 0.0416 0.0674 -0.0899 -0.0598 Subject 4 0.0392 -0.0567 -0.0832 -0.2177 -0.3215 -0.1989 Subject 5 -0.0313 -0.0426 -0.1091 0.1177 0.1446 0.0769 Subject 6 -0.0893 -0.1629 -0.1086 -0.0840 -0.0328 0.0308 Subject 7 0.1893 0.1619 0.1905 0.2948 0.1833 0.1391 Subject 8 0.1099 0.1312 0.1935 0.0999 0.0676 0.0706

48

ALL ELECTRODES

Delta Theta Alpha Beta1 Beta2 Gamma

Subject 1 0.0492 -0.1109 -0.2214 -0.3091 -0.3016 -0.1373 Subject 2 0.1468 0.1977 0.0193 0.0621 0.1710 0.2079 Subject 3 0.0992 0.0444 0.0452 0.0325 -0.0978 -0.0623 Subject 4 0.1102 -0.1092 -0.0265 -0.2021 -0.3225 -0.2148 Subject 5 -0.0899 -0.0632 -0.0822 0.1826 0.1877 0.0799 Subject 6 -0.0444 -0.1754 -0.0289 -0.0954 -0.0546 -0.0186 Subject 7 0.1565 0.511 -0.2935 -0.4505 -0.2641 0.2656 Subject 8 0.1797 0.0263 -0.2011 -0.1139 -0.0712 0.0494

MIDLINE ELECTRODES

Delta Theta Alpha Beta1 Beta2 Gamma

Subject 1 0.0708 -0.1294 -0.1598 -0.3576 -0.2513 -0.0922 Subject 2 -0.0566 0.0486 0.0611 0.038 0.0312 0.1284 Subject 3 0.0957 -0.0589 0.0104 -0.0444 -0.1019 -0.0077 Subject 4 0.2609 0.0712 0.1101 -0.1078 -0.0334 -0.2131 Subject 5 -0.1335 -0.1013 0.0862 0.2127 0.227 0.0841 Subject 6 -0.0591 -0.1209 0.0546 0.0898 -0.124 0.0603 Subject 7 0.1954 0.0332 -0.3716 -0.5343 -0.2933 0.1541 Subject 8 -0.1157 -0.0535 -0.1632 -0.4341 -0.2125 0.1399

49

CORRELATION COEFFICIENTS FOR ALL THE SUBJECTS IN THE RECOVERY CONDITION

OCCIPITAL ELECTRODES

Delta Theta Alpha Beta1 Beta2 Gamma

Subject 1 -0.1868 -0.0091 0.2078 0.1092 0.1210 -0.1016 Subject 2 -0.0514 0.1215 0.3836 0.2685 0.1291 0.1879 Subject 3 0.0323 0.0783 0.3359 0.0871 -0.0572 -0.0924 Subject 4 0.0694 0.0591 0.1519 0.0125 0.0980 -0.1176 Subject 5 -0.1110 -0.1181 -0.1557 0.0435 0.0267 -0.0770 Subject 6 0.0896 -0.0802 -0.2016 -0.0265 -0.0707 0.0844 Subject 7 -0.1620 0.1093 -0.0615 0.0621 0.0457 -0.0689 Subject 8 -0.0196 -0.1297 -0.1556 -0.1739 -0.0859 0.0182

CENT-PARIET ELECTRODES

Delta Theta Alpha Beta1 Beta2 Gamma

Subject 1 -0.1817 -0.1405 0.1503 -0.0900 0.0945 -0.1159 Subject 2 0.0405 0.0411 0.3853 -0.0591 -0.1582 -0.0944 Subject 3 0.1681 0.1911 0.3514 0.0423 0.0405 -0.0940 Subject 4 -0.0798 -0.0587 0.1402 0.0470 -0.0450 -0.1710 Subject 5 0.0278 0.0827 0.1888 0.2323 0.1065 -0.0761 Subject 6 0.0316 -0.1349 -0.2657 -0.2240 -0.1740 0.1571 Subject 7 0.0260 0.2232 0.0593 -0.0199 -0.0706 0.1020 Subject 8 0.0239 0.0223 -0.0507 -0.1607 -0.2342 -0.0689

FRONTAL ELECTRODES

Delta Theta Alpha Beta1 Beta2 Gamma

Subject 1 -0.1599 -0.1443 0.0277 -0.0649 -0.0850 -0.1281 Subject 2 0.0168 0.0286 0.1328 -0.0888 -0.1709 -0.0913 Subject 3 -0.0691 0.1525 0.2056 0.0724 -0.0376 0.0375 Subject 4 -0.0607 -0.0915 -0.0698 -0.1496 -0.1231 -0.1337 Subject 5 0.0882 -0.1517 -0.0314 -0.0362 -0.0660 -0.1154 Subject 6 0.0979 0.0854 -0.0667 0.0454 0.0203 0.1977 Subject 7 -0.2050 -0.0820 0.0977 0.0952 -0.1682 -0.0877 Subject 8 0.0947 0.0727 0.0697 0.0384 0.1101 0.0996

50

ALL ELECTRODES

Delta Theta Alpha Beta1 Beta2 Gamma

Subject 1 -0.1595 -0.1078 0.3633 -0.0940 0.0470 -0.1130 Subject 2 0.1283 0.1178 0.3486 -0.0541 -0.2145 -0.0964 Subject 3 0.0671 0.1327 0.4041 0.1896 0.0691 -0.0870 Subject 4 -0.0356 -0.0325 0.1336 -0.1148 -0.0495 -0.1155 Subject 5 0.0187 -0.0841 0.1934 0.1846 0.1584 -0.0826 Subject 6 0.0594 -0.1127 -0.2536 0.0540 0.0706 0.1893 Subject 7 -0.0877 0.0467 -0.0435 0.0421 -0.1080 0.0635 Subject 8 0.0792 0.0139 -0.0612 -0.1218 -0.0911 0.1093

MIDLINE ELECTRODES

Delta Theta Alpha Beta1 Beta2 Gamma

Subject 1 -0.1882 -0.1043 0.3088 -0.0746 -0.0673 -0.1265 Subject 2 0.0187 0.147 0.3655 0.1876 -0.1075 -0.0746 Subject 3 -0.1061 0.1595 0.191 0.1403 0.0699 0.0272 Subject 4 0.0363 -0.0317 0.0503 0.0545 0.1209 -0.172 Subject 5 0.155 0.0934 0.181 0.2732 0.0833 -0.1672 Subject 6 0.0421 -0.1623 -0.2814 0.0695 -0.0392 0.1401 Subject 7 -0.076 -0.0692 -0.0405 -0.0179 -0.0253 -0.0456 Subject 8 0.0712 0.0023 -0.0404 -0.0758 -0.1527 0.0877

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APPENDIX 5. t-test results for theta, alpha, beta1 and beta2 bands in all electrode locations during the wakefulness condition (Bonferroni correction

applied)

Band Mean Std t Theta 0.153 0.202 0.0699Alpha 0.096 0.115 0.0500Beta1 0.147 0.120 0.0100Beta2 0.152 0.121 0.0094

Cent-Pariet

Band Mean Std t Theta 0.085 0.084 0.0237Alpha 0.072 0.170 0.2665Beta1 0.073 0.086 0.0466Beta2 0.071 0.057 0.0098

Frontal

Band Mean Std t Theta 0.098 0.131 0.0722Alpha 0.029 0.086 0.3582Beta1 0.024 0.136 0.6249Beta2 0.023 0.090 0.4913

Occipital

Band Mean Std t Theta 0.102 0.171 0.1367Alpha 0.036 0.118 0.4198Beta1 0.122 0.140 0.0440Beta2 0.083 0.090 0.0346

All

Band Mean Std t Theta 0.083 0.150 0.1617Alpha 0.022 0.146 0.6794Beta1 0.077 0.146 0.1777Beta2 0.090 0.108 0.0506

Midline

52

Acknowledgments

I want to thank all the Coma Science Group team for their welcome and support during

the realization of this work. Professor Laureys for allowing me to perform this stage in

their group, M Noirhomme for his supervision, constant presence and their valuable

advice in the realization of this work, Athene Demertzi for her help and for sharing her

joie de vivre with me, Didier Ledoux for his advice on statistical treatment of data,

Andrea Soddu for their valuable suggestions and discussions about the results, Camille

Chatelle who was always there to ease my work at times of surcharge and finally, to all

for making me so nice to spend some time in their company.