Thesis
Reference
Brain Functional Connectivity and Network alterations in patients with focal epilepsy
CARBONI, Margherita
Abstract
Epilepsy is a chronic neurological disorder characterized by recurrent and unpredictable seizures. Electroencephalography (EEG) is fundamental in the diagnosis of patients. The extension and the properties of the epileptic network and of the entire pathological epileptic brain still remain under investigation. We investigated the connectivity changes during spike in patients that will or will not recover after surgery. We studied the brain networks properties, in patients with epilepsy compared to healthy controls. Finally, we used simultaneous intracranial-scalp-EEG to investigate the different network proprieties during perods with and without cortical spikes. We have consistently shown stronger ability of the network to propagate pathological activity in patients that won't recover after surgery and in patients as compared to controls. Also, in the same patient the ability of the network to integrate tha patological information is higher during cortical spike as compared to periods without epileptic activity.
CARBONI, Margherita. Brain Functional Connectivity and Network alterations in
patients with focal epilepsy. Thèse de doctorat : Univ. Genève et Lausanne, 2020, no. Neur.
279
DOI : 10.13097/archive-ouverte/unige:142245 URN : urn:nbn:ch:unige-1422457
Available at:
http://archive-ouverte.unige.ch/unige:142245
Disclaimer: layout of this document may differ from the published version.
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Professeur Serge Vulliémoz, directeure de thèse Professeur Christoph M. Michel, co-directeure de thèse
TITRE DE LA THESE
Brain Functional Connectivity and Network alterations in patients with focal epilepsy.
THESE Présentée à la Faculté des Medicine de l’Université de Genève
pour obtenir le grade de Docteure en Neurosciences
par
Margherita CARBONI
De l’Italie Thèse N° 279
Genève
Editeur ou imprimeur : Université de Genève 2020
DOCTORAT EN NEUROSCIENCES des Universités de Genève
et de Lausanne
UNIVERSITÉ DE GENÈVE FACULTÉ DES MÉDECINE
“Ricorda che l'umiltà apre tutte le porte e che la conoscenza ti renderà più forte”
Alla mia famiglia e ai miei amici, che sono la parte migliore di me.
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Summary
Abstract ... 3
Résumé en français ... 4
Acknowledgments ... 7
Aims of the thesis project ... 8
Chapter 1 - Introduction ... 10
1. Epilepsy ... 10
1.1 Temporal Lobe and Extra-Temporal Lobe Epilepsy ... 10
1.2 Pharmaco-resistance ... 11
1.3 Epilepsy as a network disease ... 11
2. Presurgical evaluation ... 12
2.1 Long-term scalp video-EEG ... 12
2.2 Structural MRI ... 13
2.3 Functional Imaging ... 13
2.4 Intracranial EEG ... 14
2.5 Neuropsychological and Psychiatric assessment ... 14
2.6 Predicting surgical outcome ... 15
3. EEG ... 16
3.1 EEG acquisition and scalp field maps ... 16
3.2 Brain activity recorded with EEG ... 17
3.3 EEG in epilepsy ... 17
3.3.1 Seizures ... 18
3.3.2 Interictal discharges ... 18
4. Electrical Source Imaging (ESI) ... 19
4.1 The importance of the number and location of electrodes ... 21
4.2 The extraction of brain region time-series ... 22
4.3 Magnetic Source Imaging ... 23
5. Brain Connectivity ... 24
5.1 Directed Functional connectivity based on Granger Causality ... 25
5.2 The importance of performing functional connectivity in the source space ... 26
5.3 Connectivity in Epilepsy ... 27
6. Graph Theory ... 29
6.1 Global Network Properties in the brain ... 30
6.2 Physiological Resting State Networks ... 31
Basis of the Main Studies ... 32
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Chapter 2 - Brief summary of the results ... 33
Study 1 - The network integration of epileptic activity in relation to surgical outcome... 33
Study 2 - Abnormal directed connectivity of resting state networks in focal epilepsy ... 35
Study 3 – Brain activity with or without epileptic spikes: a simultaneous scalp and intracranial EEG study ... 36
Study 4 – High-density EEG source localization of interictal discharges: How many electrodes and which time point? ... 41
Chapter 3 - Discussion ... 42
1. Studying pre-operative features to predict patients’ outcome: from single focus to network prospective ... 43
1.1 Limitations and future perspectives to predict patients’ outcome ... 45
2. Studying resting state features in epileptic patients as compared to healthy controls ... 45
2.1 Limitations and future perspectives to study patients’ resting state activity in EEG ... 47
3. Revealing the underlying epileptic activity ... 48
3.1 Limitations and future perspectives to study the underlying epileptic activity ... 49
4. Studying different electrodes setup to improve clinical and research practices ... 50
5. Parallel projects ... 51
Chapter 4 - Future developments ... 52
References ... 54
Articles – Main Studies ... 75
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Abstract
Epilepsy is a chronic neurological disorder characterized by recurrent and unpredictable seizures (Fisher et al. 2014). Recently, it has been shown that human epilepsy is, more in general, a disorder of large neural network functions that are structurally connected (Spencer 2002). It has been suggested that in many patients, rather than a single region, a complex network drives the seizure onset and maintains the interictal epileptiform discharges (IEDs) (Richardson 2012). This concept is of extreme clinical relevance, since any structural or functional change of the network or any electrical, biochemical or metabolic activity in any part of the network can modify seizure expression.
Electroencephalography (EEG) plays a central role in the diagnosis and management of patients with seizure disorders and contributes to the multi-axial diagnosis of epilepsy (Smith 2005). Up to now, many techniques have been used in order to study large-scale brain networks, such as intracranial EEG (iEEG), functional Magnetic Resonance Imaging (fMRI), and nuclear medicine (PET, SPECT).
One-third of patients with focal epilepsy become drug resistant and one of the treatment options is the surgical removal of the epileptogenic zone (Berg 2009; Sillanpaa 2000). Unfortunately, this is sometimes difficult to determine: it is thought to overlap with the seizure onset zone (SOZ) and with the irritative zone (IZ). It is important to note that the epileptogenic zone is a “conceptual zone” that cannot be determined pre-operatively while the SOZ and IZ can be determined and are used as surrogate markers for the EZ. Some patients become seizure-free even though the seizure onset zone is not entirely removed and some patients continue having seizures in spite of complete removal of the same zone (Lüders and Comair 2001). This suggests that possibly other drivers and mechanisms, most likely network-dependent, influence the post-surgical outcome.
The topological organization of brain networks is important for the brain’s overall function, performance and behavior (Rubinov and Sporns 2010). All neuronal networks can be represented as graphs (or, more specifically, digraphs), i.e., ordered collections of vertices and edges with distinctly non-random features (Sporns et al. 2000). We are specifically interested in identifying which sub-regions of this space are associated with particular kinds of altered functional dynamics in epilepsy.
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In the following introductory section, I describe the current state of the art:
In section 1 I give a definition of epilepsy and the main electrophysiological alteration related to it;
I also describe the concept of epilepsy as a network disorder.
In section 2 I describe the context in which our contribution could be applied: the presurgical evaluation of pharmacoresistant patients.
In section 3 I describe the tool used, the EEG, and its clinical application in epilepsy.
In section 4 I describe how source activity can be reconstructed from the scalp: I detail some methodological aspects that have been systematically addressed during the thesis.
In section 5 I describe the connectivity analysis, one of the possible analytical methods to study the relation between different brain areas.
In section 6 I describe graph theory, one way to model the brain networks: the nodes are the different brain regions, the edges are the connectivity values between them.
Résumé en français
L'épilepsie est une affection neurologique chronique qui se caractérise par des crises récurrentes et imprévisibles (Fisher et al. 2014). Il a récemment été montré que l’épilepsie est sous-tendue par un dysfonctionnement des réseaux structuraux cérébraux. Ces derniers semblent connectés tant sur le plan fonctionnel que sur le plan structurel (Spencer 2002). Des résultats obtenus auprès de nombreux patients suggèrent que l’origine de la crise ainsi que les décharges épileptiformes (IED) ne sont pas liées à une seule région mais plutôt à l’interaction d’un ensemble de réseaux (Richardson 2012). La pertinence de cette perception de l’épilepsie est capitale sur le plan clinique. Chaque changement structurel, fonctionnel du réseau ou de son équilibre électrique, biochimique ou métabolique, a le potentiel de modifier l’expression de la crise.
L’électroencéphalographie (EEG) joue un rôle central dans le diagnostic et le suivi des patients souffrant d’épilepsie et contribue à poser le diagnostic. Jusqu’à présent, différentes techniques ont été utilisées dans l’étude des réseaux neuronaux, notamment l’EEG intracrânien (iEEG), l’imagerie par résonance magnétique fonctionnelle (fMRI), ainsi que certaines techniques de médecine nucléaire telles que la tomographie à émission de positrons (PET) et la tomographie à émission monophotonique (SPECT). Il est aujourd’hui communément admis qu’un tiers des patients souffrant d’épilepsie focale présentent une pharmaco-résistance. Pour ces patients, une des options thérapeutiques est la résection chirurgicale de la zone épileptogène (EZ) (Berg 2009; Sillanpaa 2000).
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Cependant, déterminer l’EZ représente souvent un défi. Il est en effet difficile de dissocier l’EZ de la zone de départ des crises (SOZ) ou de la zone irritative (IZ).
Il est important de souligner que l’EZ est une zone conceptuelle et ne peut pas être déterminée en phase préopératoire, alors que la SOZ et IZ peuvent l’être. Ces deux informations sont alors utilisées afin d’estimer l’EZ.
Après intervention chirurgicale, certains patients sont libres de crises même si la SOZ n’est pas entièrement réséquée. A l’inverse, certains patients continuent à présenter des crises malgré une résection complète de la zone (Lüders and Comair 2001). Cette dissociation suggère que d’autres mécanismes, probablement dépendants de la configuration des réseaux cérébraux, peuvent influencer le pronostic post-opératoire.
L’organisation topographique des réseaux cérébraux est cruciale pour la compréhension du fonctionnement du cerveau, tant sur le plan des aptitudes cognitives qu’au niveau comportemental (Rubinov and Sporns 2010). Tous les réseaux peuvent être représentés sous forme de graphes (ou plus spécifiquement en digraphes), soit une collection ordonnée de nœuds et connexions ayant des caractéristiques distinctes, distribuées de façon non aléatoire (Sporns et al. 2000).
Afin de contribuer aux connaissances dans ce domaine, nous avons tenté d’identifier les régions associées à une altération des types particuliers de dynamique fonctionnelle chez des patients présentant une épilepsie focale.
Dans l’introduction ci-dessous, sont décrites les connaissances actuelles sur ce thème, ainsi que l’état de la recherche.
La section 1 définit l’épilepsie et les altérations électrophysiologiques associées. Nous abordons également le thème de l’épilepsie comme une perturbation des réseaux neuronaux.
Dans la section 2, nous décrivons le contexte dans lequel notre contribution a été apportée, soit au niveau de l’évaluation pré-chirurgicale des patients avec épilepsie pharmaco-résistante.
Dans la section 3 nous décrivons la technique utilisée: l’EEG, ainsi que ses applications cliniques dans l’épilepsie.
Dans la section 4 nous résumons comment la source d’une activité électrique peut être reconstruite à partir d’électrodes placées sur le scalp: sont décrits en détail certains aspects méthodologiques que nous avons systématiquement explorés tout au long de la thèse.
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Dans la section 5 nous décrivons l’analyse de connectivité. Cette méthode permet d’investiguer la relation entre différentes régions du cerveau.
Dans la section 6 nous décrivons la théorie des graphes, un modèle mathématique permettant de modéliser le fonctionnement du cerveau. Selon ce courant, les nœuds sont représentés par les différentes régions du cerveau, et les connexions par les valeurs de connectivité entre elles.
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Acknowledgments
I would like to thank my supervisor, Prof. Dr. Serge Vulliémoz, for this entire journey in the exploration of the brain network, for the support along the way and the positive attitude at every step.
I would like to thank Prof. Christoph Michel for his advice, methodological consideration and guidance particularly with some of the technical challenges I encountered.
I would like to thank all the internal and external experts of my PhD thesis committee, it has been a pleasure for me sharing my work with each of you and receive your important comments and feedbacks.
Thanks to all the colleagues and friends at the 4th and 2th floor of the Hospital, thanks for the scientific discussions, the important advice, the everyday life: it has been really nice working with each of you.
Thanks to all the people in the FBM lab at campus biotech, you have been significant part of my entire work.
Thanks to all the people around Europe of the Sinergia consortium for the inspirational discussions.
Grazie ai miei amici di Ginevra con i quali ho condiviso tutte le emozioni e tutti i giorni di questa esperienza.
Grazie Vera, Laura, Pia, Francesca, Aimone e Lorenzo per tutte le esperienze passate insieme.
Grazie Giannarita, Michele e Giuditta per farmi sentire sempre in famiglia.
Thanks to all the friends met in Tuebingen, I have been thinking a lot about my experience back there. Thanks Marius for your friendship at every step: I told you it would have last. Grazie Chiara for the support.
Thank you Boss, you have been there for me since the beginning, you belived in my scientif skills even before me.
Grazie agli amici di sempre, specialmente ad Arianna, Lorenzo, Federica e Fabrizio per l’amicizia incondizionata, nonostante la distanza, nonostante il poco tempo, nonostante tutto.
Grazie a Edoardo che mi ha cambiato la vita.
Grazie infine, e soprattutto, alla mia Famiglia, a mia Mamma, a mio Papá, a mio Fratello e a tutti quelli che, da lontano e da vicino, mi fanno sentire a Casa ogni volta che ne ho bisogno.
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Aims of the thesis project
Epilepsy is now defined as a widespread brain network disorder with a high risk of recurrent unprovoked epileptic seizures that can be associated with impaired awareness or convulsions (Hauser et al. 1993). Therefore, a better understanding of the organization and dynamics of normal and pathological brain networks is needed, particularly for patients who are reported as pharmaco- resistant and are optimal candidates for surgical resection of the epileptogenic zone (Lüders et al.
2006).
In this thesis project, I first focus on the pre-operative global and lobar network properties during interictal epileptiform discharges (IEDs) in patients with good vs poor post-operative seizure outcome: we found a consistently increased network integration reflected by higher global, hemispheric and lobar efficiency in patients showing poor post-surgical outcome (Carboni et al.
2019).
Nevertheless, some patients do not show any IED on standard recordings and the diagnosis of epilepsy can be challenging. We, therefore, wanted to test if the alterations in network features seen during IED would hold also during task-free (resting) wakefulness epochs without IEDs as compared to healthy controls: we found that patients with focal epilepsy showed increased integration at the global level as well as in three canonical resting-state-networks as compared to controls (Carboni et al., 2020 – accepted in NeuroImage: Clinical).
Based on the two previous studies, the activity of brain networks was found to be disrupted during periods with and without any epileptic discharges visible in scalp EEG. From simultaneous recordings of scalp and invasive EEG, it was previously shown that only a minority of epileptic spikes detected by icEEG also appear on the scalp (Koessler et al. 2014; Ramantani et al. 2014). We therefore aimed at evaluating features that could reveal changes on the scalp EEG in epochs with and without underlying epileptic activity in the intracranial EEG: we found that during underlying epileptic activity, slow frequency bands such as theta and delta showed an increase in their power as compared to epochs without any underlying epileptic activity. Also, the connectivity analysis as well as the network features showed an increased integration during epochs with underlying epileptic activity. Furthermore, the averaging of epochs with underlying epileptic activity revealed a spike- like waveform on the scalp EEG: the ESI at its peak was found to be concordant with the intracranial contact marked with underlying epileptic activity (Carboni et al., in preparation).
The studies in epileptic patients have been performed on data from high density EEG but low density EEG is the electrode configurations mostly used in clinics. The additional value in the ESI localization
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of the electrodes placed below the neurocranium such as these on the cheeks and on the neck has been shown to be relevant in low density EEG (Seeck et al. 2017) but remained unknown in high density EEG. We have hypothesized that these electrodes could be especially helpful for activity arising from basal structures such as the temporal lobes. Furthermore, the best time point for interictal ESI is also debated, so we compared ESI at the IED’s half-rise to other time points: we investigated these in a side project (Vorderwulbecke*, Carboni*, et al. 2020 - under review in Clinical Neurophysiology).
In another side project, the pipeline developed for previous studies has been applied to a different type of patients and brain patterns: the GPDs frequently observed in comatose patients with ischemic-anoxic encephalopathy following cardiac arrest and associated with a poor neurological outcome recorded with low density EEG. Since little is known about the mechanisms of generation of GPD in humans, our study aim at the identification of the neuronal network generating this pattern using EEG source localization and connectivity analysis in patients with ischemic-anoxic encephalopathy. We consistently found that GPDs seem to arise from the limbic system (De Stefano*, Carboni*, et al. 2020 - under review in Resuscitation).
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Chapter 1 - Introduction
1. Epilepsy
Epilepsy is a disorder of the brain characterized by an enduring predisposition to generate epileptic seizures, and by the neurobiological, cognitive, psychological, and social consequences of this condition (Fisher et al. 2014).
Epilepsy is one of the most common neurological problems (together with migraine, stroke and Alzheimer’s disease) which affects 50 million persons worldwide at any given time. It is gender and age dependent with higher prevalence rates in men and peaks both in the first years of life and in senescence (>50 years old) (Ottman et al. 2011). Epilepsy is defined by recurrent unprovoked seizures and has neurobiological, cognitive, psychological and social implications.
It is diagnosed when a patient has:
1) At least two unprovoked seizures that are more than 24h apart.
or
2) One unprovoked seizure and a high recurrence risk over the following 10 years (Fisher et al. 2014).
In most cases, the etiology of epilepsy is difficult to assess as many factors are known to cause this disorder such as genetic influence, head trauma, brain conditions (tumors or stroke), infectious diseases, prenatal injury, and developmental disorders.
1.1 Temporal Lobe and Extra-Temporal Lobe Epilepsy
Temporal Lobe Epilepsy (TLE) is the most frequent type of pharmaco-resistant epilepsy in adults (Schuele and Lüders 2008). TLE is characterized by recurrent focal seizures arising from temporal structures (such as the hippocampus, amygdala, entorhinal cortex) and its most frequent structural pathology is unilateral hippocampal sclerosis (Wieser 2004). Pharmaco-resistant TLE is often successfully treated by surgery, however long-term relapses in up to 58% of cases have been reported (De Tisi et al. 2011), suggesting insufficient disruption of epileptic networks.
Even if TLE is the most common type of focal epilepsy, others are known as ExtraTemporal Lobe Epilepsies (ETLE). These are characterized by the epileptogenic foci outside the temporal lobe: these have a wide spectrum of semiology depending upon the site of origin such as frontal, parietal, occipital lobes (Tripathi and Dash 2014).
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1.2 Pharmaco-resistance
Anti-epileptic drugs are one option to ideally suppress seizures, and statistically 70% of the patients achieve seizure remission. Unfortunately, the remaining 30% of epilepsy patients do not respond to anti-epileptic drugs (Sillanpaa 2000), i.e., they continue to have seizures despite a well conducted anti-epileptic drug treatment with at least two adequately chosen drugs. In this case, epilepsy is said to be pharmaco-resistant (Berg 2009).
In some of these patients, one of the treatment options is the epilepsy surgery, that consists in the removal or disconnection of the epileptogenic zone, i.e. the cortical zone responsible for the generation of seizures (Lüders et al. 2006). The ultimate goal is seizure freedom, while preserving eloquent cortex areas: the accurate and patient-specific localization of the cortical area that should be removed by surgery is crucial.
1.3 Epilepsy as a network disease
In epilepsy, historically, most work has concentrated attention on the molecular, anatomical, and cellular physiological changes involved in the development of the disease (epileptogenesis) and in the initiation of seizures (ictogenesis) (Kramer and Cash 2012; Laufs 2012; Spencer 2002). The classic representation of seizures is an abnormal state of hyper-synchronization in a circuit of neurons (Kramer and Cash 2012). It is now known that the epileptogenesis is characterized by a network of widespread aberrant and compensatory changes rather than by a specific cellular or molecular abnormality. These changes have been observed in seizure-prone brain areas in animal models as well as in brain tissue surgically resected from drug-resistant epileptic patients. Several consequences are known to be an outcome of this network, such as the onset of spontaneous seizure, seizure severity and progression, histopathological changes and neurological comorbidities.
Furthermore, several functional impairments in addition to epilepsy can develop, including developmental delay, cognitive and sensory-motor deficits and drug refractoriness (Pitkänen and Lukasiuk 2009).
Modern classifications of the epilepsies recognize the network nature of the disease and define focal epilepsies as originating in network localized to one hemisphere and generalized epilepsies as originating in bilateral network (Berg et al. 2010). A different aspect of the network has been studied over the last years, mainly focusing on the way in which different brain regions communicate.
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2. Presurgical evaluation
People suffering from pharmaco-resistant epilepsy who are considered good candidates for epilepsy surgery undergo extensive evaluations to determine the likelihood that any specific surgical procedure will be effective in controlling seizures without unacceptable adverse effects.
Surgical intervention may be carried out with a high probability of success if different exams have shown two conditions to be true: (a) the area of seizure onset is consistently and repeatedly from the same brain area, such as the frontal or temporal lobe and (b) the implicated region of the brain can be safely removed without creating intolerable deficits.
Up to now, the standard exams normally performed during the presurgical work up include long- term scalp video-EEG, structural and functional neuroimaging, and neuropsychological and psychiatric assessment. If the scalp EEG or neuroimaging data leave doubt regarding the side or precise cortical localization of seizure onset, this noninvasive phase is followed by invasive diagnostics with long-term subdural electrodes or stereotaxic depth electrodes to further define the seizure onset zone and explore surgical possibilities (Zijlmans et al. 2019).
A lot of technological developments in several directions, such as in the high-field MRI and high- density EEG recordings, may soon, if not already, allow better investigation of the functional and structural alteration at the whole brain. Since focal epilepsy is now known to involve a large-scale network, the understanding of how the epileptogenic tissue connects within the brain network in order to plan optimal surgical strategies is fundamental (Chen et al. 2008; Pittau et al. 2014;
Rosenow and Lüders 2001; Wagner et al. 2011).
In what follows we describe the principles of the different techniques involved in the diagnosis and the pre-surgical planning in epilepsy.
2.1 Long-term scalp video-EEG
Long-term scalp video-EEG recording is a diagnosis technique normally performed for several days or weeks. The main aim is to capture any epileptic activity on the scalp EEG, such as seizures and interictal epileptiform activity. The video recording is important to identify specific movements or behaviors during the seizures (semiology). We will describe in further detail the importance of high- density EEG recordings and the detection of epileptic activity in a later chapter (Chapter 3- EEG).
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2.2 Structural MRI
The most efficient way to discriminate brain tissues and markers of tissue microstructural integrity in the brain is structural MRI (Bernasconi et al. 2019). This technique has become fundamental in the management of drug-resistant epilepsy, as the identification of a clear-cut lesion on structural MRI is associated with favorable seizure outcome after surgery (Jones and Cascino 2016). It has been shown that post-surgical outcome is more favorable in patients with MRI-identified hippocampal atrophy indicating mesial temporal sclerosis and less favorable in patients with unremarkable MRI.
Structural MRI can be acquired with different contrasts in order to highlight different types of tissues. In the pre-surgical evaluation, the focus is to find different types of abnormalities: T1- weighted images are used to identify hippocampus sclerosis, atrophy, focal cortical dysplasia and lesions, T2-weighted images are good to visualize edema.
2.3 Functional Imaging
When performing structural brain imaging, one scan is often enough to classify different tissue types and identify structural abnormalities. With functional brain imaging (fMRI) the interest is in evaluating abnormalities in neuronal activity over time, thus images are collected for an extended time period. During an fMRI exam, image volumes are continuously collected with a repetition time (TR) of 2–4 seconds, resulting in a total of 200–300 images for the whole time-series. fMRI uses blood-oxygenation level-dependent (BOLD) contrast to indirectly investigate neuronal activity through the measure of neuronal oxygen consumption and focal perfusion (hemodynamic) changes.
fMRI is therefore mainly used in the context of presurgical evaluation to identify the eloquent cortex such as language areas and the sensory-motor cortex (Baciu et al. 2005).
Simultaneous acquisition of EEG and fMRI allows the combination of high temporal and spatial resolution. This technique is increasingly used for the localization of interictal epileptiform activity (Bénar et al. 2006; Grouiller et al. 2011; Pittau et al. 2012; Vulliemoz et al. 2009; Zijlmans et al. 2007). By correlating the fMRI data with the appearance of epileptic activity in the EEG, it is possible to identify the source of the epileptic discharges, normally consisting of different clusters of activation and de-activation and thereby provide information for improved surgical planning and procedure.
Other functional imaging techniques are also performed to help the localization of the epileptogenic zone, such as Positron Emission Tomography (PET) or Single Photon Emission Computed Tomography (SPECT). PET and SPECT are based on the intravenous injection of radioactive tracers.
In more detail, PET is useful to detect a focal interictal hypometabolism that can extend beyond
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visible MRI lesions (Chassoux et al. 2010). SPECT is particularly important in focal epilepsy because a decreased cerebral blood flow during interictal periods on a focal area is indicative of the epileptogenic zone and a hyper-perfusion during ictal periods indicates sites of epileptic activity and seizure spread (Van Paesschen et al. 2007). The difference between ictal and interictal SPECT is known as SISCOM (O’Brien et al. 1999).
2.4 Intracranial EEG
If the non-invasive techniques are not sufficient to localize the epileptogenic zone and determine its extension, intracranial recordings might be performed (Chauvel et al. 2019; Diehl and Luders 2000; Zumsteg and Wieser 2000).
There are two types of intracranial EEG electrodes, both implying brain surgery to place them: the stereotactic EEG (sEEG) in which depth electrodes are implanted in the brain, and the subdural EEG or electro-corticogram (ECoG) in which electrodes in grids and/or strips are placed under the dura mater over the brain surface. Both types of electrodes record the local field potentials created by a group of neurons in the proximity of the electrode. The recorded signals normally have high signal- to-noise ratio because they are recorded directly from the cortex without attenuation by the skull.
With this technique, the coverage of the brain is limited at the location of the electrodes and, if the real epileptic zone is not well-targeted, rather the spread of activation than the initial onset could be seen.
2.5 Neuropsychological and Psychiatric assessment
Neuropsychological and psychiatric assessments aim to find cognitive impairments related to epilepsy (Helmstaedter and Witt 2012; Vogt et al. 2017). Up to 80% of the patients already show cognitive decline before epilepsy surgery (Äikiä et al. 1995), and up to 45% of TLE patients may experience a memory decline after surgery (Helmstaedter 2013; Sherman et al. 2011).
Neuropsychological assessments are indicative of the lateralization of different functions, such as language, verbal memory (Mungas et al. 1985) and visuo-spatial memory (Bohbot et al. 1998).
Psychiatric comorbidities, such as affective, obsessive-compulsive disorders and psychoses, are present in 33% of epileptic patients (Kanner 2016) and in these cases there are higher risk for post- operative worsening of the conditions (Foong and Flugel 2007).
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2.6 Predicting surgical outcome
Thanks to the extensive clinical workup in epileptic patients, surgery is likely to obtain seizure- freedom in up to 80% of patients (Baud et al. 2018; Choi et al. 2011; Wiebe et al. 2001).
Unfortunately, in more difficult cases, among others the non-lesional ones, poor outcome after surgery could rates up to 50% with seizures still occurring after the surgery (De Tisi et al. 2011; Yoon et al. 2003). It would, therefore, be beneficial to be able to predict in a patient-specific manner when surgery will or will not work in order to inform patients and better customize therapeutic approaches (van Mierlo et al. 2019; Sinha et al. 2017).
One of the proposed reasons for unsuccessful surgical resections is the notion that even focal epilepsies are diseases of abnormal network organization of brain areas and the connections between them (Bragin et al. 2000; Kramer and Cash 2012; Spencer 2002). Connectivity and network analysis are good candidate tools for improving the diagnosis of epilepsy and classification in the absence of visible EEG abnormalities (van Mierlo et al. 2019; Verhoeven et al. 2018). It has been already shown that connectivity could predict the diagnosis of epilepsy with sensitivity of 51% and specificity of 73% (Douw et al. 2010b) and an increased theta band connectivity revealed in magnetoencephalography could be potentially used as a biomarker for tumor-related epilepsy (Douw et al. 2010a). Furthermore, graph theory measures in intracranial studies (Lagarde et al.
2018; Van Mierlo et al. 2013; Wilke et al. 2011) as well as EEG/MEG studies have been used as quantitative measures of the epileptogenic zone and to evaluate their relations with the actually resected brain tissue or the post-surgical seizure control.
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3. EEG
The use of electroencephalography (EEG) is a central part of current investigations of the normal and pathological brain activity, and has been in use for over 80 years.
3.1 EEG acquisition and scalp field maps
Extracellular currents originate from post-synaptic activity in the dendrites of aligned pyramidal neurons, and the generation of the synchronized post-synaptic activity causes electric fields that are measurable on the scalp surface.
To record the electric signal, EEG electrodes are placed on the scalp following an internationally accepted standard position: the 10-20 or 10-10 system (Klem et al. 1999).
Each electrode has a conventional anatomical label and number: ‘F’ for Frontal, ’T’ for temporal, ‘P’
for parietal, ’O’ for Occipital, ’C’ for central, ‘A’ for Auricular regions; even numbers are on the right, odd numbers on the left hemisphere, ‘z’ stands for midline electrodes. Different numbers of electrodes are used to register the brain activity, each of them is used in different research and clinical set-up. For example, in clinical set-up, normally 27 or 32 electrodes are used and up to 256 electrodes are used in research (normally called high-density-EEG). EEG is recorded with a sampling frequency between 250 Hz and 2000 Hz, reaching a temporal resolution in the millisecond range (Michel and Murray 2012).
EEG consists of a two-dimensional array: at each time point EEG potentials can be represented as scalp field maps, also called topographies (Murray et al. 2008). EEG recordings can be recorded with a single reference: the measured voltages from the reference electrode channel are subtracted from the other electrode channels for each time sample. The choice of the reference electrode channel can differ depending on the purpose of the EEG recording: a quite common reference is Cz (placed in the center of the head). When dealing with high density EEG analysis offline, another used reference is the common average reference, in which the average over all electrodes is subtracted from the potentials of all electrodes.
Figure 1: Example of epileptic spike recorded on hd-EEG and corresponding scalp topography.
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3.2 Brain activity recorded with EEG
Since the beginning of the EEG, in 1939, scientists were talking about “alphas” and “betas” to describe the neuronal activity. The clearest activity seen at the time were periodic oscillations around 10 Hz (indeed the most common rate for neuronal activity in an awake human), Berger called these periods Alpha Waves, everything else was called beta activity. In 1936, Delta waves [< 4 Hz]
were reported by Hoagland, Rubin, and Cameron, and Gamma waves [around 30Hz] were reported by Jesper and Andrews.
Since then, the EEG rhythms have been divided in mainly 5 frequency bands (Niedermeyer 2005):
Delta [<4Hz] often seen during deep sleep, Theta [4-7Hz], Alpha [8-12 Hz] often seen during eyes- closed wakefulness, Beta [13-30 Hz], Gamma [>30 Hz] often seen during information processing, and HFOs [>200 Hz] mainly seen in intracranial recordings.
3.3 EEG in epilepsy
In 12-50% of patients with epilepsy, EEG may be normal (Van Donselaar et al. 1992; Goodin and Aminoff 1984). Long-term and repeated EEG recordings increase the chance of recording different patterns that are considered epileptiform (Noachtar and Rémi 2009).
The epileptic activity can appear clinically and /or via neurophysiological measures. While seizures usually manifest clinically, interictal epileptic discharges (IEDs) are clinically mute but appear in EEG or magnetoencephalography (MEG) recordings. In addition, there are interictal periods without any EEG- or MEG-visible epileptic activity.
According to the ILAE, "an epileptic seizure is a transient occurrence of signs and/or symptoms due to abnormal excessive or synchronous neuronal activity in the brain" (Fisher et al. 2017), while an interictal event, also called spike or IED, is an intermittent, between two seizures, abnormal EEG patterns without a manifested symptomatology (Bancaud et al. 1981).
The so-called high frequency oscillations (HFO), EEG activity above 70 Hz, might be as well EEG and MEG biomarkers for epileptogenicity (Jacobs et al. 2012, 2009).
These can occur in association with IEDs but also may precede seizures. The majority of the time, HFO are localized and remain restricted to small areas of neocortex, but sometimes they can occur over larger areas which may be more indicative of epileptogenic cortex (Lemieux et al. 2011).
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Finally, periods without visible epileptic activity are considered when the clinician does not see any pathological activity both on behavioral and EEG levels.
3.3.1 Seizures
Based on the International League against Epilepsy (ILAE) classification of 2017, seizures are defined as either focal or generalized or with unknown onset. Seizures are focal (old term: “partial”) when resulting from a network limited to one hemisphere and are generalized when originating in, and rapidly engaging, bilaterally distributed network (Fisher et al. 2017).
Seizures with clinical symptoms are often only a small proportion of all abnormal electrical activity in the brain, which includes subclinical EEG/MEG seizure patterns, interictal spikes, bursts and high- frequency oscillations. It remains unclear precisely how seizures are related to this broader range of abnormal electrical activity (Karoly et al. 2016).
3.3.2 Interictal discharges
Interictal discharges arise from the synchronous firing of a hyperexcitable population of neurons and are considered an abnormal electrical phenomenon. Their configuration is short and sharp, thus, they have been termed spikes (duration 20-80ms) or sharp waves (80-200ms). Historically it is known that they are associated with epilepsy (Ayala et al. 1973; Schulze-Bonhage et al. 2011; Ward 1959), however, over the years, there has been conflicting evidence regarding the complex relationship between spiking process and ictogenesis (Avoli et al. 2006; de Curtis and Avanzini 2001).
On the one hand, increased neural excitability promotes epileptic spikes, which may reach a critical spatial or temporal density and lead to a seizure (de Curtis and Avanzini 2001; Jensen and Yaari 1988) and predictive or causal relationships between interictal spikes and ictogenesis have been reported. There is relatively little experimental evidence supporting the conjecture that spikes precede ictal onset, in particular from studies based on human tissue recordings (Avoli et al. 2006;
de Curtis and Avanzini 2001).
On the other hand, it has been reported that the spike rate is largely unchanged or even reduced prior to seizures (Engel and Ackermann 1980; Gotman and Marciani 1985; Librizzi and De Curtis 2003). This suggests that spikes may provide some protective beneficial mechanism against seizures.
The spike is often followed by a period of hyper-polarization, which may contribute to limiting the frequency of periodic interictal activity. Alternatively, spontaneous interictal discharge may provide regulatory, low-level excitation to forestall the transition to seizure (Avoli et al. 2006; de Curtis and
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Avanzini 2001). There may also be a secondary mechanism that influences patterns of both spikes and seizures.
4. Electrical Source Imaging (ESI)
EEG and MEG are among the non-invasive methods for the study of the neuronal activity at the sub- millisecond timescale. However, the signal measured is a propagation of the neuronal activity on the scalp, and due to the ambiguity of the underlying electromagnetic problem (Helmholtz 1853), infinite different source configurations could potentially generate the same topography on the scalp (de Munck et al. 1988). Therefore, maximal activity at a certain electrode does not mean that the generator is localized in the area underlying it (Michel et al. 2004). In order to solve this problem and properly model the propagation, some a priori constraints need to be taken into account.
Several different constraints have been proposed using different mathematical, biophysical, statistical, anatomical or functional properties and producing different type of solution suitable for distinct type of data.
Electric Source Imaging involves two main processes: (a) the forward model and (b) the inverse problem.
The forward model is a description of the propagation of the electric current across the brain to the scalp (the lead-field matrix): the most important parameter is the head-model, i.e. the head volume conductor model, that takes into account the position of the solution points within the brain (sources- solution space), the position of the scalp electrodes, the head geometry, and the head tissues’ conductivity. A lot of different head-models have been proposed: (a) spherical head models in which the head is approximated by concentric spheres where each sphere corresponds to a
Figure 2: Schematic representation of scalp and source space solution at the peak of an epileptic spike recorded with hd-EEG.
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certain tissue type (Rush and Driscoll 1968) and (b) realistic head models based on the individual MRI.
For the scope of this thesis, the Locally Spherical Model with Anatomical Constrains (LSMAC) has been extensively used. These head models consist of a simplified realistic head model with consideration of scalp, skull and brain thicknesses. LSMAC uses an adaptive local spherical model sequentially at each electrode position, to generate sets of 3-layer spherical models using the conductivities and local radiuses of the scalp, skull and brain under each electrode site. This allows the real geometry between solution points and electrodes to be taken into account (Brunet et al.
2011). It has been previously shown that, as compared to realistic head models based on individual MRI, LSMAC localization of the epileptic zone has the same accuracy (Birot et al. 2014) and it has the advantage of being less computationally demanding.
The inverse problem is used to estimate the source activity, given the EEG recording on the electrodes, based on the solution of the forward problem. The inverse problem is ill-posed, i.e.
infinite source configuration can produce the same topographical map on the scalp electrodes, therefore some a priori hypotheses are needed (source model)(Michel et al. 2004).
Two main types of inverse solution methods are proposed based on different numbers of unknown sources (a) dipole source model (where the number is much smaller than the number of electrodes) and (b) distributed source models (where the number is much higher).
In the dipole source models a search is made for the best dipole position(s) and orientation(s): the assumption is that a single, or only a few, brain area(s) are responsible for the topography seen on the scalp. This could sound optimal for the localization of epileptic activity, but it is known that epileptic activity very quickly within a widespread neuronal network, which can lead to localization of propagated instead of the initial activity.
In the distributed source models, a 3D grid of dipoles (normally around 5000) are computed in a fixed position, with variable strength and either variable or fixed orientation, in the grey matter. No a priori hypotheses are made on the position and on the number of the dipoles, but some assumptions, based on physiological or mathematical information’s, are needed. Two well-known models are the Low Resolution Electromagnetic Tomography (LORETA) (Pascual-Marqui et al. 1994) and the Local Auto-Regressive Averages (LAURA) (Grave De Peralta Menendez et al. 2004).
LORETA assumes smoothness property of the neuronal activity: the a priori hypothesis behind is that neighboring sources must have similar activation and orientation, as the population of neurons must fire synchronously to produce activation (Pascual-Marqui et al. 1994).
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LAURA incorporates biophysical laws as additional constraints: it assumes that the source activity will decrease with the inverse of the cubic distance for vector fields, and with the inverse of the squared distance for potential fields (Grave De Peralta Menendez et al. 2004).
LSMAC and LAURA have been extensively used in this thesis, for comprehensive reviews on ESI as well as the advantages and drawbacks of the different algorithms, we refer the reader to (Grech et al. 2008; Michel et al. 2004, 2009). For clinical application of ESI in the context of presurgical evaluation in focal epilepsy we refer to (Brodbeck et al. 2011; Kaiboriboon et al. 2012; Mégevand et al. 2014).
Other techniques, that include regularization schemes not based on spatial constrained (as described above for LORETA) but based on the following statistical frameworks have been also proposed: (1) the Maximum Entropy on the Mean (MEM) and (2) the Hierarchical Bayesian (HB) framework (Chowdhury et al. 2013; Grova et al. 2006).
In this context two types of spatial models have been investigated. The first one is the idea that brain activity may be modeled as organized among cortical parcels, that can be active or not, when contributing to specific activity (Trujillo-Barreto et al. 2004). The second model is an extension of the spatial smoothness constraint originally proposed in LORETA but locally constrained within cortical parcels. Clustering of the brain activity into non- overlapping cortical parcels is achieved using a data driven parcellization (Chowdhury et al. 2013).
For epileptic activity, the localization accuracy of different source localization methods have been previously evaluated: the minimum norm, the minimum norm weighted by multivariate source prelocalization (MSP), cortical LORETA with or without additional minimum norm regularization, and two derivations of the Maximum Entropy of the Mean (MEM) approach have been tested.
Results showed that LORETA-based and MEM methods were able to accurately recover sources of different spatial extents. However, also several wrong sources are sometimes generated by those methods, it is therefore important to take into account the results from different localization methods when analyzing real interictal spikes (Grova et al. 2006).
4.1 The importance of the number and location of electrodes
When collecting brain signal with EEG, caps with different numbers of electrodes are available. The most commonly used range between 19 and 256 electrodes.
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When the aim of the recording is to perform source reconstruction, two main characteristics need to be taken into account: (a) the whole scalp needs to be equally covered, (b) the number of electrodes (Michel et al. 2004; Michel and Murray 2012).
It is known from the literature that increasing the electrode density in the regions where the maximal activation is expected decreases the localization power of the ESI technique: if the topographic features are not adequately sampled, none of the inverse solutions can retrieve the sources that would have generated these features (Bénar and Gotman 2001; Michel et al. 2004).
It has been previously shown, initially via simulation of single dipoles and later by localization of interictal discharges, that the number of electrodes influences source localization (Lantz et al.
2003a). There is a non-linear relation between the precision of ESI and the number of EEG electrodes: the precision increases from 25 to around 100 electrodes and then it reaches a plateau.
From low density EEG recording, mainly in clinical set-ups (Seeck et al. 2017), it has been also shown that temporal electrodes allow better identification of temporal lobe spikes.
However, the additional value of inferior electrodes for localization of the source activity is still unclear. On the one hand, due to their spatial proximity to the skull base, inferior EEG channels may help in localizing epileptic activity in basal structures such as the temporal lobes. On the other hand, located rather far from the convexity, they might be of less value for ESI in extratemporal structures.
In addition, inferior electrodes placed on the neck and facial muscles are prone to artifacts which, in turn, may reduce their value for ESI.
4.2 The extraction of brain region time-series
The estimation of the source space activity using ESI always leads to a description of the brain as a collection of around 5000 three-dimensional dipoles placed in different brain areas. When the final aim of the reconstruction is to describe the compute connectivity analysis, since the full spatial size of the data is unreasonable, the interest is to parcel the brain in different regions and extract a unique time-series representing the activity in that area.
To solve this step, in the literature, a lot of different approaches have been proposed: (a) computing the norm of all the dipoles, (b) fixing their orientation and (c) selecting one dipole time-course as representative of the signal.
When computing the absolute dipole amplitude (i.e. the norm of the signal) or the power modulation using Hilbert transformation (Baker et al. 2014; Brookes et al. 2011) while discarding the directionality, the phase information of the original signal is lost (Vidaurre et al. 2016) and
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therefore connectivity analysis cannot be applied. Others computed the average cortical activity in each ROI by means of the instantaneous average of the signed magnitude of all the dipoles within the ROI (Astolfi et al. 2007).
When imposing one direction, two steps are needed, first one should either fix the orientation (a) orthogonal to the segmented grey matter, based on the assumption that the orientation of the dipoles should resemble the orientation of the apical dendrites of the pyramidal neurons (Phillips et al. 2002), or (b) maximize the projected power (Barnes et al. 2004) or, (c) as the projection to the refined average direction across time and epochs (Coito et al. 2016). Second, an averaging to all dipole time-series within the ROI (Hassan et al. 2017) or principal component analysis (PCA) needs to be applied to obtain the representative time-series (Gruber et al. 2008; Supp et al. 2007). A common observation in these cases is a drastic amplitude reduction: some sources in the ROI may be almost perfectly parallel to each other, but inverted in orientation, leading to cancelation when averaging them.
When selecting one dipole time-course only, the closest to the geometric center of each ROI, i.e., the centroid (Adebimpe et al. 2016; Coito et al. 2015; Sperdin et al. 2018), is considered. However, the selection of only one dipole out of hundreds does not necessarily properly represent the activity in a given ROI.
4.3 Magnetic Source Imaging
Another technique that can measure neuronal activity not invasively is magnetoencephalography (MEG), which measures the brain magnetic fields. Both EEG and MEG collect signal arising from neuronal activation. There are, however, some differences between the two techniques: EEG is sensitive to both tangential and radial components of the dipolar sources, while MEG is not sensitive to only to tangential components components (Ahlfors et al. 2010). This is why, EEG can pick up the activity of deep and radial current sources in opposition to MEG [90]. However, the EEG is strongly influenced by volume conduction and by the different electrical conductivities of various head tissues that attenuate the sources electrical signal. This has a less strong influence on MEG, given that the head tissues have a constant magnetic permeability(Lopes da Silva 2013). All the ESI described methodology can also be applied in MEG, and in this case, it is called Magnetic Source Imaging (MSI).
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Among others, Minimum Norm Estimate (MNE) is one of the most commonly used inverse solutions methods in MEG. It looks for a distribution of sources with the minimum (L2-norm) current that can give the best account of the measured data. As in the case of EEG, the inverse problem is ill-posed:
MNE uses a regularization procedure that sets the balance between fitting the measured data (minimizing the residual) and minimizing the contributions of noise (Hincapié et al. 2016).
As for EEG, the localization of the generators of epileptic activity in the brain MEG signals is of particular interest during the pre-surgical investigation of epilepsy.
Using realistic simulations of epileptic activity, the Maximum Entropy on the Mean (MEM) framework has been shown to be sensitive to all spatial extents of the sources ranging (Chowdhury et al. 2013). In real data, MEM allows non-invasive localization of the seizure onset zone from both MEG and EEG (Heers et al. 2016). It has been shown that source imaging performed with MEG can provide important additional information to source imaging performed with EEG during both spikes and seizures during presurgical evaluation (Pellegrino et al. 2016).
5. Brain Connectivity
The human brain has been described as a complex system, composed by more than 1010 neurons, with great computational capacity due to the co-existence of both local circuits and long-range pathways (Hagmann et al. 2008).
In order to describe these features of the brain, a particular tool in neuroscience has been established: the connectivity analysis. Different neuroimaging tools have revealed different aspects of this, both from an anatomical/structural and functional/effective point of view.
Anatomical connectivity describes the physical connection between brain areas, quantifying the pathways among them. Structural connectivity describes the biophysical properties of the tissue that connect different regions. Anatomical and Structural connectivity together are called “human connectome”, describing the biological network of elements in the human brain (Hagmann et al.
2008). Normally to extract these information Diffusion-Weighted magnetic resonance imaging (DWI) can be used. In the white matter the mobility of water is restricted in directions perpendicular to the axons oriented along the fiber tracts. Starting from this information, using DWI, the orientation and direction uniformity of water diffusion in the brain tissue is computed and the white matter fiber tracts can be visualized and quantified.
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Effective connectivity describes the influence between two neural systems, or two different brain areas, and it is based on causal interactions among them (David et al. 2006; Friston et al. 2013, 1993, 2003). The most used methodology to study effective connectivity is the Dynamic Causal Modelling (DCM): it considers a biophysically plausible model of interacting brain regions (or nodes) with hidden states that have to be estimated (Friston et al. 2003).
Functional connectivity describes the statistical dependency between two brain areas. This interaction is estimated as directed, if it explains the directionality, or undirected. Both functional and effective connectivity are normally estimated with fMRI or EEG/MEG.
5.1 Directed Functional connectivity based on Granger Causality
One of the definitions of causality currently used, both in economics and in neuroscience field, was given by Prof. Clive W.J. Granger, in the early 1960s.
He wrote: “In the early 1960's I was considering a pair of related stochastic processes which were clearly inter-related and I wanted to know if this relationship could be broken down into a pair of one way relationships. It was suggested to me to look at a definition of causality proposed by a very famous mathematician, Norbert Wiener, so I adapted this definition (Wiener 1956) into a practical form and discussed it. Applied economists found the definition understandable and useable and applications of it started to appear. However, several writers stated that "of course, this is not real causality, it is only Granger causality." Thus, from the beginning, applications used this term to distinguish it from other possible definitions.
The basic "Granger Causality" definition is quite simple. Suppose that we have three terms, Xt , Yt , and Wt , and that we first attempt to forecast Xt+1 using past terms of Xt and Wt . We then try to forecast Xt+1 using past terms of Xt , Yt , and Wt . If the second forecast is found to be more successful, according to standard cost functions, then the past of Y appears to contain information helping in forecasting Xt+1 that is not in past Xt or Wt . In particular, Wt could be a vector of possible explanatory variables. Thus, Yt would "Granger cause" Xt+1 if (a) Yt occurs before Xt+1 ; and (b) it contains information useful in forecasting Xt+1 that is not found in a group of other appropriate variables. The definition leans heavily on the idea that the cause occurs before the effect, which is the basis of most, but not all, causality definitions.” (Granger 1980).
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In other words, signal-1 causes signal-2 if the information contained in signal-1 helps to better predict signal-2 than the information contained in the past values of signal-2 only. One should note that this causality is not reciprocal, i.e., the information transfer from signal-1 to signal-2 is different from the one from signal-2 to signal-1.
They can be investigated with autoregressive (AR) models of an appropriate model order p. The model order defines the number of past time points that are included to estimate the current value.
In AR models, the signals are represented as a linear combination of their own past plus additional uncorrelated white noise.
Granger-causality in the frequency domain have been formalized as the Directed Transfer Function (DTF) and as the Partial Directed Coherence (PDC). Connectivity measures in the frequency domain are important since EEG rhythms oscillate at different frequencies that have different roles in information processing (Arnold et al. 1998; Baccalá and Sameshima 2001; Blinowska 2011; Liao et al. 2010).
The DTF as well as PDC found many applications in the estimation of cortical connectivity (Astolfi et al. 2005) e.g., for localization of epileptic foci (Franaszczuk et al. 1994; van Mierlo et al. 2014), for estimation of EEG propagation in different sleep stages and wakefulness (Franaszczuk et al. 1994), and many others.
One of the main differences between DTF and PDC is that DTF shows not only direct, but also cascade flows, namely in case of propagation 1 → 2 → 3 it shows also propagation 1 → 3.
5.2 The importance of performing functional connectivity in the source space
It is of fundamental importance to compute functional connectivity on the source and not on the sensor/scalp space for 3 main reasons: (a) volume conduction, (b) reference problem and (c) interpretation.
The volume conduction problem (Nolte et al. 2004)describes the transmission of any source activity at the same time to different electrodes, leading to spurious connections between electrodes.
Performing connectivity on the source space partially solves this issue (He et al. 2019). Another way of minimizing it is to consider only interactions having non-zero lags, since the volume conduction is instantaneous (Daffertshofer and Stam 2007; Nolte et al. 2004).
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The reference problem (Lehmann and Michel,1990) describes the dependency of each electrode to the reference one, therefore changing the reference electrodes changes the phase and power of each electrode and therefore the frequency couplings among them. This has been previously described to not affect source space reconstruction as well as thus connectivity analysis on the source space (Geselowitz and Program 1998; Lehmann et al. 1987).
The interpretation problem is due to the previously described feature of the electric propagation: a strong or reduced activation on one electrode does not univocally mean that the area underneath is also active and this is applicable for the connectivity (Michel et al. 2004).
In this thesis, we mainly investigate the directed functional connectivity (Partial Directed Coherence based on Granger causality) in the source space of patients with epilepsy or coma, and of healthy subjects, with the aim of uncovering large-scale mechanisms describing different pathologies and impairments.
5.3 Connectivity in Epilepsy
Highly interconnected brain networks can generate a wide variety of synchronized activities, including those underlying epileptic seizures, which often emerge as an alteration of normal brain rhythms (Lehnertz et al. 2014). First evidence that network analysis might be useful to understand epilepsy came from model studies (Netoff et al. 2004). Since then, there has been growing empirical evidence for the hypothesis that changes in brain network topology might play a crucial role in epilepsy (van Mierlo et al. 2019). A more regular network topology could be related to seizure generation (Gupta et al. 2011; Ponten et al. 2009) and interictal functional networks in epilepsy patients may be characterized by increased connectivity, by a shift to a more regular topology, by changes in modular structure and prominent hub-like regions (Bartolomei et al. 2013; Chavez et al.
2010; Horstmann et al. 2010; Quraan et al. 2013). Hemispheric connectivity has been found to be altered depending on the laterality of the temporal lobe of onset (Caciagli et al. 2014; de Campos et al. 2016; Su et al. 2015), suggesting that the increased connectivity found in the contralateral hemisphere may reflect compensatory mechanisms (Bettus et al. 2009). The right temporal lobe epilepsy has been functionally described as a more bilateral functional condition than left temporal lobe epilepsy with specific connectivity alterations (de Campos et al. 2016). Ipsilateral anterior temporal structures have been identified as key drivers in both left and right temporal lobe patients but contralateral drivers has been identified only in right temporal lobe epileptic patients (Coito et
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al. 2015). Functional connectivity has been also found to be impaired in ipsilateral to the seizure focus in both right and left TLE when compared to control subjects, with strongest effect in left temporal lobe patients group (Pereira et al. 2010). On the other hand, structural connectivity studies have shown that left temporal lobe epileptic patients were much more affected than right temporal lobe epileptic patients: left patients shown greater, more diffuse structural connectivity changes, whereas patients with right patients showed changes that were primarily ipsilateral (Ahmadi et al.
2009).
In general, the extent of network changes worsens with the duration of disease, as well as the frequency of seizures (Englot et al. 2015; Morgan et al. 2015). After a first seizure, differences in the functional connectivity has been found in those who went on to develop chronic epilepsy and those who did not (Douw et al. 2010b). Patients who developed epilepsy had increased synchronization likelihood as measured by EEG (Douw et al. 2010b), and network alterations in BOLD signal did not significantly differ with and without the presence of IEDs, suggesting that these network abnormalities exist even in the absence of abnormal brain activity during the scan (Iannotti et al.
2016).
A variety of different resting-state networks have been found to be altered in temporal lobe epilepsy, including the default mode (DMN), limbic, sensorimotor and thalamic networks (Caciagli et al. 2014). The specific disruptions in connectivity and network topology associated with cognitive and behavioral impairments often seen in patients with chronic epilepsy have shifted from ‘focus’
to ‘networks’ dysfunction (Coito et al. 2016; Van Diessen et al. 2013, 2014; Lehnertz et al. 2014;
Lemieux et al. 2011).
Also, Effective connectivity has been used to study epilepsy. DCM has been mainly used in fMRI data associated with, for example, generalized spike-wave discharges and seizure propagation (Murta et al. 2012; Vaudano et al. 2013).
DCMs models for EEG data are typically considerably more complex than in DCMs for fMRI. This is because with EEG temporal information of neural activity are extracted and can only be captured with models that represent neurobiologically detailed mechanisms.
DCM models are reliable only if there is a balance between the realism of the underlying biophysical models and the feasibility of the statistical treatment.
Unfortunately, until now, the existing implementations of DCM can be used only on really precise applications and to more specific questions that are limited either by the simplifying assumptions
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of the underlying biophysical models and/or by the bounded efficiency of the associated statistical inference techniques (Lemieux et al. 2011).
6. Graph Theory
Neurons and synaptic connections have been the primary focus of research at the beginning of neuroscience history. It has been only in the late 1940s that the idea of distributed processes and strengthening of the connections when the neurons became active has become fundamental (Hebb 1950). In 1952, Ashby described the need for the brain of multiple coordinating parts to coordinate and interact with the environment (Ashby 1960).
It took almost 30 years, around the 1980 - 1990, that whole brain mapping of connections started to have an important role in the fields with the studies in soil nematodes (White et al. 1986), monkeys (Felleman and Van Essen 1991; Shipp and Zeki 11985) and humans (McClelland et al.
1988).
In the 2000s, the concept of “connectomics” was conceptualized as the idea of different brain areas structured to segregate and integrate information (Bullmore and Sporns 2009; Sporns et al. 2005).
Since then, a lot of studies have been established to map the human connectome both in healthy and pathological populations. Some network patterns have been found to exist during specific cognitive and sensory tasks, some others during resting state.
In order to study the brain’s massive and complex network, scientists needed to reduce the amount of information and to use simplified models of the brain networks. One type of brain network model is provided with modern graph theory.
A network is defined as a graph, a mathematical object defined by a set of nodes and edges/links, and network theory has been used to describe it (Bullmore and Sporns 2009; Newman 2010). The network is described as a matrix where each entry is the edge between the two nodes (brain areas), i.e. the connectivity matrix. The matrix can be weighted or unweighted (binary); it can be symmetric, leading to undirected networks, or asymmetric, leading to directed networks, i.e. the connections strength between two nodes is not the same in the two directions. In the brain networks, all the nodes are interconnected but not connected to themselves, and therefore the diagonal of the adjacency matrix consists of zeros.
In this framework, different network properties can be described and grouped in different classes:
• Edge measures: a measure relating to each edge.
