Thesis
Reference
Neural network plasticity in the human brain
RIZK, Sviatlana
Abstract
The human brain is highly organized within networks. Functionally related neural-assemblies communicate by oscillating synchronously. Intrinsic brain activity contains information on healthy and damaged brain functioning. This thesis investigated the relationship between functional networks and behavior. Furthermore, we assessed functional network plasticity after brain damage and as a result of brain stimulation. In different groups of patients we observed reduced functional connectivity between regions involved with the coordination of the affected behavior and the rest of the brain. Network coherence in patients and healthy participants correlated linearly with corresponding behavioral performance. Diverse disease-related mechanisms of neuroplasticity were identified. Based on these findings we conclude that network imaging with EEG reveals important information on neural organization and is correlated with behavior. This opens new possibilities for clinics in general and in neurorehabilitation in particular. Our results suggest new perspectives for the implication of TMS as a part of a neuro-rehabilitative program.
RIZK, Sviatlana. Neural network plasticity in the human brain . Thèse de doctorat : Univ.
Genève et Lausanne, 2013, no. Neur. 104
URN : urn:nbn:ch:unige-307503
DOI : 10.13097/archive-ouverte/unige:30750
Available at:
http://archive-ouverte.unige.ch/unige:30750
Disclaimer: layout of this document may differ from the published version.
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(Logo of your faculty)
Faculté des Sciences
DOCTORAT EN NEUROSCIENCES des Universités de Genève
et de Lausanne
UNIVERSITÉ DE GENÈVE FACULTÉ DES SCIENCES
Professeur Armin Schnider, directeur de thèse
PD Dr. med. Adrian G. Guggisberg, co-directeur de thèse
TITRE DE LA THESE
NEURAL NETWORK PLASTICITY IN THE HUMAN BRAIN
THESE Présentée à la Faculté des Sciences de l’Université de Genève
pour obtenir le grade de Docteure en Neurosciences
par
Sviatlana (DUBOVIK) RIZK
de Minsk
Thèse N° 104
Genève
Editeur ou imprimeur : Université de Genève
2013
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ACKNOWLEDGEMENTS
First and foremost, I would like to express my sincere gratitude to my supervisor PD Dr. med. Adrian G. Guggisberg for his continuous support of my PhD study and research; for his patience, motivational skills, enthusiasm and his seemingly never-ending scientific knowledge. His guidance has helped me throughout my research period and throughout writing this thesis. I could not imagine having a better advisor and mentor for my PhD.
Besides my mentor, I would like to thank in particular Prof. Armin Schnider for his valuable comments and remarks and to the rest of my thesis committee: Prof. Thomas Koenig, PD Dr. med. Serge Vulliemoz, and Prof. Christoph Michel for their time and consideration of my work.
I owe sincere and earnest gratitude to Prof. Leonardo Cohen and PhD Ethan Buch for offering me a one-month training opportunity at their laboratory of Human Cortical Physiology and Stroke
Neurorehabilitation at the National Institute of Health in Washington DC. These experiences gave me the chance to learn innovative methods in structural connectivity analyses, through their expertise. My sojourn resulted in an interesting collaboration on exciting new projects which, I hope, will continue in future.
I would also like to thank my fellow labmates in Geneva: Julia Fellrath, Anaïs Mottaz, Aurélie Manuel- Stocker, Chiara Liverani, Aurélie Bouzerda-Wahlen and ex-fellow Louis Nahum for the stimulating discussions and for all the fun we have had over the last three years.
I would like to thank my family: my parents Valentina and Vladimir, my brother Sergey and his family and my sister Irina for their support and love during all the years of my studying.
Last but not least, I would like to thank my loving husband Benoît and his family for their patience and care.
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ABSTRACT (ENGLISH)
The adult human brain is not a set of single independent regions, but a highly organized system of coordinated networks (Greicius et al., 2003; Fox et al., 2005). Even when at rest, the brain is not inactive. Functionally related neural assemblies communicate with each other by oscillating synchronously. The strength of spatio-temporal coupling between neural groups can be recorded by using several different measures such as, for instance, coherence. Consequently, intrinsic brain activity and neural functional connectivity (FC) can be studied at rest and can be an important source of information on healthy and damaged brain functioning.
This thesis aimed to investigate the relationship between functional networks and behavior.
Furthermore, we assessed functional network plasticity after brain damage and as a result of brain stimulation. Two groups of patients, including one group who had suffered from ischemic stroke and one with Alzheimer‟s disease, and a group of young healthy participants, took part in three different sub-projects using electroencephalography (EEG) and transcranial magnetic stimulation (TMS).
In different groups of patients we observed reduced functional connectivity between regions involved with the coordination of the affected behavior and the rest of the brain. The presence of the lesion alone could not explain this decrease in FC. Network coherence in patients and healthy participants correlated linearly with corresponding behavioral performance. Diverse disease-related mechanisms of neuroplasticity were identified. In addition, magnetic stimulation in a group of healthy participants showed that connectivity within functional networks can be modulated in a direct way, and that the individual state of FC prior to stimulation can predict the induced effect.
Based on these findings we can conclude that network imaging with EEG can reveal important information on neural organization and is correlated with behavior. This opens new possibilities for clinics in general and in neurorehabilitation in particular. Whole brain connectivity can be investigated using data from five minutes of EEG recording at rest. The resting state approach does not require any active participation; it can be used also in severely affected patients and even in patients in a coma or under anesthesia. Furthermore, our results suggest new perspectives for the implication of TMS as a part of a neuro-rehabilitative program.
Future research should concentrate on the predictive value of network analyses, which could improve diagnosis and treatment of patients. Repeated assessment of FC in patients alongside the recovery process could likewise provide further insight into the mechanisms of brain plasticity. Finally, a lot can be learned from network analyses when one combines different neuroimaging techniques (i.e. EEG, fMRI, DTI and TMS).
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ABSTRACT (FRENCH)
Le cerveau humain est un organe constitué de réseaux neuronaux complexes, générateurs de connections infinies. Même au repos, il ne reste pas inactif. Les aires fonctionnelles communiquent entre elles par oscillations synchronisées. La force de connexion entre groupes neuronaux peut être analysée, d‟un point de vue spatio-temporel, par des indicateurs tels que la cohérence. L‟activité intrinsèque cérébrale et la connectivité fonctionnelle neuronale peuvent donc être étudiées au repos et participer à l‟amélioration des données scientifiques relatives au fonctionnement du cerveau sain et pathologique.
L‟objectif de cette thèse est d‟approfondir la relation entre réseaux neuronaux fonctionnels et comportement. Nous avons ainsi étudié la plasticité de ces réseaux fonctionnels après lésion cérébrale et après stimulation transcranienne. Deux groupes de patients, l‟une atteinte de séquelles d‟accidents vasculaires cérébraux et l‟une de maladie d‟Alzheimer, ainsi qu‟un groupe de jeunes adultes volontaires sains, ont participé à trois sous-projets différents utilisant électroencéphalogramme (EEG) et stimulation transcranienne par champ magnétique (TMS).
Dans deux groupes de patients, nous avons constaté une réduction de connectivité fonctionnelle entre les régions impliquées dans la coordination du comportement atteint et le reste du cerveau. La présence de la lésion ne pouvait pas expliquer à elle seule la réduction de connectivité fonctionnelle.
La cohérence du réseau fonctionnel neuronal chez les malades et les volontaires sains présentait une corrélation linéaire avec leur performance comportementale correspondante. Plusieurs mécanismes de neuroplasticité liés aux pathologies ont été identifiés. De surcroît, la stimulation transcranienne par champ magnétique dans le groupe de jeunes volontaires sains a montré que la connectivité intrinsèque à un réseau fonctionnel pouvait être modulée de manière ciblée, et que la connectivité fonctionnelle de base individuelle avant stimulation pouvait prédire l‟effet induit.
Ces résultats nous permettent d‟affirmer que l‟imagerie des réseaux neuronaux avec électroencéphalographie peut révéler des informations fiables et pertinentes sur l‟organisation neuronale et peut prédire le comportement, ouvrant de nouvelles perspectives cliniques, notamment dans le domaine de la neuroréhabilitation. La connectivité du cerveau dans sa globalité peut être analysée par cinq minutes d‟EEG enregistrées au repos. L‟approche par analyses de données au repos ne sollicite aucune participation active et peut être utilisée chez des patients atteints de pathologies lourdes, y compris ceux plongés dans le coma ou sous anesthésie. De plus, nos résultats suggèrent de nouvelles perspectives pour l‟implication des TMS comme élément significatif des thérapies de neuroréhabilitation.
Des études ultérieures pourraient se concentrer sur la valeur prédictive des analyses de réseaux neuronaux, qui peuvent améliorer la performance diagnostique et la prise en charge thérapeutique des patients. De nombreux travaux sur l‟évaluation de la connectivité fonctionnelle pourraient à l‟avenir permettre de mieux comprendre les mécanismes de plasticité neuronale. En conclusion,
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l‟analyse des réseaux neuronaux peut être d‟une grande aide diagnostique, prédictive et thérapeutique si l‟on combine les différentes techniques de neuroimagerie (EEG, TMS, IRM fonctionnelle et tractographie).5
TABLE OF CONTENTS
ACKNOWLEDGEMENTS ... 1
ABSTRACT (ENGLISH)
... 2
ABSTRACT (FRENCH)
... 3
TABLE OF CONTENTS
... 5
TABLE OF ILLUSTRATIONS ... 7
LIST OF ABBREVIATIONS ... 8
CHAPTER 1 INTRODUCTION ... 9
1. Neural Networks
... 9
1.1. Resting State Networks (RSNs)
... 10
1.2. Default Mode Network
... 11
2. Functional Connectivity (FC) ... 13
2.1. fMRI... 14
2.2. EEG... 15
2.3. Network modelling
... 17
Graph Theoretical Approach
... 17
Small-world Network
... 18
Granger Causality ... 18
Dynamic Causal Modelling ... 19
3. Neuroplasticity ... 21
3.1. fMRI studies
... 22
3.2. EEG and MEG studies
... 25
3.3. TMS studies
... 26
4. Description and objectives of the project ... 29
CHAPTER 2 RESULTS ... 31
2.1. The behavioral significance of synchronous oscillations in resting-state networks after brain damage ... 31
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2.2. Adaptive Reorganization of Electrical Network Interactions in Alzheimer‟s Disease
... 32
2.3. Network mechanisms of responsiveness to continuous theta-burst stimulation ... 33
CHAPTER 3 DISCUSSION AND CONCLUSIONS ... 34
1. Imaginary coherence and functional networks ... 34
2. Alpha Rhythm Coherence
... 36
2.1. Electrical signature of behavior
... 36
Why alpha?
... 37
Other frequency bands ... 39
2.2. Alpha-band coherence and disease ... 39
3. Brain plasticity ... 43
3.1. Interhemispheric Competition
... 43
3.2. Adaptive Mechanisms
... 45
4. Functional network stimulation
... 47
5. Clinical Significance ... 50
6. Conclusions ... 51
7. Future Perspectives ... 52
CHAPTER 4 REFERENCES
... 53
Appendix A ... 70
Appendix B ... 89
Appendix C... 98
7
TABLE OF ILLUSTRATIONS
Figure 1 ... 35
Figure 2
... 37
Figure 3
... 41
Figure 4
... 42
Figure 5 ... 48
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LIST OF ABBREVIATIONS
BOLD cTBS DCM DMN DTI EEG ERD ERS FC fMRI GC IC LP MEP MPC PCC PET RSN rTMS TMS TPJ vmPFC
Blood oxygen level dependent Continuous Theta-Burst Stimulation Dynamic Causal Modelling
Default mode network Diffusion Tensor Imagery Electroencephalography
Event-related desynchronization Event-related synchronizationFunctional Connectivity
Functional Magnet Resonance Imagery Granger Causality
Imaginary Coherence Lateral Parietal Cortex Motor evoked potential Medial Prefrontal Cortex Posterior Cingulate
Positron-Emission Tomography Resting State Network
Repetitive Transcranial Magnet Stimulation Transcranial Magnet Stimulation
Temporo-parietal Junction
Ventro-medial Prefrontal Cortex
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CHAPTER 1 INTRODUCTION
1. Neural Networks
For decades, the premise that cognitive functions can be mapped onto locally segregated brain regions has been commonplace in neuroscience. The neural basis of human behavior, including perception, cognition and emotion, was widely assessed by functional brain imaging (Raichle &
Snyder, 2007). The relationship between the brain and behavior was analyzed by measuring task- evoked changes in brain activities. These studies focused on brain regions which displayed task- related increases in neural activity. The activity of interest was traditionally induced by an experimental task, compared to a baseline state, typically rest or a control task with reduced cognitive demand (Greicius et al., 2003). However, the observation that the resting brain displays spontaneous intrinsic fluctuations puts in question the assumption that the resting-state can be used as a silent baseline (Gusnard & Raichle, 2001). In addition, the task-inducing approach neglects the fact that even highly specialized brain regions never work in isolation but are organized within networks. In fact, groups of regions were found to display synchronous activations during task performance. Moreover, the same patterns of synchronization across regions were observed outside of any activation task, meaning that regions belonging to network maintain their interactions even at rest. The dynamics of the neural network can therefore be studied in a resting brain and analyzed by quantifying synchrony or coherence between its functional nodes.
Although the main interest of this thesis is connectivity analysis of resting brain, it is important to mention that networks can be likewise studied with task-evoked designs. To this end, the dynamic network modelling like e.g. dynamic causal modelling (DCM) is particularly convenient. The aim of this method is to estimate, and to make inferences about the coupling among brain areas and how that coupling is influenced by changes in experimental context (Friston et al., 2003). The dynamic network approach is fundamentally different from the traditional activation studies since it not only localizes task-evoked activity, but seeks to reconstruct changes and relationships in neuronal dynamics induced by the experimental input. When juxtaposed against resting-state connectivity approach, network analysis in a task-inducing setting enables investigation of neural dynamics that are distributed throughout a system of connected anatomical nodes in response to a specific stimulation. DCM and some other common network models are discussed at greater length in the separate chapter 2.3.
„Network Modelling‟ below.
This chapter comprises two sections. The first section gives a brief overview of the literature on resting state functional networks. In the second section, the concept of default mode network (DMN) is briefly introduced.
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1.1. Resting State Networks (RSNs)
Despite the fact that intrinsic, resting-state brain activity was reported already in the first EEG recording in humans by Hans Berger (1929) (Michel & Murray, 2012), it was only when physiologically meaningful resting-state fluctuations were described in fMRI that it attracted a lot of attention of the scientific community.
The first observations of the presence of coherent correlated BOLD fluctuations were published by Biswal et al. (1995) who observed that spontaneous slow signal fluctuations were highly correlated between motor regions (Biswal et al., 1995) but not between motor areas and non-motor areas. In the following years, these findings were confirmed by other studies showing that it is possible to distinguish several different groups of brain regions displaying simultaneous synchronous signal fluctuations at rest (Lowe et al., 1998; Damoiseaux et al., 2006). These networks were found to have a similar topography as the patterns of activation previously found to underlie a range of cognitive tasks (Cordes et al., 2000; Greicius et al., 2003; Vincent et al., 2007). In addition, the strength of coupling in a given network was reported to be related linearly to behavioral performance in tasks relying on this network (Wang et al., 2010; Koyama et al., 2011). These groups of regions were called resting state networks (RSNs) (Fox et al., 2005; Damoiseaux et al., 2006).
RSNs can be described as collections of brain regions that display synchronous patterns of activity at rest. The spatial patterns of these networks are highly reproducible across subjects and recording sessions (Damoiseaux et al., 2006). They are shaped by anatomical connectivity but display additional variation over time (Greicius et al., 2009; Honey et al., 2009; Teipel et al., 2010). In addition to these synchronous fluctuations, spontaneous activity between different networks can be anti-correlated (Fox et al., 2005; Fransson, 2005) thus suggesting a functional segregation between them. In other words, neuro-anatomical networks are organized through both correlated spontaneous fluctuations within a network and anti-correlated activities between networks (Fox et al., 2005). The exact mechanisms of such coordinated interplay between them are yet to be elucidated.
In a PET study, Raichle and colleagues (Raichle et al., 2001) demonstrated that the brain maintains a sustained level of brain activity which can be locally suspended or increased depending on current task demands. Spontaneous activity in RSN seems to consume a disproportionally high amount of energy (Raichle & Mintun, 2006). Indeed, Sokoloff et al. (1955) demonstrated that cognitive activity is associated with only small deviations (less than 5%) from the brain‟s resting metabolic activity level (Sokoloff et al., 1955). Thus, brain activity is not primarily triggered by a task, but instead is highly intrinsic, changing only slightly in response to stimuli (Gusnard & Raichle, 2001).
The existence of intrinsic brain networks, observed for the first time in fMRI, was complemented by electrophysiological studies suggesting that brain networks are dynamic structures evolving on multiple temporal and spatial scales (Laufs et al., 2003; de Pasquale et al., 2010). For instance, Mantini et al. (2007) found correlations between six resting state fMRI networks and the EEG power variations of traditional electrophysiological rhythms of human brain activity (Mantini et al., 2007).
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Other comparative studies using EEG and MEG reported similar correspondence between the fMRI RSNs and electrophysiological signal (Laufs et al., 2003; de Pasquale et al., 2010). By using inverse solution and independent component analysis (ICA), Brooks et al. (2011) could reconstruct 8 RSNs whose spatial topography strikingly corresponded to the haemodynamic RSNs described previously (Brookes et al., 2011). The attempts to identify an electrophysiological signature of fMRI networks found the closest spatial match with slow cortical potentials and beta-/gamma power fluctuations (He et al., 2008; Brookes et al., 2011). Considered together, the data confirmed the neuronal nature of the fMRI signal and showed that EEG dynamics at rest hold information about the functional state of the brain.fMRI resting state networks seem to be relatively stable since they strongly overlap with connectional anatomy (Greicius et al., 2003; Fox et al., 2005; Damoiseaux et al., 2006). It appears that they are only weakly modified by transitive states such as sleep or anesthesia (Vincent et al., 2007; Greicius et al., 2008). EEG and MEG RSNs, on the other hand, are more transient and are potentially more susceptible to behavioral diversification. Electrical networks are flexible entities responding with full engagement to environmental stimuli or to cognitive states (de Pasquale et al., 2010). Large-scale networks could be segregated by means of EEG microstates, proposed units of the human brain electrical activity otherwise known as „atoms of thoughts‟ (Lehmann et al., 1998). Microstate sequences reveal scale-free, self-similar dynamics which seem to be organized in a similar topography as fMRI RSNs (Britz et al., 2010). Consequently, they might represent the basis for the rapid reorganization and adaptation of the functional networks in response to the high cognitive demands of environment (Van de Ville et al., 2010). Lewis et al. (2012) reported that after a loss of consciousness, local neuronal networks could be spatially reproduced but in slower frequencies (Lewis et al., 2012). Therefore, electrophysiological network organization is characterized by the great ability to respond rapidly and to tune in to environmental demands. Thus, it might be suggested that such functional networks contain important information on the encoding of individual cognition and the information processing.
Overall, functional networks can be observed in a resting state, not only in fMRI data, but also in direct recordings of neural oscillations assessed with EEG/MEG. Both signals seem to converge to a great extent in their spatial configuration within identified networks and both seem to contain information on the functional state of the brain. In comparison to fMRI, EEG and MEG signals provide a richer frequency pattern, and therefore can be a source of important information on neuronal dynamics and brain functioning.
1.2. Default Mode Network
The term „default mode network‟ (DMN) refers to a particular RSN. It is comprised of the following regions: the posterior cingulate (PCC), the precuneus, the medial prefrontal cortex (MPF) and the
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lateral parietal cortex (LP) (Gusnard et al., 2001; Fox & Raichle, 2007). This structure could be reproduced in other studies using different techniques (fMRI, PET, EEG/MEG) and different methodology (independent component analysis (ICA), seed-based analyses), confirming altogether that DMN is a stable and replicable formation (Gusnard et al., 2001; Greicius et al., 2003; Fox et al., 2005). However, the critical evidence that the observed activity has a neuronal nature has originated from the studies combining functional MRI and EEG/MEG. A number of studies have reported significant correlations between the haemodynamic activities in DMN regions and electrophysiological measures of neuronal activity, affirming that they are fundamentally a neuronal, not only a vascular, phenomenon (Hlinka et al., 2010).Despite extensive research, the functional role of DMN continues to be somewhat ambiguous. It was proposed that intrinsic activity in these regions reflects inner attention (Zhang & Raichle, 2010), internally-generated mental activity (Gusnard et al., 2001) or that the regions are involved in episodic memory (Greicius et al., 2004). Disturbances in DMN were reported in a number of pathological states, for example amongst patients with depression, schizophrenia and Alzheimer‟s disease.
Moreover, its activity was shown to be linearly related to the severity of the disorder (Zhang & Raichle, 2010). However, spontaneous BOLD correlation patterns corresponding to DMN were persistent across different resting states, including sleep (Horovitz et al., 2008) and light sedation (Greicius et al., 2008; Lewis et al., 2012). It was discovered that DMN could be even reproduced in anesthetized monkeys (Vincent et al., 2007). Therefore, spontaneous activation patterns seem to be an intrinsic property of the brain, rather than a result of mental activity.
Individual resting state seems to affect general cognitive functioning. The interpersonal disparity in DMN activation observed in healthy subjects was shown to be related to the differences in activation patterns during cognitive activity. Spontaneous BOLD fluctuations found at rest do not disappear during task performance. Their individual states contribute to inter-trial variability in BOLD responses and in behavior (Fox & Raichle, 2007).
Taken together, the discovery of RSNs and DMN set a new milestone in brain research, and generated numerous studies into the actual activity of the resting brain. The observation of the intrinsic resting state activity and its networks‟ organization has opened up new possibilities for brain investigation.
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2. Functional Connectivity (FC)
Many of the existing neuroimaging techniques permit an assessment of the brain‟s network organization. Connectivity can be studied with positron-emission tomography (PET), functional MRI, EEG or MEG. Each of these techniques provide information on different spatial and temporal scales (Koenig et al., 2005) and describe different aspects of network communication.
Unlike structural connectivity which describes anatomical fibers, FC reflects the neural communication between regions that can occur at a distance, also in the absence of direct physical bonds1.
Animal studies have shown that interregional neuronal communication is associated with a synchronization of oscillations in distinct groups of neural assemblies (Gray et al., 1989; Engel et al., 1992). Therefore, FC can be quantified by measures of similarity and synchronization between activities in different brain regions. Two or more regions are supposed to be functionally connected if they show similar and synchronous oscillations (Varela et al., 2001). FC can be quantified with several different algorithms; their main principle mostly involves the search for covariance among measurements of neuronal activity (Friston, 2000).
FC can be applied to both resting-state and task-state studies (Zhang & Raichle, 2010). The crucial advantage of the network approach over task-evoked imaging remains the fact that the recordings can be done at rest, or even in coma or under general anesthesia (Vincent et al., 2007; Greicius et al., 2008). Connectivity can, as a result, be assessed without the necessity of task performances that depend on the collaboration of the participants. Another advantage of the resting state FC is the ability to screen multiple functional networks simultaneously, which dramatically reduces the acquisition time needed for an assessment of the patients‟ functional network status.
This chapter outlines the most common techniques used for the network analyses, fMRI and EEG/MEG. The main focus, however, will be on EEG since this technique was a primary aspect of this project. The concluding paragraph features major approaches currently used for the network modelling.
1 Even though FC is known to be restricted by the anatomical structures, the exact correspondence between anatomic and functional connectivity is still under research (Sporns, O., Chialvo, D.R., Kaiser, M. & Hilgetag, C.C.
(2004) Organization, development and function of complex brain networks. Trends Cogn Sci, 8, 418-425.
Honey, C.J., Sporns, O., Cammoun, L., Gigandet, X., Thiran, J.P., Meuli, R. & Hagmann, P. (2009) Predicting human resting-state functional connectivity from structural connectivity. Proc Natl Acad Sci U S A, 106, 2035- 2040.)
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2.1. fMRI
The greatest asset of fMRI is its high spatial resolution (in mm) allowing clear visualization of in depth brain structures. The signal used in this technique is based on changes in local blood circulation and in oxygen metabolism. The main assumption is that blood flow changes can adjust glucose and oxygen delivery to the variable energy demands of the brain. The local neuronal activity can therefore be inferred from the change in observed haemodynamic activity (Jäncke, 2005).
Unlike in traditional imaging studies, fMRI FC does not rely on a comparison of experimental and baseline conditions. Instead, it detects interregional temporal correlations of BOLD signal fluctuations.
Regions with high degree of temporal correlation in BOLD signal fluctuations are presumed to constitute a neural network (Greicius et al., 2003). A consistent finding is that regions with similar functionality (regions that are similarly modulated by various task paradigms) tend to be correlated in their spontaneous BOLD activity. Moreover, regions with apparently opposing functionality have been found to be negatively correlated or anti-correlated in their spontaneous fluctuations (e.g. DMN vs.
functional networks) (Fox et al., 2005).
Different methods of connectivity analyses have been proposed. The two approaches discussed below can be applied to both fMRI and EEG/MEG data, but they are mainly used for connectivity analyses in fMRI. These two approaches will be further discussed in this section.
In the seed-based correlation approach, a „seed‟ region of interest (ROI) is defined, and its BOLD signal time course is then extracted from the data. Subsequently, time courses are extracted from every voxel in the brain and correlations are computed utilizing data that records the seed‟s time course and each voxel‟s time course, resulting in a correlation map. The time courses from many seed regions are obtained and a correlation matrix is constructed. A clustering algorithm is then used to determine which regions are most closely related and can be grouped in a network (Zhang & Raichle, 2010).
Another popular technique to analyze connectivity data is known as independent component analysis (ICA). In contrast to the seed-based method, it does not require a priori definition of seed regions. An ICA algorithm decomposes the BOLD data set into components that, in a statistical sense, are maximally independent. The challenge of using ICA is that it requires prior definition of the number of components to produce. In addition, the user must determine which components reflect noise and which neuro-anatomical signal they must use. The result of ICA is a set of spatially unique maps, with different independent components constituting different RSNs (Zhang & Raichle, 2010).
Despite the advances in previous years in the fMRI field, it was argued that it is incorrect to use it as direct evidence of underlying neural activity because of the hemodynamic nature of the fMRI‟s signal.
Instead, the fMRI data should provide complementary information on the activity of underlying neuronal assembly (Raichle & Mintun, 2006).
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This is the reasoning behind scientists‟ active efforts to ascertain electrical correlates between the fMRI BOLD and EEG signals. The conclusion from the comparative studies so far is that fMRI RSNs are, at best, correlated with local field potentials (LFPs) in the range of the slow frequencies (delta and infra-slow fluctuations) (He et al., 2008) and beta-/gamma power (Brookes et al., 2011). Future studies should improve our understanding of the complex relationship between fMRI and EEG signals.2.2. EEG
Synchronous neural oscillations evolve continuously on the timescale of milliseconds (Buzsaki &
Draguhn, 2004; Michel & Murray, 2012). EEG is an appropriate technique to characterize the spatio- temporal dynamics of these neural processes since it enables recording of numerous electrophysiological sources with high temporal resolution. From such recordings, it is then possible to measure functional interactions between neural assemblies (Koenig et al., 2005). Similar to the measures of fMRI FC, the assumption here is that the simultaneous activity of anatomically distinct brain regions working together can be represented by a statistical dependency between signals originating from these regions (Nolte et al., 2004).
EEG is able to measure direct activity of the underlying neural tissue. More precisely, scalp EEG records changes of polarization in underlying patches of grey matter. Synchronous synaptic inputs polarize the neural activity either in an oscillatory fashion or as transient evoked activity. The cortex is organized in cortical columns with different layers and with large pyramidal cells aligned perpendicularly to the cortical surface. This organization is characterized by synaptic connections between neurons of diverse laminae and of different structures. The local fields detected by surface EEG reflect therefore not only the activity of uppermost parts of the apical dendrits, but also activities of deeper layers and structures (Brandeis, 2009). This makes it particularly difficult to relate the scalp potential to a specific distribution of the sources. The real activity of the signal sources cannot be concluded directly from the sensor recordings due to ambiguity of the inverse problem (Michel et al., 2004). By making a priori assumptions, relatively accurate localizations can be achieved. The more appropriate these assumptions are, the more trustworthy the source estimations (Michel et al., 2004).
Secondly, the value of the FC can be distorted by volume conduction and crosstalk among voxels which arise from spatial leakage of the inverse solution (Guggisberg et al., 2011).
Both of these problems have recently received a great deal of attention (Nolte et al., 2004; Sekihara et al., 2004; Dalal et al., 2008; Guggisberg et al., 2011). Resolutions such as the adaptive inverse solution (beamformer) and imaginary coherence (IC) were proposed to reduce this problem in connectivity analyses.
1. Coherent sources were shown to be localized with reasonable accuracy from EEG data with modern inverse solutions such as beamformers (Guggisberg et al., 2011) and weighted minimum norm estimates (Ghuman et al., 2011). The beamformer algorithm consists of a creation of an adaptive spatial filter that depends on the signal‟s characteristics using the
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temporal covariance of the EEG data. This enables more precise and focal source localization (Sekihara et al., 2004; Dalal et al., 2008; Guggisberg et al., 2008). The beamformer approaches aim to estimate the activity of a source by minimizing the interference of other surrounding simultaneously active sources.2. Spatial leakage is an artifact arising from the fact that neural activity reconstructed at a given location is always contaminated by the activity from the adjacent regions. This artifact can result in biased FC estimations. Beamformers reduce this artifact by producing more focal source reconstructions, but they cannot eliminate it completely (Guggisberg, 2012).
IC is one of many existing measures of FC. Unlike the more common magnitude squared coherence, IC ignores spectral similarities between two signals that occur with zero time delay (Nolte et al., 2004). This has the advantage that it is robust to volume conduction2 and spatial leakage because these artificial similarities occur with zero time-delay and are therefore removed. This enables us to use IC for the localization of cortico-cortical interactions (Guggisberg et al., 2008; Guggisberg et al., 2011). On the other hand, IC ignores a portion of true interactions which could lead to less reliable localizations of interactions than those of other FC estimates. However, it is presumed that cortico-cortical coherence will rarely occur at zero lag due to the amount of time needed for neural transmission. By disregarding information of the real component of coherence (similarities between two signals with zero time delay), we prefer to „stay on the safe side‟ by only giving consideration to connectivity that has been unaffected by spatial leakage. (Nolte et al., 2004; Guggisberg et al., 2008;
Guggisberg et al., 2011).
There are many other FC metrics measuring different aspects of synchrony. For instance, one can quantify phase-locking of two signals while ignoring their amplitudes (Lachaux et al., 1999; Stam et al., 2006). Alternatively, it is possible to correlate amplitudes or amplitude envelopes between signals.
Global field synchronization (GFS) is another connectivity method that permits to measure a global FC as a function of EEG frequency bands (Koenig et al., 2001). There are also information theoretical approaches for quantifying functional interactions such as mutual information. Many of these metrics necessitate supplementary procedures to control for spatial leakage. One of commonly used techniques for this purpose is a subtraction of noise FC (simulated e.g. with Monte Carlo method) from the original data FC (Ghuman et al., 2011).
2 Artificially high coherence between two regions can be observed even in absence of actual connectivity of underlying cortices. This is due to the fact that activity at any cortical area is observed instantaneously (zero- lag) by all scalp electrodes (Pascual-Marqui, R.D., Lehmann, D., Koukkou, M., Kochi, K., Anderer, P., Saletu, B., Tanaka, H., Hirata, K., John, E.R., Prichep, L., Biscay-Lirio, R. & Kinoshita, T. (2011) Assessing interactions in the brain with exact low-resolution electromagnetic tomography. Philos Trans A Math Phys Eng Sci, 369, 3768- 3784.)
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As a direct consequence, EEG data can be used for the analyses of coherent functional networks. In comparison to fMRI, EEG has the disadvantage of lower spatial resolution, but the advantage that it can assess actual neural oscillations at different spectral frequencies. Furthermore, EEG recordings are less expensive and more widely available than fMRI or MEG recordings. Unlike MEG, EEG may also be recorded alongside fMRI (Britz et al., 2010).In Dubovik et al. (2012), Dubovik et al. (2013) and Rizk et al. (submitted) we used EEG to assess neural brain activity of participants. We took advantage of the latest advances in the EEG field and in its network approach and implemented these elements in our research. In order to estimate neural resting-state oscillations in the grey matter from surface recordings, we used the beamformer inverse solution.
To minimize the distorting effect of the artifacts arising from the use of inverse solution (spatial leakage), we used the IC (Nolte et al., 2004) as an index of FC.
NUTMEG was used for assessment of networks. This is a freely available software written as a part of open-source toolbox for Matlab (NUTMEG: http://nutmeg.berkley.edu/) (Dalal et al., 2011).
2.3. Network modelling
Independently from the technique used (EEG/MEG, fMRI, DTI), the inferred communication between brain regions can be graphically represented as a set of connections constituting networks. Usually, schematic illustration of a network implies nodes and connections between them (links, edges), altogether creating a complex functional or structural system. We can describe this system by characterizing different aspects like, e.g., its size, lengths and number of its connections, the architecture of its connections (topology) or its dynamics (the development of interactions between nodes over time) (Sporns et al., 2004). Accordingly, a range of theoretical models have been proposed attempting to reconstruct real-life neural network approximations through linear or non-linear statistical dependencies (functional connectivity) (Zhou et al., 2009a) or causal interactions (effective connectivity) (Friston et al., 2003).
Graph Theoretical Approach
Graph theory describes the topology of complex networks as a group of nodes or vertices and the edges between them (Bullmore & Sporns, 2009). Within this approach, two global properties of networks can be studied: the level of their segregation (organization in groups or clusters) and their integration (global efficiency). Diverse parameters of node centrality assess the importance of individual nodes for the communication within a network. To name only few of numerous existing measures, degree of centrality is a common measure of the number of connections linking a node to
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the rest of the network; closeness centrality describes the average shortest path length from one node to all other nodes in the network; average path length across nodes indicates the expected distance.(Rubinov & Sporns, 2010). Depending on the connectivity measure (e.g. coherence, Granger causality) the final graphs can be represented as undirected, directed or weighted graphs.
Each step of graph analysis entails choices that can have an impact on the final results and must be carefully informed by the experimental question (Bullmore & Sporns, 2009). First of all, the reconstruction of nodes in individual brain networks is highly determined by the data set and by a choice of brain mapping method or atlas. For example, in EEG or MEG studies nodes can be defined either on the sensor level (as single electrodes) or on the source level (voxels). Second, the choice of connectivity metric is important. Only effective connectivity measures will permit estimation of the graph directionality. Finally, different parameters of network can be calculated. They must be subsequently statistically tested against the distribution of the same parameters in random networks containing the same number of nodes and connections (Bullmore & Sporns, 2009).
Small-world Network
The small-world model represents a global concept useful for studying complex natural and artificial networks (Bassett & Bullmore, 2006; Sporns & Honey, 2006). It has been analyzed extensively in social and life sciences and seems to be highly widespread in natural systems. Indeed, small-world networks have provided important insights into cellular metabolism and transcriptional regulation (Sporns and Honey, 2006); small-world organization has been found in structural neural networks in animal models (Sporns & Kotter, 2004). Structural and functional brain networks in humans were also shown to have small-world attributes (Sporns et al., 2004; Bassett & Bullmore, 2006; Honey et al., 2009). Characteristic for such networks is an optimal balance of integrated and segregated information processing (Sporns & Honey, 2006). Furthermore, they are generally associated with global and local parallel information processing and low wiring costs (Bassett & Bullmore, 2006).
In context of the graph theory, small-world network can be seen as a special case of highly efficient network organization.
Granger Causality
Activity in a brain region can directly or indirectly exert influence on the activity of another brain region.
Such causal interactions are described by the concept of effective connectivity (Friston & Dolan, 2010). Granger causality (GC) analysis is a statistical approach introduced first in econometrics. It determines causal dependencies by measuring whether one time series can be used to forecast another. In other words, GC tries to establish a statistical dependence between a local measurement of neural activity and measurement of activity elsewhere in the past (Friston et al., 2013). Hence, this approach can be used to extract information about the dynamics and directionality of the signal. GC can be used in task-evoked and resting state studies, revealing the directionality of the information
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flow in a stimulated (Chen et al., 2009; Zhou et al., 2009b) or unstimulated setting (Stevens et al., 2009; Liao et al., 2010).This approach has provided useful descriptions of directed FC in many electrophysiological studies (Bernasconi & Konig, 1999; Brovelli et al., 2004). It can be applied to standard EEG and MEG signals, either at the source (Barrett et al., 2012) or at the sensor level (Brovelli et al., 2004). The application of GC to fMRI data is more controversial due to the slow dynamics and regional variability of the haemodynamic response. However, careful consideration of methodological issues has permitted some valuable applications (Wen et al., 2012).
Dynamic Causal Modelling
Dynamic casual modelling (DCM) is probably the most popular approach for the assessment of effective connectivity. It incorporates the basic assumption that neural activity propagates through brain networks as in an input-state-output system with causal interactions mediating unobservable (hidden) neuronal dynamics (Friston et al., 2013). The modelling of both, neuronal and observed input- output dependencies is then reconciled in a generative model which can be optimized post hoc according to the data and studied question (Friston & Dolan, 2010). The neuronal models are formulated so as to explain how data are caused in terms of a network of distributed sources. These sources are assumed to communicate with each other through connections and influence the dynamics of hidden states (Friston & Dolan, 2010). In this way a number of models can be created.
Subsequently each model is tested on how well it explains the data. Finally, the models are compared against each other. Model comparison permits to explore a wide diversity of explanatory designs and to find optimal architectures or networks. Having selected the best model (or subset of models), it is possible then to get access to the coupling parameters defining the network (Friston et al., 2003).
Even though DCM is best known through its application to fMRI, recent advances have focused on modeling of electrophysiological dynamics. Different data (e.g. ERPs or induced responses) can be modelled by using DCM (Kiebel et al., 2006), and it has even been applied to resting state recordings (Friston et al., 2011) demonstrating its universality.
In contrast to GC, which models dependency among observed responses, DCM models couplings among the hidden states generating observations. Despite this fundamental difference, the two approaches can be viewed as complementary (Friston et al., 2013).
Network modeling is a useful and efficient tool to study neural networks. It yields informative, intuitive and highly visually compelling results. A wealth of studies in healthy and diseased participants as well as studies combining different models reported meaningful and important findings on the network structure and information flow. In spite of these advantages, it is important to be aware that the choice and application of the mathematical models to the real data represent a simplified approximation and is highly dependent on a priori assumptions. Therefore, the interpretation of the results should be made only with necessary carefulness.
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None of the described above models was the subject of this thesis project. At the current stage of knowledge, we preferred to stick to exploratory approach. We therefore focused on simple measures which were already shown to be behaviorally meaningful. Functional and effective network modelling could be the next step in our future research.21
3. Neuroplasticity
The adult human brain is a plastic organ capable of adaptation and reorganization when faced with learning, injury and recovery (Kantak et al., 2012). This ability of the brain, to adjust to a changing environment, is called neuroplasticity. Neuroplasticity is an intrinsic property of the nervous system that continues throughout a human‟s life span and plays an important role in maturity, development and the acquisition of new skills (Nudo, 2006).
Much of our knowledge on neuroplasticity comes from animal research. Animal studies have provided insights into cellular and molecular events underlying spontaneous returns of behavior which may occur after a stroke (Nudo, 2007). A unilateral infarct is associated with a number of growth-related, restructuring events, which are sometimes observed bilaterally. These events include structural changes in axons, dendrites, and synapses and increased activation and migration of endogenous neural cells (Wieloch & Nikolich, 2006; Nudo, 2007). Many of these events can be found in the peri- infarct area, whilst some others, in multiple brain regions including homologues in the contra-lesional hemisphere and remote regions are generally connected to the site of injury (Carmichael, 2006).
Cortical representation maps can expand after an injury, in response to behavioral recovery and training (Frost et al., 2003). These principles of plasticity have been, in the majority of cases, confirmed in humans (Nudo, 2006; Wieloch & Nikolich, 2006; Cramer, 2008).
Our knowledge on human brain plasticity has witnessed a sizeable expansion over the last few decades, particularly with the help of the development of neuroimaging techniques (Sharma & Cohen, 2010). Studies on healthy volunteers showed that during the practice of a particular task, whilst subjects learn the new skill, the learning process is associated with dynamic changes in neural networks. Studies on patients following brain damage and subsequent recovery revealed that cortical areas remote from the structural damage can reorganize to facilitate recovery and learning (Ward &
Cohen, 2004; Cramer, 2008; Grefkes & Fink, 2011).
These findings emphasize the importance of investigating the mechanisms of neuroplasticity in neurological disorders. Even years after injury, the human brain still retains the capacity to reorganize itself in response to training and learning (Sharma & Cohen, 2012). Understanding and influencing neuroplasticity is therefore crucial in order to find better therapies and to improve training for individual patients.
The following chapter delineates the main principles of neural plasticity described in the literature.
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3.1. fMRI studies
Task-based functional neuroimaging contributed greatly to current knowledge on brain plasticity in stroke patients. Ischemic stroke is often associated with loss of cognitive and/or motor function. In some patients, these deficits recover spontaneously within three months following the stroke‟s onset.
(Ward et al., 2003b; Corbetta et al., 2005). This makes stroke convenient for studying mechanisms and patterns of functional reorganization in several functional domains such as motor function (Loubinoux et al., 2003; Ward & Cohen, 2004), language (Buckner et al., 1996; Saur et al., 2006; Saur et al., 2010; Meinzer et al., 2011), and attention (Corbetta et al., 1998; Corbetta et al., 2005).
Three main mechanisms of neuroplasticity were highlighted (Cramer, 2008):
(1) somatotopic shifts within intact cortical regions;
(2) increased activity in brain regions distant from, but connected to the stroke zone;
(3) increased activity in the contralesional hemisphere.
Unaffected brain tissue surrounding the lesion in some cases seems to be able to adapt to the loss of function. Such somatotopic shifts in representational maps surrounding the infarcted zone were observed both in animal and in human studies. Specifically, motor and somatosensory cortices were found to display structural and functional reorganization as a result of training and recovery. Neural growth and restructuring or remapping (meaning accommodation of the lost function by adjacent survived neural cells) seem to be important biological mechanisms of this compensational mechanism (Frost et al., 2003; Ward, 2005; Nudo, 2006; Schaechter et al., 2006).
The activation of more extensive cortical areas involving peri-lesional regions was consistently reported in stroke patients (Feydy et al., 2002; Ward et al., 2003b; Saur et al., 2006). This widespread activation was initially thought to reflect the recruitment of adjacent cortical regions to compensate for the deficit. However, several studies have reported that persistent over-activation is negatively associated with function and recovery (O'Shea et al., 2007; Bestmann et al., 2010; Riecker et al., 2010). Conversely, the best recovery of affected behavior was associated with a normalization of activity in the affected areas (Pizzamiglio et al., 1998; Warburton et al., 1999; Ward et al., 2003a;
Zemke et al., 2003). Such plastic changes seem therefore to evolve over time. Within a few weeks following brain damage, functions of the lesioned tissue are reduced and adjacent areas are extensively recruited during task performance, which is expressed by the increased activation. Over the course of recovery (up to three months after stroke), the affected regions resume their functioning and recover a normal activation pattern (Feydy et al., 2002; Dong et al., 2007; Marshall et al., 2009).
This mechanism has been observed in motor (Feydy et al., 2002), language function (Cao et al., 1999;
Warburton et al., 1999) and in spatial attention (Corbetta et al., 2005).
Another type of brain responses that might contribute to brain recovery includes a shift of activation toward the contralesional hemisphere (Raboyeau et al., 2008). Similarly to increased activations in
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regions surrounding lesion, the best behavioral outcomes several months after stroke have been associated with the greatest return of brain function toward the normal state of activation (Warburton et al., 1999; Zemke et al., 2003; Corbetta et al., 2005; Dong et al., 2007).The introduction of the network approach generated a wealth of clinical research that revealed new aspects of neuroplasticity. In light of the network approach, the lesion impairs behavioral functions because it disrupts communication in brain networks that are relatively specific to particular behavioral domains, yet spatially distributed in the brain. The degree of initial disorganization and the dynamics of reorganization of these functional brain networks may determine the amount of acute impairment and then the level of recovery, respectively (Carter et al., 2012). Evidence of such network dysfunctions and reorganizations come, once again, from studies in patients with focal lesions such as stroke. For instance, preserved connectivity between regions of the language network was found to be indicative of better performance amongst aphasic stroke patients in language tasks (Warren et al., 2009).
Recovery from visuo-spatial neglect was shown to be correlated with a restitution of inter-hemispheric FC between the left and right dorsal parietal cortices (He et al., 2007). Hence, functional MRI data across different functional systems suggest that functional outcome after a stroke depends on the connectivity within the corresponding functional network.
Lesions of neural tissue cause disturbances in distributed spontaneous brain activity. This disturbance has an impact on the way in which the networks are being recruited during active behavior and can explain why the reorganization of networks can be observed not only during cognitive activity, but also at rest (Carter et al., 2012). Indeed, Carter et al. 2010 could predict the motor performance of paretic stroke patients from the coherence of spontaneous BOLD fluctuations between left and right motor cortices (Carter et al., 2010). Resting state connectivity analyses were also actively used to assess behavioral deficits in other disorders. Alzheimer disease patients were found to have disrupted connectivity between several frontal and parietal areas and the hippocampus, which is known to be implicated in memory (Gusnard et al., 2001; Greicius et al., 2004; Zhang & Raichle, 2010). The severity of disconnection was associated with the disease progression, meaning that the more the hippocampus was disconnected, the worse patients performed in memory tests (Zhang & Raichle, 2010). Thus, resting state brain connectivity contains important information on individual functional state. This makes it a useful tool for assessment of behavior that can be applied even in severely affected patients. There is hope that the identification of disease-specific group differences will allow us to gain a better understanding of the functional abnormalities underlying different diseases (Fox &
Greicius, 2010).
All in all, fMRI activation and connectivity studies revealed several important mechanisms of neuroplasticity related to brain dysfunctions and to recovery processes. The resting state approach has several advantages in clinical research as compared to traditional activation studies, as it can provide access to whole brain analysis even in patients with severe deficits. Connectivity analysis is a promising tool that might allow understanding on whether the regional interaction is disrupted, and
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what patterns can distinguish between a positive versus a negative outcome. Therefore, multi-network assessment may be key to the understanding and prognosis of recovery (Carter et al., 2012).25
3.2. EEG and MEG studies
Neural reorganization is multifaceted and is not limited to changes in spatial distribution that can be best studied with fMRI. Considering the dynamic nature of neural oscillations, it is probable that EEG and MEG recordings contain important information on plasticity that cannot be captured with fMRI or PET (Westlake et al., 2012).
Many findings support the idea that neuronal assemblies can synchronize and/or desynchronize within specific frequencies and within a specific spatiotemporal patterns in response to sensory, motor and cognitive tasks (Klimesch, 1996; Rodriguez et al., 1999; Neuper & Pfurtscheller, 2001; Buzsaki &
Draguhn, 2004). It was proposed that oscillatory synchrony is one of the mechanisms that could explain the existence of transient neuronal interactions. This transient, synchronous activity between multiple brain regions represents the dynamic manifestations of neurocognitive networks for specific cognitive functions. The ability of neuronal assemblies to synchronize with each other is ostensibly dependent on the coupling strength and distribution of natural frequencies (Buzsaki & Draguhn, 2004).
The synchrony observed in whole-scalp EEG suggests that similar mechanisms may occur at a global level by connecting different brain regions through synchronous, rhythmic discharges and forming transient, functional, neurocognitive networks (Koenig et al., 2005). Moreover, we know today that functional electrophysiological networks can be observed even at rest (Brookes et al., 2011).
Investigation of electrophysiological networks allowed for more informative analyses of neural plasticity than in traditional activation studies. Whereas previously a lot of EEG studies concentrated on task- evoked local signal analysis or analyses of power frequency in groups of patients, the connectivity approach made it possible to look into the integrity of functional connections in different electrophysiological rhythms. Indeed, patterns of disconnectivity were shown to contain important clinical information in neurologic and psychiatric diseases. For instance, in groups of Alzheimer‟s disease patients, reduction of FC was reported between close and distant EEG channels in alpha, beta, and gamma bands (Berendse et al., 2000; Babiloni et al., 2006) while coherence of slow frequencies was shown to be increased over temporo-parietal, temporo-central and frontal electrodes (Stam et al., 2006; Alonso et al., 2011). In patients with schizophrenia, deviations in sensor coherence between frontal and temporal lobes in beta-rhythm were reported (Strelets et al., 2002). Band-specific changes associated with brain disease and recovery were noted in a group of patients with acquired brain injuries (Castellanos et al., 2010). Gerloff (2006) reported that there was a reduced cortico- cortical connectivity in the lesioned hemisphere amongst stroke patients who had recovered from the chronic phase successfully after their stroke. At the same time, the contralateral non-affected hemisphere was found to have a relatively increased connectivity (Gerloff et al., 2006). In a group of tumor patients Bartolomei et al. (2006) observed that several patients had missing connections, which were found in broad band frequency analyses and specifically in the gamma band frequency (Bartolomei et al., 2006). Briefly, these results showed that connectivity is a useful technique by which one can investigate network changes related to disease and recovery. However, when connectivity is
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assessed at the sensor level, we cannot conclude exactly which regions are affected by the rapid field spread of sources to many EEG sensors.The availability of inverse solutions helped to overcome this problem. It became possible to estimate neural resting-state oscillations in the grey matter from surface recordings and to calculate the FC between actual brain areas (Gross et al., 2001; Guggisberg, 2012). By applying inverse solution to the MEG data it was shown for the first time that electrophysiological FC can be used for a pre-surgical evaluation of eloquent regions in proximity of the tumor (Guggisberg et al., 2008). Functionally critical brain areas were shown to interact with numerous other regions even when they were at rest.
Dysfunctional brain areas showed a decrease in resting state connectivity. Brain areas with low resting state connectivity in the alpha range (7-13 Hz) could be surgically resected at a low risk level for ensuing functional deficits, demonstrating that noninvasive means of measuring connectivity are capable of predicting the functionality of the brain tissue in patients with focal lesions. The comparison of these FC maps with invasive, intraoperative electrical stimulation (IES) confirmed an excellent negative predictive value of the FC maps, giving hope for the possibility of replacing invasive procedures in certain patients with brain tumors (Martino et al., 2011).
Cortical synchronicity may be affected in different ways by different disease processes and might therefore be useful for diagnostic and predictive purposes. Some of the attempts to identify disease- specific signatures of the resting state networks oscillations reported promising results (Georgopoulos et al., 2007). Nevertheless, several questions remain unanswered. It is, for example, still unclear whether the changes observed in patients represent adaptive versus maladaptive brain reorganization or a compensation mechanism of lost behavior. Furthermore, the precise relationship between network coherence and behavior in different oscillation frequencies is unknown.
In Dubovik et al. (2012), we addressed the relationship between FC and behavior in 20 stroke patients and 19 healthy controls. This study established the existence of a linear relationship between connectivity in alpha-band and standardized clinical scores of language, spatial attention and motor deficits (Dubovik et al., 2012). This finding was subsequently confirmed in healthy elderly subjects and in young adults (Dubovik et al., 2013; Rizk, accepted).
In Dubovik et al. (2013) we analyzed disease-specific changes in functional networks in patients suffering from mild Alzheimer‟s disease. We observed that adaptive network reorganization was associated with a partial preservation of cognitive functions (Dubovik et al., 2013).
3.3. TMS studies
The coupling within and between networks should be able to reconfigure dynamically in order to support ongoing processing demands. Transcranial magnetic stimulation (TMS) can be used to modulate cortical activity and can therefore be applied to explore electrical network dynamics (Eldaief et al., 2011). EEG is an ideal technique for the investigation of TMS‟ influences on networks. It enables
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the assessment of both spatial and spectral changes in network interactions, which is crucial for examining frequency-specific effects of TMS.TMS is a suitable tool to study changes within and across cortical networks. Repetitive TMS (rTMS) can induce transitory alterations in local cortical activity. Therefore, by stimulating a cortical target region it should be possible to stimulate its connections. Local stimulation to a network node is supposed to propagate transsynaptically to distant but interconnected nodes with high spatial specificity (Paus, 2005; Hampson & Hoffman, 2010a). Indeed, previous fMRI studies have shown that rTMS affects not only stimulated areas but also other nodes from the same functional network (Hubl et al., 2008; Grefkes & Fink, 2011). Hence, TMS can be used as a non-invasive approach with which to study human cerebral plasticity.
Different stimulation protocols have been proposed. Depending on the protocol, TMS can induce either an increase or a reduction of neural activity and a facilitation or suppression of corresponding behavior. Single-pulse paradigms were shown to induce a strong facilitation of spontaneous and visual-evoked spiking activity shortly after the TMS-pulse, which was followed by the subsequent suppression of activity (Moliadze et al., 2003). Another stimulation paradigm, paired-pulse TMS, involves the application of a conditioning stimulus pulse prior to the test stimulus delivered. It has been extensively studied in the motor cortex. The main principle of this stimulation is the alteration of the motor evoked potential (MEP) with the conditioning stimulus. This permits us to infer that there is a functional interaction between the target of the conditioning stimulus and the location of the test stimulus. Another important stimulation method is rTMS, which involves the delivery of trains of TMS pulses, to produce changes in cortical excitability that persist beyond the duration of stimulus. The exact mechanisms leading to alterations in excitability are unknown, but they are believed to involve processes similar to synaptic long-term potentiation and long-term-depression (Fitzgerald et al., 2003;
Thickbroom, 2007).
More recently, Huang et al. (2005) developed a patterned repetitive stimulation protocol to rapidly induce long-lasting changes in cortical plasticity. The classic theta-burst rTMS stimulation (TBS) paradigm consists of three pulses at 50 Hz, repeated every 200 msec (i.e. at 5 Hz). In the continuous protocol, a 40 second train of uninterrupted theta-burst stimulation is applied, resulting in a decrease in MEP amplitude of over 40%, with suppression persisting for as long as 60 min. In the intermittent theta-burst protocol, a 2 second train of theta-burst stimulation is repeated every 10 seconds. The MEP amplitude in the experiment of Huang et al. (2005) was increased by up to 75%, with the facilitation lasting for about 15-20 minutes (Huang et al., 2005; Nyffeler et al., 2006). EEG studies have suggested that theta-burst effects on evoked responses persisted for up to 90 minutes longer than for conventional rTMS protocols (Shafi et al., 2013).
In general, the effects of TBS on neuroplasticity were shown to depend on several factors including stimulation parameters: the strength of the magnetic flux, the shape of stimulation coil, intensity, inter- burst frequency, the frequency of pulses within each pulse (Goldsworthy et al., 2012); the distance and angle between the coil and the cortical surface; the region of stimulation, neural architecture of the stimulated neural tissue and its baseline activity (Nyffeler et al., 2008; Capotosto et al., 2012). Yet,
