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 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.
10
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).
11
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
12
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.