Spontaneous brain activity during wakeful rest is the summary of a dynamic mixture of brain states compatible with responsiveness, action planning and execution, the ability to
(re-)direct attention and the processing of information in higher-order cortices. This contrasts more homogeneous brain activity induced by a specific task set. In the wakeful state, the brain generates a large number of neural processes that interact as a complex regulatory network and which can be grouped into functional modules characterized by anatomical connectivity and co-varying levels of neural activity. The scope here is to briefly introduce work on spontaneous brain activity during wakeful rest, i.e. a task-free condition, rather than to present an exhaustive enumeration of the wealth of processes occurring spontaneously, such as sensory processing and homeostatic regulation. Given that the experimental setting underlying the resting state actually represents a non-condition, i.e. is defined by the absence of a specific task, the resting state concept at first sight remains cloudy.
Methodologically, findings are discussed which were obtained with electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), either acquired separately or simultaneously. While the EEG signal reflects the electrical activity of cortical neuronal populations in the kHz range, the blood oxygen level-dependent (BOLD) fMRI signal reflects hemodynamic changes associated with neural activity covering the whole brain at a low temporal sampling rate (usually < 1 Hz).
5.1.1 Resting state brain activity: EEG
Long before spontaneous brain activity moved into the focus of (imaging) neuroscience (Biswal, Yetkin et al. 1995) it already represented a common condition during which patients were examined: the clinical “Routine EEG”. In this context, spontaneous ongoing EEG activity is sufficiently informative for the identification of focal pathology, epileptic activity,
encephalopathy, or the degree of wakefulness; and hence a task is not required and potentially not feasible to be performed universally by any patient.
The EEG hallmark of spontaneous brain activity during wakefulness is the alpha rhythm, an amplitude-modulated 8-12 per second oscillation with the largest amplitudes during a relaxed eyes-closed condition and at occipital electrodes. This rhythm was already described in the publication of the first EEG recordings in 1929 (Berger 1929). The amplitude of the alpha rhythm diminishes almost immediately upon eye-opening or with the onset of a cognitive task (Berger 1929). This reduction in amplitude is interpreted as a
desynchronization of the oscillatory generators, in other words, the generators oscillate synchronously during rest and desynchronize with the onset of processing. The alpha rhythm was considered an 'idling rhythm' (Pfurtscheller, Stancak et al. 1996), indicating a default pattern of cortical activity when the corresponding area is task-free, but ready to react. A contrasting interpretation states that the negative correlation of alpha band amplitude and task engagement reflects an active inhibitory process (Jensen and Mazaheri 2010). During wakeful rest, the main oscillations besides the alpha rhythm are the beta and gamma rhythms, related to a spectral peak in the 13-30 Hz range (beta frequency band) and a broadband activity in the 30-80 Hz range (gamma frequency band) of resting state EEG spectra (Freeman 2004). Beta-gamma band activity is generally associated with attention and active cortical processing (Freeman 2004). In order to describe the brain state of wakeful rest, it is necessary to characterize related states such as drowsiness and sleep. Similar to eye opening or cognitive processing, the EEG correlate of transitions to states of reduced
vigilance is the desynchronization of the occipital alpha rhythm and, to a lesser extent, the appearance of slower oscillations (Loomis, Harvey et al. 1935; Davis, Davis et al. 1937;
Rechtschaffen and Kales 1968; AASM 2007). During this transition to sleep, the EEG shows low amplitude activity without distinct peaks in the frequency distribution. The similarity of low alpha amplitude patterns associated with reduced vigilance and those observed regularly in certain neuropsychiatric disorders was also discussed by Roth (Roth 1961). He noted that
vigilance fluctuations are common during EEG recordings of healthy subjects and that the EEG of wakefulness is markedly non-stationary. The preceding discussion shows that alpha band amplitude cannot be interpreted as a vigilance marker on its own, since drowsiness on the one side, and engagement in a cognitive task or in sensory processing on the other, are likewise reflected by a marked decrease in occipital alpha activity. These results were summarized and quantified as a bell-shaped relationship between the vigilance level and alpha band amplitude by Ota (Ota, Toyoshima et al. 1996).
5.1.2 Resting state brain activity: fMRI
In the context of fMRI, the term “resting state” was coined. It describes spontaneous brain activity during wakefulness, which occurs in a task-free condition when minimal systematic confounds arise from task-related activations.
Resting state-specific activation patterns can be analyzed in different ways: a) Statistically contrasting the between-task (resting state) condition against the task condition ('reverse subtraction') yields a set of regions termed task-specific deactivations (Raichle and Snyder 2007), b) It can be studied using data driven methods, mainly independent component analysis (ICA), to identify coherent and mutually independent activity patterns (Beckmann, DeLuca et al. 2005), c) using non-fMRI modalities such as EEG derived measures (Laufs 2008), surface EMG (van Rootselaar, Renken et al. 2007) or other physiological measurements (de Munck, Goncalves et al. 2008) as regressors in a generalized linear model of the BOLD signal.
In the early years of resting state research, the search for task-specific deactivations yielded a set of brain regions termed the default mode network (DMN) (Raichle, MacLeod et al. 2001), including the medial prefrontal cortex, the posterior cingulate cortex, the precuneus and parts of the parietal cortex. This set of regions has been accredited special importance as it appears to be independent of the task against which the resting state condition is contrasted (Buckner, Andrews-Hanna et al. 2008). Complementary to the default mode network, another, regionally non-overlapping network positively correlated to tasks was found and termed the anti-correlated network (Fox and Raichle 2007; Raichle and Snyder 2007).
Functionally, activity in the DMN has been related to the processing of internal or self-related
information while the anticorrelated network has been associated with attention and
working memory (Buckner, Andrews-Hanna et al. 2008). Closer inspection of DMN dynamics showed that DMN activity is reorganized, rather than deactivated during task initiation and performance (Fransson 2006) and that brain activity during relaxed wakefulness
spontaneously switches between modes that were interpreted as an introspective (default) mode and an alert mode with the readiness to process changes in the internal or external environment (Fransson 2005).
Because of its link to self-related information processing, the role of the DMN during wakefulness was investigated in a number of studies. The results of these studies show that DMN activity at least partially reflects intrinsic patterns of brain activity unrelated to
consciousness, as shown by intact DMN activity in states of reduced or absent consciousness (sleep, coma, anaesthesia) (Boly, Phillips et al. 2008). Likewise, the combination of fMRI and fibre tract visualization using diffusion tensor imaging showed that the DMN as well as other resting state networks are reflected in the intrinsic white matter connectivity of the brain, i.e.
that functional networks are at least partially determined by anatomy (van den Heuvel and Hulshoff Pol 2010).
When extracting resting state networks from fMRI time series using ICA, it is generally observed that different subsystems of the brain spontaneously activate and deactivate without apparent external stimuli conditioning these systems to engage or disengage (Beckmann, DeLuca et al. 2005). The identified subsystems were found to match networks characteristically involved in task processing, among them visual cortices, the auditory and sensorimotor systems, and the executive control network (Beckmann, DeLuca et al. 2005).
Using voxel-wise functional connectivity analysis, the set of networks representing functional brain modules could be reproduced, and further networks of still unknown functional relevance were described (Power, Cohen et al. 2011).