Resting-state brain networks: literature review and clinical applications

Neurol Sci (2011) 32:773 785 DOI 10.1007/s10072-011-0636-y REVIEW ARTICLE Resting-state brain networks: literature review and clinical applications Cristina Rosazza Ludovico Minati Received: 21 January 2011 / Accepted: 13 May 2011 / Published online: 11 June 2011 Ó Springer-Verlag 2011 Abstract This review focuses on resting-state functional connectivity, a functional MRI technique which allows the study of spontaneous brain activity generated under resting conditions. This approach is useful to explore the brain s functional organization and to examine if it is altered in neurological or psychiatric diseases. Resting-state functional connectivity has revealed a number of networks which are consistently found in healthy subjects and represent specific patterns of synchronous activity. In this review, we examine the behavioral, physiological and neurological evidences relevant to this coherent brain activity and, in particular, to each network. The investigation of functional connectivity appears promising from a clinical perspective, considering the amount of evidence regarding the importance of spontaneous activity and that resting-state paradigms are inherently simple to implement. We also discuss some examples of existing clinical applications, such as in Alzheimer s disease, and emerging possibilities such as in pre-operative mapping and disorders of consciousness. Keywords Functional connectivity Resting state Spontaneous brain activity Coherence Cognitive correlates Clinical applications C. Rosazza (&) L. Minati Scientific Department, Fondazione IRCCS Istituto Neurologico Carlo Besta, via Celoria, 11, 20133 Milan, Italy e-mail: cristina.rosazza@istituto-besta.it Introduction In the past 20 years, functional magnetic resonance (fmri) has gained widespread acceptance as a powerful tool for mapping brain function. fmri is typically used to study small changes in the blood oxygen level-dependent (BOLD) signal which are induced by performance of a task or by administration of a stimulus. In the past decades, research efforts concentrated on identifying regions specialized in given cognitive tasks, but more recently the interest has shifted toward a wider prospective aiming at understanding how brain multiple regions interact with one another and how this leads to behavioral phenomena. The recent advances in functional neuroimaging have provided new tools to study the brain, according to this view, that is, as a network of interacting regions. Functional connectivity represents a novel approach of fmri which enables to investigate the neural activity of regions that are functionally connected even when they are anatomically distant. Functional connectivity can be defined as the synchrony of neural activity among regions. Areas of the brain which exhibit signal fluctuations correlated in time are assumed to be functionally connected. These BOLD signal fluctuations occur at low frequencies (\0.1 Hz) and have been observed throughout the brain. Functional connectivity can be studied during the performance of active tasks, such as finger tapping or visual stimulation, as well as during resting state, a condition in which the participant is not performing any active task and is simply instructed to remain still, with eyes closed or open while fixating a cross. In fact, it is well known that under resting conditions the brain is engaged in spontaneous activity which is not attributable to specific inputs or to the generation of specific output, but is intrinsically originated. The brain under normal physiological conditions is

774 Neurol Sci (2011) 32:773 785 never idle, but always remains neuro-electrically and metabolically active. The concept of functional synchrony is not new. Historically, the study of functional connectivity developed in the 80s, in the context of electroencephalography (EEG) [45]. The importance of functional connectivity arises from the observation of long-range EEG coherencies across cortical regions and between the hemispheres, e.g., [74]. The coherencies that are observed throughout the whole cortex appear to originate from a relatively small number of interacting regions and processes [64]. Clearly, EEG and fmri are very different in that, the temporal resolution of EEG is considerably higher and comparable to the timescale of neural events, whereas that of fmri is limited to the order of several seconds by the hemodynamic response lag. As a consequence, using fmri it is only possible to characterize coherent activity in very low-frequency bands, i.e., \0.1 Hz, which are not directly associated with the EEG activity in this frequency range. However, it appears that a correspondence does exist at a more abstract level these low-frequency BOLD signal fluctuations track spontaneous changes in the main EEG rhythms. Indeed, this correspondence is expected, since the BOLD and EEG signals share a common neurophysiological correlate, the local field potentials (LFP). In particular, it is hypothesized that the observed BOLD signal fluctuations are due to lowfrequency LFPs and low-frequency modulations of highfrequency LFPs [88]. The observed correspondence between EEG and fmri phenomena can be typified by a simple example. The default mode network (DMN), a widespread circuit described below, displays an activity pattern that inversely correlates with arousal and effort. The EEG displays a similar phenomenon consisting of decrease in occipital alpha rhythm; however, the exact relationship between EEG- and fmri-based connectivity needs to be explored further, as the empirical evidence is still scant, e.g., [78] and a very limited literature is available in terms of theoretical models. Multiple analysis techniques are used to look at the datasets. The most commonly employed ones are the region-of-interest (ROI) based analysis and the independent component analysis (ICA). The first is based on the extraction of the time-course of the BOLD signal from a pre-defined ROI and subsequent identification of the regions showing a significant correlation with the ROI. This time-course correlation analysis produces functional connectivity maps showing which areas are connected with the given ROI and to what extent. This method represents the most straightforward way to study the functional connectivity, as the results are relatively clear and simple to interpret [42, 48]. ICA, by contrast, is a statistical technique that does not involve any a priori assumption and allows the exploration of multiple whole-brain networks. ICA uses a mathematical algorithm to decompose a set of signals into independent components also known as source signals. In particular, on the basis of the measured signals, ICA can reveal the hidden sources which have generated them, under the assumptions that sources are statistically independent. When applied to fmri, ICA is able to extract from the BOLD time series a number of independent components which are spatial maps associated with the time courses of the signal sources [79]. Each component can be interpreted as a network of similar BOLD activity. This may correspond to an actual neural functional network or in some cases to common physiological activity or neuroimaging artefacts. The studies using ICA have shown a high level of consistency in the reported components suggesting that ICA is a powerful technique that can be applied to the study of multiple connectivity patterns [19, 37, 107]. However, a possible disadvantage of this technique is that the components are not always easy to interpret. ROI-based analysis and ICA represent two very different approaches of data analysis: the former is a hypothesisdriven method, which relies on the a priori definition of ROIs and which generates results that are limited to the given ROIs. In contrast, ICA is an exploratory, hypothesisfree method, which is designed to examine the general patterns of functional connectivity across brain regions and which generates results not at inter-regional level such the ROI-based analysis, but at network level. Even though ROI-based analysis and ICA are very different in terms of methodology, the studies comparing the two techniques have provided similar results [8, 75, 77, 94, 96, 108] and the functional networks that the two methods generate are quite overlapping [5 7, 21, 26, 27, 29, 80, 99, 106, 107]. This is expected, as they represent the same underlying connectivity, reflecting the synchrony of neural activity over spatially distinct regions. However, one needs to bear in mind the potential differences between the two techniques related to the effect of physiological activity, systemic changes and potential scanner drifts which can induce diffusely coherent signal fluctuations. While ICA attempts to segregate specific components on the basis of their statistical features, ROI-based analysis is non-selective and may therefore be more sensitive to signal contamination by generators of non-neural origin. In practice, this discrepancy is attenuated by the fact that the time courses are typically pre-processed by means of baseline removal, removal of movement-related variance and bandpass filtering. The first report of resting-state fmri is by Biswal et al. [6]. The authors demonstrated that under resting conditions the BOLD signal fluctuations, measured in the left sensorimotor cortex, were correlated with fluctuations in the contralateral sensorimotor cortex, in the right

Neurol Sci (2011) 32:773 785 775 Fig. 1 Independent component analysis (ICA) spatial maps, generated from the analysis of 40 healthy subjects, shown on a cortical rendering and standard brain views [94]. This figure shows the most frequently reported resting-state components, including 1 DMN, 2 sensorimotor component, 3 executive control component, 4 mesial visual component, 5 and 6 lateralized fronto-parietal components, 7 auditory component, 8 temporo-parietal component supplementary motor area and in right premotor areas, a set of regions constituting the well-known sensorimotor network. Nowadays the most studied network is the DMN, a system of areas involving the precuneus/posterior cingulate, the lateral parietal area and the mesial prefrontal

776 Neurol Sci (2011) 32:773 785 cortex [48, 89]. It has been demonstrated that this network is more intensely engaged under resting conditions and relatively de-activated whenever the participant is involved in active tasks: for this reason, it has been called the default mode network [98]. An important question is what these spontaneous fluctuations represent. The issue is still unanswered, as the meaning and function of this low-frequency signal are unclear. A reasonable hypothesis is that these fluctuations reflect spontaneous cognitive processes. In the absence of a task or a stimulus attracting our attention, we naturally tend to think on the recent past, to imagine future events or simply to wander with our thoughts. However, even though unconstrained cognitive processes are likely to contribute to spontaneous fluctuations, they are unlikely to be the sole source. In fact, the spontaneous activity is observed across various behavioral states including different resting conditions [108] and task performance, [53] and, as examined below, persists in different consciousness states including sleep [60], anesthesia [50] and in disorders of consciousness [109]. Further, it has been observed in monkeys [110] as well as in rats [61]. Spontaneous fluctuations are also found across virtually all brain regions, including sensorimotor areas which are typically not involved in associative processing. Finally, strongly coherent spontaneous activity among regions is believed to reflect which areas typically interact with one other. The regions which tend to be engaged simultaneously throughout daily tasks generally maintain synchronous spontaneous activity even in resting conditions. Indeed, the temporal coherence of some components has been shown to be modulated by performing an active task immediately prior to resting-state study: this evidence suggests that the recent experience as well as consolidated abilities can leave a memory trace and that, more generally, spontaneous fluctuations may be involved in memory consolidation [2, 56, 102]. Overall, these data point to a more complex interpretation, that is, the pattern of correlated activity is likely to reflect a combination of conscious activity and internal neural dynamics, which are essential for the emergence of behavioral functions and are also present in the absence of a behavioral correlate [14]. Another important issue regards what supports this spontaneous BOLD activity. These spatially distinct brain regions are maintained functionally connected by a structure of anatomical connections that enables ongoing communication among areas. These anatomical connections consist of white matter tracts that both directly (with monosynaptic connections) and indirectly (with multisynaptic connections) interconnect brain regions [51]. Combining functional connectivity with diffusion tensor imaging (DTI), an MRI technique that allows the study of white matter fiber bundles, a number of studies has suggested a direct association between functional and structural connectivity [30]. However, even if functional connectivity reflects structural connectivity, the two connectivity measures are not completely coupled: for instance, functional connectivity is observed between regions which lack direct anatomical projections, for instance between the right and left amygdala [93], which indicates that functional correlations are mediated by indirect structural connections. Moreover, as it will be discussed below, functional connectivity can be modulated by a cognitive task, despite structural connectivity remains unchanged [2]. Description of resting-state components Using both ROI-based analysis and ICA, functional connectivity studies have reported a number of networks that result to be strongly functionally connected during rest [5, 7, 21, 29, 94, 99, 106, 108]. As shown in Fig. 1, the key networks, also referred as components, which are more frequently reported include: the DMN, the sensorimotor component, the executive control component, up to three visual components, two lateralized fronto-parietal components, the auditory component and the temporo-parietal component. As already reported, these resting-state networks consist of anatomically separated, but functionally connected regions displaying a high level of correlated BOLD signal activity. These networks result to be quite consistent across studies, despite differences in the data acquisition and analysis techniques. Importantly, most of these resting-state components represent known functional networks, that is, regions that are known to share and support cognitive functions [26]. For instance, the pattern of resting-state functional connectivity displayed by the executive control component has been associated with the performance on a corresponding executive functioning task [96]. Discovering the functional correlate of a component is particularly valuable as it shows a direct link between resting-state functional connectivity patterns and behavior. Default mode network This signal component is identifiable in: (1) precuneus/ posterior cingulate, (2) lateral parietal cortex, (3) mesial prefrontal cortex. The DMN is the component that has by far received the most attention throughout the clinical and research community [15, 48, 89, 91]. This set of regions is typically observed to be more intensely activated during the rest and relatively de-activated during the demanding tasks requiring focused attention such as working memory tasks and visuo-spatial tasks [25, 48]. Activation of the DMN regions tend to correlate negatively with the areas that increase activity during such demanding tasks [43].

Neurol Sci (2011) 32:773 785 777 Several studies have demonstrated that precuneus/posterior cingulate shows a strong correlation in its activation pattern with the other members of the DMN (the mesial prefrontal cortex and the lateral parietal area) and these three regions together form the core of the DMN [15, 44]. The precuneus/posterior cingulate node seems to be particularly important, because it appears to be directly related with the other nodes of the network and putatively acts as a mediator of intrinsic connectivity across these regions. This is likely because it is one of the most intensively interconnected regions in the whole brain [22, 52]. This interpretation is in fact coherent with the recent view that the precuneus is essential for introspective processes as well as for awareness [15]. The DMN has been associated to unconstrained cognitive processes, given that it is generally observed more active during passive cognitive states than during active tasks. Its activity has been associated to introspective mental processes, to the tendency of human minds to wander, to our ability to rethink about the recent past and to imagine future events [15]. Other studies suggest that the DMN is also active when focused attention is directed away from external stimuli and generally relaxed. For example, the studies of transient lapses of attention induced by demanding cognitive tasks have demonstrated that when participants are slower in responding due to distraction or when they make mistakes, the activity of DMN is increased principally in the precuneus [70, 112]. However, the DMN has been found to be engaged also during the performance of active tasks. For example, Hampson et al. [53] have demonstrated that two regions of the DMN, the precuneus and the mesial frontal gyrus, were functionally connected during the rest as well as during an active-working memory task. Importantly, the working memory performance was positively correlated with the intensity of functional connectivity observed throughout the DMN. This suggests that the DMN may facilitate or monitor performance of active tasks, rather simply deactivate during active tasks. Further studies have supported this view showing that the DMN connectivity is related to working memory, being modulated by different working memory loads [36] and also correlated to individual task performance [38] in normal subjects. In general, a significant correlation between functional connections and behavior suggests that the study of resting-state functional connectivity might be a powerful tool for investigating individual differences and their related anatomy. This has also clinical implications: if resting-state connectivity among regions of the DMN is a marker of a given cognitive ability, the strength of this connection may be of diagnostic value for different clinical conditions and diseases. According to some investigations, the DMN comprises also the hippocampi and it is involved in the consolidation of episodic memory [49]. In support of this view, in the studies on AD the DMN has been found to be affected by reduced functional connectivity and atrophy [15, 49], as described later with respect to the clinical applications of functional connectivity. Also in mild cognitive impairment (MCI), patients showed decreased network-level connectivity in the DMN compared to controls [100]. A recent study by Hasson et al. [56] has demonstrated that the connectivity pattern throughout the DMN varies in response to a cognitive task performed immediately prior to the data acquisition. The intensity of functional connectivity throughout this network was significantly correlated with the level of performance in the task. This relevant finding demonstrates that not only the demands of a current task determine the synchronized activity observed in a given region, but also the characteristics of the task performed just before the data acquisition modulate this coherent activity. Spontaneous correlations have also been observed throughout the DMN across various states of altered consciousness, including sleep [59, 60] and anesthesia [13, 50]. Indeed, coherent BOLD signal fluctuations were found to persist within the DMN during reduced levels of consciousness like light sleep, as shown by the EEG recordings [59, 60]. Interestingly, during deep sleep, the coherence of spontaneous activity within DMN generally persisted, but a significant reduction in the connectivity between the medial prefrontal cortex and parietal regions was observed. The reduction of correlation between frontal and parietal areas of the DMN might reflect the changes in consciousness induced by deep sleep. Finally, spontaneous BOLD correlations have also been found in anaesthetized monkeys, in regions corresponding to the DMN [110]. The resemblance between the human and monkey DMN correlation maps is striking and suggests that some components of the network may be conserved across species. Furthermore, this suggests that the DMN is not only associated to conscious mental activity, but also reflects intrinsic properties of the brain to some extent. Overall, we can conceptualize that coherent activity throughout the DMN: (1) emerges principally under resting conditions, but is also present during the performance of active tasks; (2) has been observed across different states of consciousness and also in non human primates; (3) is influenced by the characteristics of the task performed just before the data acquisition. As mentioned before, the exact nature of these temporally coherent fluctuations remains to be clarified. It appears likely that all the hypotheses listed above are, to some extent, valid, but it remains to clarify to what extent each of these distinct aspects is influential in determining the dynamics of the system.

778 Neurol Sci (2011) 32:773 785 Sensorimotor component This component involves (1) precentral gyrus, (2) postcentral gyrus (3) supplementary motor area. This network is characterized by the engagement of regions that anatomically correspond to motor as well as sensory areas. However, the correspondence between this network and sensorimotor functions is not only based on anatomy, but also on the functional evidences. Indeed, it has been observed that the activity of the sensorimotor cortex in resting state shows a degree of hemispheric lateralization which correlates with the lateralization of activity in the same regions that emerges during an active finger tapping task [33]. This demonstrates that the sensorimotor network is associated with functionally relevant neural activity, that is, the spontaneous fluctuations observed in this network are likely to reflect the neural activity which subserves active motor tasks. An important question is why spontaneous and task-evoked activity patterns are so similar. One possibility is that regions that tend to be activated together during active tasks show correlations in their spontaneous activity, like a memory of previous coordinated processing [40]. Visual components Throughout literature up to three distinct components have been reported: Component 1 is characterized by the activity in mesial visual areas, namely striate cortex and extra-striate regions typically mesial, such as lingual gyrus, Component 2 is associated with lateral visual areas such as the occipital pole and occipito-temporal regions, Component 3 is associated with activity in the striate cortex and in polar visual areas. The different number of visual components observed across studies may, in part, be related to the effects that the choice of decomposition parameters and the intensity of physiological and other artefactual signal components have on ICA decomposition. For example, it has been shown that increasing model order leads to branching of some components, such as the visual network [1]. Specific techniques are available that enable one to assess the stability of ICA decomposition, such as ICASSO [57]. The visual component number 2 has been investigated in a recent resting-state fmri study [102]. This study has demonstrated that resting-state BOLD fluctuations were modulated by a visual task performed prior to the data acquisition. Even the sole exposure to visual stimuli can modulate the subsequent functional connectivity, specifically in the regions that are relevant to the visual tasks. This evidence supports the view that resting-state BOLD fluctuations have dynamic components that are experiencedependent and which may have a role in memory consolidation. This is conceptually in line with findings for the DMN reported by Hasson et al. [56] and results obtained for the lateralized fronto-parietal component reported by Albert et al. [2] that will be described below. Executive control component This signal component is identifiable in: (1) medial frontal gyrus, (2) superior frontal gyrus, (3) anterior cingulate cortex. These regions, which in some cases also include lateral parietal areas, are generally involved in tasks relying on executive functions, such as control processes and working memory. As before, it has been investigated whether this network was specifically linked to a specific cognitive function. The study by Seeley et al. [96] has shown that intrinsic connectivity throughout this network is correlated with the performance on the trail-making test, a neuropsychological exam tapping executive functioning. The results demonstrate a link between individual differences in intrinsic connectivity and the variability observed in the fundamental features of cognitive functioning. Lateralized fronto-parietal components Throughout the literature, one commonly finds two strongly lateralized components, one predominantly in the right hemisphere and the other in the left hemisphere usually with a specular pattern. This involves: (1) the inferior frontal gyrus, (2) the medial frontal gyrus, (3) the precuneus, (4) the inferior parietal (5) the angular gyrus. Although most of these resting-state components tend to represent known functional networks, that is, regions that are known to share a cognitive function, the role of this network remains less clear, also because prefrontal and parietal regions are closely coupled in a wide range of cognitive processes. The fronto-parietal component has been associated to different functions, i.e., memory [29], language [99], attention [32, 43] and visual [34] processes. A resting-state functional connectivity study which investigated language and reading networks found a map of correlations to Broca s area which included the medial frontal gyrus and, more weakly, the angular gyrus [54]. Although the activation pattern included a large portion of occipito-temporal cortex and did not overlap entirely with the lateralized fronto-parietal component described here, their findings are relevant as they show a connectivitybehavior relationship. In particular, the strength of functional connectivity between Broca s area and angular gyrus during both reading and rest was significantly correlated with reading ability.

Neurol Sci (2011) 32:773 785 779 A recent investigation has shown that resting-state activity in the fronto-parietal network was specifically affected by prior sensorimotor learning [2]. The authors measured resting-state spontaneous fluctuations before and after the execution of a sensorimotor task and showed that the network was modulated by motor learning. Here, the temporal coherency was enhanced after learning, reflecting increased functional connectivity. These results are relevant as they suggest that a recently acquired motor ability can leave a memory trace that is measurable as a functional connectivity change in the rest condition after learning. As before, this finding is consistent with the view that synchronous BOLD fluctuations are modulated by the recent experience and may be involved in memory consolidation. Auditory component This component involves: (1) superior temporal gyrus, (2) Heschl s gyrus, (3) Insula, (4) postcentral gyrus. Cordes et al. [26] used resting-state functional connectivity to compare the resulting maps with those obtained using a text-listening task. Task-based fmri showed an extensive region of the superior temporal gyrus which overlapped well with the regions identified with restingstate fmri. However, the correspondence was only anatomical, as it was not demonstrated any link between the neural activity of the resting-state network and the language task. The map of this component overlaps nicely with the pattern of resting-state functional connectivity observed by Koyama et al. [65]. That pattern was associated to readingrelated areas and involved the superior temporal gyrus, area known to be implicated in speech perception. However, the map of this component is different from the pattern observed by Turken and Dronkers [105] for the posterior superior temporal sulcus, given that it apparently did not include the Heschl s gyrus, the insula and the postcentral gyrus. Temporo-parietal component This component involves: (1) inferior frontal gyrus, (2) medial temporal gyrus, (3) superior temporal gyrus, (4) angular gyrus. This component is characterized by the engagement of regions typically associated to language processing. The connectivity map of this network was consistent with the resting-state functional connectivity study of regions involved in reading, which identified the posterior middle temporal gyrus and the inferior frontal gyrus as important loci of functional interaction among five different reading networks [65]. Indeed, the posterior middle temporal gyrus, which has been found to be important for language comprehension [35], displayed a resting-state functional connectivity pattern which overlapped considerably with that of the temporo-parietal component [105]. Overall, these results support the view that patterns of resting-state functional connectivity reflect an intrinsic functional organization underlying cognitive processes and in this case language processes. Why study resting-state functional connectivity for clinical applications? Overall relevance The study of functional connectivity in resting state is important for two distinct reasons. The first is a theoretical one: spontaneous activity is the most metabolic demanding component of neural activity, which consumes more than 80% of the brain s energy [90]. This baseline neuronal activity supports neural signaling processes subserving the integration of information originating from internal as well as external phenomena. It has been estimated that the additional energy consumption associated with the taskevoked activity is surprisingly small, often less than 5% [88] and that, in general, cognitive functions consume a relatively small fraction of the brain s energy budget [90]. Therefore, when one aims to have a comprehensive evaluation of brain function, the analysis of spontaneous activity is at least as important as stimulus-evoked activity. The second reason is practical: resting-state studies do not rely on active participation by the patients, therefore, they may be the sole form of functional imaging suitable with uncooperative populations, where an adequate level of performance may be difficult to attain. Patient immobility is essential also for resting-state study, since movementrelated artefacts and signal components can significantly impair the detection of spontaneous activity components. However, resting-state fmri can be performed also in patients who need to be sedated to attain an acceptable level of immobility [50, 63]. Moreover, resting-state fmri is free from the potentially confounding effects of differences in the level of task performance. While functional connectivity has been initially used as a research tool to investigate the functional architecture of the brain in healthy volunteers, a host of potential clinical applications rapidly emerged shortly after the first publications. A number of research groups has now applied resting-state functional connectivity to study various neurological and psychiatry disorders (for clinical reviews, see [41, 97, 116]. In clinical settings, this technique has been found to be particularly useful to detect differences between patients and controls and, more importantly, to

780 Neurol Sci (2011) 32:773 785 correlate the resting-state differences to clinical variables. Highly consistent findings have been obtained for some diseases like AD (see below), multiple sclerosis [12, 76, 92] and amyotrophic lateral sclerosis [83, 103], while for other disorders like schizophrenia (see below) results are more discordant. A detailed analysis of resting-state abnormalities for each clinical disease is beyond the scope of this work. However, in this section, we present some examples of the clinical applicability of this technique, including the most commonly studied clinical applications such as AD, and some emerging applications, such as presurgical mapping and disorders of consciousness. Alzheimer s disease One of the first clinical applications of resting-state fmri has been in the context of AD. Early identification of the people at risk of AD has become a priority [81]. One of the first studies which used functional connectivity in this clinical context was by Li et al. [71]. The authors examined AD and MCI patients, hypothesizing that the accumulation of brain pathology could affect the spontaneous fluctuations of neural activity resulting in reduced synchrony across regions. In fact, using an ROI-based approach, AD patients were reported to have impaired functional connectivity in both hippocampi, compared to control subjects. For MCI patients, the reduction was significantly less severe than for AD patients, but it was pathological compared to controls. Importantly, this reduction in functional connectivity was correlated with the loss of cognitive ability, thus suggesting that this technique might be of great potential clinical value. Subsequently, by means of ICA, Greicius et al. [49] studied hippocampal connectivity in relation to other brain regions within the DMN. AD patients were found to have a disrupted DMN, in particular, the deficit of functional connectivity was evident in the posterior cingulate and in the hippocampi. This finding could explain the hypometabolism that is normally found in posterior cingulate in PET studies of AD [82]. Even in MCI patients at risk of converting to AD, the DMN connectivity has been found to be significantly reduced compared to healthy participants [100]. In particular, functional connectivity between the hippocampi and the posterior cingulate was present in controls but not in patients, likely reflecting the consequence of early degeneration. In contrast, MCI patients demonstrated no change in other networks extracted with ICA. Investigations that built on the original work by Li et al. [71] further demonstrated that important functional connections within the DMN are disrupted in AD [3, 100, 101, 111]. Interestingly, young and healthy subjects carrying the apolipoprotein E e4 allele, a genetic risk factor for AD, exhibited a modulation of the DMN that was absent in noncarriers, even if it was an increase rather than a decrease of functional connectivity [39]. The results suggest that the ApoE e4 allele modulates brain function, in particular throughout the memory system, decades before the onset of any clinical symptoms and its influence can be detected even in young, asymptomatic adults by measuring resting-state functional connectivity [81]. Another hypothesis considers the role of some brain regions with an extremely high number of connections, such as posterior cingulate, which act as relay hub for information processing. In these areas, it is hypothesized that the accumulation of AD pathology may be accelerated due to their elevated metabolic rate [16 18]. Schizophrenia Another field where resting-state functional connectivity has been applied is schizophrenia [20]. This is a severe psychiatric disorder characterized by altered perception of reality and associated with structural brain abnormalities [28]. Patients suffer from disturbances of thought, hallucinations, loss of emotion, as well as cognitive deficits including language, memory and executive functions [66]. Most of the work on schizophrenia focused on the DMN: since one of the main symptoms is the disturbance of thought, the association between schizophrenia and DMN appears straightforward, as the network is involved in internal thinking and mental simulation. It is conceivable to hypothesize that patients have difficulties in the control of the DMN: although this issue is still poorly understood, there seems to be a dynamic competition between the DMN and brain systems supporting focused attention [43]. The complex symptoms observed in schizophrenia could arise from a disruption of the interactions between the DMN and other competing systems, resulting in an overactive DMN [15]. This hypothesis would be in line with the observation that for these patients imagination and reality do not always have clear boundaries. In support of this view, a number of studies have reported a general increase of connectivity within the DMN in schizophrenia [55, 114, 117]. The work by Garrity et al. [46] demonstrated increased DMN activity for patients with schizophrenia compared to controls and importantly, showed that subregions of the network, including medial frontal gyrus and precuneus, correlated with severity of positive symptoms. Another study which used ICA demonstrated in these patients an increased connectivity for a number of components, including the DMN [62]. However, other groups found that the disease was mainly associated with a decrease in connectivity, in particular, between precuneus and cerebellum [9] in the thalamocortical connectivity [113], and in the amygdalofrontal connectivity [58]. Overall, the resting-state

Neurol Sci (2011) 32:773 785 781 functional connectivity results in schizophrenia are not consistent across studies, likely because distinct and contrasting physiological processes could exist at different scales [116]. Furthermore, other factors might limit the reproducibility of results: the existence of dissimilar disease subtypes, various effects of medications and the use of very different methods to study schizophrenia [72]. For these reasons, at least for now existing results should be taken cautiously, as they neither allow to identify nor distinguish the clinical diagnostic patterns observed in schizophrenia. Presurgical planning Resting-state functional connectivity has also been applied in the context of presurgical planning. While the importance of traditional task-based fmri for pre-operative mapping is well established, the potential relevance of resting-state analysis has emerged only recently. Up to date, a few studies have explored the feasibility of the approach in this area. Liu et al. [73] studied six patient candidates to brain surgery with tumors or epileptic foci near the motor cortex, and compared the resting-state functional connectivity maps with the task-elicited activations of the motor cortex. Functional connectivity was measured with ROI-based analysis, selecting hand and tongue motor areas as ROIs. The results obtained with the two methods were highly comparable at level of single subjects and showed that resting-state functional connectivity is able to localize hand and tongue regions of the motor cortex precisely. One of the six patients was further studied with direct cortical stimulation: functional connectivity activation maps displayed hand and tongue regions overlapping with the localization of the electrodes that disrupted hand and tongue movements. Another investigation by Zhang et al. [115] studied four patients with tumors infiltrating sensory and motor cortices, and compared resting-state functional connectivity to task-based fmri and cortical stimulation mapping (CSM). As before, resting-state functional connectivity was able to localize sensorimotor areas in accordance with CSM for each individual patient. In some cases, resting-state functional connectivity proved to be even more reliable than task-based fmri, as for one patient task-evoked fmri failed for unknown reasons, and for another patient it displayed an artefactual activation, absent with functional connectivity fmri. However, further studies with larger patient populations are needed to confirm these results. Indeed, these studies combining fmri with CSM investigated presurgical motor function mapping only, and they did not consider if resting-state fmri can be likewise useful for language and memory mapping. Disorders of consciousness Another emerging application of resting-state functional connectivity is in the context of the disorders of consciousness. These include: coma, defined as a state of unarousable unresponsiveness; vegetative state, characterized by loss of awareness of self and environment despite clear signs of wakefulness (e.g., preserved sleep wake cycles); minimally conscious state, where patients show limited, but clear evidence of awareness; locked-in syndrome, where wakefulness and awareness are largely preserved but patients are unable to generate speech, and purposeful limb and facial movements [47, 68, 87]. A critical problem with these patients is that the clinical diagnosis is based on the behavior exhibited by the patient and can be inaccurate in up to 40% of cases [4, 95]. In the last decade, functional imaging techniques have been used to explore whether some types of cognitive processing were still available in patients suffering from disorders of consciousness and for a few cases it was shown that the technique detected residual cognitive abilities and signs of conscious awareness in patients in vegetative state [69, 84, 86]. Indeed, functional imaging, including task-based fmri and resting-state functional connectivity, has been used with the purpose of obtaining diagnostically and prognostically relevant information, complementing clinical evaluation and neurophysiological studies as well as structural MRI [10, 24, 67, 68, 87]. Due to the etiological heterogeneity of these patients, the involvement of visual, motor and sensory pathways can be very variable: as a consequence, the study of brain activity under resting state appears as a solution of minimum complexity to assess the integrity of intrinsic brain function in these patients. To date, the literature remains limited. Cauda et al. [23] considered resting-state functional connectivity in three vegetative state patients and, using ICA, showed that the DMN was present but with reduced intensity. In one patient, other functional networks such as the visual and the motor components were reported to be intact, but no illustration was provided. An interesting preliminary finding is that patients seemed to show more left lateralized activation on resting-state component maps, compared to controls. This may be related to the view that self-awareness processes are critically dependent on the integrity of the right hemisphere [31]. The study by Boly et al. [11] has also demonstrated the presence of the DMN in a vegetative state patient, albeit with a significant reduced connectivity compared to controls. Distant regions, as for instance the prefrontal cortex or the inferior frontal gyrus, were found to correlate, as well as anticorrelate, with the precuneus. However, thalamo-cortical connections were absent. By contrast, in a brain dead patient neither thalamo-cortical connections nor long-range cortico-cortical functional connectivity were observed

782 Neurol Sci (2011) 32:773 785 across regions. The absence of thalamo-cortical connectivity in the vegetative state patient suggests that the integrity of a thalamo-cortical network might be essential for the emergence of consciousness [85, 104]. Vanhaudenhuyse et al. [109] conducted a relevant resting-state functional connectivity study, including patients in coma and vegetative state as well as minimally conscious and locked-in syndrome patients. Using ICA, the authors demonstrated that it was possible to identify the DMN, even though its intensity was reduced with respect to controls, a finding consistent with the previous investigations. Importantly, the authors demonstrated that the intensity of DMN connectivity was proportional to the level of impaired consciousness and the peak significance for this statistical comparison was found in the precuneus. Overall, the literature of applications of resting-state functional connectivity to disorders of consciousness remains scant and studies tend to focus exclusively on the DMN. The observation of a significant correlation between the strength of DMN connectivity and level of conscious awareness is a relevant finding which provides motivation for further consideration of this technique alongside more established neuroimaging and neurophysiological approaches, with the purpose of integrating clinical scores. As introduced above, these findings are in line with the view that there are multiple sources of these long-range temporal coherences. Some of these seem to be independent of the level of consciousness and are likely associated with the structural architecture of the brain; others seem to be more specifically linked to more cognitive processes. Summary and future prospects Resting-state functional connectivity is an emerging fmri technique based on the study of coherences in BOLD signal fluctuations. It enables the identification of multiple circuits forming specific functional networks. The recent studies have demonstrated important links between specific connections within brain networks and cognitive functions. Clinical investigations have shown that intrinsic activity can distinguish patients from healthy subjects and that the strength of connections can correlate with disease severity. Further studies are necessary to understand the functional role of each network and, more generally, to better clarify the relationship between resting-state activity components and behavior. In the context of clinical applications, more work is needed to identify the potential diagnostic and prognostic contribution of this approach, in particular, with respect to emerging applications such as pre-operative planning and the study of disorders of consciousness. In addition, the future investigations will need to clarify the correspondence between alterations in structural and functional connectivity, to address the relevance of other components beyond the DMN, and to develop means of representing the interactions among distinct functional networks. Acknowledgments We thank Dr. Maria Grazia Bruzzone and Dr. Davide Sattin for useful advice on the clinical applications and general revisions to the manuscript. Conflict of interest All authors declare that they do not have any real or perceived conflicts of interest pertaining to the present study. References 1. 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