Brain Imaging and Behavior DOI 10.1007/s11682-015-9417-1

REVIEW ARTICLE

Functional connectivity in disorders of consciousness: methodological aspects and clinical relevance Silvia Marino 1,2 & Lilla Bonanno 1 & Antonio Giorgio 1

# Springer Science+Business Media New York 2015

Abstract This is a Quick Guide about the role of the functional connectivity in the Disorders of Consciousness (DOC). Recent studies on resting state (RS) in DOC, by using functional magnetic resonance imaging (fMRI), showed that functional connectivity is severely impaired above all in the default mode network (DMN). In the vegetative and minimally conscious state, DMN integrity seems to correlate with the level of remaining consciousness, offering the possibility of using this information for diagnostic and prognostic purposes. Although the two principal approaches used in the RS analysis showed several methodological difficulties, especially in DOC patients, functional brain imaging is currently being validated as a valuable addition to the standardized clinical assessments that are already in use. Keywords Disorders of consciousness . Functional magnetic resonance imaging . Outcome . Resting state

Introduction Consciousness is a multifaceted concept that can be divided into two main components: arousal (ie, wakefulness, or vigilance) and awareness (eg, awareness of the environment and

* Silvia Marino [email protected] 1

Neurobioimaging Laboratory, IRCCS Centro Neurolesi BBonino-Pulejo^, S.S. 113, Via Palermo, C.da Casazza, 98124 Messina, Italy

2

Department of Biomedical Sciences and Morphological and Functional Imaging, University of Messina, Messina, Italy

of the self). A diagnosis of vegetative state (VS) is supported if the patient demonstrates no evidence of awareness of self or environment, no evidence of sustained, reproducible, purposeful or voluntary behavioral response to visual, auditory, tactile or noxious stimuli and critically no evidence of language comprehension or expression. Indeed, the subject in a minimally conscious state (MCS) shows partial preservation of awareness of self and environment, responding intermittently, but reproducibly, to verbal command and therefore demonstrating some degree of basic language comprehension. The locked-in syndrome describes patients who are awake and conscious but selectively deefferented, i.e., have no means of producing speech, limb, or facial movements (Bodart et al. 2013). The unresponsive wakefulness syndromes (VS, MCS, and the functional locked-in syndrome) have been also defined by using new and innovative neuroimaging techniques. Diffusion tensor imaging, positron emission tomography, fMRI, MR spectroscopy, electroencephalography, and transcranial magnetic stimulation techniques have all provided important insights into the field of Disorders of Consciousness (DOC) (Marino and Bramanti 2009; De Salvo et al. 2012). This has led to a better understanding of these patients' clinical conditions, such as a better differential diagnosis, outcome evaluation and to the development of new therapeutic and communication tools. However, low sensitivity and artifacts problems need to be solved in order to bring these new technologies to the single-patient level. In addition, new ethics questions have arisen and need to be investigated by multi-scale and multicenter studies. Sequences such as diffusion tensor imaging and MR spectroscopy seems promising to reliably predict outcome in chronic patients with severe Traumatic Brain Injury (TBI). Passive, active, and resting-state functional neuroimaging paradigms are recently being validated to increase the

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differentiation between unconscious–vegetative and minimally conscious patients. In particular, resting state functional MRI (RS-fMRI) is a relatively new method for evaluating regional interactions that occur when a subject performs no active task (Soddu et al. 2011). The resting state (RS) paradigm seems to be particularly appealing for clinical studies, because it does not require sophisticated experimental setup to administer external stimuli and the patient’s contribution is not needed. There is a growing consensus that the assessment of DOC patients should include techniques that do not rely on overt motor responses (Demertzi et al. 2014);. In fact, several studies were reported about the use of RS-fMRI, especially in noncommunicative subjects (Hannawi et al. 2015; Höller et al. 2014, Andronache et al. 2013; Mäki-Marttunen et al. 2013; Demertzi et al. 2014; Vanhaudenhuyse et al. 2010). Numerous studies demonstrate that RS-fMRI spatial patterns are closely related with neural subsystems revealed by task activation (Di Perri et al. 2013; Riganello et al. 2015; Soddu et al. 2012). Regions that co-activate with a seed region in different tasks tend to be positively correlated with the seed region at rest. A map constructed from a single seed shows a specific pattern of correlation across the brain. The spatial patterns of correlation can also be used to create extensive systems/network level descriptions of functional interactions across brain regions that can be compared with anatomical connectivity descriptions, and task-evoked functional activations. Several methods have been proposed to acquire and analyze RS-fMRI data. This entails optimization of many aspects of data acquisition (scan duration, spatial resolution, spatial smoothing during pre-processing) and data analysis (seed-based and independent component analysis [ICA] approaches). The aim of this review is to focus on a) the functional connectivity of cortical and sub-cortical networks in DOC patients; b) the methodological aspects of RS-fMRI in DOC patients; c) the clinical relevance of RS-fMRI in DOC patients.

Functional connectivity of default-mode, fronto-parietal and thalamus networks and methodological aspects of RS-fMRI in DOC patients There are two main approaches for analyzing RS-fMRI, seed voxel and ICA. The seed-voxel approach consists of extracting the blood oxygen level-dependent (BOLD) time course from a region of interest (ROI). In this case, signal from only a certain voxel or cluster of voxels known as the seed or ROI are used to calculate correlations with other voxels of the brain. This provides a

precise and detailed evaluation of specific connectivity in brain areas of interest (Margulies et al. 2007). Recently, the seed approach was integrated with graph theory method. In the latter, the RS BOLD time series for each of the ROIs extracted from the network under investigation are correlated among each other giving a correlation matrix which can be represented as a weighted graph (Zhang et al. 2014) This method has been widely used and seems to give reliable results. On the other hand, using the posterior cingulate cortex as a seed region, functional connectivity analyses have showed not only positive correlations in the Default Mode Network (DMN) but also negative correlations in another RS network related to attentional processes. This aspect could be related to the fact that the human brain is intrinsically organized into dynamic, anti-correlated functional networks. Some authors (Murphy et al. 2009) reported that analyses of negatively correlated networks in RS-data, due to a global signal regression, is probably the cause of anticorrelations. Resting state is an approach that did not require a priori knowledge of task signal modulations. The degree of negative correlation depends on the extent of the ROI and the signal-tonoise ratio (SNR) of the fluctuations (Murphy et al. 2009). The other RS-fMRI method, ICA, does not require an a priori definition of ROIs. Indeed, ICA analyzes the entire BOLD dataset and decomposes it into statistically independent spatio-temporal components. A number of studies showed that ICA is an innovative tool, which can simultaneously extract a variety of different coherent neuronal networks and separate them from other signal modulations, such as those induced by head motion or other physiological confounds (e.g., cardiac and respiratory pulsations). The ICA approach is also able to automatically identify a RS network even in presence of a distorted brain or regions with unexpected location (Heine et al. 2012). Indeed, in DOC patients the presence of brain atrophy secondary to hydrocephalus and hemorrhagic and traumatic events could significantly deform the skull and/ or the brain and a normalization step could not be sufficient for performing a seed analysis, because of the difficulty in selecting a proper seed region. Another important issue in the study of spontaneous BOLD signal fluctuations, especially in the case of patients with a highly reduced neuronal activity, is the possibility of signal contamination by artifact and noise. In fact, when examinations are performed with non-collaborative patients, it is easy to expect several confound components in RS examination. The ICA approach offers the advantage of better reducing physiological noise (Fingelkurts et al. 2012). ICA studies, however, often showed a debated bias: the estimated independent components (ICs), in fact, have to be matched across subjects and one may need to deal with several numbers of ICs which were extracted for different subjects, either if a fixed number of ICs is not empirically chosen before

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ICA was performed. To our knowledge, several technical approaches have been proposed, such as principal component back-projection techniques and more recently, general linear model dual regression approaches (Filippini et al. 2009). Recently, some authors used graph theoretical methods to explore brain network topology in RS-fMRI of severely impaired consciousness patients (Achard et al. 2012). They found evidence for a radical reorganization of high degree or highly efficient “hub” nodes. Cortical regions that were hubs of healthy brain networks had typically become nonhubs of brain networks and vice versa, in DOC patients. These results seem to show that global topological properties of complex brain networks should be conserved under different clinical conditions and that consciousness should depend on the anatomical location of hub nodes in human brain networks. In VS patients these networks are severely impaired. In a recent study, ICA and seed based analysis were found to be significantly impacted by data preprocessing settings. These findings underline the importance of performing highgrade preprocessing, including rejection of outlier volumes, ventricle masking, removal of movement-related and global signal covariance. DMN hypoconnectivity is a condition which was repoterd in VS (Di Perri et al. 2013). Some authors proposed that these preprocessing procedures should be adopted to reduce the probability of wrongly inferring that DMN activity is absent, with potential implications for clinical DOC management (Andronache et al. 2013). The DMN is defined as a set of areas, including the posterior cingulated cortex/precuneus, the anterior cingulate cortex/ mesofrontal cortex and the temporo-parietal junctions, all areas that seem to show a higher activity at rest than during attention-demanding tasks. The DMN has received attention as a potential marker for large-scale integrative processes related to memory consolidation and awareness. In addition, the DMN has been suggested to be involved in various aspects of self-referential processing, inner or task-unrelated thoughts, self-reflective thinking in term of future planning or behavior simulation. Recent studies have shown that the clinical severity of VS and MCS patients seem to be reflected in the DMN residual functional connectivity level (Demertzi et al. 2014; Vanhaudenhuyse et al. 2010; Crone et al. 2011). Boly et al. (2009) showed a difference of functional connectivity in the DMN between a VS and a brain dead patient. In the VS subject, the authors noted the absence of corticothalamic functional connectivity, but preserved corticocortical connectivity within DMN, whereas no significant anticorrelations were identified in the brain dead patient. These results seem to show how the DMN signal fluctuations are related to conscious thoughts and others derived from unconsciousness processing.

Other studies tested if the resting-state connectivity pattern in DMN was different in the different levels of DOC. Vanhaudenhuyse et al. (2010) showed an exponential correlation between DMN connectivity and the level of consciousness. The authors found a correlation in the posterior cingulate cortex/precuneus, which seems to differentiate minimally conscious from unconscious patient. Crone et al. (2011) studied deactivation of the DMN in DOC patients. They showed that the deactivation in medial regions is reduced in MCS and absent in VS patients when compared to normal controls. In addition, the clinical scales they used seem to show a high correlation with the deactivation. Cauda et al. (2009) showed impairment of DMN in 3 VS patients, showing decreased connectivity in several brain regions (including dorsolateral prefrontal and anterior cingulate cortex). Recently, some authors (Laureys and Schiff 2012) proposed a method of recovery of consciousness focusing on the connectivity between and within frontal and parietal regions influenced by thalamus circuits. In fact, several studies showed a severe impairment in functional connectivity in a widespread fronto-parietal network in DOC patients (Boly et al. 2009; Cauda et al. 2009). Different studies showed the important role of thalamus. Crone et al. (2014) investigated brain network properties in DOC patients and identified alterations between normal controls and patients on a global level but also in the fronto-parietal and thalamus networks, while only the precuneus area showed differences between patient groups. It is noted that the level of consciousness is correlated with the disruption in the precuneus, medial, and frontal regions. The role of the thalamus seems to be reduced in patients with poor outcome or in those with no recovery of consciousness. Finally, a more drastic approach potentially solving the movement problem is to mildly sedate the patients (Heine et al. 2012). It has been shown that sedatives only partially reduce the connectivity in the DMN. Accordingly, it could become feasible to compare brain connectivity between mildly sedated DOC patients and mildly sedated healthy controls.

Clinical relevance of RS-fMRI in DOC patients Awareness is the principal issue in DOC patients. It is composed of two coexistent, anticorrelated networks (Golland et al. 2008; Demertzi et al. 2013). Laureys et al. (2007) proposed that the two anti-correlated components (external and internal awareness) may account for the phenomenological awareness and consciousness complexity. Internal awareness refers to self-awareness that is achieved by perceiving the internal world of thoughts, feelings, commentary and daydreaming, whereas external awareness, or ‘self-sense’, is the perception of the external world through the five senses.

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Internal awareness is linked to the activity of midline anterior and posterior cortices, while the external awareness involves a widespread lateral fronto-temporo-parietal associative cortical network. It is known that RS-fMRI acquisitions are easy to perform and could have a potential role and translation into clinical practice (Di Perri et al. 2014). The clinical interest of RS-fMRI studies is related to the possibility of investigating higher-order cognitive networks, such as the DMN, without requiring patients' collaboration, that cannot be achieved in VS and MCS patients. Although a great deal of data are not available thus far, this technique could be interesting to test the functional integrity of principal brain structures and could be useful in distinguishing unconscious–vegetative from minimally conscious patients. In fact, recent evidence mainly based on hemodynamic measures suggests that the impairment of functional connections between different brain areas may help clarify the neuronal dysfunction occurring in patients with DOC (He et al. 2014). Recently, some authors studied the weighted global connectivity (WGC) in VS and MCS patients, using areas of significant WGC differences as ‘seed regions’ in a secondary connectivity analysis. They showed extended functional networks in both MCS and healthy subjects, whereas no such functional connections were observed in VS. These results demonstrate the potential of functional connectivity MRI as a clinical tool for differential diagnosis in disorders of consciousness (Kotchoubey et al. 2013). Pharmacologically, sedation should be avoided, because activity in the cortical network might be decreased. However, when it is impossible to perform the MRI examination due to movement artifacts, we should use propofol, which is considered the only medication associated with a modulation of consciousness that can be efficiently revealed by tracking the patterns of co-activation in the posterior cingulate cortex, an area with a central role in the dynamical connectivity at rest (Amico et al. 2014). This quick guide of heterogeneous literature on this topic suggests that resting functional imaging studies can provide valuable prognostic information. Ovadia-Caro et al. (2012) examined resting-state inter-hemispheric connectivity in three homotopic ROI. They found reduction in inter-hemispheric functional connectivity in impaired awareness subjects as compared to normal controls. In addition, functional connectivity was correlated with the level of consciousness. Significantly, one of the three patients whose connectivity indices were comparable to the controls, was diagnosed as locked-in syndrome. These results seem to suggest that RS-functional connectivity could be used as a complementary measure in the diagnosis of DOC patients. All the studies included in the present guide employed different patient assessment methods and different analysis methodologies. Future efforts should focus on large multicenter

cohort studies with standardized behavioral and neuroimaging paradigms. At present, the field of neurorehabilitation lacks evidence-based treatment for disorders of consciousness such as VS. Functional neuroimaging could help measure objectively the effect of pharmacological and non-pharmacological therapeutic interventions. Finally, the medical community needs to define an ethical framework that permits the study of brain function and plasticity in these non-communicative and severely brain damaged patients unable to provide consent.

Conclusion In the last decade, the progress of neuroimaging techniques has led to substantial developments in the understanding of consciousness. Studies in both DOC and anesthesia have shown that altered states of consciousness are related to a complex dysfunctionality in the connectivity architecture of the brain. Disruption of connectivity in the fronto-parietal associative networks and increased connectivity between networks involving subcortical structures seem to occur. Finally, improvements in motion artifact removal and spatial normalization are needed before R-fMRI data can be used as proper biomarkers in DOC. However, the development of RS-fMRI studies could improve the diagnosis and prognosis in the absence of DOC patients' active collaboration in data acquisition.

Conflict of interest Silvia Marino, Lilla Bonanno, and Antonio Giorgio declare that they have no conflicts of interest.

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