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Resting state activity in patients with disorders of consciousness
Andrea Soddu1, Audrey Vanhaudenhuyse1, Athena Demertzi1, Marie-Aurélie Bruno1, Luaba
Tshibanda2, Haibo Di3, Mélanie Boly1,2, Steven Laureys1,2, Quentin Noirhomme1
1Coma Science Group, Cyclotron Research Centre, University of Liège, Belgium
2Neurology Department, CHU Sart Tilman Hospital, University of Liège, Belgium
3Vegetative State and Consciousness Science Institute, Hangzhou Normal University, China
Corresponding author:
Andrea Soddu
Coma Science Group, Cyclotron Research Centre, University of Liège, Belgium
Email: [email protected]
Tel: +32 4 366 23 35; Fax: +32 4 366 29 46
Vanhaudenhuyse A: [email protected]
Demertzi A: [email protected]
Bruno MA: [email protected]
Tshibanda L: [email protected]
Di H: [email protected]
Boly M: [email protected];
Laureys S: [email protected];
Noirhomme Q: [email protected];
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Abstract
Recent progress in studying spontaneous brain activity has demonstrated activity patterns that
emerge without any task performance or sensory stimulation, holding promise for the study of
higher order associative network functionality. Additionally, such advances are argued to be
relevant in pathology, such as disorders of consciousness (DOC; i.e., coma, vegetative and
minimally conscious states). Recent studies on resting state activity in DOC, measured with
functional Magnetic Resonance Imaging (fMRI) techniques, show that the functional
connectivity is disrupted in the task-negative or the default mode network. However, the two
main approaches employed in the analysis of resting–state functional connectivity data (i.e.,
hypothesis-driven seed-voxel and data driven Independent Component Analysis) present
multiple methodological difficulties, especially in non-collaborative DOC patients.
Improvements in motion artifact removal and spatial normalization are needed before fMRI
resting state data can be used as proper biomarkers in severe brain injury. However, we
anticipate that such developments will boost clinical “resting state” fMRI studies, allowing for
easy and fast acquisitions and ultimately improve the diagnosis and prognosis in the absence
of DOC patients’ active collaboration in data acquisition.
Key Words: Coma, consciousness, resting state, spontaneous activity, functional magnetic
resonance imaging, default network, vegetative state.
Introduction
According to present-day understanding, the vacuum state (also called the ‘quantum
vacuum'), is by no means a “simple empty space” (Lambrecht 2002) or some “absolutely
empty void” (Ray 1991). In fact, the modern theory of quantum mechanics tells us that the
vacuum state contains fleeting electromagnetic waves and particles that pop into and out of
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existence. Thanks to this new theory of vacuum it is now possible to predict with a
remarkable precision the most “intimate” properties of the atoms. If a proper theory of
vacuum in physics was an irreplaceable step to develop a comprehensive modern theory of
the “particle” world, we hypothesize that in neuroscience to develop a proper theory of what
is called the “baseline” activity of the brain could be a critical step in a deeper understanding
of the “neuronal” world.
Much of what is currently known about brain function derives from studies in which a
task or stimulus is administered and the resulting changes in neuronal activity and behavior
are measured. Recent studies on brain energy metabolism, however, have shown that task-
related increases in neuronal metabolism are usually small when compared to resting energy
consumption (Fox and Raichle, 2007). Hence, it becomes clear that in order for one to fully
investigate the brain’s activity, one also needs to study its baseline energy consuming
features. Recent progress in studying spontaneous brain activity (Biswal et al., 1995; Cordes
et al., 2000; Damoiseaux et al., 2006; Fox and Raichle, 2007; Fox et al., 2005; Greicius et al.,
2003; Lowe et al., 1998; Mitra et al., 1997; Nir et al., 2006; Vincent et al., 2007; Xiong et al.,
1999; Beckmann et al., 2005) has demonstrated activity patterns that emerge with no task
performance or sensory stimulation, holding promise for the study of higher order associative
network functionality and its potential implication in stimulus-induced activity (e.g. see Boly
et al., 2007) and pathology (Boly et al., 2008a,b, 2009; Greicius et al., 2004; Rombouts et al.,
2009).
In this paper we will review studies on resting state activity measured with functional
Magnetic Resonance Imaging (fMRI) techniques in patients with disorders of consciousness
(DOC; coma, vegetative and minimally conscious states). While progress has been made in
describing DOC from the clinical perspective (Majerus et al, 2005), we will here focus on
examining DOC patients from the point of view of recent developments deriving from studies
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on the healthy human brain. In our view, new insights obtained by studying Blood Oxygen
Level Dependent (BOLD) resting state activity (Raichle et al., 2001; Raichle and Snyder,
2007) are relevant to a better understanding of DOC, allowing for a better diagnosis, as well
as potential prognosis and treatment (Giacino et al., 2006; Laureys et al., 2006; Schiff, 2006a,
2006b).
Disorders of consciousness
Disorders of consciousness (DOC) encompass a spectrum of clinical conditions
involving profound disruption in global consciousness due to massive brain lesions (Bernat,
2006; Giacino et al., 2002; Laureys et al., 2004; Schiff, 2006b). Clinical characterization of
the different DOC is based on two main distinct components of human consciousness: arousal
and awareness (Plum and Posner, 1972). If arousal refers to the behavioral alternation of sleep
and wakefulness, awareness refers to the collective thoughts and feelings of an individual
(Laureys, 2005). Coma is characterized by the absence of arousal and hence of awareness.
Vegetative state patients are aroused but unaware of the environment and of themselves
(Jennett and Plum, 1972). Minimally conscious state patients show reproducible behavioral
evidence of awareness of environment or self but are unable to reliably communicate (Giacino
et al., 2002). Locked-in syndrome patients (Plum and Posner, 1972) are fully conscious but
are completely paralyzed except for small movements of the eyes or eyelids. Behavioral
assessment is one of the main methods used to detect awareness in severely brain injured
patients recovering from coma (Majerus et al., 2005). However, detecting unambiguous signs
of conscious perception in such patients can be very challenging as it is illustrated by the high
frequency of misdiagnosis (up to 43%) in these patients (Andrews et al., 1996; Childs and
Mercer, 1996; Schnakers et al., 2009b). New technical methods such as fMRI offer specific
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assessment procedures and the possibility to determine objectively whether an unresponsive
patient is aware without explicit verbal or motor response.
Spontaneous fMRI activity patterns as a diagnostic tool in DOCs
The potentially powerful approach of functional brain imaging and particularly of
fMRI has been shown in the diagnosis (Hirsch, 2005; Laureys et al., 2000; Schiff et al., 2005)
and prognosis (Di et al., 2008; Coleman 2009) of DOC. However, in the absence of a full
understanding of the neural correlates of consciousness, even a near-to-normal activation in
response to passive stimulations (e.g. auditive, tactile or visual stimuli) cannot be considered
as a proof of preserved awareness. Instead, all that can be inferred is that a specific brain
region is, to some degree, still able to activate and process relevant sensory stimuli. In the
absence of a cerebral activation during passive fMRI paradigms, it could be argued that the
patient may have intact cognition but failing peripheral sensory systems (e.g., may be deaf or
with hearing impairments) or, for visual stimuli, did not look at or fixate the presented visual
targets (the latter needing visual eye tracking devices which are difficult to calibrate in non-
collaborative patients). Resting state fMRI acquisitions are easy to perform (i.e., do not need
auditory, visual or somatosensory stimulation equipment in the fMRI environment) and could
have a potentially broader and faster translation into clinical practice. Indeed, recent progress
in investigating spontaneous brain activity showed the clinical relevance of these studies in
the absence of the patients’ collaboration, particularly important in vegetative and minimally
conscious patients. Cauda and co-workers (2009), for example, showed a dysfunctional
default mode network (defined as a set of area encompassing posterior-cingulate/precuneus,
anterior cingulated/mesiofrontal cortex and tempero-parietal junctions, showing more activity
at rest than during attention-demanding tasks; Raichle et al., 2001) in three patients in a
vegetative state. They found decreased connectivity in several brain regions, including the
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dorsolateral prefrontal cortex and anterior cingulated cortex, especially in the right
hemisphere. Boly and co-workers (2009) demonstrated absent cortico-thalamic functional
connectivity (i.e. cross correlation of BOLD signal between precuneal areas and medial
thalamus) but partially preserved cortico-cortical connectivity within the default network in a
vegetative state patient studied 2.5 years following cardio-respiratory arrest. In this patient,
anticorrelations could also be observed between posterior cingulate/precuneus and a
previously identified task-positive cortical network, but both correlations and anticorrelations
were significantly reduced as compared to healthy controls. In the same study, a brain death
patient studied two days after a massive cranial hemorrhage and evolution to a comatose state,
showed no residual functional connectivity. Brain death is an irreversible coma with absent
brainstem reflexes – complimentary examinations such as the electroencephalogram shows an
isoelectric pattern and (MR) angiography shows absent intracranial arterial flow (Wijdicks,
1995). For brain death, measuring a non-zero BOLD signal in a brain lacking any neuronal
activity and arterial blood flow indicates the artifactual nature of the signal. In fact, it could be
shown that head motion (see figure 1), due to the arterial blood flow up the lower carotid
level, was the main source of the measured BOLD signal as confirmed by comparing its
power spectrum with the power spectrum of the motion curves obtained by the alignment
procedure of the echo planar images. The absence of any connectivity pattern in brain death
as published by Boly et al. (2009) is an important indication that the generally used
preprocessing technique (Fox et al., 2005) employed in resting state data and seed-voxel
connectivity analyses approach, as also applied for this study, are quite effective and reliable.
In a more comprehensive study (Vanhaudenhuyse et al., 2009), fourteen non-
communicative brain-damaged patients and fourteen healthy controls participated in a resting
state fMRI protocol. Connectivity was investigated by using probabilistic independent
component analysis (ICA) and an automated template-matching component selection
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approach. Functional connectivity in all default network areas was found to be non-linearly
correlated with the degree of consciousness, ranging from healthy volunteers and locked-in
syndrome, to minimally conscious, vegetative and comatose patients. Furthermore,
connectivity in the precuneus was found to be significantly stronger in minimally conscious
patients compared to vegetative state patients, while locked-in syndrome patients’ default
network connectivity was shown to be not significantly different from healthy control
subjects.
Methodological issues in fMRI resting state assessment
There are two main ways to analyze resting–state functional connectivity MRI (rs-
fcMRI): (1) hypothesis-driven seed-voxel (Fox et al., 2005) and (2) data driven Independent
Component Analysis (ICA) approaches (McKeown et al., 1998) – each offering their own
advantages and limitations. The seed-voxel approach consists in extracting the BOLD time
course from a region of interest and determines the temporal correlation between this signal
(seed) and the time course from all other brain voxels (Fox et al., 2005). To better visualize
the correlation pattern and better analyze its properties, the seed approach can be integrated
with graph-theory methods (e.g., Fair et al., 2008; Hagmann et al., 2008). In graphical rs-
fcMRI representations, the resting-state BOLD time series for each of the region of interest
(ROI) extracted from the network under investigation are correlated among each other giving
a correlation matrix which can be represented as a weighted graph. To reduce spurious
variance unlikely to reflect neuronal activity, the BOLD signal is pre-processed by temporal
band-pass filter, spatial smoothing, and by regressing out head motion curves, whole brain
signal and ventricular and white matter signal (and each of their first-order derivative terms)
(Fox et al., 2005). This method, which is quite straightforward and gives very intuitive results
has been widely adopted and seems to give very consistent results (Fox and Raichle, 2007).
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On the other hand, it has raised some controversial issues mostly related to the pre-processing
procedure, especially concerning the regressing out of the global activity from the BOLD
signal (an important step to obtain zero connectivity in brain death, Boly et al. 2009) which
might induce spurious anti-correlations (Fox et al., 2009; Murphy et al., 2009).
Contrary to the seed-voxel connectivity-assessment, ICA-based analyses (McKeown
et al., 1998) do not require an a priori definition of regions of interest. ICA analyzes the entire
BOLD dataset and decomposes it into components that are maximally statistically
independent (Hyvarinen et al., 2001). A number of studies have shown that ICA is a powerful
tool which can simultaneously extract a variety of different coherent neuronal networks (De
Luca et al., 2006; Esposito et al., 2008; Greicius et al., 2003; Greicius and Menon, 2004;
Greicius et al., 2004; McKeown et al., 1998) and separate them from other signal
modulations, such as those induced by head motion or physiological confounds (e.g., cardiac
pulsation, respiratory cycle and slow changes in the depth and rate of breathing, Birn et al.,
2008; Perlbarg et al., 2007; Perlbarg and Marrelec, 2008).
Patients with chronic DOC develop brain atrophy and secondary hydrocephalus (i.e.,
ex-vaquo dilated ventricles) and traumatic brain injuries and focal hemorrhages will largely
deform the brain. This implies that even if a normalization procedure of the brain has been
performed, the selection of a proper seed region becomes difficult and requires visual
inspection of an expert eye. Instead, the ICA approach (as illustrated in the minimally
conscious patient shown in figure 2 is able to automatically identify a cerebral network even if
the brain is distorted and if the regions are not localized as expected. To recognize the
network (even if it involves only half a brain as in the reported case) becomes an easier task.
Of course the problem of automatically selecting the right independent component in cases
like the one reported remains a delicate issue. Simply performing a similarity test (Esposito
2005) has shown to be not successful for choosing the right component (e.g., the component
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presented in figure 2 was selected by visual inspection). Different approaches could be
implemented, such as masking, which can restrict the similarity test only to the part of the
brain which by expert inspection of structural imaging or other neuroimaging data are
considered less damaged and, hence, still able to present a pattern of activity similar to
healthy subjects.
Another important issue in the study of spontaneous BOLD signal fluctuations,
especially for patients that show a significantly reduced neuronal activity, is the possible
contamination by artifact and noise sources (Birn et al., 2006; Chuang and Chen, 2001;
Cordes et al., 2000). When dealing with non-collaborative patients, major confounds in
resting state fMRI acquisitions and analysis are movement, pulse and respiration artifacts.
ICA seems to offer the advantage to better isolate physiological artifacts from the neuronal
components and this is why data-driven approaches are now being commonly adopted in this
field (Beall and Lowe, 2007; Birn et al., 2008; Perlbarg et al., 2007). Clinical studies should
preferably perform heart rate and respiratory rate recording (Gray et al., 2009) and
simultaneous real movement monitoring which would improve resting state analyses.
Conclusions and future directions
Recent progress in the study of spontaneous brain activity not only sheds new light on
the understanding of the human brain functionality but offers new opportunities for clinical
investigations of the most disparate neurological pathologies. The world of DOC research
started taking advantage from the study of spontaneous activity and studies of resting
metabolism and spontaneous electrical activity at the scalp level in patients with disorder of
consciousness can now be supported by BOLD activity studies during rest. What makes fMRI
approaches very appealing is the large diffusion in medical facilities of new generation MRI
scanners, able now not only to perform structural but also functional sequences. Easy as a
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resting state protocol using a common echo planar imaging sequence may appear, and thus
suitable for clinical purposes, rapid transient “clonic” motions during the acquisition (which
induce big artifacts on the BOLD measurements) and DOC patients’ highly deformed brains
(making comparisons with healthy normal brains difficult) challenge data analysis and
interpretation of these data.
Recent progress in data analysis has made it possible to increase our understanding of
the BOLD signal properties and its spatial patterns in cohorts of healthy subjects, where head
motions are controllable and the brain’s structure does not have a too large between subject
variability. For clinical purposes, we need to go from group level analysis to reliable
individual assessments. Before the technique can be employed in clinical routine as a
diagnostic or prognostic biomarker at the single subject level, we will need to tackle the
problems of spatial normalization (e.g., novel warping methods, Ashburner, 2007) and
movement correction.
Acknowledgment: SL is Senior Research Associate; AS, MB, QN are Post-doctoral Fellows
and MAB is Research Fellow at the Fonds de la Recherche Scientifique (FRS). AV was
funded by French Speaking Community Concerted Research Action (ARC 06/11-340). AD is
funded by the DISCOS Marie-Curie Research Training Network. This work was also
supported by European Commission (Mindbridge, DECODER, COST-CATIA), McDonnell
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Foundation, Mind Science Foundation, FRS, Reine Elisabeth Medical Foundation and
University and University Hospital of Liège.
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Figure 1: Independent component (IC) selected as default mode in brain death (female, age
50 y). The selection criteria were based on similarity testing with an averaged default mode
map obtained from an independent control group of 20 healthy volunteers (black and white
contour). Similarity value was 0.11. (a) Spatial map of the IC selected as default mode; (b)
Motion curves illustrating translation parameters (in mm) for x (red), y (green) and z (blue)
and rotation (in °) for pitch (yellow), roll (purple) and yaw (cyan) parameters together with
the time course of the IC selected as default mode; (c) Power spectrum of the motion curves
and of the time course of the IC selected as default mode; (d) Graphical representation (i.e,
fingerprint) of default mode temporal properties showing five frequency bands, temporal
entropy and one-lag autocorrelation and spatial properties (spatial entropy, skewness, kurtosis
and clustering) (normalized values).
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Figure 2: Independent component selected as default mode in a healthy subject (male, age 39
y), a locked-in syndrome patient (female, age 24 y with a brainstem stroke), a minimally
conscious state (MCS) patient (male, age 24 y post-traumatic) and a vegetative state (VS)
patient (male, age 87 y post-traumatic). The selection criteria were based on similarity testing
with an averaged default mode map obtained from an independent control group of 20 healthy
volunteers (black and white contour). Similarity values were respectively 0.65, 0.52 and 0.10.
Note that for the presented minimally conscious patient this method failed to select the proper
default mode because of the very asymmetrical brain damage and network selection; hence, it
was based on visual inspection of both the spatial maps and time courses.
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