Reduction in inter-hemispheric connectivity in disorders of consciousness.

Abstract

Clinical diagnosis of disorders of consciousness (DOC) caused by brain injury poses great challenges since patients are often behaviorally unresponsive. A promising new approach towards objective DOC diagnosis may be offered by the analysis of ultra-slow (<0.1 Hz) spontaneous brain activity fluctuations measured with functional magnetic resonance imaging (fMRI) during the resting-state. Previous work has shown reduced functional connectivity within the "default network", a subset of regions known to be deactivated during engaging tasks, which correlated with the degree of consciousness impairment. However, it remains unclear whether the breakdown of connectivity is restricted to the "default network", and to what degree changes in functional connectivity can be observed at the single subject level. Here, we analyzed resting-state inter-hemispheric connectivity in three homotopic regions of interest, which could reliably be identified based on distinct anatomical landmarks, and were part of the "Extrinsic" (externally oriented, task positive) network (pre- and postcentral gyrus, and intraparietal sulcus). Resting-state fMRI data were acquired for a group of 11 healthy subjects and 8 DOC patients. At the group level, our results indicate decreased inter-hemispheric functional connectivity in subjects with impaired awareness as compared to subjects with intact awareness. Individual connectivity scores significantly correlated with the degree of consciousness. Furthermore, a single-case statistic indicated a significant deviation from the healthy sample in 5/8 patients. Importantly, of the three patients whose connectivity indices were comparable to the healthy sample, one was diagnosed as locked-in. Taken together, our results further highlight the clinical potential of resting-state connectivity analysis and might guide the way towards a connectivity measure complementing existing DOC diagnosis.

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Reduction in Inter-Hemispheric Connectivity in Disorders
of Consciousness
Smadar Ovadia-Caro1,2, Yuval Nir3, Andrea Soddu4, Michal Ramot1, Guido Hesselmann1,5,
Audrey Vanhaudenhuyse4, Ilan Dinstein1, Jean-Flory L. Tshibanda4, Melanie Boly4, Michal Harel1,
Steven Laureys4, Rafael Malach1*
1Department of Neurobiology, Weizmann Institute of Science, Rehovot, Israel, 2Berlin School of Mind and Brain, Humboldt University, Berlin, Germany, 3Department of
Psychiatry, University of Wisconsin-Madison, Madison, Wisconsin, United States of America, 4Coma Science Group, Cyclotron Research Center and Neurology department,
University of Lie `ge, Lie `ge, Belgium, 5Department of Psychiatry, Charite ´ Campus Mitte, Berlin, Germany
Abstract
Clinical diagnosis of disorders of consciousness (DOC) caused by brain injury poses great challenges since patients are often
behaviorally unresponsive. A promising new approach towards objective DOC diagnosis may be offered by the analysis of
ultra-slow (,0.1 Hz) spontaneous brain activity fluctuations measured with functional magnetic resonance imaging (fMRI)
during the resting-state. Previous work has shown reduced functional connectivity within the ‘‘default network’’, a subset of
regions known to be deactivated during engaging tasks, which correlated with the degree of consciousness impairment.
However, it remains unclear whether the breakdown of connectivity is restricted to the ‘‘default network’’, and to what
degree changes in functional connectivity can be observed at the single subject level. Here, we analyzed resting-state inter-
hemispheric connectivity in three homotopic regions of interest, which could reliably be identified based on distinct
anatomical landmarks, and were part of the ‘‘Extrinsic’’ (externally oriented, task positive) network (pre- and postcentral
gyrus, and intraparietal sulcus). Resting-state fMRI data were acquired for a group of 11 healthy subjects and 8 DOC
patients. At the group level, our results indicate decreased inter-hemispheric functional connectivity in subjects with
impaired awareness as compared to subjects with intact awareness. Individual connectivity scores significantly correlated
with the degree of consciousness. Furthermore, a single-case statistic indicated a significant deviation from the healthy
sample in 5/8 patients. Importantly, of the three patients whose connectivity indices were comparable to the healthy
sample, one was diagnosed as locked-in. Taken together, our results further highlight the clinical potential of resting-state
connectivity analysis and might guide the way towards a connectivity measure complementing existing DOC diagnosis.
Citation: Ovadia-Caro S, Nir Y, Soddu A, Ramot M, Hesselmann G, et al. (2012) Reduction in Inter-Hemispheric Connectivity in Disorders of Consciousness. PLoS
ONE 7(5): e37238. doi:10.1371/journal.pone.0037238
Editor: Pedro Antonio Valdes-Sosa, Cuban Neuroscience Center, Cuba
Received July 18, 2011; Accepted April 18, 2012; Published May 22, 2012
Copyright: ? 2012 Ovadia-Caro et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This study was supported by the Israel Science Foundation 160/07 and ISF-Bikura. Grants and the Helen Kimmel Award for Innovative Research to
Rafael Malach. The study was supported by the Belgian Funds for Scientific Research (FRS) (http://www2.frs-fnrs.be/), European Commission (DECODER) (http://
www.decoder.i1.psychologie.uni-wuerzburg.de/?Partners), McDonnell Foundation (http://www.jsmf.org/), Mind Science Foundation (http://www.mindscience.
org/), University Hospital of Lie `ge (http://www.chu.ulg.ac.be/jcms/r_127200/internet-accueil) and University of Lie `ge (http://www.ulg.ac.be/cms/c_5000/home)
for Steven Laureys. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: rafi.malach@gmail.com
Introduction
The coupling between conscious awareness and its external
motor manifestation is so pervasive that it is difficult to
comprehend the devastating state of fully conscious patients who
are unable to respond. Severe brain injury can lead to such cases,
termed the ‘‘locked-in’’ syndrome (LIS). As a result of motor
disconnection, it is challenging to differentiate such cases from
those in which awareness itself is disrupted – termed vegetative
state (VS) or minimally conscious state (MCS). The differential
diagnosis between VS and MCS is even more challenging and up
to 40% misdiagnosis has been reported [1,2,3,4]. The method of
choice for diagnosis of conscious status has been careful bedside
observations, which are challenging due to fluctuation in arousal,
motor deficits and other deficits attributed to the injury, such as
aphasia. This method, due to its subjective nature could partly
contribute to the misdiagnosis rate [5]. Recent studies have
demonstrated that fMRI may provide some DOC patients with a
means for communication through blood oxygen level dependent
(BOLD) signals evoked by mental imagery, even in the complete
absence of motor outputs [6,7]. However, this method relies on
patient cooperation as well as attentional capacity and may not be
suitable for the general patient population. Even more problematic
is prognosis, the ability to predict which patients have better
chances of recovery. These challenges highlight the urgent need
for an objective physiological measure complementing current
evaluation tools.
Recently, a series of studies uncovered a robust phenomenon
that offers exciting potential for a complementary diagnosis of
unresponsive patients. Even in the absence of intentional sensory-
motor tasks, the human cortex manifests high-amplitude ultra-slow
(,0.1 Hz) fluctuations in its BOLD signals that reflect distinct
functional systems [8,9,10]. These spontaneous fluctuations show
anatomical specificity in that correlations (also termed functional-
connectivity) are more pronounced between the functionally
related cortical regions (e.g. right and left auditory cortices) than
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between functionally unrelated cortical regions (e.g. ‘Extrinsic’/
‘task-positive’ and ‘default-mode’ networks [11]). A particularly
striking and consistent aspect of this connectivity is the correlation
across homotopic sites in the two hemispheres [9,12,13,14]. More
recently, a likely neuronal correlate of these spontaneous BOLD
fluctuations was found in ultra-slow modulations of neuronal firing
rates and gamma power in local field potentials [12,15,16].
Although the functional role of the ultra-slow spontaneous
fluctuations remains unclear, they could potentially aid clinical
diagnosis. Indeed, such fluctuations and their network correlations
were shown to be altered in several neurological and neuropsy-
chiatric disorders [10,17,18,19,20]. The spontaneous nature of
ultra-slow fluctuations, emerging without the need for intentional
cooperation, makes them ideally suited as a complementary
diagnostic tool in DOC. It has been recently shown that
connectivity within the default-network [21], a subset of regions
that are deactivated during externally oriented tasks, is negatively
correlated with the degree of clinical consciousness impairment
[22], see also [23]. In these studies resting–state connectivity
within the default mode network (DMN) was assessed using
probabilistic independent component analysis in DOC patients
and an alteration in the spatial extent of the DMN was found at
the group level [22] and at the individual level [23].
Although it has been suggested that the DMN is associated with
basic functions related to consciousness [24], such as self-related
processes [25,26], it is not clear whether the reduction in
connectivity is restricted to the default-mode network or rather
extends into externally oriented regions. Furthermore, in order to
use connectivity analyses for the diagnosis of these patients, it will
have to be established how reliable these measures are on a
subject-by-subject basis.
Here, we examined resting-state inter-hemispheric connectivity
in three homotopic regions of interest, which were easily identified
based on anatomical landmarks, and were part of the externally
oriented network. We found reduction in inter-hemispheric
functional connectivity in impaired awareness subjects as com-
pared to intact awareness subjects. In addition, functional
connectivity was correlated with the level of consciousness and
was found to deviate from the healthy sample in in 5/8 patients
using a single case statistical test. Importantly, of the three patients
whose connectivity indices were comparable to the healthy
sample, one was diagnosed as locked-in. These results suggest
that resting-state functional connectivity might prove beneficial in
the future as a complementary measure in the diagnosis of DOC
patients.
Materials and Methods
The study was approved by the ethics committee of the Faculty
of Medicine at the University of Lie `ge, Belgium. Written informed
consent for healthy volunteers and patients was obtained from all
subjects and legal guardians, respectively.
Subjects
Nineteen subjects participated in the study. Eleven healthy
subjects with no neurological or psychiatric history were recruited
(age 28.864.5 years). Eight neurological patients (age 53.4616.7
years) were evaluated using the CRS-R scale [27], and a diagnosis
of locked-in syndrome (LIS, n=1), minimally conscious state
(MCS, n=2), vegetative state (VS, n=2), coma (n=2), or brain
death (BD, n=1) was established. The LIS subject was diagnosed
using the CRS-R and the FOUR scales [28]. BD diagnosis was
established when CRS-R testing showed no brain stem reflexes,
and was further confirmed by a physician conducting apnea tests
[29] as well as EEG recordings [30]. Recovery was assessed using
the Glasgow Outcome Scale, GOS [31]. The etiology of brain
injuries was traumatic (n=1), anoxic (n=2), due to cerebral
vascular accidents (CVA, n=2), hemorrhagic (n=1), meningitis
(n=1), or meningioma (n=1). See Table S1 for further clinical
details.
Data
The data used for this project were also used for two other
published studies that have applied different methods of analysis
and addressed functional connectivity in different cortical
networks. Data from fifteen subjects were used in a study
published by Vanhaudenhuyse and colleagues [22], which
addressed default network connectivity using Independent Com-
ponent Analysis. The brain dead subject has also been analyzed in
a study by Boly and colleagues [32], who addressed functional
connectivity in the default mode network. Data from the three
remaining subjects (two controls and one MCS patient) were not
used in any previously published work.
Functional imaging
Functional magnetic resonance imaging (fMRI) data were
obtained in a 10 minute resting-state scan using a Siemens Tim
Trio 1.5T scanner at the University Hospital Centre CHU-Sart
Tilman in Lie `ge, Belgium. Healthy subjects were instructed to lie
still and keep their eyes closed for the duration of the scan, with no
overt task being imposed. No sedation was applied in patients.
Three-dimensional functional images using blood oxygen level
dependent (BOLD) contrast were obtained with a gradient echo
planar imaging (EPI) sequence (TR=3000 ms, TE=30 ms, 36
slices; voxel size: 3.7563.7563.6 mm, flip angle 90u). T1-weighted
anatomical images were acquired using a 3D MPRAGE sequence
(TR=1670 ms, TE=4.5 ms, TI=1000 ms, 144 slices, voxel size:
1.260.961.4 mm, flip angle 8u). Subjects with excessive head
motion (.1 mm translation, .1 deg rotation) were excluded from
the analysis; nine subjects (7 patients; 2 healthy controls)
considered for MRI were excluded during acquisition due to
excessive movement in the scanner. DOC patients tend to exhibit
involuntary movements due to increased muscle tone. As our
patients were not sedated during the MR scan, we had to exclude
7 patients from the analysis.
fMRI preprocessing
FMRI data were preprocessed using BrainVoyager QX 1.9
(Brain Innovation, Maastricht, The Netherlands) and comple-
mentary software written in MATLAB R2009b (The MathWorks,
USA). The first two images of each functional scan were discarded
to avoid T1 saturation effects. Preprocessing of functional scans
included 3D motion correction, linear trend removal, and spatial
smoothing using a Gaussian filter kernel of 8 mm full-width-at-
half-maximum (FWHM). For all further analysis, data were band-
pass filtered between 0.01 and 0.08 Hz. Twenty volumes were
removed from the beginning and the end of the scan to avoid edge
artifacts induced by the filtering, leaving 158 volumes for the
analysis. Several sources of spurious variance were removed from
the signal time-course of each voxel through linear regression [33]:
1) the average signal from each subject’s ventricles, 2) the average
signal from each subject’s white matter voxels, and 3) the average
signal from each subject’s grey matter voxels (‘‘global signal’’).
Data were normalized to the Talairach coordinate system [34],
and the cortical surface was reconstructed for each subject as
described previously [35]. Inflated and flattened cortical maps
were used to visualize statistical parametric maps.
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ROI definitions
We focused on long-distance inter-hemispheric correlations
because these are less susceptible to local noise sources, such as
local blood flow modulations [8,12]. Regions of interest (ROIs)
serving as ‘‘seeds’’ for the inter-hemispheric correlations were
manually identified in the pre- and post-central gyrus (preCG,
postCG) and in the intra-parietal sulcus (IPS) of the right
hemisphere. This choice of ROIs was guided by multiple
considerations in addition to the goal of assessing connectivity
outside the DMN. First, previous studies indicate that inter-
hemispheric correlations are reliably observed in these regions
[14,36]. Second, correlations between regions on the lateral
cortical mantle were less affected by correlated noise due to shared
vascular supply, movement artifacts, or spatial spread of midline
signals [12]. Third, and most importantly, we anticipated the
potential use of such analysis in routine clinical procedures and
thus selected regions that are easily identifiable through simple
anatomical landmarks.
PostCG and preCG ROIs were defined based on the
localization of the ‘‘hand knob’’ on the central sulcus, known to
be largely consistent with the motor hand area [37]. Areas from
the gyri anterior and posterior to this knob were defined as preCG
(209.4645.9 voxels, isotropic voxel size: 36363 mm) and postCG
(208.4643 voxels) ROIs, respectively. The ROI in IPS (83.9631.9
voxels) was defined based on the intersection of the post-central
sulcus with the intra-parietal sulcus [38]. In addition, we
confirmed that the results were robust to the precise delineation
of the ROIs, by demonstrating a tight correlation with results
based on ROIs marked by an independent researcher (see
Material S1 for further details).
fMRI data analysis
For each subject, three resting-state functional connectivity
maps were computed within the framework of the General Linear
Model [39], using ‘‘seed’’ time-courses sampled from right
hemisphere ROIs as regressors (preCG, postCG, IPS). Fits to
the model were evaluated after removing the auto-regression
factor [40]. Unless otherwise stated, the resulting statistical
parametric maps were thresholded at p,0.01. Correction for
multiple comparisons at the cluster level was performed using the
AlphaSim plugin for BrainVoyager QX [41]. Subjects were
divided into two groups based on their level of awareness [42]: the
‘intact awareness’ group comprising healthy and LIS subjects and
the ‘impaired awareness’ group comprising MCS, VS, coma and
brain-dead patients. Second-level statistical analysis across subjects
within each group was performed using a random-effects analysis.
The resulting three resting-state functional connectivity group
maps were projected on an inflated and flattened 3D reconstruc-
tion of the cortical surface. The difference between the group
maps was evaluated using two-sample t-tests. Thus, the resulting t-
test maps (Figure S2) reflect regions that show significantly
different resting-state functional connectivity in the two groups.
Note that the two-sample t-test accounts for the different sample
sizes of the two groups by weighting the variance terms. The t-test
maps were thresholded at p,0.05. Finally, an inter-hemispheric
correlation index (ICD) was computed for each subject by
averaging across the three inter-hemispheric correlations of
spontaneous BOLD fMRI activity between homotopic ROIs
(i.e., left and right postCG/preCG/IPS). To test for a relationship
between individual ICD values and the level of consciousness, we
calculated the non-parametric Spearman correlation coefficient.
To test whether individual ICD scores from patients significantly
deviate from the healthy subjects (used as normative sample), we
applied a t-test specifically developed for single case studies [43].
This modified t-statistic by Crawford & Howell tests for the rarity
or abnormality of a patient’s score, using the standard deviation of
a group of healthy subjects (as the normative sample of size N) as
an estimate for the population standard deviation and N-1 degrees
of freedom.
Results
We examined correlations between spontaneous BOLD activity
in selected cortical regions of interest (ROIs) of the ‘‘Extrinsic’’-
externally oriented, task-positive network [11,44] and all other
cortical regions. Subjects were tentatively grouped into intact and
impaired awareness (see methods for further details). Figure 1
shows the correlation map computed for a ‘‘seed’’ ROI in the right
preCG. In the intact awareness group (Figure 1A), activity in the
preCG significantly correlated with neighboring somato-sensory
cortex, and with the homotopic ‘‘mirror’’ region in the left
hemisphere. Activity in the cingulate sulcus was also significantly
correlated with that of the preCG (Figure S1). Negative
correlations were observed in the posterior cingulate cortex,
lateral temporal cortex, and inferior parietal lobule, which are
commonly referred to as the default-mode network [45,46] or
‘‘intrinsic’’ network [11,25]. In the impaired awareness group,
significant correlations were restricted to immediately neighboring
cortex, while inter-hemispheric correlations were largely absent
(Figure 1B). This drastic reduction of inter-hemispheric correla-
tions was confirmed by statistically comparing the group maps
(Figure S2) taking into account the different sample sizes. Highly
similar results were observed for ‘‘seed’’ regions in the right intra-
parietal sulcus (IPS, Figure S3) and the right posterior central
gyrus (postCG, Figure S4).
To obtain a quantitative measure of the inter-hemispheric
correlations in each subject, we introduced an inter-hemispheric
correlation index (ICD). This index represents the average inter-
hemispheric correlations between the pre-defined ROIs (for details
see materials and methods). We found that ICD values were
decreased in the majority of DOC patients (Figure 2). Individual
ICD scores were found to significantly correlate with the degree of
consciousness, ranging from brain dead, coma, VS, MCS, LIS, to
healthy controls (Spearman’s correlation coefficient r=.61,
p=.0057). Since a decrease of resting-state connectivity with age
has been reported for the DMN [[47]; but see [48]], we confirmed
that lower ICD values in the patient group did not reflect age
differences. Indeed, the correlation between age and ICD value for
all participants was not statistically significant. (Spearman’s
correlation coefficient r=2.36, p=.128). In addition, when using
age as a control variable in a partial correlation analysis, the
correlation between ICD and the level of consciousness remained
significant (r=.52, p=.027). We further addressed this concern by
analyzing data from additional 11 healthy controls that were
matched for age (53.54615.97 years). Resting-state data and
structural scans were downloaded from a freely available online
source (http://fcon_1000.projects.nitrc.org/indi/pro/nki.html),
and identical preprocessing and analysis was performed (see
Figure S5, Table S2 and Material S2 for further details). The
results show that when individual ICD values of DOC patients
were tested against the normative sample of age-matched controls
(see below), highly similar statistical results were obtained as found
in the younger control group.
To test whether individual ICD scores from patients signifi-
cantly deviate from the healthy subjects (used as normative
sample), we used a t-test specifically developed for single case
studies [43]. As shown in Table 1, individual ICD values from 5/8
patients were significantly different (p,.05) from the average ICD
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value of the healthy controls. In addition to the ICD measure, the
disruption of inter-hemispheric correlations in DOC patients
could also be discerned to a large extent in individual BOLD time
courses (Figure 3) and single-subject maps (Figure S6).
Taken together, these results demonstrate that impaired
awareness was associated with reduced inter-hemispheric correla-
tions and was largely evident at the single subject level. The three
patients with non-significant difference from the healthy control
group were the LIS patient (which was expected to have normal
ICD since his consciousness level is identical to a healthy control),
one Coma patient (which showed a trend towards significance,
p=.0517) and one VS patient (which would be expected to have
decreased correlation values). The ICD value of this VS patient,
studied seven days after anoxic brain damage was 0.65, well within
the range of healthy controls (0.5660.1; mean 6 SD). Impor-
tantly, shortly after the brain imaging (13 days), this patient
progressed to a state of MCS and later (40 days post scan),
recovered consciousness, reaching a state of ‘‘moderate disability’’
according to the Glasgow Outcome Scale [31]. In our sample, this
was the only patient to improve in diagnosis, (see Glasgow
Outcome Scale for all patients in Table S1), one patient remained
in a MCS and the rest of the patients did not survive.
Importantly, inter-operator variability was low; control analysis
based on ROIs drawn by a separate fMRI researcher yielded a
significant correlation (r=0.65, p,0.01, Figure S7) between the
ICD scores obtained by these two independent investigators,
supporting of the future potential of this method for clinical use
(see Table S3 and Material S1 for further information).
Discussion
In this study, we show reduced inter-hemispheric connectivity
between homologous cortical regions within the ‘‘Extrinsic’’, task
positive network using resting-state fMRI in DOC patients. The
observed decrease in connectivity was significantly correlated with
Figure 1. Correlations between spontaneous BOLD fluctuations in right pre-central gyrus and other cortical regions. Group
correlation maps between a ‘‘seed’’ region in the pre-central gyrus (preCG) and all other cortical voxels, projected on inflated left (LH) and right (RH)
hemispheres (lateral view). (a) Correlations of spontaneous activity in the intact awareness group (n=12). (b) Correlations in the impaired awareness
group (n=7). Red arrow, ‘‘seed’’ region location. Blue arrow, homotopic ‘‘mirror’’ regions in the left hemisphere. Note that inter-hemispheric
correlations are largely absent in the impaired awareness group. Abbreviations: CS, central sulcus; LS, lateral sulcus; IPS, intra-parietal sulcus.
doi:10.1371/journal.pone.0037238.g001
Figure 2. Inter-hemispheric Correlation Index (ICD) in individ-
ual subjects. Subjects are separated on the x-axis depending on their
clinical state (patients in red and healthy controls in black). The solid
line represents the mean ICD value in the healthy controls group and
the dashed line represents the mean-2*standard deviation. Abbrevia-
tions: H, healthy; L, locked-in syndrome; V, vegetative state; M,
minimally conscious state; C, coma; B, brain death.
doi:10.1371/journal.pone.0037238.g002
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the degree of consciousness impairment, and was evident to a large
extent at the single subject level.
Inter-hemispheric correlations, or symmetry, between homolo-
gous regions is one of the prominent characteristics of resting-state
fMRI and have been demonstrated in healthy populations using
various techniques, such as region of interest based analysis [9,44],
independent component analysis (ICA) [14,36], and whole brain
approaches [49,50]. Inter-hemispheric symmetry appears to be a
ubiquitous characteristic of brain anatomy [51] and function [52].
Inter-hemispheric connections, in analogous fashion to within-
hemisphere connections, play a role in the integration of
information and coordination between the two hemispheres. Not
surprisingly, alteration in inter-hemispheric correlation has been
demonstrated in various diseases and behavioral impairments
[53,54,55,56,57] illustrating the importance of intactness of
communication/synchronization for the normal functioning of
the brain. Although the link between reduction in connectivity
within the DMN and DOC has been shown previously [22,23], it
has remained unclear whether a reduction in connectivity is
restricted to this network alone. Our results demonstrate a
significant reduction in connectivity between homotopic regions
belonging to the ‘‘Extrinsic’’, task positive network. This finding
raises the question of how widespread the reduction of connec-
tivity is in DOC patients. Since reduction in connectivity is not
restricted to one network, it seems, however, that DOC might
reflect a more global impairment in functional connectivity and in
the integrity of different circuits. In other words, it may be that
reduced connectivity within specific cortical networks may affect
specific behaviors but is not sufficient to affect the overall level of
consciousness. Along this line, it has been shown in stroke patients
that a reduction in connectivity that is specific to the attention
network is reflected behaviorally as neglect symptoms, but not in
the level of consciousness [58]. Indeed, reduction in connectivity
within one network might not be a sufficient marker for the
diagnosis of DOC patients, and a whole brain analysis might be
better suited to test more global impairments.
The exact source of the reduction in connectivity is still not fully
understood. However, it is beyond the scope of our paper to
investigate whether the striking disruption in the inter-hemispheric
correlations observed in DOC patients is due to a widespread
cortical, subcortical or white matter damage. Although structural
damage will evidently lead to loss of connectivity, especially in the
immediate time following injury [59], but see [60] and [61], such
reduction may be linked to synaptic changes well below the
resolution of brain imaging and could also be influenced by
plasticity changes following the injury through sub-cortical
connections [60]. In addition, reduction in functional connectivity
following structural damage has been reported for areas that
appear structurally intact [58,62]. Recently, Bruno et al. reported
a case of ‘‘functional hemispherectomy’’ in two DOC patients,
with near-normal DMN components in one hemisphere, given a
structural and metabolic deficit in the other hemisphere [63]. This
result highlights the need for a multimodal neuroimaging
approach, as one of the challenges related to connectivity
measures in the population of DOC patients will be to conceive
a quantification of the heterogeneous damage typically observed in
Figure 3. Correlations between spontaneous BOLD signal time-
courses across hemispheres. (a) Anatomical locations of regions-of-
interest (ROIs) in the right pre-central gyrus (red arrow) and the
homotopic ROI in the left hemisphere (blue arrow). (b) Time-courses in
a healthy subject exhibit high correlation (r=0.77). (c) Time-courses in
the vegetative patient who recovered consciousness exhibit high
correlation (r=0.73). (d) Time-courses in an impaired awareness patient
exhibit low correlation (r=20.02). Red and blue time-courses denote
signals from the right and left hemispheres, respectively normalized to
percent signal change. Abbreviations: CS, central sulcus; LS, lateral
sulcus; IPS, intra-parietal sulcus.
doi:10.1371/journal.pone.0037238.g003
Table 1. ICD values and Crawford and Howell test results.
Consciousness level ICD valuet(10) p – value
LIS 0.4757
20.8417 0.2098
MCS1 0.1033
24.3682 0.0007*
MCS2 0.2877
22.6219 0.0128*
VS1 0.1126
24.2799 0.0008*
VS2 0.64890.798 0.2217
Coma1 0.3113
22.3987 0.0187*
Coma2 0.3753
21.7923 0.0517
Brain Death 0.1506
23.91960.0014*
Control10.3683--
Control20.4592--
Control3 0.4894--
Control40.5156--
Control50.5415--
Control6 0.5875--
Control70.6047--
Control8 0.6261--
Control90.6308--
Control10 0.6803--
Control110.7072--
doi:10.1371/journal.pone.0037238.t001
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this population, taking into account asymmetric structural damage
which poses a challenge for the ICD measure proposed in our
study.
On a more general scope, the inference one can make about the
cognitive level from spontaneous fMRI fluctuations remains
controversial. On the one hand, spontaneous fluctuations can
appear in the absence of any task or intentional activity, are
detectable during anesthesia [64,65], and even accentuated during
sleep [12,66]. On the other hand, other evidence implicates a
contribution of ultra-slow fluctuations in perceptual decision
making [67,68] and motor control [69]. Furthermore, the fact
that the spatial organization of spontaneous ultra-slow activity
replicates task-related activity of functional networks [8,9,14]
suggests that their presence may reflect a hebbian co-activation
process [70], and conversely, their disruption may thus be due to a
reduction in network functionality.
As to the clinical significance of our findings, the results point to
the potential usefulness of the ICD in diagnosing individual cases
of impaired awareness. In order to validate such measure for
future clinical use, a larger population of DOC patients and
healthy subjects need to be tested, thus also allowing for an
estimation of the ICD measure’s specificity and sensitivity. The
fact that we did not observe the lowest ICD value in the brain
dead patient further emphasizes the need for a larger sample to
separate ‘‘true’’ ICD values from ICD values generated by
spurious noise. In a recent paper by Boly and colleagues, using a
seed based approach in the default mode network, a brain dead
patient failed to show any significant correlations in a whole brain
map [32].
The unexpected recovery of the VS patient showing a normal-
level ICD, points to the promising possibility that the ICD index
may serve, at least in specific cases, as a prognostic measure for
recovery from DOC. However, this single observation is of course
far from providing a conclusive demonstration and should be
taken at this stage only as a catalyst for a wide-scope search for
additional similar cases.
To conclude, we propose the ICD index as a measure of
symmetry in functional connectivity that can be used as a
diagnostic marker in DOC. This measure has the advantage of
relying on spontaneous fMRI signal fluctuations and thus does not
depend on patient cooperation, which is often absent in DOC
patients.
Supporting Information
Material S1
(DOC)
Inter-operator variability analysis.
Material S2
control group.
(DOCX)
Comparison of the ICD to an aged-matched
Figure S1
fluctuations in right pre-central gyrus and all other
cortical regions. Group correlation maps between a ‘‘seed’’
region in the pre-central gyrus (preCG) and all other cortical
voxels. (a) Correlations of spontaneous activity in the intact
awareness group (n=12) projected on inflated hemispheres as seen
from a lateral view (top left) and a medial view (top right), as well
as a flat format (bottom). (b) Correlations in the impaired
awareness group (n=7). Format as above. Red arrow, ‘‘seed’’
region location. Blue arrow, ‘‘mirror’’ regions in the left
hemisphere. Note that inter-hemispheric correlations are largely
absent in the impaired awareness group. Abbreviations: LH, left
hemisphere; RH, right hemisphere; CS, central sulcus; LS, lateral
Correlations between spontaneous BOLD
sulcus; IPS, intra-parietal sulcus; CinS, cingulate sulcus; POS,
parieto-occipital sulcus.
(PNG)
Figure S2
BOLD correlations between intact- and impaired-aware-
ness groups. Statistical maps of two-sample t-tests (see Methods)
comparing BOLD signal correlations in the two subject groups
(intact, n=12; impaired, n=7) separately for each voxel. Maps
are projected on inflated cortical surfaces as seen from lateral (top)
and medial (bottom) views in each panel. Panels show differences
in BOLD correlations of spontaneous activity with a ‘‘seed’’ in the
(a) right intra-parietal sulcus, (b) right post-central gyrus, and (c)
right pre-central gyrus. Note that in all maps, significant
differences were found in contralateral ‘‘mirror’’ sites (yellow
ellipses in the left hemisphere), as well as in the vicinity of seed
regions. Abbreviations: LH, left hemisphere; RH, right hemi-
sphere; IPS, intra-parietal sulcus; CS, central sulcus.
(PNG)
Voxel-by-voxel differences in Spontaneous
Figure S3
fluctuations in right intra-parietal sulcus and all other
cortical regions. Group correlation maps between a ‘‘seed’’
region in the intra-parietal sulcus (IPS) and all other cortical
voxels. (A) Correlations of spontaneous activity in the intact
awareness group (n=12) projected on inflated hemispheres as seen
from a lateral view (top left) and a medial view (top right), as well
as a flat format (bottom). (B) Correlations in the impaired
awareness group (n=7). Format as above. Red arrow, ‘‘seed’’
region location. Blue arrow, ‘‘mirror’’ regions in the left
hemisphere. Note that inter-hemispheric correlations are largely
absent in the impaired awareness group. Abbreviations: LH, left
hemisphere; RH, right hemisphere; CS, central sulcus; LS, lateral
sulcus; IPS, intra-parietal sulcus; CinS, cingulate sulcus; POS,
parieto-occipital sulcus.
(PNG)
Correlations between spontaneous BOLD
Figure S4
fluctuations in right post-central gyrus and all other
cortical regions. Group correlation maps between a ‘‘seed’’
region in the post-central gyrus (postCG) and all other cortical
voxels. (A) Correlations of spontaneous activity in the intact
awareness group (n=12) projected on inflated hemispheres as seen
from a lateral view (top left) and a medial view (top right), as well
as a flat format (bottom). (B) Correlations in the impaired
awareness group (n=7). Format as above. Red arrow, ‘‘seed’’
region location. Blue arrow, ‘‘mirror’’ regions in the left
hemisphere. Note that inter-hemispheric correlations are largely
absent in the impaired awareness group. Abbreviations: LH, left
hemisphere; RH, right hemisphere; CS, central sulcus; LS, lateral
sulcus; IPS, intra-parietal sulcus; CinS, cingulate sulcus; POS,
parieto-occipital sulcus.
(PNG)
Correlations between Spontaneous BOLD
Figure S5
individual subjects in all three groups. Subjects are
separated on the x-axis depending on their group (controls1:
non-aged matched group; controls2: aged-matched group, and
patients). Abbreviations: (*) refers to the VS patient who regained
consciousness shortly after scan (VS2 in the supplementary tables),
(+) refers to the Locked-in patient.
(TIF)
Inter-hemispheric Correlation Index (ICD) in
Figure S6
maps (seed: right PreCG) ordered according to the ICD
values. Correlation maps with a ‘‘seed’’ time-course in the right
pre-central gyrus (pre-CG) are shown in flat, left hemisphere
Single subject inter-hemispheric correlation
Reduced Connectivity in Disorders of Consciousness
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Page 7

(‘‘mirror site’’) cortical format for each subject separately. (a)
Location of seed (red ellipse) and location of the ‘‘mirror site’’ in
the contra-lateral hemisphere (black arrow). (b) Intact awareness
group, (c) Impaired awareness group. Abbreviations: LH, left
hemisphere; RH, right hemisphere; CS, central sulcus; LS, lateral
sulcus; I, individual ICD value; VS*, vegetative state patient who
recovered consciousness shortly following our study; LIS, locked-in
syndrome.
(PNG)
Figure S7
ICD values as computed on a subsample of 11 subjects by two
independent operators drawing the ROIs. Abbreviations: CCC,
concordance correlation coefficient; r, Pearson correlation.
(TIF)
Inter-operator correlation of ICD measure.
Table S1
imaging data of patients. Abbreviations: LIS, locked-in
syndrome; VS, vegetative state; MCS, minimally conscious state.
(DOCX)
Clinical, electrophysiological and structural
Table S2
results for the age-matched sample. Abbreviations: LIS,
ICD values and Crawford and Howell test
locked-in syndrome; VS, vegetative state; MCS, minimally
conscious state.
(DOCX)
Table S3
IPS, intra-parietal sulcus; preCG, pre-central gyrus; postCG, post-
central gyrus.
(DOCX)
Inter-operator variability data. Abbreviations:
Acknowledgments
The authors would like to thank Avital Hahamy who accepted to draw the
regions of interest used for the inter-operator analyses, and Dr. Daniel
Margulies for his insightful comments on an earlier version of the
manuscript.
Author Contributions
Conceived and designed the experiments: RM YN SL MB. Performed the
experiments: AS MB AV JFLT SL. Analyzed the data: SOC GH MH AS
YN AV MB JFLT MR ID. Contributed reagents/materials/analysis tools:
GH YN MR ID. Wrote the paper: SOC RM GH YN.
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