Intrinsic Brain Activity in ASC, Religioznawstwo, Doświadczenie religijne
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Intrinsic Brain Activity in Altered States
of Consciousness
How Conscious Is the Default Mode of Brain Function?
M. B
OLY
,
a
,
b
C. P
HILLIPS
,
a
L. T
SHIBANDA
,
c
A. V
ANHAUDENHUYSE
,
a
M. S
CHABUS
,
a
,
d
T. T. D
ANG
-V
U
,
a
,
b
G. M
OONEN
,
b
R. H
USTINX
,
e
P. M
AQUET
,
a
,
b
AND
S. L
AUREYS
a
,
b
a
Coma Science Group, Cyclotron Research Center, University of Liege, Liege, Belgium
b
Neurology Department, CHU Hospital, Liege, Belgium
c
Radiology Department, CHU Hospital, Liege, Belgium
d
University of Salzburg, Department of Physiological Psychology, Salzburg, Austria
e
Nuclear Medicine Department, CHU Hospital, Liege, Belgium
Spontaneous brain activity has recently received increasing interest in the neuroimaging com-
munity. However, the value of resting-state studies to a better understanding of brain–behavior
relationships has been challenged. That altered states of consciousness are a privileged way to
study the relationships between spontaneous brain activity and behavior is proposed, and com-
mon resting-state brain activity features observed in various states of altered consciousness are
reviewed. Early positron emission tomography studies showed that states of extremely low or high
brain activity are often associated with unconsciousness. However, this relationship is not absolute,
and the precise link between global brain metabolism and awareness remains yet difficult to assert.
In contrast, voxel-based analyses identified a systematic impairment of associative frontoparieto–
cingulate areas in altered states of consciousness, such as sleep, anesthesia, coma, vegetative
state, epileptic loss of consciousness, and somnambulism. In parallel, recent functional magnetic
resonance imaging studies have identified structured patterns of slow neuronal oscillations in the
resting human brain. Similar coherent blood oxygen level–dependent (BOLD) systemwide patterns
can also be found, in particular in the default-mode network, in several states of unconsciousness,
such as coma, anesthesia, and slow-wave sleep. The latter results suggest that slow coherent spon-
taneous BOLD fluctuations cannot be exclusively a reflection of conscious mental activity, but may
reflect default brain connectivity shaping brain areas of most likely interactions in a way that
transcends levels of consciousness, and whose functional significance remains largely in the dark.
Key words:
functional neuroimaging; resting state; disorders of consciousness; vegetative state
Introduction
known as the “default network.” Furthermore, Raichle
et al.
2
showed that most brain areas at rest manifest a
high level of “default” functional activity. This work
has called attention to the importance of intrinsic func-
tional activity in assessing brain behavior relationships,
and has now been extended in several functional mag-
netic resonance imaging (fMRI) studies.
An ongoing controversy concerns the value and in-
terpretability of resting-state studies and their contri-
bution to a better understanding of brain–behavior
relationships.
3
,
4
It has been suggested that intrinsic
brain activity would have a limited role for behav-
ioral outcomes. In this view, observations made un-
der resting conditions have no privileged status as a
fundamental metric of brain functioning, and the link
between the processing taking place at rest and its
physiology would be one without direct relevance to
In recent years, there has been a growing interest
from the neuroscientific community concerning spon-
taneous brain activity and its relation to cognition and
behavior. The concept of a “default mode of brain
function” arose from the need to explain consistent
brain-activity decreases in a set of areas during cogni-
tive processing as compared to a passive resting base-
line.
1
These areas, encompassing the posterior cingu-
late cortex/precuneus, the medial prefrontal cortex,
and bilateral temporoparietal junctions, began to be
Address for correspondence: Melanie Boly, Cyclotron Research Center,
B30, Allee du 6 aout, Sart Tilman, 4000 Liege, Belgium.
mboly@student.ulg.ac.be
Ann. N.Y. Acad. Sci. 1129: 119–129 (2008).
C
2008 New York Academy of Sciences.
doi: 10.1196/annals.1417.015
119
120
Annals of theNewYorkAcademyof Sciences
neuroscience. In contrast, the aims of cognitive neuro-
science would be best served by the study of specific
task manipulations, rather than of rest.
3
In response to this criticism, Raichle and Snyder
5
argued that “there is likely much more to brain func-
tion than that revealed by experiments manipulating
momentary demands of the environment.” In their
view, a first argument in this direction is the cost of in-
trinsic brain activity, which far exceeds that of evoked
activity.
6
Indeed, relative to the high rate of ongoing
or “basal” brain metabolism,
6
the amount dedicated
to task-evoked regional imaging signals is remarkably
small (estimated to be less than 5%). The brain contin-
uously expends a considerable amount of energy, even
in the absence of a particular task (i.e., when a subject is
awake and at rest). A significant fraction of the energy
consumed by the brain (quite possibly the majority) has
been shown to be a result of functionally significant
spontaneous neuronal activity.
7
From this cost-based
analysis of brain functional activity, it seems reasonable
to conclude that intrinsic activity may be as significant,
if not more so, than evoked activity in terms of over-
all brain function.
6
Another argument for the interest
of studying spontaneous brain activity is the striking
degree of functional organization exhibited by this in-
trinsic activity.
5
The first clue of this organization is the
consistent activity decreases in default network during
cognitive tasks.
1
Even more striking data recently arose
from fMRI blood oxygen level–dependent (BOLD)
connectivity studies in awake resting subjects, which
will be discussed later in this chapter. Maps of spon-
taneous network correlations have also been proposed
to provide tools for functional localization, for the un-
derstanding of clinical conditions such as Alzheimer’s
disease
8
,
9
and autism,
10
or for the study of com-
parative anatomy between primate species.
11
How-
ever, the functional significance of the observed pat-
terns of intrinsic brain activity remains actually poorly
understood.
We here propose that disorders of consciousness
are a privileged way to investigate the links be-
tween spontaneous brain activity and behavior. These
states are indeed mainly characterized by the alter-
ation of intrinsic brain activity, which induces dra-
matic changes in the contents of awareness and
responses to environmental stimuli and demands.
We will illustrate our view, reviewing common fea-
tures of spontaneous brain-activity patterns in al-
tered states of consciousness, as shown by metabolic
positron emission tomography (PET) data as well
as recent BOLD fMRI studies. We will also discuss
methodological issues of resting-state neuroimaging
experiments.
Consciousness as a Multidimensional
Concept
Consciousness has two major components: aware-
ness (i.e., the content of consciousness) and arousal (i.e.,
the level of consciousness).
12
Arousal and awareness
are usually positively correlated: when your arousal
decreases, so does your awareness [rapid eye move-
ment (REM) sleep being a notable exception]. Aware-
ness can also be divided into two components: self-
awareness and external awareness. Self- and external
awareness usually behave in an anti-correlated man-
ner. When you are engaged in self-related processes,
you are less receptive to environmental demands, and
vice versa.
13
,
14
A number of studies have compared
brain activation in circumstances that do or do not give
rise to consciousness in either of its two main senses of
awareness and arousal. Very few groups, however, have
studied situations in which wakefulness and arousal are
dissociated.
The vegetative state (VS) is a classic example of a
dissociated state of unconsciousness. VS patients are
fully aroused, but are unaware of themselves and their
environment. They can show automatic reactions like
moving their eyes, head, and limbs in a meaning-
less manner, and may even grimace, cry, or smile (al-
beit never contingently upon specific external stimuli).
Some patients might evolve toward full recovery or re-
main in the minimally conscious state,
15
where some
nonreflexive or nonmeaningful behaviors are shown,
but patients are still unable to communicate. In addi-
tion to their clinical and ethical importance, the study
of vegetative and minimally conscious states offers a
still widely unexploited means of studying human con-
sciousness.
12
In contrast to other unconscious states,
such as general anesthesia and deep sleep, where im-
pairment in arousal cannot be disentangled from im-
pairment in awareness, these states represent a unique
lesional approach enabling us to identify the neural
correlates of (un)awareness.
PET Studies of Brain Metabolism in
Altered States of Consciousness
PET studies modulating arousal, and hence aware-
ness, bymeans of anaesthetic drugs such as halothane
16
or propofol
17
have shown a drop in global brain
metabolism to around half of normal values. Similar
global decreases in metabolic activity are observed in
deep slow-wave sleep,
18
although in rapid eye move-
ment (REM) sleep brain metabolism returns to normal
waking values. On average, grey-matter metabolism
Boly et al.: IntrinsicBrainActivity inAlteredStates ofConsciousness
121
FIGURE 1.
Relationships between global brain
metabolism and awareness. The link between global brain
energy consumption and awareness is complex. Evidence
exists that both states of extremely low and extremely high
global brain metabolism are associated with small amounts
of awareness. An intermediate level of brain metabolism,
corresponding to a proper balance between inhibitory and
excitatory neural activity, seems to be necessary to allow
the genesis of awareness.
perpolarized state of the system, is maximized in condi-
tions of “intermediate” neural activity, and decreases
in states where the neural activity is extremely high
and near-synchronous.
27
It has been proposed that a
proper balance between excitatory and inhibitory ac-
tivity would be necessary to allow neurons to respond
appropriately to correlational changes in their input,
and to establish the functional connectivity as required
for a particular cognitive task or behavior.
29
In the
same line, Raichle and Gusnard suggested that a large
part of the brain’s default activity could be devoted to
ongoing synaptic processes associated with the main-
tenance of this balance.
30
In this view, global brain
metabolism would have a potentially decisive role by
allowing the presence of conscious perception or be-
havior.
The equivocal link between global brain activity and
consciousness is, however, further challenged by the
fact that in some patients who subsequently recovered
from a VS to normal consciousness, global metabolic
rates for glucose metabolism did not show substantial
changes.
31
Moreover, some awake healthy volunteers
have global brain metabolism values comparable to
those observed in some patients in a VS.
12
Inversely,
some well-documented vegetative patients have shown
close to normal global cortical metabolism.
32
These
data led us to focus rather on regional metabolism
in our quest for a better understanding of the links
between consciousness and resting-brain activity.
Voxel-based statistical analyses have sought to iden-
tify regions showing metabolic dysfunction in VS pa-
tients as compared with the conscious resting state
in healthy controls. These studies have identified a
systematic metabolic dysfunction, not in one brain
region but in a wide frontoparietal network en-
compassing the polymodal associative cortices in
VS: lateral and medial frontal regions bilater-
ally, parietotemporal and posterior parietal ar-
eas bilaterally, posterior cingulated, and precuneal
cortices,
12
,
21
known to be the most active “by
default” in resting nonstimulated conditions.
33
In con-
trast, arousal structures (encompassing the peduncu-
lopontine reticular formation, the hypothalamus, and
the basal forebrain) are relatively preserved in these
patients.
19
The same frontoparietal functional im-
pairment is found in various other states of un-
consciousness, that is, in sleep,
34
coma,
35
general
anesthesia,
36
generalized seizures,
37
or in other dis-
sociated unconscious states like absence seizures,
38
,
39
complex partial seizures,
40
or somnambulism.
41
F
IGURE
2 illustrates the involvement of the frontopari-
etal cortical network in awareness, while arousal rather
relies on subcortical structures.
is 50–70% of the normal range in comatose patients
of traumatic or hypoxic origin.
19
In VS, that is, in
“arousal without awareness,” global brain metabolic
activity also decreases to about 50% of normal lev-
els.
20
,
21
As vegetative patients are fully aroused, global
brain metabolism seems to correlate with awareness
rather than with arousal in altered consciousness states.
However, contradictory data exist concerning the
positive correlation between global brain metabolism
and levels of consciousness. First, not all anesthet-
ics suppress global cerebral metabolism. Some stud-
ies have also reported that ketamine, a so-called dis-
sociative anesthetic agent, increases global cerebral
metabolism and fast EEG rhythms at doses associated
with a loss of consciousness.
22
,
23
In the same line, in
comatose patients with traumatic diffuse axonal injury,
hyperglycolysis, leading to increased brainmetabolism,
has sometimes been reported.
19
Another counterex-
ample is the loss of consciousness induced by gener-
alized epilepsia
24
and some absence seizures,
25
where
global brain metabolism is diffusely increased. Finally,
a similar return from abnormally high global brain
metabolism to a normal balance of activation and in-
hibition could be evoked in rare cases of zolpidem-
evoked (a GABAergic agent) clinical improvement in
VS patients.
26
Looking at these data as a whole (summarized in
F
IG
. 1), the complex relationships between global brain
metabolism and awareness could be linked to Tononi’s
theory of information integration.
27
,
28
This theory
claims that consciousness is reflected in a system’s ca-
pacity to integrate information, and proposes a way—
the value
φ
is very low in the case of an hy-
—to measure such a capacity.
28
Computer
simulation shows that
φ
122
Annals of theNewYorkAcademyof Sciences
FIGURE 2.
(Left) Consciousness has two main components: arousal, or the level of consciousness, and awareness,
corresponding to the contents of consciousness per se. Arousal and awareness are usually positively correlated. However,
they involve different brain structures. Arousal involves the activity of subcortical structures encompassing brain-stem reticular
formation, hypothalamus, and basal forebrain. Awareness is related to the activity of a widespread set of frontoparietal
associative areas, both on the convexity and on the midline. (Right) Awareness can in turn be divided into two main
components: self and external awareness. In healthy volunteers, self- and external awareness are usually negatively
correlated. Similarly, the frontoparietal awareness network can in turn be divided into two sub-systems, involved in self-
and external awareness. Self-awareness networks encompass the posterior cingulate/precuneal cortices, medial frontal
cortex, and bilateral temporoparietal junctions. The external awareness network encompasses lateral frontal and parietal
cortices. In healthy volunteers, self- and external awareness networks usually show an anticorrelated pattern of activity.
These findings emphasize the importance of fron-
toparietal association areas in consciousness, and are
in line with the global workspace theory as introduced
by Baars.
42
,
43
This theory views the brain as a mas-
sive parallel set of specialized processors. Conscious-
ness might be a gateway to brain integration, enabling
access between otherwise separate neuronal functions.
In such a system, coordination and control may take
place by way of a central information exchange, al-
lowing some processors—such as sensory systems in
the brain—to distribute information to the system as
a whole. According to Baars,
35
frontoparietal associa-
tion areas would be an ideal candidate for being the
global workspace processor.
As reported below, awareness can in turn be divided
in two main components: self- and external aware-
ness. In the same line, F
IGURE
2 illustrates that fron-
toparietal network can be subdivided in areas involved
in external awareness, and in self-awareness. External
awareness network activity is crucial for conscious ex-
ternal stimuli perception, as documented in healthy
awake volunteers.
13
,
44
Self-awareness network encom-
passes the so-called “default network” and has been
involved in various aspects of self-related processes.
45
In awake healthy volunteers, self- and external aware-
ness networks usually show an anticorrelated pattern
of activity. Anticorrelations between self- and external
awareness networks have indeed been observed dur-
ing cognitive tasks,
46
sensory perception,
13
as well as
in studies of resting-state brain activity.
45
In contrast, in
most states of altered consciousness, both the activity
of subnetworks is similarly impaired.
In addition to activity in frontoparietal network,
awareness seems also to relate to the functional
connectivity within this network, and with the tha-
lami. Functional disconnections in long-range cortico–
cortical (between laterofrontal and midline-posterior
areas) and cortico–thalamo–cortical (between nonspe-
cific thalamic nuclei and lateral and medial frontal
cortices) pathways have been identified in the veg-
etative state.
21
,
47
In the same line, disruptions of
thalamo–cortical
48
and cortico–cortical
49
connectiv-
ity have been reported during other unconscious states
like sleep or anesthesia. Moreover, recovery from VS
is accompanied by a functional restoration of the fron-
toparietal network
31
and some of its cortico–thalamo–
cortical connections.
47
These results are in line with Dehaene and
Changeux’s recent computational model of the re-
lationships between spontaneous brain activity and
external stimuli awareness.
50
This model emphasizes
the importance of both thalamo–cortical and cortico–
cortical cerebral connections to create patterns of spon-
taneous brain activity hypothesized to allow conscious
perception.
Methodological Considerations in the
Study of Spontaneous Brain Activity
Using Functional Magnetic Rersonance
Up to now, a large majority of functional neu-
roimaging focused on brain activity elicited by exter-
nal stimuli or “evoked responses.” Likewise, current
Boly et al.: IntrinsicBrainActivity inAlteredStates ofConsciousness
123
mathematical tools have been mainly devised for the
analysis of data acquired during external stimulation
and not for spontaneous activity. Since the late 1990s,
brain activity fluctuations in the default resting state
have received increasing interest. Analytical tools have
therefore been developed for the processing of sponta-
neous fMRI (and electroencephalographic) data.
Spontaneous brain activity is by definition not trig-
gered by external stimuli. We therefore have no control
and, importantly, no a priori knowledge about when a
“spontaneous event” occurs. The problemof analyzing
spontaneous data is thus twofold, as we must simulta-
neously determine the “where” and “when” of brain
activity. Knowing one or the other, that is, location or
timing, allows a more complete characterization of the
spontaneous activity. Most methods rely on this idea,
that is, make assumptions about the timing or location
of some activity pattern to determine the location or
timing, respectively, of the activated brain system.
The BOLD signal recorded with fMRI allows the
mapping of brain activity with good spatial resolution
(submillimetric) but poor timing (around 1 s, at best),
due to the physiological origin of the hemodynamic
signal. Unfortunately, the signal recorded also contains
some noise on top of the brain signal. This noise com-
ponent has typically two origins: the scanner itself (such
as scanner instability)
51
and nonneural physiological
fluctuations due to, for example, cardiac or respiratory
artefacts.
52
,
53
Before investigating spontaneous brain
activity, it is necessary to correct the fMRI data for
these artefacts. One option to account for part of the
noise is to use a high sampling rate. With a very short
repetition time, the higher-frequency spurious (mainly
cardiac and respiratory) signal will not be aliased and
can be directly filtered out.
54–56
There are technical
limitations, though, as a compromise must be found
between speed of acquisition, field of view, and spatial
resolution. High-pass filtering is the method of choice
to remove the slowly varying “scanner drift” signal but,
of course, as for any filtering method, this removes pos-
sible information carrying signals. Alternatively, linear
regression can be employed to remove nonneural sig-
nal from the data.
52
,
57
For example, if physiological
parameters are measured along side the fMRI acqui-
sition, any BOLD signal correlated with (functions of)
these measurements can be regressed out. Other re-
gressors generated directly from the fMRI data are
also possible, such as the mean BOLD signal over all
voxels (usually called “global”), or the BOLD signal
from areas where there should be little or no neural
activity (e.g., the ventricles or white matter). As hinted
by its name, “linear regression” assumes a linear rela-
tionship between the “noise regressor” and the noise
part of the signal in all the voxels. If, by chance, some
neural signal was also correlated with the “noise regres-
sor,” that part of the signal would be lost for further
analysis.
Finally, “independent component analysis” (ICA)—
a relatively new approach—is capable of directly sepa-
rating the signals of interest (due to brain activity) from
the noise.
58
,
59
This approach is discussed later on in
more detail. The goal of the described procedures is
to ensure that the further analyzed signal is neurobi-
ologically meaningful and corresponds to the sponta-
neous brain activity of interest. We now focus on ways
to identify and characterize patterns of spontaneous
activity.
The most straightforward approach is correlation
or “functional connectivity” analyses. After choosing
a “seed region,” that is, a region of interest, the time
course of the BOLD signal is extracted (averaged over
the region or the first principal component) and a cor-
relation coefficient is calculated for all the other vox-
els, providing a correlation map. This method has the
advantage of being simple, sensitive, and easily inter-
pretable, but is limited to one seed region at a time.
45
,
60
Results rely heavily on the a priori choice of seed region
and provide no information about the causality of the
observed correlated activation. Indeed the activity in
two disconnected areas can be correlated because they
are driven by a third independent area. To study the
interaction between two seed regions, “physiophysio-
logical interaction” models are useful.
61
These mod-
els provide evidence for the interaction between dis-
tributed brain systems: voxels whose correlation with
one seed region is modulated by the other seed region
are highlighted.
A more mathematically sophisticated approach to
analyze spontaneous fMRI data is the previously men-
tioned ICA. ICA is a data-driven “blind source sep-
aration” algorithm that tries to decompose the entire
data set into components, spatial and temporal, that
are statistically independent.
62
–
64
The way statistical
independence is defined and reached leads to different
flavors of ICA decompositions. The main advantage
of ICA is the direct extractions of spatial maps, with
their associated time course: the sources of interest,
that is, spontaneous brain activity, can be automati-
cally separated from the noise components. However,
there remain two major difficulties with ICA. First,
the number of components to be extracted has to
be defined a priori, and results are highly dependent
on that chosen number. Second, components are not
ranked during the decomposition. It is the investiga-
tor’s duty to manually and subjectively decide, based
on his or her experience, knowledge, or priors, which
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