NATURE HUMAN BEHAVIOUR 1, 0069 (2017) | DOI: 10.1038/s41562-017-0069 | www.nature.com/nathumbehav 1
PUBLISHED: 24 APRIL 2017 | VOLUME: 1 | ARTICLE NUMBER: 0069
© 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.
Evidence for a large-scale brain system supporting
allostasis and interoception in humans
Ian R. Kleckner1*, Jiahe Zhang1, Alexandra Touroutoglou2,
4, Lorena Chanes1,
4, Chenjie Xia3,
W. Kyle Simmons6,
7, Karen S. Quigley1,
8, Bradford C. Dickerson3,
† and Lisa Feldman Barrett1,
Large-scale intrinsic brain systems have been identified for exteroceptive senses (such as sight, hearing and touch). We intro-
duce an analogous system for representing sensations from within the body, called interoception, and demonstrate its relation
to regulating peripheral systems in the body, called allostasis. Employing the recently introduced Embodied Predictive
Interoception Coding (EPIC) model, we used tract-tracing studies of macaque monkeys, followed by two intrinsic functional
magnetic resonance imaging samples (N=280 and N=270) to evaluate the existence of an intrinsic allostatic–interoceptive
system in the human brain. Another sample (N=41) allowed us to evaluate the convergent validity of the hypothesized
allostatic–interoceptive system by showing that individuals with stronger connectivity between system hubs performed bet-
ter on an implicit index of interoceptive ability related to autonomic fluctuations. Implications include insights for the brain’s
functional architecture, dissolving the artificial boundary between mind and body, and unifying mental and physical illness.
The brain contains intrinsic systems for processing exterocep-
tive sensory inputs from the world, such as vision, audition
and proprioception/touch1. Accumulating evidence indicates
that these systems work via the principles of predictive coding2–7,
in which sensations are anticipated and then corrected by sensory
inputs from the world. The brain, as a generative system, models
the world by predicting, rather than reacting to, sensory inputs.
Predictions guide action and perception by continually constructing
possible representations of the immediate future based on their
prior probabilities relative to the present context8,9. We and others
have recently begun to study the hypothesis that ascending sensory
inputs from the organs and systems within the body’s internal milieu
are similarly anticipated and represented (autonomic visceral and
vascular function, neuroendocrine fluctuations and neuroimmune
function)10–16. These sensations are referred to as interoception17–19.
Engineering studies of neural design20, along with physiologi-
cal evidence21, indicate that the brain continually anticipates the
body’s energy needs in an efficient manner and prepares to meet
those needs before they arise (for example, physical movements
to cool the body’s temperature before it gets too hot). This process
is called allostasis20–22. Allostasis is not a condition or state of the
body — it is the process by which the brain efficiently maintains
energy regulation in the body. Allostasis is defined in terms of pre-
diction, and recent theories propose that the prediction of intero-
ceptive signals is necessary for successful allostasis10,15,23–25. Thus, in
addition to the ascending pathways and brain regions important for
interoception17,18,26,27, recent theoretical discussions11 have proposed
the existence of a distributed intrinsic allostatic–interoceptive sys-
tem in the brain (analogous to the exteroceptive systems). A full
investigation of the predictive nature of an allostatic–interoceptive
brain system requires multiple studies under various conditions.
Here, we identify the anatomical and functional substrates for a
unified allostatic–interoceptive system in the human brain and
report an association between connectivity within this system and
individual differences in interoceptive-related behaviour during
allostatically relevant events.
We first review tract-tracing studies of non-human animals that
provide the anatomical substrate for our hypothesis that the brain
contains a unified, intrinsic system for allostasis and interoception.
Next, we present evidence of this hypothesized system in humans
using functional connectivity analyses on three samples of task-
independent (‘resting state’) functional magnetic resonance imaging
(fMRI) data (also called ‘intrinsic’ connectivity). We then present
brain–behaviour evidence to validate the hypothesized allostatic–
interoceptive system by using an implicit measure of interoception
during an allostatically challenging task. Finally, we summarize
empirical evidence to show that this allostatic–interoceptive sys-
tem is a domain-general system that supports a wide range of psy-
chological functions including interoception, emotion, memory,
reward and cognitive control28,29. That is, whatever else this system
might be doing — remembering, directing attention and so on — it
is also predictively regulating the body’s physiological systems in the
service of allostasis to achieve those functions23.
Our work synthesizes anatomical and functional brain studies
that together provide evidence of a single brain system — comprising
the salience and default mode networks — that supports not just
allostasis but a wide range of psychological functions (such as emo-
tion, pain, memory and decision-making) that can all be explained
by their reliance on allostasis. To our knowledge, this evidence
and our simple yet powerful explanation has not been presented
despite the fact that many functional imaging studies show that
the salience and default mode networks support a wide range of
1Department of Psychology, Northeastern University, 105–107 Forsyth Street, Boston, Massachusetts 02115, USA. 2Department of Neurology,
Massachusetts General Hospital and Harvard Medical School, 15 Parkman Street, Boston, Massachusetts 02114, USA. 3Athinoula A. Martinos Center
for Biomedical Imaging, 149 13th Street, Charlestown, Massachusetts 02129, USA. 4Psychiatric Neuroimaging Division, Department of Psychiatry,
Massachusetts General Hospital and Harvard Medical School, 55 Fruit Street Boston, Massachusetts 02114, USA. 5Frontotemporal Disorders Unit,
Department of Neurology, Massachusetts General Hospital and Harvard Medical School, 55 Fruit Street Boston, Massachusetts 02114, USA.
6Laureate Institute for Brain Research, 6655 South Yale Avenue, Tulsa, Oklahoma 74136, USA. 7School of Community Medicine, The University of Tulsa,
4502 East 41st Street, Tulsa, Oklahoma 74135, USA. 8Edith Nourse Rogers Memorial VA Hospital, 200 Springs Road, Bedford, Massachusetts 01730, USA.
†These authors jointly supervised this work. *e-mail: firstname.lastname@example.org; email@example.com
© 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved. © 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.
2 NATURE HUMAN BEHAVIOUR 1, 0069 (2017) | DOI: 10.1038/s41562-017-0069| www.nature.com/nathumbehav
ARTICLES NATURE HUMAN BEHAVIOUR
psychological functions (that is, they are domain-general30; see
previous reviews28,29). Our paper provides the groundwork for
a theoretical and empirical framework for making sense of these
findings in an anatomically principled way. Our key hypotheses
and results are summarized in Table1.
Anatomical evidence supporting the proposed allostatic–
Over three decades of tract-tracing studies of the macaque mon-
key brain clearly demonstrate an anatomical substrate for the pro-
posed flow of the brain’s prediction and prediction error signals.
Specifically, anatomical studies indicate a flow of information
within the laminar gradients of these cortical regions according to
the structural model of corticocortical connections in ref. 31 (for a
review, see ref. 32). In addition, this structural model of corticocor-
tical connections has been seamlessly integrated with a predictive
coding framework11,12. Unlike other models of information flow that
work in specific regions of cortex, the structural model successfully
predicts information flow in frontal, temporal, parietal and occipital
cortices33–37. Accordingly, prediction signals flow from regions with
less laminar development (for example agranular regions) to regions
with greater laminar development (for example granular regions),
whereas prediction error signals flow in the other direction.
In our recently developed theory of interoception, the EPIC
model11, we integrated the active inference approach to predictive
coding38–40 with the structural model of ref. 31 to hypothesize that
less-differentiated agranular and dysgranular visceromotor cortices
in the cingulate cortex and anterior insula initiate visceromotor
predictions through their cascading connections to the hypothal-
amus, the periaqueductal grey (PAG) and other brainstem nuclei
known to control the body’s internal milieu41–44 (also see ref. 32;
red pathways in Fig. 1); simultaneously, the cingulate cortex and
anterior insula send the anticipated sensory consequences of those
visceromotor actions (that is, interoceptive predictions) to the
more granular primary interoceptive cortex in the dorsal mid to
posterior insula (dmIns/dpIns18,45,46; blue solid pathways in Fig.1).
Using this logic, we identified a key set of cortical regions with
visceromotor connections that should form the basis of our unified
system for interoception and allostasis (we also included one subcor-
tical region, the dorsal amygdala (dAmy), in this analysis because of
the role of the central nucleus in visceromotor regulation; see Methods
for details). This evidence is summarized in Table2. As predicted
by our EPIC model, most of the key visceromotor regions in the
proposed interoceptive system do, in fact, have monosynaptic, bidi-
rectional connections to primary interoceptive cortex, reinforcing
the hypothesis that they directly exchange interoceptive predic-
tion and prediction error signals. We also confirmed that these
visceromotor cortical regions do indeed monosynaptically project
to the subcortical and brainstem regions that control the internal
milieu (that is, the autonomic nervous system, immune system and
neuroendocrine system), such as the hypothalamus, PAG, parabra-
chial nucleus (PBN), ventral striatum, and nucleus of the solitary
tract (NTS) (Table2, right column).
Next, we tested for evidence of these connections in functional
data from human brains. Axonal connections between neurons,
both direct (monosynaptic) and indirect (for example, disynap-
tic) connections, are closely reflected in intrinsic brain systems
(see previous reviews47,48). As such, we tested for evidence of these
connections in functional connectivity analyses on two samples of
low-frequency, blood oxygenation-level dependent (BOLD) signals
during task-independent (that is, ‘resting state’) fMRI scans col-
lected on human participants (discovery sample, N= 280, 174
female, mean age= 19.3 years, s.d.= 1.4 years; replication sample,
N= 270, 142 female, mean age = 22.3 years, s.d. = 2.1 years).
Table 1 | Summary of this study’s hypotheses, predictions or questions, and results.
EPIC hypothesis Experimental prediction Result in the current study
Interoception and visceromotor
control are part of a unified
brain system that supports
Primary interoceptive cortex (for example, dmIns/
dpIns) is anatomically and functionally connected to
agranular and dysgranular visceromotor hubs of the
cortex (for example, sgACC, pACC, aMCC).
The interoceptive and visceromotor hubs are anatomically
connected in monkeys (Table2). The interoception and
visceromotor hubs are functionally connected in humans
(Fig.2, Supplementary Table 1). Coordinates for human hubs
are shown in Table3.
The allostatic–interoceptive system also includes
subcortical and brainstem visceromotor regions.
Previously established subcortical and brainstem visceromotor
regions (for example, hypothalamus and PAG) are part
of the unified system for allostasis/interoception (Fig.4,
Supplementary Fig. 6).
The allostatic–interoceptive brain system contains
The allostatic–interoceptive system comprises two established
large-scale brain networks that contain the majority of limbic
cortices: the salience network and the default mode networks
(Fig.3, Supplementary Fig. 3).
Connectivity in the allostatic–interoceptive system
is related to an implicit performance measure of
interoception in humans.
The correspondence between sympathetic arousal
(electrodermal activity) and experienced arousal during
an allostatically challenging task is related to functional
connectivity within the allostatic–interoceptive system in
humans (Supplementary Fig. 8).
system is domain-general.
The allostatic–interoceptive system sits at the core
of the brain’s computational architecture.
Many hubs of the allostatic–interoceptive system have
been previously identified as members of the ‘rich club’,
which are the most densely connected within the brain and
therefore help to constitute the brain’s ‘neural backbone’ for
coordinating neural synchrony (Fig.3, Supplementary Table 4).
Brain activity and connectivity in the allostatic–
interoceptive system is associated with a variety
of psychological functions.
Both the default mode network and the salience network
support various mental phenomena across major
psychological domains (for example, cognition, emotion,
perception and action; Fig.5).
Other hypotheses, such as the computational dynamics of the proposed allostatic–interoceptive network, are beyond the scope of this study. pACC,pregenual anterior cingulate cortex.
NATURE HUMAN BEHAVIOUR 1, 0069 (2017) | DOI: 10.1038/s41562-017-0069 | www.nature.com/nathumbehav 3
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NATURE HUMAN BEHAVIOUR
We then examined the validity of these connections in a third inde-
pendent sample of participants (N= 41, 19 female, mean age= 33.5
years, s.d.= 14.1 years), following which we situated these findings
in the larger literature on network function.
Cortical and amygdalar intrinsic connectivity supporting a
unified allostatic–interoceptive system in humans. Our seed-
based approach estimated the functional connectivity between a
set of voxels of interest (the seed) and the voxels in the rest of
the brain as the correlation between the low-frequency portion of
their BOLD signals over time, producing a discovery map for each
seed region. Starting with the anatomical regions of interest speci-
fied by the EPIC model, and verified in the anatomical literature,
we selected seed regions guided by previously published func-
tional studies. We selected two groupings of voxels in primary
interoceptive cortex (dpIns and dmIns) that consistently showed
increased activity during task-dependent fMRI studies of intero-
ception (Table3, first and second rows). We selected seed regions
for cortical visceromotor regions and the dAmy using related
studies (Table3, remaining rows). As predicted, the voxels in the
primary interoceptive cortex and visceromotor cortices showed
statistically significant intrinsic connectivity (Fig. 2; replication
sample Supplementary Fig. 1). The dpIns was intrinsically con-
nected to all visceromotor areas of interest (seven two-tailed, one-
sample t-tests were each significant at P< 10−7; Supplementary
Table 1), and the dmIns was intrinsically connected to most of
them (Supplementary Table 1). The discovery and replication
samples demonstrated high reliability for connectivity profiles of
all seeds (η2 mean= 0.99, s.d.= 0.004).
Next, we computed η2 for all pairs of maps to determine their
spatial similarity49 (mean= 0.56, s.d.= 0.17), and then performed
k-means clustering of the η2 similarity matrix to determine the
configuration of the system. Results indicated that the allostatic–
interoceptive system is composed of two intrinsic networks con-
nected in a set of overlapping regions (Fig. 3; replication sample,
Supplementary Fig. 2). The spatial topography of one network
resembled an intrinsic network commonly known as the default
mode network (Supplementary Figs 3 and 4; for a review, see
ref. 50). The second network resembled an intrinsic network com-
monly known as the salience network51,52 (Supplementary Figs 3
and 4), the cingulo-opercular network53 or the ventral attention
network54. Resemblance was confirmed quantitatively by compar-
ing the percentage overlap in our observed networks to recon-
structions of the default mode and salience networks reported
elsewhere55 (Supplementary Table 2). Other cortical regions within
the interoceptive system shown in Fig.3(for example, dorso medial
prefrontal cortex, middle frontal gyrus), not listed in Table2, sup-
port visceromotor control by direct anatomical projections to
the hypothalamus and PAG (Supplementary Table 3), support-
ing our hypothesis that this system plays a fundamental role in
visceromotor control and allostasis.
Subcortical, hippocampal, brainstem and cerebellar connectivity
supporting a unified allostatic–interoceptive system in humans.
Using a similar analysis strategy, we assessed the intrinsic connec-
tivity between the cortical and dorsal amygdalar seeds of interest
and the thalamus, hypothalamus, cerebellum, the entire amygdala,
hippocampus, ventral striatum, PAG, PBN and NTS. The observed
functional connections with these cortical and amygdalar seeds,
which regulate energy balance, strongly suggest that the proposed
allostatic–interoceptive system itself also regulates energy bal-
ance (see Supplementary Discussion for details). All results repli-
cated in our independent sample (N= 270; Supplementary Fig. 5,
η2 mean= 0.98, s.d.= 0.008). Figure4 illustrates the connectivity
between the default mode and salience networks and the non-
cortical targets in the discovery sample. Supplementary Fig. 6
shows connectivity between the individual cortical and amyg-
dalar seed regions listed in Table2. We also observed specificity
in the proposed allostasis/interoception system: non-visceromotor
brain regions that are unimportant to interoception and allostasis,
such as the superior parietal lobule (Supplementary Fig. 7), did not
show functional connectivity to the subcortical regions of interest.
The cortical hubs of the allostatic–interoceptive system also
overlapped in their connectivity to non-cortical regions involved in
allostasis (purple in Fig.4), including the dAmy, the hypothalamus,
the PBN and two thalamic nuclei — the ventromedial posterior
nucleus, and both the medial and lateral sectors of the medio-
dorsal nucleus (which shares strong reciprocal connections with
medial and orbital sectors of the frontal cortex, the lateral sector of
the amygdala, and other parts of the basal forebrain; for a review,
see ref. 56). Additionally, the connector hubs shared projections in
the cerebellum and hippocampus (see Fig.4).
Taken together, our intrinsic connectivity analyses failed to con-
firm only five monosynaptic connections (8%) that were predicted
Figure 1 | We identified key visceromotor cortical regions (in red) that
provide cortical control of the body’s internal milieu. The regions include
the aMCC (also called dorsal anterior cingulate cortex41,42), pregenual
anterior cingulate cortex (pACC), sgACC (for a review of the cingulate,
see ref. 176) and the vaIns (also called agranular insula43,183 or posterior
orbitofrontal cortex193); these regions have a less-developed laminar
structure (that is, they are agranular or dysgranular32,176). We also included
the dAmy because it contains the central nucleus which is also involved in
visceromotor control (for a review, see ref. 145). Primary interoceptive cortex
spans the dmIns to the dpIns17 along a dysgranular to granular194 gradient
(green regions). Previous work11 summarized preliminary tract-tracing
evidence, supporting the EPIC model, demonstrating that allostasis and
interoception are maintained within an integrated system involving limbic
cortices (in red) that initiate visceromotor directions to the hypothalamus
and brainstem nuclei (for example, PAG, PBN and NTS; citations in
Table2) to regulate the autonomic, neuroendocrine and immune systems
(red paths). These visceromotor control regions (less-developed laminar
organization) also send anticipated sensory consequences of visceromotor
changes (as interoceptive prediction signals) to primary interoceptive
cortex (more-developed laminar organization; solid blue paths). The
incoming sensory inputs from the internal milieu of the body are carried
along the vagus nerve and small-diameter C and Aδ fibres (dashed green
path) to primary interoceptive cortex in the dorsal sector of the mid to
posterior insula (for a review, see ref. 17); comparisons between prediction
signals and ascending sensory input results in interoceptive prediction error.
Current interoceptive predictions can be updated by passing prediction
error signals to visceromotor regions (dashed blue paths); prediction errors
are learning signals and also adjust subsequent predictions. (For simplicity,
ascending feedback to visceromotor regions is not shown.)
© 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved. © 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.
4 NATURE HUMAN BEHAVIOUR 1, 0069 (2017) | DOI: 10.1038/s41562-017-0069| www.nature.com/nathumbehav
ARTICLES NATURE HUMAN BEHAVIOUR
from non-human tract-tracing studies: hypothalamus–dAmy,
hypothalamus–dpIns, PAG–dAmy, PAG–medial ventral anterior
insula (mvaIns) and NTS–subgenual anterior cingulate cortex
(sgACC). This is approximately what we would expect by chance;
however, there are several factors that might account for why these
predicted connections did not materialize in our discovery and
replication samples. First, all discrepancies involved the sgACC,
PAG or hypothalamus, whose BOLD data exhibit poor signal-to-
noise ratio because of their small size and their proximity to white
matter or pulsating ventricles and arteries57. Second, individual dif-
ferences in anatomical structure can make inter-subject alignment
challenging, particularly in 3 T imaging of the brainstem where
clear landmarks are not always available. Of the connections that
did not replicate, one involved the anterior insula; there is some
disagreement in the macaque anatomical literature as to the exact
location of the anterior insula45,58–60, which might help to explain any
lack of correspondence between intrinsic and tract-tracing findings
that we observed.
Validating the functions of the allostatic–interoceptive system
in humans. The allostatic–interoceptive system reported in Fig. 3
was replicated in the validation sample (η2 mean= 0.84, s.d.= 0.05
compared with discovery-sample cortical maps; η2 mean= 0.76,
s.d. = 0.07 compared with discovery-sample subcortical maps).
These η2 values are respectable and demonstrate adequate reliabil-
ity of the system according to conventional psychometric theory,
although the lower η2 values are likely to be due to the smaller sam-
ple size, which magnifies the effects of poor signal-to-noise ratio in
subcortical regions. Convergent validity for the proposed allostatic–
interoceptive system was demonstrated, in that individuals with
stronger functional connectivity within the system also reported
greater arousal while viewing images that evoked greater activity
in the sympathetic nervous system. Participants viewed 90 evoca-
tive photos known to induce a range of autonomic nervous system
changes and corresponding feelings of arousal61, as well as changes
in BOLD activity within these regions62,63. We predicted, and found,
that individuals showing stronger intrinsic connectivity within the
Table 2 | Summary of tract-tracing study results in non-human animals, demonstrating anatomical connections between cortical
visceromotor and primary interoceptive sensory regions, as well as between cortical and non-cortical visceromotor regions
Visceromotor regions Subcortical and brainstem visceromotor
To dpIns/dmIns To vaIns To sgACC
(BA 24, 32)
To amygdala To other subcortical and
– Case A, Fig. 1
of ref. 146
Not evident†Case 1, Fig. 5
of ref. 156
Case B, Fig. 3
of ref. 157
Case 2, Fig. 3 of
Fig. 1 of ref. 60
PAG: not observed159
Ventral striatum 162
From vaIns‡Case C, Fig. 4 of ref. 146
Case A, Fig. 1
of ref. 157
Fig. 8 of
Case 1, Fig. 5
of ref. 156
Case 2, Fig 6
of ref. 156
Case A, Fig. 1
of ref. 157
Case A, Fig. 1 of
Case 103, Fig. 3 of
Fig. 2, Table 2 of
Not evident§Case M707167 – Case 1, Fig. 5
of ref. 156
Fig. 2A of
Case 3, Fig. 7
of ref. 156
Fig. 3A of
Case 103, Fig. 3
of ref. 164
Fig. 5 of ref. 147
(BA 24, 32)
Not evident§Case M776167 Fig. 1 of
– Case 3, Fig. 7
of ref. 156
Fig. 3A of
Case 103, Fig. 3
of ref. 164
Fig. 5 of ref. 147
Ventral striatum (cat)173
Case C, Fig. 4 of
Case A, Fig. 1
of ref. 146
Fig. 4 of
Case 1, Fig. 5
of ref. 156
Fig. 2A of
– Case 103, Fig. 3
of ref. 164
Fig. 5 of ref. 147
PBN: not present175
From amygdala Case C, Fig. 4 of
Lateral basal nucleus,
Case 5, Fig. 6 of ref. 147
Case A, Fig. 1
of ref. 146
Case 4, Fig. 5
of ref. 147
Fig. 6 of
Fig. 13 of
Fig. 6 of ref. 147 – Hypothalamus43
Note: connectivity evidence is in monkeys unless otherwise indicated (for example rats, cats). Some connections from dpIns/dmIns to the NTS are unclear, owing to ambiguity in how ref. 161 reported
subregions of the insula.
*We did not assess projections from subcortical and brainstem regions to cortical regions because we only wanted to determine whether the cortical regions support visceromotor control. †Connection
from dpIns/dmIns to sgACC not evident in several monkey studies157,168,179–181 that have the potential to show it. ‡The medial portion of the vaIns exhibits connectivity with subcortical and brainstem regions,
but not the lateral portion of the vaIns43,182. §Connection from sgACC to dpIns/dmIns and from pACC to dpIns/dmIns not evident in several monkey studies146,167,179,180 that have the potential to show them,
although weak, direct connectivity is evident in a recent tractography study in humans183 (Fig.5). Moreover, connections between sgACC, pACC and dpIns have been observed in intrinsic functional
connectivity analyses in humans (for example, Fig. 6 of ref. 184). The discrepancy between human findings and the tract-tracing studies in monkeys failing to show connectivity might reflect an expansion
of Brodmann area (BA) 24 anterior and ventral to the corpus callosum in humans relative to monkeys and/or the presence of connections between BAs 25/32 and the posterior insula in humans that
do not exist in monkeys (H. Evrard, personal communication). BA,Brodmann area.
NATURE HUMAN BEHAVIOUR 1, 0069 (2017) | DOI: 10.1038/s41562-017-0069 | www.nature.com/nathumbehav 5
NATURE HUMAN BEHAVIOUR
allostatic–interoceptive system (specifically, connectivity between
dpIns and anterior midcingulate cortex (aMCC)) also demon-
strated a stronger concordance between objective and subjective
measures of bodily arousal while viewing allostatically relevant
images (P= 0.003; see Supplementary Fig. 8; see Supplementary
Discussion for details).
There were three reasons for demonstrating the convergent validity
of the proposed allostatic–interoceptive system using this task. First,
there is a decades-old body of research indicating that interoception
enables the subjective experience of arousal64–66. Thus, the amount
of joint information shared by an objective, psychophysiological
measure of visceromotor change (skin conductance) and the sub-
jective experience of arousal (self-report ratings) is an implicit,
behavioural measure of interoceptive ability. Indeed, individuals
with more accurate interoceptive ability exhibit a stronger corre-
spondence between subjective arousal and physiological arousal
in response to similar evocative photos67. Second, explicit reports
of interoceptive performance on heartbeat detection tasks68–70
are complex to interpret neurally because they require synthesiz-
ing and comparing information from other systems (somatosensory
system71, frontoparietal control systems and, for heartbeat detec-
tion, the auditory system); in addition, these tasks are sometimes
too hard (yielding floor effects) or have questionable validity70.
At this juncture, it is tempting to ask whether the unified allo-
static–interoceptive system is specific to allostasis and interocep-
tion. From our perspective, this is the wrong question to be asking.
The past two decades of neuroscience research have brought us
to the brink of a paradigm shift in understanding the workings of
the brain, setting the stage to revolutionize brain:mind mapping.
Neuroscience research is increasingly acknowledging that brain
networks have a one (network) to many (function) mapping28–30,72–74.
Our findings contribute to this discussion: a brain system that is
fundamental to allostasis and interoception is not unique to those
functions, but instead is also important for a wide range of psycho-
logical phenomena that span cognitive, emotional and perceptual
domains (Fig.5). This finding is not a failure of reverse inference;
it suggests a functional feature of how the brain works.
The integrated allostatic–interoceptive brain system is a complex
cortical and subcortical system consisting of connected intrin-
sic networks. Our work demonstrates a single brain system that
supports not just allostasis but also a wide range of psychologi-
cal phenomena (emotions, memory, decision-making, pain) that
can all be explained by their reliance on allostasis. Other studies
have already shown that regions controlling physiology are also
regions that control emotion. In fact, this was Papez’s original logic
for assuming that the ‘limbic system’ was functionally for emo-
tion. This paper goes beyond this observation. Regions control-
ling inner body physiology lie in networks that also support social
affiliation, pain, judgements, empathy, reward, addiction, memory,
stress, craving and decision-making, among others (Fig. 5). More
and more, functional imaging studies30 are finding that the salience
and default mode networks are domain-general (see previous
reviews28,29). Our paper provides the groundwork for a theoretical
and empirical framework for making sense of these findings in an
anatomically principled way.
Our investigation was strengthened by our theoretical frame-
work (the EPIC model11), the converging evidence from structural
studies of the brain (tract-tracing studies in monkeys plus the
well-validated structural model of information flow), our use of
multiple methods (intrinsic connectivity in humans, as well as brain–
behaviour relationships) and our ability to replicate the system in
three separate samples totalling over 600 human participants. Our
results are consistent with prior anatomical and functional stud-
ies that have investigated portions of this system at cortical and
subcortical levels17,18,26,27,75–78, including evidence that limbic corti-
cal regions control the brainstem circuitry involved with allostatic
functions such as cardiovascular control, respiratory control and
thermoregulatory control79, as well as prior investigations that
focused on the intrinsic connectivity of individual regions such as
the insula80, the cingulate cortex81, the amygdala82 and the ventro-
medial prefrontal cortex83; but our results go beyond these prior
studies in several ways. First, we observed an often-overlooked
finding when interpreting the functional significance of certain
brain regions: the dorsomedial prefrontal cortex, the ventrolateral
prefrontal cortex, the hippocampus and several other regions have
both a structural and functional pattern of connectivity that indi-
cates their role in visceromotor control. A second often-overlooked
finding is that relatively weaker connectivity patterns (for example
between the visceromotor sgACC and the primary interoceptive
cortex) are reliable, and future studies may find that they are of func-
tional significance. Third, we demonstrated behavioural relevance
of connectivity within this network, something that prior studies of
large-scale autonomic control networks have yet to test75–77.
Taken together, our results strongly support the EPIC model’s
hypothesis that visceromotor control and interoceptive inputs are
integrated within one unified system11, as opposed to the traditional
view that the cerebral cortical regions sending visceromotor signals
and those that receive interoceptive signals are organized as two
segregated systems, similar to the corticospinal skeletomotor effer-
ent system and the primary somatosensory afferent system.
Perhaps most importantly, the allostatic–interoceptive system has
been shown to have a role in a wide range of psychological phenom-
ena, suggesting that allostasis and interoception are fundamental
features of the nervous system. Anatomical, physiological and signal
processing evidence suggests that a brain did not evolve for rational-
ity, happiness or accurate perception; rather, all brains accomplish the
same core task20: to efficiently ensure resources for physiological sys-
tems within an animal’s body (its internal milieu) so that an animal
can grow, survive, thrive and reproduce. That is, the brain evolved
to regulate allostasis21. All psychological functions performed in the
service of growing, surviving, thriving and reproducing (such as
remembering, emoting, paying attention or deciding) require the
efficient regulation of metabolic and other biological resources.
Our findings add an important dimension to the existing obser-
vations that the default mode and salience networks serve as a high-
capacity backbone for integrating information across the entire
brain84. Diffusion tensor imaging studies indicate, for example,
that these two networks contain the highest proportion of hubs
Table 3 | Seeds used for intrinsic connectivity analyses
Seed Type of region predicted by
dpIns Primary interoceptive cortex Granular 36, − 32, 16185
dmIns Primary interoceptive cortex Dysgranular 41, 2, 3186
sgACC Visceromotor control Agranular 2, 14, − 6187
pACC Visceromotor control Agranular 13, 44, 0185
aMCC Visceromotor control Agranular 9, 22, 33188
mvaIns Visceromotor control Agranular 30, 16, − 14189
lvaIns Sensory integration Agranular 44, 6, − 15188
dAmy Visceromotor control N/A 27, 3, − 12190
Note: all seeds are in the right hemisphere. Evidence for cortical lamination comes from ref. 42
(see also refs 191,192).
Each anatomical region of interest was represented by one 4-mm-radius seed except for the
vaIns, which required a medial and a lateral seed (mvaIns and lateral vaIns (lvaIns), respectively)
to capture the previously established functional distinction between the medial visceromotor
network (containing mvaIns) and the orbital sensory integration network (containing lvaIns) in the
orbitofrontal cortex182. MNI, Montreal Neurological Institute.
6 NATURE HUMAN BEHAVIOUR 1, 0069 (2017) | DOI: 10.1038/s41562-017-0069| www.nature.com/nathumbehav
ARTICLES NATURE HUMAN BEHAVIOUR
regions, are the most powerful predictors in the brain11,32. Indeed,
hub regions in these networks display a pattern of connectivity
that positions them to easily send prediction signals to every other
sensory system in the brain12,32.
The fact that default mode and salience networks are concur-
rently regulating and representing the internal milieu, while they
are routinely engaged in a wide range of tasks spanning cognitive,
belonging to the brain’s ‘rich club’, defined as the most densely
interconnected regions in the cortex73,85 (several of which are con-
nector hubs within the allostatic–interoceptive system; see Fig. 3
and Supplementary Table 4). All other sensory and motor networks
communicate with the default mode and salience networks, and
potentially with one another, through these hubs1,85. The agranular
hubs within the two networks, which are also visceromotor control
Human seed region Monkey anatomical connectivity Human functional connectivity
Figure 2 | Eight regions (‘seeds’) used to estimate the unified allostasis/interoceptive system connecting the cortical and amygdalar visceromotor
regions and primary interoceptive regions. The left column shows the seed region for each discovery map on a human brain template. The middle column
summarizes the anatomical connectivity derived from anterograde and/or retrograde tracers injected into macaque brains at a location homologous to the
human seed (asterisks with blue arrows). The right column shows the human intrinsic connectivity discovery maps depicting all voxels whose time course
is correlated with that of the seed (ranging from P< 10−5 in red to P< 10−40 in yellow, uncorrected, N= 280). To avoid type I and type II errors, which are
enhanced with the use of stringent statistical thresholds195, we opted to separate signal from random noise using replication, according to the mathematics
of classical measurement theory147. These results were replicated in a second sample, N= 270 participants, indicating that they are reliable and cannot be
attributed to random error (Supplementary Fig. 1). Functional connectivity to the entire amygdala and other subcortical regions are shown in Fig.4.
The monkey anatomical connectivity figures were coloured red to visualize results, and some were mirrored to match the orientation of the human brain
maps. Tract-tracing figures (middle column) adapted with permission from: sgACC, ref. 168, John Wiley and Sons; pACC and aMCC, ref. 156, Elsevier;
dAmy, ref. 164, Elsevier; mvaIns, lateral vaIns (lvaIns), dmIns and dpIns, ref. 157, John Wiley and Sons. The figures from ref. 156 were adapted to show the
insula in its lateral view.
NATURE HUMAN BEHAVIOUR 1, 0069 (2017) | DOI: 10.1038/s41562-017-0069 | www.nature.com/nathumbehav 7
NATURE HUMAN BEHAVIOUR
perceptual and emotion domains, all of which involve value-based
decision-making and action30,86–90, suggests a provocative hypoth-
esis for future research: whatever other psychological functions
the default mode and salience networks are performing during any
given brain state, they are simultaneously maintaining or attempt-
ing to restore allostasis and are integrating sensory representations
of the internal milieu with the rest of the brain. Therefore, our
results, when situated in the published literature, suggest that the
default mode and salience networks create a highly connected func-
tional ensemble for integrating information across the brain, with
interoceptive and allostatic information at its core, even though it
may not be apparent much of the time.
When understood in this framework, our current findings do
more than just add more functions to the ever-growing list attributed
to the default mode and salience networks (which currently spans
cognition, attention, emotion, perception, stress and action28,30).
Our results offer an anatomically plausible computational hypoth-
esis for a set of brain networks that have long been observed but the
functions of which have not been fully understood. The observa-
tion that allostasis (regulating the internal milieu) and interocep-
tion (representing the internal milieu) are at the anatomical and
functional core of the nervous system18,20 offers a generative avenue
for further behavioural hypotheses. For example, it has recently
been observed that many of the visceromotor regions within the
unified allostatic–interoceptive system contribute to the ability of
‘SuperAgers’ to perform memory and executive function tasks like
much younger people91.
Furthermore, our findings also help to shed light on two psycho-
logical concepts that are constantly confused in the psychological and
neuroscience literatures: affect and emotion. If, whatever else your
brain is doing — thinking, feeling, perceiving, moving — it is also
regulating your autonomic nervous system, your immune system and
your endocrine system, then it is also continually representing the
interoceptive consequences of those physical changes. Interoceptive
sensations are usually experienced as lower-dim ensional feelings of
affect23,92. As such, the properties of affect — valence and arousal93,94
— can be thought of as basic features of consciousness95–101 that,
importantly, are not unique to instances of emotion.
Perhaps the most valuable aspect of our findings is in moving
beyond traditional domain-specific or ‘modular’ views of brain
structure/function relationships102, which assume a significant
degree of specificity in the functions of various brain systems.
A growing body of evidence requires that these traditional modular
views be abandoned28,103,104 in favour of models that acknowledge
that neural populations are domain-general or multi-use. The idea
of domain-generality even applies to primary sensory networks, as
evidenced by the fact that multisensory processing occurs in brain
regions that are traditionally considered unimodal (for example, the
auditory cortex responding to visual stimulation105,106). The absence
of specificity in brain structure/function relationships is not a mea-
surement error or some biological dysfunction, but a useful feature
that reflects core principles of biological degeneracy that are also
evident in the genome, the immune system and every other biologi-
cal system shaped by natural selection107.
No study is without limitations. First, there are potential issues
in identifying homologous regions between monkey and human
brains47; nonetheless, we still found evidence for most of the mono-
synaptic connections predicted by the EPIC model. Second, we used
Hubs connecting networks 1 and 2
ITG Temporal pole
Rich club hubs
Figure 3 | The unified allostatic–interoceptive system is composed of two large-scale intrinsic networks that share several hubs. Networks of the unified
allostatic–interoceptive system are shown in red and blue, and hubs are shown in purple; for coordinates, see Supplementary Table 4. Hubs belonging to
the ‘rich club’ are shown in yellow. Rich club hubs figure adapted with permission from ref. 85, Society for Neuroscience. All maps result from the sample
of 280 participants binarized at P< 10−5 uncorrected from a one-sample two-tailed t-test. These results were replicated in a second sample, N= 270
participants, indicating that they are reliable and cannot be attributed to random error (Supplementary Fig. 2). IFG,inferior frontal gyrus; ITG,inferior
temporal gyrus; PHG,parahippocampal gyrus; pMCC,posterior midcingulate cortex; postCG,postcentral gyrus; STS,superior temporal sulcus.
Thalamus: z = 8
Ventral striatum: y = 12
PAG: z = –12* PBN: z = –26*
Hypothalamus: y = –8
Amygdala: y = 2 Hippocampus: x = 28
Cerebellum: z = –32*
Default mode network
NTS: z = –50*
z = –50
z = –12
z = –32
z = –26
Figure 4 | Subcortical connectivity of the two integrated intrinsic
networks within the allostatic–interoceptive system (N=280; P<0.05
uncorrected). These results were replicated in a second sample of N= 270
(Supplementary Fig. 5). x, y and z refer to the MNI coordinates in mm.
8 NATURE HUMAN BEHAVIOUR 1, 0069 (2017) | DOI: 10.1038/s41562-017-0069| www.nature.com/nathumbehav
ARTICLES NATURE HUMAN BEHAVIOUR
an indirect measure of brain connectivity in humans (functional
connectivity analyses of low-frequency BOLD data acquired at rest)
that reflects both direct and indirect connections and can, in prin-
ciple, inflate the extent of an intrinsic network47. Moreover, low-fre-
quency BOLD correlations may reflect vascular rather than neural
effects in the brain108. Nonetheless, our results exhibit specificity:
the integrated allostatic–interoceptive system conforms to well-
established salience and default mode networks and is remarkably
consistent with both cortical and subcortical connections repeatedly
observed in tract-tracing studies of non-human animals. Third,
although our fMRI procedures were not optimized to identify sub-
cortical and brainstem structures and study their connectivity (for
examples of optimization, see refs 57,75,76,109), we nonetheless observed
92% of the predicted connectivity results. Finally, many studies
find that activities in the default mode and salience networks have
an inverse or negative relationship (sometimes referred to as ‘anti-
correlated’), meaning that as one network increases its neural
activity relative to baseline, the other decreases. Such findings and
interpretations have recently been challenged on both statistical and
theoretical grounds110 (see Supplementary Discussion). In fact, when
global signal is not removed in pre-processing, the two networks can
show a pattern of positive connectivity111. Fourth, our demonstra-
tion of a brain/behaviour relationship (using the evocative pictures)
was merely a preliminary evaluation of how individual differences in
the function of this system are related to individual differences
in behaviour. Additionally, our use of electrodermal activity as a
Social fear Physical fear Atypical emotions Emotion Emotion concepts
Social aliation Chronic pain Trait judgements Empathy Moral judgements
Reward Smoking addiction Memory Prospection Association Concepts
Default mode network
Atypical emotions Aect Eortful recall Executive attn Atrophy, stress
integration Thermal pain Alcohol craving
Empathy Decision-making Errors Word form
during aversion Hot spots
Theory of mind
Figure 5 | The default mode and salience networks each support a wide array of psychological functions. Evidence for this comes from a literature review
of psychological or other states that are sensitive to functional or structural features of these networks. These results are consistent with the idea that the
default mode (a) and salience (b) networks are domain-general networks that support interoception and allostasis, which we propose are key processes
that contribute to all psychological functions. Each sub-figure shows a set of results from an independent study, reproduced with permission from: a,
atypical emotions, ref. 197, Oxford Univ. Press; emotion, ref. 198, Elsevier; emotion concepts, ref. 199, Elsevier; subjective value, ref. 200, Oxford Univ. Press;
social affiliation, ref. 201, Society for Neuroscience; chronic pain, ref. 202, PLOS; trait judgements and theory of mind, ref. 203, Elsevier; empathy, ref. 204,
Frontiers; moral judgements, ref. 205, Oxford Univ. Press; reward, ref. 206, Elsevier; smoking addiction, ref. 207, Elsevier; memory and prospection, ref. 208,
The Royal Society; association, ref. 209, Wiley; concepts, ref. 210, Oxford Univ. Press; b, atypical emotions, ref. 197, Oxford Univ. Press; affect, ref. 211, Oxford Univ.
Press; effortful recall, ref. 212, Wiley; executive attention, ref. 213, © 2007 National Academy of Sciences; atrophy and stress (chronic, yellow; current, red), ref. 214,
Elsevier; atrophy and mental illness, ref. 122, American Medical Association; interoception, ref. 215, Wiley; recognition memory, ref. 216, Frontiers; bilingualism,
ref. 217, Elsevier; multimodal integration, ref. 1, Society for Neuroscience; thermal pain, ref. 218, Elsevier; alcohol craving, ref. 219, Wiley; empathy, ref. 220, AAAS;
decision-making, ref. 221, Frontiers; errors, ref. 222, Society for Neuroscience; word form (yellow), ref. 223, Society for Neuroscience; propranolol during aversion,
ref. 224, AAAS; hot spots, ref. 225, Guildford Press. Data used to make the sub-figures showing social and physical fear (a) taken from ref. 196.
NATURE HUMAN BEHAVIOUR 1, 0069 (2017) | DOI: 10.1038/s41562-017-0069 | www.nature.com/nathumbehav 9
NATURE HUMAN BEHAVIOUR
measure of sympathetic nervous system activity is arguably too
specific because different components of the sympathetic nervous
system react differently112, and peripheral sensations associated with
changes in electrodermal activity might not be processed by the
interoceptive brain circuitry that we are studying here, thus compli-
cating the interpretation of our results. However, we did not intend
to assess a specific neural pathway carrying information about elec-
trodermal activity, and we believe that — despite their limitations
— our results are useful and hypothesis-generating. Future work is
needed for a more thorough understanding of this and other brain–
behaviour relationships involving this system.
This project is one in a series of studies to precisely test the EPIC
model, including its predictive coding features (not just the anatomi-
cal and functional correlates as shown here). Future research must
focus on the ongoing dynamics by which the default mode and
salience networks support allostasis and interoception, including the
predictions that they issue to other sensory and motor systems. It is
possible, for example, that both networks use past experience in a
generative way to issue prediction signals, but that the default mode
network generates an internal model of the world via multisensory
predictions (consistent with previous work113–115), whereas the salience
network issues predictions, as precision signals, to tune this model
with prediction error (consistent with the salience network’s role in
attention regulation and executive control; for example refs51,116,117).
Unexpected sensory inputs that are anticipated to have allostatic
implications (that is, likely to impact survival, offering reward or
threat) will be encoded as ‘signal’ and learned, so as to support allo-
stasis better in the future, with all other prediction error treated as
‘noise’ and safely ignored118 (for discussion, see ref. 119). These and
other hypotheses regarding the flow of predictions and prediction
errors in the brain (for example incorporating the cerebellum, ven-
tral striatum and thalamus24) can be tested using new methods such
laminar MRI scanning at high (7 T) magnetic field strengths120.
Future research that provides a more mechanistic understand-
ing of how the default mode and salience networks support intero-
ception and allostasis will also reveal insights into the mind–body
connections at the root of mental and physical illness and their
comorbidities. For example, in illness, the neural representations of
the world that underlie action and experience may be directed more
by predicted allostatic relevance of information than by the need for
accuracy and completeness in representing the environment. Indeed,
atrophy and dysfunction within parts of the interoceptive system
are considered common neurobiological substrates for mental and
physical illness121–123, including depression124, anxiety125, addiction126,
chronic pain127, obesity128 and chronic stress129,130. By contrast,
increased cortical thickness in the MCC is linked to the preserved
memory of ‘SuperAgers’ relative to their more typically performing
elderly peers131,132, suggesting a potential mechanism for how exercise
(via the sustained visceromotor regulation it requires) benefits cog-
nitive function in aging133 and why certain activities, such as mind-
fulness or contemplative practice, can be beneficial134,135. Ultimately,
a better understanding of how the mind is linked to the physical state
of the body through allostasis and interoception may help to resolve
some of the most critical health problems of our time, such as the
comorbidities among mental and physical disorders related to meta-
bolic syndrome (for example depression and heart disease136) or how
chronic stress speeds cancer progression137, as well as offering key
insights into the emergence of public health issues related to addic-
tion and mental illness, such as opioid use138 and suicides139.
Participants. Discovery and replication samples. We randomly selected 660
participants (365 female, 55%, 18–30 years) from 1,000 healthy participants
described in previous work55,140. e 1,000 participants were native
English-speaking adults, 18–35 years, with normal or corrected-to-normal
vision, and reported no history of neurological or psychiatric conditions.
We removed 79 participants (11%) owing to head motion and outlying voxel
intensities; we removed 31 more participants (4.7%) owing to lack of signal in
superior and lateral parts of the brain (see section on analysis of fMRI data).
Our nal dataset of 550 participants was randomly divided into a discovery sample
of N= 280 (174 female, 62%, mean= 19.3 years, s.d.= 1.4 years) and a replication
sample of N= 270 (142 female, 53%, mean= 22.3 years, s.d.= 2.1 years).
We also randomly selected 150 participants (75 female, 50%, mean= 22.5,
s.d.= 2.0 years) from the N= 1,000 in order to generate maps of the established
default mode and salience networks.
Validity sample. We selected all 66 young and middle-aged participants (33 female,
18–60 years, mean= 34.8 years, s.d.= 13.8 years) from an existing dataset of 111
participants (56 female, 18–81 years, mean= 46.6 years, s.d.= 18.9 years)
recruited from the Boston area during 2012–2014 for a study examining age-
related changes in how affect supports memory141. Only 41 participants
(14 female, 47%, 20–60 years, mean= 33.8 years, s.d.= 14.1 years) had both high-
quality fMRI BOLD data and sufficient electrodermal activity changes according
to previously established procedures (see Analysis sections). Specifically,
12 participants exhibited excessive head motion and outlying voxel intensities, and
16 participants lacked electrodermal responses. Participants were right-handed,
native English speakers and had normal or corrected-to-normal vision. None
reported any history of neurological or psychiatric condition, learning disability
or serious head trauma. Participants did not smoke and did not ingest substances
(such as beta-blockers or anti-cholinergic medications) that interfere with
Sample size. No pre-specified effect size was known, so we used a large portion of a
third-party dataset (N= 660) and the maximum size of a second dataset collected
in our laboratory with young and middle-aged adults (N= 66).
Procedure. Discovery and replication samples. Participants provided written
informed consent in accordance with the guidelines set by the institutional review
boards of Harvard University or Partners Healthcare. Participants completed MRI
structural and resting-state scans and other tasks unrelated to the current analysis.
MRI data were acquired at Harvard and the Massachusetts General Hospital across
a series of matched 3 T Tim Trio scanners (Siemens, Erlangen, Germany) using
a 12-channel phased-array head coil. Structural data included a high-resolution
multi-echo T1-weighted magnetization-prepared gradient-echo image (multi-echo
MP-RAGE). Parameters for the structural scan were as follows: repetition time
(TR)= 2,200 ms, inversion time (TI)= 1,100 ms, echo time (TE)= 1.54 ms for
image 1 to 7.01 ms for image 4, ip angle (FA)= 7°, voxel size 1.2 × 1.2 × 1.2 mm
and eld of view (FOV)= 230 mm. e functional resting state scan lasted 6.2 min
(124 time points). e echo planar imaging (EPI) parameters for functional
connectivity analyses were as follows: TR= 3,000 ms, TE= 30 ms, FA= 85°, voxel
size 3 × 3 × 3 mm, FOV= 216 mm and 47 axial slices collected with interleaved
acquisition and no gap between slices.
Validity sample. Participants provided consent in accordance with the institutional
review board. Data were acquired on separate sessions across several days. The first
session consisted of a 6-min seated baseline assessment of peripheral physiology,
the EXAMINER cognitive battery142, a second 6-min seated baseline, the evocative
images task and other tasks. Only the evocative images task is relevant for this study.
Electrodes were placed on the chest, hands and face to record electrocardiogram,
electrodermal activity and facial electromyography, respectively. A belt with a
piezoelectric sensor was secured on the chest to record respiration. Only the
electrodermal activity data are reported here. Electrodermal activity was recorded
using disposable electrodermal electrodes (containing isotonic paste) affixed to
the thenar and hypothenar eminences of the left hand. Data were collected using
BioLab v3.0.13 (Mindware Technologies, Gahanna, OH, USA). Participants sat
upright in a comfortable chair in a dimly lit room. Ninety full-colour photos were
selected from the International Affective Picture System (IAPS) and used to induce
affective experiences61. The pictures were selected based on normative ratings
of pleasure/displeasure (valence) and arousal experienced when viewing them
(unpleasant–high arousal, pleasant–high arousal, unpleasant–low arousal,
pleasant–low arousal, neutral valence–low arousal; Supplementary Table 5).
Participants viewed the photos sequentially on a 120 × 75-cm high-definition
screen 2 metres away. Photos were grouped into three blocks of 30 each, with the
order of the photos within each block fully randomized. For each trial, participants
viewed an IAPS photo for 6 seconds, and then rated their experience for valence
and arousal using the self-assessment manikin (SAM143). Only the arousal ratings
are relevant to this report, and they ranged from 1 (‘very calm’) to 5 (‘very
activated’). A variable inter-trial interval of 10–15 seconds followed the rating prior
to presentation of the next picture. Before beginning the task, participants were
familiarized with the SAM rating procedure and practised by rating five pictures.
The photos and rating scales were administered via E-Prime (Psychology Software
Tools, Pittsburgh, PA).
The second laboratory testing session involved MRI scanning, consisting of a
structural scan, resting state scan and other tasks unrelated to the present report
(presented elsewhere141). MRI data were acquired using a 3 T Tim Trio scanner
(Siemens, Erlangen, Germany) with a 12-channel phased-array head coil. Structural
data included a high-resolution T1-weighted MP-RAGE with TR= 2,530 ms,
10 NATURE HUMAN BEHAVIOUR 1, 0069 (2017) | DOI: 10.1038/s41562-017-0069| www.nature.com/nathumbehav
ARTICLES NATURE HUMAN BEHAVIOUR
TE= 3.48 ms, FA= 7° and 1 × 1 × 1-mm isotropic voxels. The functional resting-
state scan lasted 6.40 min (76 time points). The EPI parameters were as follows:
TR= 5,000 ms, TE= 30 ms, FA= 90°, 2 × 2 × 2-mm voxels and 55 axial slices
collected with interleaved acquisition and no gap between slices. Participants were
instructed to keep their eyes open without fixating and remain as still as possible.
Selection of regions in the interoceptive/allostatic system. We selected several
cortical regions with established visceromotor connections, including regions in
the insula and ACC (Table1). We also included the dAmy in our system because
its central nucleus is known to have key visceromotor functions (for a review, see
ref. 144); the dAmy, being a subcortical region, does not have a laminar structure,
but there are connections between the amygdala and primary interoceptive
cortex (dmIns/dpIns60,145,146) that are predicted by the EPIC model (using Barbas’s
structural model of information flow within the cortex). Similarly, the anterior
cingulate cortex (ACC), a key limbic visceromotor region, is connected with the
amygdala in a pattern consistent with the EPIC model hypothesis that the ACC
sends visceromotor prediction signals to the central nucleus (the ACC primarily
sends output from its deep layers and receives input from the amygdala in its
upper layers147). Currently, there are insufficient data to test the EPIC model
hypothesis that amygdala projections terminate in the upper layers of dmIns/dpIns
and that the amygdala receives inputs from its deep layers, as these data are not
available in prior tract-tracing studies involving the insula and amygdala60,145,146.
Analysis of task-independent (‘resting-state’) fMRI data. Quality assessment.
We applied established censoring protocols for head motion and outlying signal
intensities using AFNI (https://afni.nimh.nih.gov/afni/) following ref. 148 and
described in the following three steps. First, we disqualied an fMRI volume if
AFNI’s ‘enorm motion’ derivative parameter (derived from afni_proc.py) was
greater than 0.3 mm. Second, we disqualied an fMRI volume if the fraction
of voxels with outlying signal intensity (AFNI’s 3dToutcount command) was
greater than 0.05. ird, if a volume surpassed either criterion, we removed that
volume, the prior volume and the next two volumes. In a separate procedure, we
disqualied discovery and replication participants who lost more than 10% of their
124 volumes owing to either criterion (79 participants, 11%). Quality assessment
for surface-based processing required removing 31 additional participants
(4.7%) owing to a lack of signal in the most superior and lateral parts of the brain,
which would result in incomplete group connectivity maps; no participants were
removed for this reason in the validity sample. In the validity sample, we removed
participants who lost more than 40% of their 76 volumes, removing 12 participants
(18%); we used a more lenient threshold because of the small sample size (N= 66).
e fraction of volumes censored per participant using the aforementioned
approach140 yielded nearly identical results to another established censoring
approach149 as implemented in AFNI’s afni_restproc.py script.
Preprocessing. We applied standard Freesurfer preprocessing steps to both samples
of resting-state data (http://surfer.nmr.mgh.harvard.edu). These included removal
of the first four volumes, motion correction, slice timing correction, resampling to
the MNI152 cortical surface (left and right hemispheres) and MNI305 subcortical
volume (2-mm isotropic voxels), spatial smoothing (6 mm full-width at half-
maximum (FWHM), surface and volume separately) and temporal filtering
(0.01-Hz high-pass filter and 0.08-Hz low-pass filter). We did not use global signal
regression in order to prevent spurious negative correlations (‘anti-correlated
networks’), which can interfere with interpreting the connectivity results110.
Functional connectivity analysis. We estimated cortical connectivity using
surface-based analyses, affording more sensitive and reliable discovery maps
and reducing artifacts around sulcal and opercular borders by registering each
participant’s native space to MNI152 space via Freesurfer’s reconstruction of each
participant’s cortical surfaces150. The surface-based intrinsic analyses also allowed
us to incorporate the selected subcortical seed (dAmy), but did not allow us to
analyse connectivity to subcortical structures more broadly. We first created a
4-mm-radius sphere centred on the MNI coordinates identified in Table3 and
found the vertex on the MNI152 pial surface that is closest to the spherical seed.
We then smoothed this single vertex by 4 mm on the surface and mapped the
resulting cortical label to each individual subject’s cortex. The individual cortical
label was projected back into the subject’s native volumetric space to calculate
the averaged time series within the seed. For the subcortical seed (dAmy), we
directly projected the spherical seed into each subject’s native volumetric space
and extracted its time course. On the subject level, we ran a voxel-wise regression
on left and right hemispheres of MNI152 and subcortical volume of MNI305
to compute the partial correlation coefficient and correlation effect size of the
seed time series, taking into account several nuisance variables: cerebrospinal
fluid signal, white matter signal, motion correction parameters and a fifth-order
polynomial. On the group level, we concatenated the contrast effect size maps
from all subjects and ran a general linear model analysis to test whether the group
mean differed from zero. This yielded final group maps that showed regions whose
fluctuations significantly correlated with the seed’s BOLD time series.
To estimate cortical–subcortical connectivity, we used a more liberal statistical
threshold compared with the analyses of corticocortical connectivity. The smaller
size of subcortical regions, as well as their anatomical placement, renders their
signal noisier and less reliable57, yielding relatively smaller estimates of intrinsic
connectivity. Thus, guided by classical measurement theory151, we relied on
replication to determine which connectivity values were meaningful.
k-means cluster analysis of discovery maps. First, we computed the 8 × 8 η2
similarity matrix for each pair of maps49. Based on visual inspection of the
eight maps, we used k-means clustering with k= 2 and k= 3 using the kmeans
function in MATLAB (Mathworks, Natick, MA). Our results confirmed that k= 2
captured the default mode versus salience distinction across these maps, whereas
k= 3 further divided the ‘salience cluster’ into two sub-categories depending on
whether somatosensory cortices are included. Because sub-categories within the
salience network were not important to our study goals, we used the k= 2
Identification of the interoceptive system networks. We confirmed that Network 1
is the established default mode network (for a review, see ref. 50) and Network 2 is
the established salience network51,52. The reference maps were constructed using
coordinates obtained from previous work55 as follows. Using a random sample of
N= 150, we created a mask of the default mode network by conjoining functional
connectivity maps from two hubs in the default mode network55: a 4-mm seed
at the dorsomedial prefrontal cortex (MNI 0, 50, 24) and a 4-mm seed at the
posterior cingulate cortex (MNI 0, − 64, 40). We likewise created a mask of the
salience network by conjoining functional connectivity maps from two bilateral
hubs in the salience network (labelled as the ventral attention network in ref. 55):
4-mm seeds at the left and right supramarginal gyrus (MNI ± 60, − 30, 28) and
4-mm seeds at the left and right anterior insula (MNI ± 40, 12, − 4). We thresholded
our maps to P< 10−5 uncorrected (as in all our analyses) and we thresholded the
default mode and salience networks to z(r) > 0.05 where z is the Fisher’s r-to-z
transformation. We then calculated the percentage of each established network
(default mode or salience) that covered each of our networks (Network 1 or 2),
and the complementary measure: the percentage of each of our networks
(Network 1 or 2) that covered each established network (default mode or salience).
These calculations used only the right hemisphere.
Reliability analyses. We used η2 as an index of reliability because it shows similarity
between maps while discounting scaling and offset effects49. An η2 value of 1
indicates spatially identical maps, while an η2 value of 0.5 indicates statistically
independent maps. For each of our eight cortical and amygdalar seeds, we
calculated η2 between the discovery and replication samples using the effect size
(gamma) maps generated by the group-level general linear model analysis.
Then we calculated the mean and s.d. of the eight η2 values across all seeds to
index overall similarity between samples. This was done separately for the cortical
and subcortical maps. We repeated the same procedure to compare the reliability
between the discovery and validation samples.
Analysis of the evocative images task. We analysed electrodermal activity data
using Electrodermal Activity Analysis v3.0.21 (Mindware). For each 6-second
trial when the photo was visible, we measured the number of event-related skin
conductance responses (SCRs) according to best practices152. We considered a
SCR to be event-related if both the response onset and peak occurred between 1
and 6 seconds after stimulus onset, with an amplitude ≥ 0.01 μ S. It is commonly
observed that a substantial proportion of healthy adults produce relatively few
if any SCRs153. We disqualified 16 of our 66 participants (24%) because they
generated event-related SCRs during fewer than 5% of the evocative photo trials.
We analysed our data using the number of SCRs (as opposed to the amplitude
of the SCRs) per prior work from our group (for example ref. 154) and others
(for example ref. 155).
Multilevel linear modelling to assess correspondence between objective physiological
and subjective arousal during an allostatically relevant task. We used HLM v7.01
with robust parameter estimates (Scientific Software International; Skokie, IL).
Level-1 of the model estimated the linear relationship (slope and intercept) between
physiological arousal (number of event-related SCRs) and subjective arousal
(1= ‘very calm’ to 5= ‘very activated’) in response to each of 90 photos. Thus, the
model was adjusted for mean individual reactivity. Level-2 estimated the extent
to which intrinsic connectivity between viscerosensory and visceromotor regions
(for example dpIns–aMCC) moderated the relationship between objective and
subjective arousal (that is, moderated the slope of the Level 1 model). All variables
were unstandardized. Level-1 variables were group-mean centred (for each
participant) and Level-2 variables were grand-mean centred (across participants).
Data availability. The data that support the findings of this study are available
from the corresponding authors upon request.
Code availability. The code to analyse data is available from the corresponding
authors upon request.
Received 27 May 2016; accepted 13 February 2017;
published 24 April 2017
NATURE HUMAN BEHAVIOUR 1, 0069 (2017) | DOI: 10.1038/s41562-017-0069 | www.nature.com/nathumbehav 11
NATURE HUMAN BEHAVIOUR
1. Sepulcre, J., Sabuncu, M. R., Yeo, T. B., Liu, H. & Johnson, K. A. Stepwise
connectivity of the modal cortex reveals the multimodal organization of the
human brain. J. Neurosci. 32, 10649–10661 (2012).
2. Rao, R. P. & Ballard, D. H. Predictive coding in the visual cortex: a functional
interpretation of some extra-classical receptive-eld eects. Nat. Neurosci. 2,
3. Chennu, S. et al. Expectation and attention in hierarchical auditory
prediction. J. Neurosci. 33, 11194–11205 (2013).
4. Shipp, S. e importance of being agranular: a comparative account of visual
and motor cortex. Phil. Trans. R. Soc. Lond. B 360, 797–814 (2005).
5. Zelano, C., Mohanty, A. & Gottfried, J. A. Olfactory predictive codes and
stimulus templates in piriform cortex. Neuron 72, 178–187 (2011).
6. Kusumoto-Yoshida, I., Liu, H., Chen, B. T., Fontanini, A. & Bonci, A. Central
role for the insular cortex in mediating conditioned responses to anticipatory
cues. Proc. Natl Acad. Sci. USA 112, 1190–1195 (2015).
7. Adams, R. A., Shipp, S. & Friston, K. J. Predictions not commands: active
inference in the motor system. Brain Struct. Funct. 218, 611–643 (2013).
8. Clark, A. Whatever next? Predictive brains, situated agents, and the future of
cognitive science. Behav. Brain Sci. 36, 181–204 (2013).
9. Friston, K. e free-energy principle: a unied brain theory? Nat. Rev.
Neurosci. 11, 127–138 (2010).
10. Pezzulo, G., Rigoli, F. & Friston, K. Active inference, homeostatic regulation
and adaptive behavioural control. Prog. Neurobiol. 134, 17–35 (2015).
11. Barrett, L. F. & Simmons, W. K. Interoceptive predictions in the brain.
Nat. Rev. Neurosci. 16, 419–429 (2015).
12. Chanes, L. & Barrett, L. F. Redening the role of limbic areas in cortical
processing. Trends Cogn. Sci. 20, 96–106 (2016).
13. Seth, A. K. Interoceptive inference, emotion, and the embodied self.
Trends Cogn. Sci. 17, 565–573 (2013).
14. Seth, A. K., Suzuki, K. & Critchley, H. D. An interoceptive predictive
coding model of conscious presence. Front. Psychol. 2, 395–410 (2012).
15. Gu, X. & FitzGerald, T. H. Interoceptive inference: homeostasis and
decision-making. Trends Cogn. Sci. 18, 269–270 (2014).
16. Allen, M. & Friston, K. J. From cognitivism to autopoiesis:
towards a computational framework for the embodied mind. Synthese http://
17. Craig, A. D. How do you feel? Interoception: the sense of the physiological
condition of the body. Nat. Rev. Neurosci. 3, 655–666 (2002).
18. Craig, B. How Do You Feel? An Interoceptive Moment with Your
Neurobiological Self (Princeton Univ. Press, 2014).
19. Sherrington, C. in Textbook of Physiology (ed. Schäfer, E. A.) 920–1001
20. Sterling, P. & Laughlin, S. Principles of Neural Design (MIT Press, 2015).
21. Sterling, P. Allostasis: a model of predictive regulation. Physiol. Behav. 106,
22. McEwen, B. S. & Stellar, E. Stress and the individual. Mechanisms leading to
disease. Arch. Intern. Med. 153, 2093–2101 (1993).
23. Barrett, L. F. How Emotions Are Made: e Secret Life of the Brain (Houghton
Miin Harcourt, 2017).
24. Barrett, L. F. e theory of constructed emotion: an active inference account
of interoception and categorization. Soc. Cogn. Aect. Neurosci. http://dx.doi.
25. Barrett, L. F., Quigley, K. S. & Hamilton, P. An active inference theory
of allostasis and interoception in depression. Phil. Trans. R. Soc. B 371,
26. Damasio, A. & Carvalho, G. B. e nature of feelings: evolutionary and
neurobiological origins. Nat. Rev. Neurosci. 14, 143–152 (2013).
27. Critchley, H. D. & Harrison, N. A. Visceral inuences on brain and behavior.
Neuron 77, 624–638 (2013).
28. Barrett, L. F. & Satpute, A. B. Large-scale brain networks in aective and
social neuroscience: towards an integrative functional architecture of the
brain. Curr. Opin. Neurobiol. 23, 361–372 (2013).
29. Anderson, M. L. Aer Phrenology: Neural Reuse and the Interactive Brain
(MIT Press, 2014).
30. Yeo, B. T. et al. Functional specialization and exibility in human association
cortex. Cereb. Cortex 25, 3654–3672 (2015).
31. Barbas, H. & Rempel-Clower, N. Cortical structure predicts the pattern of
corticocortical connections. Cereb. Cortex 7, 635–646 (1997).
32. Barbas, H. General cortical and special prefrontal connections: principles
from structure to function. Annu. Rev. Neurosci. 38, 269–289 (2015).
33. Rempel-Clower, N. L. & Barbas, H. e laminar pattern of connections
between prefrontal and anterior temporal cortices in the rhesus monkey is
related to cortical structure and function. Cereb. Cortex 10, 851–865 (2000).
34. Medalla, M. & Barbas, H. Specialized prefrontal ‘auditory elds’: organization
of primate prefrontal-temporal pathways. Front. Neurosci. 8, 77 (2014).
35. Medalla, M. & Barbas, H. Diversity of laminar connections linking
periarcuate and lateral intraparietal areas depends on cortical structure.
Eur. J. Neurosci. 23, 161–179 (2006).
36. Hilgetag, C. C. & Grant, S. Cytoarchitectural dierences are a key
determinant of laminar projection origins in the visual cortex. Neuroimage
51, 1006–1017 (2010).
37. Goulas, A., Uylings, H. B. & Stiers, P. Mapping the hierarchical layout of the
structural network of the macaque prefrontal cortex. Cereb. Cortex 24,
38. Bastos, A. M. et al. Canonical microcircuits for predictive coding. Neuron 76,
39. Adams, R. A., Stephan, K. E., Brown, H. R., Frith, C. D. & Friston, K. J.
e computational anatomy of psychosis. Front. Psychiatry 4, 47 (2013).
40. Shipp, S., Adams, R. A. & Friston, K. J. Reections on agranular architecture:
predictive coding in the motor cortex. Trends Neurosci. 36, 706–716 (2013).
41. Weston, C. S. Another major function of the anterior cingulate cortex: the
representation of requirements. Neurosci. Biobehav. Rev. 36, 90–110 (2012).
42. Vogt, B. A. Pain and emotion interactions in subregions of the cingulate
gyrus. Nat. Rev. Neurosci. 6, 533–544 (2005).
43. Ongur, D., An, X. & Price, J. L. Prefrontal cortical projections to the
hypothalamus in macaque monkeys. J. Comp. Neurol. 401, 480–505 (1998).
44. Vogt, B. A., Vogt, L., Farber, N. B. & Bush, G. Architecture and
neurocytology of monkey cingulate gyrus. J. Comp. Neurol. 485,
45. Nieuwenhuys, R. e insular cortex: a review. Prog. Brain Res. 195,
46. Avery, J. A. et al. A common gustatory and interoceptive representation in
the human mid-insula. Hum. Brain Mapp. 36, 2996–3006 (2015).
47. Hutchison, R. M. & Everling, S. Monkey in the middle: why non-human
primates are needed to bridge the gap in resting-state investigations.
Front. Neuroanat. 6, 29 (2012).
48. Deco, G., Jirsa, V. K. & McIntosh, A. R. Emerging concepts for the dynamical
organization of resting-state activity in the brain. Nat. Rev. Neurosci. 12,
49. Cohen, A. L. et al. Dening functional areas in individual human brains
using resting functional connectivity MRI. Neuroimage 41, 45–57 (2008).
50. Raichle, M. E. e brain's default mode network. Annu. Rev. Neurosci. 38,
51. Touroutoglou, A., Hollenbeck, M., Dickerson, B. C. & Barrett, L. F.
Dissociable large-scale networks anchored in the right anterior
insula subserve aective experience and attention. Neuroimage 60,
52. Seeley, W. W. et al. Dissociable intrinsic connectivity networks for salience
processing and executive control. J. Neurosci. 27, 2349–2356 (2007).
53. Dosenbach, N. U., Fair, D. A., Cohen, A. L., Schlaggar, B. L. & Petersen, S. E.
A dual-networks architecture of top-down control. Trends Cogn. Sci. 12,
54. Corbetta, M. & Shulman, G. L. Control of goal-directed and stimulus-driven
attention in the brain. Nat. Rev. Neurosci. 3, 201–215 (2002).
55. Yeo, B. T. et al. e organization of the human cerebral cortex estimated by
intrinsic functional connectivity. J. Neurophysiol. 106, 1125–1165 (2011).
56. Nieuwenhuys, R., Voogd, J. & van Huijzen, C. in e Human Central Nervous
System Ch. 8, 253–279 (Springer, 2008).
57. Brooks, J. C., Faull, O. K., Pattinson, K. T. & Jenkinson, M. Physiological
noise in brainstem fMRI. Front. Hum. Neurosci. 7, 623 (2013).
58. Craig, A. D. How do you feel — now? e anterior insula and human
awareness. Nat. Rev. Neurosci. 10, 59–70 (2009).
59. Ongur, D. & Price, J. L. e organization of networks within the orbital and
medial prefrontal cortex of rats, monkeys and humans. Cereb. Cortex 10,
60. Hoistad, M. & Barbas, H. Sequence of information processing for emotions
through pathways linking temporal and insular cortices with the amygdala.
Neuroimage 40, 1016–1033 (2008).
61. Lang, P. J., Bradley, M. M. & Cuthbert, B. N. International Aective Picture
System (IAPS): Aective Ratings of Pictures and Instruction Manual Technical
Report A-8 (Univ. of Florida, 2008).
62. Moriguchi, Y. et al. Dierential hemodynamic response in aective circuitry
with aging: an fMRI study of novelty, valence, and arousal. J. Cogn. Neurosci.
23, 1027–1041 (2011).
63. Weierich, M. R., Wright, C. I., Negreira, A., Dickerson, B. C. &
Barrett, L. F. Novelty as a dimension in the aective brain. Neuroimage 49,
64. Damasio, A. R. Descartes' Error: Emotion, Reason, and the Human Brain
65. Wiens, S., Mezzacappa, E. S. & Katkin, E. S. Heartbeat detection and the
experience of emotions. Cogn. Emot. 14, 417–427 (2000).
66. Barrett, L. F., Quigley, K. S., Bliss-Moreau, E. & Aronson, K. R. Interoceptive
sensitivity and self-reports of emotional experience. J. Pers. Soc. Psychol. 87,
67. Dunn, B. D. et al. Listening to your heart: how interoception shapes
emotion experience and intuitive decision making. Psychol. Sci. 21,
12 NATURE HUMAN BEHAVIOUR 1, 0069 (2017) | DOI: 10.1038/s41562-017-0069| www.nature.com/nathumbehav
ARTICLES NATURE HUMAN BEHAVIOUR
68. Whitehead, W. E., Drescher, V. M., Heiman, P. & Blackwell, B. Relation of
heart rate control to heartbeat perception. Biofeedback Self-Regul. 2,
69. Schandry, R. Heart beat perception and emotional experience.
Psychophysiology 18, 483–488 (1981).
70. Kleckner, I. R., Wormwood, J. B., Simmons, W. K., Barrett, L. F. & Quigley,
K. S. Methodological recommendations for a heartbeat detection-based
measure of interoceptive sensitivity. Psychophysiology 52, 1432–1440 (2015).
71. Khalsa, S. S., Rudrauf, D., Feinstein, J. S. & Tranel, D. e pathways of
interoceptive awareness. Nat. Neurosci. 12, 1494–1496 (2009).
72. Lindquist, K. A. & Barrett, L. F. A functional architecture of the human
brain: emerging insights from the science of emotion. Trends Cogn. Sci. 16,
73. van den Heuvel, M. P. & Sporns, O. Rich-club organization of the human
connectome. J. Neurosci. 31, 15775–15786 (2011).
74. McIntosh, A. R. Contexts and catalysts: a resolution of the localization and
integration of function in the brain. Neuroinformatics 2, 175–182 (2004).
75. Beissner, F., Schumann, A., Brunn, F., Eisentrager, D. & Bar, K. J. Advances
in functional magnetic resonance imaging of the human brainstem.
Neuroimage 86, 91–98 (2014).
76. Bar, K. J. et al. Functional connectivity and network analysis of midbrain
and brainstem nuclei. Neuroimage 134, 53–63 (2016).
77. Edlow, B. L., McNab, J. A., Witzel, T. & Kinney, H. C. e structural
connectome of the human central homeostatic network. Brain Connect. 6,
78. Dum, R. P., Levinthal, D. J. & Strick, P. L. Motor, cognitive, and aective
areas of the cerebral cortex inuence the adrenal medulla. Proc. Natl Acad.
Sci. USA 113, 9922–9927 (2016).
79. Blessing, W. W. & Benarroch, E. E. in e Human Nervous System (eds Mai,
J. K. & Paxinos, G.) 1058–1073 (Academic, 2012).
80. Simmons, W. K. et al. Keeping the body in mind: insula functional
organization and functional connectivity integrate interoceptive,
exteroceptive, and emotional awareness. Hum. Brain Mapp. 34,
81. Margulies, D. S. et al. Mapping the functional connectivity of anterior
cingulate cortex. Neuroimage 37, 579–588 (2007).
82. Bickart, K. C., Dickerson, B. C. & Barrett, L. F. e amygdala as a
hub in brain networks that support social life. Neuropsychologia 63,
83. Smith, D. V., Clithero, J. A., Boltuck, S. E. & Huettel, S. A. Functional
connectivity with ventromedial prefrontal cortex reects subjective value
for social rewards. Soc. Cogn. Aect. Neurosci. 9, 2017–2025 (2014).
84. van den Heuvel, M. P., Kahn, R. S., Goni, J. & Sporns, O. High-cost,
high-capacity backbone for global brain communication. Proc. Natl Acad.
Sci. USA 109, 11372–11377 (2012).
85. van den Heuvel, M. P. & Sporns, O. An anatomical substrate for
integration among functional networks in human cortex. J. Neurosci. 33,
86. Mesulam, M. M. From sensation to cognition. Brain 121,
87. Zold, C. L. & Hussain Shuler, M. G. eta oscillations in visual cortex
emerge with experience to convey expected reward time and experienced
reward rate. J. Neurosci. 35, 9603–9614 (2015).
88. Satpute, A. B. et al. Involvement of sensory regions in aective experience:
a meta-analysis. Front. Psychol. 6, 1860 (2015).
89. Vuilleumier, P. How brains beware: neural mechanisms of emotional
attention. Trends Cogn. Sci. 9, 585–594 (2005).
90. Allen, M. et al. Anterior insula coordinates hierarchical processing of tactile
mismatch responses. Neuroimage 127, 34–43 (2016).
91. Sun, F. W. et al. Youthful brains in older adults: preserved neuroanatomy in
the default mode and salience networks contributes to youthful memory in
superaging. J. Neurosci. 36, 9659–9668 (2016).
92. Barrett, L. F. & Bliss-Moreau, E. Aect as a psychological primitive.
Adv. Exp. Soc. Psychol. 41, 167–218 (2009).
93. Barrett, L. F. & Russell, J. A. Structure of current aect: controversies and
emerging consensus. Curr. Dir. Psychol. Sci. 8, 10–14 (1999).
94. Kuppens, P., Tuerlinckx, F., Russell, J. A. & Barrett, L. F. e relation
between valence and arousal in subjective experience. Psychol. Bull. 139,
95. Damasio, A. R. e Feeling of What Happens: Body and Emotion in the
Making of Consciousness (Houghton Miin Harcourt, 1999).
96. Dreyfus, G. & ompson, E. in e Cambridge Handbook of Consciousness
(Zelazo, P. D., Moscovitch, M. & ompson, E.) 89–114 (Cambridge Univ.
97. Edelman, G. M. & Tononi, G. A Universe of Consciousness: How Matter
Becomes Imagination (Basic Books, 2000).
98. James, W. e Principles of Psychology Vol. 1 (Dover, 1890/2007).
99. Searle, J. R. e Rediscovery of the Mind (MIT Press, 1992).
100. Searle, J. R. Mind: A Brief Introduction (Oxford Univ. Press, 2004).
101. Wundt, W. Outlines of Psychology (Wilhelm Engelmann, 1897).
102. Fodor, J. A. e Modularity of Mind: An Essay on Faculty Psychology
(MIT Press, 1983).
103. Li, D., Christ, S. E. & Cowan, N. Domain-general and domain-specic
functional networks in working memory. Neuroimage 102,
104. Fuster, J. M. e Module: Crisis of a Paradigm (Cell, 2000).
105. Kayser, C., Petkov, C. I., Augath, M. & Logothetis, N. K. Functional
imaging reveals visual modulation of specic elds in auditory cortex.
J. Neurosci. 27, 1824–1835 (2007).
106. Liang, M., Mouraux, A., Hu, L. & Iannetti, G. D. Primary sensory
cortices contain distinguishable spatial patterns of activity for each sense.
Nat. Commun. 4, 1979 (2013).
107. Edelman, G. M. & Gally, J. A. Degeneracy and complexity in biological
systems. Proc. Natl Acad. Sci. USA 98, 13763–13768 (2001).
108. Tong, Y. et al. Systemic low-frequency oscillations in bold signal vary with
tissue type. Front. Neurosci. 10, 313 (2016).
109. Satpute, A. B. et al. Identication of discrete functional subregions
of the human periaqueductal gray. Proc. Natl Acad. Sci. USA 110,
110. Murphy, K., Birn, R. M., Handwerker, D. A., Jones, T. B. &
Bandettini, P. A. e impact of global signal regression on resting state
correlations: are anti-correlated networks introduced? Neuroimage 44,
111. Raz, G. et al. Functional connectivity dynamics during lm viewing reveal
common networks for dierent emotional experiences. Cogn. Aect. Behav.
Neurosci. 16, 709–723 (2016).
112. Morrison, S. F. Dierential control of sympathetic outow. Am. J. Physiol.
Regul. Integr. Comp. Physiol. 281, R683–698 (2001).
113. Buckner, R. L. e serendipitous discovery of the brain's default network.
Neuroimage 62, 1137–1145 (2012).
114. Mesulam, M. e evolving landscape of human cortical connectivity:
facts and inferences. Neuroimage 62, 2182–2189 (2012).
115. Hassabis, D. & Maguire, E. A. e construction system of the brain.
Phil. Trans. R. Soc. Lond. B 364, 1263–1271 (2009).
116. Power, J. D. et al. Functional network organization of the human brain.
Neuron 72, 665–678 (2011).
117. Menon, V. & Uddin, L. Q. Saliency, switching, attention and control:
a network model of insula function. Brain Struct. Funct. 214,
118. Li, S. S. & McNally, G. P. e conditions that promote fear learning:
prediction error and pavlovian fear conditioning. Neurobiol. Learn. Mem.
108, 14–21 (2014).
119. Barrett, L. F. How Emotions Are Made: e New Science of the Mind and
Brain (Houghton Miin Harcourt, 2016).
120. Guidi, M., Huber, L., Lampe, L., Gauthier, C. J. & Moller, H. E. Lamina-
dependent calibrated bold response in human primary motor cortex.
Neuroimage 141, 250–261 (2016).
121. Crossley, N. A. et al. e hubs of the human connectome are
generally implicated in the anatomy of brain disorders. Brain 137,
122. Goodkind, M. et al. Identication of a common neurobiological substrate
for mental illness. JAMA Psychiatry 72, 305–315 (2015).
123. Menon, V. Large-scale brain networks and psychopathology: a unifying
triple network model. Trends Cogn. Sci. 15, 483–506 (2011).
124. Harshaw, C. Interoceptive dysfunction: toward an integrated framework
for understanding somatic and aective disturbance in depression.
Psychol. Bull. 141, 311–363 (2015).
125. Paulus, M. P. & Stein, M. B. Interoception in anxiety and depression.
Brain Struct. Funct. 214, 451–463 (2010).
126. Naqvi, N. H. & Bechara, A. e insula and drug addiction: an interoceptive
view of pleasure, urges, and decision-making. Brain Struct. Funct. 214,
127. Farmer, M. A., Baliki, M. N. & Apkarian, A. V. A dynamic network
perspective of chronic pain. Neurosci. Lett. 520, 197–203 (2012).
128. Mayer, E. A. Gut feelings: the emerging biology of gut–brain
communication. Nat. Rev. Neurosci. 12, 453–466 (2011).
129. Radley, J., Morilak, D., Viau, V. & Campeau, S. Chronic stress and brain
plasticity: mechanisms underlying adaptive and maladaptive changes and
implications for stress-related cns disorders. Neurosci. Biobehav. Rev. 58,
130. Gianaros, P. J. & Wager, T. D. Brain–body pathways linking psychological
stress and physical health. Curr. Dir. Psychol. Sci. 24, 313–321 (2015).
131. Gefen, T. et al. Morphometric and histologic substrates of cingulate
integrity in elders with exceptional memory capacity. J. Neurosci. 35,
132. Rogalski, E. J. et al. Youthful memory capacity in old brains: anatomic and
genetic clues from the northwestern superaging project. J. Cogn. Neurosci.
25, 29–36 (2013).
NATURE HUMAN BEHAVIOUR 1, 0069 (2017) | DOI: 10.1038/s41562-017-0069 | www.nature.com/nathumbehav 13
NATURE HUMAN BEHAVIOUR
133. Angevaren, M., Aufdemkampe, G., Verhaar, H. J., Aleman, A. & Vanhees, L.
Physical activity and enhanced tness to improve cognitive function in
older people without known cognitive impairment. Cochrane Database Syst
Rev. http://dx.doi.org/10.1002/14651858.CD005381.pub3 (2008).
134. Farb, N. et al. Interoception, contemplative practice, and health.
Front. Psychol. 6, 763 (2015).
135. Tang, Y. Y., Holzel, B. K. & Posner, M. I. e neuroscience of mindfulness
meditation. Nat. Rev. Neurosci. 16, 213–225 (2015).
136. ombs, B. D. et al. Prevalence of depression in survivors of acute
myocardial infarction. J. Gen. Intern. Med. 21, 30–38 (2006).
137. Moreno-Smith, M., Lutgendorf, S. K. & Sood, A. K. Impact of stress on
cancer metastasis. Future Oncol. 6, 1863–1881 (2010).
138. Kolodny, A. et al. e prescription opioid and heroin crisis: a public health
approach to an epidemic of addiction. Annu. Rev. Public Health 36,
139. Turecki, G. & Brent, D. A. Suicide and suicidal behaviour. Lancet 387,
140. Buckner, R. L., Krienen, F. M., Castellanos, A., Diaz, J. C. & Yeo, B. T. e
organization of the human cerebellum estimated by intrinsic functional
connectivity. J. Neurophysiol. 106, 2322–2345 (2011).
141. Touroutoglou, A., Andreano, J. M., Barrett, L. F. & Dickerson, B. C. Brain
network connectivity–behavioral relationships exhibit trait-like properties:
evidence from hippocampal connectivity and memory. Hippocampus 25,
142. Kramer, J. H. et al. NIH EXAMINER: conceptualization and
development of an executive function battery. J. Int. Neuropsychol. Soc. 20,
143. Bradley, M. M. & Lang, P. J. Measuring emotion: the self-assessment
manikin and the semantic dierential. J. Behav. er. Exp. Psychiatry 25,
144. Bohus, B. et al. e neurobiology of the central nucleus of the amygdala in
relation to neuroendocrine and autonomic outow. Prog. Brain Res. 107,
145. Mufson, E. J., Mesulam, M. M. & Pandya, D. N. Insular interconnections
with the amygdala in rhesus monkey. Neuroscience 6, 1231–1248 (1981).
146. Mufson, E. J. & Mesulam, M. M. Insula of the old world monkey. II:
aerent cortical input and comments on the claustrum. J. Comp. Neurol.
212, 23–37 (1982).
147. Ghashghaei, H. T., Hilgetag, C. C. & Barbas, H. Sequence of information
processing for emotions based on the anatomic dialogue between prefrontal
cortex and amygdala. Neuroimage 34, 905–923 (2007).
148. Jo, H. J. et al. Eective preprocessing procedures virtually eliminate
distance-dependent motion artifacts in resting state fMRI. J. Appl. Math.
2013, 935154 (2013).
149. Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L. & Petersen, S. E.
Spurious but systematic correlations in functional connectivity mri
networks arise from subject motion. Neuroimage 59, 2142–2154 (2012).
150. Tucholka, A., Fritsch, V., Poline, J. B. & irion, B. An empirical
comparison of surface-based and volume-based group studies in
neuroimaging. Neuroimage 63, 1443–1453 (2012).
151. Cronbach, L. J., Rajaratnam, N. & Gleser, G. C. eory of generalizability:
a liberalization of reliability theory. Br. J. Stat. Psychol. 16, 137–163 (1963).
152. Boucsein, W. et al. Publication recommendations for electrodermal
measurements. Psychophysiology 49, 1017–1034 (2012).
153. Schell, A. M., Dawson, M. E. & Filion, D. L. Psychophysiological correlates
of electrodermal lability. Psychophysiology 25, 619–632 (1988).
154. Xia, C., Touroutoglou, A., Quigley, K. S., Barrett, L. F. & Dickerson, B. C.
Salience network connectivity modulates skin conductance responses in
predicting arousal experience. J. Cogn. Neurosci. http://dx.doi.org/10.1162/
155. Fredrikson, M. et al. Functional neuroanatomical correlates of
electrodermal activity: a positron emission tomographic study.
Psychophysiology 35, 179–185 (1998).
156. Morecra, R. J. et al. Cytoarchitecture and cortical connections of the
anterior cingulate and adjacent somatomotor elds in the rhesus monkey.
Brain Res. Bull. 87, 457–497 (2012).
157. Mesulam, M. M. & Mufson, E. J. Insula of the old world monkey. III:
eerent cortical output and comments on function. J. Comp. Neurol. 212,
158. Cavdar, S. et al. e aerent connections of the posterior hypothalamic
nucleus in the rat using horseradish peroxidase. J. Anat. 198, 463–472
159. An, X., Bandler, R., Ongur, D. & Price, J. L. Prefrontal cortical projections
to longitudinal columns in the midbrain periaqueductal gray in macaque
monkeys. J. Comp. Neurol. 401, 455–479 (1998).
160. Saper, C. B. Reciprocal parabrachial–cortical connections in the rat.
Brain Res. 242, 33–40 (1982).
161. Saper, C. B. Convergence of autonomic and limbic connections in the
insular cortex of the rat. J. Comp. Neurol. 210, 163–173 (1982).
162. Fudge, J. L., Breitbart, M. A., Danish, M. & Pannoni, V. Insular and
gustatory inputs to the caudal ventral striatum in primates. J. Comp. Neurol.
490, 101–118 (2005).
163. Carmichael, S. T. & Price, J. L. Connectional networks within the orbital
and medial prefrontal cortex of macaque monkeys. J. Comp. Neurol. 371,
164. Aggleton, J. P., Burton, M. J. & Passingham, R. E. Cortical and subcortical
aerents to the amygdala of the rhesus monkey (Macaca mulatta). Brain
Res. 190, 347–368 (1980).
165. Stefanacci, L. & Amaral, D. G. Some observations on cortical inputs to the
macaque monkey amygdala: an anterograde tracing study. J. Comp. Neurol.
451, 301–323 (2002).
166. Chikama, M., McFarland, N. R., Amaral, D. G. & Haber, S. N. Insular
cortical projections to functional regions of the striatum correlate with
cortical cytoarchitectonic organization in the primate. J. Neurosci. 17,
167. Chiba, T., Kayahara, T. & Nakano, K. Eerent projections of infralimbic and
prelimbic areas of the medial prefrontal cortex in the Japanese monkey,
Macaca fuscata. Brain Res. 888, 83–101 (2001).
168. Vogt, B. A. & Pandya, D. N. Cingulate cortex of the rhesus monkey. II.
Cortical aerents. J. Comp. Neurol. 262, 271–289 (1987).
169. Rempel-Clower, N. L. & Barbas, H. Topographic organization of
connections between the hypothalamus and prefrontal cortex in the rhesus
mo nke y. J. Comp. Neurol. 398, 393–419 (1998).
170. Freedman, L. J., Insel, T. R. & Smith, Y. Subcortical projections of
area 25 (subgenual cortex) of the macaque monkey. J. Comp. Neurol. 421,
171. Terreberry, R. R. & Neafsey, E. J. Rat medial frontal cortex: a visceral motor
region with a direct projection to the solitary nucleus. Brain Res. 278,
172. van der Kooy, D., McGinty, J. F., Koda, L. Y., Gerfen, C. R. & Bloom, F. E.
Visceral cortex: a direct connection from prefrontal cortex to the solitary
nucleus in rat. Neurosci. Lett. 33, 123–127 (1982).
173. Room, P., Russchen, F. T., Groenewegen, H. J. & Lohman, A. H. Eerent
connections of the prelimbic (area 32) and the infralimbic (area 25)
cortices: an anterograde tracing study in the cat. J. Comp. Neurol. 242,
174. Pandya, D. N., Van Hoesen, G. W. & Mesulam, M. M. Eerent
connections of the cingulate gyrus in the rhesus monkey. Exp. Brain Res.
42, 319–330 (1981).
175. Vogt, B. A. & Palomero-Gallagher, N. in e Human Nervous System
(eds Mai, J. K. & Paxinos, G.) Ch. 25, 943–987 (Academic, 2012).
176. Haber, S. N., Kim, K. S., Mailly, P. & Calzavara, R. Reward-related cortical
inputs dene a large striatal region in primates that interface with
associative cortical connections, providing a substrate for incentive-based
learning. J. Neurosci. 26, 8368–8376 (2006).
177. Price, J. L. & Amaral, D. G. An autoradiographic study of the projections
of the central nucleus of the monkey amygdala. J. Neurosci. 1,
178. Fudge, J. L., Kunishio, K., Walsh, P., Richard, C. & Haber, S. N. Amygdaloid
projections to ventromedial striatal subterritories in the primate.
Neuroscience 110, 257–275 (2002).
179. Morecra, R. J. & Van Hoesen, G. W. Convergence of limbic input to
the cingulate motor cortex in the rhesus monkey. Brain Res. Bull. 45,
180. Saleem, K. S., Kondo, H. & Price, J. L. Complementary circuits connecting
the orbital and medial prefrontal networks with the temporal, insular,
and opercular cortex in the macaque monkey. J. Comp. Neurol. 506,
181. Barbas, H., Ghashghaei, H., Dombrowski, S. M. & Rempel-Clower, N. L.
Medial prefrontal cortices are unied by common connections with
superior temporal cortices and distinguished by input from memory-related
areas in the rhesus monkey. J. Comp. Neurol. 410, 343–367 (1999).
182. Ongur, D., Ferry, A. T. & Price, J. L. Architectonic subdivision of the
human orbital and medial prefrontal cortex. J. Comp. Neurol. 460,
183. Ghaziri, J. et al. e corticocortical structural connectivity of the human
insula. Cereb. Cortex http://dx.doi.org/10.1093/cercor/bhv308 (2015).
184. Wiech, K., Jbabdi, S., Lin, C. S., Andersson, J. & Tracey, I. Dierential
structural and resting state connectivity between insular subdivisions and
other pain-related brain regions. Pain 155, 2047–2055 (2014).
185. Gianaros, P. J. & Sheu, L. K. A review of neuroimaging studies of
stressor-evoked blood pressure reactivity: emerging evidence for a
brain-body pathway to coronary heart disease risk. Neuroimage 47,
186. Kurth, F., Zilles, K., Fox, P. T., Laird, A. R. & Eickho, S. B. A link
between the systems: functional dierentiation and integration within the
human insula revealed by meta-analysis. Brain Struct. Funct. 214,
© 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.
14 NATURE HUMAN BEHAVIOUR 1, 0069 (2017) | DOI: 10.1038/s41562-017-0069| www.nature.com/nathumbehav
ARTICLES NATURE HUMAN BEHAVIOUR
187. Gianaros, P. J. et al. An inammatory pathway links atherosclerotic
cardiovascular disease risk to neural activity evoked by the cognitive
regulation of emotion. Biol. Psychiatry 75, 738–745 (2014).
188. Wager, T. D. et al. Brain mediators of cardiovascular responses to
social threat. Part I: reciprocal dorsal and ventral sub-regions of
the medial prefrontal cortex and heart-rate reactivity. Neuroimage 47,
189. Harper, R. M. et al. fMRI responses to cold pressor challenges in
control and obstructive sleep apnea subjects. J. Appl. Physiol. 94,
190. Gianaros, P. J. et al. Individual dierences in stressor-evoked blood pressure
reactivity vary with activation, volume, and functional connectivity of the
amygdala. J. Neurosci. 28, 990–999 (2008).
191. Zilles, K. & Amunts, K. in e Human Nervous System (eds Mai, J. K. &
Paxinos G.) Ch. 23, 836–895 (Elsevier, 2012).
192. Petrides, M. & Pandya, D. N. in e Human Nervous System (eds Mai, J. K.
& Paxinos, G.) Ch. 26, 988–1011 (Elsevier, 2012).
193. Barbas, H. Specialized elements of orbitofrontal cortex in primates.
Ann. NY Acad. Sci. 1121, 10–32 (2007).
194. Mesulam, M. M. & Mufson, E. J. Insula of the old world monkey. I.
Architectonics in the insulo-orbito-temporal component of the paralimbic
brain. J. Comp. Neurol. 212, 1–22 (1982).
195. Yarkoni, T. Big correlations in little studies: inated fMRI correlations
reect low statistical power — commentary on Vul et al. (2009). Persp.
Psychol. Sci. 4, 294–298 (2009).
196. Wilson-Mendenhall, C. D., Barrett, L. F., Simmons, W. K. & Barsalou, L. W.
Grounding emotion in situated conceptualization. Neuropsychologia 49,
197. Wilson-Mendenhall, C. D., Barrett, L. F. & Barsalou, L. W. Variety in
emotional life: within-category typicality of emotional experiences is
associated with neural activity in large-scale brain networks. Soc. Cogn.
Aect. Neurosci. 10, 62–71 (2015).
198. Kober, H. et al. Functional grouping and cortical-subcortical interactions in
emotion: A meta-analysis of neuroimaging studies. Neuroimage 42,
199. Skerry, A. E. & Saxe, R. Neural representations of emotion are organized
around abstract event features. Curr. Biol. 25, 1945–1954 (2015).
200. Clithero, J. A. & Rangel, A. Informatic parcellation of the network involved
in the computation of subjective value. Soc. Cogn. Aect. Neurosci. 9,
201. Bickart, K. C., Hollenbeck, M. C., Barrett, L. F. & Dickerson, B. C. Intrinsic
amygdala-cortical functional connectivity predicts social network size in
humans. J. Neurosci. 32, 14729–14741 (2012).
202. Baliki, M. N., Mansour, A. R., Baria, A. T. & Apkarian, A. V. Functional
reorganization of the default mode network across chronic pain conditions.
PLoS ONE 9, e106133 (2014).
203. Schurz, M., Radua, J., Aichhorn, M., Richlan, F. & Perner, J. Fractionating
theory of mind: a meta-analysis of functional brain imaging studies.
Neurosci. Biobehav. Rev. 42, 9–34 (2014).
204. Morelli, S. A. & Lieberman, M. D. e role of automaticity and attention in
neural processes underlying empathy for happiness, sadness, and anxiety.
Front. Hum. Neurosci. 7, 160 (2013).
205. Chiong, W. et al. e salience network causally inuences default mode
network activity during moral reasoning. Brain 136, 1929–1941 (2013).
206. Liu, X., Hairston, J., Schrier, M. & Fan, J. Common and distinct networks
underlying reward valence and processing stages: a meta-analysis
of functional neuroimaging studies. Neurosci. Biobehav. Rev. 35,
207. Engelmann, J. M. et al. Neural substrates of smoking cue reactivity:
a meta-analysis of fMRI studies. Neuroimage 60, 252–262 (2012).
208. Schacter, D. L. & Addis, D. R. e cognitive neuroscience of constructive
memory: remembering the past and imagining the future. Phil. Trans. R.
Soc. Lond. B 362, 773–786 (2007).
209. Bar, M., Amino, E., Mason, M. & Fenske, M. e units of thought.
Hippocampus 17, 420–428 (2007).
210. Fernandino, L. et al. Concept representation reects multimodal
abstraction: A framework for embodied semantics. Cereb. Cortex 26,
211. Touroutoglou, A., Lindquist, K. A., Dickerson, B. C. & Barrett, L. F.
Intrinsic connectivity in the human brain does not reveal networks for
'basic' emotions. Soc. Cogn. Aect. Neurosci. 10, 1257–1265 (2015).
212. Dhanjal, N. S. & Wise, R. J. Frontoparietal cognitive control
of verbal memory recall in alzheimer's disease. Ann. Neurol. 76,
213. Dosenbach, N. U. et al. Distinct brain networks for adaptive and stable task
control in humans. Proc. Natl Acad. Sci. USA 104, 11073–11078 (2007).
214. Ansell, E. B., Rando, K., Tuit, K., Guarnaccia, J. & Sinha, R. Cumulative
adversity and smaller gray matter volume in medial prefrontal, anterior
cingulate, and insula regions. Biol. Psychiatry 72, 57–64 (2012).
215. Caseras, X. et al. Anatomical and functional overlap within the insula and
anterior cingulate cortex during interoception and phobic symptom
provocation. Hum. Brain Mapp. 34, 1220–1229 (2013).
216. Wolf, D. H. et al. Striatal intrinsic reinforcement signals during recognition
memory: relationship to response bias and dysregulation in schizophrenia.
Front. Behav. Neurosci. 5, 81 (2011).
217. Grady, C. L., Luk, G., Craik, F. I. & Bialystok, E. Brain network
activity in monolingual and bilingual older adults. Neuropsychologia 66,
218. Derbyshire, S. W., Whalley, M. G., Stenger, V. A. & Oakley, D. A. Cerebral
activation during hypnotically induced and imagined pain. Neuroimage 23,
219. Feldstein Ewing, S. W., Filbey, F. M., Sabbineni, A., Chandler, L. D. &
Hutchison, K. E. How psychosocial alcohol interventions work: a
preliminary look at what fMRI can tell us. Alcohol. Clin. Exp. Res. 35,
220. Singer, T. et al. Empathy for pain involves the aective but not sensory
components of pain. Science 303, 1157–1162 (2004).
221. Kirk, U., Downar, J. & Montague, P. R. Interoception drives increased
rational decision-making in meditators playing the ultimatum game.
Front. Neurosci. 5, 49 (2011).
222. FitzGerald, T. H., Schwartenbeck, P. & Dolan, R. J. Reward-related activity
in ventral striatum is action contingent and modulated by behavioral
relevance. J. Neurosci. 34, 1271–1279 (2014).
223. Fernandino, L., Humphries, C. J., Conant, L., Seidenberg, M. S. & Binder, J. R.
Heteromodal cortical areas encode sensory-motor features of word
meaning. J. Neurosci. 36, 9763–9769 (2016).
224. Hermans, E. J. et al. Stress-related noradrenergic activity
prompts large-scale neural network reconguration. Science 334,
225. Clark-Polner, E., Wager, T. D., Satpute, A. B. & Barrett, L. F. in e
Handbook of Emotion (eds Barrett, L. F., Lewis, M. & Haviland-Jones, J. M.)
146–165 (Guilford Press, 2016).
We thank M. A. Garcia-Cabezas for comments and advice on neuroanatomy, and
H. Evrard for discussions on anatomical connectivity. This research was supported
by funds from the National Institutes on Aging (R01 AG030311) to L.F.B. and B.C.D.,
the US Army Research Institute for the Behavioral and Social Sciences Contracts
(W5J9CQ-11-C-0046 and W5J9CQ-12-C-0049) to L.F.B., the National Cancer Institute
(U01 CA193632) to L.F.B., the National Institute of Mental Health Ruth L. Kirschstein
National Research Service Award (F32MH096533) to I.R.K., the National Cancer
Institute (UG1 CA189961 and R25 CA102618) to support I.R.K., the National Institutes
of Mental Health (K01MH096175-01) and Oklahoma Tobacco Research Center grants to
W.K.S., a Fyssen Foundation postdoctoral fellowship and Alicia Koplowitz Foundation
short-term fellowship to L.C. and the Fonds de recherche sante Quebec fellowship award
to C.X. The views, opinions and findings contained in this paper are those of the authors
and shall not be construed as an official Department of the Army position, policy or
decision, unless so designated by other documents. The funders had no role in study
design, data collection and analysis, decision to publish or preparation of the manuscript.
The study was designed and analysed by all the authors, and the manuscript was written
by I.R.K. and L.F.B. with comments and edits from other authors.
Supplementary information is available for this paper.
Reprints and permissions information is available at www.nature.com/reprints.
Correspondence and requests for materials should be addressed to I.R.K. or L.F.B.
How to cite this article: Kleckner, I. R. et al. Evidence for a large-scale brain system
supporting allostasis and interoception in humans. Nat. Hum. Behav. 1, 0069 (2017).
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