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The Brain’s Default Network
Anatomy, Function, and Relevance to Disease
RANDY L. BUCKNER,a,b,c,d,eJESSICA R. ANDREWS-HANNA,a,b,c
AND DANIEL L. SCHACTERa
aDepartment of Psychology, Harvard University, Cambridge, Massachusetts, USA
bCenter for Brain Science, Harvard University, Cambridge, Massachusetts, USA
cAthinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital,
Boston, Massachusetts, USA
dDepartment of Radiology, Harvard Medical School, Boston, Massachusetts, USA
eHoward Hughes Medical Institute, Chevy Chase, Maryland 20815, USA
Thirty years of brain imaging research has converged to define the brain’s default network—a
novel and only recently appreciated brain system that participates in internal modes of cog-
nition. Here we synthesize past observations to provide strong evidence that the default net-
work is a specific, anatomically defined brain system preferentially active when individuals are
not focused on the external environment. Analysis of connectional anatomy in the monkey sup-
ports the presence of an interconnected brain system. Providing insight into function, the default
network is active when individuals are engaged in internally focused tasks including autobio-
graphical memory retrieval, envisioning the future, and conceiving the perspectives of oth-
ers. Probing the functional anatomy of the network in detail reveals that it is best understood
as multiple interacting subsystems. The medial temporal lobe subsystem provides informa-
tion from prior experiences in the form of memories and associations that are the building
blocks of mental simulation. The medial prefrontal subsystem facilitates the flexible use of
this information during the construction of self-relevant mental simulations. These two sub-
systems converge on important nodes of integration including the posterior cingulate cortex.
The implications of these functional and anatomical observations are discussed in relation to
possible adaptive roles of the default network for using past experiences to plan for the fu-
ture, navigate social interactions, and maximize the utility of moments when we are not oth-
erwise engaged by the external world. We conclude by discussing the relevance of the default
network for understanding mental disorders including autism, schizophrenia, and Alzheimer’s
disease.
Key words: default mode; default system; default network; fMRI; PET; hippocampus; memory;
schizophrenia; Alzheimer
Introduction
A common observation in brain imaging research
is that a specific set of brain regions—referred to as
the default network—is engaged when individuals are
left to think to themselves undisturbed (Shulman et al.
1997, Mazoyer et al. 2001, Raichle et al. 2001). Prob-
ing this phenomenon further reveals that other kinds of
situations, beyond freethinking, engage the default net-
work. For example, remembering the past, envisioning
Address for correspondence: Dr. Randy Buckner, Harvard University,
William James Hall, 33 Kirkland Drive, Cambridge, MA 02148.
rbuckner@wjh.harvard.edu
future events, and considering the thoughts and per-
spectives of other people all activate multiple regions
within the default network (Buckner & Carroll 2007).
These observations prompt one to ask such questions
as: What do these tasks and spontaneous cognition
share in common? and what is the significance of
this network to adaptive function? The default net-
work is also disrupted in autism, schizophrenia, and
Alzheimer’s disease, further encouraging one to con-
sider how the functions of the default network might
be important to understanding diseases of the mind
(e.g., Lustig et al. 2003, Greicius et al. 2004, Kennedy
et al. 2006, Bluhm et al. 2007).
Motivated by these questions, we provide a com-
prehensive review and synthesis of findings about the
Ann. N.Y. Acad. Sci. 1124: 1–38 (2008). C
!2008 New York Academy of Sciences.
doi: 10.1196/annals.1440.011 1
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Annals of the New York Academy of Sciences
brain’s default network. This review covers both ba-
sic science and clinical observations, with its content
organized across five sections. We begin with a brief
history of our understanding of the default network
(section I). Next, a detailed analysis of the anatomy
of the default network is provided including evidence
from humans and monkeys (section II). The follow-
ing sections concern the role of the default network in
spontaneous cognition, as commonly occurs in passive
task settings (section III), as well as its functions in active
task settings (section IV). While recognizing alterna-
tive possibilities, we hypothesize that the fundamental
function of the default network is to facilitate flexi-
ble self-relevant mental explorations—simulations—
that provide a means to anticipate and evaluate up-
coming events before they happen. The final section
of the review discusses emerging evidence that relates
the default network to cognitive disorders, including
the possibility that activity in the default network aug-
ments a metabolic cascade that is conducive to the
development of Alzheimer’s disease (section V).
I. A Brief History
The discovery of the brain’s default network was
entirely accidental. Evidence for the default network
began accumulating when researchers first measured
brain activity in humans during undirected mental
states. Even though no early studies were explicitly de-
signed to explore such unconstrained states, relevant
data were nonetheless acquired because of the com-
mon practice of using rest or other types of passive
conditions as an experimental control. These stud-
ies revealed that activity in specific brain regions in-
creased during passive control states as compared to
most goal-directed tasks. In almost all cases, the explo-
ration of activity during the control states occurred as
an afterthought—as part of reviews and meta-analyses
performed subsequent to the original reports, which
focused on the goal-directed tasks.
Early Observations
A clue that brain activity persists during undirected
mentation emerged from early studies of cerebral
metabolism. It was already known by the late 19th
century that mental activity modulated local blood
flow (James 1890). Louis Sokoloff and colleagues (1955)
used the Kety-Schmidt nitrous oxide technique (Kety
& Schmidt 1948) to ask whether cerebral metabolism
changes globally when one goes from a quiet rest state
to performing a challenging arithmetic problem—a
task that demands focused cognitive effort. To their
surprise, metabolism remained constant. While not
FIGURE 1. An early image of regional cerebral blood
flow (rCBF) at rest made by David Ingvar and colleagues
using the nitrous oxide technique. The image shows data av-
eraged over eight individuals to reveal a “hyperfrontal” ac-
tivity pattern that Ingvar proposed reflected “spontaneous,
conscious mentation” (Ingvar 1979). Ingvar’s ideas antici-
pate many of the themes discussed in this review (see Ingvar
1974, 1979, 1985).
their initial conclusion, the unchanged global rate
of metabolism suggests that the rest state contains
persistent brain activity that is as vigorous as that
when individuals solve externally administered math
problems.
The Swedish brain physiologist David Ingvar was
the first to aggregate imaging findings from rest task
states and note the importance of consistent, region-
ally specific activity patterns (Ingvar 1974, 1979, 1985).
Using the xenon 133 inhalation technique to measure
regional cerebral blood flow (rCBF), Ingvar and his
colleagues observed that frontal activity reached high
levels during rest states (FIG. 1). To explain this unex-
pected phenomenon, Ingvar proposed that the “hy-
perfrontal” pattern of activity corresponded “to undi-
rected, spontaneous, conscious mentation, the ‘brain
work,’ which we carry out when left alone undisturbed”
(Ingvar 1974). Two lasting insights emerged from Ing-
var’s work. First, echoing ideas of Hans Berger (1931),
his work established that the brain is not idle when left
undirected. Rather, brain activity persists in the ab-
sence of external task direction. Second, Ingvar’s ob-
servations suggested that increased activity during rest
is localized to specific brain regions that prominently
include prefrontal cortex.
The Era of Task-Induced Deactivation
Ingvar’s ideas about resting brain activity remained
largely unexplored for the next decade until positron
emission tomography (PET) methods for brain imag-
ing gained prominence. PET had finer resolution and
Buckner
et al.:
The Brain’s Default Network
3
sensitivity to deep-brain structures than earlier meth-
ods and, owing to the development of isotopes with
short half-lives (Raichle 1987), typical PET studies in-
cluded many task and control conditions for compar-
ison. By the mid-1990s several dozen imaging studies
were completed that examined perception, language,
attention, and memory. Scans of rest-state brain ac-
tivityawere often acquired across these studies for a
control comparison, and researchers began routinely
noticing brain regions more active in the passive con-
trol conditions than the active target tasks—what at
the time was referred to as “deactivation.”
The term “deactivation” was used because analyses
and image visualization were referenced to the target,
experimental task. Within this nomenclature, regions
relatively more active in the target condition (e.g., read-
ing, classifying pictures) compared to the control task
(e.g., passive fixation, rest) were labeled “activations”;
regions less active in the target condition than the con-
trol were labeled “deactivations.” Deactivations were
present and often the most robust effect in many early
PET studies. One form of deactivation for which early
interest emerged was activity reductions in unattended
sensory modalities because of its theoretical relevance
to mechanisms of attention (e.g., Haxby et al. 1994,
Kawashima et al. 1994, Buckner et al. 1996). A second
form of commonly observed deactivation was along the
frontal and posterior midline during active, as com-
pared to passive, task conditions. There was no initial
explanation for these mysterious midline deactivations
(e.g., Ghatan et al. 1995, Baker et al. 1996).
A particularly informative early study was con-
ducted while exploring brain regions supporting
episodic memory. Confronted with the difficult issue of
defining a baseline state for an autobiographical mem-
ory task, Andreasen and colleagues (1995) explored
the possibility that spontaneous cognition makes an
important contribution to rest states. Much like other
studies at the time, the researchers included a rest con-
dition as a baseline for comparison to their target con-
ditions. However, unlike other contemporary studies,
they hypothesized that autobiographical memory (the
experimental target of the study) inherently involves in-
ternally directed cognition, much like the spontaneous
cognition that occurs during “rest” states. For this rea-
son, Andreasen and colleagues explored both the rest
aPET and functional MRI (fMRI) both measure neural activity indi-
rectly through local vascular (blood flow) changes that accompany neu-
ronal activity. PET is sensi tive to cha nges in blood flow directly (Raichle
1987). fMRI is sensitive to changes in oxygen concentration in the blood
which tracks blood flow (Heeger and Ress 2002). For simplicity, we refer
to these methods as measuring brain activity in this review.
and memory tasks referenced to a third control con-
dition that involved neither rest nor episodic memory.
Their results showed that similar brain regions were
engaged during rest and memory as compared to the
nonmemory control. In addition, to better understand
the cognitive processes associated with the rest state,
they informally asked their participants to subjectively
describe their mental experiences.
Two insights originated from this work that fore-
shadow much of the present review’s content. First,
Andreasen et al. (1995) noted that the resting state
“is in fact quite vigorous and consists of a mixture
of freely wandering past recollection, future plans, and
other personal thoughts and experiences.” Second, the
analysis of brain activity during the rest state revealed
prefrontal midline regions as well as a distinct poste-
rior pattern that included the posterior cingulate and
retrosplenial cortex. As later studies would confirm,
these regions are central components of the core brain
system that is consistently activated in humans during
undirected mental states.
Broad awareness of the common regions that be-
come active during passive task states emerged with
a pair of meta-analyses that pooled extensive data to
reveal the functional anatomy of unconstrained cogni-
tion. In the first study, Shulman and colleagues (1997)
conducted meta-analysis of task-induced deactivations
to explicitly determine if there were common brain re-
gions active during undirected (passive) mental states.
They pooled data from 132 normal adults for which an
active task (word reading, active stimulus classification,
etc.) could be directly compared to a passive task that
presented the same visual words or pictures but con-
tained no directed task goals. Using a similar approach,
Mazoyer et al. (2001) aggregated data across 63 nor-
mal adults that included both visually and aurally cued
active tasks as compared to passive rest conditions.
These two analyses revealed a remarkably consis-
tent set of brain regions that were more active during
passive task conditions than during numerous goal-
directed task conditions (spanning both verbal and
nonverbal domains and visual and auditory condi-
tions). The results of the Shulman et al. (1997) meta-
analysis are shown in FIGURE 2. This image displays
the full cortical extent of the brain’s default network.
The broad generality of the rest activity pattern across
so many diverse studies reinforced the intriguing pos-
sibility that a common set of cognitive processes was
used spontaneously during the passive-task states. Mo-
tivated by this idea, Mazoyer et al. (2001) explored
the content of spontaneous thought by asking partici-
pants to describe their musings following the scanned
rest periods. Paralleling the informal observations by
4
Annals of the New York Academy of Sciences
FIGURE 2. The brain’s default network was originally
identified in a meta-analysis that mapped brain regions
more active in passive as compared to active tasks (of-
ten referred to as task-induced deactivation). The displayed
positron emission tomography (PET) data include nine stud-
ies (132 participants) from Shulman et al. (1997; rean-
alyzed in Buckner et al. 2005). Images show the me-
dial and lateral surface of the left hemisphere using a
population-averaged surface representation to take into ac-
count between-subject variability in sulcal anatomy (Van Es-
sen 2005). Blue represents regions most active in passive
task settings.
Ingvar and Andreasen et al., they noted that the im-
aged rest state is associated with lively mental activity
that includes “generation and manipulation of men-
tal images, reminiscence of past experiences based on
episodic memory, and making plans” and further noted
that the subjects of their study “preferentially reported
autobiographical episodes.”
Emergence of the Default Network as Its
Own Research Area
The definitive recent event in the explication of
the default network came with the a series of publi-
cations by Raichle, Gusnard, and colleagues (Raichle
et al. 2001, Gusnard & Raichle 2001, Gusnard et al.
2001). A dominant theme in the field during the pre-
vious decade concerned how to define an appropriate
baseline condition for neuroimaging studies. This focus
on the baseline state was central to the evolving con-
cept of a default network. Many argued that passive
conditions were simply too unconstrained to be useful
as control states. Richard Frackowiak summarized this
widely held concern: “To call a ‘free-wheeling’ state,
or even a state where you are fixating on a cross and
dreaming about anything you like, a ‘control’ state,
is to my mind quite wrong” (Frackowiak 1991). (For
recent discussion of this ongoing debate see Morcom
and Fletcher 2007, Buckner & Vincent 2007, Raichle
& Snyder 2007). As a result of this uneasiness in inter-
preting passive task conditions, beyond the few earlier
studies mentioned, there was a general trend not to
thoroughly report or discuss the meaning of rest state
activity.
Raichle, Gusnard, and colleagues reversed this trend
dramatically with three papers in 2001 (Raichle et al.
2001, Gusnard & Raichle 2001, Gusnard et al. 2001).
Their papers directly considered the empirical and
theoretical implications of defining baseline states and
what the specific pattern of activity in the default net-
work might represent. Several lasting consequences on
the study of the default network emerged. First, they
distinguished between various forms of task-induced
deactivation and separated deactivations defining the
default network from other forms of deactivation (in-
cluding attenuation of activity in unattended sensory
areas). Second, they compiled a considerable array of
findings that drew attention to the specific anatomic
regions linked to the default network and what their
presence might suggest about its function. A key in-
sight was that the medial prefrontal regions consistently
identified as part of the default network are associated
with self-referential processing (Gusnard et al. 2001,
Gusnard & Raichle 2001). Most importantly, the pa-
pers brought to the forefront the exploration of the
default network as its own area of study (including pro-
viding its name, which, as of late 2007, has appeared as
a keyword in 237 articles). Our use of the label “default
network” in this review stems directly from their label-
ing the baseline rest condition as the “default mode.”b
Their reviews made clear that the default network is
to be studied as a fundamental neurobiological system
with physiological and cognitive properties that distin-
guish it from other systems.
The default network is a brain system much like the
motor system or the visual system. It contains a set
of interacting brain areas that are tightly functionally
bRefe rence s to t he de fa ult m od e appea r in t he li te rat ure on co gn iti on
prior to the introduction of the concept as an explanation for neural and
metabolic phenomena. Giambra (1995), for example, noted that “Task-
unrelated images and thoughts may represent the normal default mode
of operation of the self-aware.” Thus, the concept of a default mode is
converged upon from both cognitive and neurobiological perspectives.
Buckner
et al.:
The Brain’s Default Network
5
TABLE 1. Core regions associated with the brain’s default network
REGION ABREV INCLUDED BRAIN AREAS
Ve n t r a l m e d i a l p r e f r o n t a l c o r t e x v M P F C 2 4 , 1 0 m / 1 0 r / 1 0 p , 3 2 a c
Posterior cingulate/retosplenial cortex PCC/Rsp 29/30, 23/31
Inferior parietal lobule IPL 39, 40
Lateral temporal cortex†LTC 21
Dorsal medial prefrontal cortex dMPFC 24, 32ac, 10p, 9
Hippocampal formation†† HF+Hippocampus proper,EC, PH
Notes: Region, abbreviation, and approximate area labels for the core regions associated with the default network in humans. Labels
correspond to those originally used by Brodmann for humans with updates by Petrides and Pandya (1994), Vogt et al. (1995), Morris
et al. (2000), and ¨
Ong¨
ur et al. (2003). Labels should be considered approximate because of the uncertain boundaries of the areas and
the activation patterns. †LTC is particularly poorly characterized in humans and is therefore the most tentative estimate. ††HF+
includes entorhinal cortex (EC) and surrounding cortex (e.g., parahippocampal cortex; PH).
connected and distinct from other systems within the
brain. In the remainder of this review, we define the
default network in more detail, speculate on its func-
tion both during passive and active cognitive states,
and evaluate accumulating data that suggest that un-
derstanding the default network has important clinical
implications for brain disease.
II. Anatomy of the Default Network
The anatomy of the brain’s default network has been
characterized using multiple approaches. The default
network was originally identified by its consistent ac-
tivity increases during passive task states as compared
to a wide range of active tasks (e.g., Shulman et al.
1997, Mazoyer et al. 2001, FIG. 2). A more recent ap-
proach that identifies brain systems via intrinsic activity
correlations (e.g., Biswal et al. 1995) has also revealed
asimilarestimateoftheanatomyofthedefaultnet-
work (Greicius et al. 2003, 2004). More broadly, the
default network is hypothesized to represent a brain
system (or closely interacting subsystems) involving
anatomically connected and interacting brain areas.
Thus, its architecture should be critically informed by
studies of connectional anatomy from nonhuman pri-
mates and other relevant sources of neurobiological
data.
In this section, we review the multiple approaches
to defining the default network and consider the spe-
cific anatomy that arises from these approaches in the
context of architectonic and connectional anatomy in
the monkey. We highlight two observations. First, all
neuroimaging approaches converge on a similar es-
timate of the anatomy of the default network that
is largely consistent with available information about
connectional anatomy (TABLE 1). Second, the intrin-
sic architecture of the default network suggests that it
comprises multiple interacting hubs and subsystems.
These anatomic observations provide the foundation
on which the upcoming sections explore the functions
of the default network.
Blocked Task-Induced Deactivation
Because PET imaging requires about a minute of
data accumulation to construct a stable image, the
brain’s default network was initially characterized us-
ing blocked task paradigms. Within these paradigms,
extended epochs of active and passive tasks were com-
pared to one another. During these epochs brain ac-
tivity was averaged over blocks of multiple sequential
task trials—hence the label “blocked.” Shulman et al.
(1997) and Mazoyer et al. (2001) published two semi-
nal meta-analyses based on blocked PET methods to
identify brain regions consistently more active during
passive tasks as compared to a wide range of active
tasks. Tasks spanned verbal and nonverbal domains
(Shulman et al. 1997) and auditory and visual modal-
ities (Mazoyer et al. 2001). In total, data from 195
subjects were aggregated across 18 studies in the two
meta-analyses.
FIGURE 2 displays the original data of Shulman et al.
visualized on the cortical surface to illustrate the topog-
raphy of the default network; the data from Mazoyer
et al. (not shown) are highly similar. FIGURE 3shows
a third meta-analysis of blocked task data from a se-
ries of 4 fMRI data sets from 92 young-adult subjects
(Shannon 2006). In this meta-analysis of fMRI data,
the passive tasks were all visual fixation and the active
tasks involved making semantic decisions on visually
presented words (data from Gold & Buckner 2002,
Lustig & Buckner 2004). Across all the variations, a
consistent set of regions increases activity during pas-
sive tasks when individuals are left undirected to think
to themselves.
6
Annals of the New York Academy of Sciences
FIGURE 3. The brain’s default network is converged upon by multiple, distinct fMRI approaches.
(A) Each row of images shows a different fMRI approach for defining the default network: blocked
task-induced deactivation (top row), event-related task-induced deactivation (middle row), and functional
connectivity with the hippocampal formation (bottom row). Within each approach, the maps represent a
meta-analysis of multiple data sets thereby providing a conservative estimate of the default network (see
text). Colors reflect the number of data sets showing a significant effect within each image (color scales
to the right). (B) The convergence across approaches reveals the core regions within the default network
(legend at the bottom). Z labels correspond to the transverse level in the atlas of Talairach and Tournoux
(1988). Left is plotted on the left. Adapted from Shannon (2006).
Buckner
et al.:
The Brain’s Default Network
7
Event-Related, Task-Induced Deactivation
An alternative to defining the anatomy of the de-
fault network based on blocked tasks is to perform a
similar analysis on individual task events. Rapid event-
related fMRI makes possible such an analysis by pre-
senting task trials at randomly jittered time intervals,
typically 2 to 10 seconds apart. The reason to perform
such an analysis is the possibility that extended epochs
are required to elicit activity during passive epochs, as
might be the case if blocked task-induced deactivations
arise from slowly evolving signals or sustained task sets
that are not modulated on a rapid time frame (e.g.,
Dosenbach et al. 2006).
FIGURE 3illustratestheresultsofameta-analysisof
studies from Shannon (2006) that uses event-related
fMRI data to define the default network. In total, data
from 49 subjects were pooled for this analysis. The
data are based on semantic and phonological classifi-
cation tasks from Kirchhoff et al. (2005; n =28) as well
as a second sample of event-related data that also in-
volved semantic classification (Shannon 2006; n=21).
As can be appreciated visually, the default network de-
fined based on event-related data is highly similar to
that previously reported using blocked data. Thus, the
differential activity in the default network between pas-
sive and active task states can emerge rapidly, on the
order of seconds or less.
Functional Connectivity Analysis
Afinalapproachtodefiningthefunctionalanatomy
of the default network is based on the measurement of
the brain’s intrinsic activity. At all levels of the ner-
vous system from individual neurons (Tsodyks et al.
1999) and cortical columns (Arieli et al. 1995) to whole-
brain systems (Biswal et al. 1995, De Luca et al. 2006),
there exists spontaneous activity that tracks the func-
tional and anatomic organization of the brain. The
patterns of spontaneous activity are believed to re-
flect direct and indirect anatomic connectivity (Vincent
et al. 2007a) although additional contributions may
arise from spontaneous cognitive processes (as will be
described in a later section). In humans, low-frequency,
spontaneous correlations are detectable across the
brain with fMRI and can be used to characterize
the intrinsic architecture of large-scale brain systems,
an approach often referred to as functional connec-
tivity MRI (Biswal et al. 1995, Haughton & Biswal
1998; see Fox & Raichle 2007 for a recent review).
Motor (Biswal et al. 1995), visual (Nir et al. 2006),
auditory (Hunter et al. 2006), and attention (Fox et
al. 2006) systems have been characterized using func-
tional connectivity analysis (see also De Luca et al.
2006).
Greicius and colleagues (2003, 2004) used such an
analysis to map the brain’s default network (see also Fox
et al. 2005, Fransson 2005, Damoiseaux et al. 2006,
Vincent et al. 2006). Functional connectivity analysis
is particularly informative because it provides a means
to assess locations of interacting brain regions within
the default network in a manner that is independent
of task-induced deactivation. In their initial studies,
Greicius et al. measured spontaneous activity from the
posterior cingulate cortex, a core region in the default
network, and showed that activity levels in the remain-
ing distributed regions of the system are all correlated
together. Their map of the default network, based on
intrinsic functional correlations, is remarkably similar
to that originally generated by Shulman et al. (1997)
based on PET deactivations.
An important further observation from analyses of
intrinsic activity is that the default network includes
the hippocampus and adjacent areas in the medial
temporal lobe that are associated with episodic mem-
ory function (Greicius et al. 2004). In fact, many of
the major neocortical regions constituting the default
network can be revealed by placing a seed region in
the hippocampal formation and mapping those corti-
cal regions that show spontaneous correlation (Vincent
et al. 2006). FIGURE 3showsamapofthedefaultnet-
work as generated from intrinsic functional correla-
tions with the hippocampal formation in four inde-
pendent data sets.
Convergence across Approaches for
Defining the Default Network
Is there convergence between the three distinct ap-
proaches for defining the anatomy of the default net-
work described above? To answer this question, the
overlap among the multiple methods for defining de-
fault network anatomy is displayed on the bottom panel
of FIGURE 3. The convergence reveals that the default
network comprises a distributed set of regions that
includes association cortex and spares sensory and
motor cortex. In particular, medial prefrontal cortex
(MPFC), posterior cingulate cortex/retrosplenial cor-
tex (PCC/Rsp), and the inferior parietal lobule (IPL)
show nearly complete convergence across the 18 data
sets.
Several more specific observations are apparent
from this analysis of overlap. First, the hippocampal
formation (HF) is shown to be involved in the de-
fault network regardless of which approach is used
(task-induced deactivation or functional connectivity
analysis) but, relative to the robust posterior mid-
line and prefrontal regions, the HF is less promi-
nent using the approach of task-induced deactivations.
8
Annals of the New York Academy of Sciences
FIGURE 4. The default network in the monkey defined using functional connectivity analysis. A seed
was placed in the posterior midline (indicated by asterisk) and the regions showing correlated activity
were mapped. The left image shows the medial surface, the middle image a transverse section through
parietal cortex, and the right image a coronal section through the hippocampal formation. Left is plotted
on the left. Adapted from Vincent et al. (2007a).
Second, multiple default network regions are function-
ally correlated with the HF, reinforcing the notion that
the medial temporal lobe is included in the network.
Overlap is not perfect, however, with some indications
of more extensive recruitment during passive cogni-
tive states, including both in posterior parietal cortex
and in prefrontal cortex. These details will be shown
to be informative when subsystems within the default
network are discussed. Third, lateral temporal cortex
(LTC) extending into the temporal pole is consistently
observed across approaches but, like the HF, is less
robust. Together these observations tentatively define
the core anatomical components of the default network
(TABLE 1).
Insights from Comparative Anatomy
Important insights into the organization of human
brain systems have been provided by comparative stud-
ies in the monkey. Vincent et al. (2007a) recently used
functional connectivity analysis to show that the major
default network regions in posterior cortex have pu-
tative monkey homologues including PCC/Rsp, IPL,
and the HF (FIG. 4, see also Rilling et al. 2007). In
addition, architectonic maps reveal many similarities
between human and monkey anatomy in the vicinity
of the default network (e.g., Petrides & Pandya 1994,
Morris et al. 2000, ¨
Ong¨
ur & Price 2000, Vogt et al.
2001). Motivated by these recent observations, we pro-
vide here a detailed analysis of the architectonics and
connectional anatomy of the default network, while
recognizing that there may be fundamental differences
in humans. As a means to simplify our analysis, we fo-
cus on areas that fall within PCC/Rsp and MPFC and
their anatomic relationships with other cortical regions
and the HF. Potentially important subcortical connec-
tions, such as to the striatal reward pathway and the
amygdala, are not covered. Even with this simplifica-
tion, the details of the anatomy are complex and one is
immediately confronted with the observation that each
of the activated regions, as defined based on human
functional neuroimaging data, extends across multiple
brain areas that have distinct architecture and connec-
tivity. Progress will require significantly more detailed
analysis of the anatomic extent and locations of default
network regions in humans. Nonetheless, using avail-
able data we provide an initial analysis of the anatomy
recognizing that it is provisional and incomplete.
Posterior cingulate cortex (PCC) and restrosple-
nial cortex (Rsp) have been extensively studied in the
macaque monkey and recently so with focus on di-
rect comparison to human anatomy (e.g., Morris et al.
2000, Vogt et al. 2001). The PCC and Rsp fall along
the posterior midline and exist within a region that
contains at least three contiguous, but distinct, sets of
areas: Rsp (areas 29/30), PCC (areas 23/31), and pre-
cuneus (area 7m). Rsp is just posterior to the corpus
callosum and, in humans, extends along the ventral
bank of the cingulate gyrus (Morris et al. 2000, Vogt
et al. 2001). In macaques, Rsp is much smaller and
does not encroach onto the cingulate gyrus (Morris
et al. 1999, Kobayashi & Amaral 2000). Just poste-
rior to Rsp, along the main portion of the cingulate
gyrus, is PCC. The precuneus, a region often cited as
being involved in the default network, comprises the
posterior and dorsal portion of the medial parietal
lobe and includes area 7m (Cavanna & Trimble 2006,
Parvizi et al. 2006). As an ensemble, these three struc-
tures are sometimes referred to as “posteriomedial
Buckner
et al.:
The Brain’s Default Network
9
cortex,” and each structure is interconnected with the
others (e.g., Parvizi et al. 2006, Kobayashi & Amaral
2003).
The predominant extrinsic connections to and from
the posteriomedial cortex differ by area. Collectively,
the connections are widespread and, much like other
association areas, are consistent with a role in infor-
mation integration. Specifically, Rsp is heavily inter-
connected with the HF and parahippocampal cortex,
receiving nearly 40% of its extrinsic input from the me-
dial temporal lobe (Kobayashi & Amaral 2003, see also
Suzuki & Amaral 1994, Morris et al. 1999). Rsp also
projects back to the medial temporal lobe as well as
prominently to multiple prefrontal regions (Kobayashi
& Amaral 2007, FIG. 5). PCC area 23 has reciprocal
connections with the medial temporal lobe and robust
connections with prefrontal cortex and parietal cortex
area 7a—an area at or near the putative homologue of
the human default network region IPL (Kobayashi &
Amaral 2003, 2007, FIG. 5). The medial temporal lobe
also has modest, but consistent, connections with area
7a (Suzuki & Amaral 1994, Clower et al. 2001, Lavenex
et al. 2002). Thus, PCC/Rsp provides a key hub for
overlapping connections between themselves, the me-
dial temporal lobe, and IPL—three of the distributed
regions that constitute the major posterior extent of
the default network.
An unresolved issue is whether the lateral projection
zone of PCC/Rsp is restricted to area 7a in humans
or extends to areas 39/40. Macaque PCC has recipro-
cal projections to superior temporal sulcus (STS) and
the superior temporal gyrus (STG; see also Kobayashi
& Amaral 2003). Analysis of the default network in
macaques provides indication that the network’s lat-
eral extent includes STG (Vincent et al. 2007a). Com-
plicating the picture, IPL is greatly expanded in hu-
mans, including areas 39/40 (Culham & Kanwisher
2001, Simon et al. 2002, Orban et al. 2006) that are
closely localized to the lateral parietal region identified
by human neuroimaging as being within the default
network (see Caspers et al. 2006). A recent analysis of
cortical expansion between the macaque and human
brain based on mapping of 23 presumed homologies
revealed that IPL is among the regions of greatest in-
crease (Van Essen & Dieker 2007). Thus, these lateral
parietal and temporo-parietal areas, which are not as
well characterized as PCC/Rsp, are extremely interest-
ing in light of their anatomic connections, involvement
in the default network, and potential evolutionary ex-
pansion in humans.
The connectional anatomy of area 7m in the pre-
cuneus is difficult to understand in relation to the
default network even though it is often included in
the default network. One possibility is that area 7m is
simply not a component of the default network. Ref-
erences to precuneus in the neuroimaging literature
are often used loosely to label the general region that
includes PCC area 29/30. Precuneus area 7m pre-
dominantly connects with occipital and parietal areas
linked to visual processing and frontal areas associated
with motor planning (Cavada & Goldman-Rakic 1989,
Leichnetz 2001). Moreover, medial temporal lobe re-
gions that have extensive projections to PCC and Rsp
show minimal connections to area 7m. Connections
do exist between area 7m and the PCC, which may
be the basis for the extensive activation patterns some-
times observed along the posterior midline, but we
suspect that area 7m is not a core component of the
network.
Reinforcing this impression, close examination of
the many maps that define the human default net-
work in this review shows that the posterior medial
extent of the network usually does not encroach on the
edge of the parietal midline (where area 7m is located,
Scheperjans et al. 2007). This boundary is labeled ex-
plicitly in FIGURE 7byanasterisk.Themiddlepanel
of FIGURE 18 shows a particularly clear example of the
separation between task-induced deactivation of PCC
and its dissociation from the region at or near area 7m.
Another example of dissociation between the default
network and area 7m can be found in Vogeley et al.
(2004; their Figure 2A versus 2B). For all these reasons,
we provisionally conclude that area 7m in precuneus
is not part of the default network.
The second hub of the default network, MPFC, en-
compasses a set of areas that lie along the frontal mid-
line (Petrides & Pandya 1994, ¨
Ong¨
ur & Price 2000).
Human MPFC is greatly expanded relative to the mon-
key ( ¨
Ong¨
ur et al. 2003, FIG. 6). Two differences are no-
table. First, macaque area 32 is pushed ventrally and
rostrally in humans to below the corpus callosum (la-
beled by ¨
Ong¨
ur et al. as area 32pl in the human based
on Brodmann’s original labeling of this area in mon-
key as the “prelimbic area”). Human area 32ac cor-
responds to Brodmann’s dorsal “anterior cingulate”
area. Second, human area 10 is quite large and fol-
lows the rostral path of anterior cingulate areas 24
and 32ac much like typical activation of MPFC in the
default network. This is relevant because commonly
referenced maps based on classic architectonic analy-
ses restrict this area to frontalpolar cortex (e.g., Petrides
& Pandya 1994). Some evidence suggests that area 10
is disproportionately expanded in humans even when
contrasted to great apes, suggesting specialization
during recent hominid evolution (Semendeferi et al.
2001).
10
Annals of the New York Academy of Sciences
FIGURE 5. Monkey anatomy suggests that the default network includes multiple, distinct association
areas, each of which is connected to other areas within the network. Illustrated are two examples of output
(efferent) and input (afferent) connections for posterior cingulate/retrosplenial cortex (PCC/Rsp) and
parahippocampal cortex (PH). (A) Output connections from Rsp (areas 29 and 30) and PCC (area 23)
are displayed. Lines show connections to distributed areas; thickness represents the connection strength.
Rsp and PCC are heavily connected with the medial temporal lobe (HF, hippocampal formation; PH,
parahippocampal cortex), the inferior parietal lobule (IPL) extending into superior temporal gyrus (STG),
and prefrontal cortex (PFC). Numbers in the diagram indicate brain areas. Adapted from Kobayashi and
Amaral (2007). (B) Input and output connections to and from PH to medial prefrontal cortex (MPFC) are
displayed. Adapted from Kondo et al. (2005).
Given these details, MPFC activation within the
default network is estimated to encompass human
areas 10 (10 m, 10 r, and 10 p), anterior cingu-
late (area 24/32ac), and area 9 in prefrontal cor-
tex. The closest homologues to these areas in the
monkey—the medial prefrontal network—show re-
ciprocal connections with the PCC, Rsp, STG, HF,
and the perirhinal/parahippocampal cortex; sen-
sory inputs are nearly absent (Barbas et al. 1999,
Price 2007). These connectivity patterns closely
Buckner
et al.:
The Brain’s Default Network
11
FIGURE 6. Architectonic areas within medial prefrontal cortex (MPFC) are illustrated for the monkey
and human. The human MPFC is greatly expanded relative to the macaque monkey. This expansion is
depicted by the triangle and asterisk that plot putative homologous areas between species based on
¨
Ong¨
ur et al. (2003). Area 32 in the macaque is homologous with area 32pl in the human. Area 24c is
expanded and homologous to the caudal part of area 32ac in human. The MPFC region activated within
the human default network likely corresponds to frontalpolar cortex and its rostral expansion (areas 10m,
10r, and 10p), anterior cingulate (areas 24 and 32ac), and the rostral portion of prefrontal area 9.
Because of differences in functional properties, we sometimes differentiate in this review between dorsal
and ventral portions of MPFC (dMPFC and vMPFC). Adapted with permission from ¨
Ong¨
ur et al. (2003).
parallel areas implicated as components of the default
network.
At the broadest level, an important principle
emerges from considering these anatomic details: the
default network is not made up of a single monosynap-
tically connected brain system. Rather, the architec-
ture reveals a series of interconnected subsystems that
converge on key “hubs,” in particular the PCC, that
are connected with the medial temporal lobe memory
system. In the next section, we explore evidence for
these subsystems from functional connectivity analysis
in humans.
The Default Network Comprises Interacting
Subsystems
The default network comprises a set of brain regions
that are coactivated during passive task states, show in-
trinsic functional correlation with one another, and are
connected via direct and indirect anatomic projections
as estimated from comparison to monkey anatomy.
However, there is also clear evidence that the brain
regions within the default network contribute special-
ized functions that are organized into subsystems that
converge on hubs.
One way to gain further insight into the organiza-
tion of the default network is through detailed anal-
ysis of the functional correlations between regions.
FIGURE 7 plots maps of the intrinsic correlations as-
sociated with three separate seed regions within the
default network in humans: the hippocampal forma-
tion including a portion of parahippocampal cortex
(HF+), dMPFC, and vMPFC. The hubs—PCC/Rsp,
vMPFC, and IPL—are revealed as the regions showing
complete overlap across the maps. HF+forms a sub-
system that is distinct from other major components
of default network including the dMPFC: both are
strongly linked to the core hubs of the default network
but not to each other. We suspect further analyses will
reveal more subtle organizational properties. Of note,
the map of the default network’s hubs and subsystems
shown in FIGURE 7 bears a striking resemblance to the
original map of Shulman et al. (1997, FIG.2) and up-
dates the description of the network to show that it
comprises at least two interacting subsystems.
Normative estimates of the correlation strengths
between regions within the default network are pro-
vided in FIGURE 8. The bottom panel of FIGURE 8
is a graph analytic visualization of the correlation
strengths using a spring-embedding algorithm to clus-
ter strongly correlated regions near each other and po-
sition weakly correlated regions away from each other.
This graphical representation illustrates the separation
12
Annals of the New York Academy of Sciences
FIGURE 7. Hubs and subsystems within the default net-
work are mapped using functional connectivity analysis.
This map was produced by seeding three separate re-
gions (dMPFC, vMPFC, HF+)andplottingtheoverlapof
the functional correlations across the three regions (legend
is at bottom; threshold for each map is r =.07). Data are
high-resolution rest data (2mm voxels) from 40 participants
(mean age =22 years; 16 male) collected at 3-Tesla using
a12-channelheadcoil(datafromAndrews-Hannaetal.
2007b). Three observations are notable. First, the com-
bined map is remarkably similar to the original estimate
of the default network from PET task-induced deactivation
(see FIG. 2). Second, PCC/Rsp, IPL, and vMPFC represent
anatomic hubs in the default network to which all other re-
gions are correlated. Third, dMPFC and HF+, which are
both strongly correlated with the hub regions, are not cor-
related with each other, indicating that they are part of dis-
tinct subsystems. A further interesting feature is that area 7m
within the precuneus (indicated by asterisk) is not part of the
default network. The black line near the asterisk represents
the approximate boundary between areas 7m and 23/31
(estimated boundary based on Vogt & Laureys 2005).
of the medial temporal lobe subsystem. The analysis
also reveals that the medial temporal subsystem is less
strongly associated with the core of the default net-
work that is centered on MPFC and PCC. However,
it is important to note that the correlational strengths
associated with the medial temporal lobe are gener-
ally weaker than those observed for the distributed
neocortical regions. As shown in FIGURE 3, the most
robust correlations linked to the medial temporal lobe
overlap the default network. It is presently unclear
how to interpret the quantitatively lower overall lev-
els of correlations associated with the medial temporal
lobe. Functional understanding of the default network
should seek to explain both the distinct contributions
of the interacting subsystems and the role of their close
interaction. Of interest, infants do not show the struc-
tured interactions between the default network regions,
suggesting that the network develops in toddlers or chil-
dren (Fransson et al. 2007). At the other end of the age
spectrum, it has recently been shown that advanced
aging is associated with disrupted correlations across
large-scale brain networks including the default net-
work (Andrews-Hanna et al. 2007a, Damoiseaux et
al. in press). Thus, the correlation strengths presented
in FIGURE 8 are only representative of normal young
adults. An interesting topic for future research will be
to understand the developmental course of the default
network as well as the functional implications of its late
life disruption.
Vascular and Other Alternative Explanations
for the Anatomy of the Default Network
Given the reproducibility of the specific anatomy of
the default network, an important question to ask is
whether the pattern can be accounted for by some al-
ternative explanation that is not linked to neural archi-
tecture. One possibility is that the observed anatomy
reflects a vascular pattern—either draining veins, a
global form of “blood stealing” whereby active regions
achieve blood flow increases at the expense of nearby
regions, or some other poorly understood mechanism
of vascular regulation. The methods that have revealed
the default network are based on hemodynamic mea-
sures of blood flow that are indirectly linked to neu-
ral activity (Raichle 1987, Heeger & Ress 2002). This
issue is particularly relevant for analyses based on in-
trinsic correlations because slow fluctuations in vascu-
lar properties track breathing as well as oscillations
in intracranial pressure. Wise et al. (2004) recently
measured fMRI correlations with the slow fluctua-
tions in the partial pressure of end-tidal carbon diox-
ide that accompany breathing. Their results convinc-
ingly demonstrate correlated, spatially specific fMRI
responses suggesting that fMRI patterns can reflect
vascular responses to breathing (see also Birn et al.
2006). While the spatial patterns associated with res-
piration do not closely resemble the default network,
the results of Wise and colleagues are a reminder that
a vascular account should be explored further.
One reason to be skeptical of a vascular ac-
count is that the default network is also identified
using measures of resting glucose metabolism. In a
Buckner
et al.:
The Brain’s Default Network
13
FIGURE 8. (top) Functional correlation strengths are listed for multiple regions within the default network. Each of the
regions is displayed on top with the strengths of the region-to-region correlations indicated below (r-values were computed
using procedures identical to Vincent et al. 2006). Regions are plotted on the averaged anatomy of the participant
group (MNI/ICBM152 atlas with Z coordinates displayed). (bottom) The regions of the default network are graphically
represented with lines depicting correlation strengths. The positioning of nodes is based on a spring-embedding algorithm
that positions correlated nodes near each other. The structure of the default network has a core set of regions (red) that are
all correlated with each other. LTC is distant because of its weaker correlation with the other structures. The medial temporal
lobe subsystem (blue) includes both the hippocampal formation (HF) and parahippocampal cortex (PHC). This subsystem is
correlated with key hubs of the default network including PCC/Rsp, vMPFC, and IPL. The dMPFC is negatively correlated
with the medial temporal lobe subsystem suggesting functional dissociation. Graph analytic visualization provided by
Alexander Cohen and Steven Petersen.
particularly informative study, Vogt and colleagues
(2006) used [18F]flourodeoxyglucose (FDG) PET to
explore anatomy associated with the default net-
work. Critically, FDG-PET measures neuronal activ-
ity through glucose metabolism independent of vas-
cular coupling. Vogt et al. first defined regions within
the PCC (ventral PCC and dorsal PCC) and Rsp in
postmortem human tissue samples. They then mea-
sured resting state glucose metabolism in each of these
regions across 163 healthy adults and correlated the
14
Annals of the New York Academy of Sciences
obtained values across the brain to yield metabolism-
based maps of functional correlation. A quite remark-
able pattern emerged: ventral PCC showed correlation
with the main components of the default network in-
cluding vMPFC and IPL (see their Figure 7, panel B).
Moreover, this pattern was preferential to ventral PCC,
suggesting that the posterior hub of the default network
may be even more circumscribed than the fMRI data
suggest, which have implicated the broader region in-
cluding dorsal PCC and Rsp. Directly relevant to the
question of whether a vascular explanation can ac-
count for the default network’s anatomy, these results
were obtained without relying on vascular coupling.
Glucose Metabolism and the Oxygen
Extraction Fraction
Metabolic properties of the default network also set
the network apart from other brain systems (Raichle
et al. 2001). In particular, regions within the default
network show disproportionately high resting glucose
metabolism relative to other brain regions as mea-
sured using FDG-PET (e.g., Minoshima et al. 1997,
Gusnard & Raichle 2001, see FIG.17) as well as high
regional blood flow (Raichle et al. 2001). For example,
Minoshima et al. (1997, see their Figure 1) mapped
resting glucose metabolism in healthy older adults ref-
erenced to the pons, allowing visualization of regional
variation across the cortex. Along the midline, normal-
ized glucose metabolism in PCC was about 20% higher
than in most other brain regions. However, high glu-
cose metabolism was not selective only to the default
network—a region at or near primary visual cortex
also showed high resting metabolism. To our knowl-
edge, there has been no systematic investigation of rest-
ing glucose metabolism within default network regions
as contrasted to regions outside the network; however,
all reported exploratory maps of glucose metabolism
converge on the observation that the posterior mid-
line near PCC is a region of disproportionately high
metabolism (e.g., Minoshima et al. 1997, Figure 1, Gus-
nard & Raichle 2001, Figure 1). Intriguingly, the re-
gions within the default network that show high resting
metabolism are also those affected in Alzheimer’s dis-
ease, something that will be discussed extensively in
the final section of this review. To foreshadow this fi-
nal discussion, the possibility will be raised that high
levels of baseline activity and metabolism (glycolysis)
in the default network are conducive to the forma-
tion of pathology associated with Alzheimer’s disease
(Buckner et al. 2005).
A second metabolic property that has been explored
in connection with the default network is regional oxy-
gen utilization. In their seminal paper that drew at-
tention to the default network, Raichle et al. (2001)
mapped the ratio of oxygen used locally to oxygen de-
livered by blood flow. This ratio, referred to as the oxy-
gen extraction fraction (OEF), decreases during height-
ened neural activity because the increased flow of
blood into a region exceeds oxygen use (see Raichle &
Mintun 2006). Raichle and colleagues (2001) hypoth-
esized that an absolute physiological baseline could
be shown to exist if OEF remained constant during
passive (rest) task states, suggesting that task-induced
deactivations within the default network are physiolog-
ically dissimilar from other forms of transient neuronal
activity increase. While an intriguing possibility, there
are several observations that suggest OEF within the
default network does change at rest. First, OEF de-
creases were noted by Raichle et al. (2001) in several
default network regions at rest when each was tested
individually at the p<0.05 level of statistical signifi-
cance. Second, regional variation in OEF across the
default network was correlated from one data set to
the next (r =.89) indicating systematic modulation; a
constant OEF across regions would show zero corre-
lation from one data set to the next. The modulation
was quantitatively small, however, with OEF values
of most regions falling within 5 to 10% of the other
regions. Further exploration will be required to deter-
mine if there is an absolute metabolic state that defines
a baseline within the default network or whether there
are meaningful variations across regions. In the next
section, we will specifically explore the possibility that
the special properties that arise in the default network
associate with its role in spontaneous cognition during
freethinking.
III. Spontaneous Cognition
Human beings spend nearly all of their time in some kind
of mental activity, and much of the time their activity con-
sists not of ordered thought but of bits and snatches of in-
ner experience: daydreams, reveries, wandering interior
monologues, vivid imagery, and dreams. These desultory
concoctions, sometimes unobtrusive but often moving,
contribute a great deal to the style and flavor of being
human. Their very humanness lends them great intrinsic
interest; but beyond that, surely so prominent a set of
activities cannot be functionless. (Klinger 1971 p. 347)
A shared human experience is our active inter-
nal mental life. Left without an immediate task that
demands full attention, our minds wander jumping
from one passing thought to next—what William James
(1890) called the “stream of consciousness.” We muse
about past happenings, envision possible future events,
and lapse into ideations about worlds that are far from
Buckner
et al.:
The Brain’s Default Network
15
our immediate surroundings. In lay terms, these are
the mental processes that make up fantasy, imagina-
tion, daydreams, and thought. A central issue for our
present purposes is to understand to what degree, if
any, the default network mediates these forms of spon-
taneous cognition. The observation that the default
network is most active during passive cognitive states,
when thought is directed toward internal channels,
encourages serious consideration of the possibility that
the default network is the core brain system associated
with spontaneous cognition, and further that people
have a strong tendency to engage the default network
during moments when they are not otherwise occupied
by external tasks. In considering the relationship be-
tween the default network and spontaneous cognition,
it is worth beginning with a short review of spontaneous
cognition itself.
Descriptions of human nature have alluded to the
prominence of private mental experience since the
classical period. In a whimsical description, Plato por-
trayed Socrates as “capable of standing all day in the
market place lost in thought and oblivious of the ex-
ternal world,” leading Aristophanes to coin the phrase
“his head is in the clouds” (Singer 1966). Experimental
study of internal mental life originated within the psy-
chological movement of introspection in the late 19th
century. Developed by Wilhelm Wundt and continued
by the American psychologist Edward Titchener, in-
trospective methods required participants to describe
the contents of their internal mental experience. The
premise of introspection was that conscious elements
and attributes are sufficient to describe the mind. The
focus on behaviorism during much of the 20th century,
which emphasized measurement of the external factors
that control behavior, caused a marked decline in the
study of thought in mainstream science. The behavior-
ists rejected the methods of introspection because they
relied on subjective report leading to a global “morato-
rium on the study of inner experience” (Klinger 1971).
The dark ages of spontaneous cognition ended in
1966 with a seminal publication by Jerome Singer that
described an extensive empirical research program on
the topic of daydreaming (see also Antrobus et al.
1970, Klinger 1971, Singer 1974). Several important
advances emerged from this work. First, behavioral
instruments were developed for the measurement of
spontaneous cognition that correlated with such fac-
tors as individual differences in cognition, physiological
measures and eye movements, and were also predic-
tive of response patterns on varied tasks (e.g., Singer
& Schonbar 1961, Singer et al. 1963, Antrobus et al.
1966, Antrobus 1968, Antrobus et al. 1970). Second,
spontaneous cognition was observed to be quite com-
mon: 96% of individuals report daydreaming daily.
Moreover, the contents of daydreams were found to
include everything from mundane recounts of recent
happenings to plans and expectations about the future.
Finally, this work emphasized that spontaneous cogni-
tion is healthy and adaptive, and not simply a set of dis-
tracting processes or fantasies. Singer (1966), Antrobus
et al. (1966) and later Klinger (1971) specifically sug-
gested that internal mental activity is important for
anticipating and planning the future. We will return to
this important idea later.
In the past decade, the study of spontaneous cogni-
tion has built upon these foundations and introduced
novel experimental approaches to explore the content
of people’s internal mental states (see Smallwood &
Schooler 2006 for review). Critical to understanding
the relationship between the default network and spon-
taneous cognition, measures of sampled thoughts track
default network activity. Moreover, individual differ-
ences in tendencies to engage spontaneous cognitive
processes parallel differences in default network activ-
ity. In the following section, we review these findings
and discuss their implications.
Stimulus-Independent Thoughts
A number of brain imaging studies have explored
stimulus-independent thoughts (SITs).cSITs are oper-
ationally defined as thoughts about something other
than events originating from the environment; they
are covert and not directed toward performance of the
task at hand. The most common method for measuring
SITs involves periodically probing trained participants
to indicate whether they are experiencing a SIT. Care
is taken to minimize the intrusiveness of the probe, al-
though a limitation of this approach is that the probe
nonetheless does interfere with the SIT, most typically
to terminate its occurrence (Giambra 1995). Antrobus
and colleagues (1966, 1968, 1970) showed that SITs
occur quite pervasively—during both resting epochs
and also during the performance of concurrent tasks.
Even under heavy loads of external information, most
individuals still report the presence of some SITs al-
though the number of SITs correlates inversely with
the demands of the external task.
cVarious labels have been used in the reviewed papers to describe
self-reported thought content including task-irrelevant thoughts (Antrobus
et al. 1966), stimulus-independent thoughts (SITs, Antrobus et al. 1970,
Tea sda le et a l. 19 95) , tas k-u nre l a t e d t h oug hts ( TUTs , G i a m b r a 1 98 9), a n d
task-unrelated images and thoughts (TUITs, Giambra 1995). For simplic-
ity, we use the term “stimulus-independent thoughts” or SITs throughout
the text.
16
Annals of the New York Academy of Sciences
Extending from these behavioral observations, sev-
eral imaging studies have correlated the number of
reported SITs with brain activity. In an early study,
McGuire et al. (1996) demonstrated that the frequency
of SITs estimated following various PET scans cor-
related with MPFC activity. Following a similar ap-
proach, Binder and colleagues (Binder et al. 1999,
McKiernan et al. 2003, 2006) conducted two fMRI
studies that explored the relationship between SITs and
brain activity. In both studies they measured brain ac-
tivity during rest and various tasks using typical fMRI
procedures. Then, within a mock scanning environ-
ment, they had participants perform the same tasks
while periodically probing for the presence of SITs.
This procedure allowed them to sort the fMRI tasks
based on their propensity to elicit SITs. The first study
(Binder et al. 1999) revealed that rest, as compared
to an externally oriented tone detection task, was as-
sociated with both increased default network activity,
and nearly six times more SITs. The second study
parametrically varied task difficulty across six separate
tasks such that the easiest task (easy to detect target,
slow presentation rate) produced about twice as many
SITs as the most difficult task (McKiernan et al. 2003,
2006). Referenced to rest, there was a strong corre-
lation between SITs and activity within the default
network.
Mason et al. (2007) recently extended these ap-
proaches to study individual differences. Like the ear-
lier work, they measured the propensity of rest and task
states to elicit SITs. Task demands were manipulated
using practice: a practiced variant of the task (low de-
mands, many SITs) was compared with a novel variant
(high demands, few SITs). The researchers replicated
the work of Binder and colleagues by showing that de-
fault network regions, including MPFC and PCC/Rsp,
tracked the different task states in proportion to the
numbers of produced SITs. To ascertain who among
their group was more likely to produce SITs, they ad-
ministered a daydreaming questionnaire adopted from
Singer and Antrobus (1972) that assessed general ten-
dencies to engage in internal cognition (e.g., Do you
daydream at work? When you have time on your hands
do you daydream?). There was a strong correlation
in regional default network activity with the partici-
pant’s daydreaming tendencies (FIG. 9). Those individ-
uals who showed the greatest default network activity
during the practiced task condition were self-described
daydreamers.
Taken collectively, these findings converge to suggest
that task contexts that encourage SIT production show
the greatest default network activity; furthermore, in-
dividuals who daydream most show increased default
FIGURE 9. The default network is most active in indi-
viduals who report frequent mindwandering, suggesting a
functional role in spontaneous cognition. Activity estimates
are plotted for 16 subjects from PCC/Rsp (region shown in
insert) from a task contrast conducive to encouraging mind-
wandering. The activity within this region is significantly
correlated with individual self-reports of daydreaming ob-
tained outside the scanner. Adapted from data published in
Mason et al. (2007).
network activity, at least when placed in a conducive
experimental setting.
Momentary Lapses in Attention
An idea that emerges repeatedly in the study of inter-
nal mental activity is that there is competition between
resources for internal modes of cognition and focus
on the external world (Antrobus et al. 1966, 1970,
Teasdale et al. 1995). In discussing forms of attention,
William James (1890) wrote “When absorbed in in-
tellectual attention we become so inattentive to outer
things as to be ‘absent-minded,’ ‘abstracted,’ or ‘dis-
traits.’ All revery or concentrated meditation is apt to
throw us into this state” (pp. 418–419). In any given
task context, there must be assignment of priorities for
attending to external or internal channels of informa-
tion, which in turn will have consequences for task per-
formance (Singer 1966, Smallwood & Schooler 2006).
When an external task is performed, focus on internal
mental content will likely lead to mistakes or slowed
performance on the immediate task at hand. Several
studies have explored interactions between external
attention and activity within the default network.
In one investigation, Greicius and Menon (2004)
studied the dynamics of activity within the default net-
work while people were presented blocks of external
visual and auditory stimuli. They first showed that
spontaneous activity correlations across regions within
the default network continued during the stimulus
blocks. The implication of this observation is that
Buckner
et al.:
The Brain’s Default Network
17
spontaneous activity within the default network per-
sists through both experimental and rest epochs. They
further observed evidence for competition between
sensory processing and spontaneous default network
fluctuations: sensory-evoked responses were attenu-
ated in those individuals who showed the strongest
spontaneous activity correlations within the default
network.
Momentary lapses in external attention were ex-
plored directly by Weissman and colleagues (2006)
during a demanding perceptual task. Lapses in atten-
tion were defined as occurring when participants were
slow to respond. Two observations were made. First,
just prior to a lapse in attention, activity within brain
regions associated with control of attention was di-
minished, including dorsal anterior cingulate and pre-
frontal cortex. Second, during the lapse of attention
itself, activity within the default network was increased
prominently in the PCC/Rsp. These findings suggest
that transient lapses in the control of attention may
lead to a shift in attention from the external world to
internal mentation.
A related observation was made in the context of
memory encoding by Otten and Rugg (2001). Brain ac-
tivity was measured in two studies during the inciden-
tal encoding of words. The researchers found that in-
creased activity in the posterior midline near PCC/Rsp
and lateral parietal regions near IPL, among other re-
gions, predicted which words would be later forgotten.
This observation is consistent with the possibility that
transient activity increases in the default network mark
those trials on which the memorizers were distracted
from their primary task, perhaps lapsing into private
channels of thought.
Recently Li et al. (2007) tackled this possibility across
two studies using a go/no-go paradigm. In their task,
cues signaling participants to make speeded responses
were intermixed with infrequent stop signals that man-
dated the responses should be withheld. Errors oc-
curred when participants responded to stop signals.
Exploring brain activity on the trials that preceded er-
rors revealed that regions within the default network
(MPFC and PCC/Rsp, but not IPL) augmented activ-
ity just prior to errors, an effect replicated in a second
study. While again correlational, these data suggest
that when the default network is active, lapses in fo-
cused external attention occur in ways that affect task
performance.
However not all studies have found such relation-
ships. Hahn et al. (2007), for example, noted that fast
responses in a target-detection task were associated
with increased default network activity (see Figure 3
in Hahn et al. 2007). Gilbert et al. (2006, 2007) hy-
pothesized that the default network is associated with
a broadly tuned form of outward attention (“watch-
fulness”). This idea, as will be discussed more ex-
tensively in the upcoming section, is reminiscent of
Shulman and colleagues’ (1997) suggestion that the
default network participates in monitoring the ex-
ternal environment. While difficult to reconcile with
the studies discussed earlier, the hypothesis put for-
ward by Gilbert and colleagues is a reminder that ev-
idence to date is limited and correlational, and fur-
ther that opposing possibilities should be carefully
explored. Thus, while an accumulating set of obser-
vations suggest that mindwandering is linked to in-
creased activity in regions within the default network,
further exploration is warranted to determine if the
system is directly supporting the processes underlying
the stimulus-independent thoughts that accompany
mindwandering.
Spontaneous Activity Dynamics
The default network spontaneously exhibits slow
waxing and waning of activity during rest that is corre-
lated across its distributed regions (Greicius et al. 2003,
Fox et al. 2005, Fransson 2005, Damoiseaux et al.
2006, Vincent et al. 2006). FIGURE 10 illustrates this ro-
bust phenomenon for a 5-minute epoch during which a
young adult passively viewed a small fixation crosshair.
As can be seen, activity within MPFC and PCC/Rsp—
two of the most prominent components of the default
network—spontaneously modulates over time. Criti-
cally, these two regions, which are anatomically distant
from one another and supplied by separate vascular
territories, show strong correlation, thereby indicating
that the fMRI-measured activity swings arise from co-
ordinated neural activity and not from measurement
noise. The presence of fluctuations at rest—when SITs
are at their peak—raises the question of whether these
unprompted modulations reflect individual thoughts
and musings (e.g., Greicius & Menon 2004, Fox et al.
2005, Fransson 2006). In a particularly thoughtful ap-
proach to this question, Fransson (2006) showed that
correlated spontaneous activity within the default net-
work attenuates when people perform a concurrent de-
manding cognitive task (see also Shannon et al. 2006).
Such forms of tasks are known to reduce the frequency
of SITs as discussed above (Antrobus et al. 1966,
1970).
While these observations are intriguing, there are
several reasons to be cautious of presuming a sim-
ple relationship between spontaneous low-frequency
activity modulations and cognitive processes (see
Vincent et al. 2006, Fox & Raichle 2007). First,
18
Annals of the New York Academy of Sciences
FIGURE 10. Regions within the default network spontaneously increase and decrease activity in a
correlated manner. This is illustrated by plotting fMRI signal for two of the regions within the default
network (PCC/Rsp and MPFC) as an individual rests in an awake state. Note that the activity slowly
drifts about 2% and also that these intrinsic fluctuations are strongly correlated between the two regions.
However, similar spontaneous correlations are observed between regions in other brain systems bringing
into question whether this particular phenomenon is linked selectively to functional properties of the default
network, such as spontaneous cognition. Adapted from data published in Fox et al. (2005).
spontaneous activity simultaneously exists in numer-
ous brain systems including primary sensory and mo-
tor systems. It is not selectively observed in higher-
order brain systems. Rather, spontaneous activity is
pervasive (e.g., see De Luca et al. 2006). Second, spon-
taneous activity persists during sleep (Fukunaga et al.
2006, Horovitz et al. 2007) and under deep anesthe-
sia verified by concurrently acquired burst-suppression
electroencepholographic (EEG) patterns (Vincent et al.
2007a). Third, spontaneous activity is associated with
extremely slow fluctuations that are slower than would
be expected for cognitive events—less than one cy-
cle every 10 seconds (Cordes et al. 2001, De Luca et
al. 2006). Thus, while a considerable amount of data
converges on the possibility that default network ac-
tivity is associated with various forms of thought, the
specific phenomenon of intrinsic low-frequency fluctu-
ations may be incidentally related to immediate spon-
taneous thoughts (Vincent et al. 2006, Raichle 2006,
Buckner & Vincent 2007). An intermediate possibility
is that spontaneous activity fluctuations measured dur-
ing rest may reflect both intrinsic low-level physiological
processes that persist unrelated to conscious mental ac-
tivity and also spontaneous cognitive events that come
to dominate mental content when people are awake
and disengaged from their external environments. An
interesting future pursuit will be to disentangle these
phenomena that are typically concurrent in awake
states.
IV. Functions of the Default Network
A unique challenge for understanding the functions
of the brain’s default network is that the system is most
active in passive settings and during tasks that direct
attention away from external stimuli. This property in-
forms us that contributions of the default network are
suspended or reduced during commonly used active
tasks but, unfortunately, tells us little about what the
system does do. Two sources of data currently provide
information about function. First, while most directed
tasks cause task-induced deactivation within the net-
work, there are an accumulating number of tasks that
have been shown to elicit increased activity within the
default network relative to other tasks. The properties
that are common across these tasks provide some in-
sight into function. Second, the specific anatomy of the
default network constrains functional possibilities. For
example, the default network does not include primary
sensory or motor areas but does include areas associ-
ated with the medial temporal lobe memory system.
In this section, we explore two possible functions of
the network, while recognizing that it is too soon to rule
out various alternatives. One possibility is that the de-
fault network directly supports internal mentation that
is largely detached from the external world. Within this
possibility, the default network plays a role in construct-
ing dynamic mental simulations based on personal
past experiences such as used during remembering,
Buckner
et al.:
The Brain’s Default Network
19
FIGURE 11. The functions of the default network have been difficult to unravel because passive tasks,
which engage the default network, differ from active tasks on multiple dimensions. As one goes from an
active task demanding focused attention (left panel) to a passive task (right panel), there is both a change
in mental content (A) and level of attention to the external world (B). Spontaneous thoughts unrelated
to the external world increase (A). There is also a shift from focused attention to a diffuse low-level of
attention (B). Hypotheses about the functions of the default network have variably focused on one or the
other of these two distinct correlates of internally directed cognition.
thinking about the future, and generally when imag-
ining alternative perspectives and scenarios to the
present. This possibility is consistent with a growing
number of studies that activate components of the
default network during diverse forms of self-relevant
mentalizing as well as with the anatomic observation
that the default network is coupled to memory systems
and not sensory systems. Another possibility is that
the default network functions to support exploratory
monitoring of the external environment when focused
attention is relaxed. This alternative possibility is con-
sistent with more traditional ideas of posterior parietal
function but does not explain other aspects of the data
such as the default network’s association with memory
structures. It is important to recognize that the corre-
lational nature of available data makes it difficult to
differentiate between possibilities, especially because
focus on internal channels of thought is almost always
correlated with a change in external attention (FIG. 11).
We also explore in this section an intriguing functional
property of the default network: the default network
operates in opposition to other brain systems that are
used for focused external attention and sensory pro-
cessing. When the default network is most active, the
external attention system is attenuated and vice versa.
Monitoring the External Environment:
The Sentinel Hypothesis
One possibility is that the default network plays a
role in monitoring the external environment (Ghatan
et al. 1995, Shulman et al. 1997, Gusnard & Raichle
2001, Gilbert et al. 2007, Hahn et al. 2007). The
hypothesis is that the critical difference between di-
rected task conditions, which suspend activity within
the default network, and passive conditions, which
augment activity, is the form of their attentional fo-
cus on the external world. Active tasks typically re-
quire focused attention on foveal stimuli or on another
type of predictable cue. By contrast, passive condi-
tions release the participant to broadly monitor the
external environment—what has been ter med vari-
ably an “exploratory state” (Shulman et al. 1997) or
“watchfulness” (Gilbert et al. 2007). Within this pos-
sibility, the default network is hypothesized to support
a broad low-level focus of attention when one—like a
sentinel—monitors the external world for unexpected
events.
Hahn and colleagues (2007) specifically suggest that
activity at rest “may reflect, among other functions,
the continuous provision of resources for spontaneous,
broad, and exogenously driven information gather-
ing.” By this view, task states represent the exceptional
instances when focused attention is harnessed to re-
spond to a specific, predictable event at the expense
of broadly monitoring the environment. A variation of
this idea is that external monitoring is more passive: the
default network may mark a state of awareness of the
external environment but should not be conceived of
as supporting an active exploration. Rather, the default
network may support low levels of attention that are
20
Annals of the New York Academy of Sciences
maintained in an unfocused manner while other, in-
ternally directed cognitive acts are engaged.
The sentinel hypothesis is consistent with certain
properties of the default network as well as attentional
deficits following bilateral posterior lesions. First, pre-
liminary evidence suggests that task-induced deactiva-
tion in the default network is most pronounced during
tasks that involve foveal as compared to parafoveal
or peripheral stimuli (Shulman et al. 1997). Second,
under some circumstances, performance on sensory
processing tasks correlates positively with default net-
work activity. Hahn et al. (2007), for example, observed
that the default network was linked to high levels of
performance on a target-detection task but only for
a diffuse attention condition where targets appeared
randomly at multiple possible locations. By contrast,
performance was not associated with default network
activity when attention was cued to a specific location.
Finally, bilateral lesions that extend across precuneus
and cuneus can induce Balint’s syndrome (Mesulam
2000a). Balint’s syndrome is characterized by a form
of tunnel vision. Patients can only perceive a small por-
tion of the visual world at one time and often fail to
notice the appearance of objects outside the immedi-
ate focus of attention (Mesulam 2000a). This deficit
is consistent with what might be expected if a brain
system that supported global (as opposed to focused)
attention were disrupted.
Constructing Alternative Perspectives:
The Internal Mentation Hypothesis
An alternative hypothesis about the function of the
default network is that it contributes directly to inter-
nal mentation. Self-reflective thought and judgments
that depend on inferred social and emotional content
robustly activate MPFC regions within the default net-
work (e.g., Gusnard et al. 2001, Kelley et al. 2002,
Mitchell et al. 2006). The default network also includes
connections with the HF and overlaps with regions ac-
tive during episodic remembering (e.g., Greicius et al.
2004, Buckner et al. 2005, Vincent et al. 2006). These
later observations are particularly intriguing because
we rely so heavily on memory when imagining social
scenarios and other constructed mental simulations.
Schacter and colleagues (2008), in this volume, explore
the nature of cognitive processes linked to mental sim-
ulation (see also Tulving 2005, Gilbert 2006, Buckner
&Carroll2007,Schacter&Addis2007,Schacteret
al. 2007, Hassabis & Maguire 2007, Bar 2007, Gilbert
& Wilson 2007). Here we discuss the possibility that
the default network underlies these abilities. By mental
simulation we mean here imaginative constructions of
hypothetical events or scenarios.
Evidence that the default network participates in
self-relevant mental simulation arises from the na-
ture of the paradigms that have consistently activated
the network. Particularly informative have been those
that target autobiographical remembering, theory-of-
mind, and envisioning the future (FIG. 12). During au-
tobiographical memory tasks, individuals are encour-
aged to vividly recall past episodes from their own
experiences. Such personal reminiscences are typically
experienced as rich, mental simulations of the past
event. Andreasen et al. (1995) were the first to note cor-
respondence between autobiographical memory and
the default network. In their study, autobiographical
memory retrieval (as compared to a word fluency task)
activated the major extent of the default network. Svo-
boda and colleagues (2006) recently conducted a thor-
ough meta-analysis that included 24 separate PET and
fMRI studies of autobiographical memory (see also re-
views by Maguire 2001, Cabeza & St. Jacques 2007).
In all the included studies, participants recalled expe-
riences from their personal pasts. The aggregated plot
across these studies highlights a set of regions remark-
ably similar to the default network including vMPFC,
dMPFC, PCC/Rsp, IPL, LTC, and the HF (FIGS.12
and 13).
Studies of theory of mind also reliably activate com-
ponents of the default network. Theory of mind—
also sometimes called “mentalizing”—refers to think-
ing about the beliefs and intentions of other people. In
a typical test of theory of mind, a story is presented
that requires the understanding of another person’s
perspective. Amodio and Frith (2006) provide the fol-
lowing example introduced by Wimmer and Perner
(1983):
Max eats half his chocolate bar and puts the rest away
in the kitchen cupboard. He then goes out to play in the
sun. Meanwhile, Max’s mother comes into the kitchen,
opens the cupboard and sees the chocolate bar. She puts
it in the fridge. When Max comes back into the kitchen,
where does he look for h is chocolate bar: in the cupboard,
or in the fridge?
To answer this question one must infer what Max is
thinking—an inference that is adaptive and common
to many social settings. Awareness of the mental states
of people around us is important for anticipating be-
haviors and successfully navigating social interactions.
Commencing with the study of Fletcher et al. (1995),
neuroimaging studies of theory of mind consistently re-
veal activity overlapping the default network (see Saxe
et al. 2004, Amodio & Frith 2006 for recent reviews).
FIGURE 12 shows an example using the task of Saxe
and Kanwisher (2003, data from Andrews-Hanna et
al. 2007b). In both the target and reference tasks,
Buckner
et al.:
The Brain’s Default Network
21
FIGURE 12. The default network is activated by diverse forms of tasks that require mental simulation of
alternative perspectives or imagined scenes. Four such examples from the literature illustrate the generality.
(A) Autobiographical memory: subjects recount a specific, past event from memory. (B) Envisioning the
future: cued with an item (e.g., dress), subjects imagine a specific future event involving that item. (C)
Theory of mind: subjects answer questions that require them to conceive of the perspective (belief) of
another person. (D) Moral decision making: subjects decide upon a personal moral dilemma. Data come
from prior studies and are here displayed using procedures similar to FIGURE 2. Data in A and B are from
Addis et al. (2007). Data in C uses the paradigm of Saxe and Kanwisher (2003). Data in D is from
Greene et al. (2001). Note that all the studies activate strongly PCC/Rsp and dMPFC. Active regions
also include those close to IPL and LTC, although further research will be required to determine the exact
degree of anatomic overlap. It seems likely that these maps represent multiple, interacting subsystems.
22
Annals of the New York Academy of Sciences
FIGURE 13. Meta-analysis of autobiographical mem-
ory tasks. Locations of activation during recall from autobi-
ographical memory are plotted for 24 PET and fMRI studies
on the lateral (top) and medial (middle) surfaces. A sagittal
cut illustrates the plane of the hippocampal formation (bot-
tom). Colors indicate whether the region contains high (red),
medium (green), or low (blue) convergence across studies.
Note the clear convergence with the core regions of the
default network. Adapted from Svoboda et al. (2006).
subjects read stories that required conceiving a situ-
ation like the one above about Max. In one instance,
the story was framed in relation to a person’s beliefs; in
another instance the question was about an inanimate
object. For example, a story on what a person be-
lieved about an event was compared to a similar story
about what a camera captured in a photograph. As can
be seen in FIGURE 12, this contrast activates multiple
regions within the default network, including promi-
nently dMPFC, PCC/Rsp, and a region near IPL close
to the temporo-parietal junction. In a follow-up study,
Saxe and Powell (2006) showed that certain regions in
the default network, including PCC, did not differen-
tially activate to stories about people’s bodily sensations
(being hungry, cold) or stories that contained descrip-
tions of people’s appearances. PCC was only differ-
entially responsive when stories required conceiving
another person’s thoughts.
Rilling and colleagues (2004) provide another exam-
ple of default network activity during interpersonal in-
teractions that depend on inferences about other peo-
ple’s thoughts. In their study, the participants were in-
troduced to 10 living individuals just prior to going into
the scanner. While in the scanner, they played a series
of game trials where, on each trial, they either chose
to cooperate or work against one of the other people.
The outcome on each trial was determined both by
the participant’s decision and also the choice of a pu-
tative human playing partner. For example, theplaying
partner could appear to work against the participant.
As a control, the participants performed a rewarded
control task that did not involve playing the game. In
reality, the participants were always playing a com-
puter but believed fully they were playing real people.
Brain activity in the default network differed markedly
when the individuals believed they were playing other
people as compared to the control task. Moreover, the
activity modulation occurred when they received feed-
back about what the other players chose, suggesting a
role in making inferences about other’s minds.
The third class of task involves envisioning the fu-
ture. Schacter et al. (2007, 2008) discuss in detail find-
ings from such paradigms, so we only briefly men-
tion them here. In the prototypical paradigm, par-
ticipants are given a cue and instructed to imagine
a future situation related to that cue. For example,
cued with the word “dress,” a participant in Addis et
al. (2007) reported an imagined scene that included
the following: “My sister will be finishing her under-
graduate education... And I can see myself sitting in
some kind of sundress, like yellow, and under some
trees.” Behavioral studies show that individuals are
quite adept at conceiving plausible future scenarios
that contain considerable detail and emotional con-
tent (D’Argembeau & Van der Linden 2006). Sev-
eral such studies have been reported using PET and
fMRI (Partiot et al. 1995, Okuda et al. 2003, Szpunar
et al. 2007, Addis et al. 2007, Sharot et al. 2007,
Botzung et al. 2008, D’Argembeau et al. in press).
All these studies activated regions within the default
Buckner
et al.:
The Brain’s Default Network
23
FIGURE 14. Posterior regions within the default network overlap regions that are active during
successful episodic memory retrieval. (left) Image of the default network subsystem correlated with the
hippocampal formation. These data represent the surface projection of data from FIGURE 3B. Adapted from
Vincent et al. (2006). (middle) Image of successful episodic memory retrieval. This image shows regions
with high levels of activity during episodic recollection as compared to familiarity-based recognition.
Adapted from Wagner et al. (2005). (right) Regions of convergence across the two maps extend to the
PCC/Rsp, IPL, and portions of MPFC.
network. Data from Addis et al. (2007) are plotted in
FIGURE 12 to illustrate the similarity of the activated
region to that of the default network.
An immediate question that arises based on the
above observations is: What does this generality mean?
While remembering, envisioning the future, and con-
ceiving the mental states of others are different on sev-
eral dimensions including temporal focus (e.g., past ver-
sus present) and personal perspective (e.g., self versus
another person), they all converge on similar core pro-
cesses (Buckner & Carroll 2007, FIGURE 12). In each
instance, one is required to simulate an alternative per-
spective to the present. These abilities, which are most
often studied as distinct, rely on a common set of pro-
cesses by which mental simulations are used adaptively
to imagine events beyond those that emerge from the
immediate environment.
By this hypothesis, a defining property of the default
network is its flexibility. The tasks that activate the
default network share core processes in common but
differ in terms of the content and goal to which these
processes are applied. As a further example that illus-
trates the breadth of domains that activate the default
network, Greene and colleagues (2001) explored brain
regions supporting moral decisions. Their paradigms
required individuals to evaluate whether a hypothet-
ical action was moral or immoral (Greene & Haidt
2002). They observed that certain forms of moral judg-
ment activated default network regions (Greene et al.
2001, FIG.12). In particular, the default network was
most active when evaluations included personal moral
dilemmas (e.g., Consider whether it would be morally
acceptable for you to push one person off a sinking
boat to save five others). Solving moral dilemmas may
be exactly the kind of situation where people simu-
late alternative events in the service of evaluating them
(see Moll et al. 2005 for related discussion). While not
explored to date, one wonders whether many reflec-
tive cognitive experiences—such as pride, shame, and
guilt—are built upon the capacity of the default net-
work to enable contrasts among imagined social sce-
narios and settings.
The possibility that the default network contributes
to internal channels of thought is consistent with the
subsystems that comprise its anatomy. The medial tem-
poral lobe subsystem is associated with mnemonic pro-
cesses and is activated during successful retrieval of old
information from memory (see Wagner et al. 2005 for
areview).F
IGURE 14 illustrates this functional aspect
of the medial temporal lobe subsystem by comparing
regions intrinsically correlated with the HF to regions
responding in traditional memory paradigms. There is
considerable overlap between the two approaches, es-
pecially for PCC/Rsp and IPL. Furthermore, activity
within the medial temporal lobe subsystem increases
during retrieval of strong memory traces that include
remembered associations and content details (Henson
et al. 1999, Eldridge et al. 2000, Wheeler & Buck-
ner 2004, Yonelinas et al. 2005). Taken together, these
observations suggest that this subsystem contributes
associations and relational information from memory
perhaps to provide the critical building blocks of men-
tal exploration (see also Bar 2007, Addis & Schacter
2008).
24
Annals of the New York Academy of Sciences
The second subsystem is linked to the MPFC, specif-
ically dMPFC. dMPFC is activated by many task situ-
ations that require participants to make self-referential
judgments and engage in other forms of self-relevant
mental exploration (e.g., Gusnard et al. 2001, Kelley
et al. 2002, Mitchell et al. 2006, see Adolphs 2003,
Ramnani & Owen 2004, Amodio & Frith 2006 for
relevant reviews). All the task forms noted above that
activate the complete, or near complete, default net-
work share in common that the imagined perspectives
are self-referenced. Moreover, several findings suggest
that reference to the self causes selective and preferen-
tial activity within the MPFC subsystem. For example,
Szpunar et al. (2007) noted that MPFC was strongly
activated by envisioning oneself in the past or future
but not so for considering a personally unfamiliar pub-
lic figure in a future setting. Saxe and Kanwisher (2003)
showed greater dMPFC activity for making decisions
about conceived perspectives of people as compared
to inanimate objects (e.g., a camera). G¨
uro˘
glu et al.
(2008) demonstrated increased activity in the dMPFC
and throughout the default network when individuals
made judgments about whether to approach familiar
peers versus celebrities in an imagined social setting.
Mitchell and colleagues (2006) provided a particularly
clear example of modulation along the “self” dimen-
sion. In their study, individuals made judgments about
afictitiouspersonwhowasdescribedasbeingeither
quite similar in sociopolitical views to the participant or
quite different. Judgments made about similar others
activated dMPFC to about the same degree as mak-
ing a judgment about oneself. In contrast, judgments
about people perceived as being politically different
did not activate dMPFC.d
Thus, while it is admittedly difficult to define what is
self or self-like, dMPFC is activated when the content
of an imagined setting involves social agents that are
being considered as such. Note that a subtle distinc-
tion is being drawn here: the common element that
activates dMPFC does not appear to simply be refer-
ence to a person or oneself, which can occur devoid
of elaborated context. The common element appears
to align more with thinking about the complex inter-
actions among people that are conceived of as being
social, interactive, and emotive like oneself.
Within this hypothesis, the default network thus
comprises at least two distinct interacting subsystems—
dDorsal and ventral are relative terms and are used variably depending
on which regions are being compared. This paper defines dMPFC and
vMPFC differently than did Mitchell et al. (2006). The region labeled here
as dMPFC is the region Mitchell et al. describe as being ventral.
one subsystem functions to provide information from
memory; the second participates to derive self-relevant
mental simulations. The adaptive function may be to
provide a “life simulator”—a set of interacting sub-
systems that can use past experiences to explore and
anticipate social and event scenarios (Gilbert 2006,
Gilbert & Wilson 2007). This idea is similar to a re-
cent hypothesis from Bar (2007) that the HF subsys-
tem serves to supply associations and analogies from
past experience to make predictions about upcoming
events. An open question is when mental simulation
depends on the interactions between both subsystems.
As the functional analysis reveals, the dMPFC and
medial temporal lobe are not intrinsically correlated
with one another, suggesting some level of functional
separation (FIG. 8). Certain situations draw heavily on
both subsystems such as elicited during autobiograph-
ical memory tasks and when thinking about the future.
Theory-of-mind tasks, while utilizing the dMPFC sub-
system, activate the medial temporal lobe minimally.
One possibility is that the dMPFC subsystem interacts
with the medial temporal lobe subsystem to the degree
that past episodic information is an important con-
straint on the mental simulation being derived. The
convergence of the two subsystems on common hubs,
in particular PCC, may serve to prepare the system for
these critical interactions.
Competitive Functional Interactions
When initially considering the possibility of a brain
system for internal mentation, Ingvar (1979) proposed
that such a system might work in opposition to those
specialized for sensory processing, which he termed
“sensory-gnostic.” He noted that
the low flow/activity in postcentral sensory-gnostic re-
gions appears to agree with a low general awareness of
the sensory input from the immediate surroundings, when
one is left to oneself, undisturbed, resting awake. Possibly
the lower postcentral [flow] signals that the resting con-
sciousness implies an active global inhibition of a sensory
input, as if the brain filtered out trivial information in
order to let the mind be busy with its own consciousness
(p. 20).
The idea that the brain’s default network may work
in direct opposition to other systems has received re-
cent support from the observation of strong nega-
tive activity correlations between the default network
and other systems—coined variably “dynamic equi-
librium” and “anticorrelations” (Greicius et al. 2003,
Fransson 2005, Fox et al. 2005, Golland et al. 2007,
Tian et al. 2007). For simplicity, we use the term “an-
ticorrelation” as proposed by Fox et al. (2005).
Buckner
et al.:
The Brain’s Default Network
25
FIGURE 15 illustrates the phenomenon of anticorre-
lation. As shown earlier, the distributed regions within
the default network show spontaneous correlations
with one another (see FIG.7). These intrinsic cor-
relations also exist in other brain systems including
those dedicated to external attention (as described by
Corbetta & Shulman 2002). The phenomenon of an-
ticorrelation refers to the additional observation that
these distinct brain systems show strong negative corre-
lations with one another: as activity within the default
network increases, normalized activity in the external
attention system show activity decreases.eThis finding
suggests that the brain may shift between two distinct
modes of information processing. One mode, marked
by activity within the default network, is detached from
focused attention on the external environment and is
characterized by mental explorations based on past
memories. The second mode is associated with focused
information extraction from sensory channels. These
systems may be opposed to one another and thus rep-
resent functionally competing brain systems.
The possibility of competition raises important
questions for future research—how is this competition
regulated? Is there a separate control system, perhaps
mediated by frontal cortex, thatin some manner directs
which of these two brain systems is active? Or, are the
two systems in direct competition with one another in
a way that local competitive interactions between them
and input systems define their levels of activity? While
minimal data exist to inform this question, Vincent
and colleagues (2007b) have recently reported prelim-
inary evidence for a frontal-parietal brain system that
is anatomically juxtaposed between the default net-
work and systems associated with external attention,
providing a candidate for controlling the functional
interactions between the two anticorrelated brain net-
works.
V. Relevance to Brain Disease
To this point, extensive data have been considered
that suggest humans possess a set of closely interacting
subsystems known as the default network. One hypoth-
eAdifficulttechnicalissueassociatedwithspontaneousnegativecorre-
lations arises because the activity levels are normalized to remove global
activity variation. Without such normalization, whole-brain signal fluctua-
tions dominate the local regional correlations. This form of normalization
causes the correlation strengths to be distributed around zero (Vincent
et al. 2006) forcing negative correlations to emerge. Further research will
be required to understand the contributions of normalization to negative
correlations in spontaneous activity.
FIGURE 15. Intrinsic activity suggests that the default
network is negatively correlated (anticorrelated) with brain
systems that are used for focused external visual attention.
Anticorrelated networks are displayed by plotting those
regions that negatively correlate with the default network
(shown in blue) in addition to those that positively correlate
(shown in red). These two anticorrrelated networks may par-
ticipate in distinct functions that compete with one another
for control of information processing within the brain. Data
are the same as analyzed for FIGURE 7.
esis is that, using memories and associations from past
experiences as its building blocks, the default network
participates in constructing self-relevant mental simu-
lations that are exploited by a wide range of functions
including remembering, thinking about the future, and
inferring the perspectives and thoughts of other peo-
ple. When left undisturbed, this is the network people
engage by default. The focus of the present section
is to explore the relationship of the default network
to mental disorders including autism, schizophrenia,
and Alzheimer’s disease (TABLE 2). Each of these three
clinical conditions is associated with cognitive dysfunc-
tion in domains that are linked to the default net-
work. Other disorders for which important links are
being made to the default network but are beyond the
scope of this review include depression, obsessional
disorders, attention-deficit/hyperactivity disorder, and
post-traumatic stress disorder.
26
Annals of the New York Academy of Sciences
TABLE 2. Selected papers on cognitive disorders
associated with the default network
DATA TYPE
Autism Spectrum Disorders
Castelli et al. (2002) Activity–TID
Waiter et al. (2004) Structure
Kennedy et al. (2006) Activity–TID
Cherkassky et al. (2006) Activity–fcMRI
Kennedy & Courchesna (2008) Activity–fcMRI
Schizophrenia
Harrison et al. (2007) Activity–TID
Bluhm et al. (2007) Activity–fcMRI
Garrity et al. (2007) Activity–TID/fcMRI
Zhou et al. (2007) Activity–fcMRI
Alzheimer’s Disease
Reiman et al. (1996) Metabolism
Minoshima et al. (1997) Metabolism
Herholtz et al. (2002) Metabolism
Buckner et al. (2005) PIB-PET, Structure
Scahill et al. (2002) Structure
Thompson et al. (2003) Structure
Lustig et al. (2003) Activity–TID
Celone et al. (2006) Activity–TID
Greicius et al. (2004) Activity–fcMRI
Rombouts et al. (2005) Activity–fcMRI
Wang et al. (2007) Activity–fcMRI
Sorg et al. (2007) Activity–fcMRI, Structure
Notes: Listed are example references that link disruption of
the default network with disease. Type refers to the primary
form of support in the paper for the association: Activity–TID,
Task-induced deactivation data from either PET or fMRI;
Activity–fcMRI, functional connectivity analysis from fMRI;
Structure, Structural data from MRI; Metabolism, Resting
glucose metabolism from PET; PIB-PET, amyloid binding as
measured by PET. This list is not comprehensive, especially for
metabolism studies that have a long history.
Autism Spectrum Disorders
The autism spectrum disorders (ASD) are devel-
opmental disorders characterized by impaired social
interactions and communication. Symptoms emerge
by early childhood and include stereotyped (repetitive)
behaviors. Baron-Cohen and colleagues (1985) pro-
posed that a core deficit in many children with ASD
is the failure to represent the mental states of oth-
ers, as needed to solve theory-of-mind tasks. Based
on an extensive review of the functional anatomy that
supports theory-of-mind and social interaction skills,
Mundy (2003) proposed that the MPFC may be cen-
tral for understanding the disturbances in ASD. Given
the convergent evidence presented here that suggests
the default network contributes to such functions, it
is natural to explore whether the default network is
disrupted in ASD.
Developmental disruption of the default network,
in particular disruption linked to the MPFC, might
result in a mind that is environmentally focused and
absent a conception of other people’s thoughts. The
inability to interact with others in social contexts
would be an expected behavioral consequence. It
is important to also note that such disruptions, if
identified, may not be linked to the originating de-
velopmental events that cause ASD but rather re-
flect a developmental endpoint. That is, dysfunc-
tion of the default network and associated symp-
toms may emerge as an indirect consequence of
early developmental events that begin outside the
network.
Many studies have explored whether ASD is associ-
ated with morphological differences in brain structure.
The general conclusion from this literature is that the
brain changes are complex, reflecting differences in
growth rates and attenuation of growth (see Brambilla
et al. 2003 for review). At certain developmental stages
these differences are manifest as overgrowth and at
later stages as undergrowth. Early observations have
implicated the cerebellum. A further consistent ob-
servation has been that the amygdala is increased in
volume in children with ASD (e.g., Abell et al. 1999,
Schumann et al. 2004), perhaps as a reflection of ab-
normal regulation of brain growth (Courchesne et al.
2001). While not discussed earlier because of our focus
on cortical regions, the amygdala is known to con-
tribute to social cognition (Brothers 1990, Adolphs
2001, Phelps 2006) and interacts with regions within
the default network. The amygdala has extensive pro-
jections to orbital frontal cortex (OFC) and vMPFC
(Carmichael & Price 1995).
Of perhaps more direct relevance to the default net-
work, dMPFC has shown volume reduction in sev-
eral studies of ASD that used survey methods to ex-
plore regional differences in brain volume (Abell et al.
1999, McAlonan et al. 2005). The effects are subtle
and will require further exploration, but it is note-
worthy that, of those studies that have looked, sev-
eral have noted dMPFC volume reductions in ASD.
Of interest, a study using voxel-based morphometry
to investigate grey matter differences in male ado-
lescents with ASD noted that several regions within
the default network exhibited a relative increase in
grey matter volume compared to the control pop-
ulation (Waiter et al. 2004). Because this observa-
tion has generally not been replicated in adult ASD
groups, future studies should investigate whether com-
plex patterns of overgrowth and undergrowth of the
regions within the default network exist in ASD and,
if so, whether they track behavioral improvement on
tests of social function (see also Carper & Courchesne
2005).
Buckner
et al.:
The Brain’s Default Network
27
FIGURE 16. Default network activity tracks the sever-
ity of social dysfunction in autism. An exploratory correla-
tional analysis by Kennedy et al. (2006) found that activity
within MPFC (region shown in inset) was correlated with
social impairment as measured by the Autism Diagnostic In-
terview–Revised. Individuals with autism spectrum disorder
who showed less task-induced deactivation had lower social
impairment scores. Adapted from Kennedy et al. (2006).
Kennedy and colleagues (2006) recently used fMRI
to directly explore the functional integrity of the default
network in ASD. In their study, young adults with ASD
and age-matched individuals without ASD were im-
aged during passive tasks and demanding active tasks
that elicit strong activity differences in the default net-
work. While the control participants showed the typi-
cal pattern of activity in the default network during the
passive tasks, such activity was absent in the individuals
with ASD. Direct comparison between the groups re-
vealed differences in vMPFC and PCC. Moreover, in
an exploratory analysis of individual differences within
the ASD group, those individuals with the greatest so-
cial impairment (measured using a standardized di-
agnostic inventory) were those with the most atypical
vMPFC activity levels (FIG. 16). An intriguing possibil-
ity suggested by the authors of the study and extended
by Iacoboni (2006) is that the failure to modulate the
default network in ASD is driven by differential cog-
nitive mentation during rest, specifically a lack of self-
referential processing.
Another recent study using analysis of intrinsic func-
tional correlations showed that the default network cor-
relations were weaker in ASD (Cherkassky et al. 2006).
Of note, the individuals with ASD showed differences
in a fronto-parietal network that has been recently hy-
pothesized to control interactions between the default
network and brain systems linked to external atten-
tion (Vincent et al. 2007b). These data in ASD suggest
an interesting possibility: the default network may be
largely intact in ASD but under utilized perhaps be-
cause of a dysfunction in control systems that regulate
its use.
Schizophrenia
Schizophrenia is a mental illness characterized by
altered perceptions of reality. Auditory hallucinations,
paranoid and bizarre delusions, and disorganized
speech are common positive clinical symptoms (Liddle
1987). Cognitive tests also reveal negative symptoms,
including impaired memory and attention (Kuperberg
& Heckers 2000). These symptoms lead to questions
about their relationship to the default network for a
few reasons. The first reason surrounds the association
of the default network with internal mentation. Many
symptoms of schizophrenia stem from misattributions
of thought and therefore raise the question of an associ-
ation with the default network because of its functional
connection with mental simulation. A second related
reason has to do with the broader context of control
of the default network. While still poorly understood,
there appears to be dynamic competition between the
default network and brain systems supporting focused
external attention (Fransson 2005, Fox et al. 2005,
Golland et al. 2007, Tian et al. 2007, see also
Williamson 2007). Frontal-parietal systems are can-
didates for controlling these interactions (Vincent et
al. 2007b). The complex symptoms of schizophrenia
could arise from a disruption in this control system re-
sulting in an overactive (or inappropriately active) de-
fault network. The normally strongly defined bound-
ary between perceptions arising from imagined scenar-
ios and those from the external world might become
blurry, including the boundary between self and other
(similar to that proposed by Frith 1996).
Three studies have provided preliminary data sup-
porting the possibility that the default network is
functionally overactive. Garrity and colleagues (2007)
recently reported an analysis of correlations among
default network regions in patients with schizophre-
nia. Studying a sizable data sample (21 patients and 22
controls), they explored task-associated activity modu-
lations within the default network and identified largely
similar correlations among default network regions in
patients and controls. Differences were noted in spe-
cific subregions, as were differences in the dynamics of
activity as measured from the timecourses of the fMRI
signal. Of particular interest, they noted that within
the patient group, the positive symptoms of the disease
(e.g., hallucinations, delusions, and thought confusions)
were correlated with increased default network activ-
ity during the passive epochs, including MPFC and
PCC/Rsp. In a related analysis, Harrison et al. (2007)
noted accentuated default network activity during
28
Annals of the New York Academy of Sciences
FIGURE 17. Glucose metabolism within the default network is reduced in Alzheimer’s disease. Nor-
mal resting glucose metabolism shows a disproportionately high level of metabolism in healthy individuals
as measured by FDG-PET (left). Arrows indicate high metabolism near PCC/Rsp. Alzheimer’s disease is
consistently associated with progressive reduction in glucose metabolism (hypometabolism) in specific
regions that overlap the default network (right). These data map the glucose metabolism reduction from
a cross-sectional sample of older adults across the range of mild (Mini-Mental Status Examination score,
MMSE =30), moderate (MMSE =20), and severe (MMSE =0) Alzheimer’s disease. Adapted from Mi-
noshima et al. (1997).
passive task epochs in patients with schizophrenia as
contrasted to controls, again suggesting an overactive
default network. Moreover, within the patient group,
poor performance was again correlated with MPFC
activation during the passive as compared to the active
tasks. Finally, Zhou and colleagues (2007) found that
regions constituting the default network were func-
tionally correlated with each other to a significantly
higher degree in patients than in control participants.
Thus, while the data are limited, these studies con-
verge to suggest that patients with schizophrenia have
an overactive default network, as would be expected
if the boundary between imagination and reality were
disrupted. Overactivity within the network correlates
with task performance (Harrison et al. 2007) and clin-
ical symptoms (Garrity et al. 2007).
Alzheimer’s Disease
The most compelling link between clinical dis-
ease and disruption of the default network occurs in
Alzheimer’s disease (AD). AD is a progressive dementia
typically occurring after the age of 70 and affecting ap-
proximately half of older adults above 85. Initial symp-
toms are memory difficulties, but sensitive tests often
reveal disturbances of executive function as well (e.g.,
Balota & Faust 2001). AD has been extensively studied
in living individuals using multiple imaging approaches
including measurement of glucose metabolism, mea-
surement of structural atrophy, and measurement of
intrinsic and task-evoked brain activity (TABLE 2). All
approaches converge to suggest that the default net-
work is disrupted.
The earliest evidence that the default network is
disrupted in AD comes from studies of resting glucose
metabolism. Patients with AD show a specific anatomic
pattern of reduced metabolism relative to age-matched
healthy peers (Benson et al. 1983, Kumar et al. 1991,
Herholz 1995, Minoshima et al. 1997, de Leon et al.
2001, Alexander et al. 2002, FIG. 17). The pattern of
hypometabolism bears a striking resemblance to the
regions comprising the posterior components of the
default network including PCC/Rsp, IPL, and LTC
(Buckner et al. 2005). Hypometabolism in AD pro-
gresses with the disease and correlates with mental
status (e.g., Minoshima et al. 1997, Herholz et al.
2002). Patients at genetic risk for AD also show simi-
lar metabolism differences, implying the disturbances
occur early in the course of the disease (Reiman et al.
1996).
Methods that survey atrophy across the brain in AD
have also all converged to show disruption in the de-
fault network prominently including the medial tem-
poral lobe (Scahill et al. 2002, Thompson et al. 2003,
Buckner et al. 2005). Accelerated atrophy is present
in PCC/Rsp and the medial temporal lobe at the pre-
clinical stages of the disease, again implying the default
network is disrupted early as the disease progresses
(Buckner et al. 2005). Recently, functional changes
in the default network have been explored in AD
using both analysis of task-induced deactivation (Lustig
Buckner
et al.:
The Brain’s Default Network
29
FIGURE 18. Activity within the default network is disrupted in Alzheimer’s disease. Task increases
(red) and decreases (blue) from a simple word classification task referenced to a passive baseline task are
plotted for young adults (left panel), normal older adults (middle panel), and demented older adults with
AD (right panel). The young adults show the classic pattern of task-induced deactivation within PCC/Rsp
and MPFC. The effect attenuates significantly in AD. Adapted from Lustig et al. (2003, see also Greicius
et al. 2004).
et al. 2003, Celone et al. 2006) and analysis of intrinsic
activity correlations (Greicius et al. 2004, Rombouts
et al. 2005, Celone et al. 2006, Wang et al. 2006).
Again, in all instances, disruption has been noted
consistent with the metabolic and structural changes.
FIGURE 18 shows data from Lustig et al. (2003).
Thus, by all measures the default network appears
disrupted in AD, including prominently the medial
temporal lobe susbsystem. Recently, molecular imag-
ing methods able to measure AD pathology (Klunk
et al. 2004) have revealed an even more surprising
link to the default network: pathology preferentially
accumulates in the default network even before symp-
toms emerge. In the next section, we will explore the
possibility that metabolic properties or activity pat-
terns within the default network directly relate to—
or even cause—the pathology of AD (Buckner et al.
2005).
Default Network Activity May Set the Stage
for Alzheimer’s Disease: The Metabolism
Hypothesis
AD pathology forms preferentially throughout the
default network, suggesting the unexpected possibility
that activity within the network may facilitate disease
processes (Buckner et al. 2005). The leading hypothe-
sis about the cause of AD proposes that toxic forms of
the amyloid ß protein (Aß) initiate a cascade of events
ending in synaptic dysfunction and cell death (Walsh &
Selkoe 2004, Mattson 2004). “Plaques” and “tangles”
are the residues of this pathological process. Consistent
with the clinical observation that initial symptoms of
the disease include memory impairment, the medial
temporal lobe and cortical structures linked to mem-
ory are affected early in the disease. Several theories
have offered explanations for why memory structures
are particularly vulnerable to the disease, including
ideas based on anatomy (Hyman et al. 1990) and also
the possibility that memory structures are sensitive to
toxicity because of their role in plasticity (Mesulam
2000b). Early pathological studies also implicated dis-
tributed cortical regions as vulnerable to AD (e.g., Brun
& Gustafson 1976) leading to a call to explore further
systems-level causes of the disease (Saper et al. 1987).
The discovery of the default network and the observa-
tion that it is active during rest states suggests a novel
hypothesis regarding the origins of AD.
The basic idea is that the default network’s con-
tinuous activity augments an activity-dependent or
metabolism-dependent cascade that is conducive to the
formation of AD pathology. Buckner and colleagues
(2005) referred to this idea as the “metabolism hy-
pothesis.” Maps of Aß plaques in living individuals
provide the key evidence (Klunk et al. 2004), as im-
ages of Aß plaques taken at the earliest stages of AD
show a distribution that is remarkably similar to the
anatomy of the default network (Buckner et al. 2005,
FIG. 19). About 10% of nondemented older individu-
als also show this pattern, presumably reflecting the
preclinical stage of the disease (Buckner et al. 2005,
Mintun et al. 2006a). The preferential use of the default
network throughout life may be conducive to increased
accumulation of Aß and its pathological sequelae. By
this view, memory systems may be preferentially af-
fected by the disease because these systems play a cen-
tral role in resting brain activity as part of the default
network.
Several recent observations lend support to the
metabolism hypothesis, although it should still be con-
sidered highly speculative. Of particular interest is the
discovery of a plausible biological link between neural
activity and upregulation of Aß. In a technically inno-
vative study, Cirrito and colleagues (2005) showed that
Aß levels increased following stimulation of the brain
30
Annals of the New York Academy of Sciences
FIGURE 19. Alzheimer’s disease may be causally related to default network activity. Regions mani-
festing default activity in young adults (e.g., FIGS. 2 and 7) are highly similar to those that show pathology
in early stages of the disease as measured by molecular imaging of amyloid plaques using PET (left).
These regions, in turn, appear affected by structural atrophy as measured by longitudinal MRI (right).
One possibility is that activity within the default network augments an activity-dependent or metabolism-
dependent cascade that leads to the formation of Alzheimer’s disease pathology. Adapted from Buckner
et al. (2005).
in living genetically engineered mice expressing hu-
man proteins that form the building blocks of Aß. This
observation suggests that synaptic activity can increase
the presence of extracellular Aß (see also Selkoe 2006).
A further supporting observation comes from a new
PET method to map glycolysis based on measuring
the ratio of oxygen to glucose consumption. Glycoly-
sis is the process by which glucose is metabolized into
cellular energy. The map of rest-state glycolysis corre-
lates remarkably well with the distribution of amyloid
plaques (Mintun et al. 2006a). The metabolism hy-
pothesis might also explain certain risk factors for AD.
Specifically, a genetic risk factor was recently discov-
ered that links to the enzyme GAPDH involved in
glycolysis (Li et al. 2004). If AD takes foothold ear-
liest in regions of high glycolytic metabolism within
the default network, it is possible that the explanation
for this genetic risk factor may lie in differences in
metabolic efficiency across individuals (Buckner et al.
2005).
At the most global level, the possibility that brain
activity states can influence a disease process has impli-
cations for intervention and understanding of disease.
We so often think about how aberrant molecular an d
cellular processes affect brain circuits and cognitive
processes. The present hypothesis highlights a poten-
tial influence in the opposite direction: brain activity
patterns may directly modulate the molecular cascades
that are relevant to disease. In the case of AD, rest-state
activity may accelerate the formation of pathology. In-
tervention may take the form of a therapy that modifies
glycolysis or other aspect of brain metabolism.
VI. Conclusions
The brain’s default network is a recently described
brain system that has been identified using neuroimag-
ing methods. The reviewed findings suggest properties
of the network that set it apart from other brain systems.
In particular, the default network is the most active
brain system when individuals are left to think to them-
selves undisturbed. The default network also increases
activity during mental explorations referenced to one-
self including remembering, considering hypothetical
social interactions, and thinking about one’s own fu-
ture. These properties suggest that the default net-
work functions to allow flexible mental explorations—
simulations—that provide a means to prepare for up-
coming, self-relevant events before they happen.
Analysis of connectional anatomy in the monkey
and intrinsic functional correlations between regions
in the human suggest that the default network is or-
ganized around a set of interacting subsystems that
comprise distributed association areas of the brain
(TABLE 2, FIGS. 7 and 8). The main hubs of the de-
fault network are within the MPFC cortex and along
the posterior midline including PCC. A particularly
important direction for future research will involve the
study of behavioral deficits following damage to re-
gions within the network and also the study of nonhu-
man primate models that allow causal inferences about
function to be explored.
Characterization of the default network, unlike
study of other brain systems, arose almost entirely from
correlational imaging approaches. The study of most
Buckner
et al.:
The Brain’s Default Network
31
other brain systems has been initiated by a neurological
syndrome and then probed further using animal mod-
els and neuroimaging approaches. On the one hand,
the discovery of the brain’s default network represents
a unique contribution of neuroimaging to cognitive
neuroscience. On the other hand, there have been no
lesion studies that motivate their behavioral probes
based on the recent characterization of the network,
leaving a large number of questions unanswered. Pro-
viding some information, studies of patients with le-
sions to regions overlapping the default network are
noted in the present review and also discussed in the
companion paper of Schacter et al. (2008). However,
considerably more work needs to be conducted.
A further open issue is how the default network inter-
acts with the distributed brain systems that contribute
content to the process of mental exploration. Studies
of episodic memory retrieval have shown that visual
cortex and auditory cortex are preferentially activated
during the recollection of visual objects and sounds
(e.g., Nyberg et al. 2000, Wheeler et al. 2000). Imagin-
ing the personal future, which activates the default net-
work under many contexts,has also been demonstrated
to additionally recruit the anterior temporal cortex
(Partiot et al. 1995) and the amygdala (Sharot et al.
2007, see also G¨
uro˘
glu et al. 2008) when strong emo-
tional context is a component of the upcoming episode.
Judgments about inferred emotions have been linked
to regions within the default network (e.g., Ochsner
et al. 2004, see also Maddock 1999). One possibility is
that the regions within the default network transiently
interact with sensory, motor, and emotional systems to
represent the content of the imagined event.
Germane to this possibility, Hassabis and Maguire
(2007) recently proposed that interactions among re-
gions within the default network may “facilitate the re-
trieval and integration of relevant informational com-
ponents, stored in their modality-specific cortical areas,
the product of which has a coherent spatial context,
and can then later be manipulated and visualized.”
They refer to this process as “scene construction,” a
term emphasizing that mental simulation often un-
folds in one’s mind as an imagined scene with rich vi-
sual and spatial content (see also Hassabis et al. 2007).
Vo g e l e y a n d c o l l e a g u e s ( 2 0 0 4 ) h a v e a l s o no t e d th a t r e -
gions within the default network are differentially ac-
tive depending on the perspective taken when imaging
a scene. The default network is most active when one
takes a first-person perspective centered upon one’s
own body as opposed to a third-person perspective.
Perhaps the most intriguing avenue for future ex-
ploration surrounds the implication that specific brain
systems are devoted to internal modes of cognition.
To date, cognitive and systems neuroscience has con-
cerned itself primarily with how information is ex-
tracted from sensory inputs and integrated over time
to make decisions and plan actions. Knowledge that
the default network exists reminds us that there may
be specialized brain systems that underlie our abilities
to mentally explore and anticipate future situations.
Such constructive processes may be adaptive because
they allow the brain to preexperience upcoming events
and to derive prospectively useful forms of representa-
tion that are many steps removed from their originally
encoded sources.
Relevant to this possibility, studies of neural activ-
ity in the rat hippocampus have recently revealed that
future event sequences are the beginnings of journeys
(Diba & Buz´
aki 2007) and choice points (Johhnson
& Redish 2007) providing a candidate neural mecha-
nism for evaluating the consequences of upcoming ac-
tions before they happen (see also Shapiro et al. 2006,
Buckner & Carroll 2007). In a series of recent studies,
Johnson and Redish (2007) focused on the behavior
of rats at a critical choice point in a maze where they
were confronted with a high-cost decision. The rats
had to follow a path to the right or left, and the in-
correct choice required an extended journey to obtain
another chance for reward. By recording from ensem-
bles of cells with place fields in the hippocampus, they
were able to visualize the representation of space in
the rat brain at these critical decision junctures. What
emerged was quite remarkable: when the rats paused
before their decision, the neurons fired in patterns that
swept ahead of the location, first down one choice and
then the other. This prospective coding occurred, on
average, for about 10% of the time the rats were at
the choice point. Moreover, on some trials where the
rats made decision errors, the representations of space
swept back toward the choice point and down the path
of the correct journey. Although a direct causal link to
the decision choice has yet to be uncovered, these find-
ings suggest a candidate neural mechanism by which
potential future choices can be simulated in the rat
brain in the service of planning.
The default network’s prominent use during passive
epochs may contribute adaptive function by allowing
event scenarios to be constructed, replayed, and ex-
plored to enrich the remnants of past events in order
to derive expectations about the future. This functional
role may explain why the default network increases its
activity during passive moments when the demands for
processing external information are minimal. Rather
than let the moments pass with idle brain activity, we
capitalize on them to consolidate past experience in
ways that are adaptive for our future needs.
32
Annals of the New York Academy of Sciences
Acknowledgments
We thank Justin Vincent, Avi Snyder, Peter Fransson,
Michael Greicius, Daniel Gilbert, Cindy Lustig, Moshe
Bar, Daphne Holt, Britta Hahn, Marcus Raichle, Mike
Fox, Jason Mitchell, Michael Miller, and two reviewers
for valuable discussion and comments on the paper.
Avi Snyder and Itamar Kahn provided assistance with
computational techniques for constructing the figures.
Data for figures were generously provided by Mike
Fox, Joshua Greene, Malia Mason, Jason Mitchell,
Marcus Raichle, Ben Shannon, Avi Snyder, Satoshi
Minoshima, and Justin Vincent. Ben Shannon com-
piled the data illustrated in FIGURE 3. Steve Petersen
and Alex Cohen contributed the graph analytic visu-
alization displayed in FIGURE 8. Katie Powers assisted
with manuscript preparation and Haderer & M¨
uller
Biomedical Art illustrated FIGURES 5and11.Fund-
ing was provided by the National Institute on Aging
(AG021910, JAG08441), National Institute of Mental
Health (MH060941), and the Howard Hughes Medi-
cal Institute.
Conflict of Interest
The authors declare no conflicts of interest.
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