The Entangled Brain - Specialized Research AI at MIT

The Entangled Brain

Luiz Pessoa

Abstract

■ The Entangled Brain (Pessoa, L., 2002. MIT Press) promotes
the idea that we need to understand the brain as a complex,
entangled system. Why does the complex systems perspective,
one that entails emergent properties, matter for brain science?
In fact, many neuroscientists consider these ideas a distraction.
We discuss three principles of brain organization that inform
the question of the interactional complexity of the brain: (1)
massive combinatorial anatomical connectivity; (2) highly
distributed functional coordination; and (3) networks/circuits
as functional units. To motivate the challenges of mapping
structure and function, we discuss neural circuits illustrating
the high anatomical and functional interactional complexity typ-
ical in the brain. We discuss potential avenues for testing for
network-level properties, including those relying on distributed

computations across multiple regions. We discuss implications
for brain science, including the need to characterize decentra-
lized and heterarchical anatomical–functional organization. The
view advocated has important implications for causation, too,
because traditional accounts of causality provide poor candi-
dates for explanation in interactionally complex systems like
the brain given the distributed, mutual, and reciprocal nature
of the interactions. Ultimately, to make progress understanding
how the brain supports complex mental functions, we need
to dissolve boundaries within the brain—those suggested to
be associated with perception, cognition, action, emotion,
motivation—as well as outside the brain, as we bring down
the walls between biology, psychology, mathematics, computer
science, philosophy, and so on. ■

INTRODUCTION

Neuroscience tends to study parts of the brain separately.
The Entangled Brain (Pessoa, 2022b) promotes the idea
that, instead, we need to understand the brain as a com-
plex, entangled system. Accordingly, the business of a
brain region needs to be situated in the context of multi-
region circuits: What does a brain region do “in combina-
tion” with other areas? In a sense, when one discusses
regions R1, …, R4 as part of some function, the decision
to “not” discuss other areas is fairly arbitrary. We could
have discussed the roles of regions R5, R6, and so on.
One of the main reasons we don’t is due to the limitations
of the tools available to neuroscientists, which are ill-
suited to investigating large-scale, distributed systems
(although techniques are advancing fast). As a result, we
still do not know much about collective computations
involving larger numbers of gray matter components.

The word “entangled” conjures multiple interrelated
ideas but is not intended to suggest something like
threads that are mixed together but can be separated given
enough time. The meaning is closer to “integrated,” but
single words do not do justice to the general theme per-
meating the book—for example, cars are highly integrated
systems but are designed with parts with well-defined
functions. Instead, the sense of “entangled” is one in
which brain parts dynamically assemble into coalitions
that support complex cognitive–emotional behaviors,

University of Maryland

coalitions composed of parts that jointly do their job.
Thus, an entangled system is a deeply context-dependent
one in which the function of parts (such as a brain region,
or a population of cells within a region) must be under-
stood in terms of other parts: an interactionally complex
system (as described below).

In this piece, I summarize a few of the key themes of the
argument built in The Entangled Brain, including that
brain functions need to be understood as “emergent prop-
erties.” Of course, this is not a new idea. However, neuro-
scientists still study and explain brain functions in a way
that does not heed this assertion. What is more, new gen-
erations of students learn about the nervous system in a
piecemeal fashion as if processes were fairly localizable—
if not in areas, at least in relatively simple networks.
Therefore, revisiting these issues is valuable for students
of the brain at all levels of expertise. (N.B.: Citations in the
text that follows are only illustrative, and in no way seek to
be representative. It is my hope that specific work can be
given proper credit in the ensuing discussions initiated
by this piece.)

WHAT EMERGES?

The prevailing modus operandi of science can be summa-
rized as explaining phenomena by reducing them to an
interplay of elementary units that can be investigated inde-
pendently of one another (Von Bertalanffy, 1950). Such a
“reductionistic approach” reached its zenith, perhaps,

Journal of Cognitive Neuroscience 35:3, pp. 349–360
https://doi.org/10.1162/jocn_a_01908

D
o
w
n
o
a
d
e
d

f
r
o
m
h

t
t

:
/
/

d
i
r
e
c
t
.

i
t
.

e
d
u

/
j

o
c
n
a
r
t
i
c
e
–
p
d

f
/

3
5
3
3
4
9
2
0
6
9
4
4
1

/
j

o
c
n
_
a
_
0
1
9
0
8
p
d

b
y
g
u
e
s
t

o
n
0
8
S
e
p
e
m
b
e
r
2
0
2
3

with the success of chemistry and particle physics in the
20th century. In the present century, its power is clearly
evidenced by dramatic progress in molecular biology
and genetics. At its root, this attitude to science “resolves
all natural phenomena into a play of elementary units,
the characteristics of which remain unaltered whether
they are investigated in isolation or in a complex” ( Von
Bertalanffy, 1950, p. 135).

Of course, the reductionistic framework is not the only
game in town, as everyone knows that “the whole is
greater than the sum of its parts.” Scientists study objects
that have many components that interact in manifold
ways, so figuring out the parts is not enough—or so the
saying implies. Again, in the words of one of early propo-
nents of complex systems, Von Bertalanffy, it is necessary
to investigate “not only parts but also relations of
organization resulting from a dynamic interaction” leading
to “the difference in behavior of parts in isolation and in
the whole organism” ( Von Bertalanffy, 1950, p. 135). But
what does it mean to say that parts behave differently in
isolation relative to when they are part of a system?

Enter “emergence,” a term originally coined in the
1870s to describe instances in chemistry and physiology
where new and unpredictable properties appear that are
not clearly ascribable to the elements from which they
arise. For example, when amino acids organize themselves
into a protein, the protein can carry out enzymatic func-
tions that the amino acids on their own cannot. More
importantly, they behave differently as part of the protein
than they would on their own. But it is actually more than
that. The dynamics of the system (the protein) closes off
some of the behaviors that would be open to the compo-
nents (amino acids) were they not captured by the overall
system ( Juarrero, 1999). Once folded up into a protein,
the amino acids find their activity regulated—one sense
in which they behave differently.

A possible definition of emergence is as follows: a novel,
collective property that is observed when multiple ele-
ments interact that is not readily reducible to the function
of the elements alone. Both scientifically and philosophi-
cally speaking, the friction caused by the idea of emer-
gence arises because it is actually unclear what precisely
emerges. For example, what is it about amino acids as part
of proteins that differs from free floating ones? The
question revolves around the exact status of emergent
properties. Philosophers refer to this question as the
“ontological” status of emergence, that is, one concerning
the “proper existence” of the higher-level properties. Do
emergent properties point to the existence of new laws
that are not present at the lower level? Is something
fundamentally irreducible at stake? As the philosopher
Alicia Juarrero (1999) says, it is particularly intriguing
when “systems exhibit organized and apparently novel
properties, seemingly emergent characteristics that
should be predictable in principle, but are not in fact”
(p. 6). These types of question remain by-and-large
unsolved and subject to vigorous intellectual battles (an

excellent treatment is provided by Humphreys, 2016;
see also Juarrero, 1999).

Fortunately, we do not need to crack the problem and
can instead use “lower” and “higher” levels pragmatically
when they are epistemically useful—when the theoretical
stance advances knowledge. To provide an oversimplified
example, we do not need to worry about the status be-
tween quarks and aerodynamics. Massive airplanes are of
course made of matter, which are agglomerations of
elementary particles such as quarks. But when engineers
design a new airplane, they consider the laws of aerody-
namics, the study of the motion of air, and particularly
the behavior of a solid object, such as an airplane wing,
in air—they need no training at all in particle physics!
So, there is no need to agonize about the “true” relation-
ship between aerodynamics and particle physics (e.g., can
the former be reduced to the latter?). The practical thing
to do is simply to study the former.

One could object to this example because the inherent
levels of particle physics and aerodynamics are very far
removed, one level too micro and the other too macro.
More interesting cases present themselves when the con-
stituent parts and the higher-level objects are closer to
each other, for example, the behavior of an individual
ant and the collective behavior of the ant colony, the flight
of a pelican and the V-shape pattern of the flock, or amino
acids and proteins. And of course, such is the case of the
brain.

THE BRAIN AS A COMPLEX SYSTEM?

Why does the complex systems perspective matter for
brain science? In fact, many renowned neuroscientists
consider the issues above a distraction. For example:

[A]lthough network properties of a system are a con-
venient explanation for complex responses, they tell
us little about how they actually work, and the concept
tends to stifle exploration for more parsimonious
explanations…[For example, the] highly intercon-
nected nature of the central autonomic control system
has for many years served as an impediment to assign-
ing responsibility for specific autonomic patterns [to
particular groups of neurons]. (Saper, 2002, p. 460)

Under this view, treating the brain as a complex system
is not only a temporary distraction but also an actual
impediment to progress.

One scenario that justifies the quote’s stance is if we
consider the brain to be a “near-decomposable” system.
Herbert Simon (1962) proposed that scientists are fre-
quently interested in systems exhibiting “near-complete
decomposability” (see also Bechtel & Richardson, 2010),
where intrasystem interactions are much stronger than
extrasystem ones. Engineered systems work this way,
and much research in neuroscience—lesion work in neu-
ropsychology, systems neuroscience, fMRI research, and
so on—proceeds from this vantage point. Such systems

350

Journal of Cognitive Neuroscience

Volume 35, Number 3

D
o
w
n
o
a
d
e
d

f
r
o
m
h

t
t

:
/
/

d
i
r
e
c
t
.

i
t
.

e
d
u

/
j

o
c
n
a
r
t
i
c
e
–
p
d

f
/

3
5
3
3
4
9
2
0
6
9
4
4
1

/
j

o
c
n
_
a
_
0
1
9
0
8
p
d

b
y
g
u
e
s
t

o
n
0
8
S
e
p
e
m
b
e
r
2
0
2
3

are “interactionally simple,” with parts interacting weakly
with anything considered beyond the system’s bound-
aries, under a given, “reasonable” decomposition. A trivial
example is a rock, where the atoms within the rock inter-
act strongly but weakly with atoms elsewhere. In contrast,
a system is “interactionally complex” to the extent that its
elements (under a certain decomposition) cross its
boundaries in important ways. Of course, systems exhibit
a spectrum of interactivity, from low to high (for in-depth
analysis, see Wimsatt, 2007, Chap. 9).

Biologists of the brain, indeed academics across varied
disciplines, have been repeatedly turned off by some of
the putative mystical features of emergent properties.
With this in mind, it might be advantageous to switch
the language used to, hopefully, stimulate debate along
more productive directions centered around “system
interactivity.” The question of interest then shifts toward
dissecting the types of communication and interactivity
we find in the nervous system. Temporarily, at least, it
might be productive to have “emergence” and “complex
systems” recede into the background (hopefully in way
that does not amount to a pure semantic sleight of hand).
The question we face is thus the following: What kind

of interactional system is the brain?

PRINCIPLES OF BRAIN ORGANIZATION

To address this question, consider three principles of
brain organization: (1) massive combinatorial anatomical
connectivity, (2) highly distributed functional coordina-
tion, and (3) networks/circuits as functional units.

Massive Combinatorial Anatomical Connectivity

Anatomical pathways are dominated by short-distance
connections. In fact, 70% of all the projections to a given
locus on the cortical sheet arise from within 1.5–2.5 mm
(Markov et al., 2011). Does this not dictate that processing
in the brain is local, or quasilocal?

Computational analyses of anatomical cortical pathways
gathered from a large number of studies inform this ques-
tion. Studies initially suggested that the cortex operates
as a “small-world” (Sporns & Zwi, 2004), an organization
that supports enhanced signal propagation speed and syn-
chronizability between parts, among other properties
(Barabási & Albert, 1999; Watts & Strogatz, 1998). In
small-world networks, though most of the connectivity is
local, a modest amount of long-range random connections
suffices to endow networks with these properties. Argu-
ably, the most important insight of these analyses is not
that the brain really follows a small-world organization;
after all, biological systems would not be expected to
exhibit “random” nonlocal connections (that’s a mathe-
matical construct!). Instead, the key idea is that it’s possi-
ble for a system with mostly local physical connections,
but some mid- and long-range connections, to display
“unexpected” large-scale system properties.

Indeed, cortical organization is not small-world. First,
nonlocal pathways are not random and instead target a
“core of regions.” Although different arrangements have
been proposed, they indicate that cortical signals flow
via a relatively small subset of richly interconnected and
integrated areas, at times called a “rich club.” For example,
Markov et al. (2013) identified a small subset of areas in
temporal cortex, parietal cortex, frontal cortex, and pFC
that are very highly connected structurally. It is thus likely
that brain communication relies heavily on signals being
communicated via a core (Figure 1A). Second, and surpris-
ingly, experiments indicate that cortical regions are con-
siderably more interconnected than previously believed.
Some estimates are that 60% of the possible connections
between pairs of areas are indeed observed in some corti-
cal patches (Markov et al., 2013)—clearly not a small-world
organization! The precise implications of these findings, if
confirmed, must also consider pathway strength (not only
the existence vs. absence of a connection), which varies
over several orders of magnitude, because computational
work demonstrates that the type of network organization
(is it small-world?) strongly depends on the pattern of
pathway strengths (Gallos, Makse, & Sigman, 2012).

Cortical connectivity, although important, is only one
ingredient contributing to the anatomical organization
of the CNS. In fact, the focus on cortical connections of
most of the computational work neglects major connec-
tional properties that shape the overall neuroarchitecture
(for a comprehensive treatment, see Nieuwenhuys,
Voogd, & van Huijzen, 2008). (1) The entire cortical sheet
projects to the striatum and loops back to the cortex
via the thalamus, forming the so-called BG–cortical loops
(Figure 1B). (2) The cortex and the thalamus are mas-
sively interconnected. Most of the thalamic volume is
involved in bidirectional circuits with the cortex via the
so-called higher-order regions (Sherman & Guillery,
2002). For example, the pulvinar nucleus is bidirectionally
connected to the entire cortical sheet. (3) The hypothal-
amus is frequently viewed as a “descending” controller of
autonomic functions. However, the mammalian cerebral

Figure 1. Combinatorial anatomical connectivity. (A) Computational
analysis of cortical pathways suggests that a subgroup of regions works as
a “rich club” (orange circles in the middle): a set of highly interconnected
nodes that play a major role in determining the flow of signals across the
brain. (B) The neuroarchitecture also includes multiple large-scale
connectional systems, such as via the BG, as illustrated here. Additional
systems include those involving the thalamus, the hypothalamus, the
BLA, and the cerebellum, among others.

Pessoa

351

D
o
w
n
o
a
d
e
d

f
r
o
m
h

t
t

:
/
/

d
i
r
e
c
t
.

i
t
.

e
d
u

/
j

o
c
n
a
r
t
i
c
e
–
p
d

f
/

3
5
3
3
4
9
2
0
6
9
4
4
1

/
j

o
c
n
_
a
_
0
1
9
0
8
p
d

b
y
g
u
e
s
t

o
n
0
8
S
e
p
e
m
b
e
r
2
0
2
3

cortex and the hypothalamus share massive “bidirec-
tional” connections. In particular, in rodents, there are
direct hypothalamic projections to all parts of the cortical
sheet (as well as multiple indirect connectivity systems
with cortex; Risold, Thompson, & Swanson, 1997). In pri-
mates, the hypothalamus has widespread projections to
all sectors of the pFC, including lateral sectors. (4) The
basolateral amygdala (BLA) is bidirectionally connected
with the entire cortical sheet; these connections are quite
substantial with parts of frontal and temporal cortex, lead-
ing to the suggestion that this amygdala sector be called
the “frontotemporal amygdala” (Swanson & Petrovich,
1998). (5) The cerebellum not only receives inputs from
broad swaths of the cerebral cortex but also projects to
many, if not all, of these areas. In particular, a significant
portion of the output from the dentate nucleus of the cer-
ebellum projects to nonmotor areas, including regions of
pFC and posterior parietal cortex (Bostan, Dum, & Strick,
2013). Other major connectivity systems involve the
claustrum, the septum, and the brainstem (Nieuwenhuys
et al., 2008).

Clearly, a more complete elucidation of the properties
of the connectional neuroarchitecture requires combining
both cortical and noncortical pathway systems. The over-
all picture is one of massive interconnectivity, leading
to “combinatorial” pathways between sectors. In other
words, one can go from point A to point B in a multitude
of ways. We propose that, combined, connectivity systems
spanning the entire neuroaxis (cortical forebrain, subcor-
tical forebrain, midbrain, and hindbrain) provide the basis
for both broadcasting and integration of diverse signals
linked to the external and internal worlds. Such crisscross-
ing connectional systems support the interaction and
integration of signals that are typically associated with
standard mental domains, including emotion, motivation,
perception, cognition, and action (Pessoa, 2013) but,
critically, in a manner that does not abide by putative
boundaries between these categories (see below). I pro-
pose that this general architecture supports a degree of
computational flexibility that enables animals to cope suc-
cessfully with complex and ever-changing environments.
The overall architecture may produce circuits with local
specificity while attaining large-scale sensitivity, a type of
“global-within-local design,” which likely contributes to
more sophisticated, plastic, and context-sensitive
behaviors (Pessoa, Medina, & Desfilis, 2022).

Highly Distributed Functional Coordination

The complexity of anatomical pathways allows signals to
flow across the brain in a staggeringly large set of ways.
Anatomy provides a backbone that constrains function,
but the structure–function relationship is anything but
simple when one considers the abundance of bidirectional
connections and loop-like organization (as in the BG),
combined with excitation, inhibition, and nonlinearities.
In this manner, the anatomy supports a large range of

“functional interactions,” namely, particular relationships
between signals in disparate parts of the brain (e.g., they
might fire coherently). For one, the anatomy will support
the efficient communication of signals, even when strong
direct pathways are absent, such as the functional coordi-
nation between signals in the amygdala and lateral pFC,
although the two are not strongly connected physically.
These ideas, of course, are related to the notion of func-
tional connectivity, which in its most basic form can be
indexed via the correlation coefficient between two time
series (e.g., of the amygdala and the lateral pFC).

As an illustration of functional interactions, consider an
experiment that acquired fMRI in monkeys when they
were not performing an explicit task (Grayson et al.,
2016). The study observed robust signal correlation
between the amygdala and several regions that are not
connected to it (as far as it is known). They asked, too,
whether functional connectivity was more related to direct
(monosynaptic) pathways or multipath (polysynaptic)
connectivity by undertaking graph analysis. Are there effi-
cient routes of travel between regions even when they are
not directly connected? To address this question formally,
they estimated a graph measure called “communicability”
(related to the concept of “efficiency”) and found that
amygdala functional connectivity was more closely related
to communicability than would be expected by consider-
ing only monosynaptic pathways. Their finding illustrates
that the relationship between signals in disparate parts of
the brain is not determined by structural pathways in a
straightforward manner.

Networks/Circuits as Functional Units

The combination of the prior two principles leads to the
present one. In a highly interconnected system, to under-
stand function, we need to shift away from thinking in
terms of individual brain regions: The network itself is
the functional unit, not the brain area (Figure 2A). Pro-
cesses that support behavior are not implemented by an
individual area but depend on the interaction of multiple
areas, which are dynamically recruited into multiregion
assemblies. Such functional networks are based on the
relationships between signals across disparate parts of
the brain.

But how are networks/circuits defined? Let us consider
here large-scale networks, such as those studied with
fMRI in humans and rodents (Grandjean et al., 2020; Yeo
et al., 2011). (Other examples of networks/circuits will
be discussed in the context of extinction learning below.)
The most popular partitioning schemes parse individual
elements ( brain regions or parcels) into unique
groupings—a node belongs to one and exactly one com-
munity. (A community refers to a subdivision of a larger
network, namely, a subnetwork. At times we will refer to
subnetworks as “networks,” as in “default network,” given
common usage in the literature.) Based on fMRI data in the
absence of a task, Yeo et al. (2011) described a seven-

352

Journal of Cognitive Neuroscience

Volume 35, Number 3

D
o
w
n
o
a
d
e
d

f
r
o
m
h

t
t

:
/
/

d
i
r
e
c
t
.

i
t
.

e
d
u

/
j

o
c
n
a
r
t
i
c
e
–
p
d

f
/

3
5
3
3
4
9
2
0
6
9
4
4
1

/
j

o
c
n
_
a
_
0
1
9
0
8
p
d

b
y
g
u
e
s
t

o
n
0
8
S
e
p
e
m
b
e
r
2
0
2
3

highly connected structurally. Regions that work as
“connector hubs” (Guimera & Nunes Amaral, 2005) are
distinctly interesting because they have the potential to
integrate diverse types of signals (if they receive inputs
from disparate sources) and/or to distribute signals widely
(if they project to disparate targets). They are a good
reminder that communities are not islands; regions within
a (disjoint) community have connections both within and
outside the community.

Although there are many ways to operationalize over-
lapping networks, a simple way is to allow each brain
region to participate in all communities simultaneously
but in a graded fashion. Thus, if region A does not partic-
ipate in community C1, its membership value is 0; con-
versely, a membership value of 1 indicates that it belongs
maximally to C1. It is also useful to conceive of member-
ship as a finite resource, such that it sums to 1. Applying
these notions formally, we found that functional brain net-
works based on fMRI data both when tasks are not
required and during task conditions are highly overlap-
ping (Najafi, McMenamin, Simon, & Pessoa, 2016). In
other words, a considerable fraction of regions shared
their memberships across multiple communities. Indeed,
overlapping organization has been detected via multiple
networks analysis techniques (Faskowitz, Esfahlani, Jo,
Sporns, & Betzel, 2020; Yeo, Krienen, Chee, & Buckner,
2014).

The brain is a dynamic, constantly moving object, and so
are its networks. Functional relationships between groups
of regions are constantly fluctuating based on cognitive,
emotional, and motivational demands. Paying attention
to a stimulus that is emotionally significant (say, paired
with mild shock in the past), increases functionally con-
nectivity between the visual cortex and the amygdala
(Lojowska, Ling, Roelofs, & Hermans, 2018). Performing
a challenging task in which an advance cue stimulus indi-
cates that participants may earn extra cash for performing
it correctly increases functional connectivity between the
parietal/frontal cortex (important for performing the task)
and the ventral striatum (important for reward-related
processes; Padmala & Pessoa, 2011).

A vast literature has documented such changes in
functional connectivity between pairs of regions, but dis-
tributed, large-scale changes have been observed, too
(Cole et al., 2013). For example, in the reward study just
described, the nucleus accumbens and the caudate each
increased their functional connectivity with nearly all cor-
tical regions engaged by the cue. Network analysis identi-
fied two communities, one cortical and another composed
mostly of subcortical regions (including the nucleus
accumbens and caudate). It also revealed a decrease of
modularity when potential reward cues were encoun-
tered, consistent with the notion that particular condi-
tions (in this case, the possibility of reward) reorganize
large-scale functional organization (Kinnison, Padmala,
Choi, & Pessoa, 2012). In another study, we observed a
progression of network-level changes when participants

Pessoa

353

Figure 2. From regions to networks. (A) The meaningful functional
unit is not the brain region (left) but networks of brain regions that
aggregate and disassemble as a function of time (middle and right). (B)
Illustration of network properties (functions) in a scenario in which
regions carry out well-defined “primitives” (B1) and when they do not
(B2); in the latter, the function in question needs to be understood in terms
a set of regions (blue contours). Note that in both cases, understanding the
circuit behavior (function F ) is not well approximated by considering the
individual functions, f. Instead, it is necessary to consider the coordinated
(emergent) circuit function. In B1, individual functions can be specified
based on single regions (e.g., f(R1)), but in B2 depend on more than one
region in some cases (e.g., f(R1, R2)). Boxes at the bottom indicate criteria
to determine network-level properties, as well as Type II networks (in B2).

community division of the entire cortex, where each local
patch of tissue was assigned to a single community. In
other words, the overall space was broken into disjoint
communities. Their elegant work has been very influen-
tial, and their seven-network partition has been adopted
as a sort of “canonical” division of the cortex. Whereas
discrete clusters simplify the description of a system, do
they capture the underlying organization?

Multiple types of networks (social, biological) exhibit
nontrivial “overlapping organization” (Palla, Derenyi,
Farkas, & Vicsek, 2005). For example, the study of chemi-
cal interactions reveals that a substantial fraction of pro-
teins interacts with several protein groups, indicating that
actual networks are made of interwoven sets of overlap-
ping communities. Another way to motivate overlapping
organization in networks is by considering “hub regions.”
Both structural and functional analyses of brain data have
revealed the existence of particularly well-connected
regions, called hubs. For example, as discussed above,
Markov et al. (2013) described a set of areas in temporal
cortex, parietal cortex, frontal cortex, and pFC that are very

D
o
w
n
o
a
d
e
d

f
r
o
m
h

t
t

:
/
/

d
i
r
e
c
t
.

i
t
.

e
d
u

/
j

o
c
n
a
r
t
i
c
e
–
p
d

f
/

3
5
3
3
4
9
2
0
6
9
4
4
1

/
j

o
c
n
_
a
_
0
1
9
0
8
p
d

b
y
g
u
e
s
t

o
n
0
8
S
e
p
e
m
b
e
r
2
0
2
3

experienced threat, uncovering how network organiza-
tion unfolds across time during anxious apprehension
(McMenamin, Langeslag, Sirbu, Padmala, & Pessoa,
2014), a reminder that network functional organization
must be understood dynamically, as further illustrated
by studies of time-varying functional connectivity (Lurie
et al., 2020).

The ideas of network overlap and dynamic organization
are related. If brain areas can belong to multiple networks,
what determines the strength of a region’s affiliation to a
specific network? Here, context plays a pivotal role:
region A will participate strongly in network N1 during a
certain context C1 but will be more strongly linked with
network N2 during context C2. These ideas resonate with
the “flexible hub theory” (Cole et al., 2013), where some
regions are suggested to adjust their functional connectiv-
ity patterns as a function of task demands. This conceptu-
alization brings us back to the functions of brain regions:
The processes carried out by an area will depend on its
network affiliations (i.e., the regions it clusters with) at a
given time.

Overlap and dynamics promote a view in which net-
works do not consist of fixed collections of regions but
instead are made of coalitions that form and dissolve to
meet computational needs. In contrast, in the literature,
networks frequently are described in terms of fixed sets
of nodes; for example, the “salience network” might refer
to nine bilateral regions plus the dorsal ACC (Hermans
et al., 2011). But conceptualizing networks in more
dynamic fashion is fruitful. For instance, at time t1, regions
R1, R2, R7, and R9 might form a natural cluster; at a later
time t2, regions R2, R7, and R17 might coalesce. This shift
in perspective challenges the notion of a network as a sta-
ble unit, at least for longer periods of time, and raises new
questions. At what point does a coalition of regions
become something other than network N? Conversely,
can we think of the “salience network,” for example, as a
set of regions that varies temporally (see Figure 2A).

THE INTERACTIONAL COMPLEXITY OF
FEAR EXTINCTION

We now discuss the neural circuits of fear extinction as
an example of the types of network studied by systems
neuroscientists, which also illustrates the challenges of
trying to unravel the mapping between structure and
function. When a conditioned stimulus no longer predicts
the unconditioned stimulus to which it was paired in the
past (say, a sound no longer is followed by a shock),
the conditioned stimulus gradually stops eliciting the
conditioned response. This process is called “fear extinc-
tion.” Understanding it is of potentially enormous conse-
quence given the prevalence of anxiety and other related
disorders.

The medial pFC plays an important role in regulating the
amygdala during fear extinction (Morgan, Romanski, &
LeDoux, 1993). Extinction critically depends on context,

too. For instance, a sound may no longer signal an aversive
event, but not necessarily in a completely novel environ-
ment, and the hippocampus is thought to provide such
critical contextual information to the amygdala. Another
region influencing extinction is the nucleus reuniens of
the thalamus, which allows discrimination of dangerous
from safe contexts (Ramanathan, Jin, Giustino, Payne, &
Maren, 2018). At first glance, fear extinction appears to
fit the scheme of separate contributions interacting to gen-
erate a new behavior: cognition (tied to the medial pFC)
controlling emotion (tied to the amygdala) in a top–down
fashion, with additional contributions related to the con-
text of extinction and other factors (Figure 3A). However,
such characterization does not do justice to the behavioral
and neural richness of the phenomenon.

Let’s consider a few additional findings about fear
extinction (Figure 3B). Multiple cell groups in the BLA
actually project to the medial pFC whose outputs in turn
influence amygdala signals. Some studies even have sug-
gested that the BLA is upstream of the medial pFC,
because a population of extinction neurons in the BLA
(which project to the medial pFC) increase their activity
during extinction learning (Herry et al., 2008). The medial
pFC is also the target of the hippocampus, and this input
potentiates medial pFC signals during extinction. Further-
more, the medial pFC receives substantial inputs from the
thalamus, itself a major subcortical–cortical connectivity
hub. Additional contributors to this circuit include the
ventral tegmental area, where dopamine neurons are
activated by the omission of the aversive unconditioned
stimulus during extinction (Salinas-Hernández et al.,
2018) and are suggested to influence the BLA (possibly
via indirect projections). Although the role of the medial
pFC is well established in extinction, it is likely that both
the OFC and the ventrolateral pFC are important for
behavioral regulation in the presence of aversive stimuli,

Figure 3. Fear extinction circuits. (A) Basic extinction circuit centered on
the BLA. The contributions of a few key regions are labeled with their
putative functional contributions. (B) Extended circuit, with a larger set of
brain regions believed to be involved (not intended to be comprehensive).
The blue arrows indicate indirect anatomical connectivity. The red arrows
indicate the extensive norepinephric projections of the locus coeruleus.
HIPP, hippocampus; LC, locus coeruleus; MPFC, medial pFC; THAL,
thalamus; VTA, ventral tegmental area.

354

Journal of Cognitive Neuroscience

Volume 35, Number 3

D
o
w
n
o
a
d
e
d

f
r
o
m
h

t
t

:
/
/

d
i
r
e
c
t
.

i
t
.

e
d
u

/
j

o
c
n
a
r
t
i
c
e
–
p
d

f
/

3
5
3
3
4
9
2
0
6
9
4
4
1

/
j

o
c
n
_
a
_
0
1
9
0
8
p
d

b
y
g
u
e
s
t

o
n
0
8
S
e
p
e
m
b
e
r
2
0
2
3

too (Shiba, Santangelo, & Roberts, 2016). Finally, the locus
coeruleus in the brainstem, which is a primary source of
forebrain norepinephrine, has important neuromodula-
tory effects on extinction. In fact, stress-related engage-
ment of the locus coeruleus opposes extinction (Maren,
2022). (To simplify the discussion, we described the
medial pFC as a unit, but in rodents, the main contribu-
tions during extinction involve the ventral/infralimbic
component, which probably corresponds to the ventro-
medial pFC in humans. In addition, complex microcircuits
exist within critical nodes of the circuit, such as the amyg-
dala [Whittle et al., 2021], but are not discussed here.)

Now, let us return to the initial scheme of Figure 3A.
This description of extinction instantiates a boxes-and-
arrows arrangement that is a mainstay of psychology and
neuroscience, where semantic labels can be added to
some of the interactions when their interpretation can
be distilled to a convenient concept. However, the depic-
tion is fundamentally wanting not only because it lacks
some regions and connections but also because of the
implicit assumption that well-defined functions are imple-
mented by individual regions, with their outputs being
read by downstream regions. For example, the hippocam-
pus determines context, and the medial pFC some kind of
appraisal that determines when the amygdala should be
downregulated; hence, one can place these functions at
the regions. Their outputs are then read by the amygdala
to determine what to do given the inputs.

In contrast, an alternative mode of thinking considers
how multiple regions jointly and dynamically implement
key processes (Figure 3A). For example, as discussed
above, extinction neurons in the BLA are reciprocally
connected to the medial pFC (Herry et al., 2008). Thus,
whereas they actually could be thought to be “upstream”
of the medial pFC (thus flipping the typical way of thinking
about the two regions, as indicated previously), it is
important to evaluate the possibility that the two regions
work in a coordinated fashion during extinction learning.
Another reason fear extinction should be considered a
circuit/network property is that extinction has to do with
processes that convert a fear-inducing stimulus back to a
status of neutrality—an “off” switch, if you will. However,
as animals navigate their environment, there are many
stimuli that do exactly the opposite, and they can be
considered “on” switches. Accordingly, to understand
complex behaviors, one needs to consider how defensive
behaviors are dynamically engaged and disengaged.
Even more broadly, the defensive circuits involved
intersect and interact heavily with those that promote
exploratory and appetitive behaviors. Should the animal
withdraw, stay, or approach?

In any case, even under a more constrained conceptual-
ization, the preceding discussion should help highlight
the interactional complexity of systems-level processes
neuroscientists often focus on. For one, the circuit con-
tains both unidirectional and bidirectional connections,
as well as both excitatory and inhibitory components.

The case advanced here is that the field should in fact
embrace this level of interactivity, not attempt to side step
it. Otherwise, it will continue to be no surprise how little
progress has been made in ameliorating the debilitating
impacts of fear- and anxiety-related mental health condi-
tions in the lives of so many people. Broadly speaking,
extinction can be studied in the context of Pavlovian or
instrumental learning (an example of the latter is avoid-
ance learning where the animal makes a response to avoid
foot shock). In both laboratory animals and humans, pro-
cedures to extinguish behavior (“instrumental extinction”)
elicit well-documented “side effects” (for a discussion, see
Bouton, Maren, & McNally, 2021). Examples include tem-
porary increase of the very behavior being extinguished, a
return of other behaviors previously extinguished, as well
as increased frequency of undesirable behaviors such as
aggression. The claim made here is that these examples
appear to be secondary consequences when regions are
considered as well-defined investigative units with puta-
tively specialized functions. Instead, they are exactly the
types of effects routinely found in nondecomposable,
interactionally complex systems, where cascades of inter-
actions generate “side effects.”

WHAT KIND OF NETWORK?

Let us consider two scenarios to further clarify what is
meant by “network properties.” In a Type I network, brain
regions carry out (compute) fairly specific functions. For
example, in the context of extinction, the hippocampus
determines contextual information, and the ventral teg-
mental area computes omission prediction errors. In this
scenario, a process of interest (say, fear extinction) is still
viewed as a network property that depends on the interac-
tions of the brain regions involved. That is to say, it is nec-
essary to investigate the orchestration of multiple regions
to understand how the regions, collectively, carry out the
processes of interest. Importantly, however, the collective
properties of the system are not accessible, or predictable,
from the behavior of the individual regions alone: The
multiregion function, F(R1, R2, …, Rn), is poorly character-
ized from considering f(R1), f(R2), and so on.

Poorly characterized in what sense? In a near-
decomposable system, lesion of R1, for example, will cause
a deficit to the network that is directly related to the puta-
tive function of R1. However, this is not the outcome in an
interactionally complex system. Consider multispecies
ecological systems in which the introduction of a new spe-
cies or the removal of an existing one causes completely
unexpected knock-on effects (Levine, Bascompte, Adler,
& Allesina, 2017). The claim being made here is that, in
many cases, we need to consider brain networks in much
the same way: A complex system that is not well approx-
imated by simple decompositions; F(R1, R2, …, Rn) will
not be well approximated by considering f(R1), f(R2),
…, f(Rn) (Figure 2B1).

Pessoa

355

D
o
w
n
o
a
d
e
d

f
r
o
m
h

t
t

:
/
/

d
i
r
e
c
t
.

i
t
.

e
d
u

/
j

o
c
n
a
r
t
i
c
e
–
p
d

f
/

3
5
3
3
4
9
2
0
6
9
4
4
1

/
j

o
c
n
_
a
_
0
1
9
0
8
p
d

b
y
g
u
e
s
t

o
n
0
8
S
e
p
e
m
b
e
r
2
0
2
3

Now let us turn to Type II networks, where areas do not
instantiate specific functions. Instead, two or more regions
working together instantiate the basic function of interest,
such that its implementation is distributed across regions.
It is easy to provide an example of Type II networks if we
consider computational models where undifferentiated
units are trained together to perform a function of interest.
But, are there examples of this type of situation in the
brain? Multiarea functions are exemplified by reciprocal
dynamics between the FEFs and the lateral intraparietal
area in macaques supporting persistent activity during a
delayed oculomotor task (Hart & Huk, 2020). Based,
among others, on the tight link between these areas at
the trial level, the authors suggested that the two areas be
viewed as a single functional unit (see Murray, Jaramillo, &
Wang, 2017, and Kang & Drukmann, 2020, for a computa-
tional model; see also Mejías & Wang, 2022; Figure 2B2).

In rodents, motor preparation requires reciprocal
excitation across multiple brain areas (Guo et al., 2017).
Persistent preparatory activity cannot be sustained within
cortical circuits alone but in addition requires recurrent
excitation through a thalamocortical loop. Inactivation of
the parts of the thalamus reciprocally connected to the
frontal cortex results in strong inhibition of frontal cortex
neurons. Conversely, the frontal cortex contributes major
driving excitation to the higher-order thalamus in ques-
tion. What is more, persistent activity in frontal cortex also
requires activity in the cerebellum and vice versa (Gao
et al., 2018), revealing that persistent activity during motor
planning is maintained by circuits that span multiple
regions. The claim, thus, is that persistent motor activity
is a circuit property that requires multiple brain regions.
In such case, one cannot point to a brain region (or even
a sector) and label “working memory” as residing there.
It could be argued that, in the brain, the two types of
networks discussed here—with and without well-defined
node functions—are not really distinct and that what dif-
fers is the granularity of the function. After all, if above one
could decompose the function “persistent motor activity”
into basic primitives, it is conceivable that they could be
carried out in separate regions. In such case, we would
revert back to the situation of networks with nodes that
compute well-defined functions. Put another way, a skep-
tic could quibble that, in the brain, a putative Type II net-
work is a reflection of our temporary state of ignorance.
The conjecture advanced here is that, in the brain, such
reductive reasoning will fare poorly in the long run: It is
not the case that one can develop a system of primitive
properties that, together, span the functions/processes
of interest. In many cases, network properties are not
reducible to component interactions of well-defined
subfunctions—they are inexorably distributed.

SOME IMPLICATIONS FOR BRAIN SCIENCE

In the preceding sections, we discussed one of the major
themes of The Entangled Brain: Neural processes that

accompany behavior are profitably viewed through the
lens of complex, networked systems. Here, we summarize
some of the implications of the framework to the general
goal of elucidating brain functions and how they relate to
brain parts.

Interactional Complexity

The brain is a system of interacting parts. At a local level,
say within a specific Brodmann’s region or subcortical
area, populations of neurons interact. But interactions
are not only local. Massive anatomical connectivity pro-
vides the substrate for communication crisscrossing the
entire span of the neuroaxis (hindbrain, midbrain, and
forebrain). This structural interactional complexity has
important implications for brain function: Simpler decom-
positions that insulate brain regions from one another will
capture only a slice of the contributions of the parts in
question.

Anatomical interactional complexity implies that net-
work or circuits are the functional unit of interest. This
conclusion, when considered in a broad sense, may seem
incontrovertible to many (most?) neuroscientists. The
question then is what kind of network/circuit are we study-
ing? I contend that simply enlarging the functional unit
from an area to a standard, fixed network is only a modest
step. Networks should be considered inherently overlap-
ping and dynamic. Parts of the brain (say, populations of
neurons within areas) affiliate dynamically with other ele-
ments in a highly context dependent manner driven by the
current endogenous and exogenous demands and oppor-
tunities present to the animal. Critically, network proper-
ties are novel (with respect to that of individual regions),
and key functions are distributed across regions or neuro-
nal populations.

From this perspective, it is no surprise that neuroscien-
tists are constantly discovering that brain regions partici-
pate in novel and unexpected ways in previously studied
circuits and/or processes. Examples abound, but in the
context of extinction learning, for example, a growing
number of critical contributions of the thalamus are being
discovered (Silva et al., 2021; Ramanathan et al., 2018).
Finally, the inflexible nature of laboratory testing plays
no small role in the apparent low interactional complexity
of functional brain circuits (Paré & Quirk, 2017). By
restricting the conditions under which circuits are inter-
rogated, it appears that neuronal populations, areas, and
circuits are considerably more selective for the properties
and functions investigated.

Decentralization, Heterarchy, and Causation

In many systems, and the brain is no exception, it is instinc-
tive to think that many of its important functions depend
on centralized processes; for example, the pFC may be
viewed as a convergence sector for multiple types of infor-
mation, allowing it to control behavior. The view advanced

356

Journal of Cognitive Neuroscience

Volume 35, Number 3

D
o
w
n
o
a
d
e
d

f
r
o
m
h

t
t

:
/
/

d
i
r
e
c
t
.

i
t
.

e
d
u

/
j

o
c
n
a
r
t
i
c
e
–
p
d

f
/

3
5
3
3
4
9
2
0
6
9
4
4
1

/
j

o
c
n
_
a
_
0
1
9
0
8
p
d

b
y
g
u
e
s
t

o
n
0
8
S
e
p
e
m
b
e
r
2
0
2
3

here favors PDP (Goldman-Rakic, 1988). Instead of infor-
mation flowing hierarchically to an “apex region” where
signals are integrated, information travels in multiple
directions without a strict hierarchy. An organization of
this sort is termed a “heterarchy” to emphasize the multi-
directional flow of information. As discussed previously,
this does not imply an absence of organization. The ana-
tomical backbone itself is highly structured, and several
cortical regions are important anatomical/functional core
regions (Markov et al., 2013). Other noncortical areas
are also important hubs, including the thalamus, hypothal-
amus, BLA, and parts of the midbrain, including the supe-
rior colliculus.

The decentralized nature of processing should be
understood temporally, too. When a novel stimulus
and/or context is encountered by an animal, signals might
flow first along the most direct and potent routes. How-
ever, behavior evolves temporally, and signal flow will
progress in complex, decentralized ways. In fact, the spiral-
ing pathways of the neuroarchitecture support communi-
cation and integration of signals across different spatial
extents. The processing of new stimuli always take place
against ongoing activity reflecting the immediate, recent
past (and of course the more remote past), further decou-
pling functional states from what would be anticipated by
considering the most immediate anatomical pathways.

Investigating systems that are conceptualized as rela-
tively decentralized instigates different classes of research
questions about brain and behavior. If it is the coordina-
tion between the multiple parts that leads to the proper-
ties of interest, the object of scientific studies shifts to
unraveling how such interactions work. For example,
instead of investigating how property P is encoded in area
A, the question becomes one of elucidating how the
property arises from decentralized coordination. The
coordination framework also moves the goalpost away
from deciphering what information is passed from region
to region—or relatedly how a region decodes the signals
of other regions—to how the coordinated activity of
multiregion assemblies generates signals with specific
properties.

The view has important implications for causation, too,
as the concept needs to be substantially reformulated.
Neuroscientists often operate with an implicit billiard ball
model of causation, a Newtonian scheme in which signals
in one region affect the response in another, much like
billiard balls affect each other. However, Newtonian cau-
sality provides an extremely poor candidate for explana-
tion in interactionally complex systems like the brain
because of the distributed, mutual, and reciprocal nature
of the causal contributions. This is not to promote a Lash-
leyan view of causal equipotentiality; the brain is clearly
very highly structured. So, how should one proceed?

A possible strategy to advance the understanding of
causation is to investigate circuit controllability (Tang &
Bassett, 2018; Liu & Barabási, 2016). By using tools from
network science and mathematical control theory, one

Figure 4. Network controllability. (A) Multiperturbation methods can
be used to activate and/or silence multiple brain regions simultaneously
(here R1 and R3). (B) Such perturbations can be used to attempt to
steer the trajectory of the system from some initial state, I, toward a
final target state. Here, the state of the system can be represented as a
point in four dimensions, each corresponding to the activity level at an
individual region. The temporal evolution of the system along the state
space corresponds to a trajectory.

seeks to determine the extent to which certain network
nodes can steer the system into different states. Thus, con-
trollability of future system states provides a promising
tool to understand the emergence of multipart properties.
An interesting concept from this field is the notion of
“pinning control,” where multiple inputs are applied
(“pinned”) and propagate through the system, with the
goal of defining the (future) trajectory of the system
( Wang & Chen, 2002; Figure 4). Transplanting such rea-
soning to neuroscience, one could see how it could inform
perturbation experiments. The ability to determine
specific future states will depend on simultaneously
stimulating and/or silencing sets of regions, not a single
one, in particular ways.

This perspective offers avenues for testing network-
level properties, too. To observe a certain function, F,
instantiated by a certain future circuit state requires
pinning multiple regions (or neuronal subpopulations);
pinning a single one is insufficient to attain the collective
state in question (see also Fakhar & Hilgetag, 2021, for
arguments that multiregion lesion experiments are neces-
sary). This type of approach also helps evaluate Type II
networks, where function is not well defined at the area
level. In such cases, manipulating two or more regions is
necessary for the function in question to be instantiated
(such as R1 and R3 in Figure 2B2). More generally, neuro-
science will benefit from the development of mathematical
techniques to investigate causation in complex systems, as
in other areas such as weather prediction (Runge et al.,
2019) and ecology (Sugihara et al., 2012).

Mental Categories and the Entangled Brain

Categories such as perception, cognition, action, emotion,
and motivation organize how we understand and study
brain function. But are such mental domains consistent
with the framework described here? In a nutshell, no.
The standard decomposition adopted by neuroscientists

Pessoa

357

D
o
w
n
o
a
d
e
d

f
r
o
m
h

t
t

:
/
/

d
i
r
e
c
t
.

i
t
.

e
d
u

/
j

o
c
n
a
r
t
i
c
e
–
p
d

f
/

3
5
3
3
4
9
2
0
6
9
4
4
1

/
j

o
c
n
_
a
_
0
1
9
0
8
p
d

b
y
g
u
e
s
t

o
n
0
8
S
e
p
e
m
b
e
r
2
0
2
3

requires an organization that is fairly modular, which is
inconsistent with the principles of the anatomical and
functional neuroarchitecture discussed. In general, mental
processes of interest cut across domains and do not
respect putative boundaries between traditional systems
(e.g., emotion, cognition). In fact, crisscrossing anatomical/
functional connectional systems dissolve potential lines of
demarcation.

More broadly, brains have evolved to provide adaptive
responses to problems faced by living beings, promoting
survival and reproduction. In this context, even the mental
vocabulary of neuroscience (attention, cognitive control,
etc.), with origins disconnected from the study of animal
behavior, provides problematic theoretical pillars. Instead,
approaches inspired by evolutionary considerations pro-
vide potentially better scaffolds to sort out the relation-
ships between brain structure and function (Cisek, 2022;
Pessoa et al., 2022).

FINAL THOUGHTS

Neuroscience strives to elucidate the neural underpin-
nings of behaviors and has done so in a preponderantly
reductionistic fashion for over a century and a half. The
time is ripe for transitioning into a period when a truly
dynamic and networked view of the brain takes hold.
Future research will need to strive to make progress along
several fronts: dynamics, decentralized computation, and,
yes, emergence.

Why do we need the perspective advocated here? The
claim goes back to the interactional complexity of the
brain. If some of the ideas above are correct, neuroscience
needs to stop treating the brain as a near-decomposable
system. Doing so distorts our view of the very system we’re
attempting to decipher. For example, we will think we can
make progress in understanding fear and anxiety by focus-
ing on a few regions at a time, or even isolated circuits. The
contention made here is that this strategy is deficient (see
Pessoa, 2022a).

How can the shift advocated be implemented? At least
in part, current limitations stem from the neurotechniques
available. Novel neurotechniques will play a major role. In
particular, developments that allow recording over a larger
number of regions simultaneously, as well as accomplish-
ing multiregion perturbations. At the same time, a science
of the mind–brain must stand on a solid foundation of
understanding behavior (Krakauer, Ghazanfar, Gomez-
Marin, MacIver, & Poeppel, 2017), while employing com-
putational and mathematical tools in an integral manner.
The field needs to take stock and invest on the develop-
ment of conceptual and theoretical pillars. Bigger and
shinier tools and techniques alone will not yield the neces-
sary progress; we run the risk of being able to measure
every cell (or subcellular component even) in the brain
in a theoretical vacuum. The current obsession in the field
with causation is equally problematic. Without conceptual
clarity—how should we even think of causation in highly

entangled systems?—causal explanations in fact might
miss the point.

Ultimately, to explain the cognitive–emotional brain,
we need to dissolve boundaries within the brain—
perception, cognition, action, emotion, and motivation
—as well as outside the brain, as we bring down the walls
between biology, psychology, ecology, mathematics, com-
puter science, philosophy, and so on.

Acknowledgments

The author is grateful for support from the National Institute of
Mental Health (MH071589 and MH112517) and Brad Postle for
constructive feedback on earlier versions of the article.

Reprint requests should be sent to Luiz Pessoa, University of
Maryland, College Park, MD 20742, or via e-mail: pessoa@umd.edu.

Funding Information

Luiz Pessoa, National Institute of Mental Health (https://
dx.doi.org/10.13039/100000025), grant numbers:
MH071589, MH112517.

Diversity in Citation Practices

Retrospective analysis of the citations in every article pub-
lished in this journal from 2010 to 2021 reveals a persistent
pattern of gender imbalance: Although the proportions of
authorship teams (categorized by estimated gender iden-
tification of first author/last author) publishing in the Jour-
nal of Cognitive Neuroscience ( JoCN) during this period
were M(an)/M = .407, W(oman)/M = .32, M/ W = .115,
and W/ W = .159, the comparable proportions for the arti-
cles that these authorship teams cited were M/M = .549,
W/M = .257, M/ W = .109, and W/ W = .085 (Postle and
Fulvio, JoCN, 34:1, pp. 1–3). Consequently, JoCN encour-
ages all authors to consider gender balance explicitly when
selecting which articles to cite and gives them the oppor-
tunity to report their article’s gender citation balance.

REFERENCES

Barabási, A. L., & Albert, R. (1999). Emergence of scaling in

random networks. Science, 286, 509–512. https://doi.org/10
.1126/science.286.5439.509, PubMed: 10521342
Bechtel, W., & Richardson, R. C. (2010). Discovering

complexity: Decomposition and localization as strategies in
scientific research. Cambridge, MA: MIT Press. https://doi
.org/10.7551/mitpress/8328.001.0001

Bostan, A. C., Dum, R. P., & Strick, P. L. (2013). Cerebellar

networks with the cerebral cortex and basal ganglia. Trends
in Cognitive Sciences, 17, 241–254. https://doi.org/10.1016/j
.tics.2013.03.003, PubMed: 23579055

Bouton, M. E., Maren, S., & McNally, G. P. (2021). Behavioral and
neurobiological mechanisms of Pavlovian and instrumental
extinction learning. Physiological Reviews, 101, 611–681.
https://doi.org/10.1152/physrev.00016.2020, PubMed: 32970967
Cisek, P. (2022). Evolution of behavioural control from chordates
to primates. Philosophical Transactions of the Royal Society
of London, Series B: Biological Sciences, 377, 20200522.
https://doi.org/10.1098/rstb.2020.0522, PubMed: 34957850

358

Journal of Cognitive Neuroscience

Volume 35, Number 3

D
o
w
n
o
a
d
e
d

f
r
o
m
h

t
t

:
/
/

d
i
r
e
c
t
.

i
t
.

e
d
u

/
j

o
c
n
a
r
t
i
c
e
–
p
d

f
/

3
5
3
3
4
9
2
0
6
9
4
4
1

/
j

o
c
n
_
a
_
0
1
9
0
8
p
d

b
y
g
u
e
s
t

o
n
0
8
S
e
p
e
m
b
e
r
2
0
2
3

Cole, M. W., Reynolds, J. R., Power, J. D., Repovs, G., Anticevic,
A., & Braver, T. S. (2013). Multi-task connectivity reveals
flexible hubs for adaptive task control. Nature Neuroscience,
16, 1348–1355. https://doi.org/10.1038/nn.3470, PubMed:
23892552

Fakhar, K., & Hilgetag, C. C. (2021). Systematic perturbation of
an artificial neural network: A step towards quantifying causal
contributions in the brain. bioRxiv. https://doi.org/10.1101
/2021.11.04.467251

Faskowitz, J., Esfahlani, F. Z., Jo, Y., Sporns, O., & Betzel, R. F.
(2020). Edge-centric functional network representations of
human cerebral cortex reveal overlapping system-level
architecture. Nature Neuroscience, 23, 1644–1654. https://
doi.org/10.1038/s41593-020-00719-y, PubMed: 33077948

Gallos, L. K., Makse, H. A., & Sigman, M. (2012). A small world
of weak ties provides optimal global integration of self-similar
modules in functional brain networks. Proceedings of the
National Academy of Sciences, U.S.A., 109, 2825–2830.
https://doi.org/10.1073/pnas.1106612109, PubMed: 22308319

Gao, Z., Davis, C., Thomas, A. M., Economo, M. N., Abrego,

A. M., Svoboda, K., et al. (2018). A cortico-cerebellar loop for
motor planning. Nature, 563, 113–116. https://doi.org/10
.1038/s41586-018-0633-x, PubMed: 30333626

Goldman-Rakic, P. S. (1988). Topography of cognition: Parallel
distributed networks in primate association cortex. Annual
Review of Neuroscience, 11, 137–156. https://doi.org/10.1146
/annurev.ne.11.030188.001033, PubMed: 3284439

Grandjean, J., Canella, C., Anckaerts, C., Ayrancı, G., Bougacha,
S., Bienert, T., et al. (2020). Common functional networks in
the mouse brain revealed by multi-Centre resting-state fMRI
analysis. Neuroimage, 205, 116278. https://doi.org/10.1016/j
.neuroimage.2019.116278, PubMed: 31614221

Grayson, D. S., Bliss-Moreau, E., Machado, C. J., Bennett, J.,
Shen, K., Grant, K. A., et al. (2016). The rhesus monkey
connectome predicts disrupted functional networks resulting
from Pharmacogenetic inactivation of the amygdala. Neuron,
91, 453–466. https://doi.org/10.1016/j.neuron.2016.06.005,
PubMed: 27477019

Guimera, R., & Nunes Amaral, L. A. (2005). Functional cartography
of complex metabolic networks. Nature, 433, 895–900. https://
doi.org/10.1038/nature03288, PubMed: 15729348

Guo, Z. V., Inagaki, H. K., Daie, K., Druckmann, S., Gerfen, C. R.,
& Svoboda, K. (2017). Maintenance of persistent activity in a
frontal thalamocortical loop. Nature, 545, 181–186. https://doi
.org/10.1038/nature22324, PubMed: 28467817

Hart, E., & Huk, A. C. (2020). Recurrent circuit dynamics

underlie persistent activity in the macaque frontoparietal
network. eLife, 9, e52460. https://doi.org/10.7554/eLife.52460,
PubMed: 32379044

Hermans, E. J., Van Marle, H. J., Ossewaarde, L., Henckens, M. J.,

Qin, S., Van Kesteren, M. T., et al. (2011). Stress-related
noradrenergic activity prompts large-scale neural network
reconfiguration. Science, 334, 1151–1153. https://doi.org/10
.1126/science.1209603, PubMed: 22116887

Herry, C., Ciocchi, S., Senn, V., Demmou, L., Müller, C., & Lüthi,
A. (2008). Switching on and off fear by distinct neuronal
circuits. Nature, 454, 600–606. https://doi.org/10.1038
/nature07166, PubMed: 18615015

Humphreys, P. (2016). Emergence: A philosophical account.
Oxford University Press. https://doi.org/10.1093/acprof:oso
/9780190620325.001.0001

Juarrero, A. (1999). Dynamics in action: Intentional behavior
as a complex system. Cambridge, MA: MIT Press. https://doi
.org/10.7551/mitpress/2528.001.0001

Kang, B., & Druckmann, S. (2020). Approaches to inferring
multi-regional interactions from simultaneous population
recordings. Current Opinion in Neurobiology, 65, 108–119.
https://doi.org/10.1016/j.conb.2020.10.004, PubMed: 33227602

Kinnison, J., Padmala, S., Choi, J. M., & Pessoa, L. (2012).
Network analysis reveals increased integration during
emotional and motivational processing. Journal of
Neuroscience, 32, 8361–8372. https://doi.org/10.1523
/JNEUROSCI.0821-12.2012, PubMed: 22699916

Krakauer, J. W., Ghazanfar, A. A., Gomez-Marin, A., MacIver,
M. A., & Poeppel, D. (2017). Neuroscience needs behavior:
Correcting a reductionist bias. Neuron, 93, 480–490. https://
doi.org/10.1016/j.neuron.2016.12.041, PubMed: 28182904
Levine, J. M., Bascompte, J., Adler, P. B., & Allesina, S. (2017).

Beyond pairwise mechanisms of species coexistence in
complex communities. Nature, 546, 56–64. https://doi.org/10
.1038/nature22898, PubMed: 28569813

Liu, Y. Y., & Barabási, A. L. (2016). Control principles of

complex systems. Reviews of Modern Physics, 88, 035006.
https://doi.org/10.1103/RevModPhys.88.035006

Lojowska, M., Ling, S., Roelofs, K., & Hermans, E. J. (2018).

Visuocortical changes during a freezing-like state in humans.
Neuroimage, 179, 313–325. https://doi.org/10.1016/j
.neuroimage.2018.06.013, PubMed: 29883732

Lurie, D. J., Kessler, D., Bassett, D. S., Betzel, R. F., Breakspear,
M., Kheilholz, S., et al. (2020). Questions and controversies in
the study of time-varying functional connectivity in resting
fMRI. Network Neuroscience, 4, 30–69. https://doi.org/10
.1162/netn_a_00116, PubMed: 32043043

Maren, S. (2022). Unrelenting fear under stress: Neural circuits

and mechanisms for the immediate extinction deficit.
Frontiers in Systems Neuroscience, 39, 888461. https://doi
.org/10.3389/fnsys.2022.888461, PubMed: 35520882

Markov, N. T., Ercsey-Ravasz, M., Van Essen, D. C., Knoblauch,
K., Toroczkai, Z., & Kennedy, H. (2013). Cortical high-density
Counterstream architectures. Science, 342, 1238406. https://
doi.org/10.1126/science.1238406, PubMed: 24179228
Markov, N. T., Misery, P., Falchier, A., Lamy, C., Vezoli, J.,

Quilodran, R., et al. (2011). Weight consistency specifies
regularities of macaque cortical networks. Cerebral Cortex,
21, 1254–1272. https://doi.org/10.1093/cercor/bhq201,
PubMed: 21045004

McMenamin, B. W., Langeslag, S. J., Sirbu, M., Padmala, S., &

Pessoa, L. (2014). Network organization unfolds over
time during periods of anxious anticipation. Journal of
Neuroscience, 34, 11261–11273. https://doi.org/10.1523
/JNEUROSCI.1579-14.2014, PubMed: 25143607

Mejías, J. F., & Wang, X. J. (2022). Mechanisms of distributed

working memory in a large-scale network of macaque
neocortex. eLife, 11, e72136. https://doi.org/10.7554/eLife
.72136, PubMed: 35200137

Morgan, M. A., Romanski, L. M., & LeDoux, J. E. (1993).

Extinction of emotional learning: Contribution of medial
prefrontal cortex. Neuroscience Letters, 163, 109–113. https://
doi.org/10.1016/0304-3940(93)90241-C, PubMed: 8295722

Murray, J. D., Jaramillo, J., & Wang, X. J. (2017). Working
memory and decision-making in a frontoparietal circuit
model. Journal of Neuroscience, 37, 12167–12186. https://
doi.org/10.1523/JNEUROSCI.0343-17.2017, PubMed:
29114071

Najafi, M., McMenamin, B. W., Simon, J. Z., & Pessoa, L. (2016).
Overlapping communities reveal rich structure in large-scale
brain networks during rest and task conditions. Neuroimage,
135, 92–106. https://doi.org/10.1016/j.neuroimage.2016.04
.054, PubMed: 27129758

Nieuwenhuys, R., Voogd, J., & van Huijzen, C. (2008). The
human central nervous system: A synopsis and atlas
(4th ed.). Springer Science and Business Media. https://doi
.org/10.1007/978-3-540-34686-9

Padmala, S., & Pessoa, L. (2011). Reward reduces conflict by
enhancing attentional control and biasing visual cortical
processing. Journal of Cognitive Neuroscience, 23,

Pessoa

359

D
o
w
n
o
a
d
e
d

f
r
o
m
h

t
t

:
/
/

d
i
r
e
c
t
.

i
t
.

e
d
u

/
j

o
c
n
a
r
t
i
c
e
–
p
d

f
/

3
5
3
3
4
9
2
0
6
9
4
4
1

/
j

o
c
n
_
a
_
0
1
9
0
8
p
d

b
y
g
u
e
s
t

o
n
0
8
S
e
p
e
m
b
e
r
2
0
2
3

3419–3432. https://doi.org/10.1162/jocn_a_00011, PubMed:
21452938

Palla, G., Derenyi, I., Farkas, I., & Vicsek, T. (2005). Uncovering
the overlapping community structure of complex networks in
nature and society. Nature, 435, 814–818. https://doi.org/10
.1038/nature03607, PubMed: 15944704

Paré, D., & Quirk, G. J. (2017). When scientific paradigms lead
to tunnel vision: Lessons from the study of fear. NPJ Science
of Learning, 2, 1–8. https://doi.org/10.1038/s41539-017-0007-4,
PubMed: 30294453

Pessoa, L. (2013). The cognitive–emotional brain: From
interactions to integration. Cambridge, MA: MIT Press.
https://doi.org/10.7551/mitpress/9780262019569.001.0001
Pessoa, L. (2022a). How many brain regions are needed to

elucidate the neural bases of fear and anxiety? OSF Preprints.

Pessoa, L. (2022b). The entangled brain: How perception,

cognition, and emotion are woven together. Cambridge, MA:
MIT Press. https://doi.org/10.7551/mitpress/14636.001.0001

Pessoa, L., Medina, L., & Desfilis, E. (2022). Refocusing

neuroscience: Moving away from mental categories and
towards complex behaviours. Philosophical Transactions of
the Royal Society of London, Series B: Biological Sciences,
377, 20200534. https://doi.org/10.1098/rstb.2020.0534,
PubMed: 34957851

Ramanathan, K. R., Jin, J., Giustino, T. F., Payne, M. R., & Maren,
S. (2018). Prefrontal projections to the thalamic nucleus
reuniens mediate fear extinction. Nature Communications,
9, 1–12. https://doi.org/10.1038/s41467-018-06970-z, PubMed:
30375397

Risold, P. Y., Thompson, R. H., & Swanson, L. W. (1997).
The structural organization of connections between
hypothalamus and cerebral cortex. Brain Research Reviews,
24, 197–254. https://doi.org/10.1016/S0165-0173(97)00007-6,
PubMed: 9385455

Runge, J., Bathiany, S., Bollt, E., Camps-Valls, G., Coumou, D.,
Deyle, E., et al. (2019). Inferring causation from time series
in earth system sciences. Nature Communications, 10,
1–13. https://doi.org/10.1038/s41467-019-10105-3, PubMed:
31201306

Salinas-Hernández, X. I., Vogel, P., Betz, S., Kalisch, R.,

Sigurdsson, T., & Duvarci, S. (2018). Dopamine neurons
drive fear extinction learning by signaling the omission of
expected aversive outcomes. eLife, 7, e38818. https://doi.org
/10.7554/eLife.38818, PubMed: 30421719

Saper, C. B. (2002). The central autonomic nervous system:
Conscious visceral perception and autonomic pattern
generation. Annual Review of Neuroscience, 25, 433–469.
https://doi.org/10.1146/annurev.neuro.25.032502.111311,
PubMed: 12052916

Sherman, S. M., & Guillery, R. W. (2002). The role of the

thalamus in the flow of information to the cortex.
Philosophical Transactions of the Royal Society of London,
Series B: Biological Sciences, 357, 1695–1708. https://doi.org
/10.1098/rstb.2002.1161, PubMed: 12626004

Shiba, Y., Santangelo, A. M., & Roberts, A. C. (2016). Beyond
the medial regions of prefrontal cortex in the regulation
of fear and anxiety. Frontiers in Systems Neuroscience,
10, 12. https://doi.org/10.3389/fnsys.2016.00012, PubMed:
26941618

Silva, B. A., Astori, S., Burns, A. M., Heiser, H., van den Heuvel,
L., Santoni, G., et al. (2021). A thalamo-amygdalar circuit
underlying the extinction of remote fear memories. Nature
Neuroscience, 24, 964–974. https://doi.org/10.1038/s41593
-021-00856-y, PubMed: 34017129

Simon, H. A. (1962). The architecture of complexity. Proceedings

of the American Philosophical Society, 106, 467–482.

Sporns, O., & Zwi, J. D. (2004). The small world of the cerebral
cortex. Neuroinformatics, 2, 145–162. https://doi.org/10
.1385/NI:2:2:145, PubMed: 15319512

Sugihara, G., May, R., Ye, H., Hsieh, C. H., Deyle, E., Fogarty, M.,
et al. (2012). Detecting causality in complex ecosystems.
Science, 338, 496–500. https://doi.org/10.1126/science
.1227079, PubMed: 22997134

Swanson, L. W., & Petrovich, G. D. (1998). What is the

amygdala? Trends in Neurosciences, 21, 323–331. https://doi
.org/10.1016/S0166-2236(98)01265-X, PubMed: 9720596
Tang, E., & Bassett, D. S. (2018). Colloquium: Control of

dynamics in brain networks. Reviews of Modern Physics, 90,
031003. https://doi.org/10.1103/RevModPhys.90.031003

Von Bertalanffy, L. (1950). An outline of general system theory.
British Journal for the Philosophy of Science, 1, 134–165.
https://doi.org/10.1093/bjps/I.2.134

Wang, X. F., & Chen, G. (2002). Pinning control of scale-free

dynamical networks. Physica A: Statistical Mechanics and its
Applications, 310, 521–531. https://doi.org/10.1016/S0378
-4371(02)00772-0

Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of

‘small-world’ networks. Nature, 393, 440–442. https://doi.org
/10.1038/30918, PubMed: 9623998

Whittle, N., Fadok, J., MacPherson, K. P., Nguyen, R., Botta, P.,
Wolff, S. B., et al. (2021). Central amygdala micro-circuits
mediate fear extinction. Nature Communications, 12, 1–11.
https://doi.org/10.1038/s41467-021-24068-x, PubMed:
34230461

Wimsatt, W. C. (2007). Re-engineering philosophy for limited

beings: Piecewise approximations to reality. Harvard
University Press. https://doi.org/10.2307/j.ctv1pncnrh

Yeo, B. T. T., Krienen, F. M., Chee, M. W., & Buckner, R. L.
(2014). Estimates of segregation and overlap of functional
connectivity networks in the human cerebral cortex.
Neuroimage, 88, 212–227. https://doi.org/10.1016/j
.neuroimage.2013.10.046, PubMed: 24185018

Yeo, B. T. T., Krienen, F. M., Sepulcre, J., Sabuncu, M. R.,

Lashkari, D., Hollinshead, M., et al. (2011). The Organization
of the Human Cerebral Cortex Estimated by intrinsic
functional connectivity. Journal of Neurophysiology, 106,
1125–1165. https://doi.org/10.1152/jn.00338.2011, PubMed:
21653723

360

Journal of Cognitive Neuroscience

Volume 35, Number 3

D
o
w
n
o
a
d
e
d

f
r
o
m
h

t
t

:
/
/

d
i
r
e
c
t
.

i
t
.

e
d
u

/
j

o
c
n
a
r
t
i
c
e
–
p
d