The functional neuroanatomy of bipolar disorder: A consensus model

Article (PDF Available)inBipolar Disorders 14(4):313-25 · June 2012with57 Reads
DOI: 10.1111/j.1399-5618.2012.01022.x · Source: PubMed
Functional neuroimaging methods have proliferated in recent years, such that functional magnetic resonance imaging, in particular, is now widely used to study bipolar disorder. However, discrepant findings are common. A workgroup was organized by the Department of Psychiatry, University of Cincinnati (Cincinnati, OH, USA) to develop a consensus functional neuroanatomic model of bipolar I disorder based upon the participants' work as well as that of others. Representatives from several leading bipolar disorder neuroimaging groups were organized to present an overview of their areas of expertise as well as focused reviews of existing data. The workgroup then developed a consensus model of the functional neuroanatomy of bipolar disorder based upon these data. Among the participants, a general consensus emerged that bipolar I disorder arises from abnormalities in the structure and function of key emotional control networks in the human brain. Namely, disruption in early development (e.g., white matter connectivity and prefrontal pruning) within brain networks that modulate emotional behavior leads to decreased connectivity among ventral prefrontal networks and limbic brain regions, especially the amygdala. This developmental failure to establish healthy ventral prefrontal-limbic modulation underlies the onset of mania and ultimately, with progressive changes throughout these networks over time and with affective episodes, a bipolar course of illness. This model provides a potential substrate to guide future investigations and areas needing additional focus are identified.


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Consensus Paper
The functional neuroanatomy of bipolar
disorder: a consensus model
Strakowski SM, Adler CM, Almeida J, Altshuler LL, Blumberg HP,
Chang KD, DelBello MP, Frangou S, McIntosh A, Phillips ML,
Sussman JE, Townsend JD. The functional neuroanatomy of bipolar
disorder: a consensus model.
Bipolar Disord 2012: 14: 313–325. ! 2012 The Authors.
Journal compilation ! 2012 John Wiley & Sons A S.
Objectives: Fun ctional neuroi maging methods have prolif erated in
recent years, s uch that functi onal magnetic resonance imaging, i n
particular, is now widely used to study bipolar disorder. However,
discrepant findings are common. A workgroup was organized by t he
Depart ment of Psychiatry, University of Cincinnati (Cincinnati, OH,
USA) t o develop a consensus functional neuroanatomic model of bipolar
I disorder based upon the participant s! work as well as that of others.
Methods: Representat ives from several leading bipolar disorder
neuroimaging groups were organized to present an overview of their
areas of ex pertise as well as focused r eviews of existing data . The
workgr oup then developed a consensus model of the functional
neuroanatomy of bipolar disorder based upon thes e data.
Result s: Among the participants, a general consensus emerged that
bipolar I disorder arises from abnormalities in the structure and function
of key emotional control networks in the hu man brain. Namely,
disruption in early development (e.g., white matter connectivity and
prefro ntal pruning) within brain n etworks that modula te emotional
behavior leads to decreased connect ivity among ventral prefrontal
networks and limbic brain regions, espec ially the amygdala. This
developmental failure to establish healthy ventral prefrontal–limbic
modulation und erlies the onset of mania and ultimately, with progressive
changes throughout these networks over time and w ith aective ep isodes,
a bipolar course of illness.
Conclusions: This model provides a potential substrate to guide future
investigations and areas needing ad ditional focus are identified.
Stephen M Strakowski
, Caleb M
, Jorge Almeida
, Lori L
, Hilary P Blumberg
Kiki D Chang
, Melissa P DelBello
Sophia Frangou
, Andrew
, Mary L Phillips
Jessika E Sussman
and Jennifer D
Department of Psychiatry and Behavioral
Neuroscience, University of Cincinnati College of
Medicine, Cincinnati, OH,
Department of
Psychiatry, University of Pittsburgh School of
Medicine, Pittsburgh, PA,
Department of
Psychiatry and Biobehavioral Sciences, Semel
Institute for Neuroscience and Human Behavior,
The David Geffen School of Medicine, University of
California at Los Angeles,
Department of
Psychiatry, VA Greater Los Angeles Healthcare
System, Los Angeles, CA,
Department of
Psychiatry, Yale University School of Medicine,
New Haven, CT,
Pediatric Bipolar Disorders
Research Program, Division of Child and
Adolescent Psychiatry, Department of Psychiatry
and Behavioral Sciences, Stanford University
School of Medicine, Stanford, CA, USA,
Section of
Neurobiology of Psychosis, Department of
Psychosis Studies, Institute of Psychiatry, King’s
College, London,
Division of Psych iatry, School of
Molecular and Clinical Medicine, University of
Edinburgh, Edinburgh,
Department of
Psychological Medicine, Cardiff University School
of Medicine, Cardiff, UK
doi: 10.1111/j.1399-5618.2012.01022.x
Key words: amygdala bi polar disorder
connectivity functional magnetic resonance
imagin g (fMRI) neuroimaging prefrontal cortex
Received 28 October 2011, revised and accepted
for publication 18 March 2012
Corresponding author:
Stephen M. Strakowski, M.D.
Department of Psychiatry
University of Cincinnati College of Medicine
260 Stetson Suite 3200
Cincinnati, OH 45267-0559
Fax: 513-558-0187
Bipolar Disorders 2012: 14: 313–325
! 2012 John Wiley and Sons A/S
Steady advances in neuroimaging techniques have
produced a proliferation of neuroimaging research
in bipolar disorder during the past decade.
Although discrepancies exist among these research
reports, a common theme is emerging: namely, that
bipolar disorder arises from abnormalities within
brain systems that modulate emotional behavior.
However, more comprehensive functional neuro-
anatomic models of bipolar disorder, integrating
these various neuroimaging findings, have been
relatively uncommon. With these considerations in
mind, the University of Cincinnati Department of
Psychiatry and Behavioral Neuroscience organized
a workgroup of several leading bipolar disorder
neuroimaging programs in the USA and UK to
address these issues that arose from informal
discussions among the participants. This work-
group met in Miami, FL, USA in December 2010.
The goal of this meeting was to discuss the groups!
past and new work in order to develop a consensus
regarding the functional neuroanatomy for bipolar
I disorder. The group chose to limit itself to
discussion of bipolar I disorder, given that the data
in bipolar II disorder and related conditions are
relatively sparse. The group also primarily focused
on functional magnetic resonance imaging (fMRI)
and diusion tensor imaging (DTI) studies, con-
sistent with the stated goals. All manuscripts
arising from this workgroup were subjected to a
full peer-review process and the accepted papers
comprise this special issue of Bipolar Disorders
(1–9). The International Society for Bipolar Dis-
orders (ISBD) requested this group serve infor-
mally as an ISBD task force, to which group
members agreed. This article represents a summary
of and conclusions from those discussions. Conse-
quently, this article is not intended to be viewed as
an exhaustive review; instead, it represents the
synthesis of these investigators in order to provide
suggestions and a consensus model to guide future
research in this important area.
Bipolar disorder and emotional networks
As the workgroup approached this topic, we
started with the assumption that the brain systems
most likely to underlie bipolar disorder involve
those that modulate emotional control. This
assumption was based on the long clinical history
in which bipolar disorder is considered a primary
disorder of mood. Indeed, bipolar disorder is
defined by the occurrence of mania and character-
ized by recurring aective episodes. Nonetheless,
these aective symptoms are accompanied by
changes in cognition and neurovegetative symp-
toms, so that bipolar disorder could be considered
a disorder of energy, sleep, or cognitive processes.
As discussed throughout this special issue, neuro-
imaging studies have repeatedly identified abnor-
malities within brain emotional systems in bipolar
disorder, and there is no specific evidence suggest-
ing that approaching bipolar disorder as a primary
disturbance of mood is flawed. However, as the
field advances, other systems (e.g., those involved
in sleep regulation) may be relevant, perhaps in
some subgroups, so considerations toward this end
are warranted. Nonetheless, given the best avail-
able evidence, it is parsimonious to follow the
assumption that bipolar disorder arises from dys-
function of brain networks that modulate emo-
tional behavior.
Although the specific control of emotional
function in humans is not completely defined,
two ventral prefrontal networks appear to modu-
late emotional behavior (10–13). These networks
and related brain regions are extensively reviewed
by Townsend and Altshuler (1) and Blond et al. (2)
in this issue, but will be briefly summarized here.
Namely, these two networks are similarly orga-
nized to form iterative feedback loops that process
information and modulate the amygdala and other
limbic brain areas (Fig. 1; 1, 10–13). One network
originates in the ventrolateral prefrontal cortex
and is thought to modulate external emotional
cues, such as with aective face tasks (1, 14). The
other network originates in the ventromedial
(orbitofrontal) cortex and is thought to modulate
internal emotional stimuli, namely stimuli that
arise from internal feeling states, such as para-
digms that involve inducing an emotional response
to a specific cue (e.g., sadness in response to
personalized events) (10, 15). Additionally, dier-
ent voluntary and automatic (implicit) emotion
regulation subprocesses have been identified, cen-
tered on ventrolateral and ventromedial prefrontal
cortices, respectively (16). These networks serve as
likely substrates for the functional neuroanatomy
of bipolar disorder (Fig. 1), and they served as the
primary focus for the workgroup and consequently
for this summary (1).
Discussion of functional neuroimaging
The emotional networks illustrated in Figure 1 are
iterative feedback and feedforward systems that do
not have a "starting point! per se; however, since the
amygdala is central to these networks, we will
begin discussion there. The amygdala is an evolu-
tionarily ancient structure that is responsible for
Strakowski et al.
generating flight fight responses to threats. In
humans, it is heavily innervated by an overdevel-
oped (relative to other animal species) and evolu-
tionarily recent prefrontal cortex that likely serves
to nuance the fight flight response into the com-
plex emotional behavior that defines human inter-
actions. Along with other nearby medial temporal
structures (including the paralimbic cortex and
hippocampus), the amygdala is responsible for
emotion perception and regulation [as reviewed by
Blond et al. in this issue (2); also see Chen et al.
(14)]. In fMRI studies of bipolar disorder, amyg-
dala dysfunction is commonly observed.
Indeed, one of the most consistent functional
neuroimaging findings in bipolar disorder is exces-
sive amygdala activation in response to aective
faces during mania, compared with healthy sub-
jects (17). For example, Altshuler et al. (18) used a
facial aect matching task, against a baseline
geometric form matching task (19), to study
amygdala activation in bipolar manic and healthy
subjects. The investigators observed increased left
amygdala activation in the manic group during
facial aect matching. Several other groups re-
ported similar findings in bipolar disorder individ-
uals during depression and remission and with
other tasks (14, 20–24). For example, Strakowski
et al. (25) observed increased bilateral amygdala
activation in medication-free, early-course euthy-
mic bipolar disorder patients while they performed
a simple non-emotional continuous performance
task (CPT). Patients were asymptomatic for at
least one month prior to and one month after the
fMRI scan and had also not been receiving
medications for at least one month prior to the
scan, eliminating these clinical confounds. Consis-
tent with this finding, in their meta-analysis, Chen
et al. (14) found increased amygdala activation in
euthymia when region-of-interest (ROI) studies
were examined. Increased amygdala activation
during bipolar depression has also been reported
(23), although Almeida et al. (22) found excessive
amygdala activation only in response to specific
aective expressions (mildly sad). This latter study
suggested that amygdala over-activation in bipolar
disorder may occur only in response to specific
aective cues, i.e., that it is sensitive to the salience
and valence of the aective stimuli. Indeed, other
investigators have not observed increased amyg-
dala activation during depression (26, 27) and have
suggested that amygdala over-activation may be
state dependent, as reviewed in this issue (1). This
view is not consistent, however, with a recent large
meta-analysis of fMRI studies in bipolar disorder
Even during mania, not all studies observed
increased amygdala activation in bipolar versus
healthy subjects. Strakowski et al. (28) used the
continuous performance task with emotional and
neutral distracters (CPT-END) (10) to study
Fig. 1. Schematic of the proposed ventrolateral and ventromedial prefrontal networks underlying human emotional control [adapted
with permission from Oxford University Press (17)]. G. = globus; PFC = prefrontal cortex; OFC = orbitofrontal cortex;
BA = Brodmann! s area.
Functional neuroanatomy of bipolar disorder
healthy and bipolar manic subjects. The distracters
were neutral and emotionally negative images
taken from the International Aective Pictures
System (IAPS). In this study, the manic subjects
exhibited relatively blunted amygdala response to
distracters as compared with healthy subjects. This
observation suggested that, in the context of a
predominantly attentional task, the amygdala was
dysregulated, but dierently than in facial aect
tasks, despite the emotional distracters. However,
this finding might nonetheless reflect an underlying
over-activation of the amygdala during mania (i.e.,
at baseline), which limited the additional amygdala
response that could occur, leading to an apparent
blunted activation. The authors suggested that
restricted amygdala response flexibility might con-
tribute to the apparent discrepancies in study
findings as well as the emotional dyscontrol of
bipolar illness (17, 28). Because typical fMRI
studies are limited in that absolute activation
cannot be assessed, but instead activation always
represents a relative (subtraction) measure between
two study conditions, interpreting these types of
discrepancies among studies can be dicult.
Perhaps the more parsimonious view of fMRI
findings in bipolar disorder, then, is that, indepen-
dent of the direction of abnormalities, across
bipolar mood states amygdala activation function
is abnormal in response to a variety of stimuli, and
is sensitive to both mood state and the emotional
valence of the task (14, 17, 28). This possibility
suggests that future studies of amygdala function
in bipolar disorder need to examine the impact of
mood interacting with the emotional valence of
stimuli in order to clarify the abnormal reactivity
of this structure in bipolar disorder. Nonetheless,
even with inconsistencies across studies, because
many investigators found that amygdala activation
is dierent in subjects with bipolar disorder than in
healthy subjects, even in unaected relatives of
bipolar disorder patients (29), amygdala dysfunc-
tion appears to be a feature of this condition.
As noted previously, amygdala function in the
human brain is modulated by the prefrontal cortex,
in particular, ventral prefrontal regions (i.e., the
orbitofrontal and ventrolateral prefrontal cortex).
Consequently, consistent with evidence of dysfunc-
tional amygdala activity in bipolar disorder,
abnormalities in the ventral prefrontal cortex are
also commonly observed. Specifically, decreased
fMRI activation in both lateral and medial (or-
bitofrontal) ventral prefrontal brain regions has
been reported across a variety of cognitive tasks
and mood states (e.g., 18, 28, 30–33) and also with
positron emission tomography (PET) (34). Indeed,
ventrolateral prefrontal cortex (specifically inferior
frontal gyrus) abnormalities were observed in a
recent meta-analysis of fMRI data (14). For
example, as reported in this issue, Townsend et al.
(7) studied 32 euthymic bipolar disorder subjects
and 30 healthy subjects during a Go NoGo
response inhibition task using fMRI. The euthymic
bipolar disorder group demonstrated significantly
less inferior frontal cortical activation. Similarly, in
this issue, Liu et al. (6) studied 76 bipolar disorder
subjects across a variety of mood states and 58
healthy subjects while performing a facial aect
task. Ventral cortical activation was decreased in
the bipolar disorder group independent of mood
state, although laterality dierences were observed;
i.e., patients in elevated mood states exhibited
lower activation in the right ventral prefrontal
cortex, whereas depressed patients demonstrated
decreased activation in the left. Other studies of
bipolar disorder patients who are asymptomatic
suggest that some prefrontal recovery may occur
during euthymia to compensate for amygdala over-
activation in order to maintain attentional and
memory performance (25, 35). Together these
studies suggest that amygdala dysregulation cou-
pled with ventral prefrontal under-activation may
indicate failure of healthy ventral prefrontal net-
work modulation of the limbic brain in bipolar
disorder, potentially providing a functional neuro-
anatomic basis for aective symptoms. Eectively,
with diminished prefrontal modulation, the limbic
brain is hypothesized to be dysregulated, leading to
the emotional extremes of mania, depression, and
mixed states. This hypothesis is developed in more
detail in this issue by Blond et al. (2).
Consistent with this hypothesis, studies have
reported dierences in functional connectivity be-
tween ventral prefrontal regions and the amygdala
in bipolar compared with healthy subjects. Func-
tional connectivity can be measured in several ways,
but in general it provides an assessment of how brain
activation correlates among dierent regions over
time (17). Higher temporal correlations between two
or more brain areas suggest that they are more
strongly linked, i.e., functionally connected (17).
Foland et al. (30) found decreased functional con-
nectivity between the amygdala and lateral ventral
prefrontal cortex in manic compared with healthy
subjects, a finding replicated by Chepenik et al. (36).
Abnormalities in functional connectivity between
the ventral prefrontal cortex and amygdala may also
occur during depression. Since the prefrontal cortex
remains physically connected with the limbic brain
in bipolar individuals, these findings tell us that
synchronization between the prefrontal cortex and
amygdala is disrupted in depression and mania. For
example, work by Almeida et al. (37) measuring
Strakowski et al.
eective connectivity between the amygdala and
ventromedial prefrontal cortex suggested a pattern
of abnormal bilateral disconnectivity between the
ventromedial prefrontal cortex and amygdala in
bipolar depression in response to positive emotional
(happy) faces, a pattern that did not occur in
individuals with unipolar depression. However, this
same disconnectivity was not seen with other mood
states, suggesting that the loss of prefrontal–amyg-
dala synchronization was sensitive to the valence of
the stimulus. As another example, in this issue
o-Miranda et al. (8) applied a novel discrim-
ination paradigm to use fMRI to dierentiate
between bipolar and unipolar depression. Specifi-
cally, dierential fMRI activation responses be-
tween happy and neutral faces seemed to distinguish
depressed bipolar disorder subjects from individuals
with unipolar depression and from healthy subjects.
Although these findings suggested that as fMRI and
functional connectivity techniques progress they
may develop diagnostic utility, namely, the ability to
distinguish bipolar from unipolar depression (38);
understanding how brain regions interact and net-
works function remains rudimentary. Also, as
reviewed elsewhere in this issue (2), white matter
abnormalities, observed with diusion tensor imag-
ing, in particular, suggest that impairments in
functional connectivity observed in bipolar disorder
may have a specific neuroanatomic basis, namely
loss of white matter connections among emotional
brain regions that may predate the onset of illness
and progress with recurrent aective episodes (39–
43). Again, these failures of development may put
individuals at risk for inadequate prefrontal modu-
lation of limbic brain activity, leading to dysregula-
tion of mood and the development of extreme mood
As illustrated in Figure 1, ventral prefrontal
cortical areas that modulate human emotional
function do so within a structure of relatively
independent prefrontal-striatal-pallidal-thalamic
iterative circuits (17). Consistent with the previ-
ously discussed findings in the ventral prefrontal
cortex and amygdala, investigators have com-
monly observed abnormal activation (often exces-
sive) throughout subcortical structures in these
networks, particularly during mania and euthymia
[for reviews see Marchand and Yurgelun-Todd
(44) and Blond et al. in this issue (2)]. However,
a recent meta-analysis found both putamen under-
activation and basal ganglia over-activation,
complicating this interpretation (14). Consistent
with the former, Liu et al. (6) (in this issue)
observed decreased ventral striatal responses to
happy and neutral faces across mood states in a
large group of bipolar disorder subjects. Similarly,
in this issue, Townsend et al. (7) observed de-
creased activation during a response inhibition task
in bipolar disorder subjects compared with healthy
subjects in several subcortical brain regions includ-
ing the left caudate, bilateral globus pallidus,
lateral putamen, and right thalamus. Discrepancies
among studies in relative activation dierences
between bipolar disorder and healthy subjects may,
as with the amygdala, support a model of variable
dysregulation depending on characteristics of the
sample, the task (i.e., valence and salience), or
other clinical or demographic factors. For
example, in this issue, Jogia et al. (9) observed a
significant sex-by-diagnosis interaction within the
basal ganglia during incentive decision making in
samples of bipolar disorder and healthy men and
women. Regardless, the striatum and thalamus are
critical integrative structures within prefrontal
iterative circuits and provide "cross-talk! among
otherwise relatively independent prefrontal net-
works (17). Consequently, disruption of this inte-
grative function may contribute to dysregulation of
emotional processes in bipolar disorder.
Another integrative structure that may contribute
to the functional neuroanatomy of bipolar disorder
is the anterior cingulate cortex. The anterior cingu-
late cortex sits at the intersection of dorsal (cogni-
tive) and ventral (emotional) prefrontal functions,
thereby integrating information processing (10, 28).
Specifically, the ventral anterior cingulate, including
subgenual regions, is responsive to emotional stim-
uli and the dorsal cingulate more responsive to
cognitive stimuli; the integration may therefore
occur along a gradient along this structure. Previous
work suggested that emotional processing and
cognitive processing are principally modulated by
ventral and dorsal prefrontal regions, respectively,
and interact in such a manner as to function
reciprocally; this reciprocal interaction may be
modulated by the anterior cingulate (10, 45, 46). In
imaging studies of bipolar disorder, the anterior
cingulate frequently activated dierently in bipolar
disorder than healthy subjects (e.g., 6, 18, 28, 46).
Often, but not in every study, anterior cingulate
activation was increased in bipolar disorder subjects
during mania compared with healthy subjects,
whereas the converse occurred during bipolar
depression (18, 47–49). During mania, the anterior
cingulate also demonstrated abnormal functional
connectivity with the amygdala (50). During euthy-
mia, the emotional aspects of the anterior cingulate
may be over-activated (51), whereas the more
cognitive aspects are under-activated (52), indicating
a possible dissociation within the anterior cingulate
to dierent types of stimuli in dierent bipolar mood
states (17).
Functional neuroanatomy of bipolar disorder
Taken together, fMRI studies suggest dysfunc-
tion throughout the putative emotional brain
networks illustrated in Figure 1. This observation
is important as it does not support single-region
models of bipolar disorder, but instead suggests a
more general dysregulation of these networks.
Unfortunately, fMRI results are not always con-
sistent and several factors, including the type of
cognitive task used as a probe and the mood state
of the patients, appear to impact dierences
between bipolar disorder and healthy subjects. As
reviewed elsewhere, there are relatively few studies
using fMRI to compare subjects with bipolar
disorder to other psychiatric groups, such as
subjects with schizophrenia (5) or unipolar depres-
sion (38). Nonetheless, the existing studies suggest
that disruption in these ventral prefrontal-striatal-
pallidal-thalamic iterative networks as they modu-
late the amygdala may be relatively unique to
bipolar I disorder. Clearly, more studies are needed
across diagnostic groups to substantiate this
assumption. Moreover, as noted previously, the
use of pattern recognition techniques with whole-
brain functional neuroimaging in response to
emotional stimuli shows promise as a new method
to dierentiate bipolar from unipolar depressed
individuals case by case (8).
Bipolar disorder, imaging, and developmental
Bipolar disorder most commonly begins in late
adolescence and may include pre-syndromal aec-
tive, attentional, and behavioral disruptions prior
to the first manic or depressive episode (53, 54).
Moreover, during the first few episodes, bipolar
disorder is progressive, in that euthymic periods
between aective exacerbations steadily shorten
(17). Together these clinical observations suggest
that the neurophysiology of bipolar disorder is
progressive, particularly early in the illness; conse-
quently, studies of early course and at-risk subjects
may be particularly informative toward clarifying
this neurophysiology prior to the confounding
eects of medication exposure [see in this issue
Hafeman et al. (4)] and illness course. Schneider
et al. (3) discuss in detail in this issue these study
Neuroanatomic studies of young people at-risk
for bipolar disorder (i.e., with bipolar disorder
parents) have had mixed results, but often show
few dierences compared with youth of healthy
parents (55). However, two studies found increased
brain volumes in prefrontal (56) and paraphippo-
campal cortices (57). These authors suggest that
increased cortical volume might represent failed
pruning or other developmental processes prior to
illness onset (55). Consistent with this suggestion,
several studies reported white matter abnormalities
prior to illness onset in at-risk groups. These
findings include increased white matter hyperin-
tensities (58, 59), and evidence of decreased callosal
myelination using the proxy of T1 signal intensity
in at-risk youth compared with youth of healthy
parents (60). Using DTI to examine white matter
structure in more detail, several investigators
observed abnormalities in at-risk youth, particu-
larly in white matter tracts involved in the connec-
tions among regions of the emotional networks of
Figure 1. Versace et al. (41) used DTI to study
healthy youth with bipolar disorder parents and
healthy youth with healthy parents. They found
that, even prior to any symptoms or behavioral
disturbances, the at-risk group exhibited white
matter abnormalities in the corpus callosum and
temporal white matter tracts. This finding was also
reported in a large cohort by Sprooten et al. (61).
Moreover, Frazier et al. (40) observed decreased
fractional anisotropy (FA) in the superior longitu-
dinal fasciculus in children at risk for bipolar
disorder compared with healthy subjects; decreased
FA may represent loss of bundle coherence within
these tracts. This study also examined youth who
had developed bipolar disorder; the aected youth
demonstrated significantly more widespread FA
reductions throughout prefrontal and frontal
areas. Consistent with that observation, Adler
et al. (39) found decreased FA, suggesting dis-
rupted organization, in prefrontal white matter
tracts in adolescent first-episode manic patients,
similar to findings by Kafantaris et al. (62) and
Barnea-Goraly et al. (63). Together, these studies
suggest that white matter abnormalities (i.e., con-
nections among brain regions) may exist well
before illness onset, underlying and potentially
contributing to additional developmental abnor-
malities during adolescence, including the loss of
prefrontal functional connections with the amyg-
dala. These alterations in neurodevelopment may
lead to the onset of aective dysregulation, and the
eventual onset of the first manic episode. The
paucity of longitudinal studies, however, makes
this suggestion dicult to directly evaluate.
Reduced amygdala volume has been consistently
reported in bipolar disorder compared with healthy
adolescents [for a review see Schneider et al. (3) in
this issue, as well as Pfeifer et al. (64)]. Recently,
Bitter et al. (65) found that, during the year after a
first manic episode, bipolar disorder patients failed
to demonstrate normal amygdala growth seen in
both healthy and attention-deficit hyperactivity
disorder (ADHD)-aected youth. This early course
Strakowski et al.
eect may underlie the initial development and
progression of illness. However, later in life, amyg-
dala volumes have been reported to be larger in
bipolar disorder than in healthy subjects (17, 64);
these findings suggest dysregulated amygdala
growth might be a feature of bipolar disorder
throughout the early course, although medication
eects may contribute to later-course findings (66).
In contrast, amygdala volumes do not appear to be
abnormal in at-risk youth prior to illness onset (55).
Perhaps not surprising, then, ventral prefrontal
reductions have also been found in bipolar disor-
der youth that are inversely correlated with age,
i.e., that appear to progress during this critical
developmental time (67, 68). Similar relationships
between age and regional brain structural volumes
have been observed for the striatum (69). Consis-
tent with progressive changes as the first manic
episode approaches, Gogtay et al. (70) found
decreases in subgenual cingulate cortex volumes
after, but not before, illness onset in at-risk youth,
suggesting this change may be associated with the
onset of mania. Functional imaging studies in
bipolar disorder youth typically report abnormal-
ities throughout the emotional networks of
Figure 1, similar to adults, although prefrontal
cortical function may be relatively spared in the
early course of illness (55). Together, these findings
suggest that abnormalities in the networks of
Figure 1 in bipolar disorder progress during the
early course of illness, consistent with the corre-
sponding decrease in euthymic intervals between
aective episodes (17). These clinical and neuro-
imaging features support a model of abnormal
anatomic connections between prefrontal cortex
and limbic brain structures that evolve into the
functional disconnection of mania during adoles-
cence, which progresses over time to produce a
recurrent, chronic illness of bipolar disorder. Many
of these considerations have been reviewed in detail
in this issue by Schneider et al. (3). As noted in that
review, conclusions about the specific neurodevel-
opmental trajectories that lead to bipolar disorder
remain inadequately studied and poorly under-
stood. In particular, there is a distinct need for
prospective longitudinal studies of individuals at
risk for bipolar disorder to identify deviations from
healthy neurodevelopment and the associated
behavioral manifestations of these changes.
Summary and future directions
Discussion in this workgroup led to the hypothesis
that disruption in early development (e.g., white
matter connectivity and prefrontal pruning) within
brain networks that modulate emotional behavior
(Fig. 1) leads to decreased connectivity between
ventral prefrontal networks and limbic brain
structures, including (perhaps especially) the amyg-
dala. This loss of connectivity is associated with
abnormal functional responses of emotional net-
works to various cognitive and emotional tasks in
imaging studies, as well as abnormal development
of the component brain regions (e.g., failure of the
amygdala to mature normally and disruption of
prefrontal modulation of limbic structures). Dys-
regulation of the limbic brain then leads to loss of
emotional homeostasis, resulting in mood instabil-
ity. In the absence of healthy prefrontal-striatal-
pallidal-thalamic-limbic brain networks that can
restore this homeostasis, bipolar disorder individ-
uals are at risk for developing extreme mood states
and switching among mood states, as well as
developing mixed states as dierent unregulated
systems oscillate in the absence of homeostatic
control. During euthymia, recovery of prefrontal
function, along with compensation from other
brain regions as observed by Strakowski et al. (14),
temporarily restores homeostasis; nonetheless, the
underlying functional neuroanatomic abnormali-
ties leave the bipolar disorder individual at risk for
disruption of this fragile homeostasis under even
minor stress in the face of "stably unstable!
prefrontal-striatal-thalamic-amygdala mood net-
works (17). In short, we then hypothesize that
developmental failure to establish healthy ventral
prefrontal–amygdala networks underlies the onset
of mania and ultimately, with progressive changes
throughout these networks over time, a bipolar
course of illness (17). This model provides a
potential substrate to guide future investigations
using fMRI as well as other imaging modalities.
Functional MRI is data rich; a single study will
produce thousands of measurements from voxels
throughout the brain. Consequently, investigators
are faced with the challenge of determining which
measurements signify a meaningful event. Typical
approaches (e.g., statistical parametric mapping)
involve combinations of data clustering and con-
trolling for multiple comparisons to identify voxels
that dier from other voxels and that indicate a
response to a task or dierence among subjects or
groups. However, most approaches are agnostic
and make no assumptions that activation in any
particular voxel is any more meaningful than in
any other voxel. Consequently, by its very nature,
these approaches are exploratory and better
designed for hypothesis generating than hypothesis
testing. Moreover, these approaches often provide
dicult to interpret findings in which activation or
activation dierences occur in brain regions
distinct from those that other neuroscience
Functional neuroanatomy of bipolar disorder
investigations suggest are prominently involved in
a given cognitive task or for a specific pathology
(e.g., bipolar disorder). These exploratory voxel-
wise approaches dominate the bipolar neuroimag-
ing literature, so that it is almost certain that these
limitations contribute to the variability seen in
fMRI studies of bipolar disorder.
ROI approaches address many of these short-
comings and can be used to directly test specific
functional neuroanatomic hypotheses, like those
presented here. However, these approaches are
conservative by definition, limiting discovery of
novel findings that might better explain the func-
tional neuroanatomy of bipolar disorder than our
current models. Indeed, the neural substrates
underlying behaviors impacted by bipolar disorder
are incompletely understood. Perhaps the solution
to these limitations is to combine ROI and
voxelwise techniques, thereby ensuring specific
testing of hypotheses (ROI approach) to support
or eliminate functional neuroanatomic models,
while continuing to explore alternative models
(voxelwise techniques) through novel hypothesis-
generating findings, while carefully defining the
role of each approach. Regardless, as suggested by
Logothetis (71), fMRI findings must be interpreted
within the context of known existing networks and
network functions; hence our reliance in this
discussion on the networks illustrated in Figure 1.
It is almost certain that these issues, coupled
with the heterogeneity of bipolar disorder patients
and, at times, insucient clinical rigor when
defining phenotypes, contribute to disparity among
studies. Dierences in data analytic techniques and
among cognitive paradigms further contribute
variability across studies. Medication exposure
may complicate interpretation, as reviewed in
detail in this issue by Hafeman et al. (4).
With these considerations in mind, we have
several suggestions for future research aimed at
defining the functional neuroanatomy of bipolar
Study narrowly defined groups. A significant
complication across and within studies is the
inclusion of heterogeneous bipolar and aec-
tively ill patient samples. Combining unipolar
and bipolar disorder, and then various types of
bipolar disorder (e.g., type I, type II, not
otherwise specified, etc.) may help to increase
statistical power by virtue of ease of recruitment
and increasing sample sizes, but almost cer-
tainly introduces considerable noise. Unlike
genetic studies in which a broader inclusion of
subjects might help to identify a phenotypically
variably expressing gene, there are few advan-
tages in neuroimaging to studying heteroge-
neous rather than narrowly defined samples.
Neuroimaging research benefits from well-
designed studies of similar subjects to minimize
confounds. Consequently, for the field to move
forward, attempts to define more homogeneous
clinical samples and then compare across these
samples are needed to better understand bipolar
neurophysiology. For example, there is a lack
of well-powered studies comparing bipolar I
and II disorders across dierent types of cog-
nitive probes and mood states. There are few
studies examining the impact of comorbid drug
and alcohol abuse on neuroimaging parameters,
even though these types of bipolar disorder
patients may have diering etiologies (72).
Studies of carefully defined subtypes and sub-
groups are needed to allow investigators to
better understand the impact of variable clinical
confounds and symptomatic expression in order
to interpret dierences between groups and
across studies. Additionally, studies of carefully
defined mood states to create symptomatically
homogeneous samples, to again control poten-
tial confounds of symptomatic epiphenomena,
are needed. Very large samples will be needed
for this type of work, but a number of groups
have been scanning subjects for years and are
likely developing such samples. Alternatively,
working across institutions and geography by
collaborating with common imaging ap-
proaches and cognitive paradigms might allow
these samples to be collected relatively quickly.
The Research Domain approach suggested by
the National Institute of Mental Health
(NIMH) provides one guideline toward this
type of homogeneity. These types of studies are
unquestionably harder than recruiting "all com-
ers!, but the field has moved to the point where
the easy studies have been done and advances
will only occur with extra eort.
Longitudinal studies across mood states. The
model proposed here suggests that lack of
emotional homeostatic mechanisms related to
prefrontal modulation of limbic brain underlies
mood instability, mood switching, and devel-
oping extreme mood states. Specific studies of
patients as they experience dierent mood states
and symptom domains will aid our understand-
ing of how these aective states are represented
in activation maps of the bipolar brain. There
have now been a number of studies that include
patients in several mood states within a single
sample [e.g., Liu et al. (6) in this issue], which is
one approach to identify potential state- and
trait-related activation eects. However, these
Strakowski et al.
comparisons are limited by the inevitable dif-
ferences among the subjects in the dierent
moods states. To advance this work, longitudi-
nal studies are needed within individual subjects
as they recover from mania or depression,
switch into an alternate aective episode,
develop mixed states, or achieve and maintain
euthymia. Although medication eects cannot
be ignored, they have likely been overstated [see
Hafeman et al. (4)], so that studies can be
designed around this potential confound.
Again, these types of studies are dicult, but
are a critical step to understand how brain
activation patterns change across the dynamic
course of bipolar illness.
Longitudinal studies of treatment response. In
this issue, Hafeman et al. (4) suggest that
medication eects minimally impact fMRI
brain measurement dierences between bipo-
lar disorder and healthy subjects; however,
they also additionally suggest that medication
eects may be normalizing. Studying medica-
tion eects as they relate to symptomatic
improvement provides opportunities to under-
stand how the functional neuroanatomy of
bipolar disorder changes in response to treat-
ment and symptom resolution. A few studies
to this end have been performed (4), but the
processes leading to symptom resolution
remain poorly understood. Moreover, lithium
is a unique treatment in psychiatry in that it
is relatively specific for a specific condi-
tion—namely, bipolar mania. Lithium, then,
could serve as a pharmacological probe in
bipolar disorder that may have unique eects
in this population to help define the neuro-
physiology of illness. Indeed, recruiting a
lithium-responsive sample would be one way
to narrow the phenotype. Larger studies of
the impact of dierent treatment interventions
on brain function across time and among
medications are needed to define how medi-
cations improve symptoms in bipolar disor-
der. Moreover, such studies oer the promise
of identifying treatment response markers and
predictors to eventually guide treatment. Since
most psychotropic medications have delayed
responses in bipolar mood states, studies with
scans repeated frequently in early treatment
(e.g., every two to three days during the first
two weeks) might identify initial changes in
function neuroanatomy (e.g., restoration of
ventrolateral prefrontal connectivity to the
amygdala) that predict treatment response.
These studies may also clarify how our
treatments succeed (or fail).
Studying early course progression. It is well
established that the early course of bipolar
disorder is progressive (17). These clinical
observations suggest a progression in the neu-
rophysiology of the illness as it evolves from a
single event (manic episode) to a recurring
condition that includes periods of mania,
depression, mixed states, and remission. Based
on the considerations raised in this article, we
suggest that during the early course of bipolar
illness ventral prefrontal cortical regions fail to
form typical connectivity with and modulation
of limbic brain areas to form healthy iterative
modulatory prefrontal-striatal-pallidal-tha-
lamic networks. As episodes occur, ventral
prefrontal modulation and emotional homeo-
stasis further deteriorate, leading to the illness
course progression observed. Whether this pro-
gression represents direct eects of aective
episodes or instead failure of healthy develop-
ment (or the intersection of these two) is not
known. Longitudinal studies within individual
subjects during early illness progression to
identify if and how these disruptions in devel-
opment occur are critically needed. These stud-
ies, again dicult, have the potential to provide
significant gains by controlling for many other
confounding features (e.g., long-term medica-
tion exposure, accumulated psychosocial
impairment, and substance abuse). Specific
studies are needed of the development of the
networks–shown in Figure 1–in both healthy
and at-risk youth and recent-onset bipolar
disorder subjects to address these consider-
ations. Moreover, these latter two populations
are ideal to investigate the intersection of the
eects of genetic risks and structural and
functional brain changes on illness expression
and course.
Testing specific hypotheses. Neuroimaging
studies using fMRI in bipolar disorder have
been predominantly designed using voxelwise
comparison techniques. As noted, by definition,
these are exploratory and hypothesis-generating
analytic methods. To move forward, the field
now needs to advance toward examining how
specific networks function in the various phases
and across the course of bipolar disorder using
more focused analytic models. Functional and
eective connectivity models as reviewed by
Blond et al. (2), or pattern analysis approaches
as described in the original paper by
o-Miranda et al.(8) provide suggestions
toward this end.
Advancing cognitive probes. As noted, part of
the variability across studies likely reflects
Functional neuroanatomy of bipolar disorder
dierences in how emotional networks respond
to probes of varying emotional salience and
valence. This issue becomes even more complex
across dierent mood states that might impact
these responses and relationships. Moreover,
probes have not been consistently employed
because they have been shown to specifically
activate the regions or networks of interest, but
rather have often been incorporated simply
because they have commonly been used. There
is no defined cognitive probe that identifies a
specific cognitive impairment or functional
activation abnormality for bipolar disorder in
any phase; consequently, future research eorts
need to be developed for refining and narrowing
the eects of various probes, first, in healthy
populations to ensure that the probe activates
the networks of interest, and then, across the
phases of narrowly defined bipolar disorder
groups to understand the interactions of stimuli
salience and valence. With this progress, com-
parisons across fMRI studies will become more
Integrating other imaging modalities. Blood-
oxygen-level-dependent (BOLD) fMRI, by
itself, provides important functional neuroan-
atomic information about bipolar disorder, but
is limited in its ability to understand mecha-
nisms underlying observed dierences from
healthy subjects. It is now well established that
groups of bipolar disorder subjects have dier-
ences in these BOLD fMRI patterns compared
with healthy subjects, but what these dierences
mean mechanistically is not known. To advance
this work from where in the brain problems
exist to what those problems are, integration
with other imaging (and genetic) techniques is
the necessary next step. Multi-modal imaging
integrating fMRI with MRS, connectivity mea-
sures, DTI, and other structural imaging and
techniques, particularly within the longitudinal
and narrowed designs we have suggested, oer
significant promise to really advance our under-
standing of the pathophysiology of bipolar
Comparing with other conditions. Finally, most
neuroimaging studies have compared bipolar
disorder samples to healthy subjects. Although
these studies have provided the bulk of the
information leading to the discussions in this
article, in the end, these studies tell us nothing
about the specificity of findings to bipolar
disorder versus general eects of mental illness.
Indeed, abnormalities within the networks of
Figure 1 do not appear to be unique to bipolar
disorder. Many more carefully designed studies
comparing bipolar disorder to related groups
are needed, as reviewed in this issue by Whalley
et al. (5) (schizophrenia) and as exemplified by
o-Miranda et al. (8). Again, these studies
are often more dicult as individual research
groups tend to, necessarily, maintain a narrow
diagnostic focus, so that cross-group collabo-
rations (e.g., between bipolar disorder and
schizophrenia research programs) are required.
These studies are, however, the only way that
abnormalities specific to bipolar disorder can be
In summary, we suggest that to advance our
understanding of bipolar disorder using neuroim-
aging, studies will need to move toward testing
specific abnormalities within recognized brain net-
works, such as the emotional networks of Figure 1,
ideally across mood states and over time within the
same subjects. Patient samples must be more care-
fully evaluated and narrowly defined clinically, and
contrasted with similarly narrowly defined alterna-
tive patient samples. Longitudinal appro-
aches will help to better define what brain changes
are directly associated with specific course of illness
features, which is particularly relevant for under-
standing a dynamic and progressive condition like
bipolar disorder. Similarly, longitudinal studies of
treatment response can clarify the impact that
medications have on brain imaging measures in this
patient population as well as potentially clarify
mechanisms underlying treatment response. Studies
dierentiating patients who do or do not respond to
a particular treatment may also identify treatment
response markers or predictors, as well as define
what is unique to bipolar disorder with certain
relatively specific treatments (e.g., lithium or lamo-
trigine). Studies of at-risk youth and early-course
patients provide the opportunity to define the initial
neurophysiological progression that leads to the
onset of illness, and that may, therefore, represent
the functional neuroanatomic etiology of bipolar
disorder. By framing investigations within the con-
text of developmental functional neuroanatomic
models, studies may be able to increase compara-
bility. As you read through this special issue, please
keep these considerations in mind and be challenged
to develop better approaches to advance our under-
standing of the complexities that underlie the
neurophysiology of this uniquely human condition.
As we imply here, many of the easy imaging studies
have already been done, so in order for the field to
properly advance more sophisticated study de-
signs—that may also be more expensive—will be
needed. We believe the field is growing, is strong, and
is up to this challenge.
Strakowski et al.
This workgroup was supported in part by the Department of
Psychiatry, University of Cincinnati College of Medicine,
Cincinnati, OH, USA. This manuscript was supported in part
by NIH grants P50 MH077138 (SMS); R01 MH076971 and R01
MH088371 (MLP); 2R01MH070902, 1RC1MH088366, and
R01MH069747 (HPB); 5R21MH075944, 1K24MH001848, and
1R01MH084955 (LLA); R01 MH077047 (KDC); R01
MH080973 (MPD); and R01 MH078043 (CMA).
The authors of this paper do not have any commercial
associations that might pose a conflict of interest in connection
with this manuscript.
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Functional neuroanatomy of bipolar disorder
    • "A similar, though less marked failure of de-activation, again with no activation changes, was also seen in the unaffected siblings of patients with the disorder. At first sight our failure to find activation changes in euthymic bipolar patients seems surprising, since the disorder is widely recognized as being associated with a pattern of task-related hypoactivations and hyperactivations (Strakowski et al. 2012). However, it should be noted that these changes have mostly been documented in patients in the manic or depressed phase. "
    [Show abstract] [Hide abstract] ABSTRACT: Background: Relatively few studies have investigated whether relatives of patients with bipolar disorder show brain functional changes, and these have focused on activation changes. Failure of de-activation during cognitive task performance is also seen in the disorder and may have trait-like characteristics since it has been found in euthymia. Method: A total of 20 euthymic patients with bipolar disorder, 20 of their unaffected siblings and 40 healthy controls underwent functional magnetic resonance imaging during performance of the n-back working memory task. An analysis of variance (ANOVA) was fitted to individual whole-brain maps from each set of patient-relative-matched pair of controls. Clusters of significant difference among the groups were used as regions of interest to compare mean activations/de-activations between them. Results: A single cluster of significant difference among the three groups was found in the whole-brain ANOVA. This was located in the medial prefrontal cortex, a region of task-related de-activation in the healthy controls. Both the patients and their siblings showed significantly reduced de-activation compared with the healthy controls in this region, but the failure was less marked in the relatives. Conclusions: Failure to de-activate the medial prefrontal cortex in both euthymic bipolar patients and their unaffected siblings adds to evidence for default mode network dysfunction in the disorder, and suggests that it may act as a trait marker.
    Article · Jun 2016
    • "However, evidence shows that emotions change in subjects with mania dis- order [5][6][7]. The result presented in [8] suggests, using fMRI, that abnormal modulation between ventral prefrontal and limbic regions, especially the amygdala , are likely contribute to poor emotional regulation and mood symptoms. We hipothesize that this poor emotional regulation and mood symptoms must be detected in speech, and thus the identification of patients should be possible analysing the emotional intensity in speech. "
    [Show abstract] [Hide abstract] ABSTRACT: The massive availability of digital repositories of human thought opens radical novel way of studying the human mind. Natural language processing tools and computational models have evolved such that many mental conditions are predicted by analysing speech. Transcription of interviews and discourses are analyzed using syntactic, grammatical or sentiment analysis to infer the mental state. Here we set to investigate if classification of Bipolar and control subjects is possible. We develop the Emotion Intensity Index based on the Dictionary of Affect, and find that subjects categories are distinguishable. Using classical classification techniques we get more than 75\% of labeling performance. These results sumed to previous studies show that current automated speech analysis is capable of identifying altered mental states towards a quantitative psychiatry.
    Full-text · Article · Jun 2016 · Journal of Affective Disorders
    • "Recalculation without the subsample yielded similar results as shown in the Supplement to this article. In sum, our results lend additional support to recent neuroanatomical models, suggesting altered frontal WM integrity to be relevant for behavioral dysregulation and altered neurocognitive functioning in BD-I (Strakowski et al., 2012; Phillips and Swartz, 2014). We cannot establish disease-specific markers for BD-I, but could show a link between cingulum WM integrity and risk-taking across BD-I and HC, akin to associations found for other mental disorders with similar symptom clusters. "
    [Show abstract] [Hide abstract] ABSTRACT: Objective: This study investigated how frontal white matter (WM) alterations in patients with bipolar I disorder (BD-I) are linked to motivational dysregulation, often reported in the form of risk-taking and impulsivity, and whether structure-function relations in patients might differ from healthy subjects (HC). Method: We acquired diffusion data from 24 euthymic BD-I patients and 24 controls, to evaluate WM integrity of selected frontal tracts. Risk-taking was assessed by the Cambridge Gambling Task and impulsivity by self-report with the Barratt-Impulsiveness Scale. Results: BD-I patients displayed significantly lower integrity in the right cingulum compared to HC. They also showed more risk-taking behavior and reported increased trait-impulsivity. Risk-taking was negatively associated with WM integrity in the right cingulum. Impulsivity was not related to WM integrity in investigated tracts. Together with age and sex, FA in the cingulum explained 25% of variance in risk-taking scores in all study participants. The left inferior fronto-occipital fasciculus (IFOF) was specifically predictive of risk-taking behavior in BD-I patients, but not in HC. Limitations: The employed parameters did not allow us to specify the exact origin of WM changes, nor did the method allow the analysis of specific brain subregions. Also, sample size was moderate and the sample included patients with lifetime alcohol dependence/abuse, hence effects found need replication and have to be interpreted with caution. Conclusion: Our results further strengthen recent models linking structural changes in frontal networks to behavioral markers of BD-I. They extend recent findings by showing that risk-taking is also linked to the cingulum in BD-I and HC, while other prefrontal tracts (IFOF) are specifically implicated in risk-taking behavior in BD-I patients. Meanwhile, self-reported impulsivity was not associated with WM integrity of the tracts investigated in our study.
    Article · Jan 2016
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