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192 The APA Publishing Textbook of Mood Disorders, Second Edition
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dependent (BOLD) measures and oxygen perfusion using arterial spin labeling
(ASL). BOLD imaging essentially measures the amount of oxygenated hemoglobin in
the brain as a positive signal; the level of oxygenation increases in regions of activation
because the brain is designed to oversupply blood to brain areas that are in use. ASL
works by using magnetic pulses to “label” a volume of blood that can then be followed
as it travels through the brain. MRI can also measure neurotransmitters and energy
sources that allow the brain to function using magnetic resonance spectroscopy
(MRS). MRS capitalizes on the magnetic differences of neurochemicals to assess their
concentrations. PET and SPECT can be used to measure blood flow, neurotransmitter
concentrations, and glucose metabolism by measuring the effects of radioactive me
tabolites (radiotracers) injected into the bloodstream, although these approaches are
less commonly used since MRI technology has rapidly advanced. However, PET and
SPECT use labeled isotopes to study specific brain activities (e.g., the number of dopa
mine receptors or the metabolism of glucose) that cannot yet be studied with MRI.
Detailed descriptions of these techniques exceed the scope of this chapter; interested
readers are referred to the reviews of Gonul et al. (2009) and Videbech (2000).
These neuroimaging modalities have been used to identify biological markers of
mood disorders that may be used for early identification and diagnosis and to de
velop more effective treatments. At present, however, no neuroimaging techniques
have a direct clinical application in the care of people with mood disorders.
In the sections that follow, we introduce the neural networks that are believed to
underlie key clinical features of mood disorders—the emotion processing and regu
lation, reward processing, and resting state networks (Figure 11–1)—and discuss abnormal activity and abnormal functional connectivity within these networks. Most of
the findings presented are from research utilizing functional MRI (fMRI), given that
these studies dominate the current literature. We then address gray and white matter
structural abnormalities associated with mood disorders. Findings from studies using PET and MRS that examined regional neurotransmitter concentration and neuroreceptor density are discussed in relation to larger-scale neural network models of
mood disorders. Together, these results contribute to emerging functional neuroana
tomical models of mood disorders that may eventually increase diagnostic precision,
identify markers of risk for future mood disorders, and lay the foundation for person
alized treatments.
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Abnormal Activity and Functional Connectivity
in Neural Networks Underlying Clinical Features
of Mood Disorders
Emotion Processing and Regulation Neurocircuitry
Emotion processing and regulation arise from ventral prefrontal-striatal-thalamic
networks and their interactions with the amygdala and other brain regions. More specifically, emotion processing involves a network that includes the ventrolateral and
ventromedial, dorsal, and medial prefrontal cortices; anterior cingulate cortex; amygdala; and hippocampus (see Figure 11–1A). Studies of people with major depressive
FIGURE 11–1. Key nodes in emotion processing and regulation and in reward processing neural circuitries in healthy individuals.
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(A) Key nodes in emotion processing and emotion regulation neural circuitries in healthy individuals. (B) Key nodes in reward processing neural circuitry in healthy
individuals. ACC=anterior cingulate cortex; OFC=orbitofrontal cortex; PFC= prefrontal cortex.
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disorder typically find abnormally reduced activity in these networks during exposure to positive emotional stimuli, and increased activation during exposure to negative emotional stimuli. Specifically, findings include abnormally decreased activity in
the lateral, orbital, and medial portions of the prefrontal cortex and elevated activity
in the amygdala in depressed individuals during exposure to negative emotional
stimuli (Groenewold et al. 2013; Rive et al. 2013). In contrast, activity during exposure
to positive emotional stimuli is less predictable (Groenewold et al. 2013; Rive et al.
2013). Overrecruitment of the anterior cingulate cortex in response to both positive
and negative emotional stimuli suggests that depressed individuals have difficulty
recruiting prefrontal cortical regions to regulate the overactive limbic regions, particularly during exposure to negative emotional stimuli. Functional connectivity studies, which describe patterns of coactivation between two or more neural regions, also
support this deficit in amygdala-prefrontal functional connectivity in response to
negative emotional stimuli in depressed individuals relative to healthy individuals
(Anand et al. 2005).
In individuals with bipolar disorder, this emotion processing neurocircuitry shows
heightened emotional salience even during exposure to nonemotional content. More
specifically, individuals with bipolar disorder show abnormally decreased ventrolateral prefrontal cortical activity during tasks involving emotion processing and emotion regulation, and response inhibition (Chen et al. 2011). These studies report
abnormally decreased inferior frontal cortical activity, especially in the ventrolateral
prefrontal cortex, and abnormally decreased ventrolateral prefrontal cortex–amygdala functional connectivity during different positive and negative emotion processing and emotion regulation tasks in adults with bipolar disorder across different
mood states (Strakowski et al. 2011).
Furthermore, individuals with bipolar disorder also show abnormally increased activity in the amygdala, ventral striatum, and medial prefrontal cortex and decreased
functional connectivity between the amygdala and prefrontal cortex, during exposure
to positive emotional stimuli (Phillips and Swartz 2014). Here, the studies in bipolar
disorder show abnormally increased amygdala and medial prefrontal cortex activity
(Almeida et al. 2009; Chen et al. 2011) and abnormally decreased positive bilateral or
bitofrontal cortex–amygdala effective connectivity in response to positive emotional
stimuli, especially happy faces (Almeida et al. 2009), suggesting a dysregulated emo
tional network response to these stimuli. These findings may reflect an underlying attentional bias to positive emotional stimuli in bipolar disorder, predisposing to or
associated with risk for mania. However, others found that the prefrontal-amygdalastriatal circuit distinguished depressed from manic mood states during processing of
negatively valenced information (Grotegerd et al. 2014; Man et al. 2019). In summary,
in both (unipolar) major depressive disorder and bipolar disorder, dysregulation of
emotion processing and emotion regulation networks has been consistently demonstrated through a variety of neuroimaging approaches and tasks.
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Reward Processing Neurocircuitry
Reward-related neurocircuitry is centered on medial and lateral prefrontal cortical
modulation of the ventral striatum (Figure 11–1B), primarily through dopaminergic
systems. Reward processes are related to mood state, so it is not surprising that there
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is overlap in the neural networks involved in reward and emotion processing. Reward processing includes periods of motivation, anticipation, and satiety. Consumption of rewards induces feelings of pleasure, leading to reinforcement of reward cues
and behaviors (Schultz 2006). Reward-seeking and learning tend to decrease during
depressive episodes and are heightened during manic periods. Neuroimaging stud
ies assess the neurocircuitry of reward using a variety of paradigms, including monetary, social, and appetitive rewards. These human studies (Beckmann et al. 2009;
Kumar et al. 2008; Ongür and Price 2000) have confirmed the presence of a reward
network previously identified in animal studies (Haber 2016), consisting of the ven
tral striatum with connections to the ventral tegmental area and the ventral pallidum,
which then connect (through the thalamus and anterior cingulate) to the orbitofrontal
cortex. In animal studies, reward processing tends to localize within the striatum to
the nucleus accumbens; however, in humans, this nucleus is less well delineated, so
activation occurs at the ventral striatal–putamen junction. This activation is blunted
or unsustained in depressed individuals (Zhang et al. 2013), and cortical regions that
regulate the ventral striatum, such as the anterior cingulate and middle frontal cortex,
show abnormal activation in response to rewarding stimuli and reward anticipation
(Zhang et al. 2013). Studies in bipolar disorder have reported abnormally increased
left ventrolateral prefrontal cortex and ventral striatal activity during reward anticipation in adults with bipolar disorder in different mood states (Bermpohl et al. 2010;
Nusslock et al. 2012; Phillips and Swartz 2014). However, others found no differences
in activity in the ventral striatum in response to reward receipt versus omission in
manic adults with bipolar disorder (Abler et al. 2008). Collectively, data suggest that
depression and mania may be associated with dysfunction of these neural networks
that manage rewards and reward learning, but further study is required.
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Resting State Neurocircuitry
Studies of resting state functional connectivity examine measures of contextindependent neural circuits and may represent fundamental abnormalities in the
functional neuroanatomy of mood disorders. Measuring resting state functional connectivity is based on the discovery that low-frequency (<~0.1 Hz) BOLD fluctuations
in functionally related gray matter regions show strong temporal correlations at rest
(i.e., they vary together over time). Resting state studies are useful in clinical populations because of the ease of image acquisition; however, this technique is vulnerable
to individual variations in behavior, arousal, and head motion in the scanner.
Studies during rest have identified distributed regions—including the default
mode, salience, attentional, and emotion regulation networks—that are abnormally
activated during rest in participants with MDD (Kaiser et al. 2015). The default mode
network, which comprises coactivation among the hippocampus, medial prefrontal
cortex, posterior cingulate cortex, and temporal cortex (Drevets et al. 2008), is norma
tively activated during rest. The salience network consists of coactivation between the
anterior cingulate cortex and frontoinsular regions (Chiong et al. 2013). The attentional
network consists of dorsal and ventral networks that work in concert to orient attention and consists of visual areas, inferior frontal cortex, and parietal and temporal cortices (Vossel et al. 2014). The emotion regulation network includes regulatory regions of
the anterior cingulate cortex and the lateral and medial prefrontal cortex and subcor-
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tical regions of the amygdala, insula, and caudate (Etkin et al. 2015). Findings suggest
that interference of other networks during rest may be related to mood disorder pa
thology. Specifically, in individuals with MDD during rest, abnormally low connectivity within the attentional and emotion regulation networks (Kaiser et al. 2015) and
abnormally high connectivity within the default mode and salience networks (Kaiser
et al. 2015; Sundermann et al. 2014) have been shown, and these abnormalities may
become more pronounced with repeated depressive episodes (Meng et al. 2014).
In individuals with bipolar disorder, Brady et al. (2017) found two patterns of altered functional connectivity that differentiated mood states in bipolar disorder as
well as distinguishing bipolar euthymia from a healthy population: 1) dorsal atten
tional network—the bipolar euthymia group demonstrated hypoconnectivity and the
mania group demonstrated hyperconnectivity relative to a healthy control population;
2) default mode network—the bipolar euthymic state demonstrated hypoconnectivity,
while bipolar mania demonstrated connectivity comparable to that of a healthy popu
lation between the dorsal frontal nodes and the rest of the network. Moreover, Martino
et al. (2020) found increased functional connectivity between the sensorimotor net
work and the thalamus during bipolar mania that was absent during bipolar depression. In summary, mood disorders may be associated with a specific functional neuroanatomic architecture, although the details of this architecture are still incompletely
defined.
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Abnormal Cerebral Blood Flow
Cerebral blood flow during emotion processing, reward processing, and rest shows
hypoperfusion in bipolar participants during both depressed and manic mood epi
sodes (Toma et al. 2018). Both bipolar and unipolar depressed participants showed
hypoactive cerebral blood flow during rest, especially in regions involved in emotion
processing and cognitive control, such as the anterior cingulate, frontoparietal, and
striatal regions (Toma et al. 2018; Vasic et al. 2015).
Neural Structural Abnormalities
Abnormal Gray Matter Structure
In the brain, gray matter measurements are thought to reflect the density of dendrites,
short-range axons, and glial cell bodies (Zatorre et al. 2012). Individuals with mood
disorders, relative to healthy subjects, showed decreased gray matter in cortical (anterior cingulate cortex and across the prefrontal cortex) and subcortical regions (caudate,
putamen, hippocampus, and insula), especially after recurrent affective episodes (Ar
none et al. 2012; Bore et al. 2012; Phillips and Swartz 2014). In bipolar disorder, gray
matter volume may also be used as a proxy measure of neurotrophic exposure to lithium medication, because several studies and a meta-analysis suggest that individuals
with bipolar disorder who are being treated with lithium have a greater global and
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regional (e.g., anterior cingulate gyrus) gray matter volume than individuals with bipolar disorder who are lithium-free (Sun et al. 2018), although not all investigators
have replicated these findings (see, e.g., Eker et al. 2014). These findings provide evi
dence of structural abnormalities that parallel the functional abnormalities associated
with emotion processing, emotion regulation, and reward processing neurocircuitries
in mood disorders.
Abnormal White Matter Structure Measured
With Diffusion Imaging
Diffusion imaging allows measurement of the architecture of the white matter fiber
tracts by approximating the movement of water along the axon. Greater longitudinal
diffusivity reflects high cohesion along the length of the axis, perhaps reflecting
greater myelination and higher tract organization. By contrast, greater radial diffusiv-
ity reflects greater movement of water molecules perpendicular to the tract length
and suggests a less linear structure and may reflect damaged axonal membranes
and/or myelin sheaths. Fractional anisotropy is the ratio of longitudinal diffusivity
to radial diffusivity, with high fractional anisotropy characterizing densely packed
collinear white matter fibers, along with few noncollinear fibers.
Key white matter tracts that connect the prefrontal cortical and subcortical regions
involved in emotion regulation and reward processing neural circuits include the su
perior longitudinal fasciculus, uncinate fasciculus, corpus callosum, and cingulum
(Versace et al. 2014). Meta-analyses of studies in depressed patients have shown re
ductions in fractional anisotropy in the corpus callosum extending to the body of the
corpus callosum and the left anterior limb of the internal capsule (Chen et al. 2016;
Jiang et al. 2017). Moreover, decreased uncinate fasciculus fractional anisotropy is
co nsistent ly f ound in ad ults with dep res sio n (Bracht et a l. 20 15). Findin gs of abnormal
fractional anisotropy in the medial forebrain bundle and cingulum bundle have also
been shown and may be associated with severity of depressive symptoms (Bracht et
al. 2015). Abnormally reduced fractional anisotropy in tracts such as the uncinate fas
ciculus, which connects the prefrontal cortical and subcortical regions, may underlie
some of the functional abnormalities in emotion regulation and reward processing
observed in depressed individuals. In bipolar disorder, several meta-analyses of
white matter volumes converged to identify reductions in the posterior corpus callosum (Arnone et al. 2008; Ganzola and Duchesne 2017; Kempton et al. 2008; Pezzoli et
al. 2018), and in one study, reduced volumes extended to the adjacent posterior cingulate cortex (Pezzoli et al. 2018). The observed white matter decrease seems to be a
trait marker of disease, because it is not typically correlated with other clinical vari
ables. The white matter region lies adjacent to the posterior cingulate cortex, which is
a core region of the default mode network (Greicius et al. 2003). Finally, increased
white matter hyperintensities continue to be the most replicated finding in bipolar
disorder. However, this finding lacks specificity, given that it is also seen in other psychiatric disorders, including major depressive disorder, as well as in normal aging
and in other medical illnesses that impact the brain.
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Magnetic Resonance Spectroscopy Studies
of Neurotransmitters Implicated
in Mood Disorders
Noninvasive detection of metabolites, such as choline, N-acetylaspartate, and lactate,
can be measured using MRS. Utilizing the chemically specific radiofrequency signals
within metabolites and varying the magnetic fields, MRS can estimate neurotransmit
ter concentrations in neural regions of interest. We describe here two neurotransmitters implicated in mood disorders because of their roles in inhibition and excitation:
GABA and glutamate.
GABA is the principal mediator of inhibition in the CNS. GABA concentration
deficits have been demonstrated in depressed individuals in occipital, medial pre
frontal, and anterior cingulate cortices. These deficits appeared to resolve with remission from depression but to remain low in individuals with recurrent or treatmentresistant depression (Schür et al. 2016). GABA concentrations were also negatively as
sociated with anhedonia severity (Gabbay et al. 2012). These findings suggest that
high concentrations of GABA in the cortex are needed to successfully regulate nega
tive symptoms of anhedonia.
Glutamate may also have a relationship with mood disorders. It is the principal excitatory neurotransmitter in the CNS. In depressed individuals, glutamate concentrations have been reported to be increased in the occipital cortex and decreased across the
prefrontal cortex and anterior cingulate cortex (Lener et al. 2017). Changes in gluta
mate levels after treatment for depression were not consistently associated with symptom improvement (Lener et al. 2017). Additional research is needed to understand the
relationship between glutamate concentrations in the cortex and depression.
Individuals with bipolar I disorder who were being treated with long-term pharmacotherapy showed increased glutamate and reduced N-acetylaspartate concentrations, which, combined, suggested neuronal metabolic dysfunction (Bustillo et al.
2019). Moreover, glutamate and N-acetylaspartate concentrations were also relatively
lower in the anterior cingulate cortex of bipolar individuals who had experienced
multiple episodes compared with those who were experiencing their first episode
(Borgelt et al. 2019).
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Newer Areas of Neuroimaging Research
in Mood Disorders
Neuroimaging studies have focused on specific subgroups of individuals with mood
disorders. This research includes studies comparing individuals with major depressive disorder and individuals with bipolar depression, as well as studies of specific
mood states, neural correlates, and predictors of treatment response in individuals
with mood disorders, risk for the development of mood disorders, and pediatric-onset
mood disorders. Although an in-depth discussion is beyond the scope of this chapter,
briefly, this research supports findings from studies of adults with mood disorders
and suggests that emotion processing and reward processing neurocircuitries are ab-
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normal in mood disorder pathology. It also identifies other neural regions, such as the
insula and precuneus, that may be implicated in mood disorders. However, use of
these findings as biomarkers of disease is not yet feasible. Machine learning is a prom
ising approach for identifying individual subject–level patterns of neurocircuitry abnormalities in mood disorders. These approaches can identify individuals with mood
disorders with 70%–80% accuracy. Improvements in the use of machine learning tech
nologies in conjunction with neuroimaging measures have the potential to improve
the diagnosis of psychopathology as well as identification of optimal treatment
choices.
Familial Risk and Resilience
Risk for mood disorder development can be elevated by genetic risk (i.e., having a parent or sibling with a mood disorder) or symptomatic risk (i.e., having subthreshold
symptoms or a clinically related disorder such as an anxiety disorder). Patients and
high-risk relatives showed similar abnormalities in neural activation and functional
connectivity during emotional tasks; however, resilient relatives had larger cerebral
volumes, enhanced prefrontal connectivity during tasks, and heightened functional
coactivation of the default mode network during rest (Frangou 2019). Unaffected rela
tives also showed a constellation of differences, including greater gray matter volumes
in the frontal, prefrontal, and temporal regions and hyperactivation in the amygdala,
frontal cortex, and striatum (Cattarinussi et al. 2019), suggesting neural compensation
in these individuals. In addition, higher anterior cingulate activity and greater functional connectivity with the amygdala has been highlighted as a potential marker of
genetic risk for bipolar disorder (Acuff et al. 2018).
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Pediatric Mood Disorders
The emergence of mood disorders during childhood or adolescence often portends
more severe symptoms and increased risk of suicide in comparison with mood dis
order emergence in adulthood. Neural networks that are implicated in emotion and
reward processing are also important in pediatric depression. In depressed youth,
patterns of increased activity in the ventrolateral and dorsolateral prefrontal cortex
and anterior cingulate cortex have been shown (Miller et al. 2015). In youth with bipolar disorder, abnormal patterns of increased amygdala activity, decreased ventrolateral prefrontal cortical activity, and decreased prefrontal cortical–amygdala functional
connectivity during emotion processing were reported (Frank et al. 2015). Corroborating the activity and functional connectivity findings, decreased cortical and subcortical gray and white matter measures have also been observed in youth with pediatriconset bipolar disorder (Acuff et al. 2019).
Summary
Neuroimaging studies of mood disorders have identified specific patterns of neural
network dysfunction and related structural abnormalities. Neural network abnormalities include dysfunction in bilateral prefrontal cortical (especially ventrolateral
prefrontal cortex and orbitofrontal cortex)–hippocampal–amygdala emotion process-
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ing and emotion regulation networks along with hyperactivity in predominantly leftsided ventrostriatal-ventrolateral prefrontal cortex reward processing neurocircuitry.
Additionally, parallel abnormalities in resting-state functional connectivity and gray
and white matter structure in these networks have been shown. Bioenergetic abnormalities that may be related to mitochondrial dysfunction, abnormal neuronal glial
interactions, and abnormal glucose metabolism in some of the same regions showing
gray matter loss have been reported. The combination of these functional and struc
tural abnormalities may underlie the behavioral abnormalities associated with mood
disorders, such as emotional lability, emotional dysregulation, and reward processing
dysregulation.
Future Directions in Neuroimaging Research
in Mood Disorders
Neuroimaging techniques are helping us better understand the neurocircuitry of
mood disorders and are making significant contributions to our understanding of
mood disorder pathophysiology (Gotlib and Hamilton 2008; Zhang et al. 2016). Many
consistencies across neuroimaging studies and methods are reported, although inconsistencies are also seen. Inconsistencies may be the result of imaging parameters
or analytic differences but may also represent important differences in subcategories
of mood disorders related to symptom dimensions and symptom development. Some
approaches to improving current methods include the following:
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1. Using dimensional symptomatic approaches to examine pathological behaviors
that cut across conventionally defined diagnostic categories of mood disorders
2. Incorporating longitudinal designs to identify developmental trajectories of ab-
normal neurocircuitry associated with symptoms and with risk
3. Merging multimodal neuroimaging techniques and other biological system–level
approaches to examine the impact of genetic variation and molecular-level pro
cesses on neurocircuitry development
4. Implementing pattern recognition techniques in combination with neuroimaging
methodologies to identify individual-level neurocircuitry markers that not only
help classify individuals into present diagnostic groups but also help predict indi
vidual-level clinical course
Collectively, these approaches will enhance our understanding of the complex
neuropathophysiological processes underlying abnormal behaviors associated with
mood disorders, and they may ultimately provide individual-level biological markers that have clinical utility for diagnosis and future illness prediction, and for guiding personalized treatment choices.
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