Emerging Trends in The Social and Behavioral Sciences

Depression

Item

Title

Depression

Author

Gotlib, Ian H.

Furman, Daniella J.

Research Area

Psychopathology

Topic

Mental Disorder Varieties

Abstract

Major depressive disorder (MDD) is a costly, prevalent, and recurrent psychiatric disorder that can involve significant impairment across multiple domains of functioning. In this essay, we provide an overview of the theory and research associating aberrant information processing and neural structure and function with the etiology and maintenance of MDD. We begin by highlighting the foundational work that characterizes depressed persons' cognitive and neural responses to valenced stimuli. We then examine recent efforts to clarify the nature of the temporal relation between depression and these cognitive and neural anomalies, focusing on research designed to identify abnormalities that are present before the onset on MDD and to examine the consequences of manipulating cognitive and neural anomalies. Finally, we describe several areas and questions to be examined in future research that we believe will lead both to a more comprehensive psychobiological understanding of MDD and to improvements in the assessment, diagnosis, and treatment of this disorder. In particular, we focus on the need for innovation in diagnosis, better characterization of symptom heterogeneity in MDD, on extending neural research in MDD to the study of abnormalities in larger‐scale brain networks, and on elucidating the mechanisms that underlie the successful effects of training programs designed to reduce cognitive biases in depression.

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Identifier

etrds0074

extracted text

Depression
IAN H. GOTLIB and DANIELLA J. FURMAN

Abstract
Major depressive disorder (MDD) is a costly, prevalent, and recurrent psychiatric
disorder that can involve significant impairment across multiple domains of functioning. In this essay, we provide an overview of the theory and research associating
aberrant information processing and neural structure and function with the etiology and maintenance of MDD. We begin by highlighting the foundational work that
characterizes depressed persons’ cognitive and neural responses to valenced stimuli.
We then examine recent efforts to clarify the nature of the temporal relation between
depression and these cognitive and neural anomalies, focusing on research designed
to identify abnormalities that are present before the onset on MDD and to examine the consequences of manipulating cognitive and neural anomalies. Finally, we
describe several areas and questions to be examined in future research that we believe
will lead both to a more comprehensive psychobiological understanding of MDD and
to improvements in the assessment, diagnosis, and treatment of this disorder. In particular, we focus on the need for innovation in diagnosis, better characterization of
symptom heterogeneity in MDD, on extending neural research in MDD to the study
of abnormalities in larger-scale brain networks, and on elucidating the mechanisms
that underlie the successful effects of training programs designed to reduce cognitive
biases in depression.

INTRODUCTION
Major depressive disorder (MDD) is among the most prevalent of all
psychiatric disorders and is associated with enormous personal and societal
costs (Gotlib & Hammen, 2009). Almost 20% of the American population, or
more than 30 million adults, will experience an episode of major depression
during their lifetime (Kessler et al., 2014). In addition to the two cardinal
symptoms of sadness and decreased interest or pleasure in usually enjoyable
activities, MDD is associated with psychomotor agitation or retardation,
marked weight loss, insomnia or hypersomnia, decreased appetite, fatigue,
extreme feelings of guilt or worthlessness, concentration difficulties, and
suicidal ideation. To meet Diagnostic and Statistical Manual of Mental
Disorders criteria for MDD, a subset of these symptoms, including at least
one of the two cardinal symptoms, must be present concurrently for at least
Emerging Trends in the Social and Behavioral Sciences. Edited by Robert Scott and Stephen Kosslyn.
© 2015 John Wiley & Sons, Inc. ISBN 978-1-118-90077-2.

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EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

a 2-week period. MDD is a highly recurrent disorder: 75% of depressed
patients experience more than one depressive episode in their lifetime,
often relapsing within 2 years of recovering from an episode. Further,
epidemiological studies have found depression to be associated with other
mental and physical difficulties, most often with anxiety disorders, but
also with smoking and cardiac problems. Given its prevalence, recurrence,
comorbidity, and costs, it is not surprising that the World Health Organization (2004) projects that MDD will be the single most burdensome disease in
the world in the twenty-first century.
Over the past two decades, investigators have made considerable progress
elucidating psychological and biological aspects of MDD. In particular, there
are now large bodies of research examining anomalies in cognitive functioning and in the processing of positively and negatively valenced information in depression and, more recently, aberrations in neural function and
structure. In this essay, we describe our current understanding of the psychobiological functioning of depressed individuals, focusing in particular
on abnormalities in information processing and in brain function and structure. We begin by presenting foundational research in these areas, discussing
findings from studies that have helped to form our current conceptualization of MDD. We then describe cutting-edge developments in the study of
depression—recent investigations and research directions that have begun to
sharpen, if not refocus, our picture of cognitive and neural aspects of MDD.
Finally, we discuss key issues for future research in the study of MDD, highlighting what we consider to be the most pressing needs and questions that
investigators must address and directions that researchers should take in
moving the field forward.
FOUNDATIONAL RESEARCH
In this section, we present a brief overview of theory and research that has
helped to shape our understanding of cognitive and neural aspects of MDD.
Foundational work in both of these areas has focused in large part on elucidating depressed persons’ responses to valenced stimuli in an effort to understand processes that serve to maintain or exacerbate this disorder.
COGNITIVE ASPECTS OF DEPRESSION
Cognitive theories of depression originated over 50 years ago and provided
the impetus for a large body of research [see Foland-Ross and Gotlib (2012)
and Gotlib and Joormann (2010) for reviews]. Beck (1967) posited that
depressed individuals (and, importantly, persons who are vulnerable to
developing depression) have memory representations, or schemas, that

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3

lead them to view their environment in systematically negative ways. Beck
postulated further that when these biases in cognitive processing interact
with a stressful life event, these individuals initiate a cycle of negative
automatic thoughts about the self, the world, and the future (the “cognitive
triad”) and, consequently, experience high levels of negative affect. Early
studies of cognitive functioning in MDD tested Beck’s theory by comparing the responses of depressed and nondepressed persons to self-report
measures of dysfunctional attitudes and automatic thoughts. While these
studies were important in documenting depression-related aberrations in
self-perceived cognitive functioning, it was clear that Beck’s formulation
involved the operation of cognitive processes at an “automatic” level that
was not necessarily accessible through self-report methodologies. Thus,
more recent studies have utilized more sophisticated experimental tasks
designed to examine schematic functioning. These tasks have now been
used to assess biases in attention to, interpretation of, and memory for
negatively and positively valenced stimuli in MDD, and provide the basis
for innovative treatments for this disorder.
The first studies in this area assessed reaction times of depressed and
nondepressed individuals to name the ink colors in which positive, neutral,
and negative words were printed in an emotional version of the classic
Stroop task, and found that the attention of depressed persons is “captured”
by negatively valenced stimuli. Results of subsequent studies assessing not
only attentional processing but also other aspects and stages of information
processing, such as interpretation and memory, have helped to refine this
formulation. For example, using a variety of experimental tasks, researchers
have found that depressed persons interpret ambiguous information more
negatively than do nondepressed individuals and exhibit preferential
recall of negative versus positive material. On the basis of these and other
findings, theorists have now extended cognitive formulations of depression
to include a consideration of the role of inhibitory functioning. Researchers
have posited that the attention of depressed individuals is relatively easily
and quickly captured by negative stimuli, leading this information to be
more likely than positive material to enter working memory (WM). Given
the limited capacity of the WM system, it is important for adaptive functioning that the contents of WM be updated efficiently and continually by
discarding information that is no longer relevant. Importantly, researchers
have now documented that once negative information is in WM, depressed
individuals are impaired in their ability to inhibit processing of, or remove,
this material, a difficulty that may underlie the better memory of depressed
individuals for negative than for positive stimuli, the sustained negative
affect, and the high levels of rumination, or repetitive negative thinking,
that characterize MDD (Whitmer & Gotlib, 2013).

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EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

NEUROBIOLOGICAL ASPECTS OF DEPRESSION
Since the discovery that pharmacological interventions targeting the serotonin, norepinephrine, and dopamine neurotransmitter systems reduced
symptoms of depression, considerable research has been conducted characterizing depression-associated abnormalities in neurotransmitter production
and binding, receptor density and function, and reuptake mechanisms [see
Thase (2009), for a review]. Investigators have also attempted to induce
depressive symptoms in humans and animals by selectively depleting brain
dopamine or serotonin, usually by administering a cocktail of amino acids
that lacks the components critical for producing these neurotransmitters,
and have found that reductions in serotonin levels, for example, cause
cognitive dysfunction in non-disordered individuals similar to that found in
depressed persons, including increased attention toward negative stimuli.
With advances in brain imaging technology, researchers have been able
to examine depression-related changes in regions of the brain implicated
in mood and cognitive processes (and that are sensitive to permutations
in neurotransmitter activity). In addition to volumetric and metabolic
abnormalities, investigators have identified anomalous neural responding
in depressed individuals in brain areas associated with the generation [e.g.,
amygdala, subgenual anterior cingulate cortex (sACC), and insula] and
regulation [e.g., dorsolateral prefrontal cortex (DLPFC)] of emotion, the
anticipation of rewarding outcomes and motivation of behavior (e.g., ventral
striatum), and memory formation (e.g., hippocampus).
The amygdala has been implicated in the integration of information from
the senses and viscera, particularly in the service of detecting and mobilizing responses to signs of threat in the environment. Depressed individuals
exhibit both decreased volume of, and increased glucose metabolism in, the
amygdala; further, hyper-metabolism of the amygdala in MDD is associated
with increases in plasma cortisol, a critical stress-related glucocorticoid
hormone. Researchers using functional magnetic resonance imaging (fMRI)
have documented increased amygdala responses in MDD across a wide
range of negative emotional conditions, including anticipating, viewing, and
remembering negative words and pictures. Abnormal amygdala responsivity has also been found to correlate with severity of depressive symptoms
and level of ruminative responding, suggesting that the amygdala may
contribute to the cognitive biases in MDD described above.
Investigators have associated the sACC and the insula with the induction
of negative emotions, including sadness. In addition to reports of both
decreased volume and anomalous blood flow and metabolism in the sACC
in depression, researchers have documented increased reactivity of both the
sACC and the insula to negative emotional stimuli in depressed individuals,

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5

and have found decreases in sACC activity following recovery from MDD
[see Hamilton et al. (2012), for a review]. The DLPFC, in contrast, is involved
in WM and executive control processes, and has also been implicated in
the regulation of emotion. DLPFC metabolism has been found to be lower
in depressed individuals than in healthy controls, and researchers have
documented decreased DLPFC responses as depressed persons process
negative stimuli or attempt to regulate their emotions.
The striatum has been implicated in generating responses to cues predicting
future rewards and to the receipt of unexpected rewards, and more generally,
it has been associated with responses to positive stimuli and positive mood.
Thus, it is not surprising that investigators have reported reduced striatal
response in MDD in a range of positive emotional contexts, including receipt
of monetary rewards and positive feedback, suggesting that anomalies in
this structure contribute to decreased pleasure, or anticipation of pleasure,
in depressed individuals.
Finally, the hippocampus is critical in the formation of new memories
about experienced events and in the regulation of the stress response.
Meta-analyses have documented reduced hippocampal volume and lower
levels of hippocampal activation during performance of memory tasks in
MDD, suggest that abnormalities in this structure contribute to both the
cognitive and affective difficulties experienced by depressed individuals.
CUTTING-EDGE RESEARCH
This foundational research is important in documenting consistent associations between MDD and both aberrant cognitive functioning and anomalous
neural function and structure. We know much less, however, about the temporal or causal relation of these patterns of cognitive and neural function and
neural structure to MDD; that is, we do not yet understand whether these
characteristics are symptoms of the depressed state, consequences of having
been depressed, or vulnerability factors that increase the likelihood that individuals will develop an episode of MDD. In this section we focus on research
designed to elucidate the functional nature of the relation between depression and both cognitive and neural anomalies, including studies examining
whether cognitive and neural abnormalities are present before the onset of
a depressive episode, and investigations in which researchers have manipulated cognitive or neural functioning and examined the effects on depressive
symptoms.
COGNITIVE FUNCTIONING
A growing literature is demonstrating that depression-related biases in
cognition are not necessarily correlates or consequences of the experience of

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EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

depression, but instead, could reflect a pattern of dysfunction that precedes
the initial onset of this disorder. Indeed, like depressed adults, young
individuals who are not themselves depressed but are at high risk for developing depression by virtue of having a depressed parent have been found to
exhibit negative biases in the identification and interpretation of emotional
material [see Foland-Ross and Gotlib (2012), for a review]. Moreover, similar
to depressed adults, never-disordered girls at familial risk for depression
selectively attend to negative facial expressions on the visual-probe task,
an experimental paradigm that enables the quantification of attentional
biases toward or away from emotional stimuli. It is possible, therefore, that
negative cognitive biases play a role in placing children at increased risk for
developing MDD. Indeed, we recently found that high-risk girls (daughters
of depressed mothers) who exhibited a greater attentional bias to sad faces
on the visual-probe task, and who made either less positive or more negative
interpretations on an ambiguous word completion task, were more likely
to experience a subsequent depressive episode than were high-risk girls
who exhibited weaker negative cognitive biases. Thus, biases in attention
and interpretation may represent important targets for early intervention in
MDD. In fact, investigators have begun to report promising initial findings in
using two forms of cognitive bias training (CBM)—attentional bias training
(ABT), which teaches depressed individuals to attend more to positive and
less to negative material, and interpretation bias training (IBT)—both of
which attempt to attenuate cognitive biases in order to reduce depressive
symptoms. Initial studies have documented improvement in depressive
symptoms using these techniques, although more research is needed to
draw strong conclusions about the effectiveness of these approaches.
NEUROBIOLOGICAL FUNCTIONING
Investigators have recently begun to examine the nature of the relation
between anomalous structure and function of particular brain regions and
manifestations of depression by examining whether neural abnormalities
precede the onset of depressive symptoms as risk factors for the development of MDD. Researchers have now identified abnormalities in the
structure and function of several key brain regions in individuals who
are at elevated risk for the development of depression [see Foland-Ross,
Hardin, and Gotlib (2013), for a review]. Importantly, these studies have
revealed that anomalies in high-risk individuals often mirror those that
have been documented in currently depressed individuals. For example,
investigators have found decreased volume of the hippocampus and the
DLPFC in never-depressed individuals at familial risk for MDD, as well
as decreased activation of the striatum to monetary reward. Similarly,

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7

researchers have documented abnormal activation of the amygdala during
sad mood induction and reductions in amygdala volume in individuals at
genetic risk for depression. Thus, aberrations in neural regions implicated in
attention to emotional information, in generating and regulating emotional
and stress responses, and in forming emotional memories may render
high-risk individuals less able to disengage from, or regulate the emotional
consequences of, negative or stressful life events.
A second method by which researchers are beginning to exam the nature
of the relation between symptoms and neural function is by examining
whether directly altering anomalous neural activation in depressed individuals affects clinical aspects of the disorder. In a ground-breaking study,
Mayberg et al. (2005) demonstrated that by applying electrical current
directly to white matter tracts adjacent to the sACC using a method
called deep-brain stimulation (DBS), they were able to immediately reduce
depressive symptoms in individuals with treatment-resistant depression.
Investigators have now begun to explore the feasibility of altering neural
function in circumscribed brain regions through less invasive means.
Real-time neurofeedback training (NFT) procedures, for example, are
designed to teach individuals to exert volitional control over brain states by
presenting them with continuously updated graphical representations of
brain activity during fMRI scanning or electroencephalography (EEG), and
asking them to learn to modulate these representations. Researchers in this
area have examined the ability of individuals to learn to control key areas
involved in emotional experience, such as the amygdala, insula, and sACC
(e.g., Hamilton, Glover, Hsu, Johnson, & Gotlib, 2011). Linden et al. (2012)
found improvement in depressive symptoms immediately following NFT
designed to increase activation in brain regions associated with the elicitation of positive emotions. These preliminary results suggest that anomalous
activity in critical brain regions may not simply convey risk for the development of the disorder or represent a neuropathological consequence or
marker of the disorder, but may also reflect the ongoing maintenance of
particular symptoms. Thus, NFT that targets regions of known dysfunction
in MDD may ultimately enable researchers to identify which of the neural
features of depression are causally linked to the maintenance of specific
behavioral and emotional components of this disorder.
KEY ISSUES FOR FUTURE RESEARCH
Given our current understanding of cognitive and neural aspects of MDD, it
is clear that there are a number of key issues that must be addressed in future
research. Most important are issues concerning improvements in the diagnosis of MDD, the considerable heterogeneity of the disorder, the extension of

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EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

research in MDD to the study of abnormalities in large-scale neural networks,
and the integration of cognitive and neural research in the service of elucidating the mechanisms that might underlie successful CBM. We briefly describe
each of these issues in the following sections.
DIAGNOSIS
The primary method of diagnosing MDD is the clinical interview, which
relies in large part on an individual’s ability to accurately self-report a considerable range of emotions, cognitions, and somatic experiences. Indeed, the
information provided by an individual to a physician or researcher is the only
means for determining whether he or she is currently depressed and whether
a given treatment regimen has been effective. Unfortunately, these reports
may be unreliable, unstable, and subject to the memory biases that characterize depressed individuals. In working toward innovation in the diagnosis
of MDD, researchers have begun to use automated multivariate approaches,
such as machine learning, to classify depressed and nondepressed individuals on the basis of neural activation to sad faces, neural structure, and even
vocal prosody and facial expression. Some work suggests, in addition, that
these methods can predict remission following cognitive-behavioral therapy.
Thus, the move away from self-report as the diagnostic gold standard may
dramatically alter the way in which individuals are diagnosed, treated, and
assessed for treatment response.
HETEROGENEITY
To meet criteria for a diagnosis of MDD according to DSM-IV, individuals
must have one or both of the cardinal symptoms of MDD, depressed mood
and anhedonia, but may present with up to 17 additional possible symptoms
across seven broad categories of functioning, including changes in weight,
appetite, sleep, and psychomotor function, fatigue, worthlessness, guilt,
cognitive impairment, and suicidal thoughts or self-harm. Given the heterogeneity of possible symptom profiles in individuals who meet criteria for
MDD, some emphasis has been placed on delineating reliable and clinically
relevant subtypes of the disorder that might facilitate more effective and
individually tailored interventions, by examining which symptoms and
other manifestations of disorder tend to cluster together. For example, the
DSM-IV defines the melancholic subtype of depression as an episode characterized by severe anhedonia, profound feelings of guilt (often over trivial
events), and marked psychomotor abnormalities. These symptoms have
also been associated empirically with overreliance on external cues during
cued-response tasks and abnormal neural correlates of action monitoring.

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Nonetheless, despite efforts to define reliable symptom clusters and to
identify the neurobiological and cognitive correlates of various symptoms,
we do not yet fully understand why specific symptoms cluster together.
Thus, the development of a comprehensive and neurobiologically informed
understanding of why and how symptoms and other behavioral and neural
correlates co-occur in depressed individuals is an important future step that
would help clinicians and researchers to better characterize the etiology of
subtypes of MDD and treat specific profiles of the disorder. Further, given
that genetic and other risk factors for MDD have been associated with
anomalies in cognitive, affective, and neurobiological functioning, multivariate approaches to characterizing subtypes of MDD could be extended
to inform our understanding of, and our ability to tailor interventions for,
distinct forms of psychobiological risk for the development of the disorder.
NETWORK-LEVEL NEURAL ANALYSIS
Although identifying abnormalities in the structure and activation of particular brain regions has been important in advancing our understanding of
neural aspects of MDD, we still lack a cogent, comprehensive, and therapeutically useful model of brain function and dysfunction in this disorder. In this
context, it is critical to note that massive interconnectivity among populations of neurons in the brain means that neural events seldom occur in isolation; consequently, it is important that we attempt to understand depression
from a larger, neural-network, perspective. Only recently, however, have neuroimaging analysis techniques, as well as our understanding of the architecture of the brain, advanced sufficiently to make network-level explorations
and conceptualizations of MDD feasible.
By far, the majority of neuroimaging studies of MDD use protocols that
involve the presentation to participants of affective or cognitive tasks.
While the results of these studies can inform network-level formulations
of depression, researchers using fMRI and positron emission tomography
(PET) have increasingly been investigating neural functioning in MDD
over relatively long durations in the scanner in the absence of externally
presented tasks or stimuli. This “resting state” approach has led to the
identification of abnormalities in the “default mode network” (DMN), a
cluster of medial brain regions that appears to mediate internally generated
thought processes and is typically inhibited in tasks that require subjects to
attend to cognitively engaging, external stimuli. In depressed individuals,
this network shows greater interconnectivity with the sACC (Greicius et al.,
2007), a region that, as we noted earlier, is associated with the generation of
sadness. Further, depressed individuals do not deactivate this network in
the same way that nondepressed persons do during the active processing of

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external stimuli (Sheline et al., 2009), suggesting that MDD is characterized
by difficulty inhibiting the processing of negative, internally generated,
thought content. Indeed, given these and other findings [see Hamilton,
Chen, and Gotlib (2013), for review], it is not surprising that DMN dynamics
have been associated, in depressed individuals, with the tendency to engage
in rumination (Hamilton et al., 2011). Examinations of large-scale neural
networks in MDD have now extended beyond the DMN. For example, a
growing body of work is implicating anomalous function and structure in
the salience network in MDD, a group of regions that includes the insula and
amygdala, that undergirds responding to biological relevant stimuli, and
that may subserve heightened attention toward negative stimuli (Hamilton
et al., 2012).
In addition, most investigators to date have relied on simple correlative
methods, or “functional connectivity,” to assess intrinsic network-level function in depression. Importantly, there is growing interest in examining issues
involving the temporal and directional relations among areas. For example,
multivariate Granger causality analysis is a technique that has been applied
to neuroimaging data to estimate temporal influence, or “effective connectivity,” of one brain region with respect to another region. In the first work using
this analytic method to examine neural connectivity in MDD, Hamilton et al.
(2011) found that, to a significantly greater extent in depressed than in nondepressed participants, activations in emotion generative areas are not only
mutually excitatory, but further, are associated with subsequent decreases in
brain regions associated with emotion regulation, such as the DLPFC.
Researchers have also recently begun to use graph theory to examine
large-scale brain network organization. This method provides a means for
quantifying the overall organization of brain connectivity, allowing the
brain to be depicted as a series of “nodes,” representing particular regions,
and “edges,” representing correlations in structural volume or activity
between nodes. A handful of studies have now used graph analyses to
examine network connectivity in depressed individuals, and have identified
abnormalities in both path length, that is, how many steps it takes to get
from a node to any other node in the network, and number of hubs and connections, features that may relate to the efficiency of information processing
within and between neural networks. Therefore, research that continues to
integrate this method with other connectivity- and activation-based analytic
techniques has the potential to greatly increase our understanding of the
nature of neural function and dysfunction in MDD, as well as the way in
neural anomalies may underlie deficits, biases, and difficulties in cognition
and information processing in this disorder.

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MECHANISMS UNDERLYING THE EFFECTS OF COGNITIVE BIAS MODIFICATION
Despite the promise of CBM procedures in reducing depressive symptoms,
the mechanisms that might contribute to this improvement are not yet
clear. While it is possible that ABT and IBT simply “train away biases”
and thereby improve symptoms of MDD, it is likely that the mechanisms
underlying the effects of these training procedures are more complex.
Investigators have already begun to examine the neural foundations of
traditional cognitive-behavior therapy for depression (DeRubeis, Siegle, &
Hollon, 2008), but it will be important to extend this research to elucidate
the mechanisms by which CBM achieves its beneficial effects. MacLeod and
Mathews (2012) recently distinguished between “near” and “far” transfer of
training effects of CBM. They noted that while training typically transfers to
the same task with different stimuli (near transfer), changes in functioning
on tasks that are less closely related to the training task (far transfer) are
particularly informative for our understanding of mechanisms. Initial work
indicates that the effects of ABT can transfer to alter interpretive biases,
and that similarly, the effects of IBT may influence biases in both attention
and memory. These findings suggest not only that the distinctions made by
researchers among biases in attention, interpretation, and memory need to
be reconsidered, but further, that at least some aspects of these biases share
common mechanisms of action.
We posit that there are three mechanisms in particular that underlie the
positive effects of CBM in depression: decreased attentional capture of
negative stimuli (bottom-up processing); increased inhibition of negative
material (top-down processing); and, as a consequence of these changes,
decreased negative self-referential thinking (rumination). Importantly,
as we noted earlier in this essay, all three of these constructs have been
found to distinguish depressed from nondepressed individuals. Moreover,
investigators are beginning to examine neural underpinnings of each these
mechanisms (Cooney, Joormann, Eugene, Dennis, & Gotlib, 2010; Dichter,
Felder, & Smoski, 2009; Foland-Ross et al., 2013). It will be important to
continue this line of investigation, integrating assessments of cognitive
and neural functioning in depressed individuals in order to gain a more
comprehensive understanding of, and to continue to develop and refine,
innovative treatments for this debilitating disorder.
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Greicius, M. D., Flores, B. H., Menon, V., Glover, G. H., Solvason, H. B., Kenna, H.,
… Schatzberg, A. F. (2007). Resting-state functional connectivity in major depression: Abnormally increased contributions from subgenual cingulate cortex and
thalamus. Biological Psychiatry, 62(5), 429–437.
Hamilton, J. P., Chen, G., Thomason, M. E., Schwartz, M. E., & Gotlib, I. H. (2011).
Investigating neural primacy in Major Depressive Disorder: Multivariate granger
causality analysis of resting-state fMRI time-series data. Molecular Psychiatry, 16,
763–772.
Hamilton, J. P., Furman, D. J., Chang, C., Thomason, M. E., Dennis, E., & Gotlib, I. H.
(2011). Default-mode and task-positive network activity in Major Depressive Disorder: Implications for adaptive and maladaptive rumination. Biological Psychiatry,
70, 327–333.
Hamilton, J. P., Glover, G. H., Hsu, J.-J., Johnson, R. F., & Gotlib, I. H. (2011). Modulation of subgenual anterior cingulate cortex activity with real-time neurofeedback.
Human Brain Mapping, 32, 22–31.
Hamilton, J. P., Etkin, A., Furman, D. J., Lemus, M. G., Johnson, R. F., & Gotlib, I. H.
(2012). Functional neuroimaging of Major Depressive Disorder: A meta-analysis
and new integration of baseline activation and neural response data. American
Journal of Psychiatry, 169, 693–703.
Hamilton, J. P., Chen, M. C., & Gotlib, I. H. (2013). Neural systems approaches to
understanding Major Depressive Disorder: An intrinsic functional organization
perspective. Neurobiology of Disease, 52, 4–11.
Kessler, R. C., de Jonge, P., Shahly, V., van Loo, H. M., Wang, P. S., & Wilcox, M. A.
(2014). The epidemiology of depression. In I. H. Gotlib & C. L. Hammen (Eds.),
Handbook of Depression (3rd ed., pp. 7–24). New York, NY: The Guilford Press.

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Linden, D. E. J., Habes, I., Johnston, S. J., Linden, S., Tatineni, R., Subramanian, L.,
… , Goebel, R.. (2012). Real-time self-regulation of emotion networks in patients
with depression. PLoS One, 7(6), e38115.
MacLeod, C., & Mathews, A. (2012). Cognitive bias modification approaches to anxiety. Annual Review of Clinical Psychology, 8, 189–217.
Mayberg, H. S., Lozano, A. M., Voon, V., McNeely, H. E., Seminowicz, D., Hamani, C.,
… , Kennedy, S. H. (2005). Deep brain stimulation for treatment-resistant depression. Neuron 45(5), 651–660.
Sheline, Y. I., Barch, D. M., Price, J. L., Rundle, M. M., Vaishnavi, S. N., Snyder, A. Z.,
… , Raichle, M. E. (2009). The default mode network and self-referential processes
in depression. Proceedings of the National Academy of Sciences, 106(6), 1942–1947.
Thase, M. E. (2009). Neurobiological aspects of depression. In I. H. Gotlib & C. L.
Hammen (Eds.), Handbook of depression (2nd ed., pp. 187–217). New York, NY:
Guilford Press.
Whitmer, A. J., & Gotlib, I. H. (2013). An attentional scope model of rumination.
Psychological Bulletin, 139(5), 1036–1061.
World Health Organization (2004). The global burden of disease: 2004 update. Geneva,
Switzerland: WHO.

IAN H. GOTLIB SHORT BIOGRAPHY
Ian H. Gotlib is the David Starr Jordan Professor of Psychology and Director
of the Stanford Mood and Anxiety Disorders Laboratory at Stanford University. From 2005 to 2010, Dr. Gotlib served as Senior Associate Dean for
the Social Sciences, and he has been Chair of the Department of Psychology
at Stanford since 2012. In his research, Dr. Gotlib is broadly examining
psychological and biological factors that place individuals at increased
risk for depression, as well as processes that are involved in recovery from
this disorder. Dr. Gotlib conducts research examining cognitive, social,
endocrinological, and neural factors and genetics in depressed individuals,
as well as predictors of depression in children at familial risk for developing
this disorder. He also examines the impact of innovative procedures to
reduce young children’s risk for depression. Dr. Gotlib’s research is supported largely by grants from the National Institute of Mental Health. He
has also been funded by the National Health Research Development Program, the Medical Research Council of Canada, and the Hope for Depression
Research Foundation. He has received the Distinguished Investigator Award
from the National Alliance for Research in Schizophrenia and Affective
Disorders, the Joseph Zubin Award for lifetime research contributions to
the understanding of psychopathology, the APA Award for Distinguished
Scientific Contribution, and the APS Distinguished Scientist Award. Dr.
Gotlib has published over 500 scientific articles and has written or edited
several books in the areas of depression and stress, including the Handbook

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EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

of Depression with Constance Hammen. He is a Fellow of the American
Psychological Association, the Association for Psychological Science, and
the American Psychopathological Association, and is Past President of the
Society for Research in Psychopathology.
DANIELLA J. FURMAN SHORT BIOGRAPHY
Daniella J. Furman is completing her PhD in Psychology at Stanford
University, where she works with Dr. Ian Gotlib to characterize anomalies in
brain structure, function, and connectivity associated with Major Depressive
Disorder and risk for the development of this disorder. Daniella was named
the 2012–2013 Gerald J. Lieberman Fellow in the Social Sciences; she has
also received the Smadar Levin Award from the Society for Research in
Psychopathology, the American Psychological Association Dissertation
Research Award, and a National Science Foundation Graduate Research
Fellowship.
RELATED ESSAYS
What Is Neuroticism, and Can We Treat It? (Psychology), Amantia Ametaj
et al.
Genetics and the Life Course (Sociology), Evan Charney
Peers and Adolescent Risk Taking (Psychology), Jason Chein
Delusions (Psychology), Max Coltheart
Misinformation and How to Correct It (Psychology), John Cook et al.
Problems Attract Problems: A Network Perspective on Mental Disorders
(Psychology), Angélique Cramer and Denny Borsboom
Expertise (Sociology), Gil Eyal
Controlling the Influence of Stereotypes on One’s Thoughts (Psychology),
Patrick S. Forscher and Patricia G. Devine
Emerging Evidence of Addiction in Problematic Eating Behavior (Psychology), Ashley Gearhardt et al.
Positive Emotion Disturbance (Psychology), June Gruber and John Purcell
Family Relationships and Development (Psychology), Joan E. Grusec
Insomnia and Sleep Disorders (Psychology), Elizabeth C. Mason and Allison
G. Harvey
Mental Imagery in Psychological Disorders (Psychology), Emily A. Holmes
et al.
Normal Negative Emotions and Mental Disorders (Sociology), Allan V.
Horwitz
Dissociation and Dissociative Identity Disorder (DID) (Psychology), Rafaële
J. C. Huntjens and Martin J. Dorahy

Depression

15

Computer Technology and Children’s Mental Health (Psychology), Philip C.
Kendall et al.
Cultural Neuroscience: Connecting Culture, Brain, and Genes (Psychology),
Shinobu Kitayama and Sarah Huff
Mechanisms of Fear Reducation (Psychology), Cynthia L. Lancaster and
Marie-H. Monfils
Understanding Risk-Taking Behavior: Insights from Evolutionary Psychology (Psychology), Karin Machluf and David F. Bjorklund
Evolutionary Perspectives on Animal and Human Personality (Anthropology), Joseph H. Manson and Lynn A. Fairbanks
Disorders of Consciousness (Psychology), Martin M. Monti
Social Classification (Sociology), Elizabeth G. Pontikes
Cognitive Remediation in Schizophrenia (Psychology), Clare Reeder and Til
Wykes
Cognitive Bias Modification in Mental (Psychology), Meg M. Reuland et al.
Born This Way: Thinking Sociologically about Essentialism (Sociology),
Kristen Schilt
Clarifying the Nature and Structure of Personality Disorder (Psychology),
Takakuni Suzuki and Douglas B. Samuel
Taking Personality to the Next Level: What Does It Mean to Know a Person?
(Psychology), Simine Vazire and Robert Wilson
A Gene-Environment Approach to Understanding Youth Antisocial Behavior (Psychology), Rebecca Waller et al.
Crime and the Life Course (Sociology), Mark Warr and Carmen Gutierrez
Rumination (Psychology), Edward R. Watkins
Emotion Regulation (Psychology), Paree Zarolia et al.



Depression
IAN H. GOTLIB and DANIELLA J. FURMAN

Abstract
Major depressive disorder (MDD) is a costly, prevalent, and recurrent psychiatric
disorder that can involve significant impairment across multiple domains of functioning. In this essay, we provide an overview of the theory and research associating
aberrant information processing and neural structure and function with the etiology and maintenance of MDD. We begin by highlighting the foundational work that
characterizes depressed persons’ cognitive and neural responses to valenced stimuli.
We then examine recent efforts to clarify the nature of the temporal relation between
depression and these cognitive and neural anomalies, focusing on research designed
to identify abnormalities that are present before the onset on MDD and to examine the consequences of manipulating cognitive and neural anomalies. Finally, we
describe several areas and questions to be examined in future research that we believe
will lead both to a more comprehensive psychobiological understanding of MDD and
to improvements in the assessment, diagnosis, and treatment of this disorder. In particular, we focus on the need for innovation in diagnosis, better characterization of
symptom heterogeneity in MDD, on extending neural research in MDD to the study
of abnormalities in larger-scale brain networks, and on elucidating the mechanisms
that underlie the successful effects of training programs designed to reduce cognitive
biases in depression.

INTRODUCTION
Major depressive disorder (MDD) is among the most prevalent of all
psychiatric disorders and is associated with enormous personal and societal
costs (Gotlib & Hammen, 2009). Almost 20% of the American population, or
more than 30 million adults, will experience an episode of major depression
during their lifetime (Kessler et al., 2014). In addition to the two cardinal
symptoms of sadness and decreased interest or pleasure in usually enjoyable
activities, MDD is associated with psychomotor agitation or retardation,
marked weight loss, insomnia or hypersomnia, decreased appetite, fatigue,
extreme feelings of guilt or worthlessness, concentration difficulties, and
suicidal ideation. To meet Diagnostic and Statistical Manual of Mental
Disorders criteria for MDD, a subset of these symptoms, including at least
one of the two cardinal symptoms, must be present concurrently for at least
Emerging Trends in the Social and Behavioral Sciences. Edited by Robert Scott and Stephen Kosslyn.
© 2015 John Wiley & Sons, Inc. ISBN 978-1-118-90077-2.

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EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

a 2-week period. MDD is a highly recurrent disorder: 75% of depressed
patients experience more than one depressive episode in their lifetime,
often relapsing within 2 years of recovering from an episode. Further,
epidemiological studies have found depression to be associated with other
mental and physical difficulties, most often with anxiety disorders, but
also with smoking and cardiac problems. Given its prevalence, recurrence,
comorbidity, and costs, it is not surprising that the World Health Organization (2004) projects that MDD will be the single most burdensome disease in
the world in the twenty-first century.
Over the past two decades, investigators have made considerable progress
elucidating psychological and biological aspects of MDD. In particular, there
are now large bodies of research examining anomalies in cognitive functioning and in the processing of positively and negatively valenced information in depression and, more recently, aberrations in neural function and
structure. In this essay, we describe our current understanding of the psychobiological functioning of depressed individuals, focusing in particular
on abnormalities in information processing and in brain function and structure. We begin by presenting foundational research in these areas, discussing
findings from studies that have helped to form our current conceptualization of MDD. We then describe cutting-edge developments in the study of
depression—recent investigations and research directions that have begun to
sharpen, if not refocus, our picture of cognitive and neural aspects of MDD.
Finally, we discuss key issues for future research in the study of MDD, highlighting what we consider to be the most pressing needs and questions that
investigators must address and directions that researchers should take in
moving the field forward.
FOUNDATIONAL RESEARCH
In this section, we present a brief overview of theory and research that has
helped to shape our understanding of cognitive and neural aspects of MDD.
Foundational work in both of these areas has focused in large part on elucidating depressed persons’ responses to valenced stimuli in an effort to understand processes that serve to maintain or exacerbate this disorder.
COGNITIVE ASPECTS OF DEPRESSION
Cognitive theories of depression originated over 50 years ago and provided
the impetus for a large body of research [see Foland-Ross and Gotlib (2012)
and Gotlib and Joormann (2010) for reviews]. Beck (1967) posited that
depressed individuals (and, importantly, persons who are vulnerable to
developing depression) have memory representations, or schemas, that

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3

lead them to view their environment in systematically negative ways. Beck
postulated further that when these biases in cognitive processing interact
with a stressful life event, these individuals initiate a cycle of negative
automatic thoughts about the self, the world, and the future (the “cognitive
triad”) and, consequently, experience high levels of negative affect. Early
studies of cognitive functioning in MDD tested Beck’s theory by comparing the responses of depressed and nondepressed persons to self-report
measures of dysfunctional attitudes and automatic thoughts. While these
studies were important in documenting depression-related aberrations in
self-perceived cognitive functioning, it was clear that Beck’s formulation
involved the operation of cognitive processes at an “automatic” level that
was not necessarily accessible through self-report methodologies. Thus,
more recent studies have utilized more sophisticated experimental tasks
designed to examine schematic functioning. These tasks have now been
used to assess biases in attention to, interpretation of, and memory for
negatively and positively valenced stimuli in MDD, and provide the basis
for innovative treatments for this disorder.
The first studies in this area assessed reaction times of depressed and
nondepressed individuals to name the ink colors in which positive, neutral,
and negative words were printed in an emotional version of the classic
Stroop task, and found that the attention of depressed persons is “captured”
by negatively valenced stimuli. Results of subsequent studies assessing not
only attentional processing but also other aspects and stages of information
processing, such as interpretation and memory, have helped to refine this
formulation. For example, using a variety of experimental tasks, researchers
have found that depressed persons interpret ambiguous information more
negatively than do nondepressed individuals and exhibit preferential
recall of negative versus positive material. On the basis of these and other
findings, theorists have now extended cognitive formulations of depression
to include a consideration of the role of inhibitory functioning. Researchers
have posited that the attention of depressed individuals is relatively easily
and quickly captured by negative stimuli, leading this information to be
more likely than positive material to enter working memory (WM). Given
the limited capacity of the WM system, it is important for adaptive functioning that the contents of WM be updated efficiently and continually by
discarding information that is no longer relevant. Importantly, researchers
have now documented that once negative information is in WM, depressed
individuals are impaired in their ability to inhibit processing of, or remove,
this material, a difficulty that may underlie the better memory of depressed
individuals for negative than for positive stimuli, the sustained negative
affect, and the high levels of rumination, or repetitive negative thinking,
that characterize MDD (Whitmer & Gotlib, 2013).

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NEUROBIOLOGICAL ASPECTS OF DEPRESSION
Since the discovery that pharmacological interventions targeting the serotonin, norepinephrine, and dopamine neurotransmitter systems reduced
symptoms of depression, considerable research has been conducted characterizing depression-associated abnormalities in neurotransmitter production
and binding, receptor density and function, and reuptake mechanisms [see
Thase (2009), for a review]. Investigators have also attempted to induce
depressive symptoms in humans and animals by selectively depleting brain
dopamine or serotonin, usually by administering a cocktail of amino acids
that lacks the components critical for producing these neurotransmitters,
and have found that reductions in serotonin levels, for example, cause
cognitive dysfunction in non-disordered individuals similar to that found in
depressed persons, including increased attention toward negative stimuli.
With advances in brain imaging technology, researchers have been able
to examine depression-related changes in regions of the brain implicated
in mood and cognitive processes (and that are sensitive to permutations
in neurotransmitter activity). In addition to volumetric and metabolic
abnormalities, investigators have identified anomalous neural responding
in depressed individuals in brain areas associated with the generation [e.g.,
amygdala, subgenual anterior cingulate cortex (sACC), and insula] and
regulation [e.g., dorsolateral prefrontal cortex (DLPFC)] of emotion, the
anticipation of rewarding outcomes and motivation of behavior (e.g., ventral
striatum), and memory formation (e.g., hippocampus).
The amygdala has been implicated in the integration of information from
the senses and viscera, particularly in the service of detecting and mobilizing responses to signs of threat in the environment. Depressed individuals
exhibit both decreased volume of, and increased glucose metabolism in, the
amygdala; further, hyper-metabolism of the amygdala in MDD is associated
with increases in plasma cortisol, a critical stress-related glucocorticoid
hormone. Researchers using functional magnetic resonance imaging (fMRI)
have documented increased amygdala responses in MDD across a wide
range of negative emotional conditions, including anticipating, viewing, and
remembering negative words and pictures. Abnormal amygdala responsivity has also been found to correlate with severity of depressive symptoms
and level of ruminative responding, suggesting that the amygdala may
contribute to the cognitive biases in MDD described above.
Investigators have associated the sACC and the insula with the induction
of negative emotions, including sadness. In addition to reports of both
decreased volume and anomalous blood flow and metabolism in the sACC
in depression, researchers have documented increased reactivity of both the
sACC and the insula to negative emotional stimuli in depressed individuals,

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and have found decreases in sACC activity following recovery from MDD
[see Hamilton et al. (2012), for a review]. The DLPFC, in contrast, is involved
in WM and executive control processes, and has also been implicated in
the regulation of emotion. DLPFC metabolism has been found to be lower
in depressed individuals than in healthy controls, and researchers have
documented decreased DLPFC responses as depressed persons process
negative stimuli or attempt to regulate their emotions.
The striatum has been implicated in generating responses to cues predicting
future rewards and to the receipt of unexpected rewards, and more generally,
it has been associated with responses to positive stimuli and positive mood.
Thus, it is not surprising that investigators have reported reduced striatal
response in MDD in a range of positive emotional contexts, including receipt
of monetary rewards and positive feedback, suggesting that anomalies in
this structure contribute to decreased pleasure, or anticipation of pleasure,
in depressed individuals.
Finally, the hippocampus is critical in the formation of new memories
about experienced events and in the regulation of the stress response.
Meta-analyses have documented reduced hippocampal volume and lower
levels of hippocampal activation during performance of memory tasks in
MDD, suggest that abnormalities in this structure contribute to both the
cognitive and affective difficulties experienced by depressed individuals.
CUTTING-EDGE RESEARCH
This foundational research is important in documenting consistent associations between MDD and both aberrant cognitive functioning and anomalous
neural function and structure. We know much less, however, about the temporal or causal relation of these patterns of cognitive and neural function and
neural structure to MDD; that is, we do not yet understand whether these
characteristics are symptoms of the depressed state, consequences of having
been depressed, or vulnerability factors that increase the likelihood that individuals will develop an episode of MDD. In this section we focus on research
designed to elucidate the functional nature of the relation between depression and both cognitive and neural anomalies, including studies examining
whether cognitive and neural abnormalities are present before the onset of
a depressive episode, and investigations in which researchers have manipulated cognitive or neural functioning and examined the effects on depressive
symptoms.
COGNITIVE FUNCTIONING
A growing literature is demonstrating that depression-related biases in
cognition are not necessarily correlates or consequences of the experience of

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EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

depression, but instead, could reflect a pattern of dysfunction that precedes
the initial onset of this disorder. Indeed, like depressed adults, young
individuals who are not themselves depressed but are at high risk for developing depression by virtue of having a depressed parent have been found to
exhibit negative biases in the identification and interpretation of emotional
material [see Foland-Ross and Gotlib (2012), for a review]. Moreover, similar
to depressed adults, never-disordered girls at familial risk for depression
selectively attend to negative facial expressions on the visual-probe task,
an experimental paradigm that enables the quantification of attentional
biases toward or away from emotional stimuli. It is possible, therefore, that
negative cognitive biases play a role in placing children at increased risk for
developing MDD. Indeed, we recently found that high-risk girls (daughters
of depressed mothers) who exhibited a greater attentional bias to sad faces
on the visual-probe task, and who made either less positive or more negative
interpretations on an ambiguous word completion task, were more likely
to experience a subsequent depressive episode than were high-risk girls
who exhibited weaker negative cognitive biases. Thus, biases in attention
and interpretation may represent important targets for early intervention in
MDD. In fact, investigators have begun to report promising initial findings in
using two forms of cognitive bias training (CBM)—attentional bias training
(ABT), which teaches depressed individuals to attend more to positive and
less to negative material, and interpretation bias training (IBT)—both of
which attempt to attenuate cognitive biases in order to reduce depressive
symptoms. Initial studies have documented improvement in depressive
symptoms using these techniques, although more research is needed to
draw strong conclusions about the effectiveness of these approaches.
NEUROBIOLOGICAL FUNCTIONING
Investigators have recently begun to examine the nature of the relation
between anomalous structure and function of particular brain regions and
manifestations of depression by examining whether neural abnormalities
precede the onset of depressive symptoms as risk factors for the development of MDD. Researchers have now identified abnormalities in the
structure and function of several key brain regions in individuals who
are at elevated risk for the development of depression [see Foland-Ross,
Hardin, and Gotlib (2013), for a review]. Importantly, these studies have
revealed that anomalies in high-risk individuals often mirror those that
have been documented in currently depressed individuals. For example,
investigators have found decreased volume of the hippocampus and the
DLPFC in never-depressed individuals at familial risk for MDD, as well
as decreased activation of the striatum to monetary reward. Similarly,

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7

researchers have documented abnormal activation of the amygdala during
sad mood induction and reductions in amygdala volume in individuals at
genetic risk for depression. Thus, aberrations in neural regions implicated in
attention to emotional information, in generating and regulating emotional
and stress responses, and in forming emotional memories may render
high-risk individuals less able to disengage from, or regulate the emotional
consequences of, negative or stressful life events.
A second method by which researchers are beginning to exam the nature
of the relation between symptoms and neural function is by examining
whether directly altering anomalous neural activation in depressed individuals affects clinical aspects of the disorder. In a ground-breaking study,
Mayberg et al. (2005) demonstrated that by applying electrical current
directly to white matter tracts adjacent to the sACC using a method
called deep-brain stimulation (DBS), they were able to immediately reduce
depressive symptoms in individuals with treatment-resistant depression.
Investigators have now begun to explore the feasibility of altering neural
function in circumscribed brain regions through less invasive means.
Real-time neurofeedback training (NFT) procedures, for example, are
designed to teach individuals to exert volitional control over brain states by
presenting them with continuously updated graphical representations of
brain activity during fMRI scanning or electroencephalography (EEG), and
asking them to learn to modulate these representations. Researchers in this
area have examined the ability of individuals to learn to control key areas
involved in emotional experience, such as the amygdala, insula, and sACC
(e.g., Hamilton, Glover, Hsu, Johnson, & Gotlib, 2011). Linden et al. (2012)
found improvement in depressive symptoms immediately following NFT
designed to increase activation in brain regions associated with the elicitation of positive emotions. These preliminary results suggest that anomalous
activity in critical brain regions may not simply convey risk for the development of the disorder or represent a neuropathological consequence or
marker of the disorder, but may also reflect the ongoing maintenance of
particular symptoms. Thus, NFT that targets regions of known dysfunction
in MDD may ultimately enable researchers to identify which of the neural
features of depression are causally linked to the maintenance of specific
behavioral and emotional components of this disorder.
KEY ISSUES FOR FUTURE RESEARCH
Given our current understanding of cognitive and neural aspects of MDD, it
is clear that there are a number of key issues that must be addressed in future
research. Most important are issues concerning improvements in the diagnosis of MDD, the considerable heterogeneity of the disorder, the extension of

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EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

research in MDD to the study of abnormalities in large-scale neural networks,
and the integration of cognitive and neural research in the service of elucidating the mechanisms that might underlie successful CBM. We briefly describe
each of these issues in the following sections.
DIAGNOSIS
The primary method of diagnosing MDD is the clinical interview, which
relies in large part on an individual’s ability to accurately self-report a considerable range of emotions, cognitions, and somatic experiences. Indeed, the
information provided by an individual to a physician or researcher is the only
means for determining whether he or she is currently depressed and whether
a given treatment regimen has been effective. Unfortunately, these reports
may be unreliable, unstable, and subject to the memory biases that characterize depressed individuals. In working toward innovation in the diagnosis
of MDD, researchers have begun to use automated multivariate approaches,
such as machine learning, to classify depressed and nondepressed individuals on the basis of neural activation to sad faces, neural structure, and even
vocal prosody and facial expression. Some work suggests, in addition, that
these methods can predict remission following cognitive-behavioral therapy.
Thus, the move away from self-report as the diagnostic gold standard may
dramatically alter the way in which individuals are diagnosed, treated, and
assessed for treatment response.
HETEROGENEITY
To meet criteria for a diagnosis of MDD according to DSM-IV, individuals
must have one or both of the cardinal symptoms of MDD, depressed mood
and anhedonia, but may present with up to 17 additional possible symptoms
across seven broad categories of functioning, including changes in weight,
appetite, sleep, and psychomotor function, fatigue, worthlessness, guilt,
cognitive impairment, and suicidal thoughts or self-harm. Given the heterogeneity of possible symptom profiles in individuals who meet criteria for
MDD, some emphasis has been placed on delineating reliable and clinically
relevant subtypes of the disorder that might facilitate more effective and
individually tailored interventions, by examining which symptoms and
other manifestations of disorder tend to cluster together. For example, the
DSM-IV defines the melancholic subtype of depression as an episode characterized by severe anhedonia, profound feelings of guilt (often over trivial
events), and marked psychomotor abnormalities. These symptoms have
also been associated empirically with overreliance on external cues during
cued-response tasks and abnormal neural correlates of action monitoring.

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Nonetheless, despite efforts to define reliable symptom clusters and to
identify the neurobiological and cognitive correlates of various symptoms,
we do not yet fully understand why specific symptoms cluster together.
Thus, the development of a comprehensive and neurobiologically informed
understanding of why and how symptoms and other behavioral and neural
correlates co-occur in depressed individuals is an important future step that
would help clinicians and researchers to better characterize the etiology of
subtypes of MDD and treat specific profiles of the disorder. Further, given
that genetic and other risk factors for MDD have been associated with
anomalies in cognitive, affective, and neurobiological functioning, multivariate approaches to characterizing subtypes of MDD could be extended
to inform our understanding of, and our ability to tailor interventions for,
distinct forms of psychobiological risk for the development of the disorder.
NETWORK-LEVEL NEURAL ANALYSIS
Although identifying abnormalities in the structure and activation of particular brain regions has been important in advancing our understanding of
neural aspects of MDD, we still lack a cogent, comprehensive, and therapeutically useful model of brain function and dysfunction in this disorder. In this
context, it is critical to note that massive interconnectivity among populations of neurons in the brain means that neural events seldom occur in isolation; consequently, it is important that we attempt to understand depression
from a larger, neural-network, perspective. Only recently, however, have neuroimaging analysis techniques, as well as our understanding of the architecture of the brain, advanced sufficiently to make network-level explorations
and conceptualizations of MDD feasible.
By far, the majority of neuroimaging studies of MDD use protocols that
involve the presentation to participants of affective or cognitive tasks.
While the results of these studies can inform network-level formulations
of depression, researchers using fMRI and positron emission tomography
(PET) have increasingly been investigating neural functioning in MDD
over relatively long durations in the scanner in the absence of externally
presented tasks or stimuli. This “resting state” approach has led to the
identification of abnormalities in the “default mode network” (DMN), a
cluster of medial brain regions that appears to mediate internally generated
thought processes and is typically inhibited in tasks that require subjects to
attend to cognitively engaging, external stimuli. In depressed individuals,
this network shows greater interconnectivity with the sACC (Greicius et al.,
2007), a region that, as we noted earlier, is associated with the generation of
sadness. Further, depressed individuals do not deactivate this network in
the same way that nondepressed persons do during the active processing of

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external stimuli (Sheline et al., 2009), suggesting that MDD is characterized
by difficulty inhibiting the processing of negative, internally generated,
thought content. Indeed, given these and other findings [see Hamilton,
Chen, and Gotlib (2013), for review], it is not surprising that DMN dynamics
have been associated, in depressed individuals, with the tendency to engage
in rumination (Hamilton et al., 2011). Examinations of large-scale neural
networks in MDD have now extended beyond the DMN. For example, a
growing body of work is implicating anomalous function and structure in
the salience network in MDD, a group of regions that includes the insula and
amygdala, that undergirds responding to biological relevant stimuli, and
that may subserve heightened attention toward negative stimuli (Hamilton
et al., 2012).
In addition, most investigators to date have relied on simple correlative
methods, or “functional connectivity,” to assess intrinsic network-level function in depression. Importantly, there is growing interest in examining issues
involving the temporal and directional relations among areas. For example,
multivariate Granger causality analysis is a technique that has been applied
to neuroimaging data to estimate temporal influence, or “effective connectivity,” of one brain region with respect to another region. In the first work using
this analytic method to examine neural connectivity in MDD, Hamilton et al.
(2011) found that, to a significantly greater extent in depressed than in nondepressed participants, activations in emotion generative areas are not only
mutually excitatory, but further, are associated with subsequent decreases in
brain regions associated with emotion regulation, such as the DLPFC.
Researchers have also recently begun to use graph theory to examine
large-scale brain network organization. This method provides a means for
quantifying the overall organization of brain connectivity, allowing the
brain to be depicted as a series of “nodes,” representing particular regions,
and “edges,” representing correlations in structural volume or activity
between nodes. A handful of studies have now used graph analyses to
examine network connectivity in depressed individuals, and have identified
abnormalities in both path length, that is, how many steps it takes to get
from a node to any other node in the network, and number of hubs and connections, features that may relate to the efficiency of information processing
within and between neural networks. Therefore, research that continues to
integrate this method with other connectivity- and activation-based analytic
techniques has the potential to greatly increase our understanding of the
nature of neural function and dysfunction in MDD, as well as the way in
neural anomalies may underlie deficits, biases, and difficulties in cognition
and information processing in this disorder.

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MECHANISMS UNDERLYING THE EFFECTS OF COGNITIVE BIAS MODIFICATION
Despite the promise of CBM procedures in reducing depressive symptoms,
the mechanisms that might contribute to this improvement are not yet
clear. While it is possible that ABT and IBT simply “train away biases”
and thereby improve symptoms of MDD, it is likely that the mechanisms
underlying the effects of these training procedures are more complex.
Investigators have already begun to examine the neural foundations of
traditional cognitive-behavior therapy for depression (DeRubeis, Siegle, &
Hollon, 2008), but it will be important to extend this research to elucidate
the mechanisms by which CBM achieves its beneficial effects. MacLeod and
Mathews (2012) recently distinguished between “near” and “far” transfer of
training effects of CBM. They noted that while training typically transfers to
the same task with different stimuli (near transfer), changes in functioning
on tasks that are less closely related to the training task (far transfer) are
particularly informative for our understanding of mechanisms. Initial work
indicates that the effects of ABT can transfer to alter interpretive biases,
and that similarly, the effects of IBT may influence biases in both attention
and memory. These findings suggest not only that the distinctions made by
researchers among biases in attention, interpretation, and memory need to
be reconsidered, but further, that at least some aspects of these biases share
common mechanisms of action.
We posit that there are three mechanisms in particular that underlie the
positive effects of CBM in depression: decreased attentional capture of
negative stimuli (bottom-up processing); increased inhibition of negative
material (top-down processing); and, as a consequence of these changes,
decreased negative self-referential thinking (rumination). Importantly,
as we noted earlier in this essay, all three of these constructs have been
found to distinguish depressed from nondepressed individuals. Moreover,
investigators are beginning to examine neural underpinnings of each these
mechanisms (Cooney, Joormann, Eugene, Dennis, & Gotlib, 2010; Dichter,
Felder, & Smoski, 2009; Foland-Ross et al., 2013). It will be important to
continue this line of investigation, integrating assessments of cognitive
and neural functioning in depressed individuals in order to gain a more
comprehensive understanding of, and to continue to develop and refine,
innovative treatments for this debilitating disorder.
REFERENCES
Beck, A. T. (1967). Depression: clinical, experimental, and theoretical aspects. New York,
NY: Harper & Row.
Cooney, R. E., Joormann, J., Eugene, F., Dennis, E. L., & Gotlib, I. H. (2010). Neural
correlates of rumination in depression. Cognitive, Affective, and Behavioral Neuroscience, 10, 470–478. doi:10.3758/CABN.10.4.470

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DeRubeis, R. J., Siegle, G. J., & Hollon, S. D. (2008). Cognitive therapy versus medication for depression: Treatment outcomes and neural mechanisms. Nature Reviews
Neuroscience, 9(10), 788–796.
Dichter, G. S., Felder, J. N., & Smoski, M. J. (2009). Affective context interferes with
cognitive control in unipolar depression: An fMRI investigation. Journal of Affective
Disorders, 114, 131–142.
Foland-Ross, L. C., & Gotlib, I. H. (2012). Cognitive and neural aspects of information
processing in major depressive disorder: An integrative perspective. Frontiers in
Emotion Science, November, 3 Article 489, 1–17.
Foland-Ross, L. C., Hamilton, J. P., Joormann, J., Berman, M. G., Jonides, J., & Gotlib,
I. H. (2013). The neural basis of difficulties disengaging from negative irrelevant
material in Major Depression. Psycholological Science, 24, 334–344.
Foland-Ross, L. C., Hardin, M. G., & Gotlib, I. H. (2013). Neurobiological markers
of familial risk for depression. In P. Cowen, T. Sharp & J. Lau (Eds.), Behavioral
neurobiology of depression and its treatment: Current topics in behavioral neurosciences
(Vol. 14, pp. 181–206). New York, NY: Springer.
Gotlib, I. H., & Hammen, C. L. (Eds.) (2009). Handbook of depression (2nd ed.). New
York, NY: The Guilford Press.
Gotlib, I. H., & Joormann, J. (2010). Cognition and depression: Current status and
future directions. Annual Review of Clinical Psychology, 6, 285–312.
Greicius, M. D., Flores, B. H., Menon, V., Glover, G. H., Solvason, H. B., Kenna, H.,
… Schatzberg, A. F. (2007). Resting-state functional connectivity in major depression: Abnormally increased contributions from subgenual cingulate cortex and
thalamus. Biological Psychiatry, 62(5), 429–437.
Hamilton, J. P., Chen, G., Thomason, M. E., Schwartz, M. E., & Gotlib, I. H. (2011).
Investigating neural primacy in Major Depressive Disorder: Multivariate granger
causality analysis of resting-state fMRI time-series data. Molecular Psychiatry, 16,
763–772.
Hamilton, J. P., Furman, D. J., Chang, C., Thomason, M. E., Dennis, E., & Gotlib, I. H.
(2011). Default-mode and task-positive network activity in Major Depressive Disorder: Implications for adaptive and maladaptive rumination. Biological Psychiatry,
70, 327–333.
Hamilton, J. P., Glover, G. H., Hsu, J.-J., Johnson, R. F., & Gotlib, I. H. (2011). Modulation of subgenual anterior cingulate cortex activity with real-time neurofeedback.
Human Brain Mapping, 32, 22–31.
Hamilton, J. P., Etkin, A., Furman, D. J., Lemus, M. G., Johnson, R. F., & Gotlib, I. H.
(2012). Functional neuroimaging of Major Depressive Disorder: A meta-analysis
and new integration of baseline activation and neural response data. American
Journal of Psychiatry, 169, 693–703.
Hamilton, J. P., Chen, M. C., & Gotlib, I. H. (2013). Neural systems approaches to
understanding Major Depressive Disorder: An intrinsic functional organization
perspective. Neurobiology of Disease, 52, 4–11.
Kessler, R. C., de Jonge, P., Shahly, V., van Loo, H. M., Wang, P. S., & Wilcox, M. A.
(2014). The epidemiology of depression. In I. H. Gotlib & C. L. Hammen (Eds.),
Handbook of Depression (3rd ed., pp. 7–24). New York, NY: The Guilford Press.

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Linden, D. E. J., Habes, I., Johnston, S. J., Linden, S., Tatineni, R., Subramanian, L.,
… , Goebel, R.. (2012). Real-time self-regulation of emotion networks in patients
with depression. PLoS One, 7(6), e38115.
MacLeod, C., & Mathews, A. (2012). Cognitive bias modification approaches to anxiety. Annual Review of Clinical Psychology, 8, 189–217.
Mayberg, H. S., Lozano, A. M., Voon, V., McNeely, H. E., Seminowicz, D., Hamani, C.,
… , Kennedy, S. H. (2005). Deep brain stimulation for treatment-resistant depression. Neuron 45(5), 651–660.
Sheline, Y. I., Barch, D. M., Price, J. L., Rundle, M. M., Vaishnavi, S. N., Snyder, A. Z.,
… , Raichle, M. E. (2009). The default mode network and self-referential processes
in depression. Proceedings of the National Academy of Sciences, 106(6), 1942–1947.
Thase, M. E. (2009). Neurobiological aspects of depression. In I. H. Gotlib & C. L.
Hammen (Eds.), Handbook of depression (2nd ed., pp. 187–217). New York, NY:
Guilford Press.
Whitmer, A. J., & Gotlib, I. H. (2013). An attentional scope model of rumination.
Psychological Bulletin, 139(5), 1036–1061.
World Health Organization (2004). The global burden of disease: 2004 update. Geneva,
Switzerland: WHO.

IAN H. GOTLIB SHORT BIOGRAPHY
Ian H. Gotlib is the David Starr Jordan Professor of Psychology and Director
of the Stanford Mood and Anxiety Disorders Laboratory at Stanford University. From 2005 to 2010, Dr. Gotlib served as Senior Associate Dean for
the Social Sciences, and he has been Chair of the Department of Psychology
at Stanford since 2012. In his research, Dr. Gotlib is broadly examining
psychological and biological factors that place individuals at increased
risk for depression, as well as processes that are involved in recovery from
this disorder. Dr. Gotlib conducts research examining cognitive, social,
endocrinological, and neural factors and genetics in depressed individuals,
as well as predictors of depression in children at familial risk for developing
this disorder. He also examines the impact of innovative procedures to
reduce young children’s risk for depression. Dr. Gotlib’s research is supported largely by grants from the National Institute of Mental Health. He
has also been funded by the National Health Research Development Program, the Medical Research Council of Canada, and the Hope for Depression
Research Foundation. He has received the Distinguished Investigator Award
from the National Alliance for Research in Schizophrenia and Affective
Disorders, the Joseph Zubin Award for lifetime research contributions to
the understanding of psychopathology, the APA Award for Distinguished
Scientific Contribution, and the APS Distinguished Scientist Award. Dr.
Gotlib has published over 500 scientific articles and has written or edited
several books in the areas of depression and stress, including the Handbook

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EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

of Depression with Constance Hammen. He is a Fellow of the American
Psychological Association, the Association for Psychological Science, and
the American Psychopathological Association, and is Past President of the
Society for Research in Psychopathology.
DANIELLA J. FURMAN SHORT BIOGRAPHY
Daniella J. Furman is completing her PhD in Psychology at Stanford
University, where she works with Dr. Ian Gotlib to characterize anomalies in
brain structure, function, and connectivity associated with Major Depressive
Disorder and risk for the development of this disorder. Daniella was named
the 2012–2013 Gerald J. Lieberman Fellow in the Social Sciences; she has
also received the Smadar Levin Award from the Society for Research in
Psychopathology, the American Psychological Association Dissertation
Research Award, and a National Science Foundation Graduate Research
Fellowship.
RELATED ESSAYS
What Is Neuroticism, and Can We Treat It? (Psychology), Amantia Ametaj
et al.
Genetics and the Life Course (Sociology), Evan Charney
Peers and Adolescent Risk Taking (Psychology), Jason Chein
Delusions (Psychology), Max Coltheart
Misinformation and How to Correct It (Psychology), John Cook et al.
Problems Attract Problems: A Network Perspective on Mental Disorders
(Psychology), Angélique Cramer and Denny Borsboom
Expertise (Sociology), Gil Eyal
Controlling the Influence of Stereotypes on One’s Thoughts (Psychology),
Patrick S. Forscher and Patricia G. Devine
Emerging Evidence of Addiction in Problematic Eating Behavior (Psychology), Ashley Gearhardt et al.
Positive Emotion Disturbance (Psychology), June Gruber and John Purcell
Family Relationships and Development (Psychology), Joan E. Grusec
Insomnia and Sleep Disorders (Psychology), Elizabeth C. Mason and Allison
G. Harvey
Mental Imagery in Psychological Disorders (Psychology), Emily A. Holmes
et al.
Normal Negative Emotions and Mental Disorders (Sociology), Allan V.
Horwitz
Dissociation and Dissociative Identity Disorder (DID) (Psychology), Rafaële
J. C. Huntjens and Martin J. Dorahy

Depression

15

Computer Technology and Children’s Mental Health (Psychology), Philip C.
Kendall et al.
Cultural Neuroscience: Connecting Culture, Brain, and Genes (Psychology),
Shinobu Kitayama and Sarah Huff
Mechanisms of Fear Reducation (Psychology), Cynthia L. Lancaster and
Marie-H. Monfils
Understanding Risk-Taking Behavior: Insights from Evolutionary Psychology (Psychology), Karin Machluf and David F. Bjorklund
Evolutionary Perspectives on Animal and Human Personality (Anthropology), Joseph H. Manson and Lynn A. Fairbanks
Disorders of Consciousness (Psychology), Martin M. Monti
Social Classification (Sociology), Elizabeth G. Pontikes
Cognitive Remediation in Schizophrenia (Psychology), Clare Reeder and Til
Wykes
Cognitive Bias Modification in Mental (Psychology), Meg M. Reuland et al.
Born This Way: Thinking Sociologically about Essentialism (Sociology),
Kristen Schilt
Clarifying the Nature and Structure of Personality Disorder (Psychology),
Takakuni Suzuki and Douglas B. Samuel
Taking Personality to the Next Level: What Does It Mean to Know a Person?
(Psychology), Simine Vazire and Robert Wilson
A Gene-Environment Approach to Understanding Youth Antisocial Behavior (Psychology), Rebecca Waller et al.
Crime and the Life Course (Sociology), Mark Warr and Carmen Gutierrez
Rumination (Psychology), Edward R. Watkins
Emotion Regulation (Psychology), Paree Zarolia et al.


Depression
IAN H. GOTLIB and DANIELLA J. FURMAN

Abstract
Major depressive disorder (MDD) is a costly, prevalent, and recurrent psychiatric
disorder that can involve significant impairment across multiple domains of functioning. In this essay, we provide an overview of the theory and research associating
aberrant information processing and neural structure and function with the etiology and maintenance of MDD. We begin by highlighting the foundational work that
characterizes depressed persons’ cognitive and neural responses to valenced stimuli.
We then examine recent efforts to clarify the nature of the temporal relation between
depression and these cognitive and neural anomalies, focusing on research designed
to identify abnormalities that are present before the onset on MDD and to examine the consequences of manipulating cognitive and neural anomalies. Finally, we
describe several areas and questions to be examined in future research that we believe
will lead both to a more comprehensive psychobiological understanding of MDD and
to improvements in the assessment, diagnosis, and treatment of this disorder. In particular, we focus on the need for innovation in diagnosis, better characterization of
symptom heterogeneity in MDD, on extending neural research in MDD to the study
of abnormalities in larger-scale brain networks, and on elucidating the mechanisms
that underlie the successful effects of training programs designed to reduce cognitive
biases in depression.

INTRODUCTION
Major depressive disorder (MDD) is among the most prevalent of all
psychiatric disorders and is associated with enormous personal and societal
costs (Gotlib & Hammen, 2009). Almost 20% of the American population, or
more than 30 million adults, will experience an episode of major depression
during their lifetime (Kessler et al., 2014). In addition to the two cardinal
symptoms of sadness and decreased interest or pleasure in usually enjoyable
activities, MDD is associated with psychomotor agitation or retardation,
marked weight loss, insomnia or hypersomnia, decreased appetite, fatigue,
extreme feelings of guilt or worthlessness, concentration difficulties, and
suicidal ideation. To meet Diagnostic and Statistical Manual of Mental
Disorders criteria for MDD, a subset of these symptoms, including at least
one of the two cardinal symptoms, must be present concurrently for at least
Emerging Trends in the Social and Behavioral Sciences. Edited by Robert Scott and Stephen Kosslyn.
© 2015 John Wiley & Sons, Inc. ISBN 978-1-118-90077-2.

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EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

a 2-week period. MDD is a highly recurrent disorder: 75% of depressed
patients experience more than one depressive episode in their lifetime,
often relapsing within 2 years of recovering from an episode. Further,
epidemiological studies have found depression to be associated with other
mental and physical difficulties, most often with anxiety disorders, but
also with smoking and cardiac problems. Given its prevalence, recurrence,
comorbidity, and costs, it is not surprising that the World Health Organization (2004) projects that MDD will be the single most burdensome disease in
the world in the twenty-first century.
Over the past two decades, investigators have made considerable progress
elucidating psychological and biological aspects of MDD. In particular, there
are now large bodies of research examining anomalies in cognitive functioning and in the processing of positively and negatively valenced information in depression and, more recently, aberrations in neural function and
structure. In this essay, we describe our current understanding of the psychobiological functioning of depressed individuals, focusing in particular
on abnormalities in information processing and in brain function and structure. We begin by presenting foundational research in these areas, discussing
findings from studies that have helped to form our current conceptualization of MDD. We then describe cutting-edge developments in the study of
depression—recent investigations and research directions that have begun to
sharpen, if not refocus, our picture of cognitive and neural aspects of MDD.
Finally, we discuss key issues for future research in the study of MDD, highlighting what we consider to be the most pressing needs and questions that
investigators must address and directions that researchers should take in
moving the field forward.
FOUNDATIONAL RESEARCH
In this section, we present a brief overview of theory and research that has
helped to shape our understanding of cognitive and neural aspects of MDD.
Foundational work in both of these areas has focused in large part on elucidating depressed persons’ responses to valenced stimuli in an effort to understand processes that serve to maintain or exacerbate this disorder.
COGNITIVE ASPECTS OF DEPRESSION
Cognitive theories of depression originated over 50 years ago and provided
the impetus for a large body of research [see Foland-Ross and Gotlib (2012)
and Gotlib and Joormann (2010) for reviews]. Beck (1967) posited that
depressed individuals (and, importantly, persons who are vulnerable to
developing depression) have memory representations, or schemas, that

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3

lead them to view their environment in systematically negative ways. Beck
postulated further that when these biases in cognitive processing interact
with a stressful life event, these individuals initiate a cycle of negative
automatic thoughts about the self, the world, and the future (the “cognitive
triad”) and, consequently, experience high levels of negative affect. Early
studies of cognitive functioning in MDD tested Beck’s theory by comparing the responses of depressed and nondepressed persons to self-report
measures of dysfunctional attitudes and automatic thoughts. While these
studies were important in documenting depression-related aberrations in
self-perceived cognitive functioning, it was clear that Beck’s formulation
involved the operation of cognitive processes at an “automatic” level that
was not necessarily accessible through self-report methodologies. Thus,
more recent studies have utilized more sophisticated experimental tasks
designed to examine schematic functioning. These tasks have now been
used to assess biases in attention to, interpretation of, and memory for
negatively and positively valenced stimuli in MDD, and provide the basis
for innovative treatments for this disorder.
The first studies in this area assessed reaction times of depressed and
nondepressed individuals to name the ink colors in which positive, neutral,
and negative words were printed in an emotional version of the classic
Stroop task, and found that the attention of depressed persons is “captured”
by negatively valenced stimuli. Results of subsequent studies assessing not
only attentional processing but also other aspects and stages of information
processing, such as interpretation and memory, have helped to refine this
formulation. For example, using a variety of experimental tasks, researchers
have found that depressed persons interpret ambiguous information more
negatively than do nondepressed individuals and exhibit preferential
recall of negative versus positive material. On the basis of these and other
findings, theorists have now extended cognitive formulations of depression
to include a consideration of the role of inhibitory functioning. Researchers
have posited that the attention of depressed individuals is relatively easily
and quickly captured by negative stimuli, leading this information to be
more likely than positive material to enter working memory (WM). Given
the limited capacity of the WM system, it is important for adaptive functioning that the contents of WM be updated efficiently and continually by
discarding information that is no longer relevant. Importantly, researchers
have now documented that once negative information is in WM, depressed
individuals are impaired in their ability to inhibit processing of, or remove,
this material, a difficulty that may underlie the better memory of depressed
individuals for negative than for positive stimuli, the sustained negative
affect, and the high levels of rumination, or repetitive negative thinking,
that characterize MDD (Whitmer & Gotlib, 2013).

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EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

NEUROBIOLOGICAL ASPECTS OF DEPRESSION
Since the discovery that pharmacological interventions targeting the serotonin, norepinephrine, and dopamine neurotransmitter systems reduced
symptoms of depression, considerable research has been conducted characterizing depression-associated abnormalities in neurotransmitter production
and binding, receptor density and function, and reuptake mechanisms [see
Thase (2009), for a review]. Investigators have also attempted to induce
depressive symptoms in humans and animals by selectively depleting brain
dopamine or serotonin, usually by administering a cocktail of amino acids
that lacks the components critical for producing these neurotransmitters,
and have found that reductions in serotonin levels, for example, cause
cognitive dysfunction in non-disordered individuals similar to that found in
depressed persons, including increased attention toward negative stimuli.
With advances in brain imaging technology, researchers have been able
to examine depression-related changes in regions of the brain implicated
in mood and cognitive processes (and that are sensitive to permutations
in neurotransmitter activity). In addition to volumetric and metabolic
abnormalities, investigators have identified anomalous neural responding
in depressed individuals in brain areas associated with the generation [e.g.,
amygdala, subgenual anterior cingulate cortex (sACC), and insula] and
regulation [e.g., dorsolateral prefrontal cortex (DLPFC)] of emotion, the
anticipation of rewarding outcomes and motivation of behavior (e.g., ventral
striatum), and memory formation (e.g., hippocampus).
The amygdala has been implicated in the integration of information from
the senses and viscera, particularly in the service of detecting and mobilizing responses to signs of threat in the environment. Depressed individuals
exhibit both decreased volume of, and increased glucose metabolism in, the
amygdala; further, hyper-metabolism of the amygdala in MDD is associated
with increases in plasma cortisol, a critical stress-related glucocorticoid
hormone. Researchers using functional magnetic resonance imaging (fMRI)
have documented increased amygdala responses in MDD across a wide
range of negative emotional conditions, including anticipating, viewing, and
remembering negative words and pictures. Abnormal amygdala responsivity has also been found to correlate with severity of depressive symptoms
and level of ruminative responding, suggesting that the amygdala may
contribute to the cognitive biases in MDD described above.
Investigators have associated the sACC and the insula with the induction
of negative emotions, including sadness. In addition to reports of both
decreased volume and anomalous blood flow and metabolism in the sACC
in depression, researchers have documented increased reactivity of both the
sACC and the insula to negative emotional stimuli in depressed individuals,

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and have found decreases in sACC activity following recovery from MDD
[see Hamilton et al. (2012), for a review]. The DLPFC, in contrast, is involved
in WM and executive control processes, and has also been implicated in
the regulation of emotion. DLPFC metabolism has been found to be lower
in depressed individuals than in healthy controls, and researchers have
documented decreased DLPFC responses as depressed persons process
negative stimuli or attempt to regulate their emotions.
The striatum has been implicated in generating responses to cues predicting
future rewards and to the receipt of unexpected rewards, and more generally,
it has been associated with responses to positive stimuli and positive mood.
Thus, it is not surprising that investigators have reported reduced striatal
response in MDD in a range of positive emotional contexts, including receipt
of monetary rewards and positive feedback, suggesting that anomalies in
this structure contribute to decreased pleasure, or anticipation of pleasure,
in depressed individuals.
Finally, the hippocampus is critical in the formation of new memories
about experienced events and in the regulation of the stress response.
Meta-analyses have documented reduced hippocampal volume and lower
levels of hippocampal activation during performance of memory tasks in
MDD, suggest that abnormalities in this structure contribute to both the
cognitive and affective difficulties experienced by depressed individuals.
CUTTING-EDGE RESEARCH
This foundational research is important in documenting consistent associations between MDD and both aberrant cognitive functioning and anomalous
neural function and structure. We know much less, however, about the temporal or causal relation of these patterns of cognitive and neural function and
neural structure to MDD; that is, we do not yet understand whether these
characteristics are symptoms of the depressed state, consequences of having
been depressed, or vulnerability factors that increase the likelihood that individuals will develop an episode of MDD. In this section we focus on research
designed to elucidate the functional nature of the relation between depression and both cognitive and neural anomalies, including studies examining
whether cognitive and neural abnormalities are present before the onset of
a depressive episode, and investigations in which researchers have manipulated cognitive or neural functioning and examined the effects on depressive
symptoms.
COGNITIVE FUNCTIONING
A growing literature is demonstrating that depression-related biases in
cognition are not necessarily correlates or consequences of the experience of

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EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

depression, but instead, could reflect a pattern of dysfunction that precedes
the initial onset of this disorder. Indeed, like depressed adults, young
individuals who are not themselves depressed but are at high risk for developing depression by virtue of having a depressed parent have been found to
exhibit negative biases in the identification and interpretation of emotional
material [see Foland-Ross and Gotlib (2012), for a review]. Moreover, similar
to depressed adults, never-disordered girls at familial risk for depression
selectively attend to negative facial expressions on the visual-probe task,
an experimental paradigm that enables the quantification of attentional
biases toward or away from emotional stimuli. It is possible, therefore, that
negative cognitive biases play a role in placing children at increased risk for
developing MDD. Indeed, we recently found that high-risk girls (daughters
of depressed mothers) who exhibited a greater attentional bias to sad faces
on the visual-probe task, and who made either less positive or more negative
interpretations on an ambiguous word completion task, were more likely
to experience a subsequent depressive episode than were high-risk girls
who exhibited weaker negative cognitive biases. Thus, biases in attention
and interpretation may represent important targets for early intervention in
MDD. In fact, investigators have begun to report promising initial findings in
using two forms of cognitive bias training (CBM)—attentional bias training
(ABT), which teaches depressed individuals to attend more to positive and
less to negative material, and interpretation bias training (IBT)—both of
which attempt to attenuate cognitive biases in order to reduce depressive
symptoms. Initial studies have documented improvement in depressive
symptoms using these techniques, although more research is needed to
draw strong conclusions about the effectiveness of these approaches.
NEUROBIOLOGICAL FUNCTIONING
Investigators have recently begun to examine the nature of the relation
between anomalous structure and function of particular brain regions and
manifestations of depression by examining whether neural abnormalities
precede the onset of depressive symptoms as risk factors for the development of MDD. Researchers have now identified abnormalities in the
structure and function of several key brain regions in individuals who
are at elevated risk for the development of depression [see Foland-Ross,
Hardin, and Gotlib (2013), for a review]. Importantly, these studies have
revealed that anomalies in high-risk individuals often mirror those that
have been documented in currently depressed individuals. For example,
investigators have found decreased volume of the hippocampus and the
DLPFC in never-depressed individuals at familial risk for MDD, as well
as decreased activation of the striatum to monetary reward. Similarly,

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7

researchers have documented abnormal activation of the amygdala during
sad mood induction and reductions in amygdala volume in individuals at
genetic risk for depression. Thus, aberrations in neural regions implicated in
attention to emotional information, in generating and regulating emotional
and stress responses, and in forming emotional memories may render
high-risk individuals less able to disengage from, or regulate the emotional
consequences of, negative or stressful life events.
A second method by which researchers are beginning to exam the nature
of the relation between symptoms and neural function is by examining
whether directly altering anomalous neural activation in depressed individuals affects clinical aspects of the disorder. In a ground-breaking study,
Mayberg et al. (2005) demonstrated that by applying electrical current
directly to white matter tracts adjacent to the sACC using a method
called deep-brain stimulation (DBS), they were able to immediately reduce
depressive symptoms in individuals with treatment-resistant depression.
Investigators have now begun to explore the feasibility of altering neural
function in circumscribed brain regions through less invasive means.
Real-time neurofeedback training (NFT) procedures, for example, are
designed to teach individuals to exert volitional control over brain states by
presenting them with continuously updated graphical representations of
brain activity during fMRI scanning or electroencephalography (EEG), and
asking them to learn to modulate these representations. Researchers in this
area have examined the ability of individuals to learn to control key areas
involved in emotional experience, such as the amygdala, insula, and sACC
(e.g., Hamilton, Glover, Hsu, Johnson, & Gotlib, 2011). Linden et al. (2012)
found improvement in depressive symptoms immediately following NFT
designed to increase activation in brain regions associated with the elicitation of positive emotions. These preliminary results suggest that anomalous
activity in critical brain regions may not simply convey risk for the development of the disorder or represent a neuropathological consequence or
marker of the disorder, but may also reflect the ongoing maintenance of
particular symptoms. Thus, NFT that targets regions of known dysfunction
in MDD may ultimately enable researchers to identify which of the neural
features of depression are causally linked to the maintenance of specific
behavioral and emotional components of this disorder.
KEY ISSUES FOR FUTURE RESEARCH
Given our current understanding of cognitive and neural aspects of MDD, it
is clear that there are a number of key issues that must be addressed in future
research. Most important are issues concerning improvements in the diagnosis of MDD, the considerable heterogeneity of the disorder, the extension of

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EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

research in MDD to the study of abnormalities in large-scale neural networks,
and the integration of cognitive and neural research in the service of elucidating the mechanisms that might underlie successful CBM. We briefly describe
each of these issues in the following sections.
DIAGNOSIS
The primary method of diagnosing MDD is the clinical interview, which
relies in large part on an individual’s ability to accurately self-report a considerable range of emotions, cognitions, and somatic experiences. Indeed, the
information provided by an individual to a physician or researcher is the only
means for determining whether he or she is currently depressed and whether
a given treatment regimen has been effective. Unfortunately, these reports
may be unreliable, unstable, and subject to the memory biases that characterize depressed individuals. In working toward innovation in the diagnosis
of MDD, researchers have begun to use automated multivariate approaches,
such as machine learning, to classify depressed and nondepressed individuals on the basis of neural activation to sad faces, neural structure, and even
vocal prosody and facial expression. Some work suggests, in addition, that
these methods can predict remission following cognitive-behavioral therapy.
Thus, the move away from self-report as the diagnostic gold standard may
dramatically alter the way in which individuals are diagnosed, treated, and
assessed for treatment response.
HETEROGENEITY
To meet criteria for a diagnosis of MDD according to DSM-IV, individuals
must have one or both of the cardinal symptoms of MDD, depressed mood
and anhedonia, but may present with up to 17 additional possible symptoms
across seven broad categories of functioning, including changes in weight,
appetite, sleep, and psychomotor function, fatigue, worthlessness, guilt,
cognitive impairment, and suicidal thoughts or self-harm. Given the heterogeneity of possible symptom profiles in individuals who meet criteria for
MDD, some emphasis has been placed on delineating reliable and clinically
relevant subtypes of the disorder that might facilitate more effective and
individually tailored interventions, by examining which symptoms and
other manifestations of disorder tend to cluster together. For example, the
DSM-IV defines the melancholic subtype of depression as an episode characterized by severe anhedonia, profound feelings of guilt (often over trivial
events), and marked psychomotor abnormalities. These symptoms have
also been associated empirically with overreliance on external cues during
cued-response tasks and abnormal neural correlates of action monitoring.

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9

Nonetheless, despite efforts to define reliable symptom clusters and to
identify the neurobiological and cognitive correlates of various symptoms,
we do not yet fully understand why specific symptoms cluster together.
Thus, the development of a comprehensive and neurobiologically informed
understanding of why and how symptoms and other behavioral and neural
correlates co-occur in depressed individuals is an important future step that
would help clinicians and researchers to better characterize the etiology of
subtypes of MDD and treat specific profiles of the disorder. Further, given
that genetic and other risk factors for MDD have been associated with
anomalies in cognitive, affective, and neurobiological functioning, multivariate approaches to characterizing subtypes of MDD could be extended
to inform our understanding of, and our ability to tailor interventions for,
distinct forms of psychobiological risk for the development of the disorder.
NETWORK-LEVEL NEURAL ANALYSIS
Although identifying abnormalities in the structure and activation of particular brain regions has been important in advancing our understanding of
neural aspects of MDD, we still lack a cogent, comprehensive, and therapeutically useful model of brain function and dysfunction in this disorder. In this
context, it is critical to note that massive interconnectivity among populations of neurons in the brain means that neural events seldom occur in isolation; consequently, it is important that we attempt to understand depression
from a larger, neural-network, perspective. Only recently, however, have neuroimaging analysis techniques, as well as our understanding of the architecture of the brain, advanced sufficiently to make network-level explorations
and conceptualizations of MDD feasible.
By far, the majority of neuroimaging studies of MDD use protocols that
involve the presentation to participants of affective or cognitive tasks.
While the results of these studies can inform network-level formulations
of depression, researchers using fMRI and positron emission tomography
(PET) have increasingly been investigating neural functioning in MDD
over relatively long durations in the scanner in the absence of externally
presented tasks or stimuli. This “resting state” approach has led to the
identification of abnormalities in the “default mode network” (DMN), a
cluster of medial brain regions that appears to mediate internally generated
thought processes and is typically inhibited in tasks that require subjects to
attend to cognitively engaging, external stimuli. In depressed individuals,
this network shows greater interconnectivity with the sACC (Greicius et al.,
2007), a region that, as we noted earlier, is associated with the generation of
sadness. Further, depressed individuals do not deactivate this network in
the same way that nondepressed persons do during the active processing of

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EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

external stimuli (Sheline et al., 2009), suggesting that MDD is characterized
by difficulty inhibiting the processing of negative, internally generated,
thought content. Indeed, given these and other findings [see Hamilton,
Chen, and Gotlib (2013), for review], it is not surprising that DMN dynamics
have been associated, in depressed individuals, with the tendency to engage
in rumination (Hamilton et al., 2011). Examinations of large-scale neural
networks in MDD have now extended beyond the DMN. For example, a
growing body of work is implicating anomalous function and structure in
the salience network in MDD, a group of regions that includes the insula and
amygdala, that undergirds responding to biological relevant stimuli, and
that may subserve heightened attention toward negative stimuli (Hamilton
et al., 2012).
In addition, most investigators to date have relied on simple correlative
methods, or “functional connectivity,” to assess intrinsic network-level function in depression. Importantly, there is growing interest in examining issues
involving the temporal and directional relations among areas. For example,
multivariate Granger causality analysis is a technique that has been applied
to neuroimaging data to estimate temporal influence, or “effective connectivity,” of one brain region with respect to another region. In the first work using
this analytic method to examine neural connectivity in MDD, Hamilton et al.
(2011) found that, to a significantly greater extent in depressed than in nondepressed participants, activations in emotion generative areas are not only
mutually excitatory, but further, are associated with subsequent decreases in
brain regions associated with emotion regulation, such as the DLPFC.
Researchers have also recently begun to use graph theory to examine
large-scale brain network organization. This method provides a means for
quantifying the overall organization of brain connectivity, allowing the
brain to be depicted as a series of “nodes,” representing particular regions,
and “edges,” representing correlations in structural volume or activity
between nodes. A handful of studies have now used graph analyses to
examine network connectivity in depressed individuals, and have identified
abnormalities in both path length, that is, how many steps it takes to get
from a node to any other node in the network, and number of hubs and connections, features that may relate to the efficiency of information processing
within and between neural networks. Therefore, research that continues to
integrate this method with other connectivity- and activation-based analytic
techniques has the potential to greatly increase our understanding of the
nature of neural function and dysfunction in MDD, as well as the way in
neural anomalies may underlie deficits, biases, and difficulties in cognition
and information processing in this disorder.

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MECHANISMS UNDERLYING THE EFFECTS OF COGNITIVE BIAS MODIFICATION
Despite the promise of CBM procedures in reducing depressive symptoms,
the mechanisms that might contribute to this improvement are not yet
clear. While it is possible that ABT and IBT simply “train away biases”
and thereby improve symptoms of MDD, it is likely that the mechanisms
underlying the effects of these training procedures are more complex.
Investigators have already begun to examine the neural foundations of
traditional cognitive-behavior therapy for depression (DeRubeis, Siegle, &
Hollon, 2008), but it will be important to extend this research to elucidate
the mechanisms by which CBM achieves its beneficial effects. MacLeod and
Mathews (2012) recently distinguished between “near” and “far” transfer of
training effects of CBM. They noted that while training typically transfers to
the same task with different stimuli (near transfer), changes in functioning
on tasks that are less closely related to the training task (far transfer) are
particularly informative for our understanding of mechanisms. Initial work
indicates that the effects of ABT can transfer to alter interpretive biases,
and that similarly, the effects of IBT may influence biases in both attention
and memory. These findings suggest not only that the distinctions made by
researchers among biases in attention, interpretation, and memory need to
be reconsidered, but further, that at least some aspects of these biases share
common mechanisms of action.
We posit that there are three mechanisms in particular that underlie the
positive effects of CBM in depression: decreased attentional capture of
negative stimuli (bottom-up processing); increased inhibition of negative
material (top-down processing); and, as a consequence of these changes,
decreased negative self-referential thinking (rumination). Importantly,
as we noted earlier in this essay, all three of these constructs have been
found to distinguish depressed from nondepressed individuals. Moreover,
investigators are beginning to examine neural underpinnings of each these
mechanisms (Cooney, Joormann, Eugene, Dennis, & Gotlib, 2010; Dichter,
Felder, & Smoski, 2009; Foland-Ross et al., 2013). It will be important to
continue this line of investigation, integrating assessments of cognitive
and neural functioning in depressed individuals in order to gain a more
comprehensive understanding of, and to continue to develop and refine,
innovative treatments for this debilitating disorder.
REFERENCES
Beck, A. T. (1967). Depression: clinical, experimental, and theoretical aspects. New York,
NY: Harper & Row.
Cooney, R. E., Joormann, J., Eugene, F., Dennis, E. L., & Gotlib, I. H. (2010). Neural
correlates of rumination in depression. Cognitive, Affective, and Behavioral Neuroscience, 10, 470–478. doi:10.3758/CABN.10.4.470

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DeRubeis, R. J., Siegle, G. J., & Hollon, S. D. (2008). Cognitive therapy versus medication for depression: Treatment outcomes and neural mechanisms. Nature Reviews
Neuroscience, 9(10), 788–796.
Dichter, G. S., Felder, J. N., & Smoski, M. J. (2009). Affective context interferes with
cognitive control in unipolar depression: An fMRI investigation. Journal of Affective
Disorders, 114, 131–142.
Foland-Ross, L. C., & Gotlib, I. H. (2012). Cognitive and neural aspects of information
processing in major depressive disorder: An integrative perspective. Frontiers in
Emotion Science, November, 3 Article 489, 1–17.
Foland-Ross, L. C., Hamilton, J. P., Joormann, J., Berman, M. G., Jonides, J., & Gotlib,
I. H. (2013). The neural basis of difficulties disengaging from negative irrelevant
material in Major Depression. Psycholological Science, 24, 334–344.
Foland-Ross, L. C., Hardin, M. G., & Gotlib, I. H. (2013). Neurobiological markers
of familial risk for depression. In P. Cowen, T. Sharp & J. Lau (Eds.), Behavioral
neurobiology of depression and its treatment: Current topics in behavioral neurosciences
(Vol. 14, pp. 181–206). New York, NY: Springer.
Gotlib, I. H., & Hammen, C. L. (Eds.) (2009). Handbook of depression (2nd ed.). New
York, NY: The Guilford Press.
Gotlib, I. H., & Joormann, J. (2010). Cognition and depression: Current status and
future directions. Annual Review of Clinical Psychology, 6, 285–312.
Greicius, M. D., Flores, B. H., Menon, V., Glover, G. H., Solvason, H. B., Kenna, H.,
… Schatzberg, A. F. (2007). Resting-state functional connectivity in major depression: Abnormally increased contributions from subgenual cingulate cortex and
thalamus. Biological Psychiatry, 62(5), 429–437.
Hamilton, J. P., Chen, G., Thomason, M. E., Schwartz, M. E., & Gotlib, I. H. (2011).
Investigating neural primacy in Major Depressive Disorder: Multivariate granger
causality analysis of resting-state fMRI time-series data. Molecular Psychiatry, 16,
763–772.
Hamilton, J. P., Furman, D. J., Chang, C., Thomason, M. E., Dennis, E., & Gotlib, I. H.
(2011). Default-mode and task-positive network activity in Major Depressive Disorder: Implications for adaptive and maladaptive rumination. Biological Psychiatry,
70, 327–333.
Hamilton, J. P., Glover, G. H., Hsu, J.-J., Johnson, R. F., & Gotlib, I. H. (2011). Modulation of subgenual anterior cingulate cortex activity with real-time neurofeedback.
Human Brain Mapping, 32, 22–31.
Hamilton, J. P., Etkin, A., Furman, D. J., Lemus, M. G., Johnson, R. F., & Gotlib, I. H.
(2012). Functional neuroimaging of Major Depressive Disorder: A meta-analysis
and new integration of baseline activation and neural response data. American
Journal of Psychiatry, 169, 693–703.
Hamilton, J. P., Chen, M. C., & Gotlib, I. H. (2013). Neural systems approaches to
understanding Major Depressive Disorder: An intrinsic functional organization
perspective. Neurobiology of Disease, 52, 4–11.
Kessler, R. C., de Jonge, P., Shahly, V., van Loo, H. M., Wang, P. S., & Wilcox, M. A.
(2014). The epidemiology of depression. In I. H. Gotlib & C. L. Hammen (Eds.),
Handbook of Depression (3rd ed., pp. 7–24). New York, NY: The Guilford Press.

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Linden, D. E. J., Habes, I., Johnston, S. J., Linden, S., Tatineni, R., Subramanian, L.,
… , Goebel, R.. (2012). Real-time self-regulation of emotion networks in patients
with depression. PLoS One, 7(6), e38115.
MacLeod, C., & Mathews, A. (2012). Cognitive bias modification approaches to anxiety. Annual Review of Clinical Psychology, 8, 189–217.
Mayberg, H. S., Lozano, A. M., Voon, V., McNeely, H. E., Seminowicz, D., Hamani, C.,
… , Kennedy, S. H. (2005). Deep brain stimulation for treatment-resistant depression. Neuron 45(5), 651–660.
Sheline, Y. I., Barch, D. M., Price, J. L., Rundle, M. M., Vaishnavi, S. N., Snyder, A. Z.,
… , Raichle, M. E. (2009). The default mode network and self-referential processes
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Hammen (Eds.), Handbook of depression (2nd ed., pp. 187–217). New York, NY:
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World Health Organization (2004). The global burden of disease: 2004 update. Geneva,
Switzerland: WHO.

IAN H. GOTLIB SHORT BIOGRAPHY
Ian H. Gotlib is the David Starr Jordan Professor of Psychology and Director
of the Stanford Mood and Anxiety Disorders Laboratory at Stanford University. From 2005 to 2010, Dr. Gotlib served as Senior Associate Dean for
the Social Sciences, and he has been Chair of the Department of Psychology
at Stanford since 2012. In his research, Dr. Gotlib is broadly examining
psychological and biological factors that place individuals at increased
risk for depression, as well as processes that are involved in recovery from
this disorder. Dr. Gotlib conducts research examining cognitive, social,
endocrinological, and neural factors and genetics in depressed individuals,
as well as predictors of depression in children at familial risk for developing
this disorder. He also examines the impact of innovative procedures to
reduce young children’s risk for depression. Dr. Gotlib’s research is supported largely by grants from the National Institute of Mental Health. He
has also been funded by the National Health Research Development Program, the Medical Research Council of Canada, and the Hope for Depression
Research Foundation. He has received the Distinguished Investigator Award
from the National Alliance for Research in Schizophrenia and Affective
Disorders, the Joseph Zubin Award for lifetime research contributions to
the understanding of psychopathology, the APA Award for Distinguished
Scientific Contribution, and the APS Distinguished Scientist Award. Dr.
Gotlib has published over 500 scientific articles and has written or edited
several books in the areas of depression and stress, including the Handbook

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EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

of Depression with Constance Hammen. He is a Fellow of the American
Psychological Association, the Association for Psychological Science, and
the American Psychopathological Association, and is Past President of the
Society for Research in Psychopathology.
DANIELLA J. FURMAN SHORT BIOGRAPHY
Daniella J. Furman is completing her PhD in Psychology at Stanford
University, where she works with Dr. Ian Gotlib to characterize anomalies in
brain structure, function, and connectivity associated with Major Depressive
Disorder and risk for the development of this disorder. Daniella was named
the 2012–2013 Gerald J. Lieberman Fellow in the Social Sciences; she has
also received the Smadar Levin Award from the Society for Research in
Psychopathology, the American Psychological Association Dissertation
Research Award, and a National Science Foundation Graduate Research
Fellowship.
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