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The neuroscience of depressive disorders:
A brief review of the past and some
considerations about the future
Alexander Kaltenboeck1 and Catherine Harmer1,2
Abstract
Depression is a common and debilitating mental health condition whose underlying aetiology and pathophysiology is still relatively poorly understood.
In this article, we first turn to the past and briefly review what neuroscientific investigations have taught us so far about depression. In doing so,
we cover neurochemical, neuroendocrine, immunological, functional and structural anatomical, and cognitive levels of description. We then turn our
attention to the future and discuss where the field might be moving in the years to come. We argue that future developments may rely on three
important lines of enquiry: first, the development of an integrated neuroscientific model of depression and its treatment in which different levels
of description can be mechanistically linked, and in which distinct pathophysiological trajectories leading to depressive symptomatology can be
identified. Second, the continued search for potentially overlooked pathophysiological factors, especially outside the immediate boundaries of the
brain. And third, the improvement in translation of neuroscientific insights to aid and advance clinical practice and research.
Keywords
Depression, antidepressant, neurochemistry, brain imaging, hypothalamic–pituitary–adrenal axis, cognition, emotion
Received: 12 February 2018
1 Psychopharmacology and Emotion Research Laboratory (PERL),
Department of Psychiatry, University of Oxford, Oxford, UK
2Oxford Health NHS Foundation Trust, Oxford, UK
Corresponding author:
Catherine Harmer, Psychopharmacology and Emotion Research
Laboratory (PERL), Department of Psychiatry, University of Oxford,
Oxford, OX3 7JX, UK.
Email: catherine.harmer@psych.ox.ac.uk
799269BNA0010.1177/2398212818799269Brain and Neuroscience AdvancesKaltenboeck and Harmer
research-article2018
Article
2 Brain and Neuroscience Advances
neurochemical investigations in patients with depression (Cowen,
2015).
Although first reports of impaired monoaminergic function
in depression were rather limited by the methodologies on
which they were based (e.g. measurements of the levels of neurotransmitters,
their precursors or their metabolites in plasma,
cerebrospinal fluid or post-mortem brain tissue), they have
more recently been complemented by studies using improved
methodologies, such as brain imaging with radiolabelled receptor
ligands (Cowen, 2015). It could thus be shown, for example,
that depression is associated with decreased serotonin transporter
binding in the midbrain and amygdala (Gryglewski et al.,
2014), decreased 5-HT1A receptor binding in frontal, temporal
and limbic regions (Sargent et al., 2000) and increased density
of monoamine oxidase A (Meyer et al., 2006), and all these
findings are consistent with the idea of some sort of monoaminergic
dysfunction.
Notably, some abnormalities in monoamine function have not
only been reported in patients during acute depressive episodes,
but also in remission (Cowen, 2015). This observation has led
some authors to postulate that they might actually represent the
neurobiological correlates of some trait-like vulnerability, or of
‘scars’ left by previous depressive episodes, rather than representing
markers of acute depression (Bhagwagar et al., 2008;
Cowen, 2015; Wichers et al., 2010).
Furthermore, studies exploring the effects of experimental
manipulations of monoaminergic pathway activity, for example,
through restricting the availability of the serotonin precursor
tryptophan, report that only those who had previously suffered
from depressive episodes or who have a family history of depression
develop depression-like symptoms in response to impaired
monoamine function (Ruhé et al., 2007). The inability to induce
depression in those without a vulnerability to the disorder thus
suggests that impaired monoamine pathway activity alone is
probably not sufficient to cause depressive episodes (Cowen,
2015).
Despite the main focus having been on monoamines in the
last decades, there is also accumulating evidence for changes in
other neurotransmitter systems associated with depression, specifically
the gamma-aminobutyric acid (GABA) system
(Croarkin et al., 2011; Sanacora, 2010) and the glutamate system
(Sanacora et al., 2008).
For instance, in the plasma and cerebrospinal fluid of
depressed patients, GABA levels have been repeatedly reported
to be lowered (Sanacora, 2010). Furthermore, there have been
reports of decreased density of specific GABAergic interneurons
in prefrontal and occipital cortical regions of patients with
depression (Maciag et al., 2010; Rajkowska et al., 2007) and
magnetic resonance spectroscopy studies have suggested a
decrease in GABA in occipital cortex and anterior cingulate cortex
(Cowen, 2015; Sanacora et al., 1999).
Abnormalities of glutamate in plasma, serum, cerebrospinal
fluid and brain tissue have also been described in patients with
depression (Sanacora et al., 2008) and magnetic resonance spectroscopy
imaging has yielded some evidence for decreased levels
of glutamate especially in anterior brain regions (Cowen, 2015;
Yüksel and Öngür, 2010). Furthermore, ketamine, an antagonist
at the glutamatergic N-methyl-D-aspartate (NMDA) receptor,
has repeatedly been shown to exert rapid antidepressant effects
(Kishimoto et al., 2016).
The neuroendocrine level of description
Neuroendocrine investigations have also revealed interesting
associations between endocrine functioning and clinical
depression.
It has long been noted by clinicians that patients suffering
from various endocrine disorders (e.g. Cushing’s disease) often
develop depressive symptoms (Cowen, 2015). In turn, patients
with depression have been observed to exhibit a number of
abnormalities in the hypothalamic–pituitary–adrenal (HPA) axis,
most notably subtle signs of cortisol hypersecretion. For instance,
depression has been associated with elevated 24-h blood cortisol
levels, decreased suppression of cortisol secretion after dexamethasone
administration, increased waking salivary cortisol,
increased volumes of the adrenal glands and decreased numbers
of glucocorticoid receptors both in the brain and in the periphery
(Cowen, 2015). Furthermore, depressive disorders frequently go
along with comorbidities (e.g. diabetes mellitus and cardiovascular
disease) and long-term consequences (e.g. hippocampal volume
reduction and cognitive impairments) that are consistent
with increased long-term glucocorticoid exposure (Brown et al.,
2004). Highly interesting in this context is also the finding that
increased early life stress (a known risk factor for depression)
could potentially cause hyperactivity of the HPA axis that persists
into adulthood (Pariante and Lightman, 2008).
As with neurochemical abnormalities, some HPA axis–related
abnormalities (e.g. increased waking salivary cortisol) have been
reported not only in individuals suffering from acute depression
(Bhagwagar et al., 2005) but also in those at risk for the disorder
(Mannie et al., 2007; Portella et al., 2005), which could suggest
that they also represent more of a trait-like vulnerability factor.
More recently, other endocrine systems (e.g. vasopressin,
oxytocin or melatonin) have also been implicated in the development
of depressive symptoms (Cowen, 2015; Neumann and
Landgraf, 2012; Valdes-Tovar et al., 2018). However, neither
their pathophysiological role nor their clinical relevance has been
clarified in detail.
Depressive disorders have also been associated with abnormalities
in the immune system especially an increase in levels of
inflammatory biomarkers (e.g. C-reactive protein, tumour necrosis
factor alpha or interleukin 6) (Cowen, 2015). Furthermore, it
is also known that treatment with immune system modulating
drugs (e.g. interferon alpha) can induce depressive symptoms
(Bonaccorso et al., 2002; Capuron and Miller, 2004). One causal
mechanism by which inflammatory processes have been hypothesised
to lead to the development of depression is via the
induction of a tryptophan-metabolising enzyme (indoleamine
2,3-dioxygenase), causing decreased availability of the serotonin
precursor tryptophan and increased production of the potentially
neurotoxic metabolite quinolinic acid (Cowen, 2015; Dantzer
et al., 2008). In line with these considerations, clinical trials using
anti-inflammatory treatments in patients with depression have
been conducted, and in some cases have indeed yielded promising
results (Köhler et al., 2014).
The anatomical level of description
From an anatomical perspective, depression is associated with
structural and functional abnormalities in the limbic-cortico-striato-
pallido-thalamic pathway which includes the orbitofrontal
Kaltenboeck and Harmer 3
cortex, the anterior cingulate cortex, the basal ganglia, the hippocampus,
the parahippocampus and the amygdala (Disabato
et al., 2016).
Structural anatomical abnormalities in depressed individuals
have been described for both grey matter and white matter.
Consistently reported grey matter abnormalities associated
with depression are decreased volumes of the hippocampus,
the prefrontal cortex, the orbitofrontal cortex, the (subgenual)
anterior cingulate cortex and basal ganglia structures (Bora
et al., 2012; Campbell et al., 2004; Disabato et al., 2016). It is
unclear to date what exactly causes these decreases in grey
matter volume, however, increased neuronal and glial cell
death as well as diminished adult neurogenesis presumably
play a role (Duman, 2004).
White matter lesions are especially prominent in late-life
depression and are conceptualised to be mainly of ischemic origin
(Disabato et al., 2016; Herrmann et al., 2008). A dominant
theory suggests that these white matter lesions can cause depressive
symptomatology by interrupting limbic projections to the
frontal cortex that are crucially involved in mood regulation
(Disabato et al., 2016; Herrmann et al., 2008). Interestingly,
higher levels of white matter lesions have been associated with
later onset of depression, greater clinical severity, poorer clinical
outcome and specific clinical symptoms (e.g. apathy and psychomotor
retardation) (Cowen et al., 2012), which has led some
researchers and clinicians to consider them the substrate of a
pathophysiologically special type of depression.
Functional imaging studies of patients with depression have
examined task-related and resting-state brain activity patterns.
Abnormalities of cerebral blood flow (and/or metabolism) – as
measured with single-photon emission tomography (SPET), positron
emission tomography (PET) or functional magnetic resonance
imaging (fMRI) – are relatively consistently reported for
the prefrontal cortex (orbitofrontal, dorsolateral and dorsomedial
cortex), the anterior cingulate cortex (especially the subgenual
region), the amygdala, the thalamus and basal ganglia structures
(Cowen et al., 2012; Disabato et al., 2016).
Furthermore, functional connectivity studies have revealed
evidence for various connectivity abnormalities in patients with
depression, in line with the idea that network-level deficits may
also play an important pathophysiological role. The limbiccortico-
striato-pallido-thalamic circuit encompasses several
distinct functional networks, including the cognitive control
network, the default mode network, the affective network and
the salience network, and abnormalities have been found both
within and between these networks in depressed patients
(Disabato et al., 2016).
In the cognitive control network, for instance, decreased taskrelated
activity and increased resting-state functional connectivity
have been reported (Disabato et al., 2016). In the affective
network, both increased task-related activity and increased resting-
state functional connectivity have been described (Disabato
et al., 2016). In the default mode network, increased task-related
activity has been found (Whitfield-Gabrieli and Ford, 2012).
Furthermore, there have been reports of decreased functional
connectivity between the salience network and the default mode
network, and increased functional connectivity of the dorsomedial
prefrontal cortex with the default mode network, the cognitive
control network and the affective network (Disabato et al.,
2016; Sheline et al., 2010).
The cognitive level of description
On a cognitive level, depressive disorders have been associated
with various abnormalities in information-processing. Apart
from high-level constructs postulated in psychotherapeutic models
of depression (e.g. ‘dysfunctional attitudes’ in cognitive
behavioural therapy (Beck, 2008)), abnormalities can also be
observed in more basic cognitive domains such as perception,
attention or memory, and these abnormalities are accessible to
objective measurement in neurocognitive tasks (Elliott et al.,
2011; Roiser and Sahakian, 2016; Warren et al., 2015). Broadly,
cognitive abnormalities associated with depression can be distinguished
in ‘cold’ (i.e. emotion-independent) and ‘hot’ (i.e. emotion-
dependent) cognitive impairments (Roiser et al., 2012).
‘Cold’ cognitive impairments are observable in attention,
executive function and memory, and seem to be more severe in
individuals whose depressive disorder has a more chronic or
recurrent course (Roiser and Sahakian, 2016). Interestingly, they
seem to be predictive of poor treatment response (independent
of baseline symptom severity) which has led to the suggestion
that a subset of patients with depression might benefit from
some sort of cognitive enhancement (Roiser et al., 2012; Roiser
and Sahakian, 2016).
Important ‘hot’ cognitive abnormalities associated with
depression are mood-congruent, negative biases in the processing
of emotional stimuli (Roiser et al., 2012; Roiser and Sahakian,
2016), and impaired processing of reward and punishmentrelated
experiences (Eshel and Roiser, 2010).
Negative biases are observable in behavioural tasks as differences
in performance depending on whether presented stimuli
have an emotionally negative or positive value. These biases have
been described in various cognitive domains encompassing perception,
attention, working memory and memory (Elliott et al.,
2011; Harmer and Pringle, 2016; Roiser and Sahakian, 2016).
Notably, biases in emotional information processing seem to be
not only observable in currently depressed individuals, but also in
individuals at risk for depression, such as those with a history of
depressive disorder (LeMoult et al., 2009), a first-degree relative
with depression (Le Masurier et al., 2007), certain genetic variants
(Pérez-Edgar et al., 2010) or specific personality traits (Chan et al.,
2007). An interesting phenomenon in this context is the finding
that antidepressant treatments are able to dampen negative biases
long before clinical effects on mood are usually measurable, which
has led to the suggestion that the reversal of negative biases in
emotional information processing might actually play a causal role
in antidepressant treatment effects (Harmer et al., 2009, 2017).
Another ‘hot’ cognitive abnormality observable in depressed
patients is altered reward and punishment processing (Eshel and
Roiser, 2010). It has been reported, for instance, that depressed
patients show impairments in reward and punishment based
learning (Eshel and Roiser, 2010; Roiser and Sahakian, 2016)
with hypersensitivity to negative (Elliott et al., 1997) and hyposensitivity
to positive feedback (Henriques and Davidson, 2000).
Furthermore, in line with clinical observations, patients with
depression exhibit impaired motivation in effort-based tasks
(Roiser and Sahakian, 2016). Although impairments in reward
and punishment processing have just recently become a focus of
interest in depression research, they might be of particular relevance
for understanding the complex phenomenology of anhedonia
(Roiser and Sahakian, 2016).
4 Brain and Neuroscience Advances
Understanding depression: what does
the future hold?
In the first section of this essay, we have summarised a selected
range of key findings yielded by neuroscientific investigations on
depressive disorders in the last decades. In the paragraphs that
follow, we will now highlight a few potential lines of enquiry that
could take the field forward in the future.
Linking different levels of description:
an integrated neuroscientific model of
depression
As it has become clear from the brief outline above, previous
research efforts have revealed a considerable number of insights
on various levels of description. The importance of these discoveries
notwithstanding, the current neuroscientific understanding
of depression still faces large ‘explanatory gaps’ insofar as it
remains for the most part silent about the mechanistic links
between different physiological abnormalities, cognitive impairments,
clinical symptoms and the effects of different treatments.
An important challenge for future research will therefore be to
develop an overarching theoretical framework in which findings
on different levels of description can be integrated. This should
make clear, for example, how neurotransmitter imbalances are
mechanistically linked to macroscopic decreases in hippocampal
volumes, increased task-related default mode network activity
and elevated salivary cortisol levels, or how treatments as different
as psychotherapy, psychopharmacological manipulations and
brain stimulation can all yield the same clinical effect of relieving
depressive symptoms.
We admit that this will be a difficult task with many obstacles
in the way. One major problem to overcome might be the hitherto
strong reliance of researchers on relatively crude diagnostic systems
which are primarily based on clinical symptomatology and
therefore assign the same diagnostic label to what most probably
is a range of aetiologically and pathophysiologically heterogeneous
conditions. In line with the Research Domain Criteria Project
launched by the National Institute of Mental Health (Insel, 2014;
Insel et al., 2010), a distinguished aim in developing an integrated
neuroscientific model of depression therefore has to be the
separation of distinct aetiological and pathophysiological trajectories
which, although eventually giving rise to a similar symptomatology,
involve different distal neurobiological and cognitive
mechanisms, and therefore might be differentially amenable to
specific treatments. Eventually, such an approach would likely
benefit the development of ‘precision psychiatry’, that is, the
optimal matching of available treatment options to patients based
on their individual characteristics.
Finding all culprits: extending the search
outside the brain
Another potential line of enquiry for the future could concern the
search for pathophysiologically relevant factors outside the immediate
boundaries of the brain. Although mostly ignored in neuroscientific
research in the past, there is now accumulating evidence
for important functional relationships between the brain and other
organs of the body, and dysfunctions in these relationships might
play a crucial role in the development of various psychiatric disorders.
Recent studies, for example, suggest a surprisingly close
functional relationship between gut microbiota and the brain,
implicating a whole range of bilateral signalling mechanisms
(Cryan and Dinan, 2012; Mayer et al., 2014).
What is especially interesting in the context of depressive disorders
is the fact that various gut-inhabiting bacteria are not only
able to influence the serotonergic system and GABAergic system
but also to regulate the stress response (Dash et al., 2015; Dinan
and Cryan, 2013) – all of which are aspects that have been shown
to function abnormally in depressed patients. Although still preliminary,
there is some evidence that experimental manipulations
of the gut microbiome can indeed affect emotional processes,
mood and other cognitive functions in humans and animals, and
these manipulations therefore might have the potential to become
future treatments for depressive disorders (Cryan and Dinan,
2012; Dinan and Cryan, 2013; Mayer et al., 2014).
Last but not least: aiming at a better
translation of neuroscientific insights
Finally, future research has to address the need for better translation
of neuroscientific insights into clinical practice. Despite its
recent progress, neuroscientific research has had surprisingly little
impact on the clinical treatment of patients with depression.
Future work therefore has to find opportunities to better apply
already established knowledge in clinically useful ways.
Such translational efforts do not have to aim exclusively at
major paradigm changes in clinical treatment (e.g. the development
of a completely new antidepressant) but can also tackle
comparatively ‘smaller’ problems whose solution, however,
might still have a major impact on clinical routine and future
research. To give an example, it would be highly desirable to find
reliable biomarkers of antidepressant treatment response that are
measurable early in the course of treatment. Such biomarkers
would not only facilitate predictions whether a given antidepressant
is going to be effective in an individual patient but could also
prove useful in speeding up decision processes in the development
of new antidepressant interventions.
Summary and concluding remarks
In this essay, we have briefly reviewed a selected range of key
discoveries that neuroscientific research has made on the topic of
depressive disorders in the last decades. We have shown that
depression has been linked to a wide range of abnormalities on
different levels of neuroscientific description ranging from molecules
and cells to brain circuits and cognitive mechanisms. Based
on this short overview, we have then identified three potential
lines of future scientific enquiry: first, the development of an integrated
neuroscientific model of depression (and antidepressant
treatment) that provides mechanistic links between abnormalities
(and the effects of antidepressant interventions) on different levels
of neuroscientific description and that separates distinct pathophysiological
trajectories leading to depressive symptomatology.
Second, the continuation of the search for aetiological and pathophysiological
factors implicated in the development of depression,
especially outside the immediate boundaries of the
brain. And third, a stronger focus on translational efforts that use
Kaltenboeck and Harmer 5
established basic neuroscientific insights to improve clinical practice
and research.
Declaration of conflicting interests
Alexander Kaltenboeck reports receiving research funding from the
Medical Research Council and the Department of Psychiatry, University
of Oxford. Catherine Harmer reports receiving grants from Johnson &
Johnson, UCB, and Sunovion; and personal fees from P1vital and
Lundbeck (outside this work). The views expressed are those of the
authors and not necessarily those of the NHS, the NIHR or the Department
of Health.
Funding
This project was supported by the NIHR Oxford Health Biomedical
Research Centre.
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PURPOSE
The student will review, summarize, and critique a scholarly article related to a mental health topic.
COURSE OUTCOMES
This assignment enables the student to meet the following course outcomes.
• CO 4. Utilize critical thinking skills in clinical decision-making and implementation of the nursing process for psychiatric/mental health clients. (PO 4)
• CO 5: Utilize available resources to meet self‐identified goals for personal, professional, and educational development appropriate to the mental health setting. (PO 5)
• CO 7: Examine moral, ethical, legal, and professional standards and principles as a basis for clinical decision‐making. (PO 6)
• CO 9: Utilize research findings as a basis for the development of a group leadership experience. (PO 8)
DUE DATE
Refer to Course Calendar for details. The Late Assignment Policy applies to this assignment.
TOTAL POINTS POSSIBLE: 100 points
REQUIREMENTS
1. Select a scholarly nursing or research article (published within the last five years) related to mental health nursing, which includes content related to evidence‐based practice.
*** You may need to evaluate several articles before you find one that is appropriate. ***
2. Ensure that no other member of your clinical group chooses the same article. Submit the article for approval.
3. Write a 2–3 page paper (excluding the title and reference pages) using the following criteria.
a. Write a brief introduction of the topic and explain why it is important to mental health nursing.
NR326
Mental
Health
Nursing

b. Cite statistics to support the significance of the topic.
c. Summarize the article; include key points or findings of the article.
d. Discuss how you could use the information for your practice; give specific examples.
e. Identify strengths and weaknesses of the article.
f. Discuss whether you would recommend the article to other colleagues.
g. Write a conclusion.
4. Paper must follow APA format. Include a title page and a reference page; use 12‐point Times Roman font; and include in‐text citations (use citations whenever paraphrasing, using statistics, or quoting from the article). Please refer to your APA Manual as a guide for in‐text citations and sample reference pages.
5. Submit per faculty instructions by due date (see Course Calendar); please refer to your APA Manual as a guide for in‐text citations and sample reference pages. Copies of articles from any Databases, whether PDF, MSWord, or any other electronic file format, cannot be sent via the Learning
Management System (Canvas) dropbox or through email, as this violates copyright law protections outlined in our subscription agreements. Refer to

DIRECTIONS AND ASSIGNMENT CRITERIA Assignment Criteria Points % Description
Introduction
10
10
• An effective introduction establishes the purpose of the paper.
• The introduction should capture the attention of the reader.
Article summary
30
30
Summary of article must include the following.
• Statistics to support the significance of the topic
• Key points and findings of the article
• Discussion of how information from the article could be used in your practice (give specific examples)
Article critique
30
30
Article critique must include the following.
• Strengths and weaknesses of the article
• Discussion of whether you would recommend the article to a colleague
Conclusion
15
15
The conclusion statement should be well defined and clearly stated. An effective conclusion provides analysis and/or synthesis of information, which relates to the main idea/topic of the paper. The conclusion is supported by ideas presented throughout the body of your report.
Article Selection & Approval
5
5
• Article is relevant to mental health nursing practice and is current (within 5 years of publication).
• No duplicate articles within the clinical group.
• Article submitted and approved as scholarly by instructor.
Grammar/Spelling/
Mechanics/APA format
10
10
• Correct use of Standard English grammar and sentence structure
• No spelling or typographical errors
• Document includes title and reference pages
• Citations in the text and reference page Total 100 100

Assignment Criteria Outstanding or Highest Level of Performance A (92–100%) Very Good or High Level of Performance B (84–91%) Competent or Satisfactory Level of Performance C (76–83%) Poor, Failing or Unsatisfactory Level of Performance F (0–75%)
Introduction (10 points)
• Introduction is present and distinctly establishes the purpose of paper
• Introduction is appealing and promptly captures the attention of the reader
10 points
• Introduction is present and generally establishes the purpose of paper
• Introduction has appeal and generally captures the attention of the reader
9 points Introduction is present and generally establishes the purpose of paper
8 points No introduction
0‐7 points Article summary (30 points) • Statistics presented strongly support the significance of the topic • Key points and findings of the article are clearly stated • Thoroughly discusses how information from the article could be used in your practice by giving two or more specific, relevant examples 28‐30 points • Statistics presented moderately support the significance of the topic • Key points and findings of the article are vaguely stated • Adequately discusses how information from the article could be used in your practice by giving two or more specific, relevant examples 26‐27 points • Statistics presented weakly support the significance of the topic • Key points and findings of the article are stated in a manner that is confusing or difficult to understand. • Briefly discusses how information from the article could be used in your practice by giving examples that are not specific, yet are relevant 23‐25 points • Statistics presented do not support the significance of the topic OR no statistics are presented. • Key points and findings of the article are incorrectly presented OR missing • Briefly discusses how information from the article could be used in your practice by giving examples that are neither specific, nor relevant OR implications to practice not discussed 0‐22 points