Biology of depression

The biology of depression is the attempt to identify a biochemical origin of depression, as opposed to theories that emphasize psychological or situational causes.

Scientific studies have found that different brain areas show altered activity in humans with major depressive disorder (MDD). ^[1] Further, nutritional deficiencies in magnesium, ^[2] vitamin D, ^[3] and tryptophan have been linked with depression; these deficiencies may be caused by the individual's environment, but they have a biological impact. Several theories concerning the biologically based cause of depression have been suggested over the years, including theories revolving around monoamine neurotransmitters, neuroplasticity, neurogenesis, inflammation and the circadian rhythm. Physical illnesses, including hypothyroidism and mitochondrial disease, can also trigger depressive symptoms. ^{[4] [5]}

Neural circuits implicated in depression include those involved in the generation and regulation of emotion, as well as in reward. Abnormalities are commonly found in the lateral prefrontal cortex whose putative function is generally considered to involve regulation of emotion. Regions involved in the generation of emotion and reward such as the amygdala, anterior cingulate cortex (ACC), orbitofrontal cortex (OFC), and striatum are frequently implicated as well. These regions are innervated by a monoaminergic nuclei, and tentative evidence suggests a potential role for abnormal monoaminergic activity. ^{[6] [7]}

Genetic factors

Difficulty of gene studies

Historically, candidate gene studies have been a major focus of study. However, as the number of genes reduces the likelihood of choosing a correct candidate gene, Type I errors (false positives) are highly likely. Candidate genes studies frequently possess a number of flaws, including frequent genotyping errors and being statistically underpowered. These effects are compounded by the usual assessment of genes without regard for gene-gene interactions. These limitations are reflected in the fact that no candidate gene has reached genome-wide significance. ^[8]

Gene candidates

5-HTTLPR

The 5-HTTLPR, or serotonin transporter promoter gene's short allele, has been associated with increased risk of depression; since the 1990s, however, results have been inconsistent. ^{[9] [10] [11] [12] [13]} Other genes that have been linked to a gene–environment interaction include CRHR1, FKBP5 and BDNF, the first two of which are related to the stress reaction of the HPA axis, and the latter of which is involved in neurogenesis. Candidate gene analysis of 5-HTTLPR on depression was inconclusive on its effect, either alone or in combination with life stress. ^[14]

A 2003 study proposed that a gene-environment interaction (GxE) may explain why life stress is a predictor for depressive episodes in some individuals, but not in others, depending on an allelic variation of the serotonin-transporter-linked promoter region (5-HTTLPR). ^[15] This hypothesis was widely discussed in both the scientific literature and popular media, where it was dubbed the "Orchid gene", but has conclusively failed to replicate in much larger samples, and the observed effect sizes in earlier work are not consistent with the observed polygenicity of depression. ^[16]

BDNF

BDNF polymorphisms have also been hypothesized to have a genetic influence, but early findings and research failed to replicate in larger samples, and the effect sizes found by earlier estimates are inconsistent with the observed polygenicity of depression. ^[16]

SIRT1 and LHPP

A 2015 GWAS study in Han Chinese women positively identified two variants in intronic regions near SIRT1 and LHPP with a genome-wide significant association. ^{[17] [18]}

Norepinephrine transporter polymorphisms

Attempts to find a correlation between norepinephrine transporter polymorphisms and depression have yielded negative results. ^[19]

One review identified multiple frequently studied candidate genes. The genes encoding for the 5-HTT and 5-HT_2A receptor were inconsistently associated with depression and treatment response. Mixed results were found for brain-derived neurotrophic factor (BDNF) Val66Met polymorphisms. Polymorphisms in the tryptophan hydroxylase gene was found to be tentatively associated with suicidal behavior. ^[20] A meta analysis of 182 case controlled genetic studies published in 2008 found Apolipoprotein E epsilon 2 to be protective, and GNB3 825T, MTHFR 677T, SLC6A4 44bp insertion or deletions, and SLC6A3 40 bpVNTR 9/10 genotype to confer risk. ^[21]

Circadian rhythm

Depression may be related to the same brain mechanisms that control the cycles of sleep and wakefulness.

Depression may be related to abnormalities in the circadian rhythm, ^[22] or biological clock.

A well synchronized circadian rhythm is critical for maintaining optimal health. Adverse changes and alterations in the circadian rhythm have been associated with various neurological disorders and mood disorders including depression. ^[23]

Sleep

Sleep disturbance is the most prominent symptom in depressive patients. ^[24] Studies about sleep electroencephalograms have shown characteristic changes in depression such as reductions in non-rapid eye movement sleep production, disruptions of sleep continuity and disinhibition of rapid eye movement (REM) sleep. ^[25] Rapid eye movement (REM) sleep—the stage in which dreaming occurs—may be quick to arrive and intense in depressed people. REM sleep depends on decreased serotonin levels in the brain stem, ^[26] and is impaired by compounds, such as antidepressants, that increase serotonergic tone in brain stem structures. ^[26] Overall, the serotonergic system is least active during sleep and most active during wakefulness. Prolonged wakefulness due to sleep deprivation ^[22] activates serotonergic neurons, leading to processes similar to the therapeutic effect of antidepressants, such as the selective serotonin reuptake inhibitors (SSRIs). Depressed individuals can exhibit a significant lift in mood after a night of sleep deprivation. SSRIs may directly depend on the increase of central serotonergic neurotransmission for their therapeutic effect, the same system that impacts cycles of sleep and wakefulness. ^[26]

Light therapy

Research on the effects of light therapy on seasonal affective disorder suggests that light deprivation is related to decreased activity in the serotonergic system and to abnormalities in the sleep cycle, particularly insomnia. Exposure to light also targets the serotonergic system, providing more support for the important role this system may play in depression. ^[27] Sleep deprivation and light therapy both target the same brain neurotransmitter system and brain areas as antidepressant drugs, and are now used clinically to treat depression. ^[28] Light therapy, sleep deprivation and sleep time displacement (sleep phase advance therapy) are being used in combination quickly to interrupt a deep depression in people who are hospitalized for MDD (major depressive disorder). ^[27]

Increased and decreased sleep length appears to be a risk factor for depression. ^[29] People with MDD sometimes show diurnal and seasonal variation of symptom severity, even in non-seasonal depression. Diurnal mood improvement was associated with activity of dorsal neural networks. Increased mean core temperature was also observed. One hypothesis proposed that depression was a result of a phase shift. ^[30]

Daytime light exposure correlates with decreased serotonin transporter activity, which may underlie the seasonality of some depression. ^[31]

Monoamines

Illustration of the major elements in a prototypical synapse. Synapses are gaps between nerve cells. These cells convert their electrical impulses into bursts of chemical relayers, called neurotransmitters, which travel across the synapses to receptors on adjacent cells, triggering electrical impulses to travel down the latter cells.

Monoamines are neurotransmitters that include serotonin, dopamine, norepinephrine, and epinephrine. ^[32]

Monoamine hypothesis of depression

Many antidepressant drugs acutely increase synaptic levels of the monoamine neurotransmitter, serotonin, but they may also enhance the levels of norepinephrine and dopamine. The observation of this efficacy led to the monoamine hypothesis of depression, which postulates that the deficit of certain neurotransmitters is responsible for depression, and even that certain neurotransmitters are linked to specific symptoms. Normal serotonin levels have been linked to mood and behaviour regulation, sleep, and digestion; norepinephrine to the fight-or-flight response; and dopamine to movement, pleasure, and motivation. Some have also proposed the relationship between monoamines and phenotypes such as serotonin in sleep and suicide, norepinephrine in dysphoria, fatigue, apathy, cognitive dysfunction, and dopamine in loss of motivation and psychomotor symptoms. ^[33] The main limitation for the monoamine hypothesis of depression is the therapeutic lag between initiation of antidepressant treatment and perceived improvement of symptoms. One explanation for this therapeutic lag is that the initial increase in synaptic serotonin is only temporary, as firing of serotonergic neurons in the dorsal raphe adapt via the activity of 5-HT_1A autoreceptors. The therapeutic effect of antidepressants is thought to arise from autoreceptor desensitization over a period of time, eventually elevating firing of serotonergic neurons. ^[34]

Monoamine receptors affect phospholipase C and adenylyl cyclase inside of the cell. Green arrows means stimulation and red arrows inhibition. Serotonin receptors are blue, norepinephrine orange, and dopamine yellow. Phospholipase C and adenylyl cyclase start a signaling cascade which turn on or off genes in the cell. Sufficient ATP from mitochondria is required for these downstream signalling events. The 5HT-3 receptor is associated with gastrointestinal adverse effects and has no relationship to the other monoamine receptors.

Serotonin

The serotonin "chemical imbalance" theory of depression, proposed in the 1960s, ^[35] is not supported by the available scientific evidence. ^{[35] [36]} SSRIs alter the balance of serotonin inside and outside of neurons: their clinical antidepressant effect (which is robust in severe depression ^[37] ) is likely due to more complex changes in neuronal functioning which occur as a downstream consequence of this. ^[38]

Initial studies of serotonin in depression examined peripheral measures such as the serotonin metabolite 5-Hydroxyindoleacetic acid (5-HIAA) and platelet binding. The results were generally inconsistent, and may not generalize to the central nervous system. However evidence from receptor binding studies and pharmacological challenges provide some evidence for dysfunction of serotonin neurotransmission in depression. ^[39] Serotonin may indirectly influence mood by altering emotional processing biases that are seen at both the cognitive/behavioral and neural level. ^{[40] [39]} Pharmacologically reducing serotonin synthesis, and pharmacologically enhancing synaptic serotonin can produce and attenuate negative affective biases, respectively. These emotional processing biases may explain the therapeutic gap. ^[40]

Dopamine

While various abnormalities have been observed in dopaminergic systems, results have been inconsistent. People with MDD have an increased reward response to dextroamphetamine compared to controls, and it has been suggested that this results from hypersensitivity of dopaminergic pathways due to natural hypoactivity. While polymorphisms of the D₄ and D₃ receptor have been implicated in depression, associations have not been consistently replicated. Similar inconsistency has been found in postmortem studies, but various dopamine receptor agonists show promise in treating MDD. ^[41] There is some evidence that there is decreased nigrostriatal pathway activity in people with melancholic depression (psychomotor retardation). ^[42] Further supporting the role of dopamine in depression is the consistent finding of decreased cerebrospinal fluid and jugular metabolites of dopamine, ^[43] as well as post mortem findings of altered dopamine receptor D₃ and dopamine transporter expression. ^[44] Studies in rodents have supported a potential mechanism involving stress-induced dysfunction of dopaminergic systems. ^[45]

Catecholamines

A number of lines of evidence indicative of decreased adrenergic activity in depression have been reported. Findings include the decreased activity of tyrosine hydroxylase, decreased size of the locus coeruleus, increased α₂ adrenergic receptor density, and decreased α₁ adrenergic receptor density. ^[43] Furthermore, norepinephrine transporter knockout in mice models increases their tolerance to stress, implicating norepinephrine in depression. ^[46]

One method used to study the role of monoamines is monoamine depletion. Depletion of tryptophan (the precursor of serotonin), tyrosine and phenylalanine (precursors to dopamine) does result in decreased mood in those with a predisposition to depression, but not in persons lacking the predisposition. On the other hand, inhibition of dopamine and norepinephrine synthesis with alpha-methyl-para-tyrosine does not consistently result in decreased mood. ^[47]

Monoamine oxidase

An offshoot of the monoamine hypothesis suggests that monoamine oxidase A (MAO-A), an enzyme which metabolizes monoamines, may be overly active in depressed people. This would, in turn, cause the lowered levels of monoamines. This hypothesis received support from a PET study, which found significantly elevated activity of MAO-A in the brain of some depressed people. ^[48] In genetic studies, the alterations of MAO-A-related genes have not been consistently associated with depression. ^{[49] [50]} Contrary to the assumptions of the monoamine hypothesis, lowered but not heightened activity of MAO-A was associated with depressive symptoms in adolescents. This association was observed only in maltreated youth, indicating that both biological (MAO genes) and psychological (maltreatment) factors are important in the development of depressive disorders. ^[51] In addition, some evidence indicates that disrupted information processing within neural networks, rather than changes in chemical balance, might underlie depression. ^[52]

Limitations

Since the 1990s, research has uncovered multiple limitations of the monoamine hypothesis, and its inadequacy has been criticized within the psychiatric community. ^[53] For one thing, serotonin system dysfunction cannot be the sole cause of depression. Not all patients treated with antidepressants show improvements despite the usually rapid increase in synaptic serotonin. If significant mood improvements do occur, this is often not for at least two to four weeks. One possible explanation for this lag is that the neurotransmitter activity enhancement is the result of auto receptor desensitization, which can take weeks. ^[54] Intensive investigation has failed to find convincing evidence of a primary dysfunction of a specific monoamine system in people with MDD. The antidepressants that do not act through the monoamine system, such as tianeptine and opipramol, have been known for a long time. There have also been inconsistent findings with regard to levels of serum 5-HIAA, a metabolite of serotonin. ^[55] Experiments with pharmacological agents that cause depletion of monoamines have shown that this depletion does not cause depression in healthy people. ^{[56] [57]} Another problem that presents is that drugs that deplete monoamines may actually have antidepressants properties. Further, some have argued that depression may be marked by a hyperserotonergic state. ^[58] Already limited, the monoamine hypothesis has been further oversimplified when presented to the general public. ^[59]

Receptor binding

As of 2012, efforts to determine differences in neurotransmitter receptor expression or for function in the brains of people with MDD using positron emission tomography (PET) had shown inconsistent results. Using the PET imaging technology and reagents available as of 2012, it appeared that the D₁ receptor may be underexpressed in the striatum of people with MDD. 5-HT_1A receptor binding literature is inconsistent; however, it leans towards a general decrease in the mesiotemporal cortex. 5-HT_2A receptor binding appears to be dysregulated in people with MDD. Results from studies on 5-HTT binding are variable, but tend to indicate higher levels in people with MDD. Results with D₂/D₃ receptor binding studies are too inconsistent to draw any conclusions. Evidence supports increased MAO activity in people with MDD, and it may even be a trait marker (not changed by response to treatment). Muscarinic receptor binding appears to be increased in depression, and, given ligand binding dynamics, suggests increased cholinergic activity. ^[60]

Four meta analyses on receptor binding in depression have been performed, two on serotonin transporter (5-HTT), one on 5-HT_1A, and another on dopamine transporter (DAT). One meta analysis on 5-HTT reported that binding was reduced in the midbrain and amygdala, with the former correlating with greater age, and the latter correlating with depression severity. ^[61] Another meta-analysis on 5-HTT including both post-mortem and in vivo receptor binding studies reported that while in vivo studies found reduced 5-HTT in the striatum, amygdala and midbrain, post mortem studies found no significant associations. ^[62] 5-HT_1A was found to be reduced in the anterior cingulate cortex, mesiotemporal lobe, insula, and hippocampus, but not in the amygdala or occipital lobe. The most commonly used 5-HT_1A ligands are not displaced by endogenous serotonin, indicating that receptor density or affinity is reduced. ^[63] Dopamine transporter binding is not changed in depression. ^[64]

Emotional processing and neural circuits

Emotional bias

People with MDD show a number of biases in emotional processing, such as a tendency to rate happy faces more negatively, and a tendency to allocate more attentional resources to sad expressions. ^[65] Depressed people also have impaired recognition of happy, angry, disgusted, fearful and surprised, but not sad faces. ^[66] Functional neuroimaging has demonstrated hyperactivity of various brain regions in response to negative emotional stimuli, and hypoactivity in response to positive stimuli. One meta analysis reported that depressed subjects showed decreased activity in the left dorsolateral prefrontal cortex and increased activity in the amygdala in response to negative stimuli. ^[67] Another meta analysis reported elevated hippocampus and thalamus activity in a subgroup of depressed subjects who were medication naive, not elderly, and had no comorbidities. ^[68] The therapeutic lag of antidepressants has been suggested to be a result of antidepressants modifying emotional processing leading to mood changes. This is supported by the observation that both acute and subchronic SSRI administration increases response to positive faces. ^[69] Antidepressant treatment appears to reverse mood congruent biases in limbic, prefrontal, and fusiform areas. dlPFC response is enhanced and amygdala response is attenuated during processing of negative emotions, the former of which is thought to reflect increased top down regulation. The fusiform gyrus and other visual processing areas respond more strongly to positive stimuli with antidepressant treatment, which is thought to reflect a positive processing bias. ^[70] These effects do not appear to be unique to serotonergic or noradrenergic antidepressants, but also occur in other forms of treatment such as deep brain stimulation. ^[71]

Neural circuits

One meta analysis of functional neuroimaging in depression observed a pattern of abnormal neural activity hypothesized to reflect an emotional processing bias. Relative to controls, people with MDD showed hyperactivity of circuits in the salience network (SN), composed of the pulvinar nuclei, the insula, and the dorsal anterior cingulate cortex (dACC), as well as decreased activity in regulatory circuits composed of the striatum and dlPFC. ^[72]

Rendition of the Limbic-cortical-striatal-pallidal-thalamic circuit as described by Drevets et al. 2008

A neuroanatomical model called the limbic-cortical model has been proposed to explain early biological findings in depression. The model attempts to relate specific symptoms of depression to neurological abnormalities. Elevated resting amygdala activity was proposed to underlie rumination, as stimulation of the amygdala has been reported to be associated with the intrusive recall of negative memories. The ACC was divided into pregenual (pgACC) and subgenual regions (sgACC), with the former being electrophysiologically associated with fear, and the latter being metabolically implicated in sadness in healthy subjects. Hyperactivity of the lateral orbitofrontal and insular regions, along with abnormalities in lateral prefrontal regions was suggested to underlie maladaptive emotional responses, given the regions roles in reward learning. ^{[74] [75]} This model and another termed "the cortical striatal model", which focused more on abnormalities in the cortico-basal ganglia-thalamo-cortical loop, have been supported by recent literature. Reduced striatal activity, elevated OFC activity, and elevated sgACC activity were all findings consistent with the proposed models. However, amygdala activity was reported to be decreased, contrary to the limbic-cortical model. Furthermore, only lateral prefrontal regions were modulated by treatment, indicating that prefrontal areas are state markers (i.e., dependent upon mood), while subcortical abnormalities are trait markers (i.e., reflect a susceptibility). ^[76]

Reward

While depression severity as a whole is not correlated with a blunted neural response to reward, anhedonia is directly correlated to reduced activity in the reward system. ^[77] The study of reward in depression is limited by heterogeneity in the definition and conceptualizations of reward and anhedonia. Anhedonia is broadly defined as a reduced ability to feel pleasure, but questionnaires and clinical assessments rarely distinguish between motivational "wanting" and consummatory "liking". While a number of studies suggest that depressed subjects rate positive stimuli less positively and as less arousing, a number of studies fail to find a difference. Furthermore, response to natural rewards such as sucrose does not appear to be attenuated. General affective blunting may explain "anhedonic" symptoms in depression, as meta analysis of both positive and negative stimuli reveal reduced rating of intensity. ^{[78] [79]} As anhedonia is a prominent symptom of depression, direct comparison of depressed with healthy subjects reveals increased activation of the subgenual anterior cingulate cortex (sgACC), and reduced activation of the ventral striatum, and in particular the nucleus accumbens (NAcc) in response to positive stimuli. ^[80] Although the finding of reduced NAcc activity during reward paradigms is fairly consistent, the NAcc is made up of a functionally diverse range of neurons, and reduced blood-oxygen-level dependent (BOLD) signal in this region could indicate a variety of things including reduced afferent activity or reduced inhibitory output. ^[81] Nevertheless, these regions are important in reward processing, and dysfunction of them in depression is thought to underlie anhedonia. Residual anhedonia that is not well targeted by serotonergic antidepressants is hypothesized to result from inhibition of dopamine release by activation of 5-HT_2C receptors in the striatum. ^[80] The response to reward in the medial orbitofrontal cortex (OFC) is attenuated in depression, while lateral OFC response is enhanced to punishment. The lateral OFC shows sustained response to absence of reward or punishment, and it is thought to be necessary for modifying behavior in response to changing contingencies. Hypersensitivity in the lOFC may lead to depression by producing a similar effect to learned helplessness in animals. ^[82]

Elevated response in the sgACC is a consistent finding in neuroimaging studies using a number of paradigms including reward related tasks. ^{[80] [83] [84]} Treatment is also associated with attenuated activity in the sgACC, ^[85] and inhibition of neurons in the rodent homologue of the sgACC, the infralimbic cortex (IL), produces an antidepressant effect. ^[86] Hyperactivity of the sgACC has been hypothesized to lead to depression via attenuating the somatic response to reward or positive stimuli. ^[87] Contrary to studies of functional magnetic resonance imaging response in the sgACC during tasks, resting metabolism is reduced in the sgACC. However, this is only apparent when correcting for the prominent reduction in sgACC volume associated with depression; structural abnormalities are evident at a cellular level, as neuropathological studies report reduced sgACC cell markers. The model of depression proposed from these findings by Drevets et al. suggests that reduced sgACC activity results in enhanced sympathetic nervous system activity and blunted HPA axis feedback. ^[88] Activity in the sgACC may also not be causal in depression, as the authors of one review that examined neuroimaging in depressed subjects during emotional regulation hypothesized that the pattern of elevated sgACC activity reflected increased need to modulate automatic emotional responses in depression. More extensive sgACC and general prefrontal recruitment during positive emotional processing was associated with blunted subcortical response to positive emotions, and subject anhedonia. This was interpreted by the authors to reflect a downregulation of positive emotions by the excessive recruitment of the prefrontal cortex. ^[89]

Neuroanatomy

While a number of neuroimaging findings are consistently reported in people with major depressive disorder, the heterogeneity of depressed populations presents difficulties interpreting these findings. For example, averaging across populations may hide certain subgroup related findings; while reduced dlPFC activity is reported in depression, a subgroup may present with elevated dlPFC activity. Averaging may also yield statistically significant findings, such as reduced hippocampal volumes, that are actually present in a subgroup of subjects. ^[90] Due to these issues and others, including the longitudinal consistency of depression, most neural models are likely inapplicable to all depression. ^[76]

Structural neuroimaging

GMV reductions in MDD and BD

Meta analyses performed using seed-based d mapping have reported grey matter reductions in a number of frontal regions. One meta analysis of early onset general depression reported grey matter reductions in the bilateral anterior cingulate cortex (ACC) and dorsomedial prefrontal cortex (dmPFC). ^[92] One meta analysis on first episode depression observed distinct patterns of grey matter reductions in medication free, and combined populations; medication free depression was associated with reductions in the right dorsolateral prefrontal cortex, right amygdala, and right inferior temporal gyrus; analysis on a combination of medication free and medicated depression found reductions in the left insula, right supplementary motor area, and right middle temporal gyrus. ^[93] Another review distinguishing medicated and medication free populations, albeit not restricted to people with their first episode of MDD, found reductions in the combined population in the bilateral superior, right middle, and left inferior frontal gyrus, along with the bilateral parahippocampus. Increases in thalamic and ACC grey matter was reported in the medication free and medicated populations respectively. ^[94] A meta analysis performed using "activation likelihood estimate" reported reductions in the paracingulate cortex, dACC and amygdala. ^[95]

Using statistical parametric mapping, one meta analysis replicated previous findings of reduced grey matter in the ACC, medial prefrontal cortex, inferior frontal gyrus, hippocampus and thalamus; however reductions in the OFC and ventromedial prefrontal cortex grey matter were also reported. ^[96]

Two studies on depression from the ENIGMA consortium have been published, one on cortical thickness, and the other on subcortical volume. Reduced cortical thickness was reported in the bilateral OFC, ACC, insula, middle temporal gyri, fusiform gyri, and posterior cingulate cortices, while surface area deficits were found in medial occipital, inferior parietal, orbitofrontal and precentral regions. ^[97] Subcortical abnormalities, including reductions in hippocampus and amygdala volumes, which were especially pronounced in early onset depression. ^[98]

MDD is associated with reduced FA in the ALIC and genu/body of the CC.

Multiple meta analysis have been performed on studies assessing white matter integrity using fractional anisotropy (FA). Reduced FA has been reported in the corpus callosum (CC) in both first episode medication naive, ^{[100] [101]} and general major depressive populations. ^{[99] [102]} The extent of CC reductions differs from study to study. People with MDD who have not taken antidepressants before have been reported to have reductions only in the body of the CC ^[100] and only in the genu of the CC. ^[101] On the other hand, general MDD samples have been reported to have reductions in the body of the CC, ^[101] the body and genu of the CC, ^[99] and only the genu of the CC. ^[102] Reductions of FA have also been reported in the anterior limb of the internal capsule (ALIC) ^{[100] [99]} and superior longitudinal fasciculus. ^{[100] [101]}

Functional neuroimaging

Studies of resting state activity have utilized a number of indicators of resting state activity, including regional homogeneity (ReHO), amplitude of low frequency fluctuations (ALFF), fractional amplitude of low frequency fluctuations (fALFF), arterial spin labeling (ASL), and positron emission tomography (PET) measures of regional cerebral blood flow or metabolism.

Studies using ALFF and fALFF have reported elevations in ACC activity, with the former primarily reporting more ventral findings, and the latter more dorsal findings. ^[103] A conjunction analysis of ALFF and CBF studies converged on the left insula, with previously untreated people having increased insula activity. Elevated caudate CBF was also reported ^[104] A meta analysis combining multiple indicators of resting activity reported elevated anterior cingulate, striatal, and thalamic activity and reduced left insula, post-central gyrus and fusiform gyrus activity. ^[105] An activation likelihood estimate (ALE) meta analysis of PET/SPECT resting state studies reported reduced activity in the left insula, pregenual and dorsal anterior cingulate cortex and elevated activity in the thalamus, caudate, anterior hippocampus and amygdala. ^[106] Compared to the ALE meta analysis of PET/SPECT studies, a study using multi-kernel density analysis reported hyperactivity only in the pulvinar nuclei of the thalamus. ^[72]

Brain regions

Research on the brains of people with MDD usually shows disturbed patterns of interaction between multiple parts of the brain. Several areas of the brain are implicated in studies seeking to more fully understand the biology of depression:

Subgenual cingulate

Studies have shown that Brodmann area 25, also known as subgenual cingulate, is metabolically overactive in treatment-resistant depression. This region is extremely rich in serotonin transporters and is considered as a governor for a vast network involving areas like hypothalamus and brain stem, which influences changes in appetite and sleep; the amygdala and insula, which affect the mood and anxiety; the hippocampus, which plays an important role in memory formation; and some parts of the frontal cortex responsible for self-esteem. Thus disturbances in this area or a smaller than normal size of this area contributes to depression. Deep brain stimulation has been targeted to this region in order to reduce its activity in people with treatment resistant depression. ^{[107] : 576–578  [108]}

Prefrontal cortex

One review reported hypoactivity in the prefrontal cortex of those with depression compared to controls. ^[109] The prefrontal cortex is involved in emotional processing and regulation, and dysfunction of this process may be involved in the etiology of depression. One study on antidepressant treatment found an increase in PFC activity in response to administration of antidepressants. ^[110] One meta analysis published in 2012 found that areas of the prefrontal cortex were hypoactive in response to negative stimuli in people with MDD. ^[72] One study suggested that areas of the prefrontal cortex are part of a network of regions including dorsal and pregenual cingulate, bilateral middle frontal gyrus, insula and superior temporal gyrus that appear to be hypoactive in people with MDD. However the authors cautioned that the exclusion criteria, lack of consistency and small samples limit results. ^[106]

Amygdala

The amygdala, a structure involved in emotional processing appears to be hyperactive in those with major depressive disorder. ^[108] The amygdala in unmedicated depressed persons tended to be smaller than in those that were medicated, however aggregate data shows no difference between depressed and healthy persons. ^[111] During emotional processing tasks right amygdala is more active than the left, however there is no differences during cognitive tasks, and at rest only the left amygdala appears to be more hyperactive. ^[112] One study, however, found no difference in amygdala activity during emotional processing tasks. ^[113]

Hippocampus

Atrophy of the hippocampus has been observed during depression, consistent with animal models of stress and neurogenesis. ^{[114] [115]}

Stress can cause depression and depression-like symptoms through monoaminergic changes in several key brain regions as well as suppression in hippocampal neurogenesis. ^[116] This leads to alteration in emotion and cognition related brain regions as well as HPA axis dysfunction. Through the dysfunction, the effects of stress can be exacerbated including its effects on 5-HT. Furthermore, some of these effects are reversed by antidepressant action, which may act by increasing hippocampal neurogenesis. This leads to a restoration in HPA activity and stress reactivity, thus restoring the deleterious effects induced by stress on 5-HT. ^[117]

The hypothalamic-pituitary-adrenal axis is a chain of endocrine structures that are activated during the body's response to stressors of various sorts. The HPA axis involves three structure, the hypothalamus which release CRH that stimulates the pituitary gland to release ACTH which stimulates the adrenal glands to release cortisol. Cortisol has a negative feedback effect on the pituitary gland and hypothalamus. In people with MDD this often shows increased activation in depressed people, but the mechanism behind this is not yet known. ^[118] Increased basal cortisol levels and abnormal response to dexamethasone challenges have been observed in people with MDD. ^[119] Early life stress has been hypothesized as a potential cause of HPA dysfunction. ^{[120] [121]} HPA axis regulation may be examined through a dexamethasone suppression tests, which tests the feedback mechanisms. Non-suppression of dexamethasone is a common finding in depression, but is not consistent enough to be used as a diagnostic tool. ^[122] HPA axis changes may be responsible for some of the changes such as decreased bone mineral density and increased weight found in people with MDD. One drug, ketoconazole, currently under development has shown promise in treating MDD. ^{[123] [ clarification needed ]}

Hippocampal Neurogenesis

Reduced hippocampal neurogenesis leads to a reduction in hippocampal volume. A genetically smaller hippocampus has been linked to a reduced ability to process psychological trauma and external stress, and subsequent predisposition to psychological illness. ^[124] Depression without familial risk or childhood trauma has been linked to a normal hippocampal volume but localised dysfunction. ^[125]

Animal models

Main article: Animal models of depression

A number of animal models exist for depression, but they are limited in that depression involves primarily subjective emotional changes. However, some of these changes are reflected in physiology and behavior, the latter of which is the target of many animal models. These models are generally assessed according to four facets of validity; the reflection of the core symptoms in the model; the predictive validity of the model; the validity of the model with regard to human characteristics of etiology; ^[126] and the biological plausibility. ^{[127] [128]}

Different models for inducing depressive behaviors have been utilized; neuroanatomical manipulations such as olfactory bulbectomy or circuit specific manipulations with optogenetics; genetic models such as 5-HT_1A knockout or selectively bred animals; ^[126] models involving environmental manipulation associated with depression in humans, including chronic mild stress, early life stress and learned helplessness. ^[129] The validity of these models in producing depressive behaviors may be assessed with a number of behavioral tests. Anhedonia and motivational deficits may, for example, be assessed via examining an animal's level of engagement with rewarding stimuli such as sucrose or intracranial self-stimulation. Anxious and irritable symptoms may be assessed with exploratory behavior in the presence of a stressful or novelty environment, such as the open field test, novelty suppressed feeding, or the elevated plus-maze. Fatigue, psychomotor poverty, and agitation may be assessed with locomotor activity, grooming activity, and open field tests.

Animal models possess a number of limitations due to the nature of depression. Some core symptoms of depression, such as rumination, low self-esteem, guilt, and depressed mood cannot be assessed in animals as they require subjective reporting. ^[128] From an evolutionary standpoint, the behavior correlates of defeats of loss are thought to be an adaptive response to prevent further loss. Therefore, attempts to model depression that seeks to induce defeat or despair may actually reflect adaption and not disease. Furthermore, while depression and anxiety are frequently comorbid, dissociation of the two in animal models is difficult to achieve. ^[126] Pharmacological assessment of validity is frequently disconnected from clinical pharmacotherapeutics in that most screening tests assess acute effects, while antidepressants normally take a few weeks to work in humans. ^[130]

Neurocircuits

Regions involved in reward are common targets of manipulation in animal models of depression, including the nucleus accumbens (NAc), ventral tegmental area (VTA), ventral pallidum (VP), lateral habenula (LHb) and medial prefrontal cortex (mPFC). Tentative fMRI studies in humans demonstrate elevated LHb activity in depression. ^[131] The lateral habenula projects to the RMTg to drive inhibition of dopamine neurons in the VTA during omission of reward. In animal models of depression, elevated activity has been reported in LHb neurons that project to the ventral tegmental area (ostensibly reducing dopamine release). The LHb also projects to aversion reactive mPFC neurons, which may provide an indirect mechanism for producing depressive behaviors. ^[132] Learned helplessness induced potentiation of LHb synapses are reversed by antidepressant treatment, providing predictive validity. ^[131] A number of inputs to the LHb have been implicated in producing depressive behaviors. Silencing GABAergic projections from the NAc to the LHb reduces conditioned place preference induced in social aggression, and activation of these terminals induces CPP. Ventral pallidum firing is also elevated by stress induced depression, an effect that is pharmacologically valid, and silencing of these neurons alleviates behavioral correlates of depression. ^[131] Tentative in vivo evidence from people with MDD suggests abnormalities in dopamine signalling. ^[133] This led to early studies investigating VTA activity and manipulations in animal models of depression. Massive destruction of VTA neurons enhances depressive behaviors, while VTA neurons reduce firing in response to chronic stress. However, more recent specific manipulations of the VTA produce varying results, with the specific animal model, duration of VTA manipulation, method of VTA manipulation, and subregion of VTA manipulation all potentially leading to differential outcomes. ^[134] Stress and social defeat induced depressive symptoms, including anhedonia, are associated with potentiation of excitatory inputs to dopamine D2 receptor-expressing medium spiny neurons (D2-MSNs) and depression of excitatory inputs to dopamine D1 receptor-expressing medium spiny neurons (D1-MSNs). Optogenetic excitation of D1-MSNs alleviates depressive symptoms and is rewarding, while the same with D2-MSNs enhances depressive symptoms. Excitation of glutaminergic inputs from the ventral hippocampus reduces social interactions, and enhancing these projections produces susceptibility to stress-induced depression. ^[134] Manipulations of different regions of the mPFC can produce and attenuate depressive behaviors. For example, inhibiting mPFC neurons specifically in the intralimbic cortex attenuates depressive behaviors. The conflicting findings associated with mPFC stimulation, when compared to the relatively specific findings in the infralimbic cortex, suggest that the prelimbic cortex and infralimbic cortex may mediate opposing effects. ^[86] mPFC projections to the raphe nuclei are largely GABAergic and inhibit the firing of serotonergic neurons. Specific activation of these regions reduce immobility in the forced swim test but do not affect open field or forced swim behavior. Inhibition of the raphe shifts the behavioral phenotype of uncontrolled stress to a phenotype closer to that of controlled stress. ^[135]

Altered neuroplasticity

Recent studies have called attention to the role of altered neuroplasticity in depression. A review found a convergence of three phenomena:

1. Chronic stress reduces synaptic and dendritic plasticity
2. Depressed subjects show evidence of impaired neuroplasticity (e.g. shortening and reduced complexity of dendritic trees)
3. Anti-depressant medications may enhance neuroplasticity at both a molecular and dendritic level.

The conclusion is that disrupted neuroplasticity is an underlying feature of depression, and is reversed by antidepressants. ^[136]

Blood levels of BDNF in people with MDD increase significantly with antidepressant treatment and correlate with decrease in symptoms. ^[137] Post mortem studies and rat models demonstrate decreased neuronal density in the prefrontal cortex in people with MDD. Rat models demonstrate histological changes consistent with MRI findings in humans, however studies on neurogenesis in humans are limited. Antidepressants appear to reverse the changes in neurogenesis in both animal models and humans. ^[138]

Inflammation

Various reviews have found that general inflammation may play a role in depression. ^{[139] [140]} One meta analysis of cytokines in people with MDD found increased levels of pro-inflammatory IL-6 and TNF-α levels relative to controls. ^[141] The first theories came about when it was noticed that interferon therapy caused depression in a large number of people receiving it. ^[142] Meta analysis on cytokine levels in people with MDD have demonstrated increased levels of IL-1, IL-6, C-reactive protein, but not IL-10. ^{[143] [144]} Increased numbers of T-Cells presenting activation markers, levels of neopterin, IFN-γ, sTNFR, and IL-2 receptors have been observed in depression. ^[145] Various sources of inflammation in depressive illness have been hypothesized and include trauma, sleep problems, diet, smoking and obesity. ^[146] Cytokines, by manipulating neurotransmitters, are involved in the generation of sickness behavior, which shares some overlap with the symptoms of depression. Neurotransmitters hypothesized to be affected include dopamine and serotonin, which are common targets for antidepressant drugs. Induction of indoleamine 2,3-dioxygenase by cytokines has been proposed as a mechanism by which immune dysfunction causes depression. ^[147] One review found normalization of cytokine levels after successful treatment of depression. ^[148] A meta analysis published in 2014 found the use of anti-inflammatory drugs such as NSAIDs and investigational cytokine inhibitors reduced depressive symptoms. ^[149] Exercise can act as a stressor, decreasing the levels of IL-6 and TNF-α and increasing those of IL-10, an anti-inflammatory cytokine. ^[150]

Inflammation is also intimately linked with metabolic processes in humans. For example, low levels of vitamin D have been associated with greater risk for depression. ^[151] The role of metabolic biomarkers in depression is an active research area. Recent work has explored the potential relationship between plasma sterols and depressive symptom severity. ^[152]

Oxidative stress

A marker of DNA oxidation, 8-Oxo-2'-deoxyguanosine, has been found to be increased in both the plasma and urine of people with MDD. This along with the finding of increased F2-isoprostanes levels found in blood, urine and cerebrospinal fluid indicate increased damage to lipids and DNA in people with MDD. Studies with 8-Oxo-2'-deoxyguanosine varied by methods of measurement and type of depression, but F2-isoprostane level was consistent across depression types. Authors suggested lifestyle factors, dysregulation of the HPA axis, immune system and autonomics nervous system as possible causes. ^[153] Another meta-analysis found similar results with regards to oxidative damage products as well as decreased oxidative capacity. ^[154] Oxidative DNA damage may play a role in MDD. ^[155]

Mitochondrial dysfunction

Increased markers of oxidative stress relative to controls have been found in people with MDD. ^[156] These markers include high levels of RNS and ROS which have been shown to influence chronic inflammation, damaging the electron transport chain and biochemical cascades in mitochondria. This lowers the activity of enzymes in the respiratory chain resulting in mitochondrial dysfunction. ^[157] The brain is a highly energy-consuming and has little capacity to store glucose as glycogen and so depends greatly on mitochondria. Mitochondrial dysfunction has been linked to the dampened neuroplasticity observed in depressed brains. ^[158]

Large-scale brain network theory

Instead of studying one brain region, studying large scale brain networks is another approach to understanding psychiatric and neurological disorders, ^[159] supported by recent research that has shown that multiple brain regions are involved in these disorders. Understanding the disruptions in these networks may provide important insights into interventions for treating these disorders. Recent work suggests that at least three large-scale brain networks are important in psychopathology: ^[159]

Central executive network

The central executive network is made up of fronto-parietal regions, including dorsolateral prefrontal cortex and lateral posterior parietal cortex. ^{[160] [161]} This network is involved in high level cognitive functions such as maintaining and using information in working memory, problem solving, and decision making. ^{[159] [162]} Deficiencies in this network are common in most major psychiatric and neurological disorders, including depression. ^{[163] [164]} Because this network is crucial for everyday life activities, those who are depressed can show impairment in basic activities like test taking and being decisive. ^[165]

Default mode network

The default mode network includes hubs in the prefrontal cortex and posterior cingulate, with other prominent regions of the network in the medial temporal lobe and angular gyrus. ^[159] The default mode network is usually active during mind-wandering and thinking about social situations. In contrast, during specific tasks probed in cognitive science (for example, simple attention tasks), the default network is often deactivated. ^{[166] [167]} Research has shown that regions in the default mode network (including medial prefrontal cortex and posterior cingulate) show greater activity when depressed participants ruminate (that is, when they engage in repetitive self-focused thinking) than when typical, healthy participants ruminate. ^[168] People with MDD also show increased connectivity between the default mode network and the subgenual cingulate and the adjoining ventromedial prefrontal cortex in comparison to healthy individuals, individuals with dementia or with autism. Numerous studies suggest that the subgenual cingulate plays an important role in the dysfunction that characterizes major depression. ^[169] The increased activation in the default mode network during rumination and the atypical connectivity between core default mode regions and the subgenual cingulate may underlie the tendency for depressed individual to get "stuck" in the negative, self-focused thoughts that often characterize depression. ^[170] However, further research is needed to gain a precise understanding of how these network interactions map to specific symptoms of depression.

Salience network

The salience network is a cingulate-frontal operculum network that includes core nodes in the anterior cingulate and anterior insula. ^[160] A salience network is a large-scale brain network involved in detecting and orienting the most pertinent of the external stimuli and internal events being presented. ^[159] Individuals who have a tendency to experience negative emotional states (scoring high on measures of neuroticism) show an increase in the right anterior insula during decision-making, even if the decision has already been made. ^[171] This atypically high activity in the right anterior insula is thought to contribute to the experience of negative and worrisome feelings. ^[172] In major depressive disorder, anxiety is often a part of the emotional state that characterizes depression. ^[173]

See also

• Epigenetics of depression
• The Mind Fixers
• Anne Harrington § Mind Fixers: Psychiatry's Troubled Search for the Biology of Mental Illness

References

1. Zhang, Fei-Fei; Peng, Wei; Sweeney, John A.; Jia, Zhi-Yun; Gong, Qi-Yong (November 2018). "Brain structure alterations in depression: Psychoradiological evidence". CNS Neuroscience & Therapeutics. 24 (11): 994–1003. doi:10.1111/cns.12835. ISSN   1755-5949. PMC   6489983 . PMID   29508560.
2. Serefko, Anna; Szopa, Aleksandra; Poleszak, Ewa (1 March 2016). "Magnesium and depression". Magnesium Research. 29 (3): 112–119. doi:10.1684/mrh.2016.0407 (inactive 1 November 2024). ISSN   1952-4021. PMID   27910808.
3. Geng, Chunmei; Shaikh, Abdul Sami; Han, Wenxiu; Chen, Dan; Guo, Yujin; Jiang, Pei (2019). "Vitamin D and depression: mechanisms, determination and application". Asia Pacific Journal of Clinical Nutrition. 28 (4): 689–694. doi:10.6133/apjcn.201912_28(4).0003. ISSN   1440-6047. PMID   31826364.
4. Anglin, Rebecca E.; Tarnopolsky, Mark A.; Mazurek, Michael F.; Rosebush, Patricia I. (January 2012). "The Psychiatric Presentation of Mitochondrial Disorders in Adults". The Journal of Neuropsychiatry and Clinical Neurosciences. 24 (4): 394–409. doi:10.1176/appi.neuropsych.11110345. ISSN   0895-0172. PMID   23224446.
5. CARROLL, BERNARD J. (October 2004). "Psychoneuroendocrinology: The Scientific Basis of Clinical Practice. Edited by O. M. Wolkowitz and A. J. Rothschild. (Pp. 606; $73.95; ISBN 0-88048-857-3 pb.) American Psychiatric Publishing, Inc.: Arlington, Virginia, 2003". Psychological Medicine. 34 (7): 1359–1360. doi:10.1017/S0033291704213678. ISSN   0033-2917. S2CID   73645516.
6. Kupfer DJ, Frank E, Phillips ML (17 March 2012). "Major depressive disorder: new clinical, neurobiological, and treatment perspectives". Lancet. 379 (9820): 1045–55. doi:10.1016/S0140-6736(11)60602-8. PMC   3397431 . PMID   22189047.
7. aan het Rot M, Mathew SJ, Charney DS (3 February 2009). "Neurobiological mechanisms in major depressive disorder". Canadian Medical Association Journal. 180 (3): 305–13. doi:10.1503/cmaj.080697. PMC   2630359 . PMID   19188629.
8. Levinson, Douglas F.; Nichols, Walter E. (2018). "24. Genetics of Depression". In Charney, Dennis S.; Sklar, Pamela; Buxbaum, Joseph D.; Nestler, Eric J. (eds.). Charney & Nestlers Neurobiology of Mental Illness (5th ed.). New York: Oxford University Press. p. 310.
9. Caspi, Avshalom; Sugden, Karen; Moffitt, Terrie E.; Taylor, Alan; Craig, Ian W.; Harrington, HonaLee; McClay, Joseph; Mill, Jonathan; Martin, Judy; Braithwaite, Antony; Poulton, Richie (July 2003). "Influence of Life Stress on Depression: Moderation by a Polymorphism in the 5-HTT Gene". Science. 301 (5631): 386–89. Bibcode:2003Sci...301..386C. doi:10.1126/science.1083968. PMID   12869766. S2CID   146500484.
10. Kendler KS, Kuhn JW, Vittum J, Prescott CA, Riley B (May 2005). "The interaction of stressful life events and a serotonin transporter polymorphism in the prediction of episodes of major depression: a replication". Archives of General Psychiatry. 62 (5): 529–35. doi: 10.1001/archpsyc.62.5.529 . PMID   15867106.
11. Risch N, Herrell R, Lehner T, Liang KY, Eaves L, Hoh J, Griem A, Kovacs M, Ott J, Merikangas KR (June 2009). "Interaction between the serotonin transporter gene (5-HTTLPR), stressful life events, and risk of depression: a meta-analysis". JAMA. 301 (23): 2462–71. doi:10.1001/jama.2009.878. PMC   2938776 . PMID   19531786.
12. Munafò MR, Durrant C, Lewis G, Flint J (February 2009). "Gene X environment interactions at the serotonin transporter locus". Biological Psychiatry. 65 (3): 211–19. doi:10.1016/j.biopsych.2008.06.009. PMID   18691701. S2CID   5780325.
13. Karg K, Burmeister M, Shedden K, Sen S (May 2011). "The serotonin transporter promoter variant (5-HTTLPR), stress, and depression meta-analysis revisited: evidence of genetic moderation". Archives of General Psychiatry. 68 (5): 444–54. doi:10.1001/archgenpsychiatry.2010.189. PMC   3740203 . PMID   21199959.
14. Culverhouse RC, Saccone NL, Horton AC, Ma Y, Anstey KJ, Banaschewski T, et al. (January 2018). "Collaborative meta-analysis finds no evidence of a strong interaction between stress and 5-HTTLPR genotype contributing to the development of depression". Molecular Psychiatry. 23 (1): 133–142. doi:10.1038/mp.2017.44. PMC   5628077 . PMID   28373689.
15. Nierenberg, AA (2009). "The long tale of the short arm of the promoter region for the gene that encodes the serotonin uptake protein". CNS Spectrums. 14 (9): 462–3. doi:10.1017/s1092852900023506. PMID   19890228. S2CID   24236284.
16. Border, Richard; Johnson, Emma; Evans, Luke; Smolen, Andrew; Berley, Noah; Sullivan, Patrick; Keller, Matthew (1 May 2019). "No support for historic candidate gene or candidate gene-by-interaction hypotheses for major depression across multiple large samples". American Journal of Psychiatry. 176 (5): 376–387. doi: 10.1176/appi.ajp.2018.18070881 . PMC   6548317 . PMID   30845820.
17. Converge Consortium; Bigdeli, Tim B.; Kretzschmar, Warren; Li, Yihan; Liang, Jieqin; Song, Li; Hu, Jingchu; Li, Qibin; Jin, Wei; Hu, Zhenfei; Wang, Guangbiao; Wang, Linmao; Qian, Puyi; Liu, Yuan; Jiang, Tao; Lu, Yao; Zhang, Xiuqing; Yin, Ye; Li, Yingrui; Xu, Xun; Gao, Jingfang; Reimers, Mark; Webb, Todd; Riley, Brien; Bacanu, Silviu; Peterson, Roseann E.; Chen, Yiping; Zhong, Hui; Liu, Zhengrong; et al. (2015). "Sparse whole-genome sequencing identifies two loci for major depressive disorder". Nature. 523 (7562): 588–91. Bibcode:2015Natur.523..588C. doi:10.1038/nature14659. PMC   4522619 . PMID   26176920.
18. Smoller, Jordan W (2015). "The Genetics of Stress-Related Disorders: PTSD, Depression, and Anxiety Disorders". Neuropsychopharmacology. 41 (1): 297–319. doi:10.1038/npp.2015.266. PMC   4677147 . PMID   26321314.
19. Zhao, Xiaofeng; Huang, Yinglin; Ma, Hui; Jin, Qiu; Wang, Yuan; Zhu, Gang (15 August 2013). "Association between major depressive disorder and the norepinephrine transporter polymorphisms T-182C and G1287A: a meta-analysis". Journal of Affective Disorders. 150 (1): 23–28. doi:10.1016/j.jad.2013.03.016. ISSN   1573-2517. PMID   23648227.
20. Lohoff, Falk W. (6 December 2016). "Overview of the Genetics of Major Depressive Disorder". Current Psychiatry Reports. 12 (6): 539–546. doi:10.1007/s11920-010-0150-6. ISSN   1523-3812. PMC   3077049 . PMID   20848240.
21. López-León, S.; Janssens, A. C. J. W.; González-Zuloeta Ladd, A. M.; Del-Favero, J.; Claes, S. J.; Oostra, B. A.; van Duijn, C. M. (1 August 2008). "Meta-analyses of genetic studies on major depressive disorder". Molecular Psychiatry. 13 (8): 772–785. doi: 10.1038/sj.mp.4002088 . ISSN   1476-5578. PMID   17938638.
22. Carlson, Neil R. (2013). Physiology of behavior (11th ed.). Boston: Pearson. pp. 578–582. ISBN   978-0-205-23939-9. OCLC   769818904.
23. Satyanarayanan, Senthil Kumaran; Su, Huanxing; Lin, Yi-Wen; Su, Kuan-Pin (19 October 2018). "Circadian Rhythm and Melatonin in the Treatment of Depression". Current Pharmaceutical Design. 24 (22): 2549–2555. doi:10.2174/1381612824666180803112304. PMID   30073921. S2CID   51904516.
24. Fang, Hong; Tu, Sheng; Sheng, Jifang; Shao, Anwen (April 2019). "Depression in sleep disturbance: A review on a bidirectional relationship, mechanisms and treatment". Journal of Cellular and Molecular Medicine. 23 (4): 2324–2332. doi:10.1111/jcmm.14170. PMC   6433686 . PMID   30734486.
25. Wang, Yi-Qun; Li, Rui; Zhang, Meng-Qi; Zhang, Ze; Qu, Wei-Min; Huang, Zhi-Li (31 August 2015). "The Neurobiological Mechanisms and Treatments of REM Sleep Disturbances in Depression". Current Neuropharmacology. 13 (4): 543–553. doi:10.2174/1570159x13666150310002540. PMC   4790401 . PMID   26412074.
26. Adrien J. (2003). "Neurobiological bases for the relation between sleep and depression". Sleep Medicine Reviews. 6 (5): 341–51. doi:10.1053/smrv.2001.0200. PMID   12531125.
27. Terman M (2007). "Evolving applications of light therapy". Sleep Medicine Reviews. 11 (6): 497–507. doi:10.1016/j.smrv.2007.06.003. PMID   17964200. S2CID   2054580.
28. Benedetti F, Barbini B, Colombo C, Smeraldi E (2007). "Chronotherapeutics in a psychiatric ward". Sleep Medicine Reviews. 11 (6): 509–22. doi:10.1016/j.smrv.2007.06.004. PMID   17689120.
29. Zhai, Long; Zhang, Hua; Zhang, Dongfeng (1 September 2015). "Sleep Duration and Depression Among Adults: A Meta-Analysis of Prospective Studies". Depression and Anxiety. 32 (9): 664–670. doi: 10.1002/da.22386 . ISSN   1520-6394. PMID   26047492. S2CID   19071838.
30. Germain, Anne; Kupfer, David J. (6 December 2016). "Circadian rhythm disturbances in depression". Human Psychopharmacology. 23 (7): 571–585. doi:10.1002/hup.964. ISSN   0885-6222. PMC   2612129 . PMID   18680211.
31. Savitz, Jonathan B.; Drevets, Wayne C. (1 April 2013). "Neuroreceptor imaging in depression". Neurobiology of Disease. 52: 49–65. doi: 10.1016/j.nbd.2012.06.001 . ISSN   1095-953X. PMID   22691454.
32. Carlson, Neil R. (2005). Foundations of Physiological Psychology (6th ed.). Boston: Pearson A and B. p.  108. ISBN   978-0-205-42723-9. OCLC   60880502.
33. Marchand; Valentina; Jensen. "Neurobiology of Mood disorders". Hospital Physician: 17–26.
34. Hjorth, S; Bengtsson, HJ; Kullberg, A; Carlzon, D; Peilot, H; Auerbach, SB (June 2000). "Serotonin autoreceptor function and antidepressant drug action". Journal of Psychopharmacology. 14 (2): 177–85. doi:10.1177/026988110001400208. PMID   10890313. S2CID   33440228.
35. Moncrieff, Joanna; Horowitz, Mark (20 July 2022). "Depression is probably not caused by a chemical imbalance in the brain – new study". The Conversation. Retrieved 21 July 2022.
36. Moncrieff, Joanna; Cooper, Ruth E.; Stockmann, Tom; Amendola, Simone; Hengartner, Michael P.; Horowitz, Mark A. (20 July 2022). "The serotonin theory of depression: a systematic umbrella review of the evidence". Molecular Psychiatry. 28 (8): 3243–3256. doi: 10.1038/s41380-022-01661-0 . ISSN   1476-5578. PMC   10618090 . PMID   35854107. S2CID   250646781.
37. Cipriani, Andrea; Furukawa, Toshi A.; Chaimani, Anna; Atkinson, Lauren Z.; Ogawa, Yusuke; Leucht, Stefan. (8 February 2018). "Comparative efficacy and acceptability of 21 antidepressant drugs for the acute treatment of adults with major depressive disorder: a systematic review and network meta-analysis". The Lancet. 391 (10128): 1357–1366. doi:10.1016/S0140-6736(17)32802-7. PMC   5889788 . PMID   29477251.
38. "Expert reaction to a review paper on the 'serotonin theory of depression' | Science Media Centre".
39. COWEN, P (September 2008). "Serotonin and depression: pathophysiological mechanism or marketing myth?". Trends in Pharmacological Sciences. 29 (9): 433–436. doi:10.1016/j.tips.2008.05.004. PMID   18585794.
40. Harmer, CJ (November 2008). "Serotonin and emotional processing: does it help explain antidepressant drug action?". Neuropharmacology. 55 (6): 1023–8. doi:10.1016/j.neuropharm.2008.06.036. PMID   18634807. S2CID   43480495.
41. Dunlop, Boadie W.; Nemeroff, Charles B. (1 April 2007). "The Role of Dopamine in the Pathophysiology of Depression". Archives of General Psychiatry. 64 (3): 327–37. doi:10.1001/archpsyc.64.3.327. ISSN   0003-990X. PMID   17339521. S2CID   26550661.
42. Willner, Paul (1 December 1983). "Dopamine and depression: A review of recent evidence. I. Empirical studies". Brain Research Reviews. 6 (3): 211–224. doi:10.1016/0165-0173(83)90005-X. PMID   6140979. S2CID   974017.
43. HASLER, GREGOR (4 December 2016). "Pathophysiology of Depression: Do We Have Any Solid Evidence of Interest to Clinicians?". World Psychiatry. 9 (3): 155–161. doi:10.1002/j.2051-5545.2010.tb00298.x. ISSN   1723-8617. PMC   2950973 . PMID   20975857.
44. Kunugi, Hiroshi; Hori, Hiroaki; Ogawa, Shintaro (1 October 2015). "Biochemical markers subtyping major depressive disorder". Psychiatry and Clinical Neurosciences. 69 (10): 597–608. doi: 10.1111/pcn.12299 . ISSN   1440-1819. PMID   25825158.
45. Lammel, S.; Tye, K. M.; Warden, M. R. (1 January 2014). "Progress in understanding mood disorders: optogenetic dissection of neural circuits". Genes, Brain and Behavior. 13 (1): 38–51. doi: 10.1111/gbb.12049 . ISSN   1601-183X. PMID   23682971.
46. Delgado PL, Moreno FA (2000). "Role of norepinephrine in depression". J Clin Psychiatry. 61 (Suppl 1): 5–12. PMID   10703757.
47. Ruhe, HG; Mason, NS; Schene, AH (2007). "Mood is indirectly related to serotonin, norepinephrine and dopamine levels in humans: a meta-analysis of monoamine depletion studies". Molecular Psychiatry. 12 (4): 331–359. doi: 10.1038/sj.mp.4001949 . PMID   17389902.
48. Meyer JH, Ginovart N, Boovariwala A, et al. (November 2006). "Elevated monoamine oxidase a levels in the brain: An explanation for the monoamine imbalance of major depression". Archives of General Psychiatry. 63 (11): 1209–16. doi: 10.1001/archpsyc.63.11.1209 . PMID   17088501.
49. Huang SY, Lin MT, Lin WW, Huang CC, Shy MJ, Lu RB (19 December 2007). "Association of monoamine oxidase A (MAOA) polymorphisms and clinical subgroups of major depressive disorders in the Han Chinese population". World Journal of Biological Psychiatry. 10 (4 Pt 2): 544–51. doi:10.1080/15622970701816506. PMID   19224413. S2CID   30281258.
50. Yu YW, Tsai SJ, Hong CJ, Chen TJ, Chen MC, Yang CW (September 2005). "Association study of a monoamine oxidase a gene promoter polymorphism with major depressive disorder and antidepressant response". Neuropsychopharmacology. 30 (9): 1719–23. doi: 10.1038/sj.npp.1300785 . PMID   15956990.
51. Cicchetti D, Rogosch FA, Sturge-Apple ML (2007). "Interactions of child maltreatment and serotonin transporter and monoamine oxidase A polymorphisms: depressive symptomatology among adolescents from low socioeconomic status backgrounds". Dev. Psychopathol. 19 (4): 1161–80. doi:10.1017/S0954579407000600. PMID   17931441. S2CID   32519363.
52. Castrén, E (2005). "Is mood chemistry?". Nature Reviews Neuroscience. 6 (3): 241–46. doi:10.1038/nrn1629. PMID   15738959. S2CID   34523310.
53. Hirschfeld RM (2000). "History and evolution of the monoamine hypothesis of depression". Journal of Clinical Psychiatry. 61 (Suppl 6): 4–6. PMID   10775017.
54. Davis, Kenneth L.; et al., eds. (2002). Neuropsychopharmacology : the fifth generation of progress : an official publication of the American College of Neuropsychopharmacology (5th ed.). Philadelphia, Pa.: Lippincott Williams & Wilkins. pp. 1139–1163. ISBN   9780781728379.
55. Jacobsen, Jacob P. R.; Medvedev, Ivan O.; Caron, Marc G. (5 September 2012). "The 5-HT deficiency theory of depression: perspectives from a naturalistic 5-HT deficiency model, the tryptophan hydroxylase 2Arg439His knockin mouse". Philosophical Transactions of the Royal Society B: Biological Sciences. 367 (1601): 2444–2459. doi:10.1098/rstb.2012.0109. ISSN   0962-8436. PMC   3405680 . PMID   22826344.
56. Delgado PL, Moreno FA (2000). "Role of norepinephrine in depression". J Clin Psychiatry. 61 (Suppl 1): 5–12. PMID   10703757.
57. Delgado PL (2000). "Depression: the case for a monoamine deficiency". Journal of Clinical Psychiatry. 61 (Suppl 6): 7–11. PMID   10775018.
58. Andrews, Paul W.; Bharwani, Aadil; Lee, Kyuwon R.; Fox, Molly; Thomson, J. Anderson (1 April 2015). "Is serotonin an upper or a downer? The evolution of the serotonergic system and its role in depression and the antidepressant response". Neuroscience and Biobehavioral Reviews. 51: 164–188. doi:10.1016/j.neubiorev.2015.01.018. ISSN   1873-7528. PMID   25625874. S2CID   23980182.
59. Lacasse, Jeffrey R.; Leo, Jonathan (8 November 2005). "Serotonin and Depression: A Disconnect between the Advertisements and the Scientific Literature". PLOS Medicine . 2 (12): e392. doi: 10.1371/journal.pmed.0020392 . PMC   1277931 . PMID   16268734.
60. Savitz, Jonathan; Drevets, Wayne (2013). "Neuroreceptor imaging in depression". Neurobiology of Disease. 52: 49–65. doi: 10.1016/j.nbd.2012.06.001 . PMID   22691454.
61. Gryglewski, G; Lanzenberger, R; Kranz, GS; Cumming, P (July 2014). "Meta-analysis of molecular imaging of serotonin transporters in major depression". Journal of Cerebral Blood Flow and Metabolism. 34 (7): 1096–103. doi:10.1038/jcbfm.2014.82. PMC   4083395 . PMID   24802331.
62. Kambeitz, JP; Howes, OD (1 November 2015). "The serotonin transporter in depression: Meta-analysis of in vivo and post mortem findings and implications for understanding and treating depression". Journal of Affective Disorders. 186: 358–66. doi:10.1016/j.jad.2015.07.034. PMID   26281039.
63. Wang, L; Zhou, C; Zhu, D; Wang, X; Fang, L; Zhong, J; Mao, Q; Sun, L; Gong, X; Xia, J; Lian, B; Xie, P (13 September 2016). "Serotonin-1A receptor alterations in depression: a meta-analysis of molecular imaging studies". BMC Psychiatry. 16 (1): 319. doi: 10.1186/s12888-016-1025-0 . PMC   5022168 . PMID   27623971.
64. Li, Z; He, Y; Tang, J; Zong, X; Hu, M; Chen, X (15 March 2015). "Molecular imaging of striatal dopamine transporters in major depression—a meta-analysis". Journal of Affective Disorders. 174: 137–43. doi:10.1016/j.jad.2014.11.045. PMID   25497470.
65. Bourke, Cecilia; Douglas, Katie; Porter, Richard (1 August 2010). "Processing of facial emotion expression in major depression: a review". The Australian and New Zealand Journal of Psychiatry. 44 (8): 681–696. doi:10.3109/00048674.2010.496359. ISSN   1440-1614. PMID   20636189. S2CID   20302084.
66. Dalili, M. N.; Penton-Voak, I. S.; Harmer, C. J.; Munafò, M. R. (7 December 2016). "Meta-analysis of emotion recognition deficits in major depressive disorder". Psychological Medicine. 45 (6): 1135–1144. doi:10.1017/S0033291714002591. ISSN   0033-2917. PMC   4712476 . PMID   25395075.
67. Groenewold, Nynke A.; Opmeer, Esther M.; de Jonge, Peter; Aleman, André; Costafreda, Sergi G. (1 February 2013). "Emotional valence modulates brain functional abnormalities in depression: evidence from a meta-analysis of fMRI studies". Neuroscience and Biobehavioral Reviews. 37 (2): 152–163. doi:10.1016/j.neubiorev.2012.11.015. ISSN   1873-7528. PMID   23206667. S2CID   9980163.
68. Müller, VI; Cieslik, EC; Serbanescu, I; Laird, AR; Fox, PT; Eickhoff, SB (1 January 2017). "Altered Brain Activity in Unipolar Depression Revisited: Meta-analyses of Neuroimaging Studies". JAMA Psychiatry. 74 (1): 47–55. doi:10.1001/jamapsychiatry.2016.2783. PMC   5293141 . PMID   27829086.
69. Harmer, C. J.; Goodwin, G. M.; Cowen, P. J. (31 July 2009). "Why do antidepressants take so long to work? A cognitive neuropsychological model of antidepressant drug action". The British Journal of Psychiatry. 195 (2): 102–108. doi: 10.1192/bjp.bp.108.051193 . PMID   19648538.
70. Delaveau, P; Jabourian, M; Lemogne, C; Guionnet, S; Bergouignan, L; Fossati, P (April 2011). "Brain effects of antidepressants in major depression: a meta-analysis of emotional processing studies". Journal of Affective Disorders. 130 (1–2): 66–74. doi:10.1016/j.jad.2010.09.032. PMID   21030092.
71. Pringle, A; Harmer, CJ (December 2015). "The effects of drugs on human models of emotional processing: an account of antidepressant drug treatment". Dialogues in Clinical Neuroscience. 17 (4): 477–87. doi:10.31887/DCNS.2015.17.4/apringle. PMC   4734885 . PMID   26869848.
72. Hamilton, J. Paul; Etkin, Amit; Furman, Daniella J.; Lemus, Maria G.; Johnson, Rebecca F.; Gotlib, Ian H. (1 July 2012). "Functional neuroimaging of major depressive disorder: a meta-analysis and new integration of base line activation and neural response data". The American Journal of Psychiatry. 169 (7): 693–703. doi:10.1176/appi.ajp.2012.11071105. ISSN   1535-7228. PMID   22535198.
73. Drevets, WC; Price, JL; Furey, ML (September 2008). "Brain structural and functional abnormalities in mood disorders: implications for neurocircuitry models of depression". Brain Structure & Function. 213 (1–2): 93–118. doi:10.1007/s00429-008-0189-x. PMC   2522333 . PMID   18704495.
74. Drevets, WC (April 2001). "Neuroimaging and neuropathological studies of depression: implications for the cognitive-emotional features of mood disorders". Current Opinion in Neurobiology. 11 (2): 240–9. doi:10.1016/S0959-4388(00)00203-8. PMID   11301246. S2CID   36416079.
75. Mayberg, Helen (1 August 1997). "Limbic-cortical dysregulation: a proposed model of depression". The Journal of Neuropsychiatry and Clinical Neurosciences. 9 (3): 471–481. doi:10.1176/jnp.9.3.471. ISSN   0895-0172. PMID   9276848.
76. Graham, Julia; Salimi-Khorshidi, Gholamreza; Hagan, Cindy; Walsh, Nicholas; Goodyer, Ian; Lennox, Belinda; Suckling, John (1 November 2013). "Meta-analytic evidence for neuroimaging models of depression: State or trait?". Journal of Affective Disorders. 151 (2): 423–431. doi: 10.1016/j.jad.2013.07.002 . PMID   23890584.
77. Anticevic, A; Schleifer, C; Youngsun, TC (December 2015). "Emotional and cognitive dysregulation in schizophrenia and depression: understanding common and distinct behavioral and neural mechanisms". Dialogues in Clinical Neuroscience. 17 (4): 421–34. doi:10.31887/DCNS.2015.17.4/aanticevic. PMC   4734880 . PMID   26869843.
78. Rømer Thomsen, K; Whybrow, PC; Kringelbach, ML (2015). "Reconceptualizing anhedonia: novel perspectives on balancing the pleasure networks in the human brain". Frontiers in Behavioral Neuroscience. 9: 49. doi: 10.3389/fnbeh.2015.00049 . PMC   4356228 . PMID   25814941.
79. Treadway, MT; Zald, DH (January 2011). "Reconsidering anhedonia in depression: lessons from translational neuroscience". Neuroscience and Biobehavioral Reviews. 35 (3): 537–55. doi:10.1016/j.neubiorev.2010.06.006. PMC   3005986 . PMID   20603146.
80. Sternat T, Katzman MA (1 January 2016). "Neurobiology of hedonic tone: the relationship between treatment-resistant depression, attention-deficit hyperactivity disorder, and substance abuse". Neuropsychiatric Disease and Treatment. 12: 2149–64. doi: 10.2147/NDT.S111818 . PMC   5003599 . PMID   27601909.
81. Russo, SJ; Nestler, EJ (September 2013). "The brain reward circuitry in mood disorders". Nature Reviews. Neuroscience. 14 (9): 609–25. doi:10.1038/nrn3381. PMC   3867253 . PMID   23942470.
82. Rolls, ET (September 2016). "A non-reward attractor theory of depression" (PDF). Neuroscience and Biobehavioral Reviews. 68: 47–58. doi:10.1016/j.neubiorev.2016.05.007. PMID   27181908. S2CID   8145667.
83. Miller, CH; Hamilton, JP; Sacchet, MD; Gotlib, IH (October 2015). "Meta-analysis of Functional Neuroimaging of Major Depressive Disorder in Youth". JAMA Psychiatry. 72 (10): 1045–53. doi: 10.1001/jamapsychiatry.2015.1376 . PMID   26332700.
84. Graham, J; Salimi-Khorshidi, G; Hagan, C; Walsh, N; Goodyer, I; Lennox, B; Suckling, J (November 2013). "Meta-analytic evidence for neuroimaging models of depression: state or trait?". Journal of Affective Disorders. 151 (2): 423–31. doi: 10.1016/j.jad.2013.07.002 . PMID   23890584.
85. Drevets, WC; Savitz, J; Trimble, M (August 2008). "The subgenual anterior cingulate cortex in mood disorders". CNS Spectrums. 13 (8): 663–81. doi:10.1017/S1092852900013754. PMC   2729429 . PMID   18704022.
86. Lammel, S; Tye, KM; Warden, MR (January 2014). "Progress in understanding mood disorders: optogenetic dissection of neural circuits". Genes, Brain and Behavior. 13 (1): 38–51. doi: 10.1111/gbb.12049 . PMID   23682971.
87. Groenewold, NA; Opmeer, EM; de Jonge, P; Aleman, A; Costafreda, SG (February 2013). "Emotional valence modulates brain functional abnormalities in depression: evidence from a meta-analysis of fMRI studies". Neuroscience and Biobehavioral Reviews. 37 (2): 152–63. doi:10.1016/j.neubiorev.2012.11.015. PMID   23206667. S2CID   9980163.
88. Drevets, WC; Savitz, J; Trimble, M (August 2008). "The subgenual anterior cingulate cortex in mood disorders". CNS Spectrums. 13 (8): 663–81. doi:10.1017/S1092852900013754. PMC   2729429 . PMID   18704022. Together, these data suggest the hypothesis that dysfunction of the sgACC results in understimulation of parasympathetic tone in mood disorders.
89. Rive, MM; van Rooijen, G; Veltman, DJ; Phillips, ML; Schene, AH; Ruhé, HG (December 2013). "Neural correlates of dysfunctional emotion regulation in major depressive disorder. A systematic review of neuroimaging studies". Neuroscience and Biobehavioral Reviews. 37 (10 Pt 2): 2529–53. doi:10.1016/j.neubiorev.2013.07.018. PMID   23928089. S2CID   33607901.
90. Dunlop, BW; Mayberg, HS (December 2014). "Neuroimaging-based biomarkers for treatment selection in major depressive disorder". Dialogues in Clinical Neuroscience. 16 (4): 479–90. doi:10.31887/DCNS.2014.16.4/bdunlop. PMC   4336918 . PMID   25733953.
91. Wise, T; Radua, J; Via, E; Cardoner, N; Abe, O; Adams, TM; Amico, F; Cheng, Y; Cole, JH; de Azevedo Marques Périco, C; Dickstein, DP; Farrow, TFD; Frodl, T; Wagner, G; Gotlib, IH; Gruber, O; Ham, BJ; Job, DE; Kempton, MJ; Kim, MJ; Koolschijn, PCMP; Malhi, GS; Mataix-Cols, D; McIntosh, AM; Nugent, AC; O'Brien, JT; Pezzoli, S; Phillips, ML; Sachdev, PS; Salvadore, G; Selvaraj, S; Stanfield, AC; Thomas, AJ; van Tol, MJ; van der Wee, NJA; Veltman, DJ; Young, AH; Fu, CH; Cleare, AJ; Arnone, D (October 2017). "Common and distinct patterns of grey-matter volume alteration in major depression and bipolar disorder: evidence from voxel-based meta-analysis". Molecular Psychiatry. 22 (10): 1455–1463. doi:10.1038/mp.2016.72. PMC   5622121 . PMID   27217146.
92. Bora, E; Fornito, A; Pantelis, C; Yücel, M (April 2012). "Gray matter abnormalities in Major Depressive Disorder: a meta-analysis of voxel based morphometry studies". Journal of Affective Disorders. 138 (1–2): 9–18. doi:10.1016/j.jad.2011.03.049. PMID   21511342.
93. Zhang, H; Li, L; Wu, M; Chen, Z; Hu, X; Chen, Y; Zhu, H; Jia, Z; Gong, Q (January 2016). "Brain gray matter alterations in first episodes of depression: A meta-analysis of whole-brain studies". Neuroscience and Biobehavioral Reviews. 60: 43–50. doi:10.1016/j.neubiorev.2015.10.011. PMID   26592799. S2CID   207092294.
94. Zhao, YJ; Du, MY; Huang, XQ; Lui, S; Chen, ZQ; Liu, J; Luo, Y; Wang, XL; Kemp, GJ; Gong, QY (October 2014). "Brain grey matter abnormalities in medication-free patients with major depressive disorder: a meta-analysis". Psychological Medicine. 44 (14): 2927–37. doi: 10.1017/S0033291714000518 . PMID   25065859.
95. Sacher, J; Neumann, J; Fünfstück, T; Soliman, A; Villringer, A; Schroeter, ML (October 2012). "Mapping the depressed brain: a meta-analysis of structural and functional alterations in major depressive disorder". Journal of Affective Disorders. 140 (2): 142–8. doi: 10.1016/j.jad.2011.08.001 . PMID   21890211.
96. Arnone, D; Job, D; Selvaraj, S; Abe, O; Amico, F; Cheng, Y; Colloby, SJ; O'Brien, JT; Frodl, T; Gotlib, IH; Ham, BJ; Kim, MJ; Koolschijn, PC; Périco, CA; Salvadore, G; Thomas, AJ; Van Tol, MJ; van der Wee, NJ; Veltman, DJ; Wagner, G; McIntosh, AM (April 2016). "Computational meta-analysis of statistical parametric maps in major depression". Human Brain Mapping. 37 (4): 1393–404. doi:10.1002/hbm.23108. PMC   6867585 . PMID   26854015.
97. Schmaal, L; Hibar, DP; Sämann, PG; Hall, GB; Baune, BT; Jahanshad, N; Cheung, JW; van Erp, TGM; Bos, D; Ikram, MA; Vernooij, MW; Niessen, WJ; Tiemeier, H; Hofman, A; Wittfeld, K; Grabe, HJ; Janowitz, D; Bülow, R; Selonke, M; Völzke, H; Grotegerd, D; Dannlowski, U; Arolt, V; Opel, N; Heindel, W; Kugel, H; Hoehn, D; Czisch, M; Couvy-Duchesne, B; Rentería, ME; Strike, LT; Wright, MJ; Mills, NT; de Zubicaray, GI; McMahon, KL; Medland, SE; Martin, NG; Gillespie, NA; Goya-Maldonado, R; Gruber, O; Krämer, B; Hatton, SN; Lagopoulos, J; Hickie, IB; Frodl, T; Carballedo, A; Frey, EM; van Velzen, LS; Penninx, BWJH; van Tol, MJ; van der Wee, NJ; Davey, CG; Harrison, BJ; Mwangi, B; Cao, B; Soares, JC; Veer, IM; Walter, H; Schoepf, D; Zurowski, B; Konrad, C; Schramm, E; Normann, C; Schnell, K; Sacchet, MD; Gotlib, IH; MacQueen, GM; Godlewska, BR; Nickson, T; McIntosh, AM; Papmeyer, M; Whalley, HC; Hall, J; Sussmann, JE; Li, M; Walter, M; Aftanas, L; Brack, I; Bokhan, NA; Thompson, PM; Veltman, DJ (June 2017). "Cortical abnormalities in adults and adolescents with major depression based on brain scans from 20 cohorts worldwide in the ENIGMA Major Depressive Disorder Working Group". Molecular Psychiatry. 22 (6): 900–909. doi:10.1038/mp.2016.60. PMC   5444023 . PMID   27137745.
98. Schmaal, L; Veltman, DJ; van Erp, TG; Sämann, PG; Frodl, T; Jahanshad, N; Loehrer, E; Tiemeier, H; Hofman, A; Niessen, WJ; Vernooij, MW; Ikram, MA; Wittfeld, K; Grabe, HJ; Block, A; Hegenscheid, K; Völzke, H; Hoehn, D; Czisch, M; Lagopoulos, J; Hatton, SN; Hickie, IB; Goya-Maldonado, R; Krämer, B; Gruber, O; Couvy-Duchesne, B; Rentería, ME; Strike, LT; Mills, NT; de Zubicaray, GI; McMahon, KL; Medland, SE; Martin, NG; Gillespie, NA; Wright, MJ; Hall, GB; MacQueen, GM; Frey, EM; Carballedo, A; van Velzen, LS; van Tol, MJ; van der Wee, NJ; Veer, IM; Walter, H; Schnell, K; Schramm, E; Normann, C; Schoepf, D; Konrad, C; Zurowski, B; Nickson, T; McIntosh, AM; Papmeyer, M; Whalley, HC; Sussmann, JE; Godlewska, BR; Cowen, PJ; Fischer, FH; Rose, M; Penninx, BW; Thompson, PM; Hibar, DP (June 2016). "Subcortical brain alterations in major depressive disorder: findings from the ENIGMA Major Depressive Disorder working group". Molecular Psychiatry. 21 (6): 806–12. doi:10.1038/mp.2015.69. PMC   4879183 . PMID   26122586.
99. Chen, G; Hu, X; Li, L; Huang, X; Lui, S; Kuang, W; Ai, H; Bi, F; Gu, Z; Gong, Q (24 February 2016). "Disorganization of white matter architecture in major depressive disorder: a meta-analysis of diffusion tensor imaging with tract-based spatial statistics". Scientific Reports. 6: 21825. Bibcode:2016NatSR...621825C. doi:10.1038/srep21825. PMC   4764827 . PMID   26906716.
100. Chen, G; Guo, Y; Zhu, H; Kuang, W; Bi, F; Ai, H; Gu, Z; Huang, X; Lui, S; Gong, Q (2 June 2017). "Intrinsic disruption of white matter microarchitecture in first-episode, drug-naive major depressive disorder: A voxel-based meta-analysis of diffusion tensor imaging". Progress in Neuro-psychopharmacology & Biological Psychiatry. 76: 179–187. doi:10.1016/j.pnpbp.2017.03.011. PMID   28336497. S2CID   4610677.
101. Jiang, J; Zhao, YJ; Hu, XY; Du, MY; Chen, ZQ; Wu, M; Li, KM; Zhu, HY; Kumar, P; Gong, QY (May 2017). "Microstructural brain abnormalities in medication-free patients with major depressive disorder: a systematic review and meta-analysis of diffusion tensor imaging". Journal of Psychiatry & Neuroscience. 42 (3): 150–163. doi:10.1503/jpn.150341. PMC   5403660 . PMID   27780031.
102. Wise, T; Radua, J; Nortje, G; Cleare, AJ; Young, AH; Arnone, D (15 February 2016). "Voxel-Based Meta-Analytical Evidence of Structural Disconnectivity in Major Depression and Bipolar Disorder". Biological Psychiatry. 79 (4): 293–302. doi: 10.1016/j.biopsych.2015.03.004 . PMID   25891219.
103. Zhou, M; Hu, X; Lu, L; Zhang, L; Chen, L; Gong, Q; Huang, X (3 April 2017). "Intrinsic cerebral activity at resting state in adults with major depressive disorder: A meta-analysis". Progress in Neuro-psychopharmacology & Biological Psychiatry. 75: 157–164. doi:10.1016/j.pnpbp.2017.02.001. PMID   28174129. S2CID   20054773.
104. Li, W; Chen, Z; Wu, M; Zhu, H; Gu, L; Zhao, Y; Kuang, W; Bi, F; Kemp, GJ; Gong, Q (1 March 2017). "Characterization of brain blood flow and the amplitude of low-frequency fluctuations in major depressive disorder: A multimodal meta-analysis". Journal of Affective Disorders. 210: 303–311. doi:10.1016/j.jad.2016.12.032. PMID   28068619.
105. Kühn, S; Gallinat, J (March 2013). "Resting-state brain activity in schizophrenia and major depression: a quantitative meta-analysis". Schizophrenia Bulletin. 39 (2): 358–65. doi:10.1093/schbul/sbr151. PMC   3576173 . PMID   22080493.
106. Fitzgerald, PB; Laird, AR; Maller, J; Daskalakis, ZJ (June 2008). "A meta-analytic study of changes in brain activation in depression". Human Brain Mapping. 29 (6): 683–95. doi:10.1002/hbm.20426. PMC   2873772 . PMID   17598168.
107. Carlson, Neil R. (2012). Physiology of Behavior Books a La Carte Edition (11th ed.). Boston: Pearson College Div. ISBN   978-0-205-23981-8.
108. Miller, Chris H.; Hamilton, J. Paul; Sacchet, Matthew D.; Gotlib, Ian H. (1 October 2015). "Meta-analysis of Functional Neuroimaging of Major Depressive Disorder in Youth". JAMA Psychiatry. 72 (10): 1045–1053. doi: 10.1001/jamapsychiatry.2015.1376 . ISSN   2168-6238. PMID   26332700.
109. Wessa, Michèle; Lois, Giannis (30 November 2016). "Brain Functional Effects of Psychopharmacological Treatment in Major Depression: A Focus on Neural Circuitry of Affective Processing". Current Neuropharmacology. 13 (4): 466–479. doi:10.2174/1570159X13666150416224801. ISSN   1570-159X. PMC   4790403 . PMID   26412066.
110. Outhred, Tim; Hawkshead, Brittany E.; Wager, Tor D.; Das, Pritha; Malhi, Gin S.; Kemp, Andrew H. (1 September 2013). "Acute neural effects of selective serotonin reuptake inhibitors versus noradrenaline reuptake inhibitors on emotion processing: Implications for differential treatment efficacy" (PDF). Neuroscience and Biobehavioral Reviews. 37 (8): 1786–1800. doi:10.1016/j.neubiorev.2013.07.010. ISSN   1873-7528. PMID   23886514. S2CID   15469440.
111. Hamilton, J. Paul; Siemer, Matthias; Gotlib, Ian H. (8 September 2009). "Amygdala volume in Major Depressive Disorder: A meta-analysis of magnetic resonance imaging studies". Molecular Psychiatry. 13 (11): 993–1000. doi:10.1038/mp.2008.57. ISSN   1359-4184. PMC   2739676 . PMID   18504424.
112. Palmer, Susan M.; Crewther, Sheila G.; Carey, Leeanne M. (14 January 2015). "A Meta-Analysis of Changes in Brain Activity in Clinical Depression". Frontiers in Human Neuroscience. 8: 1045. doi: 10.3389/fnhum.2014.01045 . ISSN   1662-5161. PMC   4294131 . PMID   25642179.
113. Fitzgerald, Paul B.; Laird, Angela R.; Maller, Jerome; Daskalakis, Zafiris J. (5 December 2016). "A Meta-Analytic Study of Changes in Brain Activation in Depression". Human Brain Mapping. 29 (6): 683–695. doi:10.1002/hbm.20426. ISSN   1065-9471. PMC   2873772 . PMID   17598168.
114. Cole, James; Costafreda, Sergi G.; McGuffin, Peter; Fu, Cynthia H. Y. (1 November 2011). "Hippocampal atrophy in first episode depression: a meta-analysis of magnetic resonance imaging studies". Journal of Affective Disorders. 134 (1–3): 483–487. doi:10.1016/j.jad.2011.05.057. ISSN   1573-2517. PMID   21745692.
115. Videbech, Poul; Ravnkilde, Barbara (1 November 2004). "Hippocampal volume and depression: a meta-analysis of MRI studies". The American Journal of Psychiatry. 161 (11): 1957–1966. doi:10.1176/appi.ajp.161.11.1957. ISSN   0002-953X. PMID   15514393.
116. Mahar, I; Bambico, FR; Mechawar, N; Nobrega, JN (January 2014). "Stress, serotonin, and hippocampal neurogenesis in relation to depression and antidepressant effects". Neuroscience and Biobehavioral Reviews. 38: 173–92. doi:10.1016/j.neubiorev.2013.11.009. PMID   24300695. S2CID   207090692.
117. Willner, P; Scheel-Krüger, J; Belzung, C (December 2013). "The neurobiology of depression and antidepressant action". Neuroscience and Biobehavioral Reviews. 37 (10 Pt 1): 2331–71. doi:10.1016/j.neubiorev.2012.12.007. PMID   23261405. S2CID   46160087.
118. Pariante CM, Lightman SL (September 2008). "The HPA axis in major depression: classical theories and new developments". Trends Neurosci. 31 (9): 464–468. doi:10.1016/j.tins.2008.06.006. PMID   18675469. S2CID   13308611.
119. Belvederi Murri, Martino; Pariante, Carmine; Mondelli, Valeria; Masotti, Mattia; Atti, Anna Rita; Mellacqua, Zefiro; Antonioli, Marco; Ghio, Lucio; Menchetti, Marco; Zanetidou, Stamatula; Innamorati, Marco; Amore, Mario (1 March 2014). "HPA axis and aging in depression: systematic review and meta-analysis". Psychoneuroendocrinology. 41: 46–62. doi:10.1016/j.psyneuen.2013.12.004. hdl:11567/691367. ISSN   1873-3360. PMID   24495607. S2CID   24419374.
120. Juruena, Mario F. (1 September 2014). "Early-life stress and HPA axis trigger recurrent adulthood depression". Epilepsy & Behavior. 38: 148–159. doi:10.1016/j.yebeh.2013.10.020. ISSN   1525-5069. PMID   24269030. S2CID   24251067.
121. Heim, Christine; Newport, D. Jeffrey; Mletzko, Tanja; Miller, Andrew H.; Nemeroff, Charles B. (1 August 2008). "The link between childhood trauma and depression: Insights from HPA axis studies in humans". Psychoneuroendocrinology. 33 (6): 693–710. doi:10.1016/j.psyneuen.2008.03.008. ISSN   0306-4530. PMID   18602762. S2CID   2629673.
122. Arana, G. W.; Baldessarini, R. J.; Ornsteen, M. (1 December 1985). "The dexamethasone suppression test for diagnosis and prognosis in psychiatry. Commentary and review". Archives of General Psychiatry. 42 (12): 1193–1204. doi:10.1001/archpsyc.1985.01790350067012. ISSN   0003-990X. PMID   3000317.
123. Varghese, Femina P.; Brown, E. Sherwood (1 January 2001). "The Hypothalamic-Pituitary-Adrenal Axis in Major Depressive Disorder: A Brief Primer for Primary Care Physicians". Primary Care Companion to the Journal of Clinical Psychiatry. 3 (4): 151–155. doi:10.4088/pcc.v03n0401. ISSN   1523-5998. PMC   181180 . PMID   15014598.
124. Gilbertson, M. W.; Shenton, M. E.; Ciszewski, A.; Kasai, K.; Lasko, N. B.; Orr, S. P.; Pitman, R. K. (2002). "Smaller hippocampal volume predicts pathologic vulnerability to psychological trauma". Nature Neuroscience. 5 (11): 1242–7. doi:10.1038/nn958. PMC   2819093 . PMID   12379862.
125. Vythilingam, Meena; Vermetten, Eric; Anderson, George M.; Luckenbaugh, David; Anderson, Eric R.; Snow, Joseph; Staib, Lawrence H.; Charney, Dennis S.; Bremner, J. Douglas (15 July 2004). "Hippocampal volume, memory, and cortisol status in major depressive disorder: effects of treatment". Biological Psychiatry. 56 (2): 101–112. doi:10.1016/j.biopsych.2004.04.002. ISSN   0006-3223. PMID   15231442. S2CID   34280275.
126. Krishnan, V; Nestler, EJ (2011). Animal models of depression: molecular perspectives. Current Topics in Behavioral Neurosciences. Vol. 7. pp. 121–47. doi:10.1007/7854_2010_108. ISBN   978-3-642-19702-4. PMC   3270071 . PMID   21225412.
127. Belzung, C; Lemoine, M (7 November 2011). "Criteria of validity for animal models of psychiatric disorders: focus on anxiety disorders and depression". Biology of Mood & Anxiety Disorders. 1 (1): 9. doi: 10.1186/2045-5380-1-9 . PMC   3384226 . PMID   22738250.
128. Alcantara, Lyonna F.; Praise, Eric M.; Bolanos-Guzman, Carlos A. (2018). "26. Animal Models of Mood Disorders". In Charney, Dennis; Sklar, Pamela; Nestler, Eric; Buxbaum, Joseph (eds.). Charney & Nestler's Neurobiology of Mental Illness (5th ed.). New York: Oxford University Press. pp. 329–333.
129. Yan, HC; Cao, X; Das, M; Zhu, XH; Gao, TM (August 2010). "Behavioral animal models of depression". Neuroscience Bulletin. 26 (4): 327–37. doi:10.1007/s12264-010-0323-7. PMC   5552573 . PMID   20651815.
130. Czéh, B; Fuchs, E; Wiborg, O; Simon, M (4 January 2016). "Animal models of major depression and their clinical implications". Progress in Neuro-psychopharmacology & Biological Psychiatry. 64: 293–310. doi:10.1016/j.pnpbp.2015.04.004. PMID   25891248. S2CID   207410936.
131. Yang, Y; Wang, H; Hu, J; Hu, H (February 2018). "Lateral habenula in the pathophysiology of depression". Current Opinion in Neurobiology. 48: 90–96. doi: 10.1016/j.conb.2017.10.024 . PMID   29175713.
132. Proulx, CD; Hikosaka, O; Malinow, R (September 2014). "Reward processing by the lateral habenula in normal and depressive behaviors". Nature Neuroscience. 17 (9): 1146–52. doi:10.1038/nn.3779. PMC   4305435 . PMID   25157511.
133. Belujon, P; Grace, AA (1 December 2017). "Dopamine System Dysregulation in Major Depressive Disorders". The International Journal of Neuropsychopharmacology. 20 (12): 1036–1046. doi:10.1093/ijnp/pyx056. PMC   5716179 . PMID   29106542.
134. Knowland, D; Lim, BK (5 January 2018). "Circuit-based frameworks of depressive behaviors: The role of reward circuitry and beyond". Pharmacology Biochemistry and Behavior. 174: 42–52. doi:10.1016/j.pbb.2017.12.010. PMC   6340396 . PMID   29309799.
135. Heller, AS (2016). "Cortical-Subcortical Interactions in Depression: From Animal Models to Human Psychopathology". Frontiers in Systems Neuroscience. 10: 20. doi: 10.3389/fnsys.2016.00020 . PMC   4780432 . PMID   27013988.
136. Christopher Pittenger; Ronald S Duman (2008). "Stress, Depression, and Neuroplasticity: A Convergence of Mechanisms". Neuropsychopharmacology. 33 (1): 88–109. doi: 10.1038/sj.npp.1301574 . PMID   17851537.
137. Brunoni, André Russowsky; Lopes, Mariana; Fregni, Felipe (1 December 2008). "A systematic review and meta-analysis of clinical studies on major depression and BDNF levels: implications for the role of neuroplasticity in depression". International Journal of Neuropsychopharmacology. 11 (8): 1169–1180. doi: 10.1017/S1461145708009309 . ISSN   1461-1457. PMID   18752720.
138. Serafini, Gianluca (22 June 2012). "Neuroplasticity and major depression, the role of modern antidepressant drugs". World Journal of Psychiatry. 2 (3): 49–57. doi: 10.5498/wjp.v2.i3.49 . ISSN   2220-3206. PMC   3782176 . PMID   24175168.
139. Krishnadas, Rajeev; Cavanagh, Jonathan (1 May 2012). "Depression: an inflammatory illness?". Journal of Neurology, Neurosurgery, and Psychiatry. 83 (5): 495–502. doi: 10.1136/jnnp-2011-301779 . ISSN   1468-330X. PMID   22423117.
140. Patel, Amisha (1 September 2013). "Review: the role of inflammation in depression". Psychiatria Danubina. 25 (Suppl 2): S216–223. ISSN   0353-5053. PMID   23995180.
141. Dowlati, Yekta; Herrmann, Nathan; Swardfager, Walter; Liu, Helena; Sham, Lauren; Reim, Elyse K.; Lanctôt, Krista L. (1 March 2010). "A meta-analysis of cytokines in major depression". Biological Psychiatry. 67 (5): 446–457. doi:10.1016/j.biopsych.2009.09.033. ISSN   1873-2402. PMID   20015486. S2CID   230209.
142. Dantzer, Robert; O'Connor, Jason C.; Freund, Gregory G.; Johnson, Rodney W.; Kelley, Keith W. (3 December 2016). "From inflammation to sickness and depression: when the immune system subjugates the brain". Nature Reviews Neuroscience. 9 (1): 46–56. doi:10.1038/nrn2297. ISSN   1471-003X. PMC   2919277 . PMID   18073775.
143. Hiles, Sarah A.; Baker, Amanda L.; de Malmanche, Theo; Attia, John (1 October 2012). "A meta-analysis of differences in IL-6 and IL-10 between people with and without depression: exploring the causes of heterogeneity". Brain, Behavior, and Immunity. 26 (7): 1180–1188. doi:10.1016/j.bbi.2012.06.001. hdl: 1959.13/1040816 . ISSN   1090-2139. PMID   22687336. S2CID   205862714.
144. Howren, M. Bryant; Lamkin, Donald M.; Suls, Jerry (1 February 2009). "Associations of depression with C-reactive protein, IL-1, and IL-6: a meta-analysis". Psychosomatic Medicine. 71 (2): 171–186. doi:10.1097/PSY.0b013e3181907c1b. ISSN   1534-7796. PMID   19188531. S2CID   10130027.
145. Maes, Michael (29 April 2011). "Depression is an inflammatory disease, but cell-mediated immune activation is the key component of depression". Progress in Neuro-Psychopharmacology & Biological Psychiatry. 35 (3): 664–675. doi:10.1016/j.pnpbp.2010.06.014. ISSN   1878-4216. PMID   20599581. S2CID   11653910.
146. Berk, Michael; Williams, Lana J; Jacka, Felice N; O'Neil, Adrienne; Pasco, Julie A; Moylan, Steven; Allen, Nicholas B; Stuart, Amanda L; Hayley, Amie C; Byrne, Michelle L; Maes, Michael (12 September 2013). "So depression is an inflammatory disease, but where does the inflammation come from?". BMC Medicine. 11: 200. doi: 10.1186/1741-7015-11-200 . ISSN   1741-7015. PMC   3846682 . PMID   24228900.
147. Leonard, Brian; Maes, Michael (1 February 2012). "Mechanistic explanations how cell-mediated immune activation, inflammation and oxidative and nitrosative stress pathways and their sequels and concomitants play a role in the pathophysiology of unipolar depression". Neuroscience and Biobehavioral Reviews. 36 (2): 764–785. doi:10.1016/j.neubiorev.2011.12.005. ISSN   1873-7528. PMID   22197082. S2CID   37761511.
148. Raedler, Thomas J. (1 November 2011). "Inflammatory mechanisms in major depressive disorder". Current Opinion in Psychiatry. 24 (6): 519–525. doi:10.1097/YCO.0b013e32834b9db6. ISSN   1473-6578. PMID   21897249. S2CID   24215407.
149. Köhler, Ole; Benros, Michael E.; Nordentoft, Merete; Farkouh, Michael E.; Iyengar, Rupa L.; Mors, Ole; Krogh, Jesper (1 December 2014). "Effect of anti-inflammatory treatment on depression, depressive symptoms, and adverse effects: a systematic review and meta-analysis of randomized clinical trials". JAMA Psychiatry. 71 (12): 1381–1391. doi: 10.1001/jamapsychiatry.2014.1611 . ISSN   2168-6238. PMID   25322082.
150. Medina, Johnna L.; Jacquart, Jolene; Smits, Jasper A. J. (2015). "Optimizing the exercise prescription for depression: the search for biomarkers of response". Current Opinion in Psychology. 4: 43–47. doi:10.1016/j.copsyc.2015.02.003. ISSN   2352-250X. PMC   4545504 . PMID   26309904.
151. Parker GB, Brotchie H, Graham RK (January 2017). "Vitamin D and depression". J Affect Disord. 208: 56–61. doi:10.1016/j.jad.2016.08.082. PMID   27750060.
152. Cenik B, Cenik C, Snyder MP, Brown ES (2017). "Plasma sterols and depressive symptom severity in a population-based cohort". PLOS ONE. 12 (9): e0184382. Bibcode:2017PLoSO..1284382C. doi: 10.1371/journal.pone.0184382 . PMC   5590924 . PMID   28886149.
153. Black, Catherine N.; Bot, Mariska; Scheffer, Peter G.; Cuijpers, Pim; Penninx, Brenda W. J. H. (1 January 2015). "Is depression associated with increased oxidative stress? A systematic review and meta-analysis". Psychoneuroendocrinology. 51: 164–175. doi: 10.1016/j.psyneuen.2014.09.025 . ISSN   1873-3360. PMID   25462890. S2CID   6896118.
154. Liu, Tao; Zhong, Shuming; Liao, Xiaoxiao; Chen, Jian; He, Tingting; Lai, Shunkai; Jia, Yanbin (1 January 2015). "A Meta-Analysis of Oxidative Stress Markers in Depression". PLOS ONE. 10 (10): e0138904. Bibcode:2015PLoSO..1038904L. doi: 10.1371/journal.pone.0138904 . ISSN   1932-6203. PMC   4596519 . PMID   26445247.
155. Raza MU, Tufan T, Wang Y, Hill C, Zhu MY (August 2016). "DNA Damage in Major Psychiatric Diseases". Neurotox Res. 30 (2): 251–67. doi:10.1007/s12640-016-9621-9. PMC   4947450 . PMID   27126805.
156. Liu, Tao; Zhong, Shuming; Liao, Xiaoxiao; Chen, Jian; He, Tingting; Lai, Shunkai; Jia, Yanbin (7 October 2015). "A Meta-Analysis of Oxidative Stress Markers in Depression". PLOS ONE. 10 (10): e0138904. Bibcode:2015PLoSO..1038904L. doi: 10.1371/journal.pone.0138904 . ISSN   1932-6203. PMC   4596519 . PMID   26445247.
157. M, Morris G and Berk (2015). "The many roads to mitochondrial dysfunction in neuroimmune and neuropsychiatric disorders". BMC Medicine. 13: 68. doi: 10.1186/s12916-015-0310-y . PMC   4382850 . PMID   25889215.
158. Allen, Josh; Romay-Tallon, Raquel; Brymer, Kyle J.; Caruncho, Hector J.; Kalynchuk, Lisa E. (6 June 2018). "Mitochondria and Mood: Mitochondrial Dysfunction as a Key Player in the Manifestation of Depression". Frontiers in Neuroscience. 12: 386. doi: 10.3389/fnins.2018.00386 . ISSN   1662-453X. PMC   5997778 . PMID   29928190.
159. Menon, Vinod (October 2011). "Large-scale brain networks and psychopathology: a unifying triple network model". Trends in Cognitive Sciences. 15 (10): 483–506. doi:10.1016/j.tics.2011.08.003. PMID   21908230. S2CID   26653572.
160. Seeley, W.W; et al. (February 2007). "Dissociable intrinsic connectivity networks for salience processing and executive control". The Journal of Neuroscience. 27 (9): 2349–56. doi:10.1523/JNEUROSCI.5587-06.2007. PMC   2680293 . PMID   17329432.
161. Habas, C; et al. (1 July 2009). "Distinct cerebellar contributions to intrinsic connectivity networks". The Journal of Neuroscience. 29 (26): 8586–94. doi:10.1523/JNEUROSCI.1868-09.2009. PMC   2742620 . PMID   19571149.
162. Petrides, M (2005). "Lateral prefrontal cortex: architecture and functional organization". Philosophical Transactions of the Royal Society B. 360 (1456): 781–795. doi:10.1098/rstb.2005.1631. PMC   1569489 . PMID   15937012.
163. Woodward, N.D.; et al. (2011). "Functional resting-state networks are differentially affected in schizophrenia". Schizophrenia Research. 130 (1–3): 86–93. doi:10.1016/j.schres.2011.03.010. PMC   3139756 . PMID   21458238.
164. Menon, Vinod; et al. (2001). "Functional neuroanatomy of auditory working memory in schizophrenia: relation to positive and negative symptoms". NeuroImage. 13 (3): 433–446. doi:10.1006/nimg.2000.0699. PMID   11170809. S2CID   12757905.
165. Levin, R.L.; et al. (2007). "Cognitive deficits in depression and functional specificity of regional brain activity". Cognitive Therapy and Research. 31 (2): 211–233. doi:10.1007/s10608-007-9128-z. S2CID   22374128.
166. Qin, P; Northoff, G (2011). "How is our self related to midline regions and the default mode network?". NeuroImage. 57 (3): 1221–1233. doi:10.1016/j.neuroimage.2011.05.028. PMID   21609772. S2CID   16242246.
167. Raichle, M.E.; et al. (2001). "A default mode of brain function". Proceedings of the National Academy of Sciences of the United States of America. 98 (2): 676–682. Bibcode:2001PNAS...98..676R. doi: 10.1073/pnas.98.2.676 . PMC   14647 . PMID   11209064.
168. Cooney, R.E.; et al. (2010). "Neural correlates of rumination in depression". Cognitive, Affective, & Behavioral Neuroscience. 10 (4): 470–478. doi:10.3758/cabn.10.4.470. PMC   4476645 . PMID   21098808.
169. Broyd, S.J.; et al. (2009). "Default mode brain dysfunction in mental disorders: a systematic review". Neuroscience & Biobehavioral Reviews. 33 (3): 279–296. doi:10.1016/j.neubiorev.2008.09.002. PMID   18824195. S2CID   7175805.
170. Hamani, C; et al. (15 February 2011). "The subcallosal cingulate gyrus in the context of major depression". Biological Psychiatry. 69 (4): 301–8. doi: 10.1016/j.biopsych.2010.09.034 . PMID   21145043. S2CID   35458273.
171. Feinstein, J.S.; et al. (September 2006). "Anterior insula reactivity during certain decisions is associated with neuroticism". Social Cognitive and Affective Neuroscience. 1 (2): 136–142. doi:10.1093/scan/nsl016. PMC   2555442 . PMID   18985124.
172. Paulus, M.P; Stein, M.B. (2006). "An insular view of anxiety". Biological Psychiatry. 60 (4): 383–387. doi:10.1016/j.biopsych.2006.03.042. PMID   16780813. S2CID   17889111.
173. Antony, M.M. (2009). Oxford Handbook of Anxiety and Related Disorders. Oxford University Press.

Further reading

• Naumenko, VS; Popova, NK; Lacivita, E; Leopoldo, M; Ponimaskin, EG (July 2014). "Interplay between serotonin 5-HT1A and 5-HT7 receptors in depressive disorders". CNS Neuroscience & Therapeutics . 20 (7). Wiley-Blackwell: 582–90. doi: 10.1111/cns.12247 . ISSN   1755-5949. PMC   6493079 . PMID   24935787. S2CID   13089087.
• Silić, A; Vukojević, J; Peitl, V; De Hert, M; Karlović, D (January 2022). Wegener, G (ed.). "Major depressive disorder: a possible typisation according to serotonin, inflammation, and metabolic syndrome" (PDF). Acta Neuropsychiatrica . 34 (1). Cambridge and New York: Cambridge University Press on behalf of the Scandinavian College of Neuropsychopharmacology: 15–23. doi: 10.1017/neu.2021.30 . ISSN   1601-5215. PMID   34503595. S2CID   237469505 . Retrieved 10 March 2022.
• Szafran, K; Faron-Górecka, A; Kolasa, M; Kuśmider, M; Solich, J; Zurawek, D; Dziedzicka-Wasylewska, M (November–December 2013). "Potential role of G protein-coupled receptor (GPCR) heterodimerization in neuropsychiatric disorders: a focus on depression" (PDF). Pharmacological Reports. 65 (6). Elsevier on behalf of the Institute of Pharmacology of the Polish Academy of Sciences: 1498–505. doi:10.1016/s1734-1140(13)71510-x. ISSN   1734-1140. PMID   24552997. S2CID   4726214 . Retrieved 10 March 2022.