Research: Partnership History and Mental Health over Time

Study Objective

To describe the mental health of men and women with differing histories of partnership transitions.

Design

Data from nine waves of the British Household Panel Survey, a stratified general population sample, were used to calculate age standardised ratios and 95% confidence intervals for mean General Health Questionnaire scores for groups with different partnership transition histories.

Participants

2,127 men and 2,303 women aged under 65 who provided full interviews at every survey wave.

Main Results

Enduring first partnerships were associated with good mental health. Partnership splits were associated with poorer mental health, although the reformation of partnerships partially reversed this. Cohabiting was more beneficial to men’s mental health, whereas marriage was more beneficial to women’s mental health. The more recently a partnership split had occurred the greater the negative outcome for mental health. Women seemed more adversely affected by multiple partnership transitions and to take longer to recover from partnership splits than men. Single women had good mental health relative to other women but the same was not true for single men relative to other male partnership groups.

Conclusions

Partnership was protective of mental health. Mental health was worse immediately after partnership splits, and the negative outcomes for health were longer lasting in women. Future work should consider other factors that may mediate, confound, or jointly determine the relation between partnership change and health.

Reference

Willitts, M., Benzeval, M. & Stansfield, S. (2004) Partnership History and Mental Health Over Time. Journal of Epidemiology and Community Health. 58(1), pp.53-58. https://jech.bmj.com/content/58/1/53.short.

An Overview of the Biology of Depression

Introduction

Scientific studies have found that different brain areas show altered activity in people with major depressive disorder (MDD), and this has encouraged advocates of various theories that seek to identify a biochemical origin of the disease, as opposed to theories that emphasize psychological or situational causes.

Factors spanning these causative groups include nutritional deficiencies in magnesium, vitamin D, and tryptophan with situational origin but 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.

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.

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.

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. 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.

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). 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.

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.

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.

Norepinephrine Transporter Polymorphisms

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

One review identified multiple frequently studied candidate genes. The genes encoding for the 5-HTT and 5-HT2A 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 behaviour. A meta analysis of 182 case controlled genetic studies published in 2008 found Apolipoprotein E verepsilon 2 to be protective, and GNB3 825T, MTHFR 677T, SLC6A4 44bp insertion or deletions, and SLC6A3 40 bpVNTR 9/10 genotype to confer risk.

Circadian Rhythm

Depression may be related to abnormalities in the circadian rhythm, or biological clock.

A well synchronised circadian rhythm is critical for maintaining optimal health. Adverse changes and alterations in the circadian rhythm have been associated various neurological disorders and mood disorders including depression.

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

Sleep

Sleep disturbance is the most prominent symptom in depressive patients. 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. 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, and is impaired by compounds, such as antidepressants, that increase serotonergic tone in brain stem structures. Overall, the serotonergic system is least active during sleep and most active during wakefulness. Prolonged wakefulness due to sleep deprivation 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.

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. 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. 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 hospitalised for MDD.

Increased and decreased sleep length appears to be a risk factor for depression. 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.

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

Monoamines

Monoamines are neurotransmitters that include serotonin, dopamine, norepinephrine, and epinephrine.

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.

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.[31] 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-HT1A autoreceptors. The therapeutic effect of antidepressants is thought to arise from autoreceptor desensitization over a period of time, eventually elevating firing of serotonergic neurons.

Serotonin

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 generalise to the central nervous system. However evidence from receptor binding studies and pharmacological challenges provide some evidence for dysfunction of serotonin neurotransmission in depression. Serotonin may indirectly influence mood by altering emotional processing biases that are seen at both the cognitive/behavioural and neural level. 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.

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 post-mortem studies, but various dopamine receptor agonists show promise in treating MDD. There is some evidence that there is decreased nigrostriatal pathway activity in people with melancholic depression (psychomotor retardation). Further supporting the role of dopamine in depression is the consistent finding of decreased cerebrospinal fluid and jugular metabolites of dopamine, as well as post mortem findings of altered Dopamine receptor D3 and dopamine transporter expression. Studies in rodents have supported a potential mechanism involving stress-induced dysfunction of dopaminergic systems.

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 signalling 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.

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 alpha 2 adrenergic receptor density, and decreased alpha 1 adrenergic receptor density. Furthermore, norepinephrine transporter knockout in mice models increases their tolerance to stress, implicating norepinephrine in depression.

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.

Monoamine Oxidase

An offshoot of the monoamine hypothesis suggests that monoamine oxidase A (MAO-A), an enzyme which metabolises 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. In genetic studies, the alterations of MAO-A-related genes have not been consistently associated with depression. 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. In addition, some evidence indicates that disrupted information processing within neural networks, rather than changes in chemical balance, might underlie depression.

Limitations

Since the 1990s, research has uncovered multiple limitations of the monoamine hypothesis, and its inadequacy has been criticised within the psychiatric community. 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. 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. Experiments with pharmacological agents that cause depletion of monoamines have shown that this depletion does not cause depression in healthy people. Another problem that presents is that drugs that deplete monoamines may actually have antidepressant properties. Further, some have argued that depression may be marked by a hyperserotonergic state. Already limited, the monoamine hypothesis has been further oversimplified when presented to the general public.

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 under-expressed in the striatum of people with MDD. 5-HT1A receptor binding literature is inconsistent; however, it leans towards a general decrease in the mesiotemporal cortex. 5-HT2A receptor binding appears to be unregulated 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.

Four meta analyses on receptor binding in depression have been performed, two on serotonin transporter (5-HTT), one on 5-HT1A, 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. 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. 5-HT1A 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-HT1A ligands are not displaced by endogenous serotonin, indicating that receptor density or affinity is reduced. Dopamine transporter binding is not changed in depression.

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. Depressed people also have impaired recognition of happy, angry, disgusted, fearful and surprised, but not sad faces. 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. Another meta analysis reported elevated hippocampus and thalamus activity in a subgroup of depressed subjects who were medication naïve, not elderly, and had no comorbidities. 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 sub-chronic SSRI administration increases response to positive faces. 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 or 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 the a positive processing bias. 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.

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.

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. 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).

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. The study of reward in depression is limited by heterogeneity in the definition and conceptualisations 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. 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. 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. 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-HT2C receptors in the striatum. 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 behaviour in response to changing contingencies. Hypersensitivity in the lOFC may lead to depression by producing a similar effect to learned helplessness in animals.

Elevated response in the sgACC is a consistent finding in neuroimaging studies using a number of paradigms including reward related tasks. Treatment is also associated with attenuated activity in the sgACC, and inhibition of neurons in the rodent homologue of the sgACC, the infralimbic cortex (IL), produces an antidepressant effect. Hyperactivity of the sgACC has been hypothesized to lead to depression via attenuating the somatic response to reward or positive stimuli. 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. 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.

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. Due to these issues and others, including the longitudinal consistency of depression, most neural models are likely inapplicable to all depression.

Structural Neuroimaging

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). 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. 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. A meta analysis performed using “activation likelihood estimate” reported reductions in the paracingulate cortex, dACC and amygdala.

GMV reductions in MDD and BD.

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.

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. Subcortical abnormalities, including reductions in hippocampus and amygdala volumes, which were especially pronounced in early onset depression.

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 naïve, and general major depressive populations. 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 and only in the genu of the CC. On the other hand, general MDD samples have been reported to have reductions in the body of the CC, the body and genu of the CC, and only the genu of the CC. Reductions of FA have also been reported in the anterior limb of the internal capsule (ALIC) and superior longitudinal fasciculus.

Functional Neuroimaging

Studies of resting state activity have utilised 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 labelling (ASL), and positron emission tomography measures of regional cerebral blood flow or metabolism.

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

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. 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 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. 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. 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.

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.

Prefrontal Cortex

One review reported hypoactivity in the prefrontal cortex of those with depression compared to controls. The prefrontal cortex is involved in emotional processing and regulation, and dysfunction of this process may be involved in the aetiology of depression. One study on antidepressant treatment found an increase in PFC activity in response to administration of antidepressants. One meta analysis published in 2012 found that areas of the prefrontal cortex were hypoactive in response to negative stimuli in people with MDD. 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.

Amygdala

The amygdala, a structure involved in emotional processing appears to be hyperactive in those with major depressive disorder. 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. 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. One study, however, found no difference in amygdala activity during emotional processing tasks.

Hippocampus

Atrophy of the hippocampus has been observed during depression, consistent with animal models of stress and neurogenesis.

Stress can cause depression and depression-like symptoms through monoaminergic changes in several key brain regions as well as suppression in hippocampal neurogenesis. 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.

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. Increased basal cortisol levels and abnormal response to dexamethasone challenges have been observed in people with MDD. Early life stress has been hypothesized as a potential cause of HPA dysfunction. 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. 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.

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. Depression without familial risk or childhood trauma has been linked to a normal hippocampal volume but localised dysfunction.

Animal Models

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 behaviour, 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 aetiology; and the biological plausibility.

Different models for inducing depressive behaviours have been utilised; neuroanatomical manipulations such as olfactory bulbectomy or circuit specific manipulations with optogenetics; genetic models such as 5-HT1A knockout or selectively bred animals; models involving environmental manipulation associated with depression in humans, including chronic mild stress, early life stress and learned helplessness. The validity of these models in producing depressive behaviours may be assessed with a number of behavioural 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 behaviour 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. From an evolutionary standpoint, the behaviour 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. 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.

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. 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 behaviours. Learned helplessness induced potentiation of LHb synapses are reversed by antidepressant treatment, providing predictive validity. A number of inputs to the LHb have been implicated in producing depressive behaviours. 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 behavioural correlates of depression. Tentative in vivo evidence from people with MDD suggests abnormalities in dopamine signalling. This led to early studies investigating VTA activity and manipulations in animal models of depression. Massive destruction of VTA neurons enhances depressive behaviours, 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. 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. Manipulations of different regions of the mPFC can produce and attenuate depressive behaviours. For example, inhibiting mPFC neurons specifically in the intralimbic cortex attenuates depressive behaviours. 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. 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 behaviour. Inhibition of the raphe shifts the behavioural phenotype of uncontrolled stress to a phenotype closer to that of controlled stress.

Altered Neuroplasticity

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

  • Chronic stress reduces synaptic and dendritic plasticity;
  • Depressed subjects show evidence of impaired neuroplasticity (e.g. shortening and reduced complexity of dendritic trees); and
  • 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.

Blood levels of BDNF in people with MDD increase significantly with antidepressant treatment and correlate with decrease in symptoms. Post mortem studies and rat models demonstrate decreased neuronal density in the prefrontal cortex thickness 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.

Inflammation

Various reviews have found that general inflammation may play a role in depression. One meta analysis of cytokines in people with MDD found increased levels of pro-inflammatory IL-6 and TNF-a levels relative to controls. The first theories came about when it was noticed that interferon therapy caused depression in a large number of people receiving it. 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. Increased numbers of T-Cells presenting activation markers, levels of neopterin, IFN gamma, sTNFR, and IL-2 receptors have been observed in depression. Various sources of inflammation in depressive illness have been hypothesized and include trauma, sleep problems, diet, smoking and obesity. Cytokines, by manipulating neurotransmitters, are involved in the generation of sickness behaviour, 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 indolamine-2,3 dioxygenease by cytokines has been proposed as a mechanism by which immune dysfunction causes depression. One review found normalization of cytokine levels after successful treatment of depression. A meta analysis published in 2014 found the use of anti-inflammatory drugs such as NSAIDs and investigational cytokine inhibitors reduced depressive symptoms. Exercise can act as a stressor, decreasing the levels of IL-6 and TNF-a and increasing those of IL-10, an anti-inflammatory cytokine.

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. 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.

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. Another meta-analysis found similar results with regards to oxidative damage products as well as decreased oxidative capacity. Oxidative DNA damage may play a role in MDD.

Mitochondrial Dysfunction:

Increased markers of oxidative stress relative to controls have been found in people with MDD. 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. 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.

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, 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.

Central Executive Network

The central executive network is made up of fronto-parietal regions, including dorsolateral prefrontal cortex and lateral posterior parietal cortex. This network is involved in high level cognitive functions such as maintaining and using information in working memory, problem solving, and decision making. Deficiencies in this network are common in most major psychiatric and neurological disorders, including depression. 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.

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. 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. 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. 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. 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 characterise depression. 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. 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. 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. This atypically high activity in the right anterior insula is thought to contribute to the experience of negative and worrisome feelings. In MDD, anxiety is often a part of the emotional state that characterises depression.

What was the National Survey of Mental Health and Wellbeing?

Introduction

The 2007 National Survey of Mental Health and Wellbeing (NSMHWB) was designed to provide lifetime prevalence estimates for mental disorders.

Purpose

To gain statistics on key mental health issues including the prevalence of mental disorders, the associated disability, and the use of services.

As such the NSMHWB was a national epidemiological survey of mental disorders that used similar methodology to the NCS. It aimed to answer three main questions:

  1. How many people meet DSM-IV and ICD-10 diagnostic criteria for the major mental disorders?
  2. How disabled are they by their mental disorders? and
  3. How many have seen a health professional for their mental disorder?

Background

Respondents were asked about experiences throughout their lifetime. In this survey, 12-month diagnoses were derived based on lifetime diagnosis and the presence of symptoms of that disorder in the 12 months prior to the survey interview. Assessment of mental disorders presented in this publication are based on the definitions and criteria of the World Health Organisation’s (WHO) International Classification of Diseases, Tenth Revision (ICD-10). Prevalence rates are presented with hierarchy rules applied (i.e. a person will not meet the criteria for particular disorders because the symptoms are believed to be accounted for by the presence of another disorder).

Results

  • Among the 16,015,300 people aged 16-85 years, 45% (or 7,286,600 people) had a lifetime mental disorder (i.e. a mental disorder at some point in their life).
  • More than half (55% or 8,728,700 people) of people had no lifetime mental disorders.
  • Of people who had a lifetime mental disorder:
    • 20% (or 3,197,800 people) had a 12-month mental disorder and had symptoms in the 12 months prior to the survey interview; and
    • 25% (or 4,088,800 people) had experienced a lifetime mental disorder but did not have symptoms in the 12 months prior to the survey interview.

Prevalence of 12-Month Mental Health Disorders

Prevalence of mental disorders is the proportion of people in a given population who met the criteria for diagnosis of a mental disorder at a point in time

  • Among the 3,197,800 people (or 20% of people) who had a 12-month mental disorder and had symptoms in the 12 months prior to interview:
    • 14.4% had a 12-month Anxiety disorder (includes Panic disorder (2.6%); Agoraphobia (2.8%); Social Phobia (4.7%); Generalised Anxiety Disorder (2.7%); Obsessive-Compulsive Disorder (1.9%); and Post-Traumatic Stress Disorder (6.4%))
    • 6.2% had a 12-month Affective disorder (includes Depressive Episode (4.1%) (includes severe, moderate and mild depressive episodes); Dysthymia (1.3%); and Bipolar Affective Disorder (1.8%)), and
    • 5.1% had a 12-month Substance Use Disorder (includes Alcohol Harmful Use (2.9%); Alcohol Dependence (1.4%); and Drug Use Disorders (includes harmful use and dependence) (1.4%)).
  • Note that a person may have had more than one mental disorder.
    • The components when added may therefore not add to the total shown.
    • Includes Severe Depressive Episode, Moderate Depressive Episode, and Mild Depressive Episode.
    • Includes Harmful Use and Dependence.

There were 3.2 million people who had a 12-month mental disorder. In total, 14.4% (2.3 million) of Australians aged 16-85 years had a 12-month Anxiety disorder, 6.2% (995,900) had a 12-month Affective disorder and 5.1% (819,800) had a 12-month Substance Use disorder.

Women experienced higher rates of 12-month mental disorders than men (22% compared with 18%). Women experienced higher rates than men of Anxiety (18% and 11% respectively) and Affective disorders (7.1% and 5.3% respectively). However, men had twice the rate of Substance Use disorders (7.0% compared with 3.3% for women).

The prevalence of 12-month mental disorders varies across age groups, with people in younger age groups experiencing higher rates of disorder. More than a quarter (26%) of people aged 16-24 years and a similar proportion (25%) of people aged 25-34 years had a 12-month mental disorder compared with 5.9% of those aged 75-85 years old.

You can read the full survey results here and a shorter analysis can be found here.

What is the WHO Assessment Instrument for Mental Health Systems?

Introduction

The World Health Organisation Assessment Instrument for Mental Health Systems (WHO-AIMS) is a new WHO tool for collecting essential information on the mental health system of a country or region.

Purpose

The goal of collecting this information is to improve mental health systems and to provide a baseline for monitoring the change.

What is WHO-AIMS?

WHO-AIMS is a WHO tool for collecting essential information on the mental health system of a country or region. The goal of collecting this information is to improve mental health systems and to provide a baseline for monitoring the change.

For the purpose of WHO-AIMS, a mental health system is defined as all the activities whose primary purpose is to promote, restore or maintain mental health. WHO-AIMS is primarily intended for assessing mental health systems in low and middle income countries, but is also a valuable assessment tool for high resource countries.

Note: Great care has been taken to ensure the reliability of the data presented in the WHO-AIMS country reports. Data for WHO-AIMS are collected by a team led by a focal point within the country and are, in most cases, approved by the Ministry of Health. However, since WHO is not directly responsible for the data collection, WHO cannot independently verify the accuracy of any of the data presented in these reports.

WHO-AIMS Instrument, Version 2.2

You can find country reports, sub-regional reports, and other reports here (Pan American Health Organisation (PAHO) site).

What is Observational Learning?

Introduction

Observational learning is learning that occurs through observing the behaviour of others.

It is a form of social learning which takes various forms, based on various processes. In humans, this form of learning seems to not need reinforcement to occur, but instead, requires a social model such as a parent, sibling, friend, or teacher with surroundings. Particularly in childhood, a model is someone of authority or higher status in an environment. In animals, observational learning is often based on classical conditioning, in which an instinctive behaviour is elicited by observing the behaviour of another (e.g. mobbing in birds), but other processes may be involved as well.

Human Observational Learning

Many behaviours that a learner observes, remembers, and imitates are actions that models display and display modelling, even though the model may not intentionally try to instil a particular behaviour. A child may learn to swear, smack, smoke, and deem other inappropriate behaviour acceptable through poor modelling. Albert Bandura claims that children continually learn desirable and undesirable behaviour through observational learning. Observational learning suggests that an individual’s environment, cognition, and behaviour all incorporate and ultimately determine how the individual functions and models.

Through observational learning, individual behaviours can spread across a culture through a process called diffusion chain. This basically occurs when an individual first learns a behaviour by observing another individual and that individual serves as a model through whom other individuals learn the behaviour, and so on.

Culture plays a role in whether observational learning is the dominant learning style in a person or community. Some cultures expect children to actively participate in their communities and are therefore exposed to different trades and roles on a daily basis. This exposure allows children to observe and learn the different skills and practices that are valued in their communities.

Albert Bandura, who is known for the classic Bobo doll experiment, identified this basic form of learning in 1961. The importance of observational learning lies in helping individuals, especially children, acquire new responses by observing others’ behaviour.

Albert Bandura states that people’s behaviour could be determined by their environment. Observational learning occurs through observing negative and positive behaviours. Bandura believes in reciprocal determinism in which the environment can influence people’s behaviour and vice versa. For instance, the Bobo doll experiment shows that the model, in a determined environment, affects children’s behaviour. In this experiment Bandura demonstrates that one group of children placed in an aggressive environment would act the same way, while the control group and the other group of children placed in a passive role model environment hardly shows any type of aggression.

In communities where children’s primary mode of learning is through observation, the children are rarely separated from adult activities. This incorporation into the adult world at an early age allows children to use observational learning skills in multiple spheres of life. This learning through observation requires keen attentive abilities. Culturally, they learn that their participation and contributions are valued in their communities. This teaches children that it is their duty, as members of the community, to observe others’ contributions so they gradually become involved and participate further in the community.

Influential Stages and Factors

The stages of observational learning include exposure to the model, acquiring the model’s behaviour and accepting it as one’s own.

Bandura’s social cognitive learning theory states that there are four factors that influence observational learning:

AttentionObservers cannot learn unless they pay attention to what’s happening around them. This process is influenced by characteristics of the model, such as how much one likes or identifies with the model, and by characteristics of the observer, such as the observer’s expectations or level of emotional arousal.
Retention/MemoryObservers must not only recognize the observed behaviour but also remember it at some later time. This process depends on the observer’s ability to code or structure the information in an easily remembered form or to mentally or physically rehearse the model’s actions.
Initiation/MotorObservers must be physically and/intellectually capable of producing the act. In many cases, the observer possesses the necessary responses. But sometimes, reproducing the model’s actions may involve skills the observer has not yet acquired. It is one thing to carefully watch a circus juggler, but it is quite another to go home and repeat those acts.
MotivationThe observer must have motivation to recreate the observed behaviour.


Bandura clearly distinguishes between learning and performance. Unless motivated, a person does not produce learned behaviour. This motivation can come from external reinforcement, such as the experimenter’s promise of reward in some of Bandura’s studies, or the bribe of a parent. Or it can come from vicarious reinforcement, based on the observation that models are rewarded. High-status models can affect performance through motivation. For example, girls aged 11 to 14 performed better on a motor performance task when they thought it was demonstrated by a high-status cheerleader than by a low-status model.

Some have even added a step between attention and retention involving encoding a behaviour.

Observational learning leads to a change in an individual’s behaviour along three dimensions:

  • An individual thinks about a situation in a different way and may have incentive to react to it.
  • The change is a result of a person’s direct experiences as opposed to being in-born.
  • For the most part, the change an individual has made is permanent.

Effect on Behaviour

According to Bandura’s social cognitive learning theory, observational learning can affect behaviour in many ways, with both positive and negative consequences. It can teach completely new behaviours, for one. It can also increase or decrease the frequency of behaviours that have previously been learned. Observational learning can even encourage behaviours that were previously forbidden (for example, the violent behaviour towards the Bobo doll that children imitated in Albert Bandura’s study). Observational learning can also influence behaviours that are similar to, but not identical to, the ones being modelled. For example, seeing a model excel at playing the piano may motivate an observer to play the saxophone.

Age Difference

Albert Bandura stressed that developing children learn from different social models, meaning that no two children are exposed to exactly the same modelling influence. From infancy to adolescence, they are exposed to various social models. A 2013 study found that a toddlers’ previous social familiarity with a model was not always necessary for learning and that they were also able to learn from observing a stranger demonstrating or modelling a new action to another stranger.

It was once believed that babies could not imitate actions until the latter half of the first year. However, a number of studies now report that infants as young as seven days can imitate simple facial expressions. By the latter half of their first year, 9-month-old babies can imitate actions hours after they first see them. As they continue to develop, toddlers around age two can acquire important personal and social skills by imitating a social model.

Deferred imitation is an important developmental milestone in a two-year-old, in which children not only construct symbolic representations but can also remember information. Unlike toddlers, children of elementary school age are less likely to rely on imagination to represent an experience. Instead, they can verbally describe the model’s behaviour. Since this form of learning does not need reinforcement, it is more likely to occur regularly.

As age increases, age-related observational learning motor skills may decrease in athletes and golfers. Younger and skilled golfers have higher observational learning compared to older golfers and less skilled golfers.

Observational Causal Learning

Humans use observational Moleen causal learning to watch other people’s actions and use the information gained to find out how something works and how we can do it ourselves.

A study of 25-month-old infants found that they can learn causal relations from observing human interventions. They also learn by observing normal actions not created by intentional human action.

Comparisons with Imitation

Observational learning is presumed to have occurred when an organism copies an improbable action or action outcome that it has observed and the matching behaviour cannot be explained by an alternative mechanism. Psychologists have been particularly interested in the form of observational learning known as imitation and in how to distinguish imitation from other processes. To successfully make this distinction, one must separate the degree to which behavioural similarity results from:

  • Predisposed behaviour.
  • Increased motivation resulting from the presence of another animal.
  • Attention drawn to a place or object.
  • Learning about the way the environment works, as distinguished from what we think of as.
  • Imitation (the copying of the demonstrated behaviour).

Observational learning differs from imitative learning in that it does not require a duplication of the behaviour exhibited by the model. For example, the learner may observe an unwanted behaviour and the subsequent consequences, and thus learn to refrain from that behaviour. For example, Riopelle (1960) found that monkeys did better with observational learning if they saw the “tutor” monkey make a mistake before making the right choice. Heyes (1993) distinguished imitation and non-imitative social learning in the following way: imitation occurs when animals learn about behaviour from observing conspecifics, whereas non-imitative social learning occurs when animals learn about the environment from observing others.

Not all imitation and learning through observing is the same, and they often differ in the degree to which they take on an active or passive form. John Dewey describes an important distinction between two different forms of imitation: imitation as an end in itself and imitation with a purpose.[19] Imitation as an end is more akin to mimicry, in which a person copies another’s act to repeat that action again. This kind of imitation is often observed in animals. Imitation with a purpose utilizes the imitative act as a means to accomplish something more significant. Whereas the more passive form of imitation as an end has been documented in some European American communities, the other kind of more active, purposeful imitation has been documented in other communities around the world.

Observation may take on a more active form in children’s learning in multiple Indigenous American communities. Ethnographic anthropological studies in Yucatec Mayan and Quechua Peruvian communities provide evidence that the home or community-centred economic systems of these cultures allow children to witness first-hand, activities that are meaningful to their own livelihoods and the overall well-being of the community. These children have the opportunity to observe activities that are relevant within the context of that community, which gives them a reason to sharpen their attention to the practical knowledge they are exposed to. This does not mean that they have to observe the activities even though they are present. The children often make an active decision to stay in attendance while a community activity is taking place to observe and learn. This decision underscores the significance of this learning style in many indigenous American communities. It goes far beyond learning mundane tasks through rote imitation; it is central to children’s gradual transformation into informed members of their communities’ unique practices. There was also a study, done with children, that concluded that Imitated behaviour can be recalled and used in another situation or the same.

Apprenticeship

Apprenticeship can involve both observational learning and modelling. Apprentices gain their skills in part through working with masters in their profession and through observing and evaluating the work of their fellow apprentices. Examples include renaissance inventor/painter Leonardo da Vinci and Michelangelo, before succeeding in their profession they were apprentices.

Learning Without Imitation

Michael Tomasello described various ways of observational learning without the process of imitation in animals (ethology):

ExposureIndividuals learn about their environment through close proximity to other individuals that have more experience. For example, a young dolphin learning the location of a plethora of fish by staying near its mother.
Stimulus EnhancementIndividuals become interested in an object from watching others interact with it. Increased interest in an object may result in object manipulation, which facilitates new object-related behaviours by trial-and-error learning. For example, a young killer whale might become interested in playing with a sea lion pup after watching other whales toss the sea lion pup around. After playing with the pup, the killer whale may develop foraging behaviours appropriate to such prey. In this case, the killer whale did not learn to prey on sea lions by observing other whales do so, but rather the killer whale became intrigued after observing other whales play with the pup. After the killer whale became interested, then its interactions with the sea lion resulted in behaviours that provoked future foraging efforts.
Goal EmulationIndividuals are enticed by the end result of an observed behaviour and attempt the same outcome but with a different method. For example, Haggerty (1909) devised an experiment in which a monkey climbed up the side of a cage, stuck its arm into a wooden chute, and pulled a rope in the chute to release food. Another monkey was provided an opportunity to obtain the food after watching a monkey go through this process on four separate occasions. The monkey performed a different method and finally succeeded after trial and error.

Peer Model Influences

Observational learning is very beneficial when there are positive, reinforcing peer models involved. Although individuals go through four different stages for observational learning: attention; retention; production; and motivation, this does not simply mean that when an individual’s attention is captured that it automatically sets the process in that exact order. One of the most important ongoing stages for observational learning, especially among children, is motivation and positive reinforcement.

Performance is enhanced when children are positively instructed on how they can improve a situation and where children actively participate alongside a more skilled person. Examples of this are scaffolding and guided participation. Scaffolding refers to an expert responding contingently to a novice so the novice gradually increases their understanding of a problem. Guided participation refers to an expert actively engaging in a situation with a novice so the novice participates with or observes the adult to understand how to resolve a problem.

Cultural Variation

Cultural variation can be seen by the extent of information learned or absorbed by children in non-Western cultures through learning by observation. Cultural variation is not restricted only to ethnicity and nationality, but rather, extends to the specific practices within communities. In learning by observation, children use observation to learn without verbal requests for further information, or without direct instruction. For example, children from Mexican heritage families tend to learn and make better use of information observed during classroom demonstration than children of European heritage. Children of European heritage experience the type of learning that separates them from their family and community activities. They instead participate in lessons and other exercises in special settings such as school. Cultural backgrounds differ from each other in which children display certain characteristics in regards to learning an activity. Another example is seen in the immersion of children in some Indigenous communities of the Americas into the adult world and the effects it has on observational learning and the ability to complete multiple tasks simultaneously. This might be due to children in these communities having the opportunity to see a task being completed by their elders or peers and then trying to emulate the task. In doing so they learn to value observation and the skill-building it affords them because of the value it holds within their community. This type of observation is not passive, but reflects the child’s intent to participate or learn within a community.

Observational learning can be seen taking place in many domains of Indigenous communities. The classroom setting is one significant example, and it functions differently for Indigenous communities compared to what is commonly present in Western schooling. The emphasis of keen observation in favour of supporting participation in ongoing activities strives to aid children to learn the important tools and ways of their community. Engaging in shared endeavours – with both the experienced and inexperienced – allows for the experienced to understand what the inexperienced need in order to grow in regards to the assessment of observational learning. The involvement of the inexperienced, or the children in this matter, can either be furthered by the children’s learning or advancing into the activity performed by the assessment of observational learning. Indigenous communities rely on observational learning as a way for their children to be a part of ongoing activities in the community.

Although learning in the Indigenous American communities is not always the central focus when participating in an activity, studies have shown that attention in intentional observation differs from accidental observation. Intentional participation is “keen observation and listening in anticipation of, or in the process of engaging in endeavors”. This means that when they have the intention of participating in an event, their attention is more focused on the details, compared to when they are accidentally observing.

Observational learning can be an active process in many Indigenous American communities. The learner must take initiative to attend to activities going on around them. Children in these communities also take initiative to contribute their knowledge in ways that will benefit their community. For example, in many Indigenous American cultures, children perform household chores without being instructed to do so by adults. Instead, they observe a need for their contributions, understand their role in their community, and take initiative to accomplish the tasks they have observed others doing. The learner’s intrinsic motivations play an important role in the child’s understanding and construction of meaning in these educational experiences. The independence and responsibility associated with observational learning in many Indigenous American communities are significant reasons why this method of learning involves more than just watching and imitating. A learner must be actively engaged with their demonstrations and experiences in order to fully comprehend and apply the knowledge they obtain.

Indigenous Communities of the Americas

Children from indigenous heritage communities of the Americas often learn through observation, a strategy that can carry over into adulthood. The heightened value towards observation allows children to multi-task and actively engage in simultaneous activities. The exposure to an uncensored adult lifestyle allows children to observe and learn the skills and practices that are valued in their communities. Children observe elders, parents, and siblings complete tasks and learn to participate in them. They are seen as contributors and learn to observe multiple tasks being completed at once and can learn to complete a task while still engaging with other community members without being distracted.

Indigenous communities provide more opportunities to incorporate children in everyday life. This can be seen in some Mayan communities where children are given full access to community events, which allows observational learning to occur more often. Other children in Mazahua, Mexico are known to observe ongoing activities intensely. In native northern Canadian and indigenous Mayan communities, children often learn as third-party observers from stories and conversations by others. Most young Mayan children are carried on their mother’s back, allowing them to observe their mother’s work and see the world as their mother sees it. Often, children in Indigenous American communities assume the majority of the responsibility for their learning. Additionally, children find their own approaches to learning. Children are often allowed to learn without restrictions and with minimal guidance. They are encouraged to participate in the community even if they do not know how to do the work. They are self-motivated to learn and finish their chores. These children act as a second set of eyes and ears for their parents, updating them about the community.

Children aged 6 to 8 in an indigenous heritage community in Guadalajara, Mexico participated in hard work, such as cooking or running errands, thus benefiting the whole family, while those in the city of Guadalajara rarely did so. These children participated more in adult regulated activities and had little time to play, while those from the indigenous-heritage community had more time to play and initiate in their after-school activities and had a higher sense of belonging to their community. Children from formerly indigenous communities are more likely to show these aspects than children from cosmopolitan communities are, even after leaving their childhood community

Within certain indigenous communities, people do not typically seek out explanations beyond basic observation. This is because they are competent in learning through astute observation and often nonverbally encourage to do so. In a Guatemalan footloom factory, amateur adult weavers observed skilled weavers over the course of weeks without questioning or being given explanations; the amateur weaver moved at their own pace and began when they felt confident. The framework of learning how to weave through observation can serve as a model that groups within a society use as a reference to guide their actions in particular domains of life. Communities that participate in observational learning promote tolerance and mutual understand of those coming from different cultural backgrounds.

Other Human and Animal Behaviour Experiments

When an animal is given a task to complete, they are almost always more successful after observing another animal doing the same task before them. Experiments have been conducted on several different species with the same effect: animals can learn behaviours from peers. However, there is a need to distinguish the propagation of behaviour and the stability of behaviour. Research has shown that social learning can spread a behaviour, but there are more factors regarding how a behaviour carries across generations of an animal culture.

Learning in Fish

Experiments with ninespine sticklebacks showed that individuals will use social learning to locate food.

Social Learning in Pigeons

A study in 1996 at the University of Kentucky used a foraging device to test social learning in pigeons. A pigeon could access the food reward by either pecking at a treadle or stepping on it. Significant correspondence was found between the methods of how the observers accessed their food and the methods the initial model used in accessing the food.

Acquiring Foraging Niches

Studies have been conducted at the University of Oslo and University of Saskatchewan regarding the possibility of social learning in birds, delineating the difference between cultural and genetic acquisition. Strong evidence already exists for mate choice, bird song, predator recognition, and foraging.

Researchers cross-fostered eggs between nests of blue tits and great tits and observed the resulting behaviour through audio-visual recording. Tits raised in the foster family learned their foster family’s foraging sites early. This shift – from the sites the tits would among their own kind and the sites they learned from the foster parents – lasted for life. What young birds learn from foster parents, they eventually transmitted to their own offspring. This suggests cultural transmissions of foraging behaviour over generations in the wild.

Social Learning in Crows

The University of Washington studied this phenomenon with crows, acknowledging the evolutionary tradeoff between acquiring costly information firsthand and learning that information socially with less cost to the individual but at the risk of inaccuracy. The experimenters exposed wild crows to a unique “dangerous face” mask as they trapped, banded, and released 7-15 birds at five different study places around Seattle, Washington. An immediate scolding response to the mask after trapping by previously captured crows illustrates that the individual crow learned the danger of that mask. There was a scolding from crows that were captured that had not been captured initially. That response indicates conditioning from the mob of birds that assembled during the capture.

Horizontal social learning (learning from peers) is consistent with the lone crows that recognized the dangerous face without ever being captured. Children of captured crow parents were conditioned to scold the dangerous mask, which demonstrates vertical social learning (learning from parents). The crows that were captured directly had the most precise discrimination between dangerous and neutral masks than the crows that learned from the experience of their peers. The ability of crows to learn doubled the frequency of scolding, which spread at least 1.2 km from where the experiment started to over a 5-year period at one site.

Propagation of Animal Culture

Researchers at the Département d’Etudes Cognitives, Institut Jean Nicod, Ecole Normale Supérieure acknowledged a difficulty with research in social learning. To count acquired behaviour as cultural, two conditions need must be met: the behaviour must spread in a social group, and that behaviour must be stable across generations. Research has provided evidence that imitation may play a role in the propagation of a behaviour, but these researchers believe the fidelity of this evidence is not sufficient to prove the stability of animal culture.

Other factors like ecological availability, reward-based factors, content-based factors, and source-based factors might explain the stability of animal culture in a wild rather than just imitation. As an example of ecological availability, chimps may learn how to fish for ants with a stick from their peers, but that behaviour is also influenced by the particular type of ants as well as the condition. A behaviour may be learned socially, but the fact that it was learned socially does not necessarily mean it will last. The fact that the behaviour is rewarding has a role in cultural stability as well. The ability for socially-learned behaviours to stabilise across generations is also mitigated by the complexity of the behaviour. Different individuals of a species, like crows, vary in their ability to use a complex tool. Finally, a behaviour’s stability in animal culture depends on the context in which they learn a behaviour. If a behaviour has already been adopted by a majority, then the behaviour is more likely to carry across generations out of a need for conforming.

Animals are able to acquire behaviours from social learning, but whether or not that behaviour carries across generations requires more investigation.

Hummingbird Experiment

Experiments with hummingbirds provided one example of apparent observational learning in a non-human organism. Hummingbirds were divided into two groups. Birds in one group were exposed to the feeding of a knowledgeable “tutor” bird; hummingbirds in the other group did not have this exposure. In subsequent tests the birds that had seen a tutor were more efficient feeders than the others.

Bottlenose Dolphin

Herman (2002) suggested that bottlenose dolphins produce goal-emulated behaviours rather than imitative ones. A dolphin that watches a model place a ball in a basket might place the ball in the basket when asked to mimic the behaviour, but it may do so in a different manner seen.

Rhesus Monkey

Kinnaman (1902) reported that one rhesus monkey learned to pull a plug from a box with its teeth to obtain food after watching another monkey succeed at this task.

Fredman (2012) also performed an experiment on observational behavior. In experiment 1, human-raised monkeys observed a familiar human model open a foraging box using a tool in one of two alternate ways: levering or poking. In experiment 2, mother-raised monkeys viewed similar techniques demonstrated by monkey models. A control group in each population saw no model. In both experiments, independent coders detected which technique experimental subjects had seen, thus confirming social learning. Further analyses examined copying at three levels of resolution.

The human-raised monkeys exhibited the greatest learning with the specific tool use technique they saw. Only monkeys who saw the levering model used the lever technique, by contrast with controls and those who witnessed poking. Mother-reared monkeys instead typically ignored the tool and exhibited fidelity at a lower level, tending only to re-create whichever result the model had achieved by either levering or poking.

Nevertheless, this level of social learning was associated with significantly greater levels of success in monkeys witnessing a model than in controls, an effect absent in the human-reared population. Results in both populations are consistent with a process of canalisation of the repertoire in the direction of the approach witnessed, producing a narrower, socially shaped behavioural profile than among controls who saw no model.

Light Box Experiment

Pinkham and Jaswal (2011) did an experiment to see if a child would learn how to turn on a light box by watching a parent. They found that children who saw a parent use their head to turn on the light box tended to do the task in that manner, while children who had not seen the parent used their hands instead.

Swimming Skill Performance

When adequate practice and appropriate feedback follow demonstrations, increased skill performance and learning occurs. Lewis (1974) did a study of children who had a fear of swimming and observed how modelling and going over swimming practices affected their overall performance. The experiment spanned nine days, and included many steps. The children were first assessed on their anxiety and swimming skills. Then they were placed into one of three conditional groups and exposed to these conditions over a few days.

At the end of each day, all children participated in a group lesson. The first group was a control group where the children watched a short cartoon video unrelated to swimming. The second group was a peer mastery group, which watched a short video of similar-aged children who had very good task performances and high confidence. Lastly, the third group was a peer coping group, whose subjects watched a video of similar-aged children who progressed from low task performances and low confidence statements to high task performances and high confidence statements.

The day following the exposures to each condition, the children were reassessed. Finally, the children were also assessed a few days later for a follow up assessment. Upon reassessment, it was shown that the two model groups who watched videos of children similar in age had successful rates on the skills assessed because they perceived the models as informational and motivational.

Do-as-I-do Chimpanzee

Flexible methods must be used to assess whether an animal can imitate an action. This led to an approach that teaches animals to imitate by using a command such as “do-as-I-do” or “do this” followed by the action that they are supposed to imitate. Researchers trained chimpanzees to imitate an action that was paired with the command. For example, this might include a researcher saying “do this” paired with clapping hands. This type of instruction has been utilised in a variety of other animals in order to teach imitation actions by utilising a command or request.

Observational Learning in Everyday Life

Observational learning allows for new skills to be learned in a wide variety of areas. Demonstrations help the modification of skills and behaviours.

Learning Physical Activities

When learning skills for physical activities can be anything that is learned that requires physical movement, this can include learning a sport, learning to eat with a fork, or learning to walk. There are multiple important variables that aid in modifying physical skills and psychological responses from an observational learning standpoint. Modelling is a variable in observational learning where the skill level of the model is considered. When someone is supposed to demonstrate a physical skill such as throwing a baseball the model should be able to execute the behaviour of throwing the ball flawlessly if the model of learning is a mastery model. Another model to utilise in observational learning is a coping model, which would be a model demonstrating a physical skill that they have not yet mastered or achieved high performance in. Both models are found to be effective and can be utilised depending on the what skills is trying to be demonstrated. These models can be used as interventions to increase observational learning in practice, competition, and rehabilitation situations.

Neuroscience

Recent research in neuroscience has implicated mirror neurons as a neurophysiological basis for observational learning. These specialised visuomotor neurons fire action potentials when an individual performs a motor task and also fire when an individual passively observes another individual performing the same motor task. In observational motor learning, the process begins with a visual presentation of another individual performing a motor task, this acts as a model. The learner then needs to transform the observed visual information into internal motor commands that will allow them to perform the motor task, this is known as visuomotor transformation. Mirror neuron networks provide a mechanism for visuo-motor and motor-visual transformation and interaction. Similar networks of mirror neurons have also been implicated in social learning, motor cognition and social cognition.

Clinical Perspective

Autism Spectrum Disorder

Discreet trial training (DTT) is a structured and systematic approach utilized in helping individuals with autism spectrum disorder learn. Individuals with autism tend to struggle with learning through observation, therefore something that is reinforcing is necessary in order to motivate them to imitate or follow through with the task. When utilising DTT to teach individuals with autism modelling is utilised to aid in their learning. Modelling would include showing how to reach the correct answer, this could mean showing the steps to a math equation. Utilising DTT in a group setting also promotes observational learning from peers as well.

Who was Heather Ashton?

Introduction

Heather Ashton FRCP (11 July 1929 to 15 September 2019) was a British psychopharmacologist and physician. She is best known for her clinical and research work on benzodiazepene dependence.

Biography

Chrystal Heather Champion was born in Dehradun, northern India, to Harry Champion, a British silviculturalist, and Chrystal (Parsons) Champion, a secretary. From the age of six, she attended a boarding school in Swanage, Dorset, England. When WWII began, she was evacuated to West Chester, Pennsylvania; during the crossing, her ship was attacked by a U-boat.

Ashton went on to study Medicine at Somerville College, Oxford, graduating with a First Class Honours Degree (BA) in Physiology in 1951. She earned her medical degree (DM) in 1956. She completed professional training at Middlesex Hospital. She was elected as a Fellow of the Royal College of Physicians, London, in 1975.

In 1965, Ashton joined the faculty at Newcastle University, first in the Department of Pharmacology and later in the Department of Psychiatry. From 1982 to 1994, she ran a benzodiazepine withdrawal clinic at the Royal Victoria Infirmary in Newcastle. She was on the executive committee of the North East Council on Addictions. Ashton also helped set up the British organisation Victims of Tranquillisers (VOT). She also gave evidence to British government committees on tobacco smoking, cannabis and benzodiazepines.

Ashton died on 15 September 2019 at her home in Newcastle upon Tyne, at age 90.

Research

Ashton’s developed her expertise in the effects of psychoactive drugs and the effects of substances such as nicotine and cannabis on the brain.

During the 1960s, benzodiazepines, like diazepam and temazepam, had become popular and were seen as safe and effective treatments for anxiety or insomnia. One study found that the overdose death rate among patients taking both benzodiazepines and opioids was 10 times higher than among those who only took opioids.

Ashton’s research on these drugs found that they could be used in the short term, but could lead to physical dependence over the long-term. She also recognised that this benzodiazepine withdrawal syndrome was very different from those addicted to illegal drugs. This led to her writing an important manual to help those who were trying to stop their prescribed benzodiazepine. This manual is now used all over the world. This book, Benzodiazepines: How They Work and How to Withdraw, was first published in 1999; it has become known as the Ashton Manual and has been translated into 11 languages. Ashton’s research was influential, leading to changes in prescribing practices and guidelines recommended for benzodiazepines in 2013. Her research on psychotropic drugs led to over 200 journal articles, chapters and books, including over 50 papers concerning benzodiazepines alone.

Is there an Association between Metabolic Disorder & Cognitive Impairment in Patients with Early-Stage Schizophrenia?

Research Paper Title

The Association Between Metabolic Disturbance and Cognitive Impairments in Early-Stage Schizophrenia.

Background

Cognitive impairment is one of the core symptoms of schizophrenia, which is considered to be significantly correlated to prognosis. In recent years, many studies have suggested that metabolic disorders could be related to a higher risk of cognitive defects in a general setting. However, there has been limited evidence on the association between metabolism and cognitive function in patients with early-stage schizophrenia.

Methods

In this study, the researchers recruited 172 patients with early-stage schizophrenia. Relevant metabolic parameters were examined and cognitive function was evaluated by using the MATRICS Consensus Cognitive Battery (MCCB) to investigate the relationship between metabolic disorder and cognitive impairment.

Results

Generally, the prevalence of cognitive impairment among patients in our study was 84.7% (144/170), which was much higher than that in the general population. Compared with the general Chinese setting, the study population presented a higher proportion of metabolic disturbance. Patients who had metabolic disturbance showed no significant differences on cognitive function compared with the other patients. Correlation analysis showed that metabolic status was significantly correlated with cognitive function as assessed by the cognitive domain scores (p < 0.05), while such association was not found in further multiple regression analysis.

Conclusions

Therefore, there may be no association between metabolic disorder and cognitive impairment in patients with early-stage schizophrenia.

Reference

Peng, X-J., Hei, G-R., Li, R-R., Yang, Y., Liu, C-C., Xiao, J-M., Long, Y-J., Shao, P., Huang, J., Zhao, J-P. & Wu, R-R. (2021) The Association Between Metabolic Disturbance and Cognitive Impairments in Early-Stage Schizophrenia. Frontiers in Human Neuroscience. doi: 10.3389/fnhum.2020.599720. eCollection 2020.

Developing a Longitudinal Trajectory-Based Approach to Investigating Relapse Trend Differences in Mental Health Patients

Research Paper Title

Differences in Temporal Relapse Characteristics Between Affective and Non-affective Psychotic Disorders: Longitudinal Analysis.

Background

Multiple relapses over time are common in both affective and non-affective psychotic disorders. Characterizing the temporal nature of these relapses may be crucial to understanding the underlying neurobiology of relapse.

Methods

Anonymised records of patients with affective and non-affective psychotic disorders were collected from SA Mental Health Data Universe and retrospectively analysed. To characterise the temporal characteristic of their relapses, a relapse trend score was computed using a symbolic series-based approach. A higher score suggests that relapse follows a trend and a lower score suggests relapses are random. Regression models were built to investigate if this score was significantly different between affective and non-affective psychotic disorders.

Results

Logistic regression models showed a significant group difference in relapse trend score between the patient groups. For example, in patients who were hospitalized six or more times, relapse score in affective disorders were 2.6 times higher than non-affective psychotic disorders [OR 2.6, 95% CI (1.8-3.7), p < 0.001].

Discussion

The results imply that the odds of a patient with affective disorder exhibiting a predictable trend in time to relapse were much higher than a patient with recurrent non-affective psychotic disorder. In other words, within recurrent non-affective psychosis group, time to relapse is random.

Conclusions

This study is an initial attempt to develop a longitudinal trajectory-based approach to investigate relapse trend differences in mental health patients. Further investigations using this approach may reflect differences in underlying biological processes between illnesses.

Reference

Immanuel, S.A., Schrader, G. & Bidargaddi, N. (2021) Differences in Temporal Relapse Characteristics Between Affective and Non-affective Psychotic Disorders: Longitudinal Analysis. Frontiers in Psychiatry. doi: 10.3389/fpsyt.2021.558056. eCollection 2021.

Can Lorazepam be Considered to be a Clinically Effective Means of Treating the Acutely Agitated Patient?

Research Paper Title

Treatment of Agitation With Lorazepam in Clinical Practice: A Systematic Review.

Background

Acute agitation is a frequent occurrence in both inpatient and outpatient psychiatric settings, and the use of medication to calm a patient may be warranted to mitigate the situation. Lorazepam is a benzodiazepine that is widely used for management of acute agitation. Despite its widespread use, there is remarkably little clinical evidence for the benefits of lorazepam in acute agitation.

Methods

The researchers performed a systematic review with focus on lorazepam, including all randomised clinical trials on lorazepam in mental and behavioural disorders, excluding studies on dementia and paediatric patients and in mixed conditions.

Results

A total of 11 studies met inclusion criteria, and all were in patients with mental and behavioural disorders. Most trials generally found improvements across a variety of outcomes related to agitation, although there was some disparity if specific outcomes were considered.

In the five studies with haloperidol, the combination of lorazepam and haloperidol was superior to either agent alone, but with no differences between monotherapy with the individual agents.

Conclusions

In the study comparing lorazepam to olanzapine, olanzapine was superior to lorazepam, and both were superior to placebo.

As expected, the safety of lorazepam among the different studies was consistent with its well-characterised profile with dizziness, sedation, and somnolence being the most common adverse events.

Based on this structured review, lorazepam can be considered to be a clinically effective means of treating the acutely agitated patient.

Reference

Amore, M., D’Andrea, M. & Fagiolini, F. (2021) Treatment of Agitation With Lorazepam in Clinical Practice: A Systematic Review.

Can the MHS: A Serve as a Clinically Useful Screening Tool for GAD?

Research Paper Title

A Brief Online and Offline (Paper-and-Pencil) Screening Tool for Generalized Anxiety Disorder: The Final Phase in the Development and Validation of the Mental Health Screening Tool for Anxiety Disorders (MHS: A).

Background

Generalised anxiety disorder (GAD) can cause significant socioeconomic burden and daily life dysfunction; hence, therapeutic intervention through early detection is important.

Methods

This study was the final stage of a 3-year anxiety screening tool development project that evaluated the psychometric properties and diagnostic screening utility of the Mental Health Screening Tool for Anxiety Disorders (MHS: A), which measures GAD.

Results

A total of 527 Koreans completed online and offline (i.e., paper-and pencil) versions of the MHS: A, Beck Anxiety Inventory (BAI), Generalised Anxiety Disorder-7 (GAD-7), and Penn State Worry Questionnaire (PSWQ). The participants had an average age of 38.6 years and included 340 (64.5%) females. Participants were also administered the Mini-International Neuropsychiatric Interview (MINI).

Internal consistency, convergent/criterion validity, item characteristics, and test information were assessed based on the item response theory (IRT), and a factor analysis and cut-off score analyses were conducted. The MHS: A had good internal consistency and good convergent validity with other anxiety scales.

The two versions (online/offline) of the MHS: A were nearly identical (r = 0.908). It had a one-factor structure and showed better diagnostic accuracy (online/offline: sensitivity = 0.98/0.90, specificity = 0.80/0.83) for GAD detection than the GAD-7 and BAI. The IRT analysis indicated that the MHS: A was most informative as a screening tool for GAD.

Conclusions

The MHS: A can serve as a clinically useful screening tool for GAD in Korea. Furthermore, it can be administered both online and offline and can be flexibly used as a brief mental health screener, especially with the current rise in telehealth.

Reference

Kim, S-H., Park, K., Yoon, S., Choi, Y., Lee, S-H. & Choi, K-H. (2021) A Brief Online and Offline (Paper-and-Pencil) Screening Tool for Generalized Anxiety Disorder: The Final Phase in the Development and Validation of the Mental Health Screening Tool for Anxiety Disorders (MHS: A). Frontiers in Psychology. doi: 10.3389/fpsyg.2021.639366. eCollection 2021.