Rapid antidepressant effect of ketamine has been demonstrated in many recent studies . Five RCTs have shown that a single infusion is rapidly effective and the overall response rate at 72 hours was 29% compared to 7% with saline placebo . The response usually last hours to weeks and but most patients eventually relapse. A few extended trials have produced longer periods of antidepressant response.
The study by Diamond et al (Oxford) adds to this growing literature. This open label study was to explore the safety and efficacy of repeated infusions in patients who continued other psychotropic medications. They also looked at its effect on memory ( as this is a worry with ECT) and the feasibility of providing this treatment in routine ECT clinic settings.
Methods: Treatment resistant bipolar or unipolar depression were recruited. In the first stage patients were recruited to receive ketamine infusions once a week for three weeks. In the second stage a separate group was recruited to receive ketamine infusions twice a week for three weeks . The dose of ketamine administered was 0.5 mg/kg (ideal body weight) administered intravenously over 40 minutes. If a participant responded to ketamine and then relapsed after the final infusion they entered the maintenance phase of the trial.Participants were followed up for 6 months where possible.Primary measure of mood was the Beck Depression Inventory. Memory functions were assessed comprehensively using Autobiographical Memory Interview – Short Form (AMI-SF), Autobiographical Fluency Task, The Story Recall test (for episodic memory) and ECT Memory Questionnaire (for subjective memory).Antidepressant response was defined at a ≥50% reduction in BDI score from baseline to the end of the three week treatment on day 21, and remission as a score falling within the normal range (0–10) on the BDI. 44 patients were screened, of whom 28 were included in the analysis.
8/28 (29%) patients failed to complete all the planned infusions. (2 because of acute adverse reactions during the infusion, 5 because of failure to benefit and increasing anxiety and 1 for unrelated reasons). At day 21, 8/28 (29%, or 33% of completers) had reached response criteria. In all patients that met response criteria at day 21 a response had developed before the third infusion. At day 21, half of all responders met remission criteria. Majority of patients agreed that ECT clinic is the right setting to give the infusions. There were no memory defects associated with treatment.
Side effects: Infusions were not always well tolerated. one patient experienced a marked vasovagal episode lasting 10 minutes.Two patients experienced marked anxiety and two vomited during the infusions. Three of these four elected to continue the course but none were responders.Dissociative side-effects were common, but these were generally well tolerated.
Conclusion: In treatment resistant severe depression ( who are continuing with their antidepressants) 14% attained remission at day 21 with 29% reaching response criteria. All eventual responders showed this by third infusion.
Limitations: No control group, Open label study.
Comment: The response/ remission rate is very encouraging.Close monitoring is essential during and after treatment. The observation that by third infusion we could predict eventual response would aid in selecting this treatment as a trial. Results from the maintenance phase is not available now.
Summary of the article: Ketamine infusions for treatment resistant depression: a series of 28 patients treated weekly or twice weekly in an ECT clinic Peter R Diamond et al ahead of print.Journal of Psychopharmacology 1–9. 2014
Emotional states are expressed through facial expressions. Happiness, surprise , sadness, anger, fear, and disgust are the primary emotions and the most studied ones. Combinations of primary states i.e. compound emotions are also well recognised. ( e.g.: happily surprised vs angrily surprised). Twelve compound emotions are described. Appall (feeling disgust and anger with the emphasis being on disgust), Hate (feeling of disgust and anger with emphasis on anger) and Awe ( feeling of fear and wonder with the emphasis on wonder) are three additional compound emotions.
Could computers use learning algorithms to correctly identify these emotional states? Shichuan Du, Yong Tao, and Aleix M. Martinez from Ohio State University report an interesting development in this field. They tested whether the images of the 21 facial expressions of emotions (primary plus compound) as described above are visually discriminable by the computer .
Movement patterns of muscle groups make the different emotions distinct from each other. All emotional states ( 22) on the images in the database for this study were coded using the Facial Action Coding System (FACS) (Ekman and Friesen) that makes for a clear, compact representation of the muscle activation of a facial expression. Each Action Unit codes the fundamental actions of individual or groups of muscles typically seen while producing facial expressions of emotion. Computer was trained to detect facial landmarks using 94 points on the face that define the shape of face in independent databases . The algorithm use 8742 features (dimensions) defining the shape of the face. Pixel information was used to define appearance of the face.
Basic emotions: The successful classification rates were 90% when using shape features, 92% when using appearance features, and 97 % when using both (shape and appearance).
Compound emotions: The classification accuracies for the 5060 images corresponding to the 22 categories of basic and compound emotions (plus neutral) for the 230 identities in the study database was 74 % when using shape features only, 70 % when using appearance features, and 77% when shape and appearance are combined.
Conclusion: Larger number of emotions are recognisable. Machines can make accurate assessment of emotional state as revealed by facial expression. This opens a new area of research in face recognition that will take human– computer interfaces to a new level of complexity.
Summary of the article:
Anxiety disorders are common and have a 12-month period prevalence of approximately 15%, and a life-time prevalence of approximately 21%.Primary care studies suggest approximately 50% significantly improve over 6–16 months but complete recovery is relative rare. Likelihood of recovery from GAD is significantly less than that of recovering from major depression. These disorders usually tend to run a waxing and waning course or a prolonged course, they may also ‘switch’ to other diagnoses particularly depression and somatoform disorders.What are the most evidence based treatments that should be offered to individuals suffering from anxiety disorders? British Association for Psychopharmacology (BAP)’s new guideline (2014) was released few days ago. This is an update of the 2005 guideline. The guidelines are prepared by expert consensus using graded available evidence.
Categories of evidence : Evidence from meta-analysis of randomised double-blind placebo-controlled trials are graded as I [M],Evidence from at least one randomised double-blind placebo-controlled trial as I [PCT]. Evidence from at least one randomised double-blind comparator-controlled trial (without placebo) is graded as II. Recommendation strength is graded A-S, grade A being recommendations directly based on category I evidence.(either I [M] or I [PCT]) and grade B when recommendations are directly based on category II evidence or an extrapolated recommendation from category I evidence.
GAD recommendation: Evidence-based acute treatment (grade A recommendation) : Most SSRIs (citalopram, escitalopram, paroxetine, sertraline), duloxetine, venlafaxine, pregabalin, agomelatine, quetiapine, benzodiazepines (alprazolam, diazepam, lorazepam), imipramine, buspirone, hydroxyzine and trazodone . CBT and applied relaxation also receive grade A recommendation.
Consider an SSRI for first-line pharmacological treatment [A]. SNRIs and pregabalin may be considered as alternative initial treatments if SSRIs are judged to be unsuitable [A}. Higher daily doses of pregabalin may be associated with greater response rates [A] .Treatment periods of up to 12 weeks may be needed to assess efficacy [S] but recognise that an absence of clinical benefit within four weeks warns that a response to unchanged treatment is unlikely [A]. Continue drug treatment for up to 18 more months in patients who have responded to treatment [A]. Recommend CBT over other forms of psychological treatment as it may reduce relapse rates better than other psychological treatments [C]. When stopping treatment, reduce the dose gradually over an extended period to avoid discontinuation and rebound symptoms [A]: a minimum of three months is recommended for this taper period [D].
Routinely combining drug and psychological approaches is not recommended for initial treatment as there is no consistent evidence for enhanced efficacy over each treatment given alone (A).
When initial treatments fail, consider switching to another evidence-based treatment after non-response to initial treatment (D). Consider pregabalin augmentation after a non-response to initial SSRI or SNRI treatment [A].Consider use of benzodiazepines after a non-response to SSRI, SNRI, pregabalin and buspirone treatment [S). Consider combining drug treatment and CBT if resistant.
Comments: Grade A list is longer than in 2005. If acute treatment is effective, continued treatment is suggested for longer periods ( 18 months as opposed to 6 months). New evidence supports the notion that those who do not show any response in 4 weeks are less likely to respond.
Summary of the article:
Evidence-based pharmacological treatment of anxiety disorders, post-traumatic stress disorder and obsessive-compulsive disorder: Journal of Psychopharmacology.A revision of the 2005 guidelines from the © British Association for Psychopharmacology .2014.
Autism is associated with many cortical defects. Macroscopic early brain overgrowth is reported in majority of cases. Autistic children between the ages of 2 and 16 years have abnormally heavy brains and have a relative increase of 67% in the overall number of neurons in the prefrontal cortex. Several cortical and subcortical regions show defects and dysfunction. Abnormal expression of genes and gene pathways that govern neuronal cell-cycle regulation , DNA integrity, cell differentiation, and cortical patterning in the prefrontal cortex is observed in autism. Evidence strongly suggest that autism is a neuro developmental disorder ( like lissencephaly, polymicrogyria, schizencephaly, and other several cortical heterotopias that arise from defects in cell-cycle processes).
Neuropathological studies so far are from brains of adult autism sufferers. Molecular, cellular, and organisational anomalies that are present in the brains of children with autism remain largely unstudied.Rich Stone and team of researchers examined the neocortical architecture during the early years after the onset of autism using RNA in situ hybridization with a panel of molecular markers to phenotype cortical microstructure.
42 fresh-frozen postmortem cortical tissue blocks (1 to 2 cm3 ) from children (2 to 15 years of age) with autism were studied against controls. They found focal patches of abnormal laminar cytoarchitecture and cortical dis- organization of neurons, but not glia, in prefrontal and temporal cortical tissue . These patches were identified in both dorsolateral prefrontal cortex (in 10 of 11 case samples) and posterior superior temporal cortex. Clearest signs of such abnormal expression were seen in cortical layers 4 and 5. No two patches were identical in presentation. A deficit in the expression of markers of excitatory cortical neurons was the most robust indicator of a patch region. Authors infer that regions of focal patches were not the result of a reduced number of neurons.
Presence of discrete pathological patches of abnormal laminar cytoarchitecture and disorganization in the samples of prefrontal and temporal cortexes is consistent with an early prenatal origin of autism or at least prenatal processes that may confer a predisposition to autism. Laminar disorganization could result from migration defects resulting in the failure of cells to reach their targeted destination and the accumulation of such cells in nearby regions. Patches could also reflect de novo changes early in neurodevelopmental processes, potentially in gene sequence or epigenetic state, where some progenitor cells are affected.
These patches occurred in regions mediating the functions that are disturbed in autism: ( social, emotional, communication, and language). These disorganized patches in different locations would be disrupting various functional systems and thus determine/ influence symptom expression, response to treatment, and clinical outcome.
Comments: This study reinforces the importance of early identification and intervention. The patchy nature may explain why some young children with autism show signs of improvement if treated early enough. One may also hope that the plastic infant brain may have a chance of rewiring itself to compensate for these changes.
Summary of the article: Patches of disorganisation in the neocortex of children with autism.Stoner R, Chow ML, Boyle MP, Sunkin SM, Mouton PR, Roy S, Wynshaw-Boris A, Colamarino SA, Lein ES, Courchesne E. N Engl J Med. 2014 Mar 27;370(13):
Socio-environmental risk factors for schizophrenia include unemployment, low socio-economic status and migration (individual level risk factors) as well as urbanicity, ethnic density and deprivation (neighbourhood level risk factors) .Income inequality is an established social determinant of health in general. The Wilkinsonian hypothesis asserts that health depends on the degree of income inequality in a given society. ie for any given average level of income, the more equally distributed the income is, the higher the average standard of health. Growing body of evidence support this idea. Increasing income inequality is associated with increased infant mortality rates , increased risk for cardiovascular disease , reduced life expectancy and increase in anxiety, depression and suicide .
Do income inequality increase the risk of serious mental disorder like schizophrenia? Jonathan K Burns, Andrew Tomita and Amy S Kapadia investigated ( systematic review) the association between income inequality and incidence of schizophrenia at the level of countries. Studies reporting original data on incidence of schizophrenia in general population were included.Income equality was measured using the Gini coefficient where higher coefficients indicate higher degrees of inequality.
110 studies from a total of 28 countries published between 1975 and 2011 met all inclusion criteria . Mean incidence rates for countries ranged from 5.4 (Norway) to 53.0 (Israel) per 100,000 population. Overall mean and median incidence of schizophrenia for all 107 rates was 18.5 per 100,000 (SD = 11.9; range = 1.7–67.0) and 16 per 100,000, respectively. The mean Gini coefficient for the 26 countries was 33.1 (SD = 6.4; range = 22.8–59.4)
At the country level, there is an association between increasing measures of income inequality and increasing incidence rates of schizophrenia. For every one-point increase in income equality, there was a two-point increase in incidence rate of schizophrenia. Countries characterized by a large rich–poor gap may be at increased risk of schizophrenia.
It is important to note that country-level low SES (e.g. low GDP per capita) does not correlate with median incidence rates of schizophrenia per country i.e. at the ecological level, simple poverty or low economic status may not directly increase risk for schizophrenia. Living in an unequal society does appear to be associated with increased risk for schizophrenia.
Limitations :Quality of incidence data on schizophrenia might vary by country . Apparent relationship between income inequality and risk for schizophrenia is observed at country level, applying at the individual level would be a case of the ‘ecological fallacy”. Individual level data would be needed to overcome this issue.
Conclusions: Income inequality with in a country could increase the risk of schizophrenia at country level.
Comments: The negative effects of income inequality are broader i.e. not specific to schizophrenia. The ill effects of income inequality are not restricted to those at lower ranks,they are experienced by all members of that society regardless of rank. Individual health depends not just on personals income, but also on the incomes of others around. . Effect on social cohesion might explain how unequal societies are more unwell. Politics and policy need to address social determinants of health.
Summary of the article: Income inequality and schizophrenia: Increased schizophrenia incidence in countries with high levels of income inequality. Burns JK, Tomita A, Kapadia AS. Int J Soc Psychiatry. 2014 Mar;60(2):185-96