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Tuesday, June 20, 2023

Olanzapine

From Wikipedia, the free encyclopedia
 
Olanzapine
Olanzapine.svg
Olanzapine-from-xtal-3D-balls.png
Clinical data
Trade namesZyprexa, others
AHFS/Drugs.comMonograph
MedlinePlusa601213
License data
Pregnancy
category
  • AU: C
Routes of
administration
By mouth, intramuscular injection
Drug classAtypical antipsychotic
ATC code
Legal status
Legal status
  • AU: S4 (Prescription only)
  • CA: ℞-only
  • NZ: Prescription medicine
  • UK: POM (Prescription only)
  • US: ℞-only
  • EU: Rx-only
Pharmacokinetic data
Bioavailability60-65%
Protein binding93%
MetabolismLiver (direct glucuronidation and CYP1A2 mediated oxidation)
Elimination half-life33 hours, 51.8 hours (elderly)
ExcretionUrine (57%; 7% as unchanged drug), faeces (30%)
Identifiers

CAS Number
PubChem CID
IUPHAR/BPS
DrugBank
ChemSpider
UNII
KEGG
ChEBI
ChEMBL
CompTox Dashboard (EPA)
ECHA InfoCard100.125.320 Edit this at Wikidata
Chemical and physical data
FormulaC17H20N4S
Molar mass312.44 g·mol−1
3D model (JSmol)
Melting point195 °C (383 °F)
Solubility in waterPractically insoluble in water mg/mL (20 °C)

Olanzapine (sold under the trade name Zyprexa among others) is an atypical antipsychotic primarily used to treat schizophrenia and bipolar disorder. For schizophrenia, it can be used for both new-onset disease and long-term maintenance. It is taken by mouth or by injection into a muscle.

Common side effects include weight gain, movement disorders, dizziness, feeling tired, constipation, and dry mouth. Other side effects include low blood pressure with standing, allergic reactions, neuroleptic malignant syndrome, high blood sugar, seizures, and tardive dyskinesia. In older people with dementia, its use increases the risk of death. Use in the later part of pregnancy may result in a movement disorder in the baby for some time after birth. Although how it works is not entirely clear, it blocks dopamine and serotonin receptors.

Olanzapine was patented in 1991 and approved for medical use in the United States in 1996. It is available as a generic medication. In 2020, it was the 164th most commonly prescribed medication in the United States, with more than 3 million prescriptions. Lilly also markets olanzapine in a fixed-dose combination with fluoxetine as olanzapine/fluoxetine (Symbyax).

Medical uses

It is approved by FDA for the following indications:

  • schizophrenia
  • Acute treatment of manic or mixed episodes associated with bipolar I disorder and maintenance treatment of bipolar I disorder.
  • Adjunct to valproate, carbamazepine or lithium in the treatment of manic or mixed episodes associated with bipolar I disorder
  • combination olanzapine/fluoxetine for the treatment of depressive episodes associated with bipolar I disorder.

In United Kingdom and Australia it is approved for schizophrenia, moderate to severe manic episodes, alone, or in combination with lithium or valproate and the short-term treatment of acute manic episodes associated with Bipolar I Disorder.

Schizophrenia

The first-line psychiatric treatment for schizophrenia is antipsychotic medication. Olanzapine appears to be effective in reducing symptoms of schizophrenia, treating acute exacerbations, and treating early-onset schizophrenia. The usefulness of maintenance therapy, however, is difficult to determine, as more than half of people in trials quit before the 6-week completion date. Treatment with olanzapine (like clozapine) may result in increased weight gain and increased glucose and cholesterol levels when compared to most other second-generation antipsychotic drugs used to treat schizophrenia.

Bipolar disorder

Olanzapine is recommended by the National Institute for Health and Care Excellence as a first-line therapy for the treatment of acute mania in bipolar disorder. Other recommended first-line treatments are aripiprazole, haloperidol, quetiapine, and risperidone. It is recommended in combination with fluoxetine as a first-line therapy for acute bipolar depression, and as a second-line treatment by itself for the maintenance treatment of bipolar disorder.

The Network for Mood and Anxiety Treatments recommends olanzapine as a first-line maintenance treatment in bipolar disorder and the combination of olanzapine with fluoxetine as second-line treatment for bipolar depression.

A review on the efficacy of olanzapine as maintenance therapy in patients with bipolar disorder was published by Dando & Tohen in 2006. A 2014 meta-analysis concluded that olanzapine with fluoxetine was the most effective among nine treatments for bipolar depression included in the analysis.

Other uses

Olanzapine may be useful in promoting weight gain in underweight adult outpatients with anorexia nervosa. However, no improvement of psychological symptoms was noted.

Olanzapine has been shown to be helpful in addressing a range of anxiety and depressive symptoms in individuals with schizophrenia and schizoaffective disorders, and has since been used in the treatment of a range of mood and anxiety disorders. Olanzapine is no less effective than lithium or valproate and more effective than placebo in treating bipolar disorder. It has also been used for Tourette syndrome and stuttering.

Olanzapine has been studied for the treatment of hyperactivity, aggressive behavior, and repetitive behaviors in autism.

Olanzapine is frequently prescribed off-label for the treatment of insomnia, including difficulty falling asleep and staying asleep, even though such use is not recommended. The daytime sedation experienced with olanzapine is generally comparable to quetiapine and lurasidone, which is a frequent complaint in clinical trials. In some cases, the sedation due to olanzapine impaired the ability of people to wake up at a consistent time every day. Some evidence of efficacy for treating insomnia is seen; however, side effects such as dyslipidemia and neutropenia, which may possibly be observed even at low doses, outweigh any potential benefits for insomnia that is not due to an underlying mental health condition.

Olanzapine has been recommended to be used in antiemetic regimens in people receiving chemotherapy that has a high risk for vomiting.

Specific populations

Pregnancy and lactation

Olanzapine is associated with the highest placental exposure of any atypical antipsychotic. Despite this, the available evidence suggests it is safe during pregnancy, although the evidence is insufficiently strong to say anything with a high degree of confidence. Olanzapine is associated with weight gain, which according to recent studies, may put olanzapine-treated patients' offspring at a heightened risk for neural tube defects (e.g. spina bifida). Breastfeeding in women taking olanzapine is advised against because olanzapine is secreted in breast milk, with one study finding that the exposure to the infant is about 1.8% that of the mother.

Elderly

Citing an increased risk of stroke, in 2004, the Committee on the Safety of Medicines in the UK issued a warning that olanzapine and risperidone, both atypical antipsychotic medications, should not be given to elderly patients with dementia. In the U.S., olanzapine comes with a black box warning for increased risk of death in elderly patients. It is not approved for use in patients with dementia-related psychosis. A BBC investigation in June 2008 found that this advice was being widely ignored by British doctors. Evidence suggested that the elderly are more likely to experience weight gain on olanzapine compared to aripiprazole and risperidone.

Adverse effects

The principal side effect of olanzapine is weight gain, which may be profound in some cases and/or associated with derangement in blood-lipid and blood-sugar profiles (see section metabolic effects). A 2013 meta-analysis of the efficacy and tolerance of 15 antipsychotic drugs (APDs) found that it had the highest propensity for causing weight gain out of the 15 APDs compared with an SMD of 0.74. Extrapyramidal side effects, although potentially serious, are infrequent to rare from olanzapine, but may include tremors and muscle rigidity.

Aripiprazole, asenapine, clozapine, quetiapine and olanzapine, in comparison to other antipsychotic drugs, are less frequently associated with hyperprolactinaemia. Although these drugs can cause transient or sustained hyperprolactinaemia, the risk is much lower. Owing to its partial dopaminergic agonist effect, aripiprazole is likely to reduce prolactin levels and, in some patients, can cause hypoprolactinaemia. Although olanzapine causes an early dose-related rise in prolactin, this is less frequent and less marked than that seen with haloperidol, and is usually transient. A rise in prolactin is seen in about half of patients on olanzapine compared to over 90% of those taking risperidone, and enduring increases were less frequent in those taking olanzapine.

It is not recommended to be used by IM injection in acute myocardial infarction, bradycardia, recent heart surgery, severe hypotension, sick sinus syndrome, and unstable angina.

Several patient groups are at a heightened risk of side effects from olanzapine and antipsychotics in general. Olanzapine may produce nontrivial high blood sugar in people with diabetes mellitus. Likewise, the elderly are at a greater risk of falls and accidental injury. Young males appear to be at heightened risk of dystonic reactions, although these are relatively rare with olanzapine. Most antipsychotics, including olanzapine, may disrupt the body's natural thermoregulatory systems, thus permitting excursions to dangerous levels when situations (exposure to heat, strenuous exercise) occur.

Other side effects include galactorrhea, amenorrhea, gynecomastia, and erectile dysfunction (impotence).

Drug-induced OCD

Many different types of medication can create or induce pure obsessive-compulsive disorder (OCD) in patients who have never had symptoms before. A new chapter about OCD in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (2013) now specifically includes drug-induced OCD.

Metabolic effects

The US Food and Drug Administration (FDA) requires all atypical antipsychotics to include a warning about the risk of developing hyperglycemia and diabetes, both of which are factors in the metabolic syndrome. These effects may be related to the drugs' ability to induce weight gain, although some reports have been made of metabolic changes in the absence of weight gain. Studies have indicated that olanzapine carries a greater risk of causing and exacerbating diabetes than another commonly prescribed atypical antipsychotic, risperidone. Of all the atypical antipsychotics, olanzapine is one of the most likely to induce weight gain based on various measures. The effect is dose dependent in humans and animal models of olanzapine-induced metabolic side effects. There are some case reports of olanzapine-induced diabetic ketoacidosis. Olanzapine may decrease insulin sensitivity, though one 3-week study seems to refute this. It may also increase triglyceride levels.

Despite weight gain, a large multicenter, randomized National Institute of Mental Health study found that olanzapine was better at controlling symptoms because patients were more likely to remain on olanzapine than the other drugs. One small, open-label, nonrandomized study suggests that taking olanzapine by orally dissolving tablets may induce less weight gain, but this has not been substantiated in a blinded experimental setting.

Post-injection delirium/sedation syndrome

Postinjection delirium/sedation syndrome (PDSS) is a rare syndrome that is specific to the long-acting injectable formulation of olanzapine, olanzapine pamoate. The incidence of PDSS with olanzapine pamoate is estimated to be 0.07% of administrations, and is unique among other second-generation, long-acting antipsychotics (e.g. paliperidone palmitate), which do not appear to carry the same risk. PDSS is characterized by symptoms of delirium (e.g. confusion, difficulty speaking, and uncoordinated movements) and sedation. Most people with PDSS exhibit both delirium and sedation (83%). Although less specific to PDSS, a majority of cases (67%) involved a feeling of general discomfort. PDSS may occur due to accidental injection and absorption of olanzapine pamoate into the bloodstream, where it can act more rapidly, as opposed to slowly distributing out from muscle tissue. Using the proper, intramuscular-injection technique for olanzapine pamoate helps to decrease the risk of PDSS, though it does not eliminate it entirely. This is why the FDA advises that people who are injected with olanzapine pamoate be watched for 3 hours after administration, in the event that PDSS occurs.

Animal toxicology

Olanzapine has demonstrated carcinogenic effects in multiple studies when exposed chronically to female mice and rats, but not male mice and rats. The tumors found were in either the liver or mammary glands of the animals.

Discontinuation

The British National Formulary recommends a gradual withdrawal when discontinuing antipsychotics to avoid acute withdrawal syndrome or rapid relapse. Symptoms of withdrawal commonly include nausea, vomiting, and loss of appetite. Other symptoms may include restlessness, increased sweating, and trouble sleeping. Less commonly, vertigo, numbness, or muscle pains may occur. Symptoms generally resolve after a short time.

Tentative evidence indicates that discontinuation of antipsychotics can result in psychosis. It may also result in reoccurrence of the condition that is being treated. Rarely, tardive dyskinesia can occur when the medication is stopped.

Overdose

Symptoms of an overdose include tachycardia, agitation, dysarthria, decreased consciousness, and coma. Death has been reported after an acute overdose of 450 mg, but also survival after an acute overdose of 2000 mg. Fatalities generally have occurred with olanzapine plasma concentrations greater than 1000 ng/mL post mortem, with concentrations up to 5200 ng/mL recorded (though this might represent confounding by dead tissue, which may release olanzapine into the blood upon death). No specific antidote for olanzapine overdose is known, and even physicians are recommended to call a certified poison control center for information on the treatment of such a case. Olanzapine is considered moderately toxic in overdose, more toxic than quetiapine, aripiprazole, and the SSRIs, and less toxic than the monoamine oxidase inhibitors and tricyclic antidepressants.

Interactions

Drugs or agents that increase the activity of the enzyme CYP1A2, notably tobacco smoke, may significantly increase hepatic first-pass clearance of olanzapine; conversely, drugs that inhibit CYP1A2 activity (examples: ciprofloxacin, fluvoxamine) may reduce olanzapine clearance. Carbamazepine, a known enzyme inducer, has decreased the concentration/dose ration of olanzapine by 33% compared to olanzapine alone. Another enzyme inducer, ritonavir, has also been shown to decrease the body's exposure to olanzapine, due to its induction of the enzymes CYP1A2 and uridine 5'-diphospho-glucuronosyltransferase (UGT). Probenecid increases the total exposure (area under the curve) and maximum plasma concentration of olanzapine. Although olanzapine's metabolism includes the minor metabolic pathway of CYP2D6, the presence of the CYP2D6 inhibitor fluoxetine does not have a clinically significant effect on olanzapine's clearance.

Pharmacology

Pharmacodynamics

Olanzapine was first discovered while searching for a chemical analog of clozapine that would not require hematological monitoring. Investigation on a series of thiophene isosteres on 1 of the phenyl rings in clozapine, a thienobenzodiazepine analog (olanzapine) was discovered.

Olanzapine has a higher affinity for 5-HT2A serotonin receptors than D2 dopamine receptors, which is a common property of most atypical antipsychotics, aside from the benzamide antipsychotics such as amisulpride along with the nonbenzamides aripiprazole, brexpiprazole, blonanserin, cariprazine, melperone, and perospirone.

In one study D2 receptor occupancy was 60% with low-dose olanzapine (5 mg/day) and occupancy with high dose at 83% (20 mg/day). In the usual clinical dose range of 10–20 mg/day, D2 receptor occupancy varied from 71% to 80%.

Olanzapine occupancy at 5-HT2A receptor are high at all doses (5 mg to 20 mg). It is reported that 5 mg dose of olanzapine produced a mean occupancy of 85% at 5 mg, 88% at 10 mg, and 93% at 20 mg dose.

Olanzapine had the highest affinity of any second-generation antipsychotic towards the P-glycoprotein in one in vitro study. P-glycoprotein transports a myriad of drugs across a number of different biological membranes (found in numerous body systems) including the blood–brain barrier (a semipermeable membrane that filters the contents of blood prior to it reaching the brain); P-GP inhibition could mean that less brain exposure to olanzapine results from this interaction with the P-glycoprotein. A relatively large quantity of commonly encountered foods and medications inhibit P-GP, and pharmaceuticals fairly commonly are either substrates of P-GP, or inhibit its action; both substrates and inhibitors of P-GP effectively increase the permeability of the blood–brain barrier to P-GP substrates and subsequently increase the central activity of the substrate, while reducing the local effects on the GI tract. The mediation of olanzapine in the central nervous system by P-GP means that any other substance or drug that interacts with P-GP increases the risk for toxic accumulations of both olanzapine and the other drug.

Olanzapine is a potent antagonist of the muscarinic M3 receptor, which may underlie its diabetogenic side effects. Additionally, it also exhibits a relatively low affinity for serotonin 5-HT1, GABAA, beta-adrenergic receptors, and benzodiazepine binding sites.

Although antagonistic effects of olanzapine at 5-HT2c alone is not associated with weight gain, olanzapine antagonism at histaminergic H1, and muscarinic M3 receptors have been implicated in weight gain.

The mode of action of olanzapine's antipsychotic activity is unknown. It may involve antagonism of dopamine and serotonin receptors. Antagonism of dopamine receptors is associated with extrapyramidal effects such as tardive dyskinesia (TD), and with therapeutic effects. Antagonism of muscarinic acetylcholine receptors is associated with anticholinergic side effects such as dry mouth and constipation; in addition, it may suppress or reduce the emergence of extrapyramidal effects for the duration of treatment, but it offers no protection against the development of TD. In common with other second-generation (atypical) antipsychotics, olanzapine poses a relatively low risk of extrapyramidal side effects including TD, due to its higher affinity for the 5HT2A receptor over the D2 receptor.

Antagonizing H1 histamine receptors causes sedation and may cause weight gain, although antagonistic actions at serotonin 5-HT2C and dopamine D2 receptors have also been associated with weight gain and appetite stimulation.

Pharmacokinetics

Metabolism

Olanzapine is metabolized by the cytochrome P450 (CYP) system; principally by isozyme 1A2 (CYP1A2) and to a lesser extent by CYP2D6. By these mechanisms, more than 40% of the oral dose, on average, is removed by the hepatic first-pass effect. Clearance of olanzapine appears to vary by sex; women have roughly 25% lower clearance than men. Clearance of olanzapine also varies by race; in self-identified African Americans or Blacks, olanzapine's clearance was 26% higher. A difference in the clearance is not apparent between individuals identifying as Caucasian, Chinese, or Japanese. Routine, pharmacokinetic monitoring of olanzapine plasma levels is generally unwarranted, though unusual circumstances (e.g. the presence of drug–drug interactions) or a desire to determine if patients are taking their medicine may prompt its use.

Chemistry

Olanzapine is unusual in having four well-characterised crystalline polymorphs and many hydrated forms.

Chemical synthesis

The preparation of olanzapine was first disclosed in a series of patents from Eli Lilly & Co. in the 1990s. In the final two steps, 5-methyl-2-[(2-nitrophenyl)amino]-3-thiophenecarbonitrile was reduced with stannous chloride in ethanol to give the substituted thienobenzodiazepine ring system, and this was treated with methylpiperazine in a mixture of dimethyl sulfoxide and toluene as solvent to produce the drug.

Olanzapine synthesis.svg

Society and culture

Zyprexa (olanzapine) 10 mg tablets (AU)

Regulatory status

Olanzapine is approved by the US FDA for:

  • Treatment—in combination with fluoxetine—of depressive episodes associated with bipolar disorder (December 2003).
  • Long-term treatment of bipolar I disorder (January 2004).
  • Long-term treatment—in combination with fluoxetine—of resistant depression (March 2009)
  • Oral formulation: acute and maintenance treatment of schizophrenia in adults, acute treatment of manic or mixed episodes associated with bipolar I disorder (monotherapy and in combination with lithium or sodium valproate)
  • Intramuscular formulation: acute agitation associated with schizophrenia and bipolar I mania in adults
  • Oral formulation combined with fluoxetine: treatment of acute depressive episodes associated with bipolar I disorder in adults, or treatment of acute, resistant depression in adults
  • Treatment of the manifestations of psychotic disorders (September 1996 – March 2000).
  • Short-term treatment of acute manic episodes associated with bipolar I disorder (March 2000)
  • Short-term treatment of schizophrenia instead of the management of the manifestations of psychotic disorders (March 2000)
  • Maintaining treatment response in schizophrenic patients who had been stable for about eight weeks and were then followed for a period of up to eight months (November 2000)

The drug became generic in 2011. Sales of Zyprexa in 2008 were $2.2 billion in the US and $4.7 billion worldwide.

Controversy and litigation

Eli Lilly has faced many lawsuits from people who claimed they developed diabetes or other diseases after taking Zyprexa, as well as by various governmental entities, insurance companies, and others. Lilly produced a large number of documents as part of the discovery phase of this litigation, which started in 2004; the documents were ruled to be confidential by a judge and placed under seal, and later themselves became the subject of litigation.

In 2006, Lilly paid $700 million to settle around 8,000 of these lawsuits, and in early 2007, Lilly settled around 18,000 suits for $500 million, which brought the total Lilly had paid to settle suits related to the drug to $1.2 billion.

A December 2006 New York Times article based on leaked company documents concluded that the company had engaged in a deliberate effort to downplay olanzapine's side effects. The company denied these allegations and stated that the article had been based on cherry-picked documents. The documents were provided to the Times by Jim Gottstein, a lawyer who represented mentally ill patients, who obtained them from a doctor, David Egilman, who was serving as an expert consultant on the case. After the documents were leaked to online peer-to-peer, file-sharing networks by Will Hall and others in the psychiatric survivors movement, who obtained copies, in 2007 Lilly filed a protection order to stop the dissemination of some of the documents, which Judge Jack B. Weinstein of the Brooklyn Federal District Court granted. Judge Weinstein also criticized the New York Times reporter, Gottstein, and Egilman in the ruling. The Times of London also received the documents and reported that as early as 1998, Lilly considered the risk of drug-induced obesity to be a "top threat" to Zyprexa sales. On October 9, 2000, senior Lilly research physician Robert Baker noted that an academic advisory board to which he belonged was "quite impressed by the magnitude of weight gain on olanzapine and implications for glucose."

Lilly had threatened Egilman with criminal contempt charges regarding the documents he took and provided to reporters; in September 2007, he agreed to pay Lilly $100,000 in return for the company's agreement to drop the threat of charges.

In September 2008, Judge Weinstein issued an order to make public Lilly's internal documents about the drug in a different suit brought by insurance companies, pension funds, and other payors.

In March 2008, Lilly settled a suit with the state of Alaska, and in October 2008, Lilly agreed to pay $62 million to 32 states and the District of Columbia to settle suits brought under state consumer protection laws.

In 2009, Eli Lilly pleaded guilty to a US federal criminal misdemeanor charge of illegally marketing Zyprexa for off-label use and agreed to pay $1.4 billion. The settlement announcement stated "Eli Lilly admits that between September 1999 and March 31, 2001, the company promoted Zyprexa in elderly populations as treatment for dementia, including Alzheimer's dementia. Eli Lilly has agreed to pay a $515 million criminal fine and to forfeit an additional $100 million in assets."

The outcomes described here, and their legal ramifications, were fueled by motions and appeals that were not resolved until 2010. In 2021, Gottstein summarized this tangle of legal activities, and their impact on the political landscape of psychiatry and antipsychiatry in the US, in The Zyprexa Papers.

Trade names

Olanzapine is generic and available under many trade names worldwide.

Dosage forms

Olanzapine is marketed in a number of countries, with tablets ranging from 2.5 to 20 mg. Zyprexa (and generic olanzapine) is available as an orally disintegrating "wafer", which rapidly dissolves in saliva. It is also available in 10-mg vials for intramuscular injection.

Research

Olanzapine has been studied as an antiemetic, particularly for the control of chemotherapy-induced nausea and vomiting (CINV).

In general, olanzapine appears to be about as effective as aprepitant for the prevention of CINV, though some concerns remain for its use in this population. For example, concomitant use of metoclopramide or haloperidol increases the risk for extrapyramidal symptoms. Otherwise, olanzapine appears to be fairly well tolerated for this indication, with somnolence being the most common side effect.

Olanzapine has been considered as part of an early psychosis approach for schizophrenia. The Prevention through Risk Identification, Management, and Education study, funded by the National Institute of Mental Health and Eli Lilly, tested the hypothesis that olanzapine might prevent the onset of psychosis in people at very high risk for schizophrenia. The study examined 60 patients with prodromal schizophrenia, who were at an estimated risk of 36–54% of developing schizophrenia within a year, and treated half with olanzapine and half with placebo. In this study, patients receiving olanzapine did not have a significantly lower risk of progressing to psychosis (16.1% vs 37.9%). Olanzapine was effective for treating the prodromal symptoms, but was associated with significant weight gain.

Computational neuroscience

From Wikipedia, the free encyclopedia

Computational neuroscience (also known as theoretical neuroscience or mathematical neuroscience) is a branch of neuroscience which employs mathematical models, computer simulations, theoretical analysis and abstractions of the brain to understand the principles that govern the development, structure, physiology and cognitive abilities of the nervous system.

Computational neuroscience employs computational simulations to validate and solve mathematical models, and so can be seen as a sub-field of theoretical neuroscience; however, the two fields are often synonymous. The term mathematical neuroscience is also used sometimes, to stress the quantitative nature of the field.

Computational neuroscience focuses on the description of biologically plausible neurons (and neural systems) and their physiology and dynamics, and it is therefore not directly concerned with biologically unrealistic models used in connectionism, control theory, cybernetics, quantitative psychology, machine learning, artificial neural networks, artificial intelligence and computational learning theory; although mutual inspiration exists and sometimes there is no strict limit between fields, with model abstraction in computational neuroscience depending on research scope and the granularity at which biological entities are analyzed.

Models in theoretical neuroscience are aimed at capturing the essential features of the biological system at multiple spatial-temporal scales, from membrane currents, and chemical coupling via network oscillations, columnar and topographic architecture, nuclei, all the way up to psychological faculties like memory, learning and behavior. These computational models frame hypotheses that can be directly tested by biological or psychological experiments.

History

The term 'computational neuroscience' was introduced by Eric L. Schwartz, who organized a conference, held in 1985 in Carmel, California, at the request of the Systems Development Foundation to provide a summary of the current status of a field which until that point was referred to by a variety of names, such as neural modeling, brain theory and neural networks. The proceedings of this definitional meeting were published in 1990 as the book Computational Neuroscience. The first of the annual open international meetings focused on Computational Neuroscience was organized by James M. Bower and John Miller in San Francisco, California in 1989. The first graduate educational program in computational neuroscience was organized as the Computational and Neural Systems Ph.D. program at the California Institute of Technology in 1985.

The early historical roots of the field can be traced to the work of people including Louis Lapicque, Hodgkin & Huxley, Hubel and Wiesel, and David Marr. Lapicque introduced the integrate and fire model of the neuron in a seminal article published in 1907, a model still popular for artificial neural networks studies because of its simplicity (see a recent review).

About 40 years later, Hodgkin and Huxley developed the voltage clamp and created the first biophysical model of the action potential. Hubel and Wiesel discovered that neurons in the primary visual cortex, the first cortical area to process information coming from the retina, have oriented receptive fields and are organized in columns. David Marr's work focused on the interactions between neurons, suggesting computational approaches to the study of how functional groups of neurons within the hippocampus and neocortex interact, store, process, and transmit information. Computational modeling of biophysically realistic neurons and dendrites began with the work of Wilfrid Rall, with the first multicompartmental model using cable theory.

Major topics

Research in computational neuroscience can be roughly categorized into several lines of inquiry. Most computational neuroscientists collaborate closely with experimentalists in analyzing novel data and synthesizing new models of biological phenomena.

Single-neuron modeling

Even a single neuron has complex biophysical characteristics and can perform computations (e.g.). Hodgkin and Huxley's original model only employed two voltage-sensitive currents (Voltage sensitive ion channels are glycoprotein molecules which extend through the lipid bilayer, allowing ions to traverse under certain conditions through the axolemma), the fast-acting sodium and the inward-rectifying potassium. Though successful in predicting the timing and qualitative features of the action potential, it nevertheless failed to predict a number of important features such as adaptation and shunting. Scientists now believe that there are a wide variety of voltage-sensitive currents, and the implications of the differing dynamics, modulations, and sensitivity of these currents is an important topic of computational neuroscience.

The computational functions of complex dendrites are also under intense investigation. There is a large body of literature regarding how different currents interact with geometric properties of neurons.

Some models are also tracking biochemical pathways at very small scales such as dendritic spines or synaptic clefts.

There are many software packages, such as GENESIS and NEURON, that allow rapid and systematic in silico modeling of realistic neurons. Blue Brain, a project founded by Henry Markram from the École Polytechnique Fédérale de Lausanne, aims to construct a biophysically detailed simulation of a cortical column on the Blue Gene supercomputer.

Modeling the richness of biophysical properties on the single-neuron scale can supply mechanisms that serve as the building blocks for network dynamics. However, detailed neuron descriptions are computationally expensive and this computing cost can limit the pursuit of realistic network investigations, where many neurons need to be simulated. As a result, researchers that study large neural circuits typically represent each neuron and synapse with an artificially simple model, ignoring much of the biological detail. Hence there is a drive to produce simplified neuron models that can retain significant biological fidelity at a low computational overhead. Algorithms have been developed to produce faithful, faster running, simplified surrogate neuron models from computationally expensive, detailed neuron models.

Modeling Neuron-glia interactions

Glial cells participate significantly to the regulation of neuronal activity at a cellular but also at a network level. Modeling this interaction allows to clarify the potassium cycle, so important for maintaining homeostatis and to prevent epileptic seizures. Modeling reveals the role of glial protrusions that can penetrate in some cases the synaptic cleft to interfere with the synpatic transmission and thus control synaptic communication.

Development, axonal patterning, and guidance

Computational neuroscience aims to address a wide array of questions. How do axons and dendrites form during development? How do axons know where to target and how to reach these targets? How do neurons migrate to the proper position in the central and peripheral systems? How do synapses form? We know from molecular biology that distinct parts of the nervous system release distinct chemical cues, from growth factors to hormones that modulate and influence the growth and development of functional connections between neurons.

Theoretical investigations into the formation and patterning of synaptic connection and morphology are still nascent. One hypothesis that has recently garnered some attention is the minimal wiring hypothesis, which postulates that the formation of axons and dendrites effectively minimizes resource allocation while maintaining maximal information storage.

Sensory processing

Early models on sensory processing understood within a theoretical framework are credited to Horace Barlow. Somewhat similar to the minimal wiring hypothesis described in the preceding section, Barlow understood the processing of the early sensory systems to be a form of efficient coding, where the neurons encoded information which minimized the number of spikes. Experimental and computational work have since supported this hypothesis in one form or another. For the example of visual processing, efficient coding is manifested in the forms of efficient spatial coding, color coding, temporal/motion coding, stereo coding, and combinations of them.

Further along the visual pathway, even the efficiently coded visual information is too much for the capacity of the information bottleneck, the visual attentional bottleneck. A subsequent theory, V1 Saliency Hypothesis (V1SH), has been developed on exogenous attentional selection of a fraction of visual input for further processing, guided by a bottom-up saliency map in the primary visual cortex.

Current research in sensory processing is divided among a biophysical modelling of different subsystems and a more theoretical modelling of perception. Current models of perception have suggested that the brain performs some form of Bayesian inference and integration of different sensory information in generating our perception of the physical world.

Motor control

Many models of the way the brain controls movement have been developed. This includes models of processing in the brain such as the cerebellum's role for error correction, skill learning in motor cortex and the basal ganglia, or the control of the vestibulo ocular reflex. This also includes many normative models, such as those of the Bayesian or optimal control flavor which are built on the idea that the brain efficiently solves its problems.

Memory and synaptic plasticity

Earlier models of memory are primarily based on the postulates of Hebbian learning. Biologically relevant models such as Hopfield net have been developed to address the properties of associative (also known as "content-addressable") style of memory that occur in biological systems. These attempts are primarily focusing on the formation of medium- and long-term memory, localizing in the hippocampus. Models of working memory, relying on theories of network oscillations and persistent activity, have been built to capture some features of the prefrontal cortex in context-related memory. Additional models look at the close relationship between the basal ganglia and the prefrontal cortex and how that contributes to working memory.

One of the major problems in neurophysiological memory is how it is maintained and changed through multiple time scales. Unstable synapses are easy to train but also prone to stochastic disruption. Stable synapses forget less easily, but they are also harder to consolidate. One recent computational hypothesis involves cascades of plasticity that allow synapses to function at multiple time scales. Stereochemically detailed models of the acetylcholine receptor-based synapse with the Monte Carlo method, working at the time scale of microseconds, have been built. It is likely that computational tools will contribute greatly to our understanding of how synapses function and change in relation to external stimulus in the coming decades.

Behaviors of networks

Biological neurons are connected to each other in a complex, recurrent fashion. These connections are, unlike most artificial neural networks, sparse and usually specific. It is not known how information is transmitted through such sparsely connected networks, although specific areas of the brain, such as the visual cortex, are understood in some detail. It is also unknown what the computational functions of these specific connectivity patterns are, if any.

The interactions of neurons in a small network can be often reduced to simple models such as the Ising model. The statistical mechanics of such simple systems are well-characterized theoretically. Some recent evidence suggests that dynamics of arbitrary neuronal networks can be reduced to pairwise interactions. It is not known, however, whether such descriptive dynamics impart any important computational function. With the emergence of two-photon microscopy and calcium imaging, we now have powerful experimental methods with which to test the new theories regarding neuronal networks.

In some cases the complex interactions between inhibitory and excitatory neurons can be simplified using mean-field theory, which gives rise to the population model of neural networks. While many neurotheorists prefer such models with reduced complexity, others argue that uncovering structural-functional relations depends on including as much neuronal and network structure as possible. Models of this type are typically built in large simulation platforms like GENESIS or NEURON. There have been some attempts to provide unified methods that bridge and integrate these levels of complexity.

Visual attention, identification, and categorization

Visual attention can be described as a set of mechanisms that limit some processing to a subset of incoming stimuli. Attentional mechanisms shape what we see and what we can act upon. They allow for concurrent selection of some (preferably, relevant) information and inhibition of other information. In order to have a more concrete specification of the mechanism underlying visual attention and the binding of features, a number of computational models have been proposed aiming to explain psychophysical findings. In general, all models postulate the existence of a saliency or priority map for registering the potentially interesting areas of the retinal input, and a gating mechanism for reducing the amount of incoming visual information, so that the limited computational resources of the brain can handle it. An example theory that is being extensively tested behaviorally and physiologically is the V1 Saliency Hypothesis that a bottom-up saliency map is created in the primary visual cortex to guide attention exogenously. Computational neuroscience provides a mathematical framework for studying the mechanisms involved in brain function and allows complete simulation and prediction of neuropsychological syndromes.

Cognition, discrimination, and learning

Computational modeling of higher cognitive functions has only recently begun. Experimental data comes primarily from single-unit recording in primates. The frontal lobe and parietal lobe function as integrators of information from multiple sensory modalities. There are some tentative ideas regarding how simple mutually inhibitory functional circuits in these areas may carry out biologically relevant computation.

The brain seems to be able to discriminate and adapt particularly well in certain contexts. For instance, human beings seem to have an enormous capacity for memorizing and recognizing faces. One of the key goals of computational neuroscience is to dissect how biological systems carry out these complex computations efficiently and potentially replicate these processes in building intelligent machines.

The brain's large-scale organizational principles are illuminated by many fields, including biology, psychology, and clinical practice. Integrative neuroscience attempts to consolidate these observations through unified descriptive models and databases of behavioral measures and recordings. These are the bases for some quantitative modeling of large-scale brain activity.

The Computational Representational Understanding of Mind (CRUM) is another attempt at modeling human cognition through simulated processes like acquired rule-based systems in decision making and the manipulation of visual representations in decision making.

Consciousness

One of the ultimate goals of psychology/neuroscience is to be able to explain the everyday experience of conscious life. Francis Crick, Giulio Tononi and Christof Koch made some attempts to formulate consistent frameworks for future work in neural correlates of consciousness (NCC), though much of the work in this field remains speculative. Specifically, Crick cautioned the field of neuroscience to not approach topics that are traditionally left to philosophy and religion.

Computational clinical neuroscience

Computational clinical neuroscience is a field that brings together experts in neuroscience, neurology, psychiatry, decision sciences and computational modeling to quantitatively define and investigate problems in neurological and psychiatric diseases, and to train scientists and clinicians that wish to apply these models to diagnosis and treatment.

Predictive computational neuroscience

Predictive computational neuroscience is a recent field that combines signal processing, neuroscience, clinical data and machine learning to predict the brain during coma or anesthesia. For example, it is possible to anticipate deep brain states using the EEG signal. These states can be used to anticipate hypnotic concentration to administrate to the patient.

Computational Psychiatry

Computational psychiatry is a new emerging field that brings together experts in machine learning, neuroscience, neurology, psychiatry, psychology to provide an understanding of psychiatric disorders.

Technology

Neuromorphic computing

A neuromorphic computer/chip is any device that uses physical artificial neurons (made from silicon) to do computations (See: neuromorphic computing, physical neural network). One of the advantages of using a physical model computer such as this is that it takes the computational load of the processor (in the sense that the structural and some of the functional elements don't have to be programmed since they are in hardware). In recent times, neuromorphic technology has been used to build supercomputers which are used in international neuroscience collaborations. Examples include the Human Brain Project SpiNNaker supercomputer and the BrainScaleS computer.

Early intervention in psychosis

From Wikipedia, the free encyclopedia
 

Early intervention in psychosis is a clinical approach to those experiencing symptoms of psychosis for the first time. It forms part of a new prevention paradigm for psychiatry and is leading to reform of mental health services, especially in the United Kingdom and Australia.

This approach centers on the early detection and treatment of early symptoms of psychosis during the formative years of the psychotic condition. The first three to five years are believed by some to be a critical period. The aim is to reduce the usual delays to treatment for those in their first episode of psychosis. The provision of optimal treatments in these early years is thought to prevent relapses and reduce the long-term impact of the condition. It is considered a secondary prevention strategy.

The duration of untreated psychosis (DUP) has been shown as an indicator of prognosis, with a longer DUP associated with more long-term disability.

Components of the model

There are a number of functional components of the early psychosis model, and they can be structured as different sub-teams within early psychosis services. The emerging pattern of sub-teams are currently:

Early psychosis treatment teams

Multidisciplinary clinical teams providing an intensive case management approach for the first three to five years. The approach is similar to assertive community treatment, but with an increased focus on the engagement and treatment of this previously untreated population and the provision of evidence based, optimal interventions for clients in their first episode of psychosis. For example, the use of low-dose antipsychotic medication is promoted ("start low, go slow"), with a need for monitoring of side effects and an intensive and deliberate period of psycho-education for patients and families that are new to the mental health system. In addition, research showed that family intervention for psychosis (FIp) reduced relapse rates, hospitalization duration, and psychotic symptoms along with increasing functionality in first-episode psychosis (FEP) up to 24 months. Interventions to prevent a further episodes of psychosis (a "relapse") and strategies that encourage a return to normal vocation and social activity are a priority. There is a concept of phase specific treatment for acute, early recovery and late recovery periods in the first episode of psychosis.

Early detection function

Interventions aimed at avoiding late detection and engagement of those in the course of their psychotic conditions. Key tasks include being aware of early signs of psychosis and improving pathways into treatment. Teams provide information and education to the general public and assist GPs with recognition and response to those with suspected signs, for example: EPPIC's Youth Access Team (YAT) (Melbourne); OPUS (Denmark); TIPS (Norway); REDIRECT (Birmingham); LEO CAT (London) "; STEP's Population Health approach to early detection.

The development and implementation of quantitative tools for early detection of at-risk individuals is an active research area. This includes development of risk calculators and methods for large-scale population screening.

Prodrome clinics

Prodrome or at risk mental state clinics are specialist services for those with subclinical symptoms of psychosis or other indicators of risk of transition to psychosis. The Pace Clinic in Melbourne, Australia, is considered one of the origins of this strategy, but a number of other services and research centers have since developed. These services are able to reliably identify those at high risk of developing psychosis and are beginning to publish encouraging outcomes from randomised controlled trials that reduce the chances of becoming psychotic, including evidence that psychological therapy and high doses of fish oil have a role in the prevention of psychosis. However, a meta-analysis of five trials found that while these interventions reduced risk of psychosis after 1 year (11% conversion to psychosis in intervention groups compared to 32% in control groups), these gains were not maintained over 2–3 years of follow-up. These findings indicate that interventions delay psychosis, but do not reduce the long-term risk. There has also been debate about the ethics of using antipsychotic medication to reduce the risk of developing psychosis, because of the potential harms involved with these medications.

In 2015, the European Psychiatric Association issued guidance recommending the use of the Cognitive Disturbances scale (COGDIS), a subscale of the basic symptoms scale, to assess psychosis risk; a meta-analysis conducted for the guidance found that while rates of conversion to psychosis were similar to those who meet Ultra High Risk (UHR) criteria up to 2 years after assessment, they were significantly higher after 2 years for those patients who met the COGDIS criteria. The COGDIS criteria measure subjective symptoms, and include such symptoms as thought interference, where irrelevant and emotionally unimportant thought contents interfere with the main line of thinking; thought block, where the current train of thought halts; thought pressure, where thoughts unrelated to a common topic appear uncontrollably; referential ideation that is immediately corrected; and other characteristic disturbances of attention and the use or understanding of language.

History

Early intervention in psychosis is a preventive approach for psychosis that has evolved as contemporary recovery views of psychosis and schizophrenia have gained acceptance. It subscribes to a "post Kraepelin" concept of schizophrenia, challenging the assumptions originally promoted by Emil Kraepelin in the 19th century, that schizophrenia ("dementia praecox") was a condition with a progressing and deteriorating course. The work of Post, whose kindling model, together with Fava and Kellner, who first adapted staging models to mental health, provided an intellectual foundation. Psychosis is now formulated within a diathesis–stress model, allowing a more hopeful view of prognosis, and expects full recovery for those with early emerging psychotic symptoms. It is more aligned with psychosis as continuum (such as with the concept of schizotypy) with multiple contributing factors, rather than schizophrenia as simply a neurobiological disease.

Within this changing view of psychosis and schizophrenia, the model has developed from a divergence of several different ideas, and from a number of sites, beginning with the closure of psychiatric institutions signaling a move toward community based care. In 1986, the Northwick Park study discovered an association between delays to treatment and disability, questioning the service provision for those with their first episode of schizophrenia. In the 1990s, evidence began to emerge that cognitive behavioural therapy was an effective treatment for delusions and hallucinations. The next step came with the development of the EPPIC early detection service in Melbourne, Australia in 1996 and the prodrome clinic led by Alison Yung. This service was an inspiration to other services, such as the West Midlands IRIS group, including the carer charity Rethink Mental Illness; the TIPS early detection randomised control trial in Norway; and the Danish OPUS trial. In 2001, the United Kingdom Department of Health called the development of early psychosis teams "a priority". The International Early Psychosis Association, founded in 1998, issued an international consensus declaration together with the World Health Organization in 2004. Clinical practice guidelines have been written by consensus.

Clinical outcome evidence

A number of studies have been carried out to see whether the early psychosis approach reduces the severity of symptoms, improves relapse rates, and decreases the use of inpatient care, in comparison to standard care. Advocates of early intervention for psychosis have been accused of selectively citing findings that support the benefits of early intervention, but ignoring findings that do not. It has been argued that the scientific reporting of evidence on early intervention in psychosis is characterized by a high prevalence of ‘spin’ and ‘bias’. An analysis of the summaries of articles found that 75% implied positive results, whereas examination of the findings with primary measures from these studies found that only 13% were positive. A prospective two-year follow-up of 114 patients hospitalized for a first episode psychotic illness reported 75% recovery, while 41% returned to baseline functioning, and nearly half experienced new episodes.

A systematic review investigated the effects of early intervention for psychosis:

Specialized team compared to standard care for psychosis
Summary
There is emerging, but as yet inconclusive evidence, to suggest that people in the prodrome of psychosis can be helped by some interventions. There is some support for specialized early intervention services, but further trials would be desirable, and there is a question of whether gains are maintained. There is some support for phase-specific treatment focused on employment and family therapy, but again, this needs replicating with larger and longer trials.

Evidence on cost

Studies have been published claiming that early psychosis services cost less than standard services, largely through reduced in-patient costs, and also save other costs to society. However, the claimed savings have been disputed. A 2012 systematic review of the evidence concluded that: "The published literature does not support the contention that early intervention for psychosis reduces costs or achieves cost-effectiveness".

Reform of mental health services

United Kingdom

The United Kingdom has made significant service reform with their adoption of early psychosis teams following the first service in Birmingham set up by Professor Max Birchwood in 1994 and used as a blueprint for national roll-out, with early psychosis now considered as an integral part of comprehensive community mental health services. The Mental Health Policy Implementation Guide outlines service specifications and forms the basis of a newly developed fidelity tool. There is a requirement for services to reduce the duration of untreated psychosis, as this has been shown to be associated with better long-term outcomes. The implementation guideline recommends:

  • 14 to 35 year age entry criteria
  • First three years of psychotic illness
  • Aim to reduce the duration of untreated psychosis to less than 3 months
  • Maximum caseload ratio of 1 care coordinator to 10–15 clients
  • For every 250,000 (depending on population characteristics), one team
    • Total caseload 120 to 150
    • 1.5 doctors per team
    • Other specialist staff to provide specific evidence based interventions

Australia and New Zealand

In Australia the EPPIC initiative provides early intervention services. In the Australian government's 2011 budget, $222.4 million was provided to fund 12 new EPPIC centres in collaboration with the states and territories. However, there have been criticisms of the evidence base for this expansion and of the claimed cost savings.

On August 19, 2011, Patrick McGorry, South Australian Social Inclusion Commissioner David Cappo AO and Frank Quinlan, CEO of the Mental Health Council of Australia, addressed a meeting of the Council of Australian Governments (COAG), chaired by Prime Minister Julia Gillard, on the future direction of mental health policy and the need for priority funding for early intervention. The invitation, an initiative of South Australian Premier Mike Rann, followed the release of Cappo's "Stepping Up" report, supported by the Rann Government, which recommended a major overhaul of mental health in South Australia, including stepped levels of care and early intervention.

New Zealand has operated significant early psychosis teams for more than 20 years, following the inclusion of early psychosis in a mental health policy document in 1997. There is a national early psychosis professional group, New Zealand Early Intervention for Psychosis Society (NZEIPS), organising a biannual training event, advocating for evidenced based service reform and supporting production of local resources.

Scandinavia

Early psychosis programmes have continued to develop from the original TIPS services in Norway and the OPUS randomised trial in Denmark.

North America

Canada has extensive coverage across most provinces, including established clinical services and comprehensive academic research in British Columbia (Vancouver), Alberta (EPT in Calgary), Quebec (PEPP-Montreal), and Ontario (PEPP, FEPP).

In the United States, the Early Assessment Support Alliance (EASA) is implementing early psychosis intervention throughout the state of Oregon.

In the United States, the implementation of coordinated specialty care (CSC), as a recovery-oriented treatment program for people with first episode psychosis (FEP), has become a US health policy priority. CSC promotes shared decision making and uses a team of specialists who work with the client to create a personal treatment plan. The specialists offer psychotherapy, medication management geared to individuals with FEP, family education and support, case management, and work or education support, depending on the individual's needs and preferences. The client and the team work together to make treatment decisions, involving family members as much as possible. The goal is to link the individual with a CSC team as soon as possible after psychotic symptoms begin because a longer period of unchecked and untreated illness might be associated with poorer outcomes.

Asia

The first meeting of the Asian Network of Early Psychosis (ANEP) was held in 2004. There are now established services in Singapore, Hong Kong and South Korea

Computing education

From Wikipedia, the free encyclopedia https://en.wikipedia.org/wiki/Computing_education Elementar...