Archive for category Depression

[Abstract + References] Antidepressant effect of vagal nerve stimulation in epilepsy patients: a systematic review

Abstract

Background

Vagal nerve stimulation (VNS) is an effective palliative therapy in drug-resistant epileptic patients and is also approved as a therapy for treatment-resistant depression. Depression is a frequent comorbidity in epilepsy and it affects the quality of life of patients more than the seizure frequency itself. The aim of this systematic review is to analyze the available literature about the VNS effect on depressive symptoms in epileptic patients.

Material and methods

A comprehensive search of PubMed, Medline, Scopus, and Google Scholar was performed, and results were included up to January 2020. All studies concerning depressive symptom assessment in epileptic patients treated with VNS were included.

Results

Nine studies were included because they fulfilled inclusion criteria. Six out of nine papers reported a positive effect of VNS on depressive symptoms. Eight out of nine studies did not find any correlation between seizure reduction and depressive symptom amelioration, as induced by VNS. Clinical scales for depression, drug regimens, and age of patients were broadly different among the examined studies.

Conclusions

Reviewed studies strongly suggest that VNS ameliorates depressive symptoms in drug-resistant epileptic patients and that the VNS effect on depression is uncorrelated to seizure response. However, more rigorous studies addressing this issue are encouraged.

References

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Source: https://link.springer.com/article/10.1007/s10072-020-04479-2

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[Infographic] How To Prevent An ANXIETY ATTACK

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[WEB PAGE] What to know about epinephrine and norepinephrine.

Epinephrine and norepinephrine belong to a group of compounds called catecholamines, and they act as both neurotransmitters and hormones. While these compounds have similar chemical structures, they produce different effects on the body.

Epinephrine is also known as adrenaline, while some people refer to norepinephrine as noradrenaline. Both of these substances play a role in the regulation of the sympathetic nervous system, which is the part of the autonomic nervous system that is responsible for the body’s “fight or flight” response.

In this article, we discuss the similarities and differences between epinephrine and norepinephrine, along with their functions. We also cover their medical uses and the health effects of having too much or too little of either compound in the body.

What are epinephrine and norepinephrine?

young woman looking nervous at interview
Epinephrine and norepinephrine both play a role in the body’s “fight-or-flight” response.

Epinephrine and norepinephrine are both hormones and neurotransmitters.

Hormones are chemical messengers that travel through the bloodstream. The endocrine glands and reproductive organs make and secrete a wide range of hormones to regulate the body’s organs, tissues, and cells.

Neurotransmitters are also a type of chemical messenger, but they only occur in nerve cells and travel across synapses, which are junctions where two nerve fibers meet. Nerves cells produce neurotransmitters in response to electrical impulses.

The adrenal medulla, the inner portion of the adrenal gland, regulates and secretes both epinephrine and norepinephrine in response to stress and other imbalances in the body, such as low blood pressure.

Epinephrine activates both alpha- and beta-adrenoreceptors in cells, whereas norepinephrine mainly stimulates alpha-adrenoreceptors.

We discuss the main functions of epinephrine and norepinephrine below:

Epinephrine

When the brain perceives danger, the amygdala triggers the hypothalamus to activate the autonomic nervous system.

Signals from the autonomic nervous system stimulate the adrenal gland to start pumping epinephrine into the bloodstream. People often refer to this surge of epinephrine as an adrenaline rush or the fight or flight response.

Epinephrine affects the heart, lungs, muscles, and blood vessels. Its release into the bloodstream brings about several physiological changes, such as:

  • increased heart rate and blood flow
  • faster breathing
  • raised blood sugar levels
  • increased strength and physical performance

Norepinephrine

The adrenal medulla produces norepinephrine in response to low blood pressure and stress. Norepinephrine promotes vasoconstriction, which is a narrowing of the blood vessels, and this increases blood pressure.

Like epinephrine, norepinephrine also increases the heart rate and blood sugar levels.

Effects of deficiency

Chronic stress, poor nutrition, some medications, and certain health conditions can affect the body’s ability to produce or respond to epinephrine and norepinephrine.

A rare condition called genetic dopamine beta-hydroxylase deficiency prevents the body from converting dopamine into norepinephrine.

According to a 2018 article, genetic dopamine beta-hydroxylase deficiency results from a mutation in the norepinephrine transporter gene g237c. The authors concluded that this condition might decrease sympathetic nerve activity and increase the risk of damage to the heart and blood vessels.

Low levels of epinephrine and norepinephrine can result in physical and mental symptoms, such as:

In addition, norepinephrine plays a role in focus and promotes periods of sustained attention. Low levels of norepinephrine may contribute to the development of attention deficit hyperactivity disorder (ADHD).

The following medications can increase levels of norepinephrine:

  • amphetamines, such as methylphenidate (Ritalin) and dextroamphetamine (Adderall)
  • serotonin-norepinephrine reuptake inhibitors (SNRIs), such as venlafaxine (Effexor) and duloxetine (Cymbalta)

Effects of high levels

man having his blood pressure taken
Having high levels of epinephrine or norepinephrine can cause high blood pressure.

Certain medical conditions, such as tumors, chronic stress, and obesity, can affect the adrenal glands and cause excess production of epinephrine and norepinephrine.

Symptoms of high levels of epinephrine or norepinephrine can include:

2018 research paper states that having high levels of norepinephrine can increase a person’s risk of cardiovascular and kidney damage.

An epinephrine overdose can occur in people who use epinephrine injections to treat certain medical conditions. An overdose of injected epinephrine can lead to dangerously high blood pressure, stroke, or even death.

Medical uses

Synthetic forms of epinephrine and norepinephrine have several medical uses, which we discuss below:

Epinephrine

Doctors prescribe epinephrine to treat severe medical conditions that affect the heart and airways, such as anaphylaxis.

Anaphylaxis is a severe allergic reaction that can interfere with a person’s ability to breathe, and it requires emergency medical treatment. Epinephrine counters anaphylactic shock by narrowing the blood vessels, relaxing the muscles, and opening up the airways.

It is common for people at risk of anaphylaxis to carry an epinephrine autoinjector with them at all times.

Doctors also use epinephrine to treat severe asthma attacks, cardiac arrest, and serious infections.

Norepinephrine

Norepinephrine can help raise systolic blood pressure in people who have had a heart attack.

Doctors also use norepinephrine to treat:

  • septic shock
  • neurogenic shock
  • pericardial tamponade
  • critical hypotension

Summary

Epinephrine and norepinephrine are similar chemicals that act as both neurotransmitters and hormones in the body. Both substances play an important role in the body’s fight or flight response, and their release into the bloodstream causes increased blood pressure, heart rate, and blood sugar levels.

Epinephrine acts on the alpha- and beta-adrenoreceptors in the muscles, lungs, heart, and blood vessels. Norepinephrine is a metabolite of dopamine that primarily acts on the alpha-adrenoreceptors in the blood vessels.

Source: https://www.medicalnewstoday.com/articles/325485

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[Abstract] Pharmacological and Non-Pharmacological Interventions for Depression after Moderate-to-Severe Traumatic Brain Injury: A Systematic Review and Meta-Analysis

The objective of this study was to systematically review the literature and perform a meta-analysis of randomized controlled trials (RCTs) on the effectiveness of pharmacological and non-pharmacological interventions for depression in patients with moderate-to-severe traumatic brain injury.

Databases searched were: Embase, PubMed, PsycInfo, Cochrane Central, Web of Science, and Google Scholar. Depression score on a self-report questionnaire was the outcome measure. Outcomes were collected at baseline and at the first follow-up moment. Data extraction was executed independently by two researchers. Thirteen RCTs were identified: five pharmacological and eight non-pharmacological. Although not all individual studies had significant results, the overall standardized mean difference (SMD) was −0.395, p ≤ 0.001, indicating that interventions improved the depression scores in patients with TBI.

The difference in effectiveness between pharmacological interventions and non-pharmacological interventions was not significant (ΔSMD: 0.203, p = 0.238). Further subdivision into methylphenidate, sertraline, psychological, and other interventions showed a significant difference in effectiveness between methylphenidate (ΔSMD: −0.700, p = 0.020) and psychological interventions (reference). This difference was not found if other depression outcomes in four of the included studies were analyzed. The SMD of low-quality studies did not differ significantly from moderate- and high-quality studies (ΔSMD: 0.321, p = 0.050).

Although RCTs targeting interventions for depression after TBI are scarce, both pharmacological and non-pharmacological interventions appear to be effective in treating depressive symptoms/depression after moderate-to-severe TBI. There is a need for high-quality RCTs in which the add-on effects of pharmacological and non-pharmacological interventions are investigated.

via Pharmacological and Non-Pharmacological Interventions for Depression after Moderate-to-Severe Traumatic Brain Injury: A Systematic Review and Meta-Analysis | Journal of Neurotrauma

 

 

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[Infographic] Depression

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[ARTICLE] Adaptive conjunctive cognitive training (ACCT) in virtual reality for chronic stroke patients: a randomized controlled pilot trial – Full Text

Abstract

Background

Current evidence for the effectiveness of post-stroke cognitive rehabilitation is weak, possibly due to two reasons. First, patients typically express cognitive deficits in several domains. Therapies focusing on specific cognitive deficits might not address their interrelated neurological nature. Second, co-occurring psychological problems are often neglected or not diagnosed, although post-stroke depression is common and related to cognitive deficits. This pilot trial aims to test a rehabilitation program in virtual reality that trains various cognitive domains in conjunction, by adapting to the patient’s disability and while investigating the influence of comorbidities.

Methods

Thirty community-dwelling stroke patients at the chronic stage and suffering from cognitive impairment performed 30 min of daily training for 6 weeks. The experimental group followed, so called, adaptive conjunctive cognitive training (ACCT) using RGS, whereas the control group solved standard cognitive tasks at home for an equivalent amount of time. A comprehensive test battery covering executive function, spatial awareness, attention, and memory as well as independence, depression, and motor impairment was applied at baseline, at 6 weeks and 18-weeks follow-up.

Results

At baseline, 75% of our sample had an impairment in more than one cognitive domain. The experimental group showed improvements in attention (χ2FχF2 (2) = 9.57, p < .01), spatial awareness (χ2FχF2 (2) = 11.23, p < .01) and generalized cognitive functioning (χ2FχF2 (2) = 15.5, p < .001). No significant change was seen in the executive function and memory domain. For the control group, no significant change over time was found. Further, they worsened in their depression level after treatment (T = 45, r = .72, p < .01) but returned to baseline at follow-up. The experimental group displayed a lower level of depression than the control group after treatment (Ws = 81.5, z = − 2.76, r = − .60, p < .01) and (Ws = 92, z = − 2.03, r = − .44, p < .05).

Conclusions

ACCT positively influences attention and spatial awareness, as well as depressive mood in chronic stroke patients.

Trial registration

The trial was registered prospectively at ClinicalTrials.gov (NCT02816008) on June 21, 2016.

Background

Cognitive impairments are common after stroke, with incident rates up to 78% [1]. Patients with mild cognitive impairment are at risk for developing dementia [2]. Cognitive deficits correlate with poor functional outcomes and increased risk of dependence [3], have negative effects on the patient’s quality of life [4], and alter the patient’s ability to socialize [5]. However, the current clinical practice seems to lack methods that specifically address cognitive sequelae. According to a meta-analysis that aimed at proposing recommendations for new clinical standards, currently available treatments that are used as control conditions are conventional therapies like physical therapy or occupational therapy, pseudo treatments like mental or social stimulation without therapeutic intent, as well as psychosocial interventions like psychotherapy or emotional support for individuals or groups [6]. Besides, it has been shown that cognitively impaired patients participate less in rehabilitation activities, which potentially contributes to the poorer functional outcome they display [7]. Finding effective cognitive rehabilitation methods that can be incorporated in clinical practice is therefore crucial. Numerous methods to improve cognitive deficits, for instance, specifically attention [8], memory [9], executive function [10], or spatial abilities [11], have been proposed. However, the results show mixed efficacies. A meta-analysis on the impact of attentional treatments showed an effect on divided attention in the short-term, but found no evidence for persisting effects on other attentional domains, global attention, or functional outcomes [12]. Similarly, a meta-review that investigated the effect of memory rehabilitation found that training might benefit subjective reports of memory in the short term, but shows no effect in the long term, on objective memory measures, mood, functional abilities or quality of life [13]. Ultimately, a meta-analysis over 6 Cochrane reviews shows insufficient research evidence or evidence of insufficient quality to support any recommendation for cognitive stroke rehabilitation [14]. Besides methodological issues, one limitation of existing methods could be that they focus on one deficit only, ignoring that patients typically express deficits in multiple cognitive domains [12]. A study on a large sample of heterogeneous stroke patients which aimed at linking lesions to cognitive deficits found that a given lesion location leads to cognitive impairments in several domains [15]. This emphasizes that cognitive functions rely on a network of brain regions. A lesion in one of those regions might cause a disturbance to the network, which leads to a multitude of symptoms. This is further supported by studies that revealed that pathological changes in brain structures are related to the occurrence of various cognitive deficits and symptoms for instance, in Alzheimer’s disease [16] or spatial neglect [17]. Moreover, the presence of multiple cognitive deficits seems to be a marker in patients that are at risk of developing Alzheimer’s disease later in life [18]. To what extent rehabilitation could potentially drive structural or functional changes to alleviate the symptoms of stroke is still under debate [1920]. Nevertheless, rehabilitation methods have to aid the patient in obtaining enough functionality to independently perform instrumental activities of daily living, be it through restoration of function or compensation. With this in mind, focusing on training a single cognitive skill might not be efficient because many daily tasks or jobs require several cognitive abilities for their execution [21]. For instance, most patients would like to be mobile and drive a car again after their stroke. Driving requires the individual to use selective attention to deal with the traffic, traffic signs and distractions, to be cognitively flexible to react to changing situations on the road, to visually scan the mirrors at the front, at the side, and in the back, to have a visual field that includes the sidewalks and to perform all of this while steering the car effectively in real-time [22]. Consequently, rehabilitation methods that address one specific cognitive ability only do not address the requirements of performing the activities of daily living and might not stimulate and train the underlying brain processes adequately. If a stroke leads to impairments in various cognitive domains, then these domains should be treated together to benefit a patient’s performance in everyday life.[…]

 

Continue —-> Adaptive conjunctive cognitive training (ACCT) in virtual reality for chronic stroke patients: a randomized controlled pilot trial | SpringerLink

 

Fig. 1

Fig. 1 Experimental protocol and set-up. a The protocol lasted 18 weeks in total, 6 weeks of training, and 3-months follow-up period. b The set-up of the EG in the hospital consisted of a desktop computer, a Microsoft Kinect and two wristbands with reflective markers that are worn by the patient. A Tobii EyeTracker T120 tracked the eye movement of the patient during the training. The Kinect detects the reflective markers and transposes the movement of the patient’s real arms onto the virtual arms of the avatar in the training scenarios. The patients are seated at a table, and the three training scenarios (c Complex Spheroids, d Star Constellations, and e Quality Controller) are shown on the screen always in the same order. Besides the automated adaptive difficulty mechanism and the embodied training, the system incorporates further principles of neurorehabilitation including the provision of multisensory feedback, feedback of results, variable and structured practice as well as promoting the use of the paretic limb. C Star Constellations, CG control group, D day, EG experimental group, Eval VR evaluation, Q Quality Controller, RGS Rehabilitation Gaming System, S Complex Spheroids

 

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[Infographic] Depression Isn’t Always Suicide Notes And Pill Bottles. It’s Also…

Η εικόνα ίσως περιέχει: ένα ή περισσότερα άτομα, πιθανό κείμενο που λέει "Depression Isn't Always Suicide Notes And Pill Bottles. It's Also... Spending all Faking a smile day in bed Overeating or not eating at all Cracking jokes or being the 00000000 Skipping work "class clown" 1000 to sleep 00000 Being 0000 Not showering emotionally for days at a time distant Social isolation Please check in on your friends, even the goofy ones they can hide it the best @RealDepressionProject"

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[NEWS] Brain-wave pattern can identify people likely to respond to antidepressant, study finds.

Researchers used electroencephalography and artificial intelligence to identify individuals who would likely respond to sertraline, the antidepressant marketed as Zoloft.

brain wave graphic

Researchers used electroencephalography and an algorithm to identify a brain-wave signature in individuals with depression who will most likely respond to a medication.
Andrea Danti/Shutterstock.comA new method of interpreting brain activity could potentially be used in clinics to help determine the best treatment options for depression, according to a study led by researchers at the Stanford School of Medicine.      

Stanford researchers and their collaborators used electroencephalography, a tool for monitoring electrical activity in the brain, and an algorithm to identify a brain-wave signature in individuals with depression who will most likely respond to sertraline, an antidepressant marketed as Zoloft.

paper describing the work was published today in Nature Biotechnology.

The study emerged from a decades-long effort funded by the National Institute of Mental Health to create biologically based approaches, such as blood tests and brain imaging, to help personalize the treatment of depression and other mental disorders. Currently, there are no such tests to objectively diagnose depression or guide its treatment.

“This study takes previous research showing that we can predict who benefits from an antidepressant and actually brings it to the point of practical utility,” said Amit Etkin, MD, PhD, professor of psychiatry and behavioral sciences at Stanford. “I will be surprised if this isn’t used by clinicians within the next five years.”

Instead of functional magnetic resonance imaging, an expensive technology often used in studies to image brain activity, the scientists turned to electroencephalography, or EEG, a much less costly technology.

Etkin shares senior authorship of the paper with Madhukar Trivedi, MD, professor of psychiatry at the University of Texas-Southwestern. Wei Wu, PhD, an instructor of psychiatry at Stanford, is the lead author.

The paper is one of several based on data from a federally funded depression study launched in 2011 — the largest randomized, placebo-controlled clinical trial on antidepressants ever conducted with brain imaging — which tested the use of sertraline in 309 medication-free patients. The multicenter trial was called Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care, or EMBARC. Led by Trivedi, it was designed to advance the goal of improving the trial-and-error method of treating depression that is still in use today.

“It often takes many steps for a patient with depression to get better,” Trivedi said. “We went into this thinking, ‘Wouldn’t it be better to identify at the beginning of treatment which treatments would be best for which patients?’”

Most common mental disorder

Major depression is the most common mental disorder in the United States, affecting about 7% of adults in 2017, according to the National Institute of Mental Health. Among those, about half never get diagnosed. For those who do, finding the right treatment can take years, Trivedi said. He pointed to one of his past studies that showed only about 30% of depressed patients saw any remission of symptoms after their first treatment with an antidepressant.

Amit Etkin

Amit Etkin

Current methods for diagnosing depression are simply too subjective and imprecise to guide clinicians in quickly identifying the right treatment, Etkin said. In addition to a variety of antidepressants, there are several other types of treatments for depression, including psychotherapy and brain stimulation, but figuring out which treatment will work for which patients is based on educated guessing. 

    

To diagnose depression, clinicians rely on a patient reporting at least 5 of 9 common symptoms of the disease. The list includes symptoms such as feelings of sadness or hopelessness, self-doubt, sleep disturbances — ranging from insomnia to sleeping too much — low energy, unexplained body aches, fatigue, and changes in appetite, ranging from overeating to undereating. Patients often vary in both the severity and types of symptoms they experience, Etkin said.

“As a psychiatrist, I know these patients differ a lot,” Etkin said. “But we put them all under the same umbrella, and we treat them all the same way.” Treating people with depression often begins with prescribing them an antidepressant. If one doesn’t work, a second antidepressant is prescribed. Each of these “trials” often takes at least eight weeks to assess whether the drug worked and symptoms are alleviated. If an antidepressant doesn’t work, other treatments, such as psychotherapy or occasionally transcranial magnetic stimulation, may also be tried. Often, multiple treatments are combined, Etkin said, but figuring out which combination works can take a while.

“People often feel a lot of dejection each time a treatment doesn’t work, creating more self-doubt for those whose primary symptom is most often self-doubt,” Trivedi said.

Looking for a biomarker

The EMBARC trial enrolled 309 people with depression who were randomized to receive either sertraline or a placebo.

For their study, Etkin and his colleagues set out to find a brain-wave pattern to help predict which depressed participants would respond to sertraline. First, the researchers collected EEG data on the participants before they received any drug treatment. The goal was to obtain a baseline measure of brain-wave patterns.

Next, using insights from neuroscience and bioengineering, the investigators analyzed the EEG using a novel artificial intelligence technique they developed and identified signatures in the data that predicted which participants would respond to treatment based on their individual EEG scans. The researchers found that this technique reliably predicted which of the patients did, in fact, respond to sertraline and which responded to placebo. The results were replicated at four different clinical sites.

Further research suggested that participants who were predicted to show little improvement with sertraline were more likely to respond to treatment involving transcranial magnetic stimulation, or TMS, in combination with psychotherapy.

“Using this method, we can characterize something about an individual person’s brain,” Etkin said. “It’s a method that can work across different types of EEG equipment, and thus more apt to reach the clinic.”

Etkin is on leave from Stanford, working as the founder and CEO of the startup Alto Neuroscience, a company based in Los Altos, California, that aims to build on these findings and develop a new generation of biologically based diagnostic tests to personalize mental health treatments with a high degree of clinical utility. “Part of getting these study results used in clinical care is, I think, that society has to demand it,” Trivedi said. “That is the way things get put into practice. I don’t see a downside to putting this into clinical use soon.”

Broad effort

When EMBARC was launched, it was part of a broader effort by the NIMH to push for improvements in mental health care by using advances in fields such as genetics, neuroscience and biotechnology, said Thomas Insel, MD, who served as director of that institute from 2002 to 2015.

“We went into EMBARC saying anything is possible,” Insel said. “Let’s see if we can come up with clinically actionable techniques.” He didn’t think it would take this long, but he remains optimistic.

“I think this study is a particularly interesting application of EMBARC,” he said. “It leverages the power of modern data science to predict at the individual level who is likely to respond to an antidepressant.”

In addition to improving care, the researchers said they see a possible side benefit to the use of biologically based approaches: It could reduce the stigma associated with depression and other mental health disorders that prevents many people from seeking appropriate medical care.

“I’d love to think scientific evidence will help to counteract this stigma, but it hasn’t so far,” said Insel. “It’s been over 160 years since Abraham Lincoln said that melancholy ‘is a misfortune, not a fault.’ We still have a long way to go before most people will understand that depression is not someone’s fault.” (President Lincoln suffered bouts of depression.)

Other Stanford co-authors of the paper are postdoctoral scholars Yu Zhang, PhD, and Jing Jiang, PhD; former postdoctoral scholar Gregory Fonzo, PhD; neuroscience graduate students Molly Lucas and Camarin Rolle; research assistants Carena Cornelssen and Kamron Sarhadi; clinical research coordinator Trevor Caudle; former clinical research coordinators Rachael Wright, Karen Monuszko and Hersh Trivedi; and former neuroscience graduate student Russell Toll. All Stanford authors, including Etkin, are affiliated with Veterans Affairs Palo Alto Healthcare System and the Sierra Pacific Mental Illness, Research, Education and Clinical Center in Palo Alto.

Etkin is a member of the Wu Tsai Neurosciences Institute at Stanford.

Researchers at South China University of Technology, the Netherlands Research Institute, Harvard Medical School, the New York State Psychiatric Institute, Columbia University and the Netherlands neuroCare Group also contributed to the work.

Insel is an investor in Alto Neuroscience.

The EMBARC study data are publicly available through the NIMH Data Archive.

The study was funded by the National Institutes of Health (U01MH092221, U01MH092250, R01MH103324, DP1 MH116506), the Stanford Neurosciences Institute, the Hersh Foundation, the National Key Research and Development Plan of China, and the National Natural Science Foundation of China.


Stanford Medicine integrates research, medical education and health care at its three institutions – Stanford University School of MedicineStanford Health Care (formerly Stanford Hospital & Clinics), and Lucile Packard Children’s Hospital Stanford. For more information, please visit the Office of Communication & Public Affairs site at http://mednews.stanford.edu.

via Brain-wave pattern can identify people likely to respond to antidepressant, study finds | News Center | Stanford Medicine

 

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[WEB PAGE] AI could play ‘critical’ role in identifying appropriate treatment for depression

Male doctor discussing reports with patient at desk in medical office

Image credits: Wavebreak Media Ltd – Dreamstime

Published Tuesday, February 11, 2020

A large-scale trial led by scientists at the University of Texas Southwestern (UT Southwestern) has produced a machine learning algorithm which accurately predicts the efficacy of an antidepressant, based on a patient’s neural activity.

The UT Southwestern researchers hope that this tool could eventually play a critical role in deciding which course of treatment would be best for patients with depression, as well as being part of a new generation of “biology-based, objective strategies” which make use of technologies such as AI to treat psychiatric disorders.

The US-wide trial was initiated in 2011 with the intention of better understanding mood disorders such as major depression and seasonal affective disorder (Sad). The trial has reaped many studies, the latest of which demonstrates that doctors could use computational tools to guide treatment choices for depression. The study was published in Nature Biotechnology.

“These studies have been a bigger success than anyone on our team could have imagined,” said Dr. Madhukar Trivedi, the UT Southwestern psychiatrist who oversaw the trial. “We provided abundant data to show we can move past the guessing game of choosing depression treatments and alter the mindset of how the disease should be diagnosed and treated.”

This 16-week trial involved more than 300 participants with depression, who either received a placebo or SSRI (selective serotonin reuptake inhibitor), the most common type of antidepressant. Despite the widespread prescription of SSRIs, they have been criticised for their side effects and for inefficacy in many patients.

Trivedi had previously established in another study that up to two-thirds of patients do not adequately respond to their first antidepressant, motivating him to find a way of identifying much earlier which treatment path is most likely to help the patient before they begin and potentially suffer further through ineffectual treatment.

Trivedi and his collaborators used an electroencephalogram (EEG) to measure electrical activity in the participants’ cortex before they began the treatment. This data was used to develop a machine learning algorithm to predict which patients would benefit from the medication within two months.

The researchers found that the AI accurately predicted outcomes, with patients less certain to respond to an antidepressant more likely to improve with other interventions, such as brain stimulation or therapeutic approaches. Their findings were replicated across three additional patient groups.

“It can be devastating for a patient when an antidepressant doesn’t work,” Trivedi said. “Our research is showing that they no longer have to endure the painful process of trial and error.”

Dr Amit Etkin, a Stanford University professor of psychiatry who also worked on the algorithm, added: “This study takes previous research, showing that we can predict who benefits from an antidepressant, and actually brings it to the point of practical utility.”

Next, they hope to develop an interface for the algorithm to be used alongside EEGs – and perhaps also with other means of measuring brain activity like functional magnetic resonance imaging (functional MRI, aka fMRI) or MEG – and have the system approved by the US Food and Drug Administration.

 

via AI could play ‘critical’ role in identifying appropriate treatment for depression | E&T Magazine

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[Slideshow] Myths and Facts About Depression

via Depression Myths: Overwork, Recklessness and More

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