Author Topic: The Neuroscience of Violence (and Prediction of)  (Read 30 times)

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The Neuroscience of Violence (and Prediction of)
« on: July 04, 2019, 05:06:24 AM »
These risk factors are poverty, family violence, exposure to media violence, availability of weapons, drug abuse, and membership in gangs (according to https://quizlet.com/80227268/health-ch-6-preventing-violence-lesson-1-2-what-is-violence-violence-in-schools-flash-cards/)

source: http://www.brainfacts.org/Thinking-Sensing-and-Behaving/Emotions-Stress-and-Anxiety/2016/The-Neuroscience-of-Violence

The Neuroscience of Violence

29 Apr 2016

We are on the brink of a new understanding of the neuroscience of violence. Like detectives slipping a fiber optic camera under a door, neuroscientists insert a fiber optic microcamera into the brain of an experimental animal and watch the neural circuits of rage respond during violent behavior.

Neurons genetically modified to flash bursts of light when they fire reveal where these circuits of rage are in the brain, and neuroscientists can stimulate or squelch the firing of a neuron they target by laser beam.

With the flip of a switch neuroscientists can launch an animal into a violent attack or arrest a violent battle underway by activating or quelling the firing of specific neurons in the brain’s rage circuits.

Technological advances in monitoring brainwaves and brain imaging are bringing new insight into this same circuitry at work in the human brain. These circuits of aggression are part of the brain’s threat detection mechanism embedded deep in the unconscious region of the brain where sex, thirst, and feeding are also controlled.

Violence, like all human behavior, is controlled by the brain. From the everyday road rage, to domestic violence, to a suicide bombing, the biology of anger and aggression is the root cause of most violent behavior.

Violence can activate some of the same circuits of addiction in individuals, especially males, who seek out violence.

A new study published in the March 7, 2016 issue of Nature Neuroscience, by Annegret Falkner and colleagues, identifies specific neurons in the hypothalamic attack region that are activated when male mice seek out violent aggressive encounters with other male mice.

The social implications of this new line of research are profound.  Struggling to comprehend a suicide bomber’s “thinking” or police searching for “motives” in cases where violence is driven by perceptions of threat, alienation or emotion is a search in vain.

Such violence is not driven by reason. It is driven by rage.  Violence at political rallies, terrorism, and horrifying workplace shootings bewilder us, but neuroscience research offers a new perspective on violence.

Viewing violence narrowly from the perspective of psychological dysfunction shirks the larger truth that the biological roots of rage exist in all of us.

The leading risk of death throughout the prime of life is not disease. It is violence. If you survive into old age you will most likely die from disease, but according to CDC statistics for deaths in the United States for the year 2014, life ends at the hand of another human so frequently, that from early childhood through middle-age, homicide is the third to 5th most common cause of death in all age brackets between 1-44 years.



A psychopath or a foreign terrorist is not the likely villain. The data show that the murderer is twice as likely to be your friend or acquaintance as it is to be a stranger. Deadly violence against oneself (suicide) is second only to accidental injury as the most frequent way we die between the ages of 10 and 34.

The most important factor in violence is not pathology, psychology, or politics-- it is biology. Nine out of ten people in prison for violent crime are men.

Males die from homicide at three times the rate of women. When the victim is a spouse or intimate partner women are murdered at 3.3 times the rate of men.

Males commit suicide at four times the rate of females. Violence and maleness is a biological fact that runs through the vast diversity of cultures and through our ancestral tree to other primates.

We have neural circuits of rage and violence because we need them. As a species we needed deadly violence to obtain food, to protect ourselves, our family, our group, and unfortunately we still need them today. Order in society is maintained through violence, meted out methodically by police and nations according to laws that benefit society at large, but this organized violence is founded on the same neurocircuitry of aggression wired into the human brain of every individual.

Most of the time the neural circuits of aggression are life-saving, as when a mother instantly reacts aggressively to protect her child in danger, but sometimes they misfire and violence explodes inappropriately, as in a road rage shooting.

The pressures of modern life constantly press on these triggers of rage. International communication and high-speed transportation increase opportunities for conflict between different groups of people.

Weapons of violence amplify the lethal effects of one enraged mind well beyond the power of any individual to combat with bare hands.

Add to this the toxic effects of psychoactive drugs for treating mental illnesses and drugs of abuse, compounded by the increasing stress, crowding, and sensory bombardment of the modern world, and we see the human brain struggling to cope with an environment it was never designed to confront.

The CDC statistics strongly suggest that in addition to understanding the biological basis of disease, there is a much greater unmet need for neuroscience research to understand the biological underpinnings of violent behavior.

--- --- ---

if you are interested in Machine Learning Approach to Inpatient Violence Risk Assessment Using Routinely Collected Clinical Notes in Electronic Health Records then go to https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2737104 (some excerpts follow)

Introduction

Violence in psychiatric inpatient wards remains a significant problem. A study combining data from 35 sites worldwide shows 14% to 20% of patients commit at least 1 act of violence during inpatient treatment, and surveys consistently show most practitioners being affected by violence at some point during their career.

Adverse effects on both patients’ and caregivers’ well-being, such as injury, low morale, and high absentee levels, are well known.

As an important part of managing inpatient violence, structured violence risk assessment (VRA) instruments have been proposed on the basis of a combination of static and dynamic risk factors.

Their predictive validity surpasses that of unstructured clinical judgment, and a reasonable adoption in practice has been achieved, with more than half of all risk assessments performed using an instrument.

However, meta-analyses reveal that only a small subset of risk factors for violent behavior generalize to different populations,and VRA instruments are consequently limited by the robustness of the individual factors that compose them.

In addition, the time needed to perform a structured assessment, ranging from minutes to hours, has been identified as an obstacle for daily practice. Although adopting a VRA instrument diminished the number of violent incidents in 1 randomized clinical trial, other research suggests that its benefits in practice are still moderate because of its limitations.

Developing a prognostic model based on textual data registered in patients’ electronic health records (EHRs) might offer a novel approach to improve VRA.

The fact that these data are unstructured and originally designated for treatment presents methodologic challenges but also opportunities in combating selection bias and exploring new associations.

Machine learning, a term that refers to a set of statistical techniques that learn from large and potentially noisy data sets, is eminently well suited for this kind of task.

Conclusions

In the near future, we envision that further advancements toward a data-driven (EDIT: only just a psychiatric practice ?) will be made and that EHR data will become an even more valuable asset in supporting important decisions in the clinical process.

Machine learning approaches have been able to contribute substantially in other fields of medicine, and our study provides evidence that such progress is possible in mental health care as well.

Although some crucial challenges need to be addressed before adoption is possible, this study highlights the potential value of EHR data, and clinical notes in particular, for decision support.

Such support systems may in the future be widely applied in daily practice, contributing to more effective and efficient psychiatric treatment.

see the source link for more on this study ...

seee https://omh.ny.gov/omhweb/sv/risk.htm for Violence Prevention: Risk Factors

There are known risk factors associated with potential violence toward self and others. It is important to keep in mind that none of these risk factors alone is sufficient for predicting violence, and it may be inappropriate or potentially harmful to use them simply as a checklist for an individual youth. This list should not be used to stereotype or stigmatize individual youths because they appear to fit a set of risk factors.

School risk factors

Previously brought a weapon at school
Aggressiveness in grades K-3, social isolation or hyperactivity
Truancy, getting into fights or misbehaving in class
Serious disciplinary problems
Past suspension or expulsion for aggressive behavior
Anger or frustration present in school essays or artwork
Academic failure beginning in grade school (experience of failure escalates risk rather than ability)

Personal risk factors

History of tantrums or uncontrollable angry outbursts
Past violent behavior
Characteristically resorts to name calling or cursing
Bullying of peers or younger youths
History of being bullied
A pattern of violent threats when angry
Cruelty to animals
Fire-setting
Use and abuse of alcohol or drugs
Past suicide attempts
Often depressed or has significant mood swings
Tends to blame others for personal problems
Recent experience of humiliation, loss, or rejection
Preoccupation with weapons or explosives
Poor peer relations, is on the fringe of peer group with few or no close friends
Involvement with cults or gangs
Unstructured time

Community and environmental risk factors

Extreme economic deprivation
Low neighborhood attachment and community disorganization
Access to guns or other weapons
Past destruction of property or vandalism
Few organized activities in community for youths

Family risk factors

History of family violence
History of weapon possession or use by family
Abuse of alcohol or drugs by family members
Family conflict
Youth has history of being abused
Severe or inconsistent punishment
Absence of clear expectations or standards for behavior
Lack of supervision or support from parents or caring adults

« Last Edit: July 04, 2019, 05:25:28 AM by Chip »
Over 90% of all computer problems can be traced back to the interface between the keyboard and the chair !

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