This is normal. Heart pounding, hands shaking, head packed with static. The absolute inability to process what anyone is saying, let alone respond to it. Sitting alone at home — lights off because you've been inside all day and the sun set hours ag… Engadget RSS Feed
Living in a modern age, one would think that suicide would be a less common occurrence. Sadly, that isn’t the case, and the World Health Organization (WHO) reports that worldwide suicide rates have increased by 60 percent in the last 45 years. Current statistics show that some one million people die from suicide each year, and the WHO anticipates that by 2020 global suicide rate will have increased from one every 40 seconds we see today to one every 20 seconds. That’s incredibly alarming.
That’s why a team of researchers from several institutions including Carnegie Mellon University and Harvard University developed a machine learning algorithm trained to understand neural representations of suicidal behavior, and it works with a regular functional magnetic resonance imaging (fMRI).
The researchers tested their technique in 17 patients with suicidal ideation and in 17 more that served as control. They looked for these suicidal brain patterns by watching how the patients’ brains reacted when they were presented with six keywords: death, cruelty, trouble, carefree, good, and praise.The algorithm was able to accurately identify 15 out of the 17 patients with suicidal ideation, and 16 out of the 17 control, using just the MRI scans of their brains, for an overall accuracy rate of 91 percent. The results of their study has been published in the journal Nature Human Behavior, while MedPage Today publishing a video that discusses these findings.
AI, Machine Learning, and Mental Health
At present, the best way to anticipate suicidal behavior is to directly ask a person if he’s ever thought about it. However, that’s not entirely accurate, as studies have shown that almost 80 percent of people who committed suicide denied having had suicidal tendencies during their last appointment with a mental health professional. This new algorithm can help address this issue.
It isn’t the first to use artificial intelligence (AI) to identify suicidal persons—for example, there’s Facebook’s AI and one that uses verbal and non-verbal language to spot suicidal behavior. Yet, this new algorithm offers a unique vantage point. It proves that there are differences in the brains of persons with suicidal ideation compared to those without, and these differences can be spotted with this machine learning and MRI combo. It’s not without limitations, however.
One problem with this technique is it requires the use of an MRI, which’ll be difficult to implement within the confines of a regular therapist’s office “It would be nice to see if we could possibly do this using EEG, if we could assess the thought alterations with EEG. It would be enormously cheaper. More widely used,” lead researcher Dr. Marcel Just from Carnegie Mellon told Yale University’s Francis Perry Wilson in the MedPage Today video.
Just also identified an even more crucial limitation. “If somebody didn’t want others to know what they are thinking, they can certainly block that method. They can not cooperate,” he explained. “I don’t think we have a way to get at people’s thoughts against their will.”
Still, for a mental health issue that’s as critical as suicide, machine learning might just provide a much needed help that could save the lives of more people.
Although current treatments for depression mostly focus on brain chemicals such as serotonin, scientists now think inflammation throughout the entire body (triggered by an overactive immune system) may be the root cause of the problem. Widespread inflammation, they posit, could be producing feelings of unhappiness, hopelessness, and fatigue. If so, depression may be treatable with anti-inflammatory drugs. It may also be a symptom: much like the low spirits experienced by many people when they are ill and their immune system is busy fighting infection or viruses. In the case of chronic depression, the immune system may be failing to “switch off” after an illness or trauma, leading to persistent symptoms.
A growing body of research, including scientific papers and results from clinical trials, appears to be revealing a connection between treating inflammation and alleviating depression. In late July 2017, Stanford researchers revealed that they could create a diagnostic laboratory test for myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), along with what may be a world-first treatment. This work confirmed and built on prior work connecting ME/CFS — a disease which is often associated with depression — and inflammation.
In October 2016, a major review of research on the next generation anti-inflammatory drugs (most often used to treat autoimmune disorders) revealed a definitive link between inflammation and depression. This link could present a promising new avenue of treatment. The work showed that about one-third of people with depression have higher levels of cytokines, proteins that control the way the immune system reacts. This could indicate inflammation in their brains. It also revealed that people with “overactive” immune systems are more likely to develop depression.
University of Cambridge Head of the Department of Psychiatry Professor Ed Bullmore told The Telegraph that he thinks a new field of “immuno-neurology” is coming soon. “It’s pretty clear that inflammation can cause depression,” Bullmore said at a London briefing connected to the recent Academy of Medical Sciences FORUM annual lecture. “In relation to mood, beyond reasonable doubt, there is a very robust association between inflammation and depressive symptoms. The question is does the inflammation drive the depression or vice versa or is it just a coincidence? In experimental medicine studies if you treat a healthy individual with an inflammatory drug, like interferon, a substantial percentage of those people will become depressed. So we think there is good enough evidence for a causal effect.”
One important result that could arise from this work would be more effective treatments for depression; treatments that may not need to be lifelong. Another major implication is that, should this knowledge shape the norm for understanding and treating depression, the artificial dichotomy between body and mind could be forever altered. Socially, viewing depression as a condition with a definite physical cause could also help reduce the stigma around mental illness that often prevents many from getting treatment.
Protecting undocumented immigrant mothers from deportation can dramatically improve the mental health of their US citizen children, according to a new study. The research shows that policies targeting undocumented immigrants affect more than the single individual, and carry important implications in the long-term for society as a whole.
The study, published today in Science, focuses on an immigration policy issued by the Obama administration in 2012 called Deferred Action for Childhood Arrivals, or DACA. This program temporarily protected over 780,000 undocumented immigrants from deportation. Researchers analyzed Medicaid claims data from Oregon and found that in that state, the incidence of mental illness dropped by half among children…
More than one-third of 15-year-old children in the UK could be classified as ‘extreme internet users’, or those who are online for more than six hours daily outside of school. A report from UK think-tank Education Policy Institute (EPI) states that children in the UK have a higher rate of extreme usage (37.8 percent of all UK 15 year olds) than other countries. Only Chile reported more. The think-tank examined the relation between social media use (including online time) and mental illness: While twelve percent of children who spend no time on social networking websites on a normal school day…