December 13-15, 2018

MedTech Impact 2018

Venetian/Palazzo Resort

Las Vegas, NV

(561) 893-8633

info@medtechimpact.com

Tag Archives: depression

Applying Artificial Intelligence to Suicide Prevention

This week, a group of researchers published a new study that demonstrates how a novel brain imaging technique can identify people who have suicidal thoughts, simply by presenting them with certain key words, asking them to reflect on their meanings, and using machine learning to analyze that brain activity.

The results of the study, published in the journal Nature Human Behaviour, challenge the common stereotype that suicidal people could change their perspective if they exerted more effort; the data suggests that suicidal feelings and thoughts are deeply intertwined with the way the brain processes information.

“Suicidality isn’t that you can’t cope with life; it’s that you’ve somehow gotten into a pattern of thinking that leads you to consider suicide,” states Marcel Just, a cognitive neuroscientist and the study’s lead author, and a professor of psychology at Carnegie Mellon University.

Just and his co-authors studied 34 young adults, half of whom had a history of suicidal thinking or past attempts, and half of whom did not. The participants were placed in a functional magnetic resonance imaging machine (fMRI), which measures brain activity by monitoring blood flow. The researchers then showed each person 30 words related to suicide and positive and negative feelings, including “death,” “desperate,” “carefree,” “kindness,” “trouble,” and “worried.”

To analyze the results, the researchers used machine learning to characterize people’s brain activity patterns: 91 percent of the time, it correctly determined which participant had a history of suicidal thoughts, and which did not. It also successfully identified which individuals had previously attempted suicide.

The analysis yielded critical information about which concepts led to the clearest distinctions between the groups. The brains of participants with suicidal thoughts and behavior had vastly different responses to the words “death,” “cruelty,” “trouble,” “carefree,” “good,” and “praise,” and most of those participants demonstrated high levels of self-reported depression that included a negative view of the self, world, and the future. “Our research shows that suicidal ideation is exactly the way you think about things,” Just says. “Something changed the way your brain and mind work.”

Though the study is small, it demonstrates the promise of fMRI used in tandem with machine learning, a novel approach that resolves some of the challenges of relying on imaging to make conclusions about brain activity. Machine learning makes it possible to observe statistically significant differences between patients and a control group, which has been difficult in the past.

Just asserts that if the technique remains successful in larger studies, it could become an important tool in helping doctors assess suicide risk and develop targeted treatments. If a psychologist, for example, had better information surrounding which concepts were altered in a suicidal patient, he/she could potentially tailor talk therapy or medication to positively change that person’s way of thinking.

The study’s results also raise complex questions about new technology that helps reveal what processes are occurring in our brains as we think. In a dystopian future, one could imagine the tool being used as a way to exclude people with suicidal thoughts or behavior from certain professional and private roles, including military service, political office, or even parenthood.

Just says the technology requires immense focus and participation from the subject, so it could not be forced on people — yet. How people decide to subject their thoughts to examination and whether that information is shared publicly will eventually become the “ultimate privacy question,” adds Just. In the meantime, he is hopeful that the technology, if proven successful, will give patients and their doctors meaningful ways to assess and prevent suicide risk. Just is optimistic in the human ability to influence and shape the brain with the right tools. “There’s no question that our brains are malleable,” he says. “They are the most powerful tool that mother nature gives us.”

Physical Activity & Psychological Health

While research has long confirmed the strong correlation between exercise and psychological health, a recent study utilizing cellphone data to track activities and moods has confirmed that people who move are overall more content than people who sit.

While previous epidemiological studies have found that people who are active are less prone to depression and anxiety than sedentary people, the majority of these studies solely focused on negative moods. They generally relied on people recalling how they had felt, in addition to how much they had moved or sat in the previous weeks—with little concrete, tangible data to support their recollections.

The new study used a different approach, focusing on correlations between movement and the most positive emotion: happiness. The researchers also looked at what people reported about their respective activities, comparing it with objective measures of movement.

In doing this, the team first developed a special app for Android phones: advertised as helping people understand the ways in which lifestyles choices—like physical activity—might affect moods. As the app sent random requests throughout the day, during which people were asked to enter estimations of their current moods in addition to an assessment regarding their satisfaction with life in general, they also answered additional questions about whether they had been sitting, standing, walking, running, lying down, etc.

The app also asked about the users’ moods at that moment, simultaneously gathering data from the activity monitor built into almost every smartphone available today. Essentially, it checked whether someone’s recall of his/her movement tallied with the numbers from the activity monitor. Overall, the information provided by users and the activity monitors’ data was almost exactly the same.

People using the app also reported greater levels of happiness when they had been moving in the past quarter-hour, rather than when they had been sedentary—although often, they were not engaging in rigorous, strenuous activity. Researchers also found that people who moved more frequently tended to convey greater life satisfaction than those who spent most time in a chair.

The results suggest that people who are generally more active are generally happier, and in the moments during which they are active, they are also happier. While the study does not establish causation, the findings incontrovertibly indicate that if you get up and move often, you are more likely to feel cheerful than if you do not.