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<div about="/news-events/nih-research-matters/machine-learning-identifies-suicidal-youth" typeof="sioc:Item foaf:Document" class="ds-1col node node--nih-research-matters view-mode-full node--full node--nih-research-matters--full clearfix">
<p class="publishdate"><span property="dc:date" datatype="xsd:dateTime" content="2017-11-07T00:00:00-05:00" class="date-display-single">November 7, 2017</span></p><div class='syndicate'><h1>Machine learning identifies suicidal youth</h1><div class="callout callout-with-border"><h3>At a Glance</h3>
<ul>
<li>Researchers used machine learning to analyze brain images and identify people with suicidal thoughts.</li>
<li>If validated in larger groups of people, the approach could be used to assess suicide risk and monitor response to treatments.</li>
</ul></div><div class="media-float-right"><div class="ds-1col file file-image file-image-jpeg view-mode-floated_media clearfix">
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<source srcset="https://www.nih.gov/sites/default/files/styles/floated_media_breakpoint-tiny/public/news-events/research-matters/2017/20171107-death-study.jpg?itok=ovOlfkNV&amp;timestamp=1510061089 1x" media="screen and (min-width: 0)" />
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<img src="https://www.nih.gov/sites/default/files/styles/floated_media_breakpoint-medium/public/news-events/research-matters/2017/20171107-death-study.jpg?itok=0Epbvm1W&amp;timestamp=1510061089" alt="Sad woman" title="Sad woman" />
</picture><span class="caption">Researchers have been studying ways to tell who is at risk for suicide in the hope of preventing suicides. </span><span class="credit">Tharakorn/iStock/Thinkstock</span></div>
</div><p>Suicide is a major public health concern. Over 40,000 people nationwide die by suicide each year. Suicide was the second leading cause of death in 2015 for young adults aged 18 to 26. Knowing who is at risk for suicide may help reduce the suicide rate.</p>
<p>Scientists have been testing several approaches to try to predict suicide risk. A research team led by Dr. Marcel Just at Carnegie Mellon University and Dr. David Brent at the University of Pittsburgh used fMRI to look for signatures of brain activity among young adults with suicidal thoughts. They focused on the networks of activity that represent concepts and the emotions they evoke. Their study was funded by NIHs National Institute of Mental Health (NIMH). It appeared online in <em>Nature Human Behaviour </em>on October 30, 2017.</p>
<p>The team recruited 38 young adults with current suicidal thoughts and 41 healthy controls with no history of a psychiatric disorder or suicide attempt. While in an fMRI scanner, the participants were shown 30 words for 3 seconds each related to suicide (e.g., “death,” funeral,” hopeless”), positive ideas (“bliss,” “carefree,” “comfort”), and negative ideas (“boredom, “gloom,” “worried”).</p>
<p>The scientists used data from 33 participants to train a machine-learning system to spot differences in networks of brain activity. They then tested it on brain images from 34 participants. The system correctly identified 15 of 17 suicidal people and 16 of 17 controls—an accuracy of 91%. The strongest differences between the groups, in order, were for the words “death”, “carefree,” “good,” “cruelty,” “praise,” and “trouble.” Within the group of 17 people with suicidal thoughts, the system was also able to distinguish the 9 who had previously made a suicide attempt from the 8 who hadnt with an accuracy of 94% (16 out of 17). Discriminating regions were spread across several areas of the brain.</p>
<p>The researchers also tested the approach on brain images from 21 people with suicidal thoughts who had lower quality scan data. The system could still distinguish these suicidal people from control participants with 87% accuracy.</p>
<div class="media-float-right"><div class="media media-element-container media-floated_media"><div class="ds-1col file file-image file-image-jpeg view-mode-floated_media clearfix">
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<source srcset="https://www.nih.gov/sites/default/files/styles/floated_media_breakpoint-tiny/public/news-events/research-matters/2017/20171107-brain-suicide.jpg?itok=fyHghhai&amp;timestamp=1510068180 1x" media="screen and (min-width: 0)"></source>
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<img class="media-element file-floated-media" src="https://www.nih.gov/sites/default/files/styles/floated_media_breakpoint-medium/public/news-events/research-matters/2017/20171107-brain-suicide.jpg?itok=4Z83l3Up&amp;timestamp=1510068180" alt="Brain scans show different patterns between people who had attempted suicide and control participants" title="rain scans of suicide attempter and control participant" />
</picture><span class="caption">Brain activation pattern for “death” in participants who had made a suicide attempt, left, compared to control participants, right. </span><span class="credit">Carnegie Mellon University</span></div>
</div></div>
<p>Previous studies have identified signatures of brain activity with different emotions. The researchers found that the neural signatures of the emotions sadness, shame, anger, and pride differed between people with suicidal thoughts and healthy controls for the six discriminating words. Among the 34 participants, a machine-learning system could distinguish between the groups based on emotional signatures with an accuracy of 85%.</p>
<p>“People with suicidal thoughts experience different emotions when they think about some of the test concepts,” Just explains. “For example, the concept of death evoked more shame and more sadness in the group that thought about suicide. This extra bit of understanding may suggest an avenue to treatment that attempts to change the emotional response to certain concepts.”</p>
<p>“Further testing of this approach in a larger sample will determine its generalizability and its ability to predict future suicidal behavior, and could give clinicians in the future a way to identify, monitor, and perhaps intervene with the altered and often distorted thinking that so often characterizes seriously suicidal individuals,” Brent says.</p>
<p>The researchers are also investigating other methods that correlate with the fMRI signatures but may be easier to implement in clinical settings.</p>
<p>—by Harrison Wein, Ph.D.</p></div><h2 class="label-above">Related Links</h2><ul class="spaced print-urls"><li><a href="https://www.nih.gov/news-events/nih-research-matters/biomarkers-questionnaires-predict-suicide-risk" target="_top">Biomarkers and Questionnaires Predict Suicide Risk</a></li><li><a href="https://www.nih.gov/news-events/nih-research-matters/blood-markers-may-signal-suicide-risk" target="_top">Blood Markers May Signal Suicide Risk</a></li><li><a href="https://www.nih.gov/news-events/nih-research-matters/mechanism-fast-acting-antidepressant-revealed" target="_top">Mechanism of Fast-Acting Antidepressant Revealed</a></li><li><a href="https://newsinhealth.nih.gov/2012/05/sorrow-suicide" target="_top">The Sorrow of Suicide</a></li><li><a href="https://www.nimh.nih.gov/health/topics/suicide-prevention/index.shtml" target="_top">Suicide Prevention</a></li></ul><p class="footnote"><strong class="label-inline">References:&nbsp;</strong><a href="https://www.nature.com/articles/s41562-017-0234-y">Machine learning of neural representations of suicide and emotion concepts identifies suicidal youth</a>. Marcel Adam Just, Lisa Pan, Vladimir L. Cherkassky, Dana L. McMakin, Christine Cha, Matthew K. Nock &amp; David Brent. <em>Nature Human Behaviour</em>. October 30, 2017. doi:10.1038/s41562-017-0234-y.</p><p class="footnote"><strong class="label-inline">Funding:&nbsp;</strong>NIHs National Institute of Mental Health (NIMH).</p>
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