Supported by the NIMH, they are now working to establish reliable neurocognitive markers of suicidal ideation and attempt. They will examine the differences in brain activation patterns between suicidal and non-suicidal young adults as they think about words related to suicide — such as positive and negative concepts — and use machine learning to identify neural signatures of suicidal ideation and behavior.
According to Just, “We were previously able to obtain consistent neural signatures to determine whether someone was thinking about objects like a banana or a hammer by examining their fMRI brain activation patterns. But now we are able to tell whether someone is thinking about ‘trouble’ or ‘death’ in an unusual way. The alterations in the signatures of these concepts are the ‘neurocognitive thought markers’ that our machine learning program looks for.”