Can a Computer Algorithm Identify Suicidal People from Brain Scans? The Answer Won ' t Surprise You

Just et al. 2017Death by suicide is a preventable tragedy if the suicidal individual is identified and receives appropriate treatment. Unfortunately, some suicidal individuals do not signal their intent, and others do not receive essential assistance. Youths with severe suicidal ideation are not taken seriously in many cases, and thus are not admitted to emergency rooms. A common scenario is that resources are scarce, the ER is backed up, and a cursory clinical assessment will determine who is admitted and who will be triaged. From a practical standpoint, using fMRI to determine suicide risk is a non-starter.Yet here we are, with media coverage blaring that anAlgorithm can identify suicidal people using brain scans andBrain Patterns May Predict People At Risk Of Suicide. These media pieces herald a new study claiming that fMRI can predict suicidal ideation with 91% accuracy (Just et al. 2017). The authors applied a complex algorithm (machine learning) to analyze brain scans obtained using a highly specialized protocol to examine semantic and emotional responses to life and death concepts.Let me unpack that a bit. The scans of 17 young adults with suicidal ideation (thoughts about suicide) were compared to those from another 17 participants without suicidal ideation. A computer algorithm (Gaussian Naive Bayes) was trained on the neural responses to death-related and suicide-related words, and correctly classified 15 out of 17 suicidal ideators (88% sensitivity) and 16 out of 1...
Source: The Neurocritic - Category: Neuroscience Authors: Source Type: blogs