Machine learning study finds standardized brain scan biomarker to detect depression with 66% accuracy

New Study Brings Biomarkers For Depression Closer To The Clinic (Forbes): Scientists have been studying biological signs of depression in the brain, looking for markers that could be used to identify the disorder. A team of scientists recently developed a technique using machine learning that can identify whether a given patient’s brain scan shows one of depression’s neural signatures… Clinicians currently diagnose major depressive disorder, commonly called depression, based on a patient’s reported symptoms, like changes in mood, activity enjoyment, appetite or sleeping, for example. But people with depression can show a wide range of different symptoms, which means that depression looks different in each person … Grouping people with wide-ranging symptoms under one category also masks the fact that different processes in the brain might underlie depression in different individuals. For these reasons, neuroscientists have been looking for neural signatures or biomarkers of depression… Using the brain network biomarker, their algorithm could correctly identify which participants had depression 66% of the time. While 66% accuracy may not sound high, it is an improvement on current accuracy levels of diagnosis by human clinicians, particularly general physicians who aren’t trained in psychiatry. The Study: Generalizable brain network markers of major depressive disorder across multiple imaging sites (PLOS Biology): Abstract: Many studies have highlighted the diffi...
Source: SharpBrains - Category: Neuroscience Authors: Tags: Brain/ Mental Health Technology & Innovation brain markers brain-scan clinical applicability depression EEG machine-learning neural signature neural signatures neuroscience Source Type: blogs