Study shows the power of electroencephalography and machine learning to help predict response to psychotherapy (or lack thereof) in patients with PTSD

This study investigates whether individual patient-level resting-state EEG connectivity can predict psychotherapy outcomes in PTSD. We developed a treatment-predictive EEG signature using machine learning applied to high-density resting-state EEG collected from military veterans with PTSD. The predictive signature was dominated by theta frequency EEG connectivity differences and was able to generalize across two types of psychotherapy—prolonged exposure and cognitive processing therapy. Our results also advance a biological definition of a PTSD patient subgroup who is resistant to psychotherapy, which is currently the most evidence-based treatment for the condition. The findings support a path towards clinically translatable and scalable biomarkers that could be used to tailor interventions for each individual or drive the development of novel treatments. The Study in Context: Alto Neuroscience raises $60M (equity + credit) to help fix the “trial and error” approach to psychiatric medication Precision psychiatry pioneer Alto Neuroscience raises $35M to advance digital biomarker-to-treatment platform Machine-learning study finds EEG brain signatures that predict response to antidepressant treatments The post Study shows the power of electroencephalography and machine learning to help predict response to psychotherapy (or lack thereof) in patients with PTSD appeared first on SharpBrains.
Source: SharpBrains - Category: Neuroscience Authors: Tags: Brain/ Mental Health Technology & Innovation Alto Neuroscience cognitive processing therapy digital biomarker electroencephalography posttraumatic stress disorder precision psychiatry prolonged exposure Psychotherapy PTSD Source Type: blogs