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The advent of new imaging methods and data modeling techniques has revolutionized our capacity to understand and
utilize brain signals. With these techniques, the dynamic interactions between the complex elements of the brain can be
used to effectively decode the neurological substrates of pain perception and modulation. The task to discover how intricately
connected brain networks process and modulate pain is an important scientific challenge that can leeway into novel clinical tools
and improved pain therapeutics. This talk will present evidence that brain network properties can be used to predict the time
course of pain experiences. We have reported that synchronizations in specific brain networks in particular BOLD frequency
bands forecast outcomes of placebo treatment in chronic back pain patients that were tested in clinical trial conditions. More
recently we have discovered that computationally characterized whole brain network alignments predict subsequent induced
placebo analgesia in a clinical population of knee pain patients. These findings indicate a role of network configurations in
facilitating adaptive pain modulation.
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