quantifying-pain-location-intensity-with-multimodal-pain-body

Quantifying Pain Location and Intensity with Multimodal Pain Body Diagrams

To quantify an individual&#39;s subjective pain severity, standardized pain rating scales such as the numeric rating scale (NRS), visual analog scale ...

University of California, San Francisco

Early studies of deep brain stimulation (DBS) for various neurological disorders involved a temporary trial period where implanted electrodes were externalized, in which the electrical contacts exiting the patient's brain are connected to external stimulation equipment, so that stimulation efficacy could be determined before permanent implant. As the optimal brain target sites for various diseases (i.e., Parkinson's disease, essential tremor) became better established, such trial periods have fallen out of favor. However, deep brain stimulation trial periods are experiencing a modern resurgence for at least two reasons: (1) studies of newer indications such as depression or chronic pain aim to identify new targets and (2) a growing interest in adaptive DBS tools necessitates neurophysiological recordings, which are often done in the peri-surgical period. In this review, we consider the possible approaches, benefits, and risks of such inpatient trial periods with a specific focus on developing new DBS therapies for chronic pain.

A Deep Brain Stimulation Trial Period for Treating Chronic Pain.

Chronic pain syndromes are often refractory to treatment and cause substantial suffering and disability. Pain severity is often measured through subjective report, while objective biomarkers that may guide diagnosis and treatment are lacking. Also, which brain activity underlies chronic pain on clinically relevant timescales, or how this relates to acute pain, remains unclear. Here four individuals with refractory neuropathic pain were implanted with chronic intracranial electrodes in the anterior cingulate cortex and orbitofrontal cortex (OFC). Participants reported pain metrics coincident with ambulatory, direct neural recordings obtained multiple times daily over months. We successfully predicted intraindividual chronic pain severity scores from neural activity with high sensitivity using machine learning methods. Chronic pain decoding relied on sustained power changes from the OFC, which tended to differ from transient patterns of activity associated with acute, evoked pain states during a task. Thus, intracranial OFC signals can be used to predict spontaneous, chronic pain state in patients.

Ashlyn Schmitgen

Neurological Surgery and UCSF Weill Institute of Neurosciences

University of California, San Francisco

Quantifying Pain Location and Intensity with Multimodal Pain Body Diagrams

Ashlyn Schmitgen

.css-o250i3{font-size:18px;font-family:Open Sans,sans-serif;line-height:21.6px;}Neurological Surgery and UCSF Weill Institute of Neurosciences

University of California, San Francisco

Quantifying Pain Location and Intensity with Multimodal Pain Body Diagrams

Neurological Surgery and UCSF Weill Institute of Neurosciences