Kenneth Weber II, DC, PhD, an instructor in the Department of Anesthesiology, Perioperative and Pain Medicine at Stanford University, recently spoke as part of the Logan Department of Radiology’s Chiropractic Grand Rounds.
Dr. Weber, who earned his clinical training as a chiropractor at Palmer College of Chiropractic Florida and then completed a PhD in neuroscience at Northwestern University, currently researches different neuroscience, machine-learning and clinical research techniques to better understand, treat, and prevent musculoskeletal and neurological conditions, including spinal pain.
On August 2, he addressed the topic of advancing chiropractic with advanced magnetic resonance imaging to students, faculty and staff, opening with a general description of thestructural and functional magnetic resonance imaging (fMRI)technology along withtheir advantages and disadvantages. He described how fMRI provides non-invasive mapping of the brain’s neuroanatomy andneurophysiology in the assessment of patients with chronic pain.Maladaptive neural circuitydevelopsas an adaptive response to the persistent nociception.This adaptation to central sensitization utilizes cortical and subcortical neuroplasticity, and these patterns of brain neural activity are mapped with fMRI technology. Dr. Weber discussed his research in brain-based models of clinical pain states, and has incorporated an artificial intelligence method known as machine learning to enhance models of bran responses to pain.
He also explained his extensiveresearch of spinal manipulationin healthy andclinical pain disorders, including a new development in his research: spinal cord fMRI. This technique, which Dr. Kettner said has been long hampered by technical challenges, is advancing and may provide a biomarker of spinal cord injury and disorders. In addition, simultaneous fMRI of thespinal cordcombined with functional imaging of the brainis now on the horizon.
Dr. Kettner said this corticospinal mapping will provide a perspective oflarge neuralnetworkintegration, allowing more precise understanding of chronic pain and other associated disorders, such as anxiety and depression, and their treatment outcomes.