9, 2019 -- 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 the structural
and functional magnetic resonance imaging (fMRI) technology along
with their advantages and disadvantages. He described how fMRI provides non-invasive
mapping of the brain’s neuroanatomy and neurophysiology in the assessment of
patients with chronic pain. Maladaptive neural
circuity develops as 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 extensive research of spinal
manipulation in healthy and clinical 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 the spinal cord combined with functional imaging
of the brain is now on the horizon.
Dr. Kettner said this corticospinal mapping will provide
a perspective of large neural network integration, allowing more
precise understanding of chronic pain and other associated disorders, such as
anxiety and depression, and their treatment outcomes.