Sean Mackey
Stanford University · Rheumatology
Active 1962–2024
About
Sean Mackey is the Redlich Professor and a faculty member at Stanford University, serving as a Professor of Anesthesiology, Perioperative, and Pain Medicine, and by courtesy, of Neurology at the Stanford University Medical Center. His work is associated with the Center for Artificial Intelligence in Medicine & Imaging (AIMI), where he contributes to advancing research in artificial intelligence applications in medicine and imaging. His professional focus includes pain medicine and anesthesiology, with an emphasis on integrating AI technologies to improve healthcare outcomes.
Research topics
- Medicine
- Computer Science
- Pathology
- Physical therapy
- Data Mining
- Physical medicine and rehabilitation
- Statistics
- Nuclear medicine
- Pharmacology
- Neuroscience
- Intensive care medicine
- Psychiatry
- Psychology
- Radiology
- Mathematics
- Surgery
- Medical physics
Selected publications
Generic acquisition protocol for quantitative MRI of the spinal cord
Nature Protocols · 2021 · 148 citations
- Computer Science
- Computer Science
- Medicine
Quantitative spinal cord (SC) magnetic resonance imaging (MRI) presents many challenges, including a lack of standardized imaging protocols. Here we present a prospectively harmonized quantitative MRI protocol, which we refer to as the spine generic protocol, for users of 3T MRI systems from the three main manufacturers: GE, Philips and Siemens. The protocol provides guidance for assessing SC macrostructural and microstructural integrity: T1-weighted and T2-weighted imaging for SC cross-sectional area computation, multi-echo gradient echo for gray matter cross-sectional area, and magnetization transfer and diffusion weighted imaging for assessing white matter microstructure. In a companion paper from the same authors, the spine generic protocol was used to acquire data across 42 centers in 260 healthy subjects. The key details of the spine generic protocol are also available in an open-access document that can be found at https://github.com/spine-generic/protocols . The protocol will serve as a starting point for researchers and clinicians implementing new SC imaging initiatives so that, in the future, inclusion of the SC in neuroimaging protocols will be more common. The protocol could be implemented by any trained MR technician or by a researcher/clinician familiar with MRI acquisition.
Scientific Data · 2021 · 76 citations
- Computer Science
- Computer Science
- Data Mining
In a companion paper by Cohen-Adad et al. we introduce the spine generic quantitative MRI protocol that provides valuable metrics for assessing spinal cord macrostructural and microstructural integrity. This protocol was used to acquire a single subject dataset across 19 centers and a multi-subject dataset across 42 centers (for a total of 260 participants), spanning the three main MRI manufacturers: GE, Philips and Siemens. Both datasets are publicly available via git-annex. Data were analysed using the Spinal Cord Toolbox to produce normative values as well as inter/intra-site and inter/intra-manufacturer statistics. Reproducibility for the spine generic protocol was high across sites and manufacturers, with an average inter-site coefficient of variation of less than 5% for all the metrics. Full documentation and results can be found at https://spine-generic.rtfd.io/ . The datasets and analysis pipeline will help pave the way towards accessible and reproducible quantitative MRI in the spinal cord.
Brain circuits for pain and its treatment
Science Translational Medicine · 2021 · 215 citations
- Neuroscience
- Psychology
- Medicine
Pain is a multidimensional experience with sensory-discriminative, affective-motivational, and cognitive-evaluative components. Pain aversiveness is one principal cause of suffering for patients with chronic pain, motivating research and drug development efforts to investigate and modulate neural activity in the brain’s circuits encoding pain unpleasantness. Here, we review progress in understanding the organization of emotion, motivation, cognition, and descending modulation circuits for pain perception. We describe the molecularly defined neuron types that collectively shape pain multidimensionality and its aversive quality. We also review how pharmacological, stimulation, neurofeedback, surgical, and cognitive-behavioral interventions alter activity in these circuits to relieve chronic pain.
Nature Reviews Neurology · 2020 · 475 citations
- Medicine
- Intensive care medicine
- Pharmacology
Pain medication plays an important role in the treatment of acute and chronic pain conditions, but some drugs, opioids in particular, have been overprescribed or prescribed without adequate safeguards, leading to an alarming rise in medication-related overdose deaths. The NIH Helping to End Addiction Long-term (HEAL) Initiative is a trans-agency effort to provide scientific solutions to stem the opioid crisis. One component of the initiative is to support biomarker discovery and rigorous validation in collaboration with industry leaders to accelerate high-quality clinical research into neurotherapeutics and pain. The use of objective biomarkers and clinical trial end points throughout the drug discovery and development process is crucial to help define pathophysiological subsets of pain, evaluate target engagement of new drugs and predict the analgesic efficacy of new drugs. In 2018, the NIH-led Discovery and Validation of Biomarkers to Develop Non-Addictive Therapeutics for Pain workshop convened scientific leaders from academia, industry, government and patient advocacy groups to discuss progress, challenges, gaps and ideas to facilitate the development of biomarkers and end points for pain. The outcomes of this workshop are outlined in this Consensus Statement.
Neurourology and Urodynamics · 2020 · 30 citations
- Medicine
- Physical therapy
- Physical medicine and rehabilitation
AIMS: The Multidisciplinary Approach to the Study of Chronic Pelvic Pain (MAPP) Research Network initiated a second observational cohort study-the Symptom Patterns Study (SPS)-to further investigate the underlying pathophysiology of Urologic Chronic Pelvic Pain Syndrome (UCPPS) and to discover factors associated with longitudinal symptom changes and responses to treatments. METHODS: This multisite cohort study of males and females with UCPPS features a run-in period of four weekly web-based symptom assessments before a baseline visit, followed by quarterly assessments up to 36 months. Controls were also recruited and assessed at baseline and 6 months. Extensive clinical data assessing urological symptoms, nonurological pain, chronic overlapping pain syndromes, and psychosocial factors were collected. Diverse biospecimens for biomarker and microbiome studies, quantitative sensory testing (QST) data under multiple stimuli, and structural and functional neuroimaging scans were obtained under a standardized protocol. RESULTS: Recruitment was initiated (July 2015) and completed (February 2019) at six discovery sites. A total of 620 males and females with UCPPS and 73 Controls were enrolled, including 83 UCPPS participants who re-enrolled from the first MAPP Network cohort study (2009-2012). Baseline neuroimaging scans, QST measures, and biospecimens were obtained on 578 UCPPS participants. The longitudinal follow-up of the cohort is ongoing. CONCLUSIONS: This comprehensive characterization of a large UCPPS cohort with extended follow-up greatly expands upon earlier MAPP Network studies and provides unprecedented opportunities to increase our understanding of UCPPS pathophysiology, factors associated with symptom change, clinically relevant patient phenotypes, and novel targets for future interventions.
Recent grants
Interdisciplinary Research Training in Pain and/or Substance Use Disorders
NIH · $6.4M · 2013–2029
NIH · $5.4M · 2015
NIH · $3.9M · 2023
Characterization of central pain mechanisms using simultaneous spinal cord-brain functional imaging
NIH · $2.9M · 2018–2024
NIH · $642k · 2012
Frequent coauthors
- 137 shared
Beth D. Darnall
Palo Alto University
- 82 shared
John A. Sturgeon
University of Michigan–Ann Arbor
- 79 shared
Kevin A. Johnson
Florida State University
- 74 shared
Katherine T. Martucci
Duke University
- 68 shared
Ming‐Chih Kao
Stanford University
- 59 shared
Ian Carroll
University of Freiburg
- 57 shared
Chris Mullins
National Institutes of Health
- 57 shared
Emeran A. Mayer
University of California, Los Angeles
Education
- 1996
M.D., Anesthesiology
Stanford University
- 1992
B.A., Human Biology
Stanford University
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