The PurdonLab

We are marking the beginning of our journey at Stanford University with the inaugural PurdonLab newsletter! Read all about our recent accomplishments and publications, and meet our new lab members.

Happy Holidays from the Purdon Lab! As we wrap up the year, we’re grateful for the collaborations, progress, and community that made this such a meaningful year for our lab. We’re excited to share a few highlights from the past months and to look ahead to what’s coming next.

Excited to share a new important paper about peripheral nerve blocks: while they reduce pain and opioid use right after surgery, they may lead to up to 22% more opioid use later during the hospital stay, likely due to rebound pain. @BJAJournals

Can brain waves during surgery predict who’s at higher risk after? What’s going on: Every year, millions of people die after surgery. Doctors need better ways to spot who’s most at risk—while the surgery is happening.

We are excited about the possibility of using EEG monitoring to increase system-wide efficiencies in OR scheduling, PACU resources, and both patient and caregiver satisfaction! (4/4)

We are excited that Mae’s case report has been published! Here we show that anesthetic-induced neural signals from EEG can be used as a direct measure of an Alexander’s Disease young patient’s anesthetics sensitivity. pmc.ncbi.nlm.nih.gov/articles/PMC11… (1/4)

Application of EEG monitoring decreased anesthetic wash-off and PACU recovery times by 50%! Overall, this case highlights the potential benefits EEG monitoring could provide our pediatric patients and clinical staff with almost no additional changes to anesthetic workflow. (3/4)

Rather than relying on anesthetic dosing schemes that are based on adult physiology and don't account for changes to anesthetic sensitivity associated with neurodegenerative disorders, the EEG can provide real-time feedback to minimize anesthetic exposure and improve outcomes.

We have officially started collecting data on our Awake EEG and Aging Study which looks at EEG brain activities associated with aging and preclinical Alzheimer's! Here is the study design poster that Amber and Julia presented at the Stanford Anesthesia Department Retreat

Link to full article: journals.lww.com/anesthesiology…

Thanks to everyone in the @PurdonLab @RyanNipp @BalanzaGus @rggutierrez1 @Ran_Liu_16 and teams at @MGHanesthesia @stanfordanes who helped contribute to this work!

@rvmather @RyanNipp @BalanzaGus @rggutierrez1 @Ran_Liu_16 @MGHanesthesia @stanfordanes Thank you for your contributions Rory 💯

Excited that my first 1st authored paper from my PhD research has been published in Anesthesiology! We found that neural oscillations in the alpha band of frequencies (8-12 Hz) during anesthesia can predict the risk of postop mortality up to a year after surgery. 🧵 (1/3)

@rvmather Amazing work! We definitely need more ways on how to predict postoperative mortality and adverse outcomes, and brain signaling power gives us great insights. Congrats!

Excited that our case report has been published! Here we show how the neural signals from a readily available forehead EEG sensor can be used to titrate anesthetic dosing for a child with neurodegenerative disease and decrease postop recovery times. journals.lww.com/aacr/fulltext/…

We're celebrating our 1-Year anniversary at Stanford University! Check out our next edition of our newsletter for our recent publications, new lab members, and upcoming events.

Excited to share the results of yrs of data collection @CharlesaNelson1‘s lab & great collab w/ @Lisa_Yankowitz @JerryChaoMD @rggutierrez1 & @PurdonLab. Developmental trajectories of EEG aperiodic and periodic components in children 2-to-44 months of age. rdcu.be/dNj0I

Enjoying our @UWAnesthPainMed 16th Academic evening in the beautiful UW WƎⱢƎBʔALTXʷ – Intellectual House: a huge display of the rich science that our departmental members pursue in biomedical & clinical sciences. Our 🔑 note speaker is Dr. Patrick Purdon from @stanfordanes

Currently, we are working on gathering such empirical evidence in response the the reviewer's comment.

Although we demonstrated several utilities of the methods, the reviewer (correctly) pointed out lack of empirical evidence for the approach's advantage.

In the reviewed preprint, we propose a Oscillation Component Analysis (OCA) approach, in analogy to Independent Component Analysis (ICA), in which source separation is achieved through biophysically inspired generative modeling of neural oscillations. doi.org/10.7554/eLife.…