Whitney S. Griggs, PhD

Academic Interests

I am interested in combining my interests in system neuroscience and neurosurgery to improve existing and develop new brain-machine interfaces. Through my PhD at Caltech, I am pursuing this by designing a real-time ultrasound-based brain-machine interface (in collaboration with many others). I returned to medical school in July 2023 and hope to continue this line of research during and after neurosurgery residency.

in the news

May 29, 2024	Another paper from my PhD just came out on Science Translational Medicine and we even got the cover photo! Claire Rabut, Sumner Norman, and I demonstrate a way to use ultrasound to sensitively measure human brain activity through an acoustic brain window!🔊🪟🧠 Caltech news article about it
Nov 30, 2023	A paper from my PhD just came out on Nature Neuroscience! Sumner Norman and I developed an ultrasound-based brain machine interface!🔊🧠🤖 Caltech news article about it
Jun 16, 2023	I officially graduated from Caltech! 👨‍🎓🎉 Now a one month break to relearn medicine before returning to medical school clerkships at UCLA. 👨‍⚕️🩺
Apr 12, 2023	I successfully defended my PhD thesis entitled Listening to the Internal Representation of Actions Within the Posterior Parietal Cortex! 🎉 Thank you to my mentors for helping me turn my assorted research projects into a compelling story.
Feb 24, 2023	Guest presenter brings computational neuroscience and biomedicine to campus

selected publications

Functional ultrasound imaging of human brain activity through an acoustically transparent cranial window

Claire Rabut*, Sumner L. Norman*, Whitney S. Griggs*, Jonathan J. Russin, Kay Jann, Vasileios Christopoulos, Charles Liu, Richard A. Andersen, and Mikhail G. Shapiro

Science Translational Medicine May 2024

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Visualization of human brain activity is crucial for understanding normal and aberrant brain function. Currently available neural activity recording methods are highly invasive, have low sensitivity, and cannot be conducted outside of an operating room. Functional ultrasound imaging (fUSI) is an emerging technique that offers sensitive, large-scale, high-resolution neural imaging; however, fUSI cannot be performed through the adult human skull. Here, we used a polymeric skull replacement material to create an acoustic window compatible with fUSI to monitor adult human brain activity in a single individual. Using an in vitro cerebrovascular phantom to mimic brain vasculature and an in vivo rodent cranial defect model, first, we evaluated the fUSI signal intensity and signal-to-noise ratio through polymethyl methacrylate (PMMA) cranial implants of different thicknesses or a titanium mesh implant. We found that rat brain neural activity could be recorded with high sensitivity through a PMMA implant using a dedicated fUSI pulse sequence. We then designed a custom ultrasound-transparent cranial window implant for an adult patient undergoing reconstructive skull surgery after traumatic brain injury. We showed that fUSI could record brain activity in an awake human outside of the operating room. In a video game “connect the dots” task, we demonstrated mapping and decoding of task-modulated cortical activity in this individual. In a guitar-strumming task, we mapped additional task-specific cortical responses. Our proof-of-principle study shows that fUSI can be used as a high-resolution (200 μm) functional imaging modality for measuring adult human brain activity through an acoustically transparent cranial window.
Decoding motor plans using a closed-loop ultrasonic brain–machine interface

Whitney S. Griggs*, Sumner L. Norman*, Thomas Deffieux, Florian Segura, Bruno-Félix Osmanski, Geeling Chau, Vasileios Christopoulos, Charles Liu, Mickael Tanter, Mikhail G. Shapiro, and Richard A. Andersen

Nature Neuroscience Nov 2023

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Brain–machine interfaces (BMIs) enable people living with chronic paralysis to control computers, robots and more with nothing but thought. Existing BMIs have trade-offs across invasiveness, performance, spatial coverage and spatiotemporal resolution. Functional ultrasound (fUS) neuroimaging is an emerging technology that balances these attributes and may complement existing BMI recording technologies. In this study, we use fUS to demonstrate a successful implementation of a closed-loop ultrasonic BMI. We streamed fUS data from the posterior parietal cortex of two rhesus macaque monkeys while they performed eye and hand movements. After training, the monkeys controlled up to eight movement directions using the BMI. We also developed a method for pretraining the BMI using data from previous sessions. This enabled immediate control on subsequent days, even those that occurred months apart, without requiring extensive recalibration. These findings establish the feasibility of ultrasonic BMIs, paving the way for a new class of less-invasive (epidural) interfaces that generalize across extended time periods and promise to restore function to people with neurological impairments.
Single-trial decoding of movement intentions using functional ultrasound neuroimaging

Sumner L. Norman*, David Maresca*, Vassilios N. Christopoulos*, Whitney S. Griggs, Charlie Demene, Mickael Tanter, Mikhail G. Shapiro, and Richard A. Andersen

Neuron Mar 2021

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New technologies are key to understanding the dynamic activity of neural circuits and systems in the brain. Here, we show that a minimally invasive approach based on ultrasound can be used to detect the neural correlates of movement planning, including directions and effectors. While non-human primates (NHPs) performed memory-guided movements, we used functional ultrasound (fUS) neuroimaging to record changes in cerebral blood volume with 100 μm resolution. We recorded from outside the dura above the posterior parietal cortex, a brain area important for spatial perception, multisensory integration, and movement planning. We then used fUS signals from the delay period before movement to decode the animals’ intended direction and effector. Single-trial decoding is a prerequisite to brain-machine interfaces, a key application that could benefit from this technology. These results are a critical step in the development of neuro-recording and brain interface tools that are less invasive, high resolution, and scalable.