New work out in @SpringerNature @natBME Nature BME! Blurring the lines between BCI and DBS to think creatively about new therapies, always with people at the heart of it all.

Excited to share a preprint of work done together with folks from UCSF (@littleneuro), UC Berkeley, and U of Washington, using neural decoding and adaptive DBS to selectively amplify movement when intended and reduce dyskinesia when it isn't.

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RT @biorxiv_neursci: Selective modulation of population dynamics during neuroprosthetic skill learning #biorxiv_ne….

RT @tsay_jonathan: To maintain calibration, our motor system adapts to changes in the body (e.g., fatigue) and environment (e.g., a windy d….

RT @FiveThirtyEight: Our 2020 Election Forecast:

RT @TessyMThomas: Our manuscript posted on medRxiv demonstrates the potential for simultaneous classification of gestures on both hands usi….

And BIG S/O to my incredible co-authors @c_mmerick @ivryrich Joni and Jose. Such incredible people and scientists. (9/9).

Of course, this is a preprint and we would LOVE to hear any thoughts/feedback! And S/O to some of the cool people and their work that motivated some of the questions we asked here @SpecificAmes @MarkChurchland @K_P_Cross @ScottLIMBlab (and any co-authors I couldn’t find!) (8/9).

These results may suggest two functional layers in the MC network: an effector-specific output layer and a bilaterally distributed computational layer. They’ve also proven essential for making sense of some of our other results that we hope to release soon (stay tuned!) (7/9).

Signals that were shared within single-units carried information capable of classifying target reaches for either arm and existed within a shared subspace for both arms. Separation of arm-specific signals came only in the simple form of localized variance (6/9).

This “localized” component emerged within PMd during preparation, became most pronounced following movement onset when M1 became strongly engaged, and principally involved the contralateral hemisphere. Now what about those units that liked both arms!? (5/9).

We found many units that modulated their activity during trials of either arm, but those that were dedicated to a single arm had much stronger modulation. This led to the majority of variance for each arm localizing within mutually exclusive sub-populations (4/9).

These questions constrain the set of possibilities for each hemisphere’s role in motor control, and have conflicting (or absent) answers in the existing literature. We addressed them (+ a bit more) using an instructed-delay task in NHPs while recording from bilateral PMd/M1 (3/9).

To what extent do population signals follow a traditional understanding of contralateral dominance? What’s the population-level contribution of signals that are shared within units? How do these organizational principles change across preparation and movement? (2/9).

Motor cortex canon says that one side of the brain controls the opposite side of the body. Yet, control involves a variety of sub-processes as actions are prepared for execution, and we see both hemispheres of the motor cortex engaged (to varying degrees) throughout (1/9).

Rolling out some new work. We think these results will be foundational for understanding the functional roles specific to each hemisphere of the motor cortex. See my tweet-storm below to see why we’re excited about it! (and check out the paper for details).

RT @neuroamyo: 🚨 Postdoc opportunity! 🚨Washington Research Foundation Postdoc fellowship call now open. 3 years of funding to work at one o….

RT @biorxiv_neursci: Head-mounted microendoscopic calcium imaging in dorsal premotor cortex of behaving rhesus macaque .