Selective modulation of cortical population dynamics during neuroprosthetic skill learning

Zippi, Ellen L.; You, Albert; Ganguly, Karunesh; Carmena, Jose M.

doi:10.1038/s41598-022-20218-3

Cited by 4 publications

(1 citation statement)

References 66 publications

(79 reference statements)

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“…msDyNODE outputs reconstruct well experimentally-acquired firing rate and field potential signals. Firing rate and LFP activity are simultaneously recorded in the left dorsal premotor (PMd) and primary motor cortex (M1) of rhesus macaques (N=2) while performing a center-out brain-machine interface (BMI) task [30][31][32][33][34] (Fig. 3; see Materials and Methods).…”

Section: Resultsmentioning

confidence: 99%

Multiscale effective connectivity analysis of brain activity using neural ordinary differential equations

Chang,

Chen,

Stealey

et al. 2023

Preprint

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Neural mechanisms and underlying directionality of signaling among brain regions depend on neural dynamics spanning multiple spatiotemporal scales of population activity. Despite recent advances in multimodal measurements of brain activity, there is no broadly accepted multiscale dynamical models for the collective activity represented in neural signals. Here we introduce a neurobiological-driven deep learning model, termed multiscale neural dynamics neural ordinary differential equation (msDyNODE), to describe multiscale brain communications governing cognition and behavior. We demonstrate that msDyNODE successfully captures multiscale activity using both simulations and electrophysiological experiments. The msDyNODE-derived causal interactions between recording locations and scales not only aligned well with the abstraction of the hierarchical neuroanatomy of the mammalian central nervous system but also exhibited behavioral dependences. This work offers a new approach for mechanistic multiscale studies of neural processes.

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Section: Resultsmentioning

confidence: 99%

Multiscale effective connectivity analysis of brain activity using neural ordinary differential equations

Chang,

Chen,

Stealey

et al. 2023

Preprint

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Distinct neural representations during a brain–machine interface and manual reaching task in motor cortex, prefrontal cortex, and striatum

Zippi,

Shvartsman,

Vendrell-Llopis

et al. 2023

Sci Rep

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Although brain–machine interfaces (BMIs) are directly controlled by the modulation of a select local population of neurons, distributed networks consisting of cortical and subcortical areas have been implicated in learning and maintaining control. Previous work in rodents has demonstrated the involvement of the striatum in BMI learning. However, the prefrontal cortex has been largely ignored when studying motor BMI control despite its role in action planning, action selection, and learning abstract tasks. Here, we compare local field potentials simultaneously recorded from primary motor cortex (M1), dorsolateral prefrontal cortex (DLPFC), and the caudate nucleus of the striatum (Cd) while nonhuman primates perform a two-dimensional, self-initiated, center-out task under BMI control and manual control. Our results demonstrate the presence of distinct neural representations for BMI and manual control in M1, DLPFC, and Cd. We find that neural activity from DLPFC and M1 best distinguishes control types at the go cue and target acquisition, respectively, while M1 best predicts target-direction at both task events. We also find effective connectivity from DLPFC → M1 throughout both control types and Cd → M1 during BMI control. These results suggest distributed network activity between M1, DLPFC, and Cd during BMI control that is similar yet distinct from manual control.

show abstract

Distinct neural representations during a brain-machine interface and manual reaching task in motor cortex, prefrontal cortex, and striatum

Zippi,

Shvartsman,

Vendrell-Llopis

et al. 2023

Preprint

View full text Add to dashboard Cite

Although brain-machine interfaces (BMIs) are directly controlled by the modulation of a select local population of neurons, distributed networks consisting of cortical and subcortical areas have been implicated in learning and maintaining control. Previous work in rodent BMI has demonstrated the involvement of the striatum in BMI learning. However, the prefrontal cortex has been largely ignored when studying motor BMI control despite its role in action planning, action selection, and learning abstract tasks. Here, we compare local field potentials simultaneously recorded from the primary motor cortex (M1), dorsolateral prefrontal cortex (DLPFC), and the caudate nucleus of the striatum (Cd) while nonhuman primates perform a two-dimensional, self-initiated, center-out task under BMI control and manual control. Our results demonstrate the presence of distinct neural representations for BMI and manual control in M1, DLPFC, and Cd. We find that neural activity from DLPFC and M1 best distinguish between control types at the go cue and target acquisition, respectively. We also found effective connectivity from DLPFC→M1 throughout trials across both control types and Cd→M1 during BMI control. These results suggest distributed network activity between M1, DLPFC, and Cd during BMI control that is similar yet distinct from manual control.

show abstract

Selective modulation of cortical population dynamics during neuroprosthetic skill learning

Cited by 4 publications

References 66 publications

Multiscale effective connectivity analysis of brain activity using neural ordinary differential equations

Multiscale effective connectivity analysis of brain activity using neural ordinary differential equations

Distinct neural representations during a brain–machine interface and manual reaching task in motor cortex, prefrontal cortex, and striatum

Distinct neural representations during a brain-machine interface and manual reaching task in motor cortex, prefrontal cortex, and striatum

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