Conserved structures of neural activity in sensorimotor cortex of freely moving rats allow cross-subject decoding

Melbaum, Svenja; Russo, Eleonora; Eriksson, David; Schneider, Artur; Durstewitz, Daniel; Brox, Thomas; Diester, Ilka

doi:10.1101/2021.03.04.433869

Cited by 4 publications

(4 citation statements)

References 56 publications

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“…Most of the variation was a result of differences in the exact movement during those behaviors. This finding indicates that conserved neuronal representations of behavioral categories in motor cortex (Melbaum et al, 2021) could be based on conserved neuronal representations of movements.…”

Section: Discussionmentioning

confidence: 78%

3D pose estimation enables virtual head-fixation in freely moving rats

Schneider

Zimmermann²,

Alyahyay

et al. 2022

Preprint

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The impact of spontaneous movements on neuronal activity has created the need to quantify behavior. We present a versatile framework to directly capture the 3D motion of freely definable body points in a marker-free manner with high precision and reliability. Combining the tracking with neural recordings revealed multiplexing of information in the motor cortex neurons of freely moving rats. By integrating multiple behavioral variables into a model of the neural response, we derived a virtual head-fixation for which the influence of specific body movements was removed. This strategy enabled us to analyze the behavior of interest (e.g., front paw movements). Thus, we unveiled an unexpectedly large fraction of neurons in the motor cortex with tuning to the paw movements, which was previously masked by body posture tuning. Once established, our framework can be efficiently applied to large datasets while minimizing the experimental workload caused by animal training and manual labeling.

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Section: Discussionmentioning

confidence: 78%

3D pose estimation enables virtual head-fixation in freely moving rats

Schneider

Zimmermann²,

Alyahyay

et al. 2022

Preprint

Self Cite

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“…Recently, Safaie et al used similar alignment techniques to show that latent signals in M1 were preserved across monkeys as they performed a center-out reaching task using a planar manipulandum; latent signals were also preserved in the dorsolateral striatum of mice that grasped and pulled a joystick (Safaie et al 2022). Other recent studies revealed that low-dimensional neural structure within the hippocampus and the sensorimotor cortex is preserved across rats for a variety of behaviors, ranging from locomotion along a linear track inside a maze to unconstrained movement in an arena (Rubin et al 2019, Chen et al 2021, Nieh et al 2021, Melbaum et al 2022.…”

Section: Neural Representations Of Motor Intent Are Similar Across Mo...mentioning

confidence: 96%

From monkeys to humans: observation-based EMG brain–computer interface decoders for humans with paralysis

Rizzoglio,

Altan,

et al. 2023

J. Neural Eng.

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Intracortical brain-computer interfaces (iBCIs) aim to enable individuals with paralysis to control the movement of virtual limbs and robotic arms. Because patients’ paralysis prevents training a direct neural activity to limb movement decoder, most iBCIs rely on “observation-based” decoding in which the patient watches a moving cursor while mentally envisioning making the movement. However, this reliance on observed target motion for decoder development precludes its application to the prediction of unobservable motor output like muscle activity. Here, we ask whether recordings of muscle activity from a surrogate individual performing the same movement as the iBCI patient can be used as target for an iBCI decoder. We test two possible approaches, each using data from a human iBCI user and a monkey, both performing similar motor actions. In one approach, we trained a decoder to predict the electromyographic (EMG) activity of a monkey from neural signals recorded from a human. We then contrast this to a second approach, based on the hypothesis that the low-dimensional “latent” neural representations of motor behavior, known to be preserved across time for a given behavior, might also be preserved across individuals. We “transferred” an EMG decoder trained solely on monkey data to the human iBCI user after using Canonical Correlation Analysis to align the human latent signals to those of the monkey. We found that both direct and transfer decoding approaches allowed accurate EMG predictions between two monkeys and from a monkey to a human. Our findings suggest that these latent representations of behavior are consistent across animals and even primate species. These methods are an important initial step in the development of iBCI decoders that generate EMG predictions that could serve as signals for a biomimetic decoder controlling motion and impedance of a prosthetic arm, or even muscle force directly through Functional Electrical Stimulation.

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“…Recently, Safaie et al used similar alignment techniques to show that latent signals in M1 were preserved across monkeys as they performed a center-out reaching task using a planar manipulandum; latent signals were also preserved in the dorsolateral striatum of mice that grasped and pulled a joystick 26 . Other recent studies revealed that low-dimensional neural structure within the hippocampus and the sensorimotor cortex is preserved across rats for a variety of behaviors, ranging from locomotion along a linear track inside a maze to unconstrained movement in an arena [27][28][29][30] .…”

Section: Neural Representations Of Motor Intent Are Similar Across Mo...mentioning

confidence: 99%

Monkey-to-human transfer of brain-computer interface decoders

Rizzoglio¹,

Altan²,

Ma³

et al. 2022

Preprint

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Intracortical brain-computer interfaces (iBCIs) enable paralyzed persons to generate movement, but current methods require large amounts of both neural and movement-related data to be collected from the iBCI user for supervised decoder training. We hypothesized that the low-dimensional latent neural representations of motor behavior, known to be preserved across time, might also be preserved across individuals, and allow us to circumvent this problem. We trained a decoder to predict the electromyographic (EMG) activity for a “source” monkey from the latent signals of motor cortex. We then used Canonical Correlation Analysis to align the latent signals of a “target” monkey to those of the source. These decoders were as accurate across monkeys as they were across sessions for a given monkey. Remarkably, the same process with latent signals from a human participant with tetraplegia was within 90% of the with-monkey decoding across session accuracy. Our findings suggest that consistent representations of motor activity exist across animals and even species. Discovering this common representation is a crucial first step in designing iBCI decoders that perform well without large amounts of data and supervised subject-specific tuning.

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Conserved structures of neural activity in sensorimotor cortex of freely moving rats allow cross-subject decoding

Cited by 4 publications

References 56 publications

3D pose estimation enables virtual head-fixation in freely moving rats

3D pose estimation enables virtual head-fixation in freely moving rats

From monkeys to humans: observation-based EMG brain–computer interface decoders for humans with paralysis

Monkey-to-human transfer of brain-computer interface decoders

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