Multiscale low-dimensional motor cortical state dynamics predict naturalistic reach-and-grasp behavior

Abbaspourazad, Hamidreza; Choudhury, Mahdi; Wong, Yan T.; Pesaran, Bijan; Shanechi, Maryam M.

doi:10.1038/s41467-020-20197-x

Cited by 57 publications

(176 citation statements)

References 87 publications

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“…Assuming that a large component of the firing of M1 neural populations relates to the motor commands, the ability to predict behaviour from the low-frequency LFPs hints at a relationship between the two. Such a relationship has been recently modelled as a “mode” that captures behaviour-related dynamics that are shared between the low and high frequency motor cortical LFPs and the neural population activity 83 .…”

Section: Discussionmentioning

confidence: 99%

Local field potentials reflect cortical population dynamics in a region-specific and frequency-dependent manner

Gallego-Carracedo

Chowdhury

et al. 2021

Preprint

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The spiking activity of populations of cortical neurons is well described by a small number of population-wide covariance patterns, the "latent dynamics". These latent dynamics are largely driven by the same correlated synaptic currents across the circuit that determine the generation of local field potentials (LFP). Yet, the relationship between latent dynamics and LFPs remains largely unexplored. Here, we characterised this relationship for three different regions of primate sensorimotor cortex during reaching. The correlation between latent dynamics and LFPs was frequency-dependent and varied across regions. However, for any given region, this relationship remained stable across behaviour: in each of primary motor and premotor cortices, the LFP-latent dynamics correlation profile was remarkably similar between movement planning and execution. These robust associations between LFPs and neural population latent dynamics help bridge the wealth of studies reporting neural correlates of behaviour using either type of recordings.

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Section: Discussionmentioning

confidence: 99%

Local field potentials reflect cortical population dynamics in a region-specific and frequency-dependent manner

Gallego-Carracedo

Chowdhury

et al. 2021

Preprint

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“…Such studies have greatly increased our knowledge about the neural correlates of movement, but it remains unclear how well these findings generalize to the natural movements that we often make in everyday situations 10,11 . Human upper-limb movement studies have incorporated self-cued and less restrictive movements [12][13][14][15][16] , but focusing on unstructured, naturalistic movements can enhance our knowledge of the neural basis of motor behaviors 17 , help us understand the role of neurobehavioral variability 18,19 , and aid in the development of robust brain-computer interfaces for real-world use [20][21][22][23][24][25][26] .…”

Section: Background and Summarymentioning

confidence: 99%

AJILE12: Long-term naturalistic human intracranial neural recordings and pose

Peterson

Singh

Dichter

et al. 2021

Preprint

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Understanding the neural basis of human movement in naturalistic scenarios is critical for expanding neuroscience research beyond constrained laboratory paradigms. Here, we describe our Annotated Joints in Long-term Electrocorticography for 12 human participants (AJILE12) dataset, the largest human neurobehavioral dataset that is publicly available; the dataset was recorded opportunistically during passive clinical epilepsy monitoring. AJILE12 includes synchronized intracranial neural recordings and upper body pose trajectories across 55 semi-continuous days of naturalistic movements, along with relevant metadata, including thousands of wrist movement events and annotated behavioral states. Neural recordings are available at 500 Hz from at least 64 electrodes per participant, for a total of 1280 hours. Pose trajectories at 9 upper-body keypoints were estimated from 118 million video frames. To facilitate data exploration and reuse, we have shared AJILE12 on The DANDI Archive in the Neurodata Without Borders (NWB) data standard and developed a browser-based dashboard.

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“…Combining multiple modalities in a single experimental inquiry has allowed researchers to achieve better performance in brain-machine interfaces (BMIs) and to gain new mechanistic insights. For example, mathematical models combining spiking activity with LFPs improved neural prosthetic prediction accuracy [25,[33][34][35][36]. Synergistic signals from multiple neural scales also minimized electrical and computational power requirements and increased the longevity of a BMI devices [34].…”

Section: Introductionmentioning

confidence: 99%

Multi-scale neural decoding and analysis

Lorenc

Zhu

et al. 2021

J. Neural Eng.

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Objective. Complex spatiotemporal neural activity encodes rich information related to behavior and cognition. Conventional research has focused on neural activity acquired using one of many different measurement modalities, each of which provides useful but incomplete assessment of the neural code. Multi-modal techniques can overcome tradeoffs in the spatial and temporal resolution of a single modality to reveal deeper and more comprehensive understanding of system-level neural mechanisms. Uncovering multi-scale dynamics is essential for a mechanistic understanding of brain function and for harnessing neuroscientific insights to develop more effective clinical treatment. Approach. We discuss conventional methodologies used for characterizing neural activity at different scales and review contemporary examples of how these approaches have been combined. Then we present our case for integrating activity across multiple scales to benefit from the combined strengths of each approach and elucidate a more holistic understanding of neural processes. Main results. We examine various combinations of neural activity at different scales and analytical techniques that can be used to integrate or illuminate information across scales, as well the technologies that enable such exciting studies. We conclude with challenges facing future multi-scale studies, and a discussion of the power and potential of these approaches. Significance. This roadmap will lead the readers toward a broad range of multi-scale neural decoding techniques and their benefits over single-modality analyses. This Review article highlights the importance of multi-scale analyses for systematically interrogating complex spatiotemporal mechanisms underlying cognition and behavior.

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Multiscale low-dimensional motor cortical state dynamics predict naturalistic reach-and-grasp behavior

Cited by 57 publications

References 87 publications

Local field potentials reflect cortical population dynamics in a region-specific and frequency-dependent manner

Local field potentials reflect cortical population dynamics in a region-specific and frequency-dependent manner

AJILE12: Long-term naturalistic human intracranial neural recordings and pose

Multi-scale neural decoding and analysis

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