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Waschke L, Kamp F, van den Elzen E, Krishna S, Lindenberger U, Rutishauser U, Garrett DD. Single-neuron spiking variability in hippocampus dynamically tracks sensory content during memory formation in humans. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.02.23.529684. [PMID: 36865320 PMCID: PMC9980052 DOI: 10.1101/2023.02.23.529684] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/03/2023]
Abstract
During memory formation, the hippocampus is presumed to represent the content of stimuli, but how it does so is unknown. Using computational modelling and human single-neuron recordings, we show that the more precisely hippocampal spiking variability tracks the composite features of each individual stimulus, the better those stimuli are later remembered. We propose that moment-to-moment spiking variability may provide a new window into how the hippocampus constructs memories from the building blocks of our sensory world.
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Raut RV, Rosenthal ZP, Wang X, Miao H, Zhang Z, Lee JM, Raichle ME, Bauer AQ, Brunton SL, Brunton BW, Kutz JN. Arousal as a universal embedding for spatiotemporal brain dynamics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.06.565918. [PMID: 38187528 PMCID: PMC10769245 DOI: 10.1101/2023.11.06.565918] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
Neural activity in awake organisms shows widespread and spatiotemporally diverse correlations with behavioral and physiological measurements. We propose that this covariation reflects in part the dynamics of a unified, arousal-related process that regulates brain-wide physiology on the timescale of seconds. Taken together with theoretical foundations in dynamical systems, this interpretation leads us to a surprising prediction: that a single, scalar measurement of arousal (e.g., pupil diameter) should suffice to reconstruct the continuous evolution of multimodal, spatiotemporal measurements of large-scale brain physiology. To test this hypothesis, we perform multimodal, cortex-wide optical imaging and behavioral monitoring in awake mice. We demonstrate that spatiotemporal measurements of neuronal calcium, metabolism, and blood-oxygen can be accurately and parsimoniously modeled from a low-dimensional state-space reconstructed from the time history of pupil diameter. Extending this framework to behavioral and electrophysiological measurements from the Allen Brain Observatory, we demonstrate the ability to integrate diverse experimental data into a unified generative model via mappings from an intrinsic arousal manifold. Our results support the hypothesis that spontaneous, spatially structured fluctuations in brain-wide physiology-widely interpreted to reflect regionally-specific neural communication-are in large part reflections of an arousal-related process. This enriched view of arousal dynamics has broad implications for interpreting observations of brain, body, and behavior as measured across modalities, contexts, and scales.
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Affiliation(s)
- Ryan V. Raut
- Allen Institute, Seattle, WA, USA
- Department of Physiology & Biophysics, University of Washington, Seattle, WA, USA
| | - Zachary P. Rosenthal
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Xiaodan Wang
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Hanyang Miao
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Zhanqi Zhang
- Department of Computer Science & Engineering, University of California San Diego, La Jolla, CA, USA
| | - Jin-Moo Lee
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Marcus E. Raichle
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, USA
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Adam Q. Bauer
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Steven L. Brunton
- Department of Mechanical Engineering, University of Washington, Seattle, WA, USA
| | | | - J. Nathan Kutz
- Department of Applied Mathematics, University of Washington, Seattle, WA, USA
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Rizzoglio F, Altan E, Ma X, Bodkin KL, Dekleva BM, Solla SA, Kennedy A, Miller LE. From monkeys to humans: observation-basedEMGbrain-computer interface decoders for humans with paralysis. J Neural Eng 2023; 20:056040. [PMID: 37844567 PMCID: PMC10618714 DOI: 10.1088/1741-2552/ad038e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 10/02/2023] [Accepted: 10/16/2023] [Indexed: 10/18/2023]
Abstract
Objective. 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.Approach. 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.Main results. We found that both direct and transfer decoding approaches allowed accurate EMG predictions between two monkeys and from a monkey to a human.Significance. 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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Affiliation(s)
- Fabio Rizzoglio
- Department of Neuroscience, Northwestern University, Chicago, IL, United States of America
| | - Ege Altan
- Department of Neuroscience, Northwestern University, Chicago, IL, United States of America
- Department of Biomedical Engineering, Northwestern University, Evanston, IL, United States of America
| | - Xuan Ma
- Department of Neuroscience, Northwestern University, Chicago, IL, United States of America
| | - Kevin L Bodkin
- Department of Neuroscience, Northwestern University, Chicago, IL, United States of America
| | - Brian M Dekleva
- Rehab Neural Engineering Labs, Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA, United States of America
| | - Sara A Solla
- Department of Neuroscience, Northwestern University, Chicago, IL, United States of America
- Department of Physics and Astronomy, Northwestern University, Evanston, IL, United States of America
| | - Ann Kennedy
- Department of Neuroscience, Northwestern University, Chicago, IL, United States of America
| | - Lee E Miller
- Department of Neuroscience, Northwestern University, Chicago, IL, United States of America
- Department of Biomedical Engineering, Northwestern University, Evanston, IL, United States of America
- Shirley Ryan AbilityLab, Chicago, IL, United States of America
- Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL, United States of America
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Ye J, Collinger JL, Wehbe L, Gaunt R. Neural Data Transformer 2: Multi-context Pretraining for Neural Spiking Activity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.18.558113. [PMID: 37781630 PMCID: PMC10541112 DOI: 10.1101/2023.09.18.558113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/03/2023]
Abstract
The neural population spiking activity recorded by intracortical brain-computer interfaces (iBCIs) contain rich structure. Current models of such spiking activity are largely prepared for individual experimental contexts, restricting data volume to that collectable within a single session and limiting the effectiveness of deep neural networks (DNNs). The purported challenge in aggregating neural spiking data is the pervasiveness of context-dependent shifts in the neural data distributions. However, large scale unsupervised pretraining by nature spans heterogeneous data, and has proven to be a fundamental recipe for successful representation learning across deep learning. We thus develop Neural Data Transformer 2 (NDT2), a spatiotemporal Transformer for neural spiking activity, and demonstrate that pretraining can leverage motor BCI datasets that span sessions, subjects, and experimental tasks. NDT2 enables rapid adaptation to novel contexts in downstream decoding tasks and opens the path to deployment of pretrained DNNs for iBCI control. Code: https://github.com/joel99/context_general_bci.
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Affiliation(s)
- Joel Ye
- Rehab Neural Engineering Labs, University of Pittsburgh
- Neuroscience Institute, Carnegie Mellon University
- Center for the Neural Basis of Cognition, Pittsburgh
| | - Jennifer L. Collinger
- Rehab Neural Engineering Labs, University of Pittsburgh
- Center for the Neural Basis of Cognition, Pittsburgh
- Department of Physical Medicine and Rehabilitation, University of Pittsburgh
- Department of Bioengineering, University of Pittsburgh
- Department of Biomedical Engineering, Carnegie Mellon University
| | - Leila Wehbe
- Neuroscience Institute, Carnegie Mellon University
- Center for the Neural Basis of Cognition, Pittsburgh
- Machine Learning Department, Carnegie Mellon University
| | - Robert Gaunt
- Rehab Neural Engineering Labs, University of Pittsburgh
- Center for the Neural Basis of Cognition, Pittsburgh
- Department of Physical Medicine and Rehabilitation, University of Pittsburgh
- Department of Bioengineering, University of Pittsburgh
- Department of Biomedical Engineering, Carnegie Mellon University
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