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Lo YT, Jiang L, Woodington B, Middya S, Braendlein M, Lam JLW, Lim MJR, Ng VYP, Rao JP, Chan DWS, Ang BT. Recording of single-unit activities with flexible micro-electrocorticographic array in rats for decoding of whole-body navigation. J Neural Eng 2024; 21:046037. [PMID: 38986465 DOI: 10.1088/1741-2552/ad618c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2024] [Accepted: 07/10/2024] [Indexed: 07/12/2024]
Abstract
Objective.Micro-electrocorticographic (μECoG) arrays are able to record neural activities from the cortical surface, without the need to penetrate the brain parenchyma. Owing in part to small electrode sizes, previous studies have demonstrated that single-unit spikes could be detected from the cortical surface, and likely from Layer I neurons of the neocortex. Here we tested the ability to useμECoG arrays to decode, in rats, body position during open field navigation, through isolated single-unit activities.Approach. μECoG arrays were chronically implanted onto primary motor cortex (M1) of Wistar rats, and neural recording was performed in awake, behaving rats in an open-field enclosure. The signals were band-pass filtered between 300-3000 Hz. Threshold-crossing spikes were identified and sorted into distinct units based on defined criteria including waveform morphology and refractory period. Body positions were derived from video recordings. We used gradient-boosting machine to predict body position based on previous 100 ms of spike data, and correlation analyses to elucidate the relationship between position and spike patterns.Main results.Single-unit spikes could be extracted during chronic recording fromμECoG, and spatial position could be decoded from these spikes with a mean absolute error of prediction of 0.135 and 0.090 in the x- and y- dimensions (of a normalized range from 0 to 1), and Pearson's r of 0.607 and 0.571, respectively.Significance. μECoG can detect single-unit activities that likely arise from superficial neurons in the cortex and is a promising alternative to intracortical arrays, with the added benefit of scalability to cover large cortical surface with minimal incremental risks. More studies should be performed in human related to its use as brain-machine interface.
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Affiliation(s)
- Yu Tung Lo
- Department of Neurosurgery, National Neuroscience Institute, Singapore, Singapore
- Duke-NUS Medical School, Singapore, Singapore
| | - Lei Jiang
- Department of Orthopaedic Surgery, Singapore General Hospital, Singapore, Singapore
| | | | | | | | | | - Mervyn Jun Rui Lim
- Department of Neurosurgery, National University Hospital, Singapore, Singapore
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Vincent Yew Poh Ng
- Department of Neurosurgery, National Neuroscience Institute, Singapore, Singapore
| | - Jai Prashanth Rao
- Duke-NUS Medical School, Singapore, Singapore
- Department of Neurosurgery, Singapore General Hospital, Singapore, Singapore
| | | | - Beng Ti Ang
- Department of Neurosurgery, Singapore General Hospital, Singapore, Singapore
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Meng R, Bouchard KE. Bayesian inference of structured latent spaces from neural population activity with the orthogonal stochastic linear mixing model. PLoS Comput Biol 2024; 20:e1011975. [PMID: 38669271 PMCID: PMC11078355 DOI: 10.1371/journal.pcbi.1011975] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 05/08/2024] [Accepted: 03/07/2024] [Indexed: 04/28/2024] Open
Abstract
The brain produces diverse functions, from perceiving sounds to producing arm reaches, through the collective activity of populations of many neurons. Determining if and how the features of these exogenous variables (e.g., sound frequency, reach angle) are reflected in population neural activity is important for understanding how the brain operates. Often, high-dimensional neural population activity is confined to low-dimensional latent spaces. However, many current methods fail to extract latent spaces that are clearly structured by exogenous variables. This has contributed to a debate about whether or not brains should be thought of as dynamical systems or representational systems. Here, we developed a new latent process Bayesian regression framework, the orthogonal stochastic linear mixing model (OSLMM) which introduces an orthogonality constraint amongst time-varying mixture coefficients, and provide Markov chain Monte Carlo inference procedures. We demonstrate superior performance of OSLMM on latent trajectory recovery in synthetic experiments and show superior computational efficiency and prediction performance on several real-world benchmark data sets. We primarily focus on demonstrating the utility of OSLMM in two neural data sets: μECoG recordings from rat auditory cortex during presentation of pure tones and multi-single unit recordings form monkey motor cortex during complex arm reaching. We show that OSLMM achieves superior or comparable predictive accuracy of neural data and decoding of external variables (e.g., reach velocity). Most importantly, in both experimental contexts, we demonstrate that OSLMM latent trajectories directly reflect features of the sounds and reaches, demonstrating that neural dynamics are structured by neural representations. Together, these results demonstrate that OSLMM will be useful for the analysis of diverse, large-scale biological time-series datasets.
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Affiliation(s)
- Rui Meng
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America
| | - Kristofer E. Bouchard
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America
- Scientific Data Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America
- Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, California, United States of America
- Redwood Center for Theoretical Neuroscience, University of California Berkeley, Berkeley, California, United States of America
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Leonard MK, Gwilliams L, Sellers KK, Chung JE, Xu D, Mischler G, Mesgarani N, Welkenhuysen M, Dutta B, Chang EF. Large-scale single-neuron speech sound encoding across the depth of human cortex. Nature 2024; 626:593-602. [PMID: 38093008 PMCID: PMC10866713 DOI: 10.1038/s41586-023-06839-2] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 11/06/2023] [Indexed: 01/31/2024]
Abstract
Understanding the neural basis of speech perception requires that we study the human brain both at the scale of the fundamental computational unit of neurons and in their organization across the depth of cortex. Here we used high-density Neuropixels arrays1-3 to record from 685 neurons across cortical layers at nine sites in a high-level auditory region that is critical for speech, the superior temporal gyrus4,5, while participants listened to spoken sentences. Single neurons encoded a wide range of speech sound cues, including features of consonants and vowels, relative vocal pitch, onsets, amplitude envelope and sequence statistics. Neurons at each cross-laminar recording exhibited dominant tuning to a primary speech feature while also containing a substantial proportion of neurons that encoded other features contributing to heterogeneous selectivity. Spatially, neurons at similar cortical depths tended to encode similar speech features. Activity across all cortical layers was predictive of high-frequency field potentials (electrocorticography), providing a neuronal origin for macroelectrode recordings from the cortical surface. Together, these results establish single-neuron tuning across the cortical laminae as an important dimension of speech encoding in human superior temporal gyrus.
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Affiliation(s)
- Matthew K Leonard
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
- Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Laura Gwilliams
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
- Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Kristin K Sellers
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
- Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Jason E Chung
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
- Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Duo Xu
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
- Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Gavin Mischler
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
- Department of Electrical Engineering, Columbia University, New York, NY, USA
| | - Nima Mesgarani
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
- Department of Electrical Engineering, Columbia University, New York, NY, USA
| | | | | | - Edward F Chang
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA.
- Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA.
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Sachdeva P, Bak JH, Livezey J, Kirst C, Frank L, Bhattacharyya S, Bouchard KE. Resolving Non-identifiability Mitigates Bias in Models of Neural Tuning and Functional Coupling. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.11.548615. [PMID: 37503030 PMCID: PMC10370036 DOI: 10.1101/2023.07.11.548615] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
In the brain, all neurons are driven by the activity of other neurons, some of which maybe simultaneously recorded, but most are not. As such, models of neuronal activity need to account for simultaneously recorded neurons and the influences of unmeasured neurons. This can be done through inclusion of model terms for observed external variables (e.g., tuning to stimuli) as well as terms for latent sources of variability. Determining the influence of groups of neurons on each other relative to other influences is important to understand brain functioning. The parameters of statistical models fit to data are commonly used to gain insight into the relative importance of those influences. Scientific interpretation of models hinge upon unbiased parameter estimates. However, evaluation of biased inference is rarely performed and sources of bias are poorly understood. Through extensive numerical study and analytic calculation, we show that common inference procedures and models are typically biased. We demonstrate that accurate parameter selection before estimation resolves model non-identifiability and mitigates bias. In diverse neurophysiology data sets, we found that contributions of coupling to other neurons are often overestimated while tuning to exogenous variables are underestimated in common methods. We explain heterogeneity in observed biases across data sets in terms of data statistics. Finally, counter to common intuition, we found that model non-identifiability contributes to bias, not variance, making it a particularly insidious form of statistical error. Together, our results identify the causes of statistical biases in common models of neural data, provide inference procedures to mitigate that bias, and reveal and explain the impact of those biases in diverse neural data sets.
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Affiliation(s)
- Pratik Sachdeva
- Physics Department, UC Berkeley
- Redwood Center for Theoretical Neuroscience, UC Berkeley
| | - Ji Hyun Bak
- Kavli Institute for Fundamental Neuroscience, UC San Francisco
- Biological Systems and Engineering Division, Lawrence Berkeley National Lab
| | - Jesse Livezey
- Biological Systems and Engineering Division, Lawrence Berkeley National Lab
| | - Christoph Kirst
- Kavli Institute for Fundamental Neuroscience, UC San Francisco
- Scientific Data Division, Lawrence Berkeley National Lab
- Deptartment of Anatomy, UC San Francisco
| | - Loren Frank
- Kavli Institute for Fundamental Neuroscience, UC San Francisco
- Departments of Physiology and Psychiatry, UC San Francisco
- Howard Hughes Medical Institute
| | | | - Kristofer E. Bouchard
- Redwood Center for Theoretical Neuroscience, UC Berkeley
- Kavli Institute for Fundamental Neuroscience, UC San Francisco
- Biological Systems and Engineering Division, Lawrence Berkeley National Lab
- Scientific Data Division, Lawrence Berkeley National Lab
- Helen Wills Neuroscience Institute, UC Berkeley
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