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McCarty MJ, Woolnough O, Mosher JC, Seymour J, Tandon N. The Listening Zone of Human Electrocorticographic Field Potential Recordings. eNeuro 2022; 9:ENEURO.0492-21.2022. [PMID: 35410871 PMCID: PMC9034754 DOI: 10.1523/eneuro.0492-21.2022] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Revised: 02/09/2022] [Accepted: 03/04/2022] [Indexed: 01/05/2023] Open
Abstract
Intracranial electroencephalographic (icEEG) recordings provide invaluable insights into neural dynamics in humans because of their unmatched spatiotemporal resolution. Yet, such recordings reflect the combined activity of multiple underlying generators, confounding the ability to resolve spatially distinct neural sources. To empirically quantify the listening zone of icEEG recordings, we computed correlations between signals as a function of distance (full width at half maximum; FWHM) between 8752 recording sites in 71 patients (33 female) implanted with either subdural electrodes (SDEs), stereo-encephalography electrodes (sEEG), or high-density sEEG electrodes. As expected, for both SDEs and sEEGs, higher frequency signals exhibited a sharper fall off relative to lower frequency signals. For broadband high γ (BHG) activity, the mean FWHM of SDEs (6.6 ± 2.5 mm) and sEEGs in gray matter (7.14 ± 1.7 mm) was not significantly different; however, FWHM for low frequencies recorded by sEEGs was 2.45 mm smaller than SDEs. White matter sEEGs showed much lower power for frequencies 17-200 Hz (q < 0.01) and a much broader decay (11.3 ± 3.2 mm) than gray matter electrodes (7.14 ± 1.7 mm). The use of a bipolar referencing scheme significantly lowered FWHM for sEEGs, relative to a white matter reference or a common average reference (CAR). These results outline the influence of array design, spectral bands, and referencing schema on local field potential recordings and source localization in icEEG recordings in humans. The metrics we derive have immediate relevance to the analysis and interpretation of both cognitive and epileptic data.
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Affiliation(s)
- Meredith J McCarty
- Vivian L. Smith Department of Neurosurgery, McGovern Medical School, University of Texas Health Houston, Houston, TX 77030
- Texas Institute for Restorative Neurotechnologies, University of Texas Health Science Center at Houston, Houston, TX 77030
| | - Oscar Woolnough
- Vivian L. Smith Department of Neurosurgery, McGovern Medical School, University of Texas Health Houston, Houston, TX 77030
- Texas Institute for Restorative Neurotechnologies, University of Texas Health Science Center at Houston, Houston, TX 77030
| | - John C Mosher
- Texas Institute for Restorative Neurotechnologies, University of Texas Health Science Center at Houston, Houston, TX 77030
| | - John Seymour
- Vivian L. Smith Department of Neurosurgery, McGovern Medical School, University of Texas Health Houston, Houston, TX 77030
- Texas Institute for Restorative Neurotechnologies, University of Texas Health Science Center at Houston, Houston, TX 77030
| | - Nitin Tandon
- Vivian L. Smith Department of Neurosurgery, McGovern Medical School, University of Texas Health Houston, Houston, TX 77030
- Texas Institute for Restorative Neurotechnologies, University of Texas Health Science Center at Houston, Houston, TX 77030
- Memorial Hermann Hospital, Texas Medical Center, Houston, TX 77030
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Kollo M, Racz R, Hanna ME, Obaid A, Angle MR, Wray W, Kong Y, Müller J, Hierlemann A, Melosh NA, Schaefer AT. CHIME: CMOS-Hosted in vivo Microelectrodes for Massively Scalable Neuronal Recordings. Front Neurosci 2020; 14:834. [PMID: 32848584 PMCID: PMC7432274 DOI: 10.3389/fnins.2020.00834] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2020] [Accepted: 07/16/2020] [Indexed: 01/20/2023] Open
Abstract
Mammalian brains consist of 10s of millions to 100s of billions of neurons operating at millisecond time scales, of which current recording techniques only capture a tiny fraction. Recording techniques capable of sampling neural activity at high spatiotemporal resolution have been difficult to scale. The most intensively studied mammalian neuronal networks, such as the neocortex, show a layered architecture, where the optimal recording technology samples densely over large areas. However, the need for application-specific designs as well as the mismatch between the three-dimensional architecture of the brain and largely two-dimensional microfabrication techniques profoundly limits both neurophysiological research and neural prosthetics. Here, we discuss a novel strategy for scalable neuronal recording by combining bundles of glass-ensheathed microwires with large-scale amplifier arrays derived from high-density CMOS in vitro MEA systems or high-speed infrared cameras. High signal-to-noise ratio (<25 μV RMS noise floor, SNR up to 25) is achieved due to the high conductivity of core metals in glass-ensheathed microwires allowing for ultrathin metal cores (down to <1 μm) and negligible stray capacitance. Multi-step electrochemical modification of the tip enables ultra-low access impedance with minimal geometric area, which is largely independent of the core diameter. We show that the microwire size can be reduced to virtually eliminate damage to the blood-brain-barrier upon insertion and we demonstrate that microwire arrays can stably record single-unit activity. Combining microwire bundles and CMOS arrays allows for a highly scalable neuronal recording approach, linking the progress in electrical neuronal recordings to the rapid progress in silicon microfabrication. The modular design of the system allows for custom arrangement of recording sites. Our approach of employing bundles of minimally invasive, highly insulated and functionalized microwires to extend a two-dimensional CMOS architecture into the 3rd dimension can be translated to other CMOS arrays, such as electrical stimulation devices.
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Affiliation(s)
- Mihaly Kollo
- Neurophysiology of Behaviour Laboratory, Francis Crick Institute, London, United Kingdom
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, United Kingdom
| | - Romeo Racz
- Neurophysiology of Behaviour Laboratory, Francis Crick Institute, London, United Kingdom
| | - Mina-Elraheb Hanna
- Department of Materials Science and Engineering, Stanford University, Stanford, CA, United States
- Paradromics, Inc., Austin, TX, United States
| | - Abdulmalik Obaid
- Department of Materials Science and Engineering, Stanford University, Stanford, CA, United States
| | | | - William Wray
- Neurophysiology of Behaviour Laboratory, Francis Crick Institute, London, United Kingdom
| | - Yifan Kong
- Paradromics, Inc., Austin, TX, United States
| | - Jan Müller
- ETH Zürich, Department of Biosystems Science and Engineering, Basel, Switzerland
- MaxWell Biosystems AG, Zurich, Switzerland
| | - Andreas Hierlemann
- ETH Zürich, Department of Biosystems Science and Engineering, Basel, Switzerland
| | - Nicholas A. Melosh
- Department of Materials Science and Engineering, Stanford University, Stanford, CA, United States
| | - Andreas T. Schaefer
- Neurophysiology of Behaviour Laboratory, Francis Crick Institute, London, United Kingdom
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, United Kingdom
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Cybulski TR, Boyden ES, Church GM, Tyo KEJ, Kording KP. Nucleotide-time alignment for molecular recorders. PLoS Comput Biol 2017; 13:e1005483. [PMID: 28459860 PMCID: PMC5432193 DOI: 10.1371/journal.pcbi.1005483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2016] [Revised: 05/15/2017] [Accepted: 03/24/2017] [Indexed: 11/18/2022] Open
Abstract
Using a DNA polymerase to record intracellular calcium levels has been proposed as a novel neural recording technique, promising massive-scale, single-cell resolution monitoring of large portions of the brain. This technique relies on local storage of neural activity in strands of DNA, followed by offline analysis of that DNA. In simple implementations of this scheme, the time when each nucleotide was written cannot be determined directly by post-hoc DNA sequencing; the timing data must be estimated instead. Here, we use a Dynamic Time Warping-based algorithm to perform this estimation, exploiting correlations between neural activity and observed experimental variables to translate DNA-based signals to an estimate of neural activity over time. This algorithm improves the parallelizability of traditional Dynamic Time Warping, allowing several-fold increases in computation speed. The algorithm also provides a solution to several critical problems with the molecular recording paradigm: determining recording start times and coping with DNA polymerase pausing. The algorithm can generally locate DNA-based records to within <10% of a recording window, allowing for the estimation of unobserved incorporation times and latent neural tunings. We apply our technique to an in silico motor control neuroscience experiment, using the algorithm to estimate both timings of DNA-based data and the directional tuning of motor cortical cells during a center-out reaching task. We also use this algorithm to explore the impact of polymerase characteristics on system performance, determining the precision of a molecular recorder as a function of its kinetic and error-generating properties. We find useful ranges of properties for DNA polymerase-based recorders, providing guidance for future protein engineering attempts. This work demonstrates a useful general extension to dynamic alignment algorithms, as well as direct applications of that extension toward the development of molecular recorders, providing a necessary stepping stone for future biological work.
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Affiliation(s)
- Thaddeus R. Cybulski
- Department of Physical Medicine and Rehabilitation, Rehabilitation Institute of Chicago, Northwestern University, Chicago, Illinois, United States of America
- * E-mail:
| | - Edward S. Boyden
- Media Lab, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
- McGovern Institute, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - George M. Church
- Biophysics Program, Harvard University, Boston, Massachusetts, United States of America
- Wyss Institute, Harvard University, Boston, Massachusetts, United States of America
- Department of Genetics, Harvard Medical School, Harvard University, Boston, Massachusetts, United States of America
| | - Keith E. J. Tyo
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, Illinois, United States of America
| | - Konrad P. Kording
- Department of Physical Medicine and Rehabilitation, Rehabilitation Institute of Chicago, Northwestern University, Chicago, Illinois, United States of America
- Department of Physiology, Northwestern University, Chicago, Illinois, United States of America
- Department of Applied Mathematics, Northwestern University, Evanston, Illinois, United States of America
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Muller L, Hamilton LS, Edwards E, Bouchard KE, Chang EF. Spatial resolution dependence on spectral frequency in human speech cortex electrocorticography. J Neural Eng 2016; 13:056013. [PMID: 27578414 PMCID: PMC5081035 DOI: 10.1088/1741-2560/13/5/056013] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
OBJECTIVE Electrocorticography (ECoG) has become an important tool in human neuroscience and has tremendous potential for emerging applications in neural interface technology. Electrode array design parameters are outstanding issues for both research and clinical applications, and these parameters depend critically on the nature of the neural signals to be recorded. Here, we investigate the functional spatial resolution of neural signals recorded at the human cortical surface. We empirically derive spatial spread functions to quantify the shared neural activity for each frequency band of the electrocorticogram. APPROACH Five subjects with high-density (4 mm center-to-center spacing) ECoG grid implants participated in speech perception and production tasks while neural activity was recorded from the speech cortex, including superior temporal gyrus, precentral gyrus, and postcentral gyrus. The cortical surface field potential was decomposed into traditional EEG frequency bands. Signal similarity between electrode pairs for each frequency band was quantified using a Pearson correlation coefficient. MAIN RESULTS The correlation of neural activity between electrode pairs was inversely related to the distance between the electrodes; this relationship was used to quantify spatial falloff functions for cortical subdomains. As expected, lower frequencies remained correlated over larger distances than higher frequencies. However, both the envelope and phase of gamma and high gamma frequencies (30-150 Hz) are largely uncorrelated (<90%) at 4 mm, the smallest spacing of the high-density arrays. Thus, ECoG arrays smaller than 4 mm have significant promise for increasing signal resolution at high frequencies, whereas less additional gain is achieved for lower frequencies. SIGNIFICANCE Our findings quantitatively demonstrate the dependence of ECoG spatial resolution on the neural frequency of interest. We demonstrate that this relationship is consistent across patients and across cortical areas during activity.
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Affiliation(s)
- Leah Muller
- Department of Neurological Surgery and Department of Physiology, University of California, San Francisco, 675 Nelson Rising Lane, Room 511, San Francisco, CA 94158, USA. Joint Program in Bioengineering, UC Berkeley/UC San Francisco, USA. Medical Scientist Training Program, UC San Francisco, USA
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Abstract
Direct human brain recordings have transformed the scope of neuroscience in the past decade. Progress has relied upon currently available neurophysiological approaches in the context of patients undergoing neurosurgical procedures for medical treatment. While this setting has provided precious opportunities for scientific research, it also has presented significant constraints on the development of new neurotechnologies. A major challenge now is how to achieve high-resolution spatiotemporal neural recordings at a large scale. By narrowing the gap between current approaches, new directions tailored to the mesoscopic (intermediate) scale of resolution may overcome the barriers towards safe and reliable human-based neurotechnology development, with major implications for advancing both basic research and clinical translation.
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