64-Channel Carbon Fiber Electrode Arrays for Chronic Electrophysiology

A chief goal in neuroscience is to understand how neuronal activity relates to behavior, perception, and cognition. However, monitoring neuronal activity over long periods of time is technically challenging, and limited, in part, by the invasive nature of recording tools. While electrodes allow for recording in freely-behaving animals, they tend to be bulky and stiff, causing damage to the tissue they are implanted in. One solution to this invasiveness problem may be probes that are small enough to fly under the immune system's radar. Carbon fiber (CF) electrodes are thinner and more flexible than typical metal or silicon electrodes, but the arrays described in previous reports had low channel counts and required time-consuming manual assembly. Here we report the design of an expanded-channel-count carbon fiber electrode array (CFEA) as well as a method for fast preparation of the recording sites using acid etching and electroplating with PEDOT-TFB, and demonstrate the ability of the 64-channel CFEA to record from rat visual cortex. We include designs for interfacing the system with micro-drives or flex-PCB cables for recording from multiple brain regions, as well as a facilitated method for coating CFs with the insulator Parylene-C. High-channel-count CFEAs may thus be an alternative to traditional microwire-based electrodes and a practical tool for exploring the neural code.

[1]  V. Verkhusha,et al.  Fast reversibly photoswitching red fluorescent proteins for live-cell RESOLFT nanoscopy , 2018, Nature Methods.

[2]  Ashesh K Dhawale,et al.  Automated long-term recording and analysis of neural activity in behaving animals , 2016, bioRxiv.

[3]  Matthew A. Wilson,et al.  Micro-drive Array for Chronic in vivo Recording: Tetrode Assembly , 2009, Journal of visualized experiments : JoVE.

[4]  M. C. Rezende,et al.  Evaluation of carbon fiber surface treated by chemical and cold plasma processes , 2005 .

[5]  A. Michael,et al.  Brain Tissue Responses to Neural Implants Impact Signal Sensitivity and Intervention Strategies , 2014, ACS chemical neuroscience.

[6]  Jeremy F. Magland,et al.  A Fully Automated Approach to Spike Sorting , 2017, Neuron.

[7]  Selmaan N. Chettih,et al.  Voltage imaging and optogenetics reveal behavior dependent changes in hippocampal dynamics , 2019, Nature.

[8]  Ashesh K Dhawale,et al.  Motor Cortex Is Required for Learning but Not for Executing a Motor Skill , 2015, Neuron.

[9]  Anthony M. Zador,et al.  Cellular barcoding: lineage tracing, screening and beyond , 2018, Nature Methods.

[10]  B. McNaughton,et al.  Tetrodes markedly improve the reliability and yield of multiple single-unit isolation from multi-unit recordings in cat striate cortex , 1995, Journal of Neuroscience Methods.

[11]  W. Fitch,et al.  Birds have primate-like numbers of neurons in the forebrain , 2016, Proceedings of the National Academy of Sciences.

[12]  David C. Martin,et al.  Neuronal cell loss accompanies the brain tissue response to chronically implanted silicon microelectrode arrays , 2005, Experimental Neurology.

[13]  K. Ganguly,et al.  Sleep-Dependent Reactivation of Ensembles in Motor Cortex Promotes Skill Consolidation , 2015, PLoS biology.

[14]  Daniel N Hill,et al.  Quality Metrics to Accompany Spike Sorting of Extracellular Signals , 2011, The Journal of Neuroscience.

[15]  Nancy Kopell,et al.  Close-Packed Silicon Microelectrodes for Scalable Spatially Oversampled Neural Recording , 2015, IEEE Transactions on Biomedical Engineering.

[16]  Felix Deku,et al.  Carbon fiber on polyimide ultra-microelectrodes , 2017, bioRxiv.

[17]  Q. Wu,et al.  EFFECT OF SIZING ON THE INTERFACIAL PROPERTIES OF CARBON FIBER / BMI UNDER DIFFERENT PROCESSING TEMPERATURE , 2013 .

[18]  Nicholas A. Steinmetz,et al.  Spontaneous behaviors drive multidimensional, brainwide activity , 2019, Science.

[19]  Takashi Kawashima,et al.  A robotic multidimensional directed evolution approach applied to fluorescent voltage reporters , 2017, Nature Chemical Biology.

[20]  Karl Deisseroth,et al.  Optetrode: a multichannel readout for optogenetic control in freely moving mice , 2011, Nature Neuroscience.

[21]  P. Tresco,et al.  Response of brain tissue to chronically implanted neural electrodes , 2005, Journal of Neuroscience Methods.

[22]  D. Grijpma,et al.  Development of biodegradable hyper-branched tissue adhesives for the repair of meniscus tears. , 2016, Acta biomaterialia.

[23]  Hamid Charkhkar,et al.  Improving the performance of poly(3,4-ethylenedioxythiophene) for brain-machine interface applications. , 2014, Acta biomaterialia.

[24]  Hamid Charkhkar,et al.  Chronic intracortical neural recordings using microelectrode arrays coated with PEDOT-TFB. , 2016, Acta biomaterialia.

[25]  Paras R. Patel,et al.  Ultrasmall implantable composite microelectrodes with bioactive surfaces for chronic neural interfaces. , 2012, Nature materials.

[26]  Caitlin Deane Resistance mechanisms: Watering down a warhead. , 2017, Nature chemical biology.

[27]  Richard Mooney,et al.  Neurobiology of song learning , 2009, Current Opinion in Neurobiology.

[28]  Yu-Wei Wu,et al.  Massively parallel microwire arrays integrated with CMOS chips for neural recording , 2019, Science Advances.

[29]  Konrad P. Körding,et al.  Statistical Analysis of Molecular Signal Recording , 2013, PLoS Comput. Biol..

[30]  Surya Ganguli,et al.  A theory of multineuronal dimensionality, dynamics and measurement , 2017, bioRxiv.

[31]  Samantha R Santacruz,et al.  A high-density carbon fiber neural recording array technology. , 2019, Journal of neural engineering.

[32]  Gian Nicola Angotzi,et al.  SiNAPS: An implantable active pixel sensor CMOS-probe for simultaneous large-scale neural recordings. , 2019, Biosensors & bioelectronics.

[33]  Keith B. Hengen,et al.  Firing Rate Homeostasis in Visual Cortex of Freely Behaving Rodents , 2013, Neuron.

[34]  Sergey L. Gratiy,et al.  Fully integrated silicon probes for high-density recording of neural activity , 2017, Nature.

[35]  Miguel A. L. Nicolelis,et al.  State-of-the-Art Microwire Array Design for Chronic Neural Recordings in Behaving Animals , 2008 .

[36]  Philipp J. Keller,et al.  Light-sheet functional imaging in fictively behaving zebrafish , 2014, Nature Methods.

[37]  Xiao Yang,et al.  Mesh electronics: a new paradigm for tissue-like brain probes , 2018, Current Opinion in Neurobiology.

[38]  Jakob Voigts,et al.  The flexDrive: an ultra-light implant for optical control and highly parallel chronic recording of neuronal ensembles in freely moving mice , 2013, Front. Syst. Neurosci..

[39]  Matthew A. Wilson,et al.  Micro-drive Array for Chronic in vivo Recording: Drive Fabrication , 2009, Journal of visualized experiments : JoVE.

[40]  Michael Z. Lin,et al.  Genetically encoded indicators of neuronal activity , 2016, Nature Neuroscience.

[41]  Mark F. Bear,et al.  Learned spatiotemporal sequence recognition and prediction in primary visual cortex , 2014, Nature Neuroscience.

[42]  Shay Ohayon,et al.  Open Ephys: an open-source, plugin-based platform for multichannel electrophysiology , 2017, Journal of neural engineering.

[43]  Rafael Yuste,et al.  Nanotools for neuroscience and brain activity mapping. , 2013, ACS nano.

[44]  X Tracy Cui,et al.  Effects of caspase-1 knockout on chronic neural recording quality and longevity: insight into cellular and molecular mechanisms of the reactive tissue response. , 2014, Biomaterials.

[45]  Huanan Zhang,et al.  Chronic in vivo stability assessment of carbon fiber microelectrode arrays , 2016, Journal of neural engineering.

[46]  Weijian Yang,et al.  In vivo imaging of neural activity , 2017, Nature Methods.

[47]  Brad E. Pfeiffer,et al.  Hippocampal place cell sequences depict future paths to remembered goals , 2013, Nature.

[48]  K. Pister,et al.  Open-source automated system for assembling a high-density microwire neural recording array , 2016, 2016 International Conference on Manipulation, Automation and Robotics at Small Scales (MARSS).

[49]  Chris Boldt,et al.  Creating low-impedance tetrodes by electroplating with additives. , 2009, Sensors and actuators. A, Physical.

[50]  Jelena Platisa,et al.  Genetically encoded fluorescent voltage indicators: are we there yet? , 2018, Current Opinion in Neurobiology.

[51]  Lagnajeet Pradhan,et al.  Ultrafast Two-Photon Imaging of a High-Gain Voltage Indicator in Awake Behaving Mice , 2019, Cell.

[52]  D. Hubel Tungsten Microelectrode for Recording from Single Units. , 1957, Science.

[53]  M. Nicolelis,et al.  Brain-Machine Interfaces: From Basic Science to Neuroprostheses and Neurorehabilitation. , 2017, Physiological reviews.

[54]  Hang Yu,et al.  Light-Sheet Microscopy in Neuroscience. , 2019, Annual review of neuroscience.

[55]  Jayashree Bijwe,et al.  Surface Treatment of Carbon Fibers - A Review , 2014 .

[56]  M. Carandini,et al.  Long Term Recordings with Immobile Silicon Probes in the Mouse Cortex , 2015, bioRxiv.

[57]  C. Koch,et al.  The origin of extracellular fields and currents — EEG, ECoG, LFP and spikes , 2012, Nature Reviews Neuroscience.

[58]  Eran Stark,et al.  Large-scale recording of neurons by movable silicon probes in behaving rodents. , 2012, Journal of visualized experiments : JoVE.

[59]  Takafumi Suzuki,et al.  Simultaneous recording of ECoG and intracortical neuronal activity using a flexible multichannel electrode-mesh in visual cortex , 2011, NeuroImage.

[60]  Aaron C. Koralek,et al.  Corticostriatal plasticity is necessary for learning intentional neuroprosthetic skills , 2012, Nature.

[61]  Timothy W. Dunn,et al.  Brain-wide mapping of neural activity controlling zebrafish exploratory locomotion , 2016, eLife.

[62]  William A Liberti,et al.  A carbon-fiber electrode array for long-term neural recording , 2013, Journal of neural engineering.

[63]  Georg B. Keller,et al.  Learning Enhances Sensory and Multiple Non-sensory Representations in Primary Visual Cortex , 2015, Neuron.

[64]  James J DiCarlo,et al.  A rodent model for the study of invariant visual object recognition , 2009, Proceedings of the National Academy of Sciences.

[65]  Daryl R Kipke,et al.  Advanced Neurotechnologies for Chronic Neural Interfaces: New Horizons and Clinical Opportunities , 2008, The Journal of Neuroscience.

[66]  Andreas Nieder,et al.  Associative learning rapidly establishes neuronal representations of upcoming behavioral choices in crows , 2015, Proceedings of the National Academy of Sciences.