Jorg Scholvin

Three-dimensional multiwaveguide probe array for light delivery to distributed brain circuits

To deliver light to the brain for neuroscientific and neuroengineering applications like optogenetics, in which light is used to activate or silence neurons expressing specific photosensitive proteins, optical fibers are commonly used. However, an optical fiber is limited to delivering light to a single target within the 3D structure of the brain. Here, we describe the design and fabrication of an array of thin microwaveguides, which terminates at a three-dimensionally distributed set of points, appropriate for delivering light to targets distributed in a 3D pattern throughout the brain.

Cell diversity and network dynamics in photosensitive human brain organoids

In vitro models of the developing brain such as three-dimensional brain organoids offer an unprecedented opportunity to study aspects of human brain development and disease. However, the cells generated within organoids and the extent to which they recapitulate the regional complexity, cellular diversity and circuit functionality of the brain remain undefined. Here we analyse gene expression in over 80,000 individual cells isolated from 31 human brain organoids. We find that organoids can generate a broad diversity of cells, which are related to endogenous classes, including cells from the cerebral cortex and the retina. Organoids could be developed over extended periods (more than 9 months), allowing for the establishment of relatively mature features, including the formation of dendritic spines and spontaneously active neuronal networks. Finally, neuronal activity within organoids could be controlled using light stimulation of photosensitive cells, which may offer a way to probe the functionality of human neuronal circuits using physiological sensory stimuli.

Close-Packed Silicon Microelectrodes for Scalable Spatially Oversampled Neural Recording

Objective: Neural recording electrodes are important tools for understanding neural codes and brain dynamics. Neural electrodes that are closely packed, such as in tetrodes, enable spatial oversampling of neural activity, which facilitates data analysis. Here we present the design and implementation of close-packed silicon microelectrodes to enable spatially oversampled recording of neural activity in a scalable fashion. Methods: Our probes are fabricated in a hybrid lithography process, resulting in a dense array of recording sites connected to submicron dimension wiring. Results: We demonstrate an implementation of a probe comprising 1000 electrode pads, each 9 × 9 μm, at a pitch of 11 μm. We introduce design automation and packaging methods that allow us to readily create a large variety of different designs. Significance: We perform neural recordings with such probes in the live mammalian brain that illustrate the spatial oversampling potential of closely packed electrode sites.

Heterogeneous neural amplifier integration for scalable extracellular microelectrodes

We here demonstrate multi-chip heterogeneous integration of microfabricated extracellular recording electrodes with neural amplifiers, highlighting a path to scaling electrode channel counts without the need for more complex monolithic integration. We characterize the noise and impedance performance of the heterogeneously integrated neural recording electrodes, and analyze the design parameters that enable the low-voltage neural input signals to co-exist with the high-frequency and high-voltage digital outputs on the same silicon substrate. This heterogeneous integration approach can enable future scaling efforts for microfabricated neural probes, and provides a design path for modular, fast, and independent scaling innovations in recording electrodes and neural amplifiers.

Principles of high–fidelity, high–density 3–d neural recording

New probe technologies, neural amplifier systems, and data acquisition systems enable the extracellular electrical recording of ever greater numbers of neurons in the live mammalian brain. These recordings have the potential to increase our understanding of neuronal network dynamics, but much remains uncharacterized about the possibilities and limitations of extracellular techniques. We explore these possibilities and limitations in the context of spike sorting and probe design. Spike sorting is a critical analysis step for extracellular data, which attempts to separate raw electrode traces into the activity patterns of individual neurons. Given the labor associated with manual spike sorting of large datasets, the necessity for automated spike sorting method will only increase. An automated method would ideally commit no errors in spike assignment – that is, it would associate each extracted individual neuron with all the spikes fired by a single neuron, and with no spikes not fired by that same neuron. The elimination of errors would reduce the reliance on manual validation, saving large amounts of analysis time, and also reduce downstream biases in data analyses introduced by errors in spike sorting. We explore designs for multi-electrode probes and spike sorting methods that in combination allow high accuracy in spike assignment with many neurons extracted. To this end, first, we ask if an automated spike sorting method with zero spike assignment errors is possible. Second, we explore what multi-electrode probe designs produce optimal yield using this method. n nWe have constructed a spike sorting method for the case of spatially dense high channel count extracellular recordings, first applying a well-established source separation technique called Independent Components Analysis (ICA) to continuous recordings. Second, we apply a classifier to the ICA components, keeping only putative single neuron units that are well separated from noise and other units. To test this algorithm, we simulated multielectrode probe data that encompasses many of the realistic variations and noisinesses of natural neural data, including spatial non-linearities in spike shape from individual cells. Running this algorithm against this simulated data, for a wide range of classifier parameters, we find that no spike assignment errors are committed. This result is robust to changes in neural firing rate, neural density, Gaussian noise, and increasing electrode density on the probes. n nHowever, for probes with electrode counts similar to those in commercial probes (10-50), only a handful of neurons are extracted. Exploring the space of probe designs, we find that designing probes with higher electrode density (for a fixed area) can compensate for this low yield. As the electrode density on the probe increases, the number of neurons extracted increases to some saturation. We also find that as the electrode density increases, we are able to extract neurons with spike peak magnitudes below the thresholding noise floor. n nThus, the construction of very high density multielectrode arrays, coupled to the algorithm here proposed, may yield experimental approaches for recording very large numbers of neurons in the live brain, and automatically analyzing the resulting spike trains.

Scaling models for microfabricated in vivo neural recording technologies

Microfabrication technology can enable extracellular neural recording electrodes with unprecedented wiring density, and the ability to benefit from continued CMOS technology scaling. A neural recording electrode consists of recording sites that sense electrical activity inside the brain, and wiring that routes these signals to neural amplifiers outside the brain. We here introduce a scalable circuit model for recording sites and signal routing, valid for different amplifier integration approaches. We define noise and cross-talk requirements, and analyze how future CMOS technology scaling will drive the ability to record from increasingly large number of sites in the mammalian brain. This analysis provides an important step in understanding how advances of MEMS and CMOS fabrication can be utilized in large-scale recording efforts of many thousands to possibly millions of neurons.

Scalable, Modular Three-Dimensional Silicon Microelectrode Assembly via Electroless Plating

We devised a scalable, modular strategy for microfabricated 3-D neural probe synthesis. We constructed a 3-D probe out of individual 2-D components (arrays of shanks bearing close-packed electrodes) using mechanical self-locking and self-aligning techniques, followed by electroless nickel plating to establish electrical contact between the individual parts. We detail the fabrication and assembly process and demonstrate different 3-D probe designs bearing thousands of electrode sites. We find typical self-alignment accuracy between shanks of <0.2° and demonstrate orthogonal electrical connections of 40 µm pitch, with thousands of connections formed electrochemically in parallel. The fabrication methods introduced allow the design of scalable, modular electrodes for high-density 3-D neural recording. The combination of scalable 3-D design and close-packed recording sites may support a variety of large-scale neural recording strategies for the mammalian brain.

Multiplexed neural recording along a single optical fiber via optical reflectometry

Abstract. We introduce the design and theoretical analysis of a fiber-optic architecture for neural recording without contrast agents, which transduces neural electrical signals into a multiplexed optical readout. Our sensor design is inspired by electro-optic modulators, which modulate the refractive index of a waveguide by applying a voltage across an electro-optic core material. We estimate that this design would allow recording of the activities of individual neurons located at points along a 10-cm length of optical fiber with 40-μm axial resolution and sensitivity down to 100u2009u2009μV using commercially available optical reflectometers as readout devices. Neural recording sites detect a potential difference against a reference and apply this potential to a capacitor. The waveguide serves as one of the plates of the capacitor, so charge accumulation across the capacitor results in an optical effect. A key concept of the design is that the sensitivity can be improved by increasing the capacitance. To maximize the capacitance, we utilize a microscopic layer of material with high relative permittivity. If suitable materials can be found—possessing high capacitance per unit area as well as favorable properties with respect to toxicity, optical attenuation, ohmic junctions, and surface capacitance—then such sensing fibers could, in principle, be scaled down to few-micron cross-sections for minimally invasive neural interfacing. We study these material requirements and propose potential material choices. Custom-designed multimaterial optical fibers, probed using a reflectometric readout, may, therefore, provide a powerful platform for neural sensing.We introduce a fiber-optic architecture for neural recordin g without contrast agents, and study its properties theoretically. Our sensor design is inspired by electrooptic mo dulators, which modulate the refractive index of a waveguid e by applying an electric field across an electrooptic core mat eri l, and allows recording of the activities of individual neurons located at points along a 10 cm length of optical fiber with20μm axial resolution, sensitivity down to 100μV and a dynamic range of up to 1V using commercially available optical reflectometers as re adout devices. A key concept of the design is the ability to create an “intensified” el ectric field inside an optical waveguide by applying the extracellular voltage from a neural spike over a nanoscopic distance. Implementing this concept requires the use of ultrathin high-dielectric capacitor layers. If suitable m aterials can be found – possessing favorable properties wit h respect to toxicity, ohmic junctions, and surface capacita n e – then such sensing fibers could, in principle, be scaled down to few-micron cross-sections for minimally invasive n eural interfacing. Custom-designed multi-material optic al fibers, probed using a reflectometric readout, may therefore provide a powerful platform for neural sensing.