1 Graduate School of Biomedical Engineering, University of New South Wales, Sydney, NSW, Australia, 2 Clem Jones Centre for Neurobiology and Stem Cell Research, Menzies Health Institute Queensland, Griffiths University, Gold Coast, QLD, Australia, 3 Department of Bioengineering, Imperial College London, London, United Kingdom

Intracortical electrodes for brain–machine interfaces rely on intimate contact with tissues for recording signals and stimulating neurons. However, the long-term viability of intracortical electrodes in vivo is poor, with a major contributing factor being the development of a glial scar. In vivo approaches for evaluating responses to intracortical devices are resource intensive and complex, making statistically significant, high throughput data difficult to obtain. In vitro models provide an alternative to in vivo studies; however, existing approaches have limitations which restrict the translation of the cellular reactions to the implant scenario. Notably, there is no current robust model that includes astrocytes, microglia, oligodendrocytes and neurons, the four principle cell types, critical to the health, function and wound responses of the central nervous system (CNS). In previous research a co-culture of primary mouse mature mixed glial cells and immature neural precursor cells were shown to mimic several key properties of the CNS response to implanted electrode materials. However, the method was not robust and took up to 63 days, significantly affecting reproducibility and widespread use for assessing brainmaterial interactions. In the current research a new co-culture approach has been developed and evaluated using immunocytochemistry and quantitative polymerase chain reaction (qPCR). The resulting method reduced the time in culture significantly and the culture model was shown to have a genetic signature similar to that of healthy adult mouse brain. This new robust CNS culture model has the potential to significantly improve the capacity to translate in vitro data to the in vivo responses.

Edited by:

#### Yuki Hayashida, Osaka University, Japan

Reviewed by:

Takashi D. Y. Kozai, University of Pittsburgh, United States Jeffrey R. Capadona, Case Western Reserve University, United States

#### \*Correspondence:

Rylie A. Green<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Neural Technology, a section of the journal Frontiers in Neuroscience

Received: 20 August 2019 Accepted: 02 December 2019 Published: 17 December 2019

#### Citation:

Gilmour A, Poole-Warren L and Green RA (2019) An Improved in vitro Model of Cortical Tissue. Front. Neurosci. 13:1349. doi: 10.3389/fnins.2019.01349 Keywords: brain machine interface, in vitro prediction, CNS, cell culture, neural interface response

# INTRODUCTION

Investigating the biocompatibility of brain interfacing devices using animal models is expensive, time consuming (Gilmour et al., 2016) and data yield from each animal can be limited by the tissue processing and histological methods used within a study (Woolley et al., 2011). However, existing in vitro models for investigating central nervous system (CNS)-device interactions are not a viable alternative, as they poorly represent the complex cell interactions within the CNS and provide little information on the expected in vivo response (Horvath et al., 2016; Belle et al., 2018).  An ideal solution is a cell culture model with enough complexity to enable useful insight into implant performance, while not compromising on capacity to trial multiple variables.

**44**

For neural cell culture models to be mimetic of the CNS in health and disease, mimicking cell–cell interactions is essential. Interactions both within and between individual glial and neural cell types are critical for the development, function and dysfunction of the CNS (Jäkel and Dimou, 2017). The astrocyte– microglia interaction is the most notable cell–cell interaction and it is pivotal in development, normal function, and response to damage (Liddelow et al., 2017; Yates, 2017). Astrocytes and microglia perform multiple roles in CNS development, ongoing health, and degenerative disease (Burda and Sofroniew, 2014; Pekny and Pekna, 2014; Ferreira and Bernardino, 2015; Sofroniew, 2015; Ziebell et al., 2015; Burda et al., 2016; Liddelow and Barres, 2017). Importantly, the functions of these cells evolve during development undergoing dynamic genotypic and phenotypic changes which are integral to the development of the CNS (see Reemst et al., 2016; Hasel et al., 2017 for in depth reviews). Glial cells change roles from promoting development of neural networks and myelination, to maintaining the complex function of the adult CNS. In response to injury in the mature CNS, glial cells within the wound parenchyma transition to a reactive state (Silver and Miller, 2004; Anderson et al., 2014; Gilmour et al., 2016). In this reactive state mature glial cells produce an environment which does not support redevelopment of neural networks, inhibiting neuronal cell migration and axonal growth (Smith et al., 1990; Canning et al., 1996; Fawcett and Asher, 1999; Faulkner, 2004; Sofroniew, 2009; Cregg et al., 2014; Burda et al., 2016). In contrast, immature glial cells from fetal or neonatal origins lack the ability to undergo reactive gliosis-like reactions in vivo and in vitro (Schwartz et al., 1989; Wu and Schwartz, 1998).

A number of mixed glial and neuronal cultures have been developed in an attempt to incorporate complex cell behaviors into in vitro models (Potter and DeMarse, 2001; Polikov et al., 2006; Thomson et al., 2008; Nash et al., 2011b; Boomkamp et al., 2012; Sommakia et al., 2014). It is expected that this complexity introduces improved alignment with the in vivo CNS cell response. However, these culture models often have intricate, multistep methodologies (Polikov et al., 2009), are extremely sensitive to minor modifications and require additional stimulating factors to induce reactive gliosis, limiting their value as a high-throughput assessment tool (Gilmour et al., 2016). Current models have a second limitation whereby the apparent upregulation of glial fibrillary acidic protein (GFAP) and Iba1 in astrocytes and microglia respectively in response to insult does not impact on neural health and regrowth (Polikov et al., 2006; Sommakia et al., 2014). The maturity of glial cells and their relative ability to undergo reactive gliosis has implications for the development and use of complex culture models for modeling CNS and effects of injury. In brain injury and device interactions, scar tissue is formed with glial cells being the dominant component. These cells modulate neuron and oligodendrocyte function, survival, or dieback in the surrounding tissues (Sofroniew, 2009; Burda and Sofroniew, 2014; Burda et al., 2016). In rodents, astrocytes start to express mature genotypes and phenotypes after 3–4 weeks postnatal development (Yang et al., 2013a; Reemst et al., 2016; Hasel et al., 2017) which aligns with the end of the major period of astrogenesis. In contrast the relative maturity of the glial cell populations in prior cultures (Polikov et al., 2006; Sommakia et al., 2014) is equivalent to postnatal days 7–14 (Reemst et al., 2016), at which age rodents are still undergoing neurological development. To achieve adequate glia maturity in these cultures it is estimated that glia would need to be cultured for at least 35 days. It was therefore hypothesized that a more mature population of glial cells are required to enable a CNS culture model with capacity to respond appropriately to injury and implants. The objective of this research was to develop a simple, robust and validated model of the mature rodent CNS. Such a culture could be used for better understanding cell–cell interactions in the CNS, and for mechanistic investigations into CNS injury, repair, and interactions with neural devices.

Co-culture models have been developed to enable understanding and probing of specific glial–neural or glial– glial cell interactions (Banker and Cowan, 1977; Ishikawa et al., 1996; Plenz and Aertsen, 1996; Nakanishi et al., 1999; Flanagan et al., 2002; Faria et al., 2006; Cullen et al., 2007; Wanner et al., 2008; Shimizu et al., 2011; Bogdanowicz and Lu, 2013; van Duinen et al., 2015) of defined cell populations. Previous research (Gilmour, 2018) identified mixed glial cells (MGCs) derived from neonatal mice and cultured for 21 days prior to co-culture generated a glial cell population which was capable of reactive gliosis. Co-culture can be approached by either combining cells in a single concurrent plating step or by staggering the plating to enable one population to develop, prior to addition of the second population. Previous attempts to combine glia and neurons have generally focused on step-wise combinations. One such approach has been the continuous culture of glial cells until they obtain maturity, followed by direct co-culture of neural progenitors (Gilmour, 2018). Despite showing that this culture method develops neural networks which respond to injury at the glial and neuronal level, there are a number of shortcomings limiting this method. First to obtain mature neural networks a continuous culture timeline of ≥ 45 days was required. Second, reproducibility which included failure to obtain time mated embryos at the correct time point (≈66% of failures), poor growth of MGCs after passage (≈15%) and less commonly contaminating cells overgrowing MGC cultures after passaging (≈10%) with an overall failure rate of ≈86%. As such, a more flexible and time efficient method is required to enable complex co-culture of MGCs in combination with neuroprogenitor cells. To address the long culture times and potential for mismatch in time mating, this study proposed the use of frozen mature glial populations that can be stored and reanimated to ensure flexibility and minimization of culture timeframes.

#### MATERIALS AND METHODS

All the chemicals and biological materials were obtained from Sigma-Aldrich (Australia) unless otherwise stated. MCG media consisted of 10% fetal calf serum, in DMEM with L-glutamine. DMMC and co-cultures used three types of media previously described in Thomson et al. (2008), being plating media (PM), defined media with insulin (DfM + I) and defined media without insulin (DfM).

# Co-culture Methodologies

fnins-13-01349 December 14, 2019 Time: 15:49 # 3

Co-cultures were formed through the combination of 30% MGC and 70% DMMC cells. Once in co-culture format they were fed three times per week with DfM + I for the first 12 days then transitioned to DfM thereafter. Co-cultures were grown on PLL coated glass for developing and assessing the baseline performance of the methods relative to both whole brain extract (for qPCR) and the DMMC cultures as developed by Sorensen et al. (2008) and Thomson et al. (2008).

# Primary Mixed Glia Culture (MGC)

All animal procedures were conducted in accordance with University of New South Wales animal ethics protocols (ACEC 13/44A). Postnatal 1–3 day old mouse pups were euthanized by exposure to excess gaseous isoflurane followed by decapitation. The isolation and culture of MGCs was performed as previously published in Goding et al. (2015), with the following modifications. Cultures were maintained until 80% confluence (approximately 7–10 days) in poly-L-lysine (134 ug mL−1 ) coated T75 tissue culture flasks. Once confluent cultures were trypsinised then frozen in DMEM + 10% FBS with the addition of 10% DMSO. Briefly, cultures were rinsed twice with PBS (without cations) then incubated with 3 mL 0.25% trypsin for 5 min. Trypsin was deactivated by the addition of DMEM + 10% FBS. The resulting cell suspension was centrifuged for 5 min at 290 g. Cell concentration was determined with a hemocytometer and diluted with DMEM + 10% FBS to achieve 2∗ 10ˆ6 cells mL−1 in freezing media.

# Dissociated Mixed Myelinating Culture (DMMC)

Dissociated mixed myelinating culture (DMMCs) were produced using the methods developed in Thomson et al. (2008) with minor modifications. Briefly, gestational day 13.5 pregnant mice were euthanized by an overdose of isoflurane followed by cervical dislocation. Embryos were extracted, spinal cords were removed and stripped of meninges. Harvested spinal cords were dissociated manually, followed by 20 min in 0.25% trypsin EDTA with 0.1% w/v type 1 collagenase. Stop digestion mix was added [40 µg mL−1 DNase, 250 µg mL−1 trypsin inhibitor, 3 mg mL−1 bovine serum albumin fraction V (BSA-V) dissolved in Leibovitz's L15 (Thermo Fisher Scientific, Australia)]. The cell suspension was then passed three times though a 21G needle followed by two times through a 23G needle. The cell suspension was diluted in PM and centrifuged at 290 g for 5 min. The cell pellet was resuspended in PM and cell counting was conducted with a hemocytometer. The DMMC cell suspension was then plated out at 1.5∗ 10ˆ6 cells cm−1 onto PLL coated coverslips. After 2 h the culture media was topped up to 500 µL PM with 500 µL DfM + I. Alternatively the cell suspension was used in co-cultures as described below. Cultures were fed three times per week by replacing 50% of the media, using DfM + I for the first 12 days followed by DfM thereafter.

# Layered Co-culture

Time mating was undertaken to obtain E13.5 embryos for tissue harvesting for DMMC cultures. On the day a successful plug was noted frozen MGC were thawed in a 37◦C water bath, once thawed the cell suspension was diluted with DMEM + 10% FBS then centrifuged at 290 g for 3 min. Revived cells were placed in a PLL coated T75 flask and cultured for 2 days in DMEM + 20% FBS then changed to 10% FBS. After 4 days MGC cultures were passaged and plated at 4∗ 10ˆ4 cells cm−2 in 1 mL of DMEM + 10% FBS onto PLL coated glass coverslips. Once DMMC were harvested, all media was removed from MGC cultures and DMMC were plated on top in 500 µL of PM at 1.1∗ 10ˆ5 cells cm−1 . Cultures were then fed as per DMMC protocol above.

# Concurrent Co-culture

Mice were time mated and MGC cultures were revived as above, however MGC were not thawed until day 9.5 of pregnancy (4 days prior to embryonic spinal cord harvest). After spinal cord tissue was harvested, dissociated and suspended at 4.4∗ 10ˆ5 cells mL−1 in plating media, MGC cultures were passaged from the T75 flasks and resuspended to a concentration of 1.6∗ 10ˆ5 cells mL−1 . The MGC and DMMC cell suspensions were mixed 1:1 resulting in a final concentration of 3∗ 10ˆ5 cells mL−1 , 500 µL of cell suspension was plated per coverslip. Cultures were maintained as described for DMMC above.

# Immunocytochemistry and Image Analysis

Cultures were fixed at 21, 28, and 35 days in 4% w/v formaldehyde and processed for microscopy. All primary and secondary antibodies were diluted in blocking buffer, immediately prior to use. Secondary antibodies were raised in goat and conjugated to either Dylight R or Alexa Fluor R 405, 488, 555, and 647 nm fluorophores diluted at 1:200. Primary antibodies were against GFAP (Abcam; ab134436), Iba1 (Wako; 019- 19741, RRID:AB\_839504) 200 kDa heavy chain neurofilament (Abcam; ab7795, RRID: AB\_306084) (H-NF) and proteolipid protein (PLP/DM20) from a hybridoma (RRID: AB\_2341144) (Jung et al., 1996).

All images were acquired using a Zeiss 780 laser scanning microscope (LSM) with a Plan-Apochromat 20x/0.8 M27 objective. Non-overlapping regions were captured as z-stacks, with 5 areas per sample. Images were post-processed with ImageJ software (ImageJ 1.50e, National Institutes of Health, United States) implemented on Java 1.8.0\_11 (64-bit). Individual channels were deconvolved with 15 iterations of the Richardson-Lucy algorithm implemented via "DevonvolutionLab" plugin (Soltys et al., 2001) with a theoretical point spread function (PSF) and minimal intensity background subtraction. A theoretical PSF was generated with the "Diffraction PSF 3D" plugin for ImageJ to match the dimensions of the acquired images.

The N-NF and PLP/DM20 channels were processed for colocalization to assess the level of interaction under different culture conditions. Colocalization was performed the with Coloc 2 plugin with the default settings. Threshold values generated from Coloc 2 were used as thresholds for the binary conversion of Z-stacks. Z-stacks were converted into maximum intensity projections and total coverage of each channel was expressed as a fraction of the total area in µm2 .

#### Statistical Analysis

fnins-13-01349 December 14, 2019 Time: 15:49 # 4

Statistical analysis was performed in GraphPad Prism 7.03 (GraphPad Software, La Jolla, CA, United States), all data sets were tested for outliers using ROUT method Q = 0.1 (99% confidence that data point is an outlier). A one-way ANOVA followed by Tukey's multiple comparisons test with a p < 0.05 were considered as significant.

### Quantitative PCR

Cultures for messenger ribonucleic acid (mRNA) extraction were rinsed 1x with ice cold DPBS and processed with the ReliaPrepTM RNA cell miniprep system (Promega, Australia) following manufactures instructions. The final RNA extract was eluted into 30 µL of RNase free water. RNA was stored frozen at −80◦C until conversion. 5 µL of RNA from each experimental triplicate was pooled, then 10 µL of the pooled RNA for each condition was converted into first strand complementary deoxyribonucleic acid (cDNA) using a High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems, Thermo Fisher, Australia) following manufactures instructions using Bio-Rad C1000 thermal cycler (Bio Rad, Australia). The resulting cDNA was diluted to a total final volume of 100 µL and stored at −80◦C prior to qPCR. Primers pairs (see **Table 1**) were designed with the assistance of Primer-Blast software (Ye et al., 2012) all primer sequences were then cross checked with Beacon Designer Free online tool to identify possible dimers and hairpins.

Quantitative PCR (qPCR) was run on a CFX384 TouchTM real-time PCR detection System (Bio-Rad, Australia) and output was analyzed with Bio-Rad CFX Mastro Software package (Bio-Rad). Power SYBRTM Green PCR master mix (Applied Biosystems, Thermo Fisher, Australia) was used, following manufacturer instructions, reaction volume was set to 10 µL with 2 µL of template used per well with final primer concentrations of 500 nM. Reaction cycles were repeated 40 times followed by melt curve as in **Table 2**. Each template was run in triplicate for all genes assessed. Relative fold change calculations and statistics were calculated with two reference genes using inbuilt analysis software package.

# RESULTS

# Morphological Properties and Interactions

Both the layered and concurrent co-culture methods resulted in dense myelinated neural networks which grew for 35 days (a targeted approach to ensure that the original predicted 21 day time period was sufficient for neural network maturation). Both co-culture methods resulted in significantly increased reliability and repeatability over the original continuous co-culture method, shown in **Figure 1**. The continuous co-culture had an 86% failure TABLE 1 | Primer pairs for qPCR.

#### TABLE 2 | qPCR reaction cycle settings.

rate compared to 100% success rate of both the concurrent and layered methods investigated here. Additionally, accumulations of H-NF were noted in the continuous co-cultures which indicated neural degeneration (see inset **Figure 1A**), this was not observed in either the layered or concurrent cultures at 35 days.

Comparison of the two co-culture methods with the DMMC culture at the phenotypic and genotypic levels indicate that the concurrent co-culture method resulted in greater numbers of myelinated neural axonal processes and these cultures also expressed significantly higher levels of genes associated with phosphorylated neurofilament and myelin production. **Figure 2** shows an overview heat map comparing fold difference in gene expression at each time point and condition. Representative composite images of DMMC and co-cultures at 35 days are shown in **Figures 3A–C**.

The complexity imparted to the DMMC and co-cultures by including all the major cell types of the CNS resulted in a morphologically diverse and intertwined distribution of GFAP positive astrocytes. **Figures 3A–C** shows the GFAP morphology in the astrocytes at 35 days in culture. The morphologies present in each culture type were variable across each sample, with fibrous, stellate, and protoplasmic being the most dominant morphologies. When assessed in isolation the GFAP morphologies appeared random, however the three

dominant morphologies occupied different domains when the astrocyte proximity with the other cell types were taken into consideration. Protoplasmic astrocytes were predominantly found at the interface of the culture and growth surface. Stellate astrocytes were associated with multiple nerve fibers, and the fibrous astrocytes were aligned with bundles of parallel axons. These cell-cell related morphologies are representative of in vivo interactions previously described for the different cell types (Oberheim et al., 2006, 2012; Wang and Bordey, 2008; Sofroniew and Vinters, 2010).

The majority of microglia present in all cultures were in ramified/resting states as shown in **Figures 3D–F**. The staining intensity for Iba1 in these control cultures on glass is relatively weak, which was expected as the microglia in culture conditions without insult (inflammatory or wound conditions) should not be activated. A notable observation is that the ramified branches of the microglia in the DMMC culture had a fluorescent intensity similar to that seen in the cytoplasm. Conversely in co-culture the ramified branches tended to be of a lower intensity, which suggests there are slight differences in their activation state. **Figure 4** shows the interaction between the three glial cells present in the concurrent co-cultures. It reveals a range of potential astrocyte, microglia, and oligodendrocyte interactions that are reflective of those observed in vivo (Domingues et al., 2016; Kiray et al., 2016). Note that staining of neural axons was excluded for clarity of the glial cell morphologies.

Representative images of the density and organization of axons (H-NF) and myelin (PLP/DM20) are shown in **Figures 3G–L**. The addition of mature MGC to the cocultures did not alter the organization of the H-NF positive

axons at 35 days in culture, however both co-cultures had marginally increased axonal coverage, as summarized in **Figure 5** compared to the DMMC alone. This was significant for the layered co-culture (p < 0.05) when compared with the DMMC culture. However, the contiguity of the staining was more homogenous along the lengths of the axons in the concurrent co-culture. This uniformity of axonal staining in the concurrent co-culture correlated with more consistent myelination along the lengths of the axons as shown in **Figure 3L**. The myelin coverage in the concurrent co-culture was more consistent when compared with DMMC and layered methods which had greater variance in coverage, as shown in **Figure 6**.

The colocalization of H-NF and PLP/DM20 staining yielded two important features which are directly relevant to the level of maturation and health of the cultures. Firstly, the fraction of myelinated axons as described in **Figure 7**. The concurrent coculture method consistently generated greater levels of axonal myelination when compared to the DMMC (p < 0.0001) and layered co-culture (p < 0.05). Secondly, the fraction of myelin produced which is associated with the axons as described in **Figure 8**, where lower values indicate the oligodendrocytes are less mature and are likely to be in a pre-myelinating state. Consequently, higher values indicate more mature oligodendrocytes, and indirectly more mature axons. The concurrent co-culture had significantly higher levels

FIGURE 4 | Maximum intensity projection of a 100x magnification tile scan from a concurrent co-culture demonstrating the potential interaction between microglia (red), myelin (orange), and astrocytes (green). Very fine ramified microglial processes can be seen in the upper left (arrow). Microglia can be observed in close apposition with myelin (∗ ) and astrocytes (∧) (Scale bar = 50 µm).

of myelinated axons compared to the DMMC (p < 0.01). The difference in myelination between concurrent and layered methods was not significant, although the layered co-culture exhibited greater variance between replicates.

# Gene Expression – Comparison With in vivo CNS Tissue

To enable comparisons between the cultures and the mature in vivo mouse CNS, qPCR was performed on mRNA extracted at 21, 28, and 35 days in co-culture and from samples of whole brain. Where possible the qPCR primers were designed for the same targets that were used for immunofluorescence.

FIGURE 6 | Assessment of average myelin coverage as fraction of total image area inferred from PLP/DM20 positive staining. Data acquired at 35 days in co-culture (n = 3).

FIGURE 7 | Fraction of Phosphorylated neurofilament which is colocalized with PLP/DM20 used to indicate the proportion of axonal area with a myelin sheath. Data acquired at 35 days in co-culture (n = 3, ∗p < 0.05, ∗∗∗∗p < 0.0001).

**Figure 9** compares the individual cultures to the in vivo mRNA expression of GFAP. Both co-cultures had at least fourfold more GFAP present than the whole brain control at all assessment time points. The DMMC culture had at least 2.5-fold higher expression than the brain extract. This indicates radial glia and/or immature astrocytes were possibly present in both DMMC and co-cultures. Both co-cultures had significantly more GFAP mRNA at 21, 28, and 35 days compared to the DMMC alone, with

mouse brain extract. Statistical comparisons relative to reference brain extract shown on graph only (n = 3, ∗p < 0.05, ∗∗∗p < 0.001).

differences being greater than twofold at 21 days. At subsequent time points the GFAP expression difference between the DMMC and both co-cultures decreased to 1.5-fold (p < 0.001). Despite this, the elevated levels of GFAP gene expression did not appear to impact on the levels of H-NF production or myelination. This

supports the premise that the increased GFAP expression is in part due to the presence of radial glia or immature astrocytes rather than reactive astrocytes.

**Figure 10** shows that the mRNA expression of Iba1 (microglial inflammatory factor) in the three cultures was at least threefold greater than whole brain extract. However, in contrast to GFAP, the concurrent co-cultures tended to have lower levels of expression when compared to DMMC and layered cultures. The differences between culture types was less than onefold, with the 28 and 35 day concurrent co-cultures being significantly lower than the respective DMMC cultures (p < 0.05). The elevated expression is possibly linked to the developmental role of microglia in regulating synapse formation and removal. The discrete differences in expression are in agreement with the small differences in Iba1 staining intensity.

Cdk5 is expressed in multiple CNS cells including neurons and regulates a diverse range of cellular events. Its expression is required for activation of Cdk5r1 to induce phosphorylation of heavy chain neurofilament expressed in the axonal segment of mature neurons. **Figure 11A** shows the expression of Cdk5 relative to whole brain mRNA expression. All cultures at all assessment time points are significantly different to the whole brain expression, however the relative differences in are small (<0.4-fold). Conversely, the expression of Cdk5r1 as shown in **Figure 11B** is at least 4.5-fold less (p < 0.001) in DMMC cultures when compared to whole brain extract whereas, layered co-cultures are at least fourfold less (p < 0.001) and the concurrent co-cultures are 3 to 3.3-fold less (p < 0.001).

The DMMC culture had the lowest expression of both Cdk5 and Cdk5r1 when compared to the co-culture techniques. There were no differences found between Cdk5 expression between the layered and concurrent co-culture methods. For Cdk5r1, the concurrent co-culture expression was at least onefold greater than DMMC cultures (p < 0.05 at 21 days and p < 0.001 at 28 and 35 days). Most notable though was that the concurrent cocultures showed consistent expression of Cdk5r1 over all time points, whereas both DMMC and layered cultures showed signs of downregulation at 35 days. This trend suggests the concurrent co-culture produced increased phosphorylated neurofilament formation when compared to the DMMC and layered cultures.

The production and phosphorylation of heavy chain neurofilament resulting in mature axon formation is indirectly linked to oligodendrocyte maturation and myelination (Jakovcevski et al., 2007; Simons and Nave, 2016). **Figure 12** shows the relative expression of PLP mRNA in the three culture types relative to whole brain mRNA expression. Importantly, at all assessment time points the concurrent co-culture had similar levels of PLP expression when compared with the whole brain extract. The concurrent co-culture was shown to have significantly greater PLP expression when compared to both DMMC (p < 0.001) and layered (p < 0.05) cultures. The mRNA expression of Cdk5r1 and PLP was found to support the morphological data in terms of continuity of H-NF staining in axons and the level of myelination. These results suggest that the concurrent co-culture produced a more consistent culture across the culture period and developed a mature myelinating neural network at an earlier time point. One concern with the PLP expression in the DMMC culture and layered co-culture is there was a measurable downregulation between 28 and 35 days, suggesting possible degeneration.

### DISCUSSION

The two co-culture approaches using frozen MGC cultures were proposed to reduce the total culture time and reliability of the co-culture system for modeling the CNS. Relative to the previous continuous co-culture method which required 45 days to develop mature myelinated neural networks, the concurrent and layered required significantly less time, 25 and 35 days respectively. Compared to the DMMC the concurrent co-culture required two additional steps and four more days to develop. In addition, as MGC were only revived once a mouse was successfully time mated this resulted in 100% success rate for the modified co-culture methods significantly reducing animal breeding costs. Both co-culture approaches resulted in dense networks of myelinated axons with closely associated astrocytes and microglia. The freeze-thaw process on the MGC had no identifiable impact on the subsequent cocultures. Most notably the shorter recovery time for the MGC in the concurrent co-culture approach was associated with a greater amount of myelinated neural networks at 35 days when compared with the layered and DMMC cultures. Although image analysis of the cultures revealed little difference between the co-cultures and the original DMMC culture with respect to total myelin and H-NF coverage, the concurrent co-culture resulted in increased myelination of axons. Assessment of GFAP did not reveal any notable differences between the culture types. The morphology of the Iba1 stained microglia indicated subtle differences between the DMMC and co-cultures. The microglia in both co-cultures appeared more ramified, thus suggesting a greater level of microglia maturity. This supports the hypothesis that the combination of MGC and DMMC would result in a more mature culture representative of normal CNS tissue in vivo.

The relative maturity of astrocytes plays a pivotal role in both neural network development and their ability to undergo reactive astrogliosis (Smith et al., 1990). In vivo the differentiation and maturation of astrocytes occurs via reciprocal maturation signals between astrocytes and neurons (Hasel et al., 2017). The time for which astrocytes are cultured prior to interaction with neurons and immature oligodendrocytes, impacts on their ability to myelinate axons (Ishikawa et al., 1996). Additionally, astrocyte maturity has been shown to directly impact oligodendrocyte differentiation (Ishikawa et al., 1996; Nash, 2010; Nash et al., 2011a), with increased time in isolated culture resulting in inhibition of myelination, as a consequence of absent cues from the developing neurons. Although there are no apparent differences in the GFAP morphologies present between the culture types, there are significant differences at the mRNA level. At 21 days both co-cultures had greater than twofold more GFAP mRNA relative to the DMMC culture alone. This difference decreased to 1.5-fold at 35 days. Elevated GFAP is classically associated with reactive gliosis associated with neurotrauma, diseases, or neurodegeneration (Ridet et al., 1997; Silver and Miller, 2004; Middeldorp and Hol, 2011; Gao et al., 2013; Brenner, 2014; Burda and Sofroniew, 2014; Cregg et al., 2014; Pekny et al., 2014; Liddelow and Barres, 2017). However, despite the increased mRNA expression of GFAP and its changes over time in culture, the increased levels had no measurable impact on the processes of axonal growth and phosphorylation of H-NF and subsequent myelination in the co-cultures.

In light of the apparent lack of impact of the elevated GFAP on neural network development, indicates there are a number of possible explanations for the elevated GFAP expression compared to the in vivo tissues. Firstly, the site of mRNA extraction from the CNS carries potential variability. In vivo there is regional heterogeneity in GFAP positive astrocytes (Schitine et al., 2015) which results in differential expression levels of GFAP. The in vivo tissue collection site relative to the in vitro cell population could be inherently different. Secondly, the astrocytes in the culture are likely in a mild inflammatory state resulting in increased GFAP expression (Liddelow and Barres, 2017), this is a consequence of being grown on rigid substrates such as glass and tissue culture plastic (Wilson et al., 2016). Alternatively, it is possible that this difference is an additive result of the two component cultures, DMMC and MGC, contributing to the mRNA expression, which is partly supported by the relative increase in GFAP expression of the co-cultures over the DMMC alone. Further to this, at 7 days in culture the DMMC likely consists of GFAP positive radial glia which continue to divide and differentiate into mature astrocytes (McDermott et al., 2005) and non-astrocytic cells. In vivo radial glial become prevalent in the mouse spinal cord tissue around E9.5 days (Hall and Miller, 2012) and undergo differentiation into immature astrocytes between E18 and P14 days of age (in rodents) (Reemst et al., 2016). This timeline correlates to a peak differentiation of the radial glia into astrocytes and other cell types around 10–14 days in culture from the DMMC population. Further to this, in vivo data from Riol et al. (1992) described initial increases in GFAP mRNA levels from P0 to P20 days followed by declining levels out to P60 days. Both co-cultures appeared to follow this trend after 21 days and the DMMC after 28 days. This suggests that the co-cultures

develop at a faster rate compared to the DMMC. However further research is required to map this change over the entire culture period to determine the exact difference in development time between the culture types, and how this relates to in vivo CNS development.

In conjunction with the elevated mRNA levels of GFAP, Iba1 was also at least threefold higher compared to whole brain mRNA in all culture types. Although only minor differences in expression were found between the culture types, the concurrent co-culture exhibited the lowest level of relative Iba1 expression. This elevated mRNA expression compared to whole brain was contrasted by the dominant ramified morphologies present in the co-cultures, which indicates a healthy, mature resting state (Lively and Schlichter, 2013; Ferreira and Bernardino, 2015). This increased Iba1 mRNA expression in the cultures relative to the adult mouse brain is potentially associated with the developmental roles of microglia in regulating synapse formation via pruning of unnecessary connections (Chaboub and Deneen, 2013; Tay et al., 2017). However, continuous co-cultures, described in Gilmour (2018) which were grown on different materials indicated the microglia are capable of maintaining resting phenotypes on control materials or taking on activated phenotypes in response to test materials, thus suggesting the elevated mRNA levels might not be due to immature microglia. It is also possible that the elevated mRNA is an artifact of the 2D culture format, combined with the physiological irrelevant volume of media required to maintain the metabolic requirements of the cultures. The effect of media volume and culture format has been shown previously to have significant effects on osteocytes (Yoshimura et al., 2017) and hepatocytes (Haque et al., 2016).

The phosphorylation of neurofilament is controlled through Cdk5 and the neuron specific activator Cdk5r1 (Wang et al., 2012). At 21 days in culture, the concurrent co-culture expressed significantly more Cdk5 compared to the other cultures, but this difference decreased at 28 and 35 days. As Cdk5 is associated with other processes and cell types within the developing and mature CNS (Zhu et al., 2011) the expression of Cdk5r1 combined with Cdk5 is more relevant. In vitro Cdk5r1 expression was significantly higher in the concurrent co-cultures at all time points, except 21 days when compared to the layered co-culture. This increased co-expression of Cdk5/Cdk5r1 did not result in a greater number of axons, but the axonal expression of the phosphorylated neurofilament was more contiguous. This infers the axonal processes in concurrent co-cultures are more stable and more resistant to degeneration (Sun et al., 1996; Zhu et al., 2011). The increased stability of the neural processes could be indirectly linked to the lower levels of Iba1 in the concurrent cocultures as there is less phagocytosis of degraded axons (Ekdahl, 2012).

Comparing the production of phosphorylated neurofilament in the co-cultures revealed Cdk5r1 expression was at least threefold less in concurrent co-cultures and fourfold less in both DMMC and layered cultures compared to whole brain extract. The difference between in vivo and in vitro expression could be the result of the 2D nature of the culture environment (Zare-Mehrjardi et al., 2011). The 2D environment limits the total number of axons and axon length, an observation similar to that previously made by Sun et al. (2016) in reference to the differences between 2D and 3D neural cell cultures. Although there is less Cdk5r1 in vitro than in vivo, there are similar levels of Cdk5. This is likely due to a secondary role of Cdk5 in modulating OPC differentiation into oligodendrocytes (Miyamoto et al., 2007). The concurrent co-cultures expressed similar amounts of PLP mRNA compared to adult brain extract, which correlates with the expression of Cdk5 for all culture types and time points. It has been proposed that Cdk5 interacts with OPCs promoting differentiation, although via different pathways to neurofilament phosphorylation (Miyamoto et al., 2007), but is facilitated as a secondary effect of this interaction (Yang et al., 2013b; Luo et al., 2016). Taken together the relative expression of Cdk5 and PLP is likely linked to the differentiation of OPCs into mature oligodendrocytes (Miyamoto et al., 2007), as all culture methods resulted in similar amounts of total myelin. However, the concurrent co-culture resulted in a higher level of myelin associated with axons and subsequently more myelinated axons. The process of axon myelination is complex an only partially understood, but is thought to be governed first by intrinsic actions followed by adaptive changes (Bechler et al., 2018). Oligodendrocytes have been shown to intrinsically wrap axons and axon like structures (Rosenberg et al., 2008; Tuck et al., 2016), however this initial myelination is transient unless stabilized through adaptive changes. The adaptive stabilization process is hypothesized to only occur based on interactive signals from active mature axons (Almeida, 2018). This might indicate that the combined co-culture has more mature neurons resulting in stabilized myelin sheaths compared with the layered approach.

Contrary to the expected relationship between GFAP, Cdk5/Cdk5r1 and PLP expression, the concurrent co-culture expressed the highest levels of GFAP at 21 and 28 days in culture. These time points were also associated with the highest level of axonal myelination. It was anticipated that the higher levels of GFAP expression would be associated with lower production of H-NF and PLP. In vivo H-NF is primarily found in its phosphorylated form in mature axons within the adult CNS (Wang et al., 2012) and is sparse in the developing and immature CNS (Haque et al., 2016). At present there is no known explanation for this relationship.

Although the model presented here does not include the blood–brain barrier (BBB) or peripheral immune cells, which are critical components of the in vivo response to intracortical implants and traumatic CNS injury (Polikov et al., 2005; Groothuis et al., 2014). The objective of this work was to establish a robust, rapidly maturing co-culture of the CNS which has the potential to replicate some of the hallmarks of CNS injury. Although BBB disruption is one of the key attributes of traumatic CNS injury, recent literature indicates the interplay of the peripheral immune system has greater impacts in wound progression and secondary degeneration (Evans et al., 2014; Ertürk et al., 2016; Makinde et al., 2017; Abe et al., 2018). Future studies could expand on this model through the inclusion of peripheral immune cells or immune cell conditioned media

(Haan et al., 2015) at different developmental or post-insult time points to evaluate the mechanisms of how the peripheral immune system alters CNS behavior in development and after insult.

Within the limitations of a 2D model to represent the 3D in vivo CNS, co-culturing mature MGC and DMMC populations provides a promising platform for modeling multicellular behaviors and responses to exogenous stimuli. The inclusion of a more mature glial cell population enables the culture to react in a more in vivo mimetic way. This was demonstrated in our previous research, whereby the inclusion of mature astrocytes dramatically altered the response of neural cell development and oligodendrocyte differentiation in response to different materials. The concurrent co-culture method provides a good robust model for use in wound healing studies and biomaterial assessment often conducted on less relevant culture systems. The combined co-culture improves on existing models by enabling the formation of mature neural networks within 25 days, compared to alternative methods which take > 5 weeks to reach maturity. In addition, the model does not require exogenous ECM coating of growth surfaces for cell attachment, as ECM type can affect neural progenitor differentiation and cell migration, thus impacting the overall cell behavior (Ma et al., 2008). The co-culture is completely serum free after 12 days in culture. The serum free nature enables evaluation of the cultures at the proteomic level without the confound of animal sera. Lastly approximately 150 cultures can be obtained from 2 neonatal and 6 E13.5 embryonic mice in a 24 well format.

Future work will determine to what extent the combined co-culture model is able to replicate cell behaviors relevant and consistent with the in vivo CNS injury. To achieve this, it is necessary to analyze the expression of pro and antiinflammatory cytokines and chemokines present within the cultures, relative to the native CNS in conjunction with genetic and morphological analysis.

#### CONCLUSION

The modified co-cultures both substantially increased the reliability and repeatability of the co-culture method. When the co-cultures were compared at the genotypic and phenotypic levels to the DMMC culture method, both methods resulted in improved and more rapid myelinated neural network development. The concurrent co-culture where MGCs were

#### REFERENCES

plated at the same time as DMMCs, performed the most consistently over all experimental repeats with reference to axonal coverage and myelination. Although both co-cultures had elevated GFAP and Iba1 mRNA expression at all time points relative to the DMMC this did not impact on the neural network development.

Comparing the co-cultures to whole brain extract, the layered co-culture expressed significantly decreased levels of myelin and Cdk5r1 resulting in lower neurofilament phosphorylation. The concurrent co-culture on the other had had significantly increased production of myelin similar to in vivo levels. Although it had lower levels of neurofilament phosphorylation relative to the whole brain control, although this was expected due to the spatial and ECM limitations of a 2D model. The concurrent co-culture showed consistent levels of PLP, Cdk5 and Cdk5r1 indicating that 21 days was sufficient to be considered a mature 2D in vitro model of the CNS. The concurrent co-culture method may provide a viable in vitro pre-clinical tool for assessing CNS cell responses as it mimics a more comprehensive number of properties of the mature healthy CNS, than existing in vitro models. Future work will characterize the concurrent co-culture response to physical injury and control biomaterials in order to assess its use as a tool for high-throughput pre-clinical testing of neural interfacing biomaterials.

#### DATA AVAILABILITY STATEMENT

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation, to any qualified researcher.

### ETHICS STATEMENT

The animal study was reviewed and approved by the University of New South Wales Animal Care and Ethics Committee.

### AUTHOR CONTRIBUTIONS

The research studies presented herein were the work of AG during his doctoral studies, supervised by LP-W and RG. The manuscript preparation was undertaken by AG in consultation and with direct contribution from LP-W and RG.

Bogdanowicz, D. R., and Lu, H. H. (2013). Studying cell-cell communication in co-culture. Biotechnol. J. 8, 395–396. doi: 10.1002/biot.201300054

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**Conflict of Interest:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2019 Gilmour, Poole-Warren and Green. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Restoring Somatosensation: Advantages and Current Limitations of Targeting the Brainstem Dorsal Column Nuclei Complex

Alastair J. Loutit and Jason R. Potas\*

School of Medical Sciences, UNSW Sydney, Sydney, NSW, Australia

Current neural prostheses can restore limb movement to tetraplegic patients by translating brain signals coding movements to control a variety of actuators. Fast and accurate somatosensory feedback is essential for normal movement, particularly dexterous tasks, but is currently lacking in motor neural prostheses. Attempts to restore somatosensory feedback have largely focused on cortical stimulation which, thus far, have succeeded in eliciting minimal naturalistic sensations. Yet, a question that deserves more attention is whether the cortex is the best place to activate the central nervous system to restore somatosensation. Here, we propose that the brainstem dorsal column nuclei are an ideal alternative target to restore somatosensation. We review some of the recent literature investigating the dorsal column nuclei functional organization and neurophysiology and highlight some of the advantages and limitations of the dorsal column nuclei as a future neural prosthetic target. Recent evidence supports the dorsal column nuclei as a potential neural prosthetic target, but also identifies several gaps in our knowledge as well as potential limitations which need to be addressed before such a goal can become reality.

#### Edited by:

Alejandro Barriga-Rivera, The University of Sydney, Australia

#### Reviewed by:

Solaiman Shokur, Federal Institute of Technology in Lausanne, Switzerland Aneesha Krithika Suresh, University of Chicago, United States

> \*Correspondence: Jason R. Potas<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Neural Technology, a section of the journal Frontiers in Neuroscience

Received: 22 November 2019 Accepted: 10 February 2020 Published: 28 February 2020

#### Citation:

Loutit AJ and Potas JR (2020) Restoring Somatosensation: Advantages and Current Limitations of Targeting the Brainstem Dorsal Column Nuclei Complex. Front. Neurosci. 14:156. doi: 10.3389/fnins.2020.00156 Keywords: neural coding, brain-machine interface, neuroprosthesis, cuneate, gracile, tactile, proprioception, sensory feedback

# INTRODUCTION

A current challenge in neural prosthetic development is how to artificially activate the central nervous system to restore touch and proprioceptive sensation to tetraplegic patients (Lebedev and Nicolelis, 2017). Developments in the neural prosthetics field have raised the possibility of restoring limb movement, either by functional electrical stimulation of a tetraplegic patient's own muscles (Ajiboye et al., 2017) or by facilitating control of a robotic limb (Collinger et al., 2013). In one paradigm, brain signals coding a patient's intended movement can be acquired and decoded to control a robotic limb via thought alone. Improvements in decoding algorithms and anthropomorphic robotic limb design have enabled complex, thought-controlled movements, but realistic limb movement will require restored somatosensation to facilitate closed-loop feedback control (O'Doherty et al., 2011; Delhaye et al., 2016).

Recently, human intracortical microstimulation (ICMS) has been successful in eliciting minimal naturalistic tactile and proprioceptive sensations (Flesher et al., 2016; Salas et al., 2018). In one subject, some ICMS protocols targeted in somatosensory cortex were perceived as natural

sensations such as squeezing, taps, vibration, and directional arm movement (Salas et al., 2018), whereas in another subject they were perceived as paraesthesia, buzzing, or almost natural (Flesher et al., 2016). Studies in monkeys have shown that different cortical stimulation parameters can elicit perception of variations in pressure, stimulus location, and virtual textures (Tabot et al., 2013, 2015; Kim et al., 2015), and can be used to provide artificial somatosensory feedback for movement control (O'Doherty et al., 2009; Klaes et al., 2014; O'Doherty et al., 2019). While these advances are promising, the effective restoration of natural tactile and proprioceptive feedback still faces many challenges.

One aspect requiring further investigation is whether other targets on the somatosensory neuraxis might offer advantages over the cortex for restoring somatosensory function. The complexity of neural networks in the cortex makes it a difficult region in which to target microstimulation. There has been better success in restoring somatosensory percepts in amputees by interfacing with peripheral nerves where the labeled line arrangement of afferent fibers has led to effective artificial recreation of somatosensory signals (Clark et al., 2014; Tan et al., 2015; Oddo et al., 2016; Valle et al., 2018; George et al., 2019). Users of some state-of-the-art peripheral nerve interfaces that used biomimetic stimulation approaches report that they feel as if they are grasping a real object and they can feel the intensity of the grasping force applied by the robotic hand (Valle et al., 2018). Another subject was able to determine whether the robotic arm held a golf ball or a lacrosse ball, based on their size, and discriminated the compliance of a soft foam block and hard plastic block during active manipulation with a robotic arm (George et al., 2019). The speed with which the subject could discriminate in these two tasks was significantly increased with biomimetic feedback algorithms, compared to simpler feedback algorithms using linear signal amplitude or frequency changes associated with the sensor output. Current peripheral neural prostheses outperform cortical ones for sensorimotor tasks. While integrating somatosensory feedback through ICMS is an impressive recent feat in humans, the subject still used a combination of visual and somatosensory feedback, and trained on the task for 2 years (Flesher et al., 2019).

Spinal cord injury sufferers require somatosensory signals to be recreated in the central nervous system above the site of damage, so peripheral interfaces are not appropriate for this purpose. In our view, the dorsal column nuclei (DCN, comprising the gracile and cuneate nuclei) and its complex (DCNc, comprising the DCN, external cuneate nuclei and nuclei X and Z), may be an ideal alternative target to the cortex as they are easily accessible, being located in a supraspinal position in the dorsal aspect of the brainstem medulla (**Figure 1**), and are one of the first processing sites for ascending somatosensory information from the entire body (excluding the head). As the DCNc are lower in the somatosensory processing hierarchy, it may prove easier for the brain to interpret artificial DCNc activation as naturalistic stimuli, mirroring the success of peripheral nerve interfaces. Perhaps the most crucial feature is that the DCNc are part of a distribution network that accesses not only the somatosensory cortex for conscious perception, but also other key brain regions including the cerebellum, tectum, pretectum, inferior olive, red nucleus, pontine nuclei, zona incerta, reticular formation, periaqueductal gray, and the spinal cord (**Figure 1**; Loutit et al., 2019b). Direct parallel access to these centers from the DCNc provides a distinct advantage as a neural prosthesis site over primary somatosensory cortex, which would not have the same direct access to other key sensorimotor systems. Congruently, the DCN have received attention as a prospective somatosensory neural prosthetic target. Recently, it was shown that chronically implanted microelectrode arrays in monkeys can collect stable recordings, and variations in electrical DCN stimulation can elicit behavioral responses that demonstrate perceptual discrimination (Richardson et al., 2015, 2016; Sritharan et al., 2016; Suresh et al., 2017). While these studies establish the DCNc as a potential target, there is a severe lack of fundamental knowledge of the DCNc functional organization and somatosensory signal processing, which needs to be addressed before this region can be pursued as a feasible neural prosthetic target.

Here, we review recent work on the functional organization and somatosensory-evoked signals of key contributors to the DCNc. We suggest that the DCNc show promise as a target for a somatosensory neural prosthetic device. We discuss some of the potential limitations of the DCNc as a neural prosthetic target and propose future directions that are necessary before development of a DCNc neural prosthesis can begin.

# DCNc FUNCTIONAL ORGANIZATION

Effective activation of the DCNc to elicit somatosensory percepts will require precise knowledge of its functional organization. The key components of the DCNc necessary to appreciate its potential use for neuroprosthetics are the gracile and cuneate nuclei, which are recipient of tactile (and other) inputs from lower and upper body afferents, respectively, and the external cuneate nuclei, nuclei X, and nuclei Z, which are key regions of proprioceptive inputs (summarized by **Figure 1**). For further details, we have recently performed a comprehensive review of the structural organization and the inputs and outputs of the DCNc (Loutit et al., 2019b). Interestingly, despite DCN neurons being somatotopically arranged across the coronal plane of the nuclei, evidence from our laboratory suggests that activity hotspots are spatially displaced across the surface of these nuclei when evoked from different stimulus locations (Loutit et al., 2017, 2019a). Recently, Suresh et al. (2017) also showed that macaque cuneate somatotopic maps are rostrocaudally organized, in addition to the medial-lateral and dorsal-ventral organization (Loutit et al., 2019b). Accordingly, attempts to stimulate cutaneous upper and lower body regions of the DCN, in addition to proprioceptive regions in the external cuneate nuclei, nuclei X, and nuclei Z, will have to target reasonably spatially displaced areas (Loutit et al., 2019b). Moreover, selective targeting of receptive fields to communicate contact location may require activation of neurons at different depths under the same surface region, which could prove difficult with current brain-machine-interface technologies. However, the

FIGURE 1 | Schematic diagram of the inputs and projections of the dorsal column nuclei complex and information flow of a potential neural prosthesis with brainstem somatosensory feedback. Shown are schematic views of the forebrain (coronal section, top), hindbrain (parasagittal section, bottom), and spinal cord and medulla (transverse sections, insert). The dorsal column nuclei complex (DCNc; collectively: CN, GN, ECN, X and Z) projects to many sensorimotor targets in addition to the commonly described pathway through the ventroposterior lateral nucleus (VPL) of the thalamus. Providing sensory feedback by cortical stimulation bypasses these other essential targets involved in sensorimotor function. Compared to the cortical approach, a DCNc somatosensory neural prosthesis would provide a more realistic quality sensorimotor experience by accessing these other key sensorimotor regions. Dashed lines indicate spinal cord and brainstem cross-sections shown in the insert. Insert: The DCNc receives upper and lower body cutaneous and proprioception-related afferents via the cf and gf of the dorsal columns, respectively. Some lower body proprioception-related, and mixed modality upper and lower body afferents, travel to the DCNc via the dorsal region of the lateral funiculus including, but not limited to, the dorsal spinocerebellar tract. These afferents primarily synapse in X and Z. Therefore, to adequately restore all tactile and proprioceptive elements of somatosensation, the entire DCNc may require targeting. Abbreviations: cf, cuneate fasciculus; CN, cuneate nucleus; ECN, external cuneate nucleus; gf, gracile fasciculus; GN, gracile nucleus; Po, posterior group of the thalamus; VPL, ventroposterior lateral nucleus of the thalamus; X, nucleus X; Z, nucleus Z.

different rostrocaudal sites could be exploited to activate neural populations with current multi-electrode array technologies that could otherwise not access different depths at adequate resolution under the same surface region.

Surprisingly, we also found that gracile activity hotspots evoked from bilateral nerve pairs were asymmetrically organized (Loutit et al., 2017, 2019a). This may indicate that the underlying structures that generate this activity are also asymmetrically organized. Previously, some variability in the somatotopic arrangement of hindlimbs has been shown in the gracile nuclei of cats and rats (Millar and Basbaum, 1975; Maslany et al., 1991). Little is known about lateralization in subcortical structures, but cortical lateralization related to handedness is a common phenomenon in mammals, including rats (Denenberg, 1981; Nudo et al., 1992; Dassonville et al., 1997; Hopkins and Cantalupo, 2004; Rogers, 2009). Recent evidence suggests that lateralization of cortical structures might, in part, result from gene expression asymmetries in the spinal cord (Ocklenburg et al., 2017). If this is the case, it is likely that the DCNc and other nuclei along the motor and sensory pathways between the spinal cord and the cortex will also show structural asymmetry. Preliminary data from our laboratory indicate that DCNc functional lateralization is related to paw dominance in the rat. DCNc asymmetries will need to be considered when designing a future DCNc neural prosthesis, but if each neural prosthesis is tailored to an individual, asymmetry is unlikely to be of major concern.

#### DCNc NEUROPHYSIOLOGY

To be a useful neural prosthetic target we propose that the DCNc neurophysiological characteristics must meet some preliminary conditions. Firstly, somatosensory-evoked DCNc signals must be shown to be robust and reproducible and, secondly, they must contain information that can be used to predict the location and quality of somatosensory stimuli. Signal features that reliably predict the location and quality of somatosensory stimuli indicate that they are relevant to the peripheral somatosensory event. Therefore, these features may inform the construction of artificial stimulus patterns that can activate DCNc neurons and elicit somatosensory percepts of natural quality.

Toward this goal, our laboratory has characterized somatosensory-evoked DCN surface activity (Loutit et al., 2017) and used feature-learnability (Loutit and Potas, 2019; Loutit et al., 2019a) – a machine-learning approach for evaluating the relevance of input features to the outputs – to determine the most useful signal features for predicting the location and quality of somatosensory stimuli. We consistently found DCN signal features contain a unique profile of high-frequency (HF) and low-frequency (LF) content when evoked from predominantly cutaneous nerves compared to nerves with mixed afferents from deep and cutaneous structures (Loutit et al., 2017, 2019a), and similarly, from tactile- or proprioceptive-dominated mechanical stimuli (Loutit and Potas, 2019). We extracted signal features from surface potential recordings of the DCN, and by using feature-learnability, were able to establish the relevance, or importance, of information inherently encoded in these signal features for (1) predicting the nerve or paw that was stimulated, and (2) the tactile or proprioceptive quality of the stimuli (Loutit and Potas, 2019; Loutit et al., 2019a). The best individual HF DCN signal features predicted electrically and mechanically evoked somatosensory events with 87 and 70% accuracy, respectively, while the best LF features achieved 90 and 66% accuracy, respectively (Loutit and Potas, 2019; Loutit et al., 2019a), suggesting that both frequency bands represent physiological events relevant to the somatosensory stimuli.

Before artificial stimulus features can be designed to activate the DCNc, we need a greater understanding of somatosensory information coding in the DCNc. In the following section we discuss how knowledge of DCN functional organization and signal features, such as those described above, can inform the development of a future neural prosthetic device.

# DISCUSSION

### A Potential DCNc Neural Prosthesis

Two groups have successfully achieved chronic implantation of Utah microelectrode arrays (Blackrock Microsystems) and floating microelectrode arrays in the cuneate nuclei of macaques, which were able to obtain stable recordings up to about 140 days post-implantation, and awake behaving macaques could detect amplitude-dependent DCN stimulation at 100 Hz (Richardson et al., 2015, 2016; Sritharan et al., 2016; Suresh et al., 2017). These studies have demonstrated proof of principle that chronic microelectrode array implants are stable in this region and that peripheral receptive fields can be selectively activated. However, the next key advancement will be the careful selection and testing of parameters for DCN stimulation to elicit naturalistic sensations.

The HF and LF DCN activity we have investigated is either directly recorded volleys of action potentials arriving in the DCN from afferent fibers (HF activity), or from the subsequent activation of DCN neurons (HF and LF activity). Therefore, to ensure that neural activity giving rise to both the HF and LF features is restored, consideration should be given to whether the best approach is to activate the cuneate and gracile fasciculus fibers of the dorsal column (DC), rather than DCN neurons, or perhaps both. Some interesting recent studies show that rats and monkeys can detect differences in frequency and location of epidural DC stimulation (Yadav et al., 2019, 2020), which is a potential approach for restoring somatosensory feedback and is an FDAapproved method of chronic pain management in humans. However, exclusively stimulating the DC may limit the ability to activate lower body proprioceptive, and potentially other somatosensory information that travels in the lateral funiculus, projecting to nuclei X and Z (Loutit et al., 2019b). Like the sensorimotor cortex, the DC and the DCN are both somatotopically organized and modality segregated (Whitsel et al., 1969, 1970; Niu et al., 2013; Loutit et al., 2019b), making them useful targets for signaling contact location, and different sensory qualities. To restore tactile and proprioceptive sensation

for both the upper and lower body, it will be necessary to either incorporate the lateral funiculus with DC stimulation, or target the entire DCNc.

The modular arrangement of the DCNc may be advantageous for neural prosthetic applications because each specific target relevant to the deficit region can be restored. The modularity and apparent sparsity of interconnectedness within the DCNc suggests that key regions can be specifically targeted with a neural prosthesis, without activating adjacent intact regions.  One of the challenges faced in ICMS is the large cortical surface area dedicated to processing somatosensory information from the human hand (Collinger et al., 2018), which spans approximately 4 cm along the post-central gyrus (Flesher et al., 2016). Current microelectrode arrays are relatively small (typically 4 mm × 4 mm) and therefore can only evoke sensations in small regions of the hand. While the compactness of the DCNc is advantageous, a key challenge will be increasing the number and density of electrodes used to activate the DCNc with high precision, however, this challenge is currently met with intense research effort.

Compared to attempts to restore sensation in tetraplegics, approaches to restore somatosensation in amputees with upper limb prostheses have been relatively successful, and may guide approaches in the DCNc. Peripheral nerve stimulation has been successful in eliciting percepts of contact location, pressure, proprioceptive qualities, and textural discrimination (Dhillon and Horch, 2005; Clark et al., 2014; Tan et al., 2014, 2015; Oddo et al., 2016). Typical stimulation protocols deliver trains of electrical pulses to peripheral nerves with amplitudes varying between 20–300 µA, and frequencies of 10–300 Hz (Raspopovic et al., 2014; Oddo et al., 2016; Valle et al., 2018; George et al., 2019), which are similar to those used in cortical stimulation (Johnson et al., 2013; Flesher et al., 2016; Salas et al., 2018). When linearly encoded, a higher value from a robotic force sensor produces an increased stimulation frequency or amplitude, which induces perception of increased stimulus intensity (Johnson et al., 2013; Raspopovic et al., 2014; Flesher et al., 2016; George et al., 2019).

While these linear encoders can elicit perception of changes in stimulus intensity, they are often not perceived as naturalistic by the user. Of particular interest is trying to create biomimetic artificial touch, which would create naturalistic activation patterns, and therefore naturalistic sensations, in response to spatiotemporal stimulation patterns (Saal and Bensmaia, 2015). Biomimetic stimulus patterns mimic attributes of fast- or slowly adapting afferents, by modulating stimulus frequency or amplitude at different phases of a stimulus presentation e.g. varying the stimulus at the onset, offset, static, or dynamic phases of a stimulus. Indeed, spike timing and temporal features of spike trains, independent of mean spike rates, encode a variety of tactile stimulus features (Johansson and Birznieks, 2004; Saal et al., 2009; Birznieks et al., 2010; Birznieks and Vickery, 2017; Ng et al., 2018). Such parameters have facilitated the instantaneous estimation of fingertip forces, essential for tasks like object manipulation (Khamis et al., 2015). Recent biomimetic testing has shown that spatiotemporal stimulation patterning that mimics firing patterns of different fast- and slowly adapting peripheral afferents generates more natural percepts to the user (Oddo et al., 2016; Valle et al., 2018; George et al., 2019). This is complemented by evidence suggesting that vibration and intensity can be multiplexed by peripheral neural coding, without the need to alter current intensity (Ng et al., 2019). However, a biomimetic approach may be technologically limited by the number of electrodes that can be implanted in a peripheral nerve, to selectively activate individual or small groups of afferents of different submodalities.

Saal and Bensmaia (2015) have suggested that in higher centers, it may not be necessary to selectively activate so many neurons. Their reinterpretation of peripheral afferent coding suggests that all afferent classes encode aspects of most tactile features (Saal and Bensmaia, 2014), and electrophysiological evidence suggests massive cutaneous primary afferent convergence onto multiple DCN neurons (Witham and Baker, 2011; Bengtsson et al., 2013; Jörntell et al., 2014). Therefore, it may be possible to use fewer electrodes to biomimetically activate a small sample of neurons in the DCN that convey more complex, naturalistic, tactile features than attempting to stimulate a larger number of primary afferents.

One group has shown that microstimulation applied to the DCN of macaques at 100 Hz could be detected at amplitudes of 45–80 µA (Sritharan et al., 2016), which is comparable to cortical stimulation ranges that elicit somatosensory percepts. It is unclear what perceptual qualities are elicited from DCN stimulation as there are no reported studies of DCN electrical stimulation in humans. However, evidence from the abovementioned peripheral nerve studies suggests that future attempts to stimulate the DCN may benefit from adopting a biomimetic approach.

#### Limitations

Aside from the potential benefits, there are several concerns for targeting a somatosensory neural prosthesis in the DCNc. The required surgery to place electrodes in the DCNc is more invasive than in the cortex. Currently, the surgery will likely involve cutting the trapezius, splenius capitis, and semispinalis capitis muscles of the posterior neck, whereas cutting, removal, and replacement of a section of cranium is safer and routinely performed in humans. Moreover, the primary goal for spinal cord injury patients is to restore motor control. The state-of-the-art upper limb motor prostheses for tetraplegics are driven by neural activity recorded from electrodes in the motor cortex (Collinger et al., 2013; Ajiboye et al., 2017). A single surgery is required to place both motor and somatosensory arrays on the cortex to restore sensorimotor functions. Conversely, to achieve sensory feedback using a DCNc somatosensory neuroprosthesis will require two surgeries; one in the cortex and one in the brainstem (**Figure 2** shows the proposed site). Future investigations will need to assess these risks and demonstrate that the sensory improvements achieved by a DCNc implant outweighs that which can be achieved by a cortical implant.

Current chronic DCNc electrode arrays risk being moved or damaged by head and neck movements. Several

FIGURE 2 | Proposed site for a potential dorsal column nuclei complex somatosensory neural prosthesis. Parasagittal view of a human brainstem and cerebellum (Cb). Dashed line indicates location of dorsal column nuclei complex (DCNc). As brain-machine-interface technology advances, future approaches may incorporate soft nanowire electrode "threads" that would permit stable targeted neural excitation during movement of the brainstem. Arrows indicate the DCNc region covered by the Cb, which can be easily retracted for electrode implantation if required. The gloved finger is inserted in the space proposed for surgical access, i.e. between the 1st cervical segment (C1) and the occipital bone (Oc).

failed experiments in macaques have been reported due to damaging the wire bundles that transmit electrical signals between the electrode arrays and headstages fixed to the skull, or from the arrays falling out (Suresh et al., 2017). Thus far, rigid microelectrode arrays have been used, but the development of new technologies that use less rigid array structures to accommodate movement, could solve this issue. For example, recently an approach has been developed that delivers small flexible electrode "threads" into the brain (Musk, 2019). Each thread can be targeted with micrometer precision and each array can have up to 3,072 active electrodes. New technologies such as this would permit the insertion of a network of flexible electrodes that could be sewn in place at high resolution throughout the entire DCNc (**Figure 2**), while permitting stable recordings during movement of brain tissues and without causing tissue damage.

The safety and efficacy of DCNc array insertion and electrical stimulation is also yet to be established. As described above, experiments in macaques showed that DCNc stimulation could be detected with currents in the range deemed safe to avoid neural damage (<100 µA per electrode; 20 µC/phase) (Chen et al., 2014; Rajan et al., 2015; Flesher et al., 2016). However, DCNc tissues will need to be analyzed following chronic microelectrode implantation and stimulation, to determine if the effects differ to that shown in the cortex. Moreover, penetrating electrodes and electrical stimulation in the DCNc pose a risk of damaging or activating neurons in respiratory control centers including the rostral ventrolateral medulla, the ventral respiratory column, and the nucleus of the solitary tract (Zoccal et al., 2014). While the ventral position of the first two centers are unlikely to be affected by DCNc stimulation, there is some risk of physically penetrating or activating the nucleus of the solitary tract, which is located near the DCNc ventral border. In our laboratory we have routinely inserted electrode arrays in the gracile and cuneate nuclei of rats that occasionally penetrate the ventral border without affecting any cardiorespiratory functions. The two macaque studies using chronic arrays in the DCNc also reported no issues with cardiorespiratory function either from physical penetration or from stimulation up to 100 µA (Richardson et al., 2016; Sritharan et al., 2016; Suresh et al., 2017). Nevertheless, the potential to cause adverse effects on respiratory control or coupling of cardiovascular and respiratory activities is a serious concern, and safe stimulation levels and penetration depths will need to be established.

Finally, spinal cord or peripheral nerve injury has been shown to cause changes in DCN somatotopy and is thought to be a crucial driver of some cortical somatotopic reorganization (Kambi et al., 2014). This will need to be considered in each subject, to determine how best to target microelectrode arrays.

### FUTURE DIRECTIONS

We believe that a DCNc somatosensory neural prosthesis is a goal worth pursuing and may provide advantages over cortical somatosensory neural prostheses. However, there are a number of concerns that need be addressed, regarding the safety and efficacy of placing microelectrode arrays and electrically stimulating in the DCNc, before a brainstem somatosensory neural prosthesis can be considered feasible. Compared to peripheral nerves and the somatosensory cortex, there is also a dire lack of knowledge about how somatosensory information is coded in the DCNc, which demands future efforts directed toward the understanding of how tactile and proprioceptive features are represented in DCNc neurons. The next frontier will then be to determine how to implement neural codes using a biomimetic approach to artificially stimulate the DCNc which is already connected to multiple sensorimotor systems, including conscious (likely involving the cortex) and unconscious (non-cortical) pathways. Such an approach could enable the subject to receive tactile and proprioceptive sensations from an anthropomorphic robotic limb for complete sensorimotor integration into multiple systems.

# AUTHOR CONTRIBUTIONS

Both authors wrote the manuscript.

# FUNDING

We are grateful to the Bootes Medical Research Foundation for financial support of our research and open access publication fees.

# REFERENCES

fnins-14-00156 February 26, 2020 Time: 18:8 # 7

**Conflict of Interest:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2020 Loutit and Potas. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Stimulation Strategies for Improving the Resolution of Retinal Prostheses

Wei Tong1,2,3, Hamish Meffin1,2,4, David J. Garrett3 and Michael R. Ibbotson1,2 \*

1 National Vision Research Institute, Australian College of Optometry, Carlton, VIC, Australia, 2 Department of Optometry and Vision Sciences, Melbourne School of Health Sciences, The University of Melbourne, Melbourne, VIC, Australia, 3 School of Physics, The University of Melbourne, Melbourne, VIC, Australia, 4 Department of Biomedical Engineering, The University of Melbourne, Melbourne, VIC, Australia

Electrical stimulation using implantable devices with arrays of stimulating electrodes is an emerging therapy for neurological diseases. The performance of these devices depends greatly on their ability to activate populations of neurons with high spatiotemporal resolution. To study electrical stimulation of populations of neurons, retina serves as a useful model because the neural network is arranged in a planar array that is easy to access. Moreover, retinal prostheses are under development to restore vision by replacing the function of damaged light sensitive photoreceptors, which makes retinal research directly relevant for curing blindness. Here we provide a progress review on stimulation strategies developed in recent years to improve the resolution of electrical stimulation in retinal prostheses. We focus on studies performed with explanted retinas, in which electrophysiological techniques are the most advanced. We summarize achievements in improving the spatial and temporal resolution of electrical stimulation of the retina and methods to selectively stimulate neurons with different visual functions. Future directions for retinal prostheses development are also discussed, which could provide insights for other types of neuromodulatory devices in which high-resolution electrical stimulation is required.

#### Edited by:

Tianruo Guo, University of New South Wales, Australia

#### Reviewed by:

Daniel Llewellyn Rathbun, Henry Ford Health System, United States David Tsai, University of New South Wales, Australia

#### \*Correspondence:

Michael R. Ibbotson<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Neural Technology, a section of the journal Frontiers in Neuroscience

Received: 27 November 2019 Accepted: 09 March 2020 Published: 26 March 2020

#### Citation:

Tong W, Meffin H, Garrett DJ and Ibbotson MR (2020) Stimulation Strategies for Improving the Resolution of Retinal Prostheses. Front. Neurosci. 14:262. doi: 10.3389/fnins.2020.00262 Keywords: retina, retinal ganglion cell, electrical stimulation, stimulation resolution, retinal prostheses

# INTRODUCTION

Vision is amongst the most vital tools for functioning in daily activities. In healthy eyes, light enters through the cornea and is focused by the cornea and lens, onto the retina, the light sensitive tissue lining the back of the eye (**Figure 1A**). The retina (**Figure 1B**) contains light sensitive photoreceptors, including rods and cones, which can then transduce the light into chemical and electrical signals. The signals are sent to other neurons in the retina, including bipolar cells and retinal ganglion cells (RGCs). RGCs have axons that collectively form the optic nerve and deliver neural signals to the central brain. The brain processes the signals in a series of complex ways to ultimately generate the sensation of vision.

Retinal degenerative diseases, including age-related macular degeneration (AMD) and retinitis pigmentosa (RP), are leading causes of major vision loss and blindness worldwide (Bourne et al., 2013). Approximately one in every 3,000–7,000 people is affected by RP (Ferrari et al., 2011) and over 8% of the population over 45 have evidence of macular degeneration (Wong W. L. et al., 2014). Both diseases lead to the loss of photoreceptor cells, thus depleting the ability of retinas to transduce light into useful visual signals. For both AMD and RP, currently available therapies normally only

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aim to slow down the death of photoreceptors, by providing nutritional supplements (Krishnadev et al., 2010) or through anti-vascular endothelial growth factor (anti-VEGF) injections (Ba et al., 2015) and lasers (Virgili et al., 2015), with limited available treatments for stopping the progression of the diseases or restoring vision. More recent treatments showing encouraging results include gene therapy and cell transplantation (Scholl et al., 2016). For both these therapies, several issues remain unresolved. Gene therapy currently suffers from limited recognized mutations for treatment (Hartong et al., 2006) and cell transplantation has difficulties with cell function and connectivity.

Over the last two decades, retinal prostheses that electrically stimulate surviving retinal neurons have emerged as a promising treatment for returning sight to the blind (Goetz and Palanker, 2016; Weiland et al., 2016). These devices can be categorized into three types depending on the location of the electrode arrays (**Figure 1B**). Epi-retinal devices have electrode arrays on top of the retina, in contact with the RGC layer. Sub-retinal implants are placed under the retina, closest to diseased photoreceptor layer. Suprachoroidal implants are between the sclera and choroid. Several devices have been implanted into human patients, such as Second Sight's epi-retinal Argus II (Stronks and Dagnelie, 2014), Retina Implant AG's sub-retinal Alpha AMS (Stingl et al., 2017), Bionic Vision Australia's suprachoroidal devices (Ayton et al., 2014), and Pixium Vision's epi-retinal IRIS II, and the most recent sub-retinal PRIMA. Most of the clinical results released by these consortiums have been positive: patients have reported the ability to detect light, categorize large objects from a list and even identify large letters (Zrenner et al., 2011; Humayun et al., 2012; Stingl et al., 2013; Ayton et al., 2014). Nevertheless, the visual resolution obtained from existing devices is very limited, meaning that even recognizing simple objects is challenging. Crucial abilities, such as facial recognition, are not yet possible. Snellen acuity is commonly used for describing visual acuity. A Snellen acuity of 20/20 represents normal vision, and 20/200 is defined legally blind. The best acuities reported in literatures so far from clinical trials are 20/1260 from Argus II (Humayun et al., 2012), 20/546 from Alpha-AMS (Stingl et al., 2017) and between 20/4451 and 20/21059 from BVA suprachoroidal devices (Ayton et al., 2014), all within legal blindness. The clinical results from retinal prostheses have been reviewed recently by Ayton et al. (2019).

Animal testing can evaluate and predict the performance of devices prior to clinical trials. Compared with in vivo testing, ex vivo experiments using explanted retinas are normally easier to perform, with more advanced electrophysiological approaches and have provided a large amount of important information to understand the performance of retinal prostheses. The knowledge gained from ex vivo experiments ranges from a better understanding of electrical stimulation, potential explanations of clinical observations, to the development of novel stimulation strategies. In this review, we first describe the current challenges in electrical stimulation of retinal neurons, which limit the performance of retinal prostheses. We then introduce the animal models commonly used, and recent advances in

electrophysiological tools for retinal experiments. After this, progress in the last 5 years in improving the resolution of electrical stimulation of retinal neurons is summarized. Finally, we discuss the trends for the next generation of retinal prostheses, which could provide insights to future development and guide the design of other neuromodulation devices.

# CURRENT CHALLENGES AND LIMITATIONS

The key challenges that inhibit visual function of retinal prostheses can be summarized as follows: (1) limited spatial resolution; (2) limited temporal precision; and (3) unselective activation of different visual pathways.

# Limited Spatial Resolution

Single electrode stimulation generates the perception of spots of light, referred to as phosphenes. However, patients often report phosphenes that are larger than the electrodes and distorted in shape. Ideally, stimulation of individual retinal neurons is desired to restore natural vision. There are over 1.5 million RGCs in the human retina (Harman et al., 2000) with the largest soma having a diameter of about 30 µm (Liu et al., 2017). Argus II devices stimulate with 60 electrodes, each of 200 µm in diameter (Dorn et al., 2013), Alpha AMS with 1600 electrodes, each of 30 µm (Stingl et al., 2017) and the BVA suprachoroidal devices with only 44 electrodes, each of 500 µm diameter (Ayton et al., 2014; Abbott et al., 2018). All of them are similar or far larger than the size of individual somas. There are several technical limitations to using higher density electrode arrays. For example, the impedance of electrodes increases when their size is reduced. High impedance electrodes require higher voltage stimulation drivers which consume more power. Many materials do not have suitable electrochemical properties to elicit neural activity within the safe charge injection limit.

Another common cause of low spatial confinement of activation is a gap between the electrode array and the surface of the retina. The electric field above a stimulating electrode rapidly spreads in a lateral direction with distance above the electrode resulting in a loss of spatial confinement. Epi-retinal devices are intended to stimulate RGCs, however large electrode-retina gaps after surgery have been reported (Gregori et al., 2018). Sub-retinal devices stimulate nearby inner retinal neurons and thereby take advantage of the natural signal processing by sending signals in the direction that a healthy retina would normally employ. For these devices, there is also potential separation between the inner retinal cells and the surface of the electrode array as degenerative retina often have a layer of debris as photoreceptors are replaced during degeneration. With suprachoroidal devices, the electrode/neuron separation is even larger – usually around 1 mm is expected.

Even when the placement of the electrodes is close and the size of the electrodes is comparable to the targeting neurons, there are other biological issues to be resolved. One critical problem is the activation of RGCs axon bundles (Fried et al., 2009), which is associated with patient reports of elongated phosphenes (Beyeler et al., 2019). This phenomenon occurs when electrodes not only stimulate the nearby neurons, but also errantly stimulate neurons from remote locations connected to the activated axons passing near the electrode.

# Limited Temporal Precision

In addition to localized activation, electrical stimulation with high temporal precision is required to replicate visual responses in retina. RGCs can be stimulated either directly by the electrode or indirectly through the retinal network. Network mediated stimulation may take advantages of the natural signal processing in the retina. In a subset of RGCs, their responses through network mediated stimulation were found to be similar to a natural light response, although delays of tens of ms were observed (Im and Fried, 2014). However, retinal remodeling can happen during degeneration (Jones and Marc, 2005), making it unclear if the natural signal processing function in retina is preserved or not. Compared with network mediated responses, the responses of RGCs to direct stimulation normally happen within a short delay (below 5 ms). However, the encoding of images based on the direct RGC responses requires sophisticated image processing techniques in order to account for the natural visual processing in retinal circuits.

Another problem limiting temporal performance is the loss of responses to high frequency repetitive stimulation which has been found in all types of retinal cells. In a healthy retina, photoreceptors can resolve repetitive frequencies of 20–50 Hz (Zrenner, 2013), leading to the RGCs firing at frequencies over 200 Hz (Koch et al., 2004). However, in most cases, retinal prostheses allow an image refreshment frequency of 5–20 Hz, and images "fade" after repetitive stimulation (Zrenner, 2013). Therefore, the loss of responses to high frequency repetitive stimulation may be one of the reasons for image fading.

# Unselective Activation of Different Visual Pathways

The third limitation for existing devices originates from the nonselective stimulation of the many visual pathways within retina. In general, RGCs can be classified as ON or OFF cells. The spike rates of ON cells increase when light illuminates the center of the cell's receptive field, while the spike rates of OFF cells increase at light offset. In natural vision, ON and OFF cells in any patch of visual space are not activated simultaneously as light and dark patches are segregated. To date, more than 30 types of mammalian RGCs have been identified (Baden et al., 2016), each responsible for different aspects of visual information processing such as brightness, contrast, movement and color. Current retinal prostheses stimulate all types of retinal neurons in a similar manner without any preference, which is very different from the way that a healthy retina processes images. Approaches for selective activation of different RGC types are expected to significantly improve the vision restored.

# EXPERIMENTAL METHODS

# Animal Models

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The animal models that have been used for visual processing research range from salamander to primates (including humans). The most popular models for studying the responses of retinal cells to electrical stimulation are mice, rats, rabbits and monkeys. Mammalian species share similar types of neurons in retina, e.g., photoreceptors, bipolar cells and RGCs, along with horizontal and amacrine cells, which provide lateral interactions (**Figure 1B**). However, there are also some differences between species. For example, in humans and some other mammals such as monkeys and cats, the location of the highest acuity in the retina is a small region at the center of the visual field that has the highest density of RGCs (area centralis). In rabbits, the area of highest acuity in their retina is not a single, restricted region but an elongated zone running across the retina, referred to as the visual streak. In contrast, rodents have RGCs distributed more uniformly without an obvious area centralis or visual streak.

The terminology commonly used for referring to different types of RGCs in each species differs (**Table 1**). For example, RGCs with large somas, large dendritic sizes and large receptive fields are referred to as Alpha or A cells in rodents and cats, but can also be known as Y cells in cats. These cells are similar to so called brisk transient cells in rabbits and parasol cells in primates. These cells can be further classified into ON or OFF cells, although there are even more subgroups for A cells in rodents, including sustained ON, sustained OFF and transient OFF according to their light responses. RGCs with very small somas, small dendritic sizes and also small receptive fields are known as Beta or B cells in cats and rodents, but can also be known as X cells in cats. These cells are similar to so-called brisk sustained cells in rabbits and midget cells in monkeys. In primate, the midget cells are known to be the main vehicle for generating high-resolution vision, but the function of beta cells in rodents is less clear (Sanes and Masland, 2015). Similar with alpha (A) cells, beta (B) cells also have ON and OFF responses to light illumination.

Despite the differences between retinas in rodents and primates, rodents are now the most popular species for research, in part due to their low costs and shorter breeding periods. Rodent animal models of retinal degeneration are also available, which are more relevant for studying retinal responses in terms of retinal prostheses. There are at least 15 mouse models of retinal degeneration with varying rates of photoreceptor loss, from a few days (rd1), to several months (rd10) (Chang et al., 2002). The commonly used rat models of retinal degeneration include Royal College of Surgeon (RCS), P23h and 344-ter rats (Goetz and Palanker, 2016). Photoreceptor degeneration is faster in RCS rats with complete death of photoreceptors and loss of light responses by the age of 90 days (P90) (Ryals et al., 2017). In the other two types of rats, the degeneration is slower, with light responses being found even at P500 in P23h rats (Sekirnjak et al., 2009). Depending on the stage of retinal degeneration of interest, different animal models have been used for different reasons. Abnormal spontaneous behaviors have been reported in degenerated retinas, e.g., RGCs tend to show low levels of background oscillation and bursts of spikes (Margolis and Detwiler, 2011). During electrical stimulation, such abnormal spontaneous activities lead to low signal-to-noise ratios (Choi et al., 2014). Several studies have also reported elevated thresholds for RGC stimulation in degenerated retinas (Jensen and Rizzo, 2009; Chan et al., 2011; Cho et al., 2016), although some others showed no significant differences (Sekirnjak et al., 2009; Cho et al., 2016). The differences observed between degenerated and healthy retinas further indicate the importance of using animal models with retinal degeneration for developing stimulation strategies for retinal prostheses.

# Electrophysiological Tools

Several electrophysiological tools have been applied for recording the responses of retinal neurons to electrical stimulation (**Figure 2**). To retain the integrity of the retinal circuits, experiments are normally performed using whole-mount retina, kept in a perfusion chamber with oxygenated Ames' medium at physiological temperatures between 33 and 37◦C.

#### Patch Clamping

Patch clamping (**Figures 2A,B**) is one of the most commonly used techniques for intracellular recording of neural responses to electrical stimulation in retina. Whole-cell patch clamping allows simultaneous recording from multiple ion channels by measuring membrane potentials or ionic currents. The impact of synaptic activity and individual ion channels can be studied when using various synaptic or ion channel blockers. In some studies, loose patch clamping has been used, which is less invasive and does not damage the integrity of the cell membrane (Im and Fried, 2014, 2015, 2016; Lee et al., 2017; Im et al., 2018).

To record RGCs from whole-mount retinas, it is sometimes necessary to first reveal RGCs by making small holes in the inner limiting membrane (ILM), but this does little damage to the cells (Cloherty et al., 2012). To record the responses of inner retinal neurons, retinal slice preparations have been used to gain access to the cells (Margalit and Thoreson, 2006; Cameron et al., 2013). Disadvantages of retinal slices are that they sever lateral synaptic connections and suffer from significant current shunting around the tissue during stimulation (Margalit et al., 2011). In whole-mount retina, the patch clamping of bipolar cells was made possible by first peeling off the photoreceptors using filter paper

TABLE 1 | Commonly identified RGCs and their names in different animal models.  In another work, the patch clamping on inner retinal neurons was also achieved using sharp glass pipettes without removing any layer of the retina (Tsai et al., 2017a).

Although patch clamping can provide the most information about a single neuron, due to the difficulty of the technique, it only allows simultaneous recording of small numbers of neurons, and is delicate and time consuming, which requires a great deal of training, practice and experience.

#### Extracellular Recording

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Extracellular recording is currently the only clinically viable method to measure retinal neuron signals. Ex vivo, it has been performed using either single sharp electrodes made of metals or carbon fibers, or multielectrode arrays (**Figures 2C,D**). Compared with intracellular recording, the signal-to-noise ratio from extracellular recording is lower, so it is more difficult to remove the artifacts arising from electrical stimulation. Recordings from single electrodes can only record from a small number of single neurons, while population information can be obtained using multi-electrode arrays. With the latest multielectrode array systems, it is possible to simultaneously record and classify recordings from more than 1,700 RGCs in a single experiment, using the high spatial and temporal spiking activities collected from the recording system (Tsai et al., 2017b). Such recording, at subcellular resolution, is termed electrical imaging, and its principle and application has been reviewed by Zeck et al. (2017).

#### Optical Imaging

Optical imaging using activity sensitive fluorescent dyes, mainly calcium imaging (**Figures 2E,F**), is another useful electrophysiological tool for studying the activities of neurons in retina. The dyes are first introduced into target neurons and the change of fluorescence intensity is used to infer neural activities, such as action potentials. Calcium imaging techniques for studying neural activity has been reviewed previously (Grienberger and Konnerth, 2012). The advantages of optical imaging include easy identification of soma locations and an absence of an electrical artifact, both of which reduce the burden of data analysis compared with electrical recording. Although recording of single action potentials with calcium imaging has been demonstrated (Smetters et al., 1999), this has rarely been demonstrated in the retina. Optical imaging is slow and therefore yields low temporal resolution recordings compared with electrical recording. One limitation lies in the low imaging frame rates available on most microscopes (normally around 10– 20 Hz), significantly lower than sampling frequencies used during electrical recording (∼10–40 kHz). In addition, the fluorescence

FIGURE 2 | Electrophysiological techniques for recording neural responses in retina. (A) A RGC during whole cell patch clamping. The glass pipette electrode is in contact with the RGC's soma. (B) The membrane potential of a RGC in response to electrical stimulation, with an action potential (circle), a spikelet (cross) and no response. Black triangles indicate the stimulation artifacts, which were at the time of stimulation. (C,D) Electrical image of a single RGC recorded by a multielectrode array. (C) Raw voltage traces (left) and the average waveforms (right) as a function of time recorded on the six electrodes indicated in (D). The maximum absolute amplitude of average voltage deflections from (C) are shown for each of the 519 electrodes in the hexagonal array in (D), indicated by the diameter of the dot plotted at each electrode location. Times of easily identified spikes recorded on Electrode 1 are identified as ticks in (C, left top). (E,F) Calcium imaging of a population of RGCs responding to electrical stimulation. The change of fluorescence intensities of five cells indicated in (E) to electrical stimulation are shown in (F). (A,B) Are adapted with permission from Soto-Breceda et al. (2018). (C,D) are from Li et al. (2015). (E,F) Are adapted with permission from Tong et al. (2019b).

intensity of the activity indicators needs some time to decay to their background levels following neural activity and, depending on the indicator type and strength of the neural activity, the decay may take up to several seconds.

Several techniques have been reported for large-area loading of retinal cells with calcium indicators. Behrend et al. (2009) first reported the loading of RGCs in whole-mount retinas by immersing the optic nerve stumps in dye solution, but the method failed in adult mammal retinas. Multicell bolus loading (Borghuis et al., 2011) using membrane permeable indicators was reported but uniform staining was difficult. Other methods that successfully stained RGCs in mammalian retinas include electroporation (Baden et al., 2016), dye incubation after dissolving the ILMs (Cameron et al., 2016), direct dye injection into the optic nerve (Tong et al., 2019a), and transduction with genetically encoded calcium indicators through adenoassociated viral vectors (Weitz et al., 2015). To reveal the activities of degenerated photoreceptors to electrical stimulation, incubation of retina with cell permeable dyes has been reported (Haq et al., 2018).

# RECENT PROGRESS

# Electrical Stimulation of Retinal Neurons

Electrical stimulation of retinal neurons can be delivered intracellularly or extracellularly. Intracellular stimulation works by directly injecting current into the cells, normally through patch clamping electrodes. No clinical application is currently available for intracellular stimulation. Nevertheless, intracellular stimulation is a useful approach for characterizing the intrinsic properties of neurons (Wong et al., 2012; Hadjinicolaou et al., 2016). Without contribution from the network, intracellular stimulation simplifies the study by focusing on the properties of the recorded neurons and avoiding the complexity of extracellular stimulation, in which the placement of the electrode plays a significant role.

All clinical neural implants operating today use extracellular stimulation, which works by depolarizing cells in an electric field, instead of directly injecting current into the cells (Rattay, 1999; Meffin et al., 2012). In the most common mode of RGC stimulation, a non-uniform electric field is required. The non-uniform electric field causes charges to redistribute across the membrane of an axon or dendrite and concentrates them at the point where the gradient of the electric field is the greatest along the fiber. Stimulation of bipolar cells is most common through depolarization caused by charge accumulation at synaptic terminals, which can occur even in a uniform electric field directed across the cell (Werginz and Rattay, 2016). Firing of action potentials can be initiated when the membrane depolarization exceeds a threshold. The charge redistribution may happen on the membranes of axons, somas and dendrites, which all contribute to the depolarization of the retinal neurons. For RGCs, experimental evidence indicates that the axon initial segment (AIS), which is located at the proximal end to the soma and contains a high density of sodium channels, is the most sensitive area for activation (Fried et al., 2009). The AIS has the lowest activation threshold, followed by other axonal sections and the soma, with the dendrites exhibiting the highest threshold to electrical stimulation (Fried et al., 2009; Tsai et al., 2012). In addition to RGCs, extracellular stimulation can also lead to the activation of other retinal neurons, including bipolar cells and photoreceptors in healthy retinas, which will then activate RGCs through neuro-transmitters, in the same way that the retina processes visual stimuli. With extracellular stimulation, RGCs can be activated mainly through one of three routes (**Figure 3**): (1) direct activation through the AIS; (2) direct activation via axon bundles; (3) indirect activation via the retinal network. How RGCs are activated can determine the spatial and temporal resolution of electrical stimulation,

which will be discussed in sections Spatial Resolution and Temporal Resolution.

The responses of neurons to electrical stimulation, for a fixed pulse duration, normally follows a sigmoidal function: the response efficacy increases with stimulus strength (current, voltage or charge), then reaches a maximum and saturates (Tsai et al., 2009; Meng et al., 2018; **Figure 4A**). The stimulus strength associated with 50% response efficacy is usually defined as the threshold of activation. Lowering the stimulation threshold is very important for retinal prostheses as larger thresholds consume more power and may exceed the safe limit of the electrode materials or tissue. As neurons are activated due to the electric fields generated by the electrodes, the stimulus effectiveness is greatly influenced by electrode size and the distance between electrode and neuron. Research has also indicated that stimulus effectiveness can be greatly influenced by various stimulation parameters. For example, Weitz et al. (2014) found that, for biphasic pulses, the currents required for RGC activation decreased as the interphase durations increased. Hadjinicolaou et al. (2015) and Jalligampala et al. (2017) proposed strategies for searching the most efficient stimulation parameters for RGCs activation.

Recent years have also revealed an interesting observation known as the upper threshold phenomenon (**Figure 4B**) in RGC activation, i.e., a drop of response efficacy instead of saturation when stimulus strength exceeds a certain amount. The upper thresholds phenomenon was first observed by Boinagrov et al. (2012), and then reported again in several other studies (Barriga-Rivera et al., 2017; Guo et al., 2018; Kotsakidis et al., 2018; Meng et al., 2018). Meng et al. (2018) found that 20/21 cells exhibited the upper threshold phenomenon when sufficiently high charge was injected. However, from modeling they observed different results between monophasic and biphasic stimulation. While the upper threshold in the soma was observed in simulation for both types of stimulation, the action potential in the distal axon was blocked with monophasic stimulation but not with biphasic pulses. However, the upper threshold phenomenon with biphasic stimulation has been reported in vivo by Barriga-Rivera et al. (2017) that the recorded spike rates decreased in some channels with high amplitude stimulation. This indicates that, different from Meng et al. (2018), the upper threshold phenomenon may also happen in the RGC axons by biphasic stimulation. The potential mechanisms for the upper threshold phenomenon have been discussed in detail by Guo et al. (2019). According to their discussion, the sodium channel kinetics in RGCs may play the major role in the upper threshold phenomenon.

#### Spatial Resolution

To confine electric fields generated by stimulating electrodes, attempts have been made to shape the electric fields with current focusing, e.g., replacing the remote return electrodes with local returns (Abramian et al., 2011, 2014; Flores et al., 2016, 2018; Matteucci et al., 2016; Fan et al., 2019; Tong et al., 2019a). Different local return configurations have been reported and compared, including connecting several stimulating electrodes as the return (Abramian et al., 2011, 2014; Matteucci et al., 2016), and specially designing a ring-shaped electrode surrounding the stimulating electrode as the local return (Flores et al., 2016, 2018; Fan et al., 2019; Tong et al., 2019a). A more detailed discussion about different local return configurations can be found in section Simultaneous Stimulation. Overall, the local returns have been shown to confine the activation of RGCs to a certain extent. For example, Fan et al. (2019) reported that the return provided by six neighboring electrodes can enhance the capability of 10 µm epi-retinal electrodes to activate cells near (<30 µm) the central electrode. However, this study focused only on the parasol cells, which are large in size. It remains unknown how the impact of a local return would affect spatial resolution when considering other neurons in the study, in particular the midget cells which are believed to be responsible for high acuity vision in primates. Furthermore, axon bundle activation was also neglected in this study, which is another main origin of RGC spread for epiretinal stimulation. In another investigation, Tong et al. (2019a) compared the effect of return configurations for sub-retinal stimulation and showed different results depending on pulse durations and retinal degeneration. In the healthy retina, local returns were more effective in confining RGC activation when 0.1 and 0.2 ms pulses were used in comparison with 0.5 ms pulses. However, in the degenerated retina the RGC activation patterns were similar between two return configurations, regardless of the pulse durations.

Both simulation and experimental results also indicate that more charge or current will be required for neural stimulation when using local returns, due to the decrease of electric field intensity (Tong et al., 2019a). The elevated thresholds will lead to larger power consumption and a greater charge requirement for the electrodes. To reduce thresholds whilst minimizing a loss of electric field confinement, Flores et al. (2018) proposed local returns in conjunction with pillar structured electrodes. The pillar electrodes reduced the distance between the stimulating electrodes and the target neurons. Experimentally (Ho et al., 2019), they demonstrated in vivo that 10 µm tall pillars with 55 µm pixels can lead to grating acuities of 48 ± 11 µm, which matches the linear pixel pitch of the hexagonal arrays they

used. When converting the value into human visual acuity, the result is close to 20/192, which is just within the legal blindness threshold of 20/200. Following these studies, they also proposed honeycomb-shaped electrodes for sub-retinal stimulation (Flores et al., 2019), where the stimulating electrodes sit within a deep honeycomb well, the walls of the well acting as the local returns. Experimentally (Flores et al., 2019) they demonstrated that the inner retinal cells migrated into the 25 µm deep wells after 5 weeks of implantation. No experimental stimulating results have been published using such arrays, but from simulation, the visual acuity is expected to be better than 20/100.

Merely reducing the size of the electric field is sometimes insufficient to confine the activation of retinal cells. As mentioned above, the epi-retinal stimulation using electrodes as small as 10 µm can also lead to a large spread of RGC activation due to axon bundle stimulation (Behrend et al., 2011). In another study, Grosberg et al. (2017) found that only 45% of electrodes, also 10 µm in diameter, can stimulate individual RGCs using current amplitudes below threshold for axon bundle activation. Therefore, the activation of axon bundles has been identified as one the main sources of the spread of retinal cell activation. The phenomenon is observed for both epi- and sub-retinal stimulation (Tong et al., 2019b).

Strategies for avoiding axon bundle stimulation can be divided in to two routes. The first of these involves bypassing axon bundle stimulation by indirectly stimulating RGCs (Haq et al., 2018; Weitz et al., 2015). Weitz et al. (2015; **Figure 5**) demonstrated, via calcium imaging, that epi-retinal stimulation using both 24 ms biphasic square pulses and 20 Hz sine waves could effectively confine the RGC activation pattern because that type of stimulation primarily stimulates cells in the inner nuclear layer (inner retinal neurons) which, in turn, activate RGCs via the retinal network. Haq et al. (2018) studied sub-retinal stimulation using 1 ms voltage pulses, which were found effective for the activation of both degenerated cone photoreceptors (d-Phr) and inner retinal neurons. They showed that the 3 µm tip diameter electrodes used in the study mostly stimulated d-Phr about 60 µm, and RGCs about 160 µm from the electrodes. By applying gap junction blockers, they found that both the spread of d-Phr and RGC activation could be confined. However, there are other studies reporting different results about the spatial resolution of RGC activation resulting from network stimulation. Hosseinzadeh et al. (2017) studied the spatial extent of epiretinal stimulation by focusing on the network responses from the electrodes. They found that the network responses can also spread to a large area 300–1034 µm away from the electrodes even when the electrodes were as small as 10 µm. For sub-retinal stimulation, Tong et al. (2019a,b) also reported the spread of RGC activation when using 25 ms long pulses that mainly stimulated inner retinal neurons. One possible hypothesis (Tong et al., 2019a) is that network stimulation could lead to the activation of RGC dendritic fields, which could be as large as 500 µm in certain RGC types. The discrepancy could be due to the different techniques used for recording. As discussed in section Electrophysiological Tools, typical multilelectrode arrays provide limited spatial coverage and/or resolution and the activated neurons could be out of the recording region or lie between the recording electrodes. On the other hand, calcium imaging may not have sensitivity high enough to detect single spikes and may not record every activated neurons.

Potential problems with network stimulation include its low temporal resolution (section Temporal Resolution), and the relatively higher charge thresholds required for neuron activation. With long pulses, the charge injection capacities for activation can be larger than 1 mC/cm2 (Weitz et al., 2015; Tong et al., 2019a), which will require the use of electrode materials with much large charge injection capacity than conventional materials such as platinum (charge injection capacity ∼150 µC/cm2 ). The larger charge required also consumes more power and leads to more heat generation.

The other strategy aimed at selectively activating RGCs within or near the electrodes is by increasing the difference between axon bundle and RGC soma activation (Chang et al., 2019; Tong et al., 2019a). For both epi-retinal and sub-retinal stimulation, ultrashort pulses (shorter than 0.15 ms in Chang et al., 2019, and shorter than 0.1 ms in Tong et al., 2019a) were demonstrated to be effective at avoiding axon bundle stimulation. Esler et al. (2018a) proposed to simultaneously stimulate multiple electrodes aligned with the axon bundles to minimize the bundle activation. The proposal was based on the fact that the excitable parts of RGC are the AIS. AIS are located in the RGC layer with random orientations, but the overlying axons are packed together as mostly parallel fibers. The simultaneous stimulation of electrodes parallel to the axons in the nerve fiber layer flattens the extracellular potential along the length of the axon, thus minimizing axon activation.

Human trials have shown that some patients see halo-shaped stimulation patterns from single electrodes (Humayun et al., 2003). There are also studies that provide insights to explain these halo-shaped phosphenes. Eickenscheidt and Zeck (2014) showed that the neurons with the lowest thresholds were at the edge of the stimulation electrode, where the gradient of the extracellular electric field is maximal. In another study (Barriga-Rivera et al., 2017), the halo-like phosphene shapes were explained using the upper threshold phenomenon. Here they found that neurons close to the stimulating electrodes were inhibited at amplitudes lower than the neurons far from the stimulating electrodes. The halo-shapes could also originate from network stimulation: Tong et al. (2019a) showed that long pulses tend to activate neurons further away from the stimulating electrodes compared to the RGCs within the electrodes.

# Temporal Resolution

High quality vision restoration requires the control of retinal neural activities with precise timing, on similar time scales to normal visual responses. There has been research demonstrating electrical stimulation of RGCs with temporal patterns resembling light-evoked spike trains (Jepson et al., 2014b; Wong R. C et al., 2014). For example, Jepson et al. (2014b) reported reproduction of the temporal spiking sequence to visual responses in populations of macaque monkey ON parasol cells. Similar results were reported by Wong R. C et al. (2014) in cat brisk transient cells. However, in both studies only limited types of cells were recorded and analyzed; it remains unclear how

right (somas) to left (optic disc). The colored dots at right indicate somas of the cells whose passing axons are activated by the electrode. Figure adapted with permission from Weitz et al. (2015).

the electrical stimulation of other cell types, in particular the midget cells responsible for high acuity vision, could replicate visual responses.

A good understanding of the temporal patterns of all types of retinal neurons following electrical stimulation could inform the design of stimulation strategies for retinal prostheses. In general, responses originating in RGCs show short latencies (<5 ms), those originating in the inner nuclear layer show medium latencies (3–70 ms), and the responses originating in photoreceptors show long latencies (>40 ms) (Boinagrov et al., 2014). The activation of RGCs through direct- or networkmediated stimulation depends on the electrode location, pulse duration and pulse polarity. For example, Boinagrov et al. (2014) showed that monophasic cathodic epi-retinal stimulation with short pulses (below 0.5 ms) tends to directly stimulate RGCs, while long monophasic anodic pulses (above 10 ms) with electrodes in the outer plexiform layer showed optimal selectivity for network-mediated stimulation.

The response latencies of RGCs from direct stimulation exhibit a U-shape in relation to current amplitudes (Boinagrov et al., 2014; Meng et al., 2018). Compared with direct stimulation, the network mediated responses of RGCs are normally slower and exhibit a variety of temporal response patterns depending on the types of the cells and the stimulus parameters. Im and Fried (2015) compared light and network mediated electrical responses in different types of RGCs from wild type rabbits. They showed that the response patterns to a single pulse stimulus varied between ON and OFF brisk transient or brisk sustained cells, which can also be used to infer the type of neuron recorded. The network mediated electrical responses from ON cells could resemble their light responses much better than OFF cells. Also, the stimuli that activated photoreceptors yielded better correlations than those activating bipolar cells. In a following study (Im and Fried, 2016), they examined the network-mediated responses to repetitive stimulation and also found differences between ON and OFF cells. In both

brisk transient and brisk sustained ON cells, they showed a reset phenomenon, in which each new stimulus elicited a brief burst of spikes. In contrast, OFF cells did not exhibit a reset in their responses; the responses to subsequent stimuli were diminished. Later, they demonstrated that varying stimulus durations (Im et al., 2018) could differentially modulate the responses between ON and OFF cells, providing a potential strategy for selective stimulation of different RGC types (see section Selective Activation). There are some further reports about the effects of varying stimulation patterns, such as duration, rate, current amplitudes, and waveform shapes (Im et al., 2018; Werginz et al., 2018; Lee and Im, 2019). This research mainly used wild-type animals, however, for the design of retinal prostheses, how cells in degenerated retinas respond to electrical stimulation is more relevant. Lee et al. (2017) found that the network mediated responses of ON alpha RGCs in rd10 mice showed trialto-trial variability and the variability increased over the course of retinal degeneration. More research needs to be done in the future to understand the impact of retinal degeneration.

In addition to RGCs, there is research recording directly from other types of retinal neurons. A survey of electrically evoked responses over different current amplitudes and pulse durations were performed by Tsai et al. (2017a). In this study, they found differences among 21 cell types in response to electrical stimulation, a finding which may enable preferential recruitment of certain cell types. Walston et al. (2018) studied ON-type bipolar cells in both normal and degenerated mouse retina, and reported desensitizing responses to repeated stimulation and the upper threshold phenomenon.

As previously mentioned (section Current Challenges and Limitations), fading is one critical problem in retinal prostheses and has been found to be associated with the desensitized responses of retinal cells to repetitive stimulation. The desensitization phenomenon has been observed experimentally in both direct and network mediated responses of RGCs. For network mediated responses, in the low frequency range (below 10 Hz), Im and Fried (2016) showed desensitization in OFF RGCs, but not in ON RGCs. However, Walston et al. (2018) observed desensitizing responses in ON bipolar cells at frequencies greater than 6 Hz. The cut-off frequencies of direct responses of RGCs vary among morphological types (Hadjinicolaou et al., 2016), and differed between intracellular and extracellular stimulation (Kotsakidis et al., 2018). One possible mechanism of desensitization in direct responses of RGCs is a lack of sodium channel deinactivation (Tsai et al., 2011). Other studies have emphasized the existence of electrical currents in the retina, like axo-axonal gap junctions, which could cause an inhibition in the neuron, thus preventing it from generating full action potentials (Soto-Breceda et al., 2018).

Strategies have been proposed to reduce the decay of RGCs responses during repetitive stimulation. Soto-Breceda et al. (2018) proposed the use of electrical pulses with irregular time intervals between them to replace periodic pulses that are normally used in retinal prostheses (**Figure 6**). They found that the random interpulse intervals could lead to lower adaptation rates than stimulation with constant intervals at frequencies above 50 Hz. In another study, Sekhar et al. (2016) analyzed the network mediated responses of RGCs to stimulation at 25 Hz, which would typically induce strong fading. As the retinal neurons could respond to sequences of subthreshold stimulation, they suggested the use of subthreshold sequences to minimize the fading problem.

# Selective Activation

There have been some encouraging results about selective activation of individual neurons. For example, Jepson et al. (2013) first demonstrated that it is possible to stimulate a single RGC without activating neighboring cells. Selectivity was improved by the use of local returns (Fan et al., 2019). However, there are several limitations in these studies for retinal prosthesis application. First, the electrodes used for stimulation were very small, with diameters around 10 µm, and in direct contact with the retina surface (epi-retinal stimulation). Clinically available devices use electrode sizes much larger; and there is usually some separation in space between the target neurons and the electrodes (Gregori et al., 2018). Secondly, these studies recorded and analyzed limited number of neurons within certain cell types. Jepson et al. (2013) examined the responses from midget, parasol and bistratified ganglion cells in the primate retina, while Fan et al. (2019) only examined the parasol cells. It is possible that other neuron types were also activated but not recorded or analyzed. Third limitation lies in the multielectrode array technique they used for recording, that activated neurons could be out of the recording region or lie between the recording electrodes.

While selectively stimulating individual RGCs may be too challenging for current technologies, preferential activation of selective types of RGCs can also be beneficial to the quality of the vision restored. In response to intracellular stimulation, RGCs showed similarities within the same morphological types (Wong et al., 2012; Hadjinicolaou et al., 2016; Zehra et al., 2018). The difference between morphological types indicates the possibility to selectively stimulate RGCs with intracellular stimulation. However this may have limited relevance to extracellular stimulation. Kotsakidis et al. (2018) examined the optimal range of combinations of current amplitude and frequencies (2–2048 Hz) that preferentially activate ON over OFF RGC population responses (**Figures 7A,B**), and they found the optimal ranges were very different between intracellular and extracellular stimulation.