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This project aims to greatly improve basic science understanding of oligodendrocytes and oligodendrocyte progenitor cells with respect to spatial and temporal dynamic changes around chronically implanted microelectrodes and long-term recording performance. Penetrating recording microelectrode arrays are a crucial component of numerous human neuroprosthetics. Obtaining selective, high fidelity, long-lasting readouts of brain activity is a critical technology across basic and applied neuroscience that impacts learning and memory studies as well as motor, pre-motor, and visual cortex neuroprostheses and brain-computer interfaces. However, implantation of cortical microelectrodes causes a reactive tissue response, which results in a degradation of the preferred functional single-unit performance over time, thus limiting the device capabilities. While the BBB and the role of other glial cells like microglia and astrocytes have long been studied with respect to the degradation of chronic recording performance, the role of oligodendrocytes and oligodendrocyte progenitor in this foreign body response has been understudied. A dynamic understanding of the interfaces is necessary for elucidating the mechanism(s) behind neural recording failure. Oligodendrocytes and oligodendrocyte progenitor cells have been implicated as key players in neuronal health following brain injury and numerous neurodegenerative diseases. Therefore, this work has the potential to output basic and clinical science level knowledge relevant to neural engineering, ischemia, stroke, intracortical hemorrhage, aneurysm, traumatic brain injury, and closed-loop neurostimulation.
Figure 1. Simple model of neuronal degeneration. Loss of neurotropic factors input from OLs can push neurons towards apoptosis.
Non-neuronal cells have been less studied since they are not as easy to study as neurons. Unlike non-neuronal cells, neuronal activity can be easily measured as voltage by an extracellularly placed electrode. Until very recently, non-neuronal cells around implants were only studied in discrete serial time point post-mortem histology. Large number of animals were implanted and sacrificed at different time points to capture how the average population activity changes over time. However, due to large inter-/intra- animal variability, it remained challenging to establish any dynamic relationships around implant injuries. Drawing on the previous research project, my group has adapted implantable neurotechnologies, in vivo two-photon imaging, and computation to study the dynamic activity of non-neuronal cells in the brain following implantation. This was done by my team by adapting the underlying theory for electrophysiological signal processing and statistics to greater dimension imaging data; 4-6D (Intensity, time, morphology or shape, XY(Z) or rθ(φ), and color or cellular/sub-cellular identity)1-7We showed that the presence of neurons and lack of glial scar tissue does not indicate if functionally intact microelectrodes can record neural activity8. This information suggests that there may be additional cellular players other than neurons, microglia, and astrocytes that are critical for the integration of silent neurons into the functional neural network. We demonstrate that oligodendrocytes (OLs) degeneration precedes neurodegeneration9. OLs in the brain are highly metabolically active cells, given the vast surface area of myelin lipid and myelin proteins the cell needs to turn over on a daily basis10,11, and they are the first cell types to degenerate in the presence of metabolic stress9. This, in turn, begs the question, ‘how OL degeneration influences neuronal health and functional neural network activity?’ Therefore, we pursued a pilot study to explore the role of OL on neuronal health and chronic recording performance. We postulated that disruption of the physiological relationship between axons, myelin, neurons, and oligodendrocytes could account for recording failure of brain implants (Fig. 1).To test the “necessary” role of OL, we caused a systematic loss of OL by inducing severe OL depletion with cuprizone12,13. Cuprizone is a copper chelator that causes rapid OL loss followed by demyelination, and is a very well characterized model for demyelination, especially in the multiple sclerosis field. It is specific to OL and does not affect neurons. Note: Wild-type untreated control mice recapitulated normal OL cell loss following electrode implantation. In contrast to the control group, the experimental group evaluated if OLs are necessary for chronic electrophysiology performance and if OLs impact functional neural activity.Depleting OL and myelin prior to surgical implantation resulted in significantly lower recording performance initially even though neural densities were similar, which plateaued stably across the duration of the experiment13. In contrast, with healthy (wild-type) OL and myelin, recording performance started at a significantly higher level. However, in these animals, as the OL and myelin degenerated in the microenvironment around the implant following the time course published in Frontiers in Neuroscience9, recording performance degraded and converged with the cuprizone treated group13. We published these findings in Biomaterials, which supports the hypothesis that healthy OLs are “necessary” for good recording performance13. In turn, this begs the question, ‘How does OL-depletion impact neural circuit activity and neurocomputation?’To study the impact of OL-depletion on neurocomputation, we first consolidated existing statistical methods and developed additional computational metrics14-20. These analyses were showcased in a study that compared cortical excitability between anesthesia (Ketamine or Isoflurane) and awake head-fixed conditions published in the Journal of Neurophysiology21. By applying these neurocomputational analyses21, we further demonstrated, the latency was not impacted by the OL-depletion. However, our results support the hypothesis that depletion of OL leads to increased metabolic consumption and reduce metabolic support of continuously driven neural activity (>400 ms)13.Other Future Directions. Based on these results, we are investigating the “sufficient” condition (in order to prove or disprove the mechanism that OLs are “necessary and sufficient” condition for chronic recording performance). Our current project further demonstrates that manipulating OL activity can lead to improved chronic recording performance and brain health after injury. Early results show that by treating OLs, we can chronically record (>12 wks) from brain regions that could previously only be recorded up to 3-6 wks (e.g. CA1), which will be a crucial first step in memory, plasticity, and learning electrophysiological studies in hippocampus. Using this approach to study “necessary and sufficient” condition mechanisms largely involves examining the system and system-of-systems integration after removing (knocking-out) specific pathways (enzymes, proteins, or cells) and rescuing it (through replacement or downstream activation). Given transgenic technology (Cre, CreER, AAV, GFP variants, number of promoters, and fluorescent activity indicators), disease models, and an ever-growing number of novel neurotechnologies, there are multiple lifetimes worth of research to be carried out using this approach. The most important aspect then becomes balancing the ‘most immediately feasible mechanisms to study’ and ‘most impactful to human health and/or Neural Engineering community.’
1 Eles, J. R. & Kozai, T. D. Y. In vivo imaging of calcium and glutamate responses to intracortical microstimulation reveals distinct temporal responses of the neuropil and somatic compartments in layer II/III neurons. . Biomaterials (2020).
2 Stieger, K., Eles, J. R., Ludwig, K. A. & Kozai, T. D. Y. In vivo microstimulation with cathodic and anodic asymmetric waveforms modulates spatiotemporal calcium dynamics in cortical neuropil and pyramidal neurons. Journal of Neuroscience Research (2020).
3 Wellman, S. M., Li, L., Yaxiaer, Y., McNamara, I. N. & Kozai, T. D. Revealing spatial and temporal patterns of cell death, glial proliferation, and blood-brain barrier dysfunction around implanted intracortical neural interfaces. J Frontiers in Neuroscience 13, 493 (2019).
4 Michelson, N. J., Eles, J. R., Vazquez, A. L., Ludwig, K. A. & Kozai, T. D. Y. Calcium activation of cortical neurons by continuous electrical stimulation: Frequency dependence, temporal fidelity, and activation density. J Neurosci Res 97, 620-638, doi:10.1002/jnr.24370 (2019).
5 Wellman, S. M. & Kozai, T. D. Y. In vivo spatiotemporal dynamics of NG2 glia activity caused by neural electrode implantation. Biomaterials 164, 121-133 (2018).
6 Du, Z. J. et al. Ultrasoft microwire neural electrodes improve chronic tissue integration. Acta Biomater, doi:http://dx.doi.org/10.1016/j.actbio.2017.02.010 (2017).
7 Kozai, T. D. Y. et al. Chronic tissue response to carboxymethyl cellulose based dissolvable insertion needle for ultra-small neural probes. Biomaterials 35, 9255-9268, doi:10.1016/j.biomaterials.2014.07.039 (2014).
8 Michelson, N. J. et al. Multi-scale, multi-modal analysis uncovers complex relationship at the brain tissue-implant neural interface: New Emphasis on the Biological Interface. Journal of Neural Engineering 15 (2018).
9 Wellman, S. M., Li, L., Yaxiaer, Y., McNamara, I. N. & Kozai, T. D. Revealing spatial and temporal patterns of cell death, glial proliferation, and blood-brain barrier dysfunction around implanted intracortical neural interfaces. Frontiers in Neuroscience 13, 493 (2019).
10 Wellman, S. M., Cambi, F. & Kozai, T. D. Y. The role of oligodendrocytes and their progenitors on neural interface technology: A novel perspective on tissue regeneration and repair. Biomaterials, doi:https://doi.org/10.1016/j.biomaterials.2018.08.046 (2018).
11 Amaral, A. I., Tavares, J. M., Sonnewald, U. & Kotter, M. R. in The Glutamate/GABA-Glutamine Cycle 275-294 (Springer, 2016).
12 Skripuletz, T. et al. Cortical demyelination is prominent in the murine cuprizone model and is strain-dependent. Am J Pathol 172, 1053-1061, doi:10.2353/ajpath.2008.070850 (2008).
13 Wellman, S. M. et al. Cuprizone-induced oligodendrocyte loss and demyelination impairs recording performance of chronically implanted neural interfaces. Biomaterials, 119842 (2020).
14 Kozai, T. D. Y. et al. Comprehensive chronic laminar single-unit, multi-unit, and local field potential recording performance with planar single shank electrode arrays. Journal of Neuroscience Methods 242, 15-40, doi:http://dx.doi.org/10.1016/j.jneumeth.2014.12.010 (2015).
15 Michelson, N. J. et al. Multi-scale, multi-modal analysis uncovers complex relationship at the brain tissue-implant neural interface: New Emphasis on the Biological Interface. Journal of Neural Engineering, doi:10.1088/1741-2552/aa9dae (2017).
16 Kozai, T. D. Y. et al. Chronic In Vivo Evaluation of PEDOT/CNT for Stable Neural Recordings. IEEE transactions on bio-medical engineering 63, 111-119, doi:10.1109/TBME.2015.2445713 (2016).
17 Kolarcik, C. L. et al. Elastomeric and soft conducting microwires for implantable neural interfaces. Soft Matter 11, 4847-4861, doi:10.1039/C5SM00174A (2015).
18 Alba, N. A., Du, Z. J., Catt, K. A., Kozai, T. D. Y. & Cui, X. T. In vivo electrochemical analysis of a PEDOT/MWCNT neural electrode coating. Biosensors 5, 618-646, doi:10.3390/bios5040618 (2015).
19 Kozai, T. D. et al. Mechanical failure modes of chronically implanted planar silicon-based neural probes for laminar recording. Biomaterials 37, 25-39, doi:10.1016/j.biomaterials.2014.10.040 (2015).
20 Kozai, T. D. Y. et al. Effects of caspase-1 knockout on chronic neural recording quality and longevity: Insight into cellular and molecular mechanisms of the reactive tissue response. Biomaterials 35, 9620-9634 (2014).
21 Michelson, N. J. & Kozai, T. D. Y. Isoflurane and Ketamine Differentially Influence Spontaneous and Evoked Laminar Electrophysiology in Mouse V1. Journal of Neurophysiology (2018).
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