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Classic technologies such as microelectrode arrays have contributed substantially to the field of neurocomputation and have even been deployed in humans for Brain-Computer Interfaces and for mapping brain activity for seizure localization and neurosurgeries. However, we showed that without oligodendrocyte support, there is a preferential decline in inhibitory neurocomputational power around the chronically (permanently) implanted microelectrode over the course of days to months. These unconventional findings draw focus onto the fact that the time and degree of injury response to implantation of these technologies can influence subtle changes to the neurocomputational capacity of the local neural circuits from which these microelectrode arrays are sampling, especially if control groups are not carefully considered. To understand how changes in glial activity impact changes in the neurocomputation of the cortex, we first examined neuronal and non-neuronal cell responses to microelectrode implantation injury.
We are the first to show the spatiotemporal time scale of changes to non-neuronal and neuronal subtypes following brain injuries caused by implanting devices in the brain. First, nearby neurons depolarize as the brain tissue dimples and compresses during insertion1. During this period, nearby neurons are hyperexcitable. This is followed by nearby microglia extending processes on the order of seconds to minutes to the injury site and nearby vessels that ruptured during tissue compression2 as well as the formation of axon1,3 and myelinosome blebs4. Next, astrocytes begin extending their processes on the order of hours5 followed by NG2-glia extending processes6 and pericyte activation constricting capillaries (Fig.1)3,7,8, and occluding blood flow3,7,8. This is followed by a decrease in tissue oxygenation3, increased neuronal silencing3, decreased neuronal excitability3, neuronal and oligodendrocyte degeneration4,9,10, increased expression of inhibitory transporters and ion channels3, microglia migration and encapsulation11-14, increased metabolic and oxidative stress15, astrocyte hypertrophy and cell division5, NG2 glia migration and differentiation to astrocytes and oligodendrocytes4,5,9, and accumulation of lipofuscin (Fig.2). Our results show that metabolic stress is correlated with lysosome dysfunction, which are key organelles that digest biological waste products. Without sufficient metabolic energy (ATP) to pump protons into lysosomes, their digestive potency decreases and turns into large lipofuscin that accumulates waste products.
A major contribution from our research is that neural activity dysfunction and silencing around implants are, at least in part, due to this metabolic stress and tissue deoxygenation3,15,16. Severing large arterioles during device implantation led to ischemic infarctions while implanting into deep capillary beds led to limited gliosis, but lasting neuronal silencing. 3,15,16 These silenced neurons were only activated with strong, direct electrical stimulation17, but not with natural sensory input. Interestingly, the Goldilocks zone was implanting close to arterioles without severing them. Evolutionarily speaking, it makes sense that neurons are silenced to decrease metabolic consumption while angiogenesis and wound healing restores the metabolic supply chain. Devices implanted close to arterioles have greater diffusion of oxygen and nutrients, so it is also possible that implanting close to arterioles minimizes metabolic stress.
Lastly, it has been difficult for researchers to investigate these complex issues due to large variability in electrode performance and challenges decoupling technological failures and artifact (eg. motion artifact) contamination in signal processing3,10,18,19. Therefore, to avoid dogmatizing imprecise interpretation of incomplete data20, we invest considerable effort in understanding and eliminating material and electrical artifacts that might otherwise mislead our interpretation3,19,21,22. Our lab approaches these complexities and variabilities at the brain-neurotechnology interface as an opportunity to better understand the non-neuronal cell activity and how they influence neurocomputation.
Current Directions:
The immediate translatable results are that a brain-computer interface technology company is developing an image guiding system for their ‘sewing machine inserter’ to implant individual ultrasmall microelectrodes while avoiding blood vessels. However, more broadly, our lab has uncovered growing evidence that metabolic stress events trigger and accelerate progression of brain injuries and neurodegenerative diseases, especially late-onset disease. Focal brain injuries in young (2 month old) genetically ‘vulnerable’ populations (eg. APP/PS1 or ApoE4/Trem2/APP) cause decreases in lysosome potency leading to accumulation of amyloid plaques and progression of Alzheimer’s pathology, increase in neurofibrillary tangles, and a persistent state of inflammation months before these genetically vulnerable population normally develop Alzheimer’s pathology. This is important because current Brain Computer Interface participants involved in clinical trials are not being screened for familial history of Alzheimer’s Disease, yet implantation of Deep-Brain Stimulation electrodes for treating Alzheimer’s Disease are undergoing Clinical Trials. We are currently exploring treating Alzheimer’s disease, multiple sclerosis, ALS, and SMA by using several different strategies to reduce metabolic stress by enhancing oligodendrocytes and lysosome activity, including neuromodulation and gliomodulation23.
Figure 1: Vascular occlusion. Top: Capillary constriction (yellow triangle) and occlusion (green triangle) around implanted technologies (blue). Scale = 100 µm. Bottom: Pericytes (red) activation leads to capillary (green) constriction. Scale = 10 µm.
Figure 2: Lipofuscin accumulation (yellow), and vascular (red) dysregulation around implanted neurotechnologies (blue) over 84 days. Scale = 50 µm.
Reference:
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3. 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).
4. Chen, K., Wellman, S.M., Yaxiaer, Y., Eles, J.R. & Kozai, T.D. In vivo spatiotemporal patterns of oligodendrocyte and myelin damage at the neural electrode interface. Biomaterials 268, 120526 (2021).
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17. Bettinger, C.J., et al. Recent advances in neural interfaces—materials chemistry to clinical translation. MRS Bulletin 45, 655-668 (2020).
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19. Kozai, T.D.Y., et al. Mechanical failure modes of chronically implanted planar silicon-based neural probes for laminar recording. Biomaterials 37, 25–39 (2015).
20. Malaga, K.A., et al. Data-driven model comparing the effects of glial scarring and interface interactions on chronic neural recordings in non-human primates. Journal of neural engineering 13, 016010 (2015).
21. Wellman, S.M., et al. A Materials Roadmap to Functional Neural Interface Design. Advanced Functional Materials 28, 201701269 (2018).
22. Nicolai, E.N., et al. Design choices for next-generation neurotechnology can impact motion artifact in electrophysiological and fast-scan cyclic voltammetry measurements. Micromachines 9, 494 (2018).
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