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Objective. Computational models have shown that directional electrical contacts placed within the epineurium, between the fascicles, and not penetrating the perineurium, can achieve selectivity levels similar to point source contacts placed within the fascicle. The objective of this study is to test, in a murine model, the hypothesis that directed interfascicular contacts are selective. Approach. Multiple interfascicular electrodes with directional contacts, exposed on a single face, were implanted in the sciatic nerves of 32 rabbits. Fine-wire intramuscular wire electrodes were implanted to measure electromyographic (EMG) activity from medial and lateral gastrocnemius, soleus, and tibialis anterior muscles. Main results. The recruitment data demonstrated that directed interfascicular interfaces, which do not penetrate the perineurium, selectively activate different axon populations. Significance. Interfascicular interfaces that are inside the nerve, but do not penetrate the perineurium are an alternative to intrafascicular interfaces and may offer additional selectivity compared to extraneural approaches.

Design of interface devices for effective, long-term integration into neural tissue is dependent on the biomechanical properties of the nerve membranes. Within the peripheral nerve, the two relevant connective tissue layers for interfacing are the epineurium and perineurium. Previous work has reported the forces needed to penetrate the whole nerve, but the mechanical differences between epineurium and perineurium were not reported. Design of intraneural electrodes that place electrodes within the nerve requires knowledge of the mechanics of individual tissues. This study quantified the Young's moduli and ultimate strains of the perineurium and the epineurium separately. We also measured the forces necessary to penetrate each tissue in isolation. We used a custom-built microtensile testing device to measure the Young's modulus values. The measured Young's moduli of the epineurium and the perineurium was 0.4 ± 0.1 MPa and 3.0 ± 0.3 MPa, respectively. We also measured the force required for blunt and sharp stainless steel, 100 µm diameter probes to be inserted into isolated epineurial tissue and perineurial tissue at 2 mm/s. These data provide additional guidelines for selection of materials for long-term implants that best match the tissue properties. The results will guide neural interface design such that electrodes can be placed through either the epineurium alone or both the epineurium and perineurium.

The brain is dependent on glucose as a primary energy substrate, but is capable of utilizing ketones such as beta-hydroxybutyrate (beta HB) and acetoacetate (AcAc), as occurs with fasting, prolonged starvation or chronic feeding of a high fat/low carbohydrate diet (ketogenic diet). In this study, the local cerebral metabolic rate of glucose consumption (CMRglu; microM/min/100g) was calculated in the cortex and cerebellum of control and ketotic rats using Patlak analysis. Rats were imaged on a rodent PET scanner and MRI was performed on a 7-Tesla Bruker scanner for registration with the PET images. Plasma glucose and beta HB concentrations were measured and 90-minute dynamic PET scans were started simultaneously with bolus injection of 2-Deoxy-2[18F]Fluoro-D-Glucose (FDG). The blood radioactivity concentration was automatically sampled from the tail vein for 3 min following injection and manual periodic blood samples were taken. The calculated local CMRGlu decreased with increasing plasma BHB concentration in the cerebellum (CMRGlu = -4.07*[BHB] + 61.4, r2 = 0.3) and in the frontal cortex (CMRGlu = -3.93*[BHB] + 42.7, r2 = 0.5). These data indicate that, under conditions of ketosis, glucose consumption is decreased in the cortex and cerebellum by about 10% per each mM of plasma ketone bodies.

The mammalian brain depends totally on a continuous supply of oxygen to maintain its function. It is well known that in the brain, adaptation to hypoxia occurs through both systemic and vascular changes, which may include metabolic changes. However, the local metabolic changes related to energy metabolism that occur within the cell are not well described (Harik et al., 1994; Harik et al., 1995; LaManna and Harik, 1997). Investigating the metabolic adaptations of the central nervous system to mild hypoxia provides an understanding of the key components responsible for regulating cell survival. This chapter concerns itself with the metabolic responses of the brain to mild hypoxia, that is, to physiological hypoxia. This is the range of hypoxia that can be compensated for with physiological mechanisms that directly or indirectly involve energy related metabolic pathways.