The magnitude of brain tissue micromotion relative to stationary brain implants and its impact on the viability and function of the surrounding brain tissue due to mechanical stresses is poorly understood. The central goal of this study is to characterize surface micromotion in the somatosensory cortex against stationary cylindrical implants. We used a differential variable reluctance transducer (DVRT) in adult rats (n = 6) to monitor micromotion normal to the somatosensory cortex surface. Experiments were performed both in the presence and in the absence of dura mater and displacement measurements were made at three different locations within craniotomies of two different sizes. In anesthetized rats, pulsatile surface micromotion was observed to be in the order of 10-30 microm due to pressure changes during respiration and 2-4 microm due to vascular pulsatility. Brain displacement values due to respiration were significantly lower in the presence of the dura compared to those without the dura. In addition, large inward displacements of brain tissue between 10-60 microm were observed in n = 3 animals immediately following the administration of anesthesia. Such significant micromotion can impact a wide variety of acute and chronic procedures involving any brain implants, precise neurosurgery or imaging and therefore has to be factored in the design of such procedures.

Conventional electrodes and associated positioning systems for intracellular recording from single neurons in vitro and in vivo are large and bulky, which has largely limited their scalability. Further, acquiring successful intracellular recordings is very tedious, requiring a high degree of skill not readily achieved in a typical laboratory. We report here a robotic, MEMS-based intracellular recording system to overcome the above limitations associated with form factor, scalability, and highly skilled and tedious manual operations required for intracellular recordings. This system combines three distinct technologies: (1) novel microscale, glass–polysilicon penetrating electrode for intracellular recording; (2) electrothermal microactuators for precise microscale movement of each electrode; and (3) closed-loop control algorithm for autonomous positioning of electrode inside single neurons. Here we demonstrate the novel, fully integrated system of glass–polysilicon microelectrode, microscale actuators, and controller for autonomous intracellular recordings from single neurons in the abdominal ganglion of Aplysia californica (n = 5 cells). Consistent resting potentials (<−35 mV) and action potentials (>60 mV) were recorded after each successful penetration attempt with the controller and microactuated glass–polysilicon microelectrodes. The success rate of penetration and quality of intracellular recordings achieved using electrothermal microactuators were comparable to that of conventional positioning systems. Preliminary data from in vivo experiments in anesthetized rats show successful intracellular recordings. The MEMS-based system offers significant advantages: (1) reduction in overall size for potential use in behaving animals, (2) scalable approach to potentially realize multi-channel recordings, and (3) a viable method to fully automate measurement of intracellular recordings. This system will be evaluated in vivo in future rodent studies.

This paper reviews some novel spectral analysis techniques that are useful for neurological signals in general and EEG signals in particular. First, some drawbacks and limitations of the commonly used Fast Fourier transforms (FFTs) are presented, and then alternative algorithms are outlined. An auto-regressive (AR) modeling based spectral estimation procedure is presented to overcome the problems of lower resolution and 'leakage' effects inherent in the FFT algorithm. For signals which are transient in nature or rapidly time-varying, two alternative algorithms are presented. The first is an adaptive AR parameter estimation algorithm and the second is a wavelet based time-frequency representation algorithm. Finally, a Spectral Distance measure and the Itakura distance measure are presented to quantify the differences between the spectra of two signals in a succinct manner. The application and performance of all the algorithms is illustrated using electroencephalograms (EEGs) recorded in animals during hypoxic asphyxic injury to brain.

Objective. Brain tissue undergoes dramatic molecular and cellular remodeling at the implant–tissue interface that evolves over a period of weeks after implantation. The biomechanical impact of such remodeling on the interface remains unknown. In this study, we aim to assess the changes in the mechanical properties of the brain–electrode interface after chronic implantation of a microelectrode. Approach. Microelectrodes were implanted in the rodent cortex at a depth of 1 mm for different durations—1 day (n = 4), 10–14 days (n = 4), 4 weeks (n = 4) and 6–8 weeks (n = 7). After the initial duration of implantation, the microelectrodes were moved an additional 1 mm downward at a constant speed of 10 µm s−1. Forces experienced by the microelectrode were measured during movement and after termination of movement. The biomechanical properties of the interfacial brain tissue were assessed from measured force–displacement curves using two separate models—a two-parameter Mooney–Rivlin hyperelastic model and a viscoelastic model with a second-order Prony series. Main results. Estimated shear moduli using a second-order viscoelastic model increased from 0.5–2.6 kPa (day 1 of implantation) to 25.7–59.3 kPa (after 4 weeks of implantation) and subsequently decreased to 0.8–7.9 kPa after 6–8 weeks of implantation in 6 of the 7 animals. The estimated elastic modulus increased from 4.1–7.8 kPa on the day of implantation to 24–44.9 kPa after 4 weeks. The elastic modulus was estimated to be 6.8–33.3 kPa in 6 of the 7 animals after 6–8 weeks of implantation. The above estimates suggest that the brain tissue surrounding the microelectrode evolves from a stiff matrix with maximal shear and elastic modulus after 4 weeks of implantation into a composite of two different layers with different mechanical properties—a stiff compact inner layer surrounded by softer brain tissue that is biomechanically similar to brain tissue—during the first week of implantation. Tissue micromotion-induced stresses on the microelectrode constituted 12–55% of the steady-state stresses on the microelectrode on the day of implantation (n = 4), 2–21% of the steady-state stresses after 4 weeks of implantation (n = 4), and 4–10% of the steady-state stresses after 6–8 weeks of implantation (n = 7). Significance. Understanding biomechanical behavior at the brain–microelectrode interface is necessary for the long-term success of implantable neuroprosthetics and microelectrode arrays. Such quantitative physical characterization of the dynamic changes in the electrode–tissue interface will (a) drive the design and development of more mechanically optimal, chronic brain implants, and (b) lead to new insights into key cellular and molecular events such as neuronal adhesion, migration and function in the immediate vicinity of the brain implant.

Burst suppression patterns in electroencephalograms (EEG's) have been observed in a variety of situations including recovery of a subject from a traumatic brain injury. They are associated with grave prognostic outcomes in neonates. The authors study power spectral parameters and bispectral parameters of the EEG at baseline, during early recovery from an asphyxic arrest (EEG burst patterns) and during late recovery after EEG evolves into a more continuous activity. The bicoherence indexes, which indicate the degree of phase coupling between two frequency components of a signal, are significantly higher within the /spl delta/-/spl theta/ band of the EEG bursts than in the baseline or late recovery waveforms. The bispectral parameters show a more detectable trend than the power spectral parameters. In the second part of the study, the authors looked into the possibility of higher (>2)-order nonlinearities in the EEG bursts using the diagonal slices of the polyspectrum. The diagonal elements of the polyspectrum reveal the presence of self-frequency and self-phase coupling of orders higher than two in majority of the EEG bursts studied. The bicoherence indexes and the diagonal elements of the polyspectrum strongly indicate the presence of nonlinearities of order two and in many cases higher, in the EEG generator during episodes of bursting. This indication of nonlinearity in EEG signals provides a novel quantitative measure of brain's response to injury.

Objective. The objective of this research is to characterize the mechanical interactions of (1) soft, compliant and (2) non-compliant implants with the surrounding brain tissue in a rodent brain. Understanding such interactions will enable the engineering of novel materials that will improve stability and reliability of brain implants. Approach. Acute force measurements were made using a load cell in n = 3 live rats, each with 4 craniotomies. Using an indentation method, brain tissue was tested for changes in force using established protocols. A total of 4 non-compliant, bare silicon microshanks, 3 non-compliant polyvinyl acetate (PVAc)-coated silicon microshanks, and 6 compliant, nanocomposite microshanks were tested. Stress values were calculated by dividing the force by surface area and strain was estimated using a linear stress–strain relationship. Micromotion effects from breathing and vascular pulsatility on tissue stress were estimated from a 5 s interval of steady-state measurements. Viscoelastic properties were estimated using a second-order Prony series expansion of stress–displacement curves for each shank. Main results. The distribution of strain values imposed on brain tissue for both compliant nanocomposite microshanks and PVAc-coated, non-compliant silicon microshanks were significantly lower compared to non-compliant bare silicon shanks. Interestingly, step-indentation experiments also showed that compliant, nanocomposite materials significantly decreased stress relaxation rates in the brain tissue at the interface ( p < 0.05) compared to non-compliant silicon and PVAc-coated silicon materials. Furthermore, both PVAc-coated non-compliant silicon and compliant nanocomposite shanks showed significantly reduced (by 4–5 fold) stresses due to tissue micromotion at the interface. Significance. The results of this study showed that soft, adaptive materials reduce strains and strain rates and micromotion induced stresses in the surrounding brain tissue. Understanding the material behavior at the site of tissue contact will help to improve neural implant design.