In vivo deployment of mechanically adaptive nanocomposites for intracortical microelectrodes.
نویسندگان
چکیده
We recently introduced a series of stimuli-responsive, mechanically adaptive polymer nanocomposites. Here, we report the first application of these bio-inspired materials as substrates for intracortical microelectrodes. Our hypothesis is that the ideal electrode should be initially stiff to facilitate minimal trauma during insertion into the cortex, yet become mechanically compliant to match the stiffness of the brain tissue and minimize forces exerted on the tissue, attenuating inflammation. Microprobes created from mechanically reinforced nanocomposites demonstrated a significant advantage compared to model microprobes composed of neat polymer only. The nanocomposite microprobes exhibit a higher storage modulus (E' = ~5 GPa) than the neat polymer microprobes (E' = ~2 GPa) and can sustain higher loads (~12 mN), facilitating penetration through the pia mater and insertion into the cerebral cortex of a rat. In contrast, the neat polymer microprobes mechanically failed under lower loads (~7 mN) before they were capable of insertion into cortical tissue. Further, we demonstrated the material's ability to morph while in the rat cortex to more closely match the mechanical properties of the cortical tissue. Nanocomposite microprobes that were implanted into the rat cortex for up to eight weeks demonstrated increased cell density at the microelectrode-tissue interface and a lack of tissue necrosis or excessive gliosis. This body of work introduces our nanocomposite-based microprobes as adaptive substrates for intracortical microelectrodes and potentially for other biomedical applications.
منابع مشابه
Resistive and reactive changes to the impedance of intracortical microelectrodes can be mitigated with polyethylene glycol under acute in vitro and in vivo settings
The reactive response of brain tissue to implantable intracortical microelectrodes is thought to negatively affect their recordable signal quality and impedance, resulting in unreliable longitudinal performance. The relationship between the progression of the reactive tissue into a glial scar and the decline in device performance is unclear. We show that exposure to a model protein solution in ...
متن کاملpHEMA Encapsulated PEDOT-PSS-CNT Microsphere Microelectrodes for Recording Single Unit Activity in the Brain
The long-term reliability of neural interfaces and stability of high-quality recordings are still unsolved issues in neuroscience research. High surface area PEDOT-PSS-CNT composites are able to greatly improve the performance of recording and stimulation for traditional intracortical metal microelectrodes by decreasing their impedance and increasing their charge transfer capability. This enhan...
متن کاملDemonstration of Intracortical Chronic Recording and Acute Microstimulation Using Novel Floating Neural Probes
This paper presents long-term stable multichannel recording of neural activity using novel intracortical floating probes implanted chronically in rat cortex. The novel flexible probe design approach allows recording of action potentials for at least 38 days after implantation. Furthermore the capability of the PEDOT: PSS coated microelectrodes for electrical stimulation is characterized in vitr...
متن کاملFabrication of Nanostructured Cu matrix Nanocomposites by High Energy Mechanical Milling and Spark Plasma Sintering
Spark plasma sintering (SPS) is a sintering process that is capable of sintering hard worked powders in short times. This technique was used to fabricate bulk Cu and Cu-SiC nanocomposites. Pure Cu and mixed powders of Cu including 4 vol% of SiC nanoparticles were mechanically alloyed for 25 h and sintered at 750˚C under vacuum condition by SPS method. Microstructures of the materials were chara...
متن کاملEstimation of the mean grain size of mechanically induced Hydroxyapatite based bioceramics via artificial neural network
This study focuses on the estimation of the mean grain size of mechanically induced Hydroxyapatite (HA) through the artificial neural network (ANN) model. The mean grain size of HA and HA based nanocomposites at different milling parameters were obtained from previous studies. The data were trained and tested by the neural network modeling. Accordingly, all data (55 sets) were based on the mecha...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- Journal of neural engineering
دوره 8 4 شماره
صفحات -
تاریخ انتشار 2011