Stability of the interface between neural tissue and chronically implanted intracortical microelectrodes.

The stability of the interface between neural tissue and chronically implanted microelectrodes is very important for obtaining reliable control signals for neuroprosthetic devices. Stability is also crucial for chronic microstimulation of the cerebral cortex. However, changes of the electrode-tissue interface can be caused by a variety of mechanisms. In the present study, intracortical microelectrode arrays were implanted into the pericruciate gyrus of cats and neural activities were recorded on a regular basis for several months. An algorithm based on cluster analysis and interspike interval analysis was developed to sort the extracellular action potentials into single units. We tracked these units based on their waveform and their response to somatic stimulation or stereotypical movements by the cats. Our results indicate that, after implantation, the electrode-tissue interface may change from day-to-day over the first 1-2 weeks, week-to-week for 1-2 months, and become quite stable thereafter. A stability index is proposed to quantify the stability of the electrode-tissue interface. The reasons for the pattern of changes are discussed.

[1]  E. M. Schmidt,et al.  Single-unit chronic recordings from the sensorimotor cortex of unrestrained cats during locomotion , 1978, Experimental Neurology.

[2]  Brian Everitt,et al.  Cluster analysis , 1974 .

[3]  R. R. Carter,et al.  Multiple single-unit recordings from the CNS using thin-film electrode arrays , 1993 .

[4]  D. McCreery,et al.  A microelectrode for delivery of defined charge densities , 1983, Journal of Neuroscience Methods.

[5]  Michael R. Anderberg,et al.  Cluster Analysis for Applications , 1973 .

[6]  D.B. McCreery,et al.  A characterization of the effects on neuronal excitability due to prolonged microstimulation with chronically implanted microelectrodes , 1997, IEEE Transactions on Biomedical Engineering.

[7]  E. M. Schmidt,et al.  Long-term chronic recording from cortical neurons , 1976, Experimental Neurology.

[8]  Partha P. Mitra,et al.  Automatic sorting of multiple unit neuronal signals in the presence of anisotropic and non-Gaussian variability , 1996, Journal of Neuroscience Methods.

[9]  J. S. Thomas,et al.  Operant conditioning of firing patterns in monkey cortical neurons , 1977, Experimental Neurology.

[10]  M. F. Sarna,et al.  Unsupervised waveform classification for multi-neuron recordings: a real-time, software-based system. II. Performance comparison to other sorters , 1988, Journal of Neuroscience Methods.

[11]  M. Salganicoff,et al.  Unsupervised waveform classification for multi-neuron recordings: a real-time, software-based system. I. Algorithms and implementation , 1988, Journal of Neuroscience Methods.

[12]  C. Gilbert,et al.  Axonal sprouting accompanies functional reorganization in adult cat striate cortex , 1994, Nature.

[13]  Hambrecht Ft Visual prostheses based on direct interfaces with the visual system. , 1995 .