Understanding how neurons contribute to perception, motor functions and cognition requires the reliable detection of spiking activity of individual neurons during a number of different experimental conditions. An important problem in computational neuroscience is thus to develop algorithms to automatically detect and sort the spiking activity of individual neurons from extracellular recordings. While many algorithms for spike sorting exist, the problem of accurate and fast online sorting still remains a challenging issue.

Here we present a novel software tool, called FSPS (Fuzzy SPike Sorting), which is designed to optimize:

(i) fast and accurate detection, (ii) offline sorting and (iii) online classification of neuronal spikes with very limited or null human intervention. The method is based on a combination of Singular Value Decomposition for fast and highly accurate pre-processing of spike shapes, unsupervised Fuzzy C-mean, high-resolution alignment of extracted spike waveforms, optimal selection of the number of features to retain, automatic identification the number of clusters, and quantitative quality assessment of resulting clusters independent on their size. After being trained on a short testing data stream, the method can reliably perform supervised online classification and monitoring of single neuron activity. The generalized procedure has been implemented in our FSPS spike sorting software using LabVIEW (National Instruments, USA). We evaluated the performance of our algorithm both on benchmark simulated datasets with different levels of background noise and on real extracellular recordings from premotor cortex of Macaque monkeys. The results of these tests showed an excellent accuracy in discriminating low-amplitude and overlapping spikes under strong background noise. The performance of our method is competitive with respect to other robust spike sorting algorithms.

This new software provides neuroscience laboratories with a new tool for fast and robust online classification of single neuron activity. This feature could become crucial in situations when online spike detection from multiple electrodes is paramount, such as in human clinical recordings or in brain-computer interfaces.

The purpose of this study was to evaluate changes in subthalamic nucleus (STN) neuronal activity in Parkinson's disease (PD) patients during deep brain stimulation (DBS) surgery under general anesthesia, and to compare these data with those recorded in the same subjects during previous surgery under local anesthesia.

Five patients with advanced PD, who had previously undergone bilateral STN-DBS under local anesthesia, underwent re-implantation under general anesthesia (with an anesthetic protocol based on the intravenous infusion of remifentanyl and ketamine) owing to surgical device complications. The microelectrode recording (MER) data obtained were analyzed by an off-line spike-sorting software. Neurophysiological data (number of spikes detected, mean firing rate, pause index and burst index) obtained under local and general anesthesia were then evaluated and compared by means of statistical analysis.

We found no statistically significant difference between the first and second surgical procedures in any of the neurophysiological parameters analyzed.

Bilateral STN-DBS for advanced PD with MER guidance is possible and reliable under a ketamine-based anesthetic protocol.

General anesthesia can be proposed for those patients who do not accept an "awake surgery" for clinical reasons, such as excessive fear, poor cooperation or severe "off"-medication effects.