Identification of a neurovascular signaling pathway regulating seizures in mice.

OBJECTIVE: A growing body of evidence suggests that increased blood-brain barrier (BBB) permeability can contribute to the development of seizures. The protease tissue plasminogen activator (tPA) has been shown to promote BBB permeability and susceptibility to seizures. In this study, we examined the pathway regulated by tPA in seizures. METHODS: An experimental model of kainate-induced seizures was used in genetically modified mice, including mice deficient in tPA (tPA (-/-) ), its inhibitor neuroserpin (Nsp (-/-) ), or both (Nsp:tPA (-/-) ), and in mice conditionally deficient in the platelet-derived growth factor receptor alpha (PDGFRα). RESULTS: Compared to wild-type (WT) mice, Nsp (-/-) mice have significantly reduced latency to seizure onset and generalization; whereas tPA (-/-) mice have the opposite phenotype, as do Nsp:tPA (-/-) mice. Furthermore, interventions that maintain BBB integrity delay seizure propagation, whereas osmotic disruption of the BBB in seizure-resistant tPA (-/-) mice dramatically reduces the time to seizure onset and accelerates seizure progression. The phenotypic differences in seizure progression between WT, tPA (-/-) , and Nsp (-/-) mice are also observed in electroencephalogram recordings in vivo, but absent in ex vivo electrophysiological recordings where regulation of the BBB is no longer necessary to maintain the extracellular environment. Finally, we demonstrate that these effects on seizure progression are mediated through signaling by PDGFRα on perivascular astrocytes. INTERPRETATION: Together, these data identify a specific molecular pathway involving tPA-mediated PDGFRα signaling in perivascular astrocytes that regulates seizure progression through control of the BBB. Inhibition of PDGFRα signaling and maintenance of BBB integrity might therefore offer a novel clinical approach for managing seizures.

Wireless transmission of neural signals using entropy and mutual information compression.

Two of the most critical tasks when designing a portable wireless neural recording system are to limit power consumption and to efficiently use the limited bandwidth. It is known that for most wireless devices the majority of power is consumed by the wireless transmitter and it often represents the bottleneck of the overall design. This paper compares two compression techniques that take advantage of the sparseness of the neural spikes in neural recordings using an information theoretic formalism to enhance the well-established vector quantization (VQ) algorithm. The two discriminative VQ algorithms are applied to neuronal recordings proving their ability to accurately reconstruct action potential (AP) regions of the neuronal signal while compressing background activity without using thresholds. The two operational modes presented offer distinct characteristics to lossy compression. The first approach requires no preprocessing or prior knowledge of the signal while the second requires a training set of spikes to obtain AP templates. The compression algorithms are implemented on an on-board digital signal processor (DSP) and results show that power consumption is decreased while the bandwidth is more efficiently utilized. The compression algorithms have been tested in real time on a hardware platform (PICO DSP ) enhanced with the DSP which runs the algorithm before sending the compressed data to a wireless transmitter. The compression ratios obtained range from 70:1 and 40:1 depending on the signal to noise ratio (SNR) of the input signal. The spike sorting accuracy in the reconstructed data is 95% compatible to the original neural data.

An information-theoretic approach to motor action decoding with a reconfigurable parallel architecture.

Methods for decoding movements from neural spike counts using adaptive filters often rely on minimizing the mean-squared error. However, for non-Gaussian distribution of errors, this approach is not optimal for performance. Therefore, rather than using probabilistic modeling, we propose an alternate non-parametric approach. In order to extract more structure from the input signal (neuronal spike counts) we propose using minimum error entropy (MEE), an information-theoretic approach that minimizes the error entropy as part of an iterative cost function. However, the disadvantage of using MEE as the cost function for adaptive filters is the increase in computational complexity. In this paper we present a comparison between the decoding performance of the analytic Wiener filter and a linear filter trained with MEE, which is then mapped to a parallel architecture in reconfigurable hardware tailored to the computational needs of the MEE filter. We observe considerable speedup from the hardware design. The adaptation of filter weights for the multiple-input, multiple-output linear filters, necessary in motor decoding, is a highly parallelizable algorithm. It can be decomposed into many independent computational blocks with a parallel architecture readily mapped to a field-programmable gate array (FPGA) and scales to large numbers of neurons. By pipelining and parallelizing independent computations in the algorithm, the proposed parallel architecture has sublinear increases in execution time with respect to both window size and filter order.

Wireless transmission of neuronal recordings using a portable real-time discrimination/compression algorithm.

A design challenge of portable wireless neural recording systems is the tradeoff between bandwidth and power consumption. This paper investigates the compression of neuronal recordings in real-time using a novel discriminating Linde-Buzo-Gray algorithm (DLBG) that preserves spike shapes while filtering background noise. The technique is implemented in a low power digital signal processor (DSP) which is capable of wirelessly transmitting raw neuronal recordings. Depending on the signal to noise ratio of the recording, the compression ratio can be tailored to the data to maximally preserve power and bandwidth. The approach was tested in real and synthetic data and achieved compression ratios between 184:1 and 10:1.