Physiological Motion Compensation in Patch Clamping using Electrical Bio-impedance Sensing

Patch clamping of neurons is a powerful technique used to understand the electrophysiological signals of the brain and advance research into neurological disorders. In in vivo patch clamping, a micropipette is clamped onto the membrane of a neuronal cell body. This technique is difficult and time-consuming to perform due to the challenges in approaching neurons because of their small size, the absence of visual feedback, and physiologically induced movement caused by heartbeat and breathing. This paper presents a model-based motion compensation algorithm relying solely on electrical bio-impedance (EBI) sensing. The ultimate goal is to cancel out the relative motion between the patch-pipette and the neuron to increase in vivo patch clamping efficiency. In the proposed algorithm, EBI-pipette measurements in response to physiologically induced motions are used to impose on the pipette a motion similar to that of the neuron. The model is based on the assumption that physiological motion can be approximated by a sinusoidal model with three parameters: frequency, phase, and amplitude. The developed compensation algorithm was evaluated in an experimental setup and results yielded a compensation efficiency of $(85.5\pm 3.6)\%,(81.9\ \pm 4.0)\%,(75.9\pm 1.8)\%$ for artificially imposed motions of 1 Hz, 2 Hz and 3 Hz with an amplitude of $31\ \upmu \mathrm{m}$. The algorithm also demonstrated that it can adjust its motion characterization in real time to changes in amplitude, phase, and also frequency.

I. Introduction

Neurological disorders, such as Alzheimer's disease, Parkinson's disease [1] and hearing loss [2], are the second largest cause of death and the leading cause of disability in today's aging society [3]. However, even though they are increasingly prevalent, their underlying neurological mechanisms are still largely unknown. The brain is filled with billions of interconnected neurons, which together process a vast amount of information. To better understand the functioning of the brain, it is essential for scientists to be able to accurately measure in vivo the electrophysiological activity flowing through these neurons [4].