Decoding Vagus-Nerve Activity with Carbon Nanotube Sensors in Freely Moving Rodents

Abstract

The vagus nerve is the largest autonomic nerve and a major target of stimulation therapies for a wide variety of chronic diseases. However, chronic recording from the vagus nerve has been limited, leading to significant gaps in our understanding of vagus nerve function and therapeutic mechanisms. In this study, we use a carbon nanotube yarn (CNTY) biosensor to chronically record from the vagus nerves of freely moving rats for over 40 continuous hours. Vagal activity was analyzed using a variety of techniques, such as spike sorting, spike-firing rates, and interspike intervals. Many spike-cluster-firing rates were found to correlate with food intake, and the neural-firing rates were used to classify eating and other behaviors. To our knowledge, this is the first chronic recording and decoding of activity in the vagus nerve of freely moving animals enabled by the axon-like properties of the CNTY biosensor in both size and flexibility and provides an important step forward in our ability to understand spontaneous vagus-nerve function.

Keywords: carbon nanotube; decoding; intrafascicular; intraneural; recording; vagus nerve.

Figure 1

Electrode implantation, histology, and recording methods. (A) Diagram of CNTY electrode mated with an 11-0 nylon suture with a fisherman’s knot. (B) Section of CNTY electrode deinsulated by laser. (C) Vagus nerve with two implanted CNTY electrodes. CNTY-suture knots are shown with arrows. (D/E) Diagram showing the setup for continuous recording of vagal activity and video for behavior identification. Signals travel from the implants to the headcap connector mounted on the animal’s skull, where they are digitized and amplified by the custom amplifier board shown. These signals are then routed through a commutator, which can rotate and allows the animal to move freely without twisting or pulling on the cable. From the commutator, the signals are sent to an Intan USB interface board, which is powered by an external DC-power source and finally sends the signals to a computer, where they are saved and can be viewed in real time. A video camera is manually synced to the vagal recordings. (F) Fluorescent images showing collagen + cellular encapsulation of CNTY electrodes implanted in the vagus nerve for seven days. (G) Toluidine blue-stained nerve section showing encapsulation of a CNTY electrode implanted for two weeks.

Figure 2

Diagram of data processing and analysis workflow. Vagal ENG and video are recorded simultaneously from freely moving rats. Spike sorting is used to decode spike metrics, which are analyzed with respect to animal behaviors identified from the video.

Figure 3

Spontaneous spikes recorded in freely moving animals. (A) Filtered ENG showing example recording spikes. (B–E) Example clusters sorted from recorded spikes in two animals. (F,G) Spike firing rate over recording time for two animals. Neither animal had a significant change in firing rate over time.

Figure 4

Example spiking activity related to eating. (A) Raster plot for Cluster 1.36. Grey-shaded boxes represent eating events, with dots representing spikes. (B) Raster plot for Cluster 2.1. (C) Firing rate of Cluster 1.36 relative to eating, averaged for all eating events. Red line represents the overall average firing rate of Cluster 1.36. (D) Firing rate of Cluster 2.1 relative to eating.

Figure 5

Interspike interval histograms for Cluster 1.8. (A) ISI histogram for noneating periods, which has a peak around 21 ms. (B) ISI histogram for pre-eating periods, which has a peak around 7ms, and a significantly different ISI distribution compared to noneating periods. (C) ISI histogram for eating periods, which has a peak around 23 ms and is not significantly different from noneating periods. (D) ISI histogram for post-eating periods, which has a peak around 21 ms and a secondary peak around 47 ms, and a significantly different ISI distribution compared to noneating periods.

Figure 6

Confusion matrix for classifying animal behavior based on spike firing rates. Blue-colored cells show rates of correct classification, and orange-colored cells show rates of incorrect classification, such that each row sums to 100%. Y-axis labels show the percentage of recording time spent doing each behavior. (A) Confusion matrix for the classification of behavior in Rat 1. (B) Confusion matrix for the classification of behavior in Rat 2.