Back

Manipulation of stimulus waveform and frequency permits targeted fiber activation during vagus nerve stimulation in 2 rodent species

Chang, Y.-C.; Ahmed, U.; Jayaprakash, N.; Abbas, A.; Lin, Q.; Mughrabi, I.; Guo, T.; Wu, Y.-C.; Gabalski, A. H.; Daytz, A.; Ashville, J.; Dokos, S.; Al-Abed, Y.; Zanos, S.

2021-02-01 bioengineering
10.1101/2021.01.30.428827 bioRxiv
Show abstract

Afferent and efferent vagal fibers mediate bidirectional communication between the brain and visceral organs. Small, unmyelinated C-afferents constitute the majority of vagal fibers, play critical roles in numerous interoceptive circuits and autonomic reflexes in health and disease and may contribute to the efficacy and safety of vagus nerve stimulation (VNS). Selective engagement of C-afferents with electrical stimuli has not been feasible, due to the default fiber recruitment order: larger fibers first, smaller fibers last. Here, we determine and optimize an electrical stimulus that selectively engages vagal C-afferents. Intermittent KHz-frequency electrical stimulation (KES) activates motor and, preferentially, sensory vagal neurons in the brainstem. During KES, asynchronous activity of C-afferents increases, while that of larger fibers remains largely unchanged. In parallel, KES effectively blocks excitability of larger fibers while moderately suppressing excitability of C-afferents. By compiling selectivity indices in individual animals, we find that optimal KES parameters for C-afferents are >5KHz frequency and 7-10 times engagement threshold (xT) intensity in rats, 15-25xT in mice. These effects can be explained in computational models by how sodium channel responses to KES are shaped by axonal size and myelin. Our results indicate that selective engagement of vagal C-afferents is attainable by intermittent KES.

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.