Novel Stimulation Paradigms with Temporally-Varying Parameters to Reduce Synchronous Activity at the Onset of High Frequency Stimulation in Rat Hippocampus

Abstract

Deep brain stimulation (DBS) has shown wide applications for treating various disorders in the central nervous system by using high frequency stimulation (HFS) sequences of electrical pulses. However, upon the onset of HFS sequences, the narrow pulses could induce synchronous firing of action potentials among large populations of neurons and cause a transient phase of “onset response” that is different from the subsequent steady state. To investigate the transient onset phase, the antidromically-evoked population spikes (APS) were used as an electrophysiological marker to evaluate the synchronous neuronal reactions to axonal HFS in the hippocampal CA1 region of anesthetized rats. New stimulation paradigms with time-varying intensity and frequency were developed to suppress the “onset responses”. Results show that HFS paradigms with ramp-up intensity at the onset phase could suppress large APS potentials. In addition, an intensity ramp with a slower ramp-up rate or with a higher pulse frequency had greater suppression on APS amplitudes. Therefore, to reach a desired pulse intensity rapidly, a stimulation paradigm combining elevated frequency and ramp-up intensity was used to shorten the transition phase of initial HFS without evoking large APS potentials. The results of the study provide important clues for certain transient side effects of DBS and for development of new adaptive stimulation paradigms.

Calcium activation of cortical neurons by continuous electrical stimulation: Frequency dependence, temporal fidelity, and activation density

“…In this paradigm, changes in evoked network response as a function of frequency and time over the duration of pulse train have been presumed to be presynaptically mediated, e.g., vesicular depletion at the synapse may lead to synaptic depression (Wang & Manis, ). However, stimulation of CA1 neurons via antidromic activation of efferent fibers exhibits time‐varying responses, where the transient population response at the onset of stimulation exceeds steady‐state activity during stimulation (Cai et al, ). Notably, the magnitude of the antidromically evoked population responses was found to be frequency–dependent, where higher frequencies of stimulation elicited weaker onset and steady‐state responses.…”

“…Wang & Manis, 2008). However, stimulation of CA1 neurons via antidromic activation of efferent fibers exhibits time-varying responses, where the transient population response at the onset of stimulation exceeds steady state activity during stimulation (Cai et al, 2017). Notably, the magnitude of the antidromically evoked population responses was found to be frequency dependent, where higher frequencies of stimulation elicited weaker onset and steady state responses.…”

In vivo microstimulation with cathodic and anodic asymmetric waveforms modulates spatiotemporal calcium dynamics in cortical neuropil and pyramidal neurons of male mice

“…Neuronal representation of sensory information is highly complex (Lieber & Bensmaia, 2019), and the ability to selectively recruit neuronal populations proximal to the electrode by varying pulse amplitude, frequency, or polarity could provide the ability to stimulate neural activity that matches physiological response patterns (Anderson, Duffley, Vorwerk, Dorval, & Butson, 2018; Cai et al., 2017; Delhaye, Saal, & Bensmaia, 2016; Grill, 2018; Histed, Bonin, & Reid, 2009; Hofmann, Ebert, Tass, & Hauptmann, 2011; Koivuniemi & Otto, 2011; Kuncel & Grill, 2004; Macherey, van Wieringen, Carlyon, Deeks, & Wouters, 2006; McIntyre & Grill, 2000, 2002; McIntyre, Richardson, & Grill, 2002; Michelson, Eles, Vazquez, Ludwig, & Kozai, 2019). For example, neuronal adaptation is a common response to sustained stimuli and is believed to allow the brain to better respond to transient stimuli (Wark, Lundstrom, & Fairhall, 2007).…”

Biomimetic microstimulation of sensory cortices

“…Additionally, ICMS bio-inspired algorithms worked as well as linear algorithms with less charge injection for object identification [22] and reduced artifact in the motor cortex [23], which could be beneficial for bidirectional brain-computer interfaces [24]. Similar algorithms have reduced large onset transients for high-frequency stimulation in the hippocampus and nerve [25], [26].…”