Low-frequency tibial neuromodulation excites bladder activity in humans
- School of Informatics, University of Edinburgh, United Kingdom
- School of Engineering, University of Edinburgh, United Kingdom
- Neural Technology Research Center, Iran University of Science and Technology, Iran
Figures
Figure 1 with 1 supplement
Experimental study results.
(A) Effects of low (1 Hz, group A), high (20 Hz, group C), and placebo (0 Hz, group B) TTNS stimulation on the time elapsed to first sensation of urge. Shown are raw data points and Bayesian posteriors for each group. (B) Effects of the washout period on self-reported urge intensity. Error-bars = ±95% confidence interval. Urge intensity self-reported on a scale of 0–4 (see ‘Materials and methods’), washout period was 10 min in duration. (C) Bivariate and univariate kernel density estimate plot displaying the relationship between the time elapsed before participants reported the urge to urinate (i.e., the unit reported in panel A) against the self-reported intensity of this urge (i.e., how strongly participants felt this initial urge, the data reported in panel B before the additional 10-min washout period).
Figure 1—figure supplement 1
Pilot study time to first urge ROPE analysis.
Ndraws=12,000. Shown is the difference in time to first urge between groups A and B, in seconds.
Figure 2
Behavior of the simulated bladder model.
Shown is the bladder behavior (top trace) and associated efferent neuronal activity (middle trace) recorded from a 1000-s duration simulation. Blowout boxes contain 5-s windows of the full recorded activity (highlighted in red) during filling (left box) and voiding (right box). Each raster trace was obtained from a randomly selected neuron within the pudendal, hypogastric, and pelvic units (Nneurons = 100 in each case).
Figure 3 with 2 supplements
Computationally modeling TTNS.
(A) Effects of low- (1 Hz) and high-frequency (20 Hz) TTNS on simulated bladder function compared to unmodulated (control) conditions. (B) Frequency-dependent effects of TTNS on bladder contraction. Shown is the average total bladder contraction duration for a 500-s simulated period under 21 different stimulation frequencies (0–20 Hz, in 1 Hz increments, Nrepeats = 10). Errorbar = ±95% CI. A contraction duration of 0 ms indicates that voiding was completely inhibited (i.e., no voiding events occurred during the simulated period of time). (C) Effects of disconnecting specific tibial-nerve projections on total contraction duration (for all voids over a 500-s simulation period) under low-frequency (1 Hz, i) and high-frequency (20 Hz, ii) conditions. In both cases, baseline behavior represents the unmodulated behavior of the model (i.e., where all tibial nerve projections remained intact and no tibial stimulation was applied). In contrast, the ‘all connected’ conditions describe a model configuration where all tibial nerve projections remained intact and with tibial stimulation applied at 1 Hz (i) or 20 Hz (ii). Nrepeats = 10 in each condition.
Figure 3—figure supplement 1
Simulated bladder volume under baseline and low-frequency stimulation.
Shown is the raw bladder volume from 10 independent bladder simulations (500 s each) under baseline (0 Hz, top) and 1 Hz (bottom) tibial stimulation. All tibial nerve projections remained intact in both conditions.
Figure 3—figure supplement 2
Impact of TNS on bladder capacity and void onset.
Shown is the mean bladder capacity at the time of voiding (left) and the mean simulated time elapsed before voiding commenced (right) from 10 independent bladder simulations (500 s each) under baseline (0 Hz) and 1 Hz tibial stimulation. All tibial nerve projections remained intact in both conditions. Error bars = 95% CI.
Figure 4 with 2 supplements
Experimental study methodology.
Shown is a high-level overview of the study used to validate the frequency-dependent effects of TTNS predicted by the computational model. Participants were healthy adults (18+) asked to abstain from any nicotine/caffeine for 12 h, and any fluids for 2 h before the study. Upon arrival, they were asked to ingest 750 ml water after emptying their bladder. A 30-min digestion period was employed before stimulation. Participants were pseudo-randomly allocated between groups and were blind to the condition.Created with BioRender.com.
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Figure 4—source data 1
Example urge-intensity survey.
Shown is an example of the survey that was given to the participants in printed form.
- https://cdn.elifesciences.org/articles/106174/elife-106174-fig4-data1-v1.pdf
Figure 4—figure supplement 1
Standard placement of TTNS electrodes used during pilot study.
Figure 4—figure supplement 2
Parameters used during neurostimulation.
Shown is the configuration of the DS7A neurostimulator as part of the experimental protocol. Stimulation was monophasic, with a 200 µs pulse width, max-voltage of 300 V, with a stimulation frequency that was set using a timing module built in-house that could output a trigger signal at 1 or 20 Hz.
Figure 5 with 2 supplements
Overview of the computational bladder control model.
Shown is a block diagram of the simulated neuronal circuit and bladder model. The model used a modified biophysical representation of the bladder produced previously (dashed box, Lister et al., 2024), which used the firing rates of the pelvic (blue), hypogastric (orange), and pudendal (green) efferents to calculate bladder state. Tibial input used to modulate the circuit is shown in purple. PMC: pontine micturition center; PAG: periaquaductal gray; PGN: preganglionic bladder neurons; ASC: ascending interneurons; PUD: pudendal afferent; Onuf: Onuf’s nucleus; Pel: pelvic efferent; Hyp: hypogastric efferent. Scissors represent possible severed projections. Labels 1–5 represent key regions of tibial modulation using opioidergic (1–4) or classical (5) inhibitory mechanisms.Created with BioRender.com.
Figure 5—figure supplement 1
Model performance during fitting process.
Shown is the overlap between ground-truth (blue) and simulated (orange) afferent neural activity for a subset of bladder pressure data. Model performance is shown in a random unoptimized state (left), after 200 rounds of Bayesian optimization (center), and after final manual optimization of model weights (right). The magnitude of the overlap between simulated and ground-truth data at each stage is shown as normalized root mean square error (NRMSE). Smoothed firing rate (first-order Butterworth filter, 1 Hz cutoff frequency) was normalized against the maximum recorded value in each dataset.
Figure 5—figure supplement 2
Bayesian optimization convergence plot.
Shown is a convergence plot detailing the normalized root mean square error (NRMSE) of the ground-truth vs. simulated afferent activity data at each phase of the optimization process.
Tables
Appendix 1—table 1
Final neuronal model parameters.
Where possible parameters were matched to the original specifications of the neuronal/synaptic model. Parameters that were altered by the model fitting process are marked as *.
| Parameter | Value |
|---|---|
| Membrane capacitance (Cm) | 200 pF |
| Tonic activity input current (Iap)* | 175.35 pA |
| Leak current reversal potential (EL) | –60 mV |
| Leak current reversal potential—tonically active neurons (ELTonic) | –70 mV |
| Reversal potential of excitatory current (Eex) | 0 mV |
| Reversal potential of inhibitory current (Ein) | –80 mV |
| Reversal potential of opioidergic current (Eop) | –80 mV |
| Reversal potential of adaptation current (EA) | –70 mV |
| Leak current increment (gL) | 10 nS |
| Excitatory current increment (gex)* | 0.51 nS |
| Inhibitory current increment (gin)* | 1.40 nS |
| Opioidergic current increment (δgop)* | 1.5 nS |
| Adaptation current increment (δgA) | 1 nS |
| Adaptation current increment—tonically active neurons (δgATonic) | 0 nS |
| Maximum adaptation current (g¯A)* | 10.17 nS |
| Maximum adaptation current—tonically active neurons (g¯ATonic) | 2 nS |
| Activation threshold for adaptation current (VA) | –50 mV |
| Activation threshold for adaptation current—tonically active neurons (vATonic) | –45 mV |
| Adaptation current slope (ΔA) | 5 mV |
| Post-spike reset potential (vR) | –55 mV |
| Spike threshold (vth) | –50 mV |
| Spike initiation slope (Δt) | 10 mV |
| Refractory period (ΔtRef) | 5 ms |
| Opioidergic current decay constant (τop) | 10 ms |
| Excitatory current decay constant (τex) | 5 ms |
| Inhibitory current decay constant (τin) | 10 ms |
| Neuroplastic decay constant (τSTDP) | 20 ms |
| Adaptation current decay constant—tonically active neurons (τATonic) | 40 ms |
| Adaptation current decay constant (τA) | 200 ms |
| Synaptic learning rate | 5×10−3 |
| Target postsynaptic firing rate (po) | 1 Hz |
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Low-frequency tibial neuromodulation excites bladder activity in humans
eLife 14:RP106174.
https://doi.org/10.7554/eLife.106174.3
