Variational free energy as a conceptual framework for understanding habituation in spinal cord stimulation
Year: 2026
Type: article
Abstract: Introduction: Habituation, or loss of clinical benefit over time, is a frequent problem in spinal cord stimulation (SCS). Existing programming strategies such as waveform cycling or closed-loop stimulation try to address it, but there is no common conceptual framework. This paper introduces the variational free energy (VFE) principle as a way to think about habituation in terms that connect clinical observation with computational models of the brain. Methods: We reviewed the core ideas of VFE, as formulated by Friston and colleagues-recognition density, prior, posterior, accuracy, complexity, and Kullback-Leibler divergence-and explained them with simple examples and equations. We then applied these concepts to clinical scenarios in SCS, with reference to existing literature on habituation, predictors of response, and multimodal outcomes. Results: The VFE framework treats the brain as an inference engine that balances accuracy (explaining sensory data) against complexity (the cost of changing beliefs). In this lens, habituation can occur when stimulation becomes predictable and weakly informative about the hidden state the patient cares about (e.g., bodily safety), so its precision is down-weighted and the system reverts toward the pre-existing pain model. Conversely, "rescue" and closed-loop strategies may work by restoring informative prediction errors and coupling stimulation to meaningful state changes. The asymmetry of KL divergence formalizes why relapse into pain can be easier than sustained relief. Discussion: VFE does not replace mechanistic models of SCS but offers a simple way to frame habituation that is mathematically grounded yet approachable. It may help clinicians and programmers understand why therapy sometimes fails, and why broader outcome targets and flexible programming approaches may lead to more durable benefit.
Source: Frontiers in Pain Research
Author: Alexander Taghva
Institution: University of California System
Language: English
Subfield: Anesthesiology and Pain Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,077
OpenAlex ID: W7130434849