Biology subjects

Sensinger, J.

Publications and source records attributed to Sensinger, J..

5 recordsLinked to original sources

bioRxiv2025

Modeling the Action-Perception Loop and its role in Phantom Limb Pain using Active Inference

Phantom limb pain is among the most prevalent and distressing consequences of limb amputation. Theories regarding its underlying mechanisms remain disputed, contributing to challenges in effectively treating the pain. In recent years, mathematical models grounded in the Bayesian inference framework have been used to describe various aspects of pain perception. However, pain is not only passively inferred but actively shaped through interactions with the environment--a dimension that classical Bayesian approaches typically do not capture. Because amputation disrupts both sensory input related to the limb and the ability to perform actions, a model incorporating both sensory and active components of pain may provide new insight into the mechanisms underlying phantom limb pain. To this end, we developed a model within the active inference framework, which extends Bayesian inference to include action selection. The model provides a conceptual account of how loss of limb control, ambiguity in sensory input pertaining to limb position, residual noxious input, and pre-amputation pain may contribute to the emergence and persistence of phantom limb pain. Furthermore, it offers insight into the possible mechanisms underlying common interventions and may help account for their variable efficacy across individuals. Author summaryPhantom limb pain is a condition where pain is perceived as arising from a limb that is no longer present. Despite being one of the most prevalent and distressing consequences of limb amputation, theories regarding the underlying mechanism of phantom limb pain remain disputed. Here, we present a mathematical model that investigates possible mechanisms underlying this complex pain condition. Using the active inference framework, which combines sensory perception and action selection processes, the model provides a conceptual account of how four distinct factors - loss of control of the limb, ambiguity in sensory input pertaining to limb position, residual activity in afferent nociceptive neurons, and pre-amputation pain - may contribute to the emergence and persistence of phantom limb pain following amputation. Furthermore, our model offers insight into the possible mechanisms underlying common interventions and may help explain why their efficacy varies across individuals.

neuroscience↗

bioRxiv2024

A Computational Framework for Understanding the Impact of Prior Experiences on Pain Perception and Neuropathic Pain

Pain perception is influenced not only by sensory input from afferent neurons but also by cognitive factors such as prior expectations. It has been suggested that overly precise priors may be a key contributing factor to chronic pain states such as neuropathic pain. However, it remains an open question how overly precise priors in favor of pain might arise. Here, we first verify that a Bayesian approach can describe how statistical integration of prior expectations and sensory input results in pain phenomena such as placebo hypoalgesia, nocebo hyperalgesia, chronic pain, and spontaneous neuropathic pain. Our results indicate that the value of the prior, which is determined by the internal model parameters, may be a key contributor to these phenomena. Next, we apply a hierarchical Bayesian approach to update the parameters of the internal model based on the difference between the predicted and the perceived pain, to reflect that people integrate prior experiences in their future expectations. In contrast with simpler approaches, this hierarchical model structure is able to show for placebo hypoalgesia and nocebo hyperalgesia how these phenomena can arise from prior experiences in the form of a classical conditioning procedure. We also demonstrate the phenomenon of offset analgesia, in which a disproportionally large pain decrease is obtained following a minor reduction in noxious stimulus intensity. Finally, we turn to simulations of neuropathic pain, where our hierarchical model corroborates that persistent non-neuropathic pain is a risk factor for developing neuropathic pain following denervation, and additionally offers an interesting prediction that complete absence of informative painful experiences could be a similar risk factor. Taken together, these results provide insight to how prior experiences may contribute to pain perception, in both experimental and neuropathic pain, which in turn might be informative for improving strategies of pain prevention and relief. Author summaryTo efficiently navigate the world and avoid harmful situations, it is beneficial to learn from prior pain experiences. This learning process typically results in certain contexts being associated with an expected level of pain, which subsequently influences pain perception. While this process of pain anticipation has evolved as a mechanism for avoiding harm, recent research indicates overly precise expectations of pain may in fact contribute to certain chronic pain conditions, in which pain persists even after tissue damage has healed, or even arises without any initiating injury. However, it remains an open question how prior experiences contribute to such overly precise expectations of pain. Here, we mathematically model the pain-learning-process. Our model successfully describes several counterintuitive but well-documented pain phenomena. We also make predictions of how prior experiences may contribute the perception of pain and how the same learning process could be leveraged to improve strategies of pain prevention and relief.

neuroscience↗

bioRxiv2020

Cutting through the noise: reducing bias in motor adaptation analysis

During goal-directed movements, the magnitude of error correction by a person on a subsequent movement provides important insight into a persons motor learning dynamics. Observed differences in trial-by-trial adaptation rates might indicate different relative weighting placed on the various sources of information that inform a movement, e.g. sensory feedback, control predictions, or internal model expectations. Measuring this trial-by-trial adaptation rate is not straightforward, however, since externally observed data are masked by noise from several sources and influenced by inaccessible internal processes. Adaptation to perturbation has been used to measure error adaptation as the introduced external disturbance is sufficiently large to overshadow other noise sources. However, perturbation analysis is difficult to implement in real-world scenarios, requires a large number of movement trials to accommodate infrequent perturbations, and the paradigm itself might affect the movement dynamics being observed. Here we focus on error adaptation during unperturbed and naturalistic movements. With increasing motor noise, the conventional estimation of trial-by-trial adaptation increases, a counterintuitive finding that is the consequence of systematic bias in the estimate due to noise masking the learners intention. We present an analytic solution relying on stochastic signal processing to reduce this effect of noise, producing an estimate of motor adaptation with reduced bias. The result is an improved estimate of trial-by-trial adaptation in a human learner compared to conventional methods. We demonstrate the effectiveness of the new method in analyzing simulated and empirical movement data under different noise conditions. The analytic approach is applicable across different types of movements in varied contexts and should replace the regression analysis method in future motor analysis studies. Author SummaryWhen a person makes a movement, a motor error is typically observed that then drives motor planning corrections on subsequent movements. This error correction provides insight into how the nervous system is operating, particularly in regard to how much confidence a person places in different sources of information such as sensory feedback or motor command reproducibility. Traditional analysis of movement has required carefully controlled laboratory conditions, limiting the usefulness of motor analysis in clinical and everyday environments. Here we present a new computational method that can be accurately applied to typical movements. Counterintuitive findings of the established approach are corrected by the proposed method. This method will provide a common framework for researchers to analyze movements while extending dynamic motor adaptation analysis capabilities to clinical and non-laboratory settings.

neuroscience↗

bioRxiv2017

Assessing the quality of supplementary sensory feedback using a crossmodal congruency task

Peripheral nerve interfaces show promise in making prosthetic limbs more biomimetic and ultimately more intuitive and useful for patients. However, approaches to assess these emerging technologies are limited in their scope and the insight they provide. When outfitting a prosthesis with a new feedback system it would be helpful to quantify its physiological correspondence, i.e. how well the experimental feedback mimics the perceived feedback in an intact limb. Here we present an approach to quantify physiological correspondence using a modified crossmodal congruency task. We trained 60 able-bodied subjects to control a bypass prosthesis under different feedback conditions and training durations. We find that the crossmodal congruency effect (CCE) score is sensitive to changes in feedback modality (multi-way ANOVA; F(2,48) = 6.02, p<0.05). After extended training, the CCE score increased as the spatial separation between expected and perceived feedback decreased (unpaired t-test, p<0.05). We present a model that can quantitatively estimate physiological correspondence given the CCE result and the measured spatial separation of the feedback. This quantification approach gives researchers a tool to assess an aspect of emerging augmented feedback systems that is not measurable with current motor assessments.

neuroscience↗

bioRxiv2017

The crossmodal congruency effect, a tool incorporation metric, suffers from a learning effect with repeated exposures

The incorporation of feedback into a persons body schema is well established. The crossmodal congruency effect (CCE) task is used to objectively quantify incorporation without being susceptible to experimenter biases. This visual-tactile interference task is used to calculate the CCE score as a difference in response time for incongruent and congruent trials. Here we show that this metric is susceptible to a learning effect that causes attenuation of the CCE score due to repeated task exposure sessions. We demonstrate that this learning effect is persistent, even after a 6 month hiatus in testing. Two mitigation strategies are proposed: 1. Only use CCE scores that are taken after learning has stabilized, or 2. Use a modified CCE protocol that decreases the task exposure time. We show that the modified and shortened CCE protocol, which may be required to meet time or logistical constraints in laboratory or clinical settings, reduced the impact of the learning effect on CCE results. Importantly, the CCE scores from the modified protocol were not significantly more variable than results obtained with the original protocol. This study highlights the importance of considering exposure time to the CCE task when designing experiments and suggests two mitigation strategies to improve the utility of this psychophysical assessment.

neuroscience↗