Sensitivity to Pain Expectations: A Bayesian Model of Individual Differences

AbstractThe thoughts and feelings people have about pain (referred to as ‘pain expectations’) are known to alter the perception of pain. However little is known about the cognitive processes that underpin pain expectations, or what drives the differing effect that pain expectations have between individuals. This paper details the testing of a model of pain perception which formalises the response to pain in terms of a Bayesian prior-to-posterior updating process. Using data acquired from a short and deception-free predictive cue task, it was found that this Bayesian model predicted ratings of pain better than other, simpler models. At the group level, the results confirmed two core predictions of predictive coding; that expectation alters perception and that increased uncertainty in the expectation reduces its impact on perception. The addition to the model of parameters relating to trait differences in pain expectation, improved its fit, suggesting that such traits play a significant role in perception beyond those expectations triggered by the pain cue. When model parameters were allowed to vary by participant, the model’s fit improved further. This final model produced a characterisation of each individual’s sensitivity to pain expectations. This model is relevant for the understanding of the cognitive basis of pain expectations and could potentially act as a useful tool for guiding patient stratification and clinical experimentation.

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A preoperative nomogram predicts prognosis of patients with hepatocellular carcinoma after liver transplantation: a multicenter retrospective study

BMC Cancer ◽

10.1186/s12885-021-07938-x ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Dabing Huang ◽

Yinan Shen ◽

Wei Zhang ◽

Chengxiang Guo ◽

Tingbo Liang ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Liver Transplantation ◽

Adjuvant Therapy ◽

Validation Cohort ◽

Multivariate Analyses ◽

Meld Score ◽

Blood Type ◽

Intermediate Risk ◽

Final Model ◽

Using Data

Abstract Background Although criteria for liver transplantation, such as the Milan criteria and Hangzhou experiences, have become popular, criteria to guide adjuvant therapy for patients with hepatocellular carcinoma after liver transplantation are lacking. Methods We collected data from all consecutive patients from 2012 to 2019 at three liver transplantation centers in China retrospectively. Univariate and multivariate analyses were used to analyze preoperative parameters, such as demographic and clinical data. Using data obtained in our center, calibration curves and the concordance Harrell’s C-indices were used to establish the final model. The validation cohort comprised the patients from the other centers. Results Data from 233 patients were used to construct the nomogram. The validation cohort comprised 36 patients. Independent predictors of overall survival (OS) were identified as HbeAg positive (P = 0.044), blood-type compatibility unmatched (P = 0.034), liver transplantation criteria (P = 0.003), and high MELD score (P = 0.037). For the validation cohort, to predict OS, the C-index of the nomogram was 0.874. Based on the model, patients could be assigned into low-risk (≥ 50%), intermediate-risk (30–50%), and high-risk (≤ 30%) groups to guide adjuvant therapy after surgery and to facilitate personalized management. Conclusions The OS in patients with hepatocellular carcinoma after liver transplantation could be accurately predicted using the developed nomogram.

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Population Pharmacokinetics of Doxycycline in Children

Antimicrobial Agents and Chemotherapy ◽

10.1128/aac.01508-19 ◽

2019 ◽

Vol 63 (12) ◽

Author(s):

Elizabeth J. Thompson ◽

Huali Wu ◽

Chiara Melloni ◽

Stephen Balevic ◽

Janice E. Sullivan ◽

...

Keyword(s):

Model Performance ◽

Standard Of Care ◽

Volume Of Distribution ◽

Clinical Value ◽

Final Model ◽

Childhood Infections ◽

Obesity Status ◽

Improve Model ◽

Using Data ◽

Improve Model Performance

ABSTRACT Doxycycline is a tetracycline-class antimicrobial labeled by the U.S. Food and Drug Administration for children >8 years of age for many common childhood infections. Doxycycline is not labeled for children ≤8 years of age, due to the association between tetracycline-class antibiotics and tooth staining, although doxycycline may be used off-label under severe conditions. Accordingly, there is a paucity of pharmacokinetic (PK) data to guide dosing in children 8 years and younger. We leveraged opportunistically collected plasma samples after intravenous (i.v.) and oral doxycycline doses received per standard of care to characterize the PK of doxycycline in children of different ages and evaluated the effect of obesity and fasting status on PK parameters. We developed a population PK model of doxycycline using data collected from 47 patients 0 to 18 years of age, including 14 participants ≤8 years. We developed a 1-compartment PK model and found doxycycline clearance to be 3.32 liters/h/70 kg of body weight and volume to be 96.8 liters/70 kg for all patients, comparable to values reported in adults. We estimated a bioavailability of 89.6%, also consistent with adult data. Allometrically scaled clearance and volume of distribution did not differ between children 2 to ≤8 years of age and children >8 to ≤18 years of age, suggesting that younger children may be given the same per-kilogram dosing. Obesity status and fasting status were not selected for inclusion in the final model. Additional doxycycline PK samples collected in future studies may be used to improve model performance and maximize its clinical value.

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Optimising the Transfer Alignment of Weapon INS

Journal of Navigation ◽

10.1017/s0373463303002261 ◽

2003 ◽

Vol 56 (2) ◽

pp. 323-335 ◽

Cited By ~ 31

Author(s):

Paul D. Groves

Keyword(s):

Inertial Sensor ◽

Integrated Navigation ◽

Velocity Measurements ◽

Transfer Alignment ◽

Navigation Solution ◽

Alignment Technique ◽

Alignment Process ◽

Using Data ◽

Design Options ◽

Better Than

Transfer alignment is the process of initialising and calibrating a weapon INS using data from the host aircraft's navigation system. To determine which transfer alignment technique performs best, different design options have been assessed, supported by simulation work. The dependence of transfer alignment performance on environmental factors, such as manoeuvres, alignment duration, lever arm and inertial sensor quality has also been studied. ‘Rapid’ alignment, using attitude as well as velocity measurements was found to perform better than ‘conventional’ techniques using only velocity. Innovative developments include the estimation of additional acceleration and gyro states and estimation of force dependent relative orientation, which has enabled robust alignment using wing rock manoeuvres, which do not require the pilot to change trajectory. Transfer alignment has been verified in real-time by flight trials on a Tornado aircraft. In addition, techniques have been developed to prevent transients in the aircraft integrated navigation solution following GPS re-acquisition after an outage of several minutes from disrupting the transfer alignment process.

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Predicting the longitudinal modulus of elasticity of Sitka spruce from cellulose orientation and abundance

Holzforschung ◽

10.1515/hf.2010.084 ◽

2010 ◽

Vol 64 (4) ◽

Cited By ~ 17

Author(s):

J. Paul McLean ◽

Robert Evans ◽

John R. Moore

Keyword(s):

Bulk Density ◽

Modulus Of Elasticity ◽

Microfibril Angle ◽

Sitka Spruce ◽

Picea Sitchensis ◽

Model Parameters ◽

Bending Tests ◽

Wood Stiffness ◽

Using Data ◽

Longitudinal Modulus

Abstract Sitka spruce (Picea sitchensis) is the most widely planted commercial tree species in the United Kingdom and Ireland. Because of the increasing use of this species for construction, the ability to predict wood stiffness is becoming more important. In this paper, a number of models are developed using data on cellulose abundance and orientation obtained from the SilviScan-3 system to predict the longitudinal modulus of elasticity (MOE) of small defect-free specimens. Longitudinal MOE was obtained from both bending tests and a sonic resonance technique. Overall, stronger relationships were found between the various measures of cellulose abundance and orientation and the dynamic MOE obtained from the sonic resonance measurements, rather than with the static MOE obtained from bending tests. There was only a moderate relationship between wood bulk density and dynamic MOE (R2=0.423), but this relationship was improved when density was divided by microfibril angle (R2=0.760). The best model for predicting both static and dynamic MOE involved the product of bulk density and the coefficient of variation in the azimuthal intensity profile (R2=0.725 and 0.862, respectively). The model parameters obtained for Sitka spruce differed from those obtained in earlier studies on Pinus radiata and Eucalyptus delegatensis, indicating that the model might require recalibration before it can be applied to different species.

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Beyond response output: More logical than we think

Behavioral and Brain Sciences ◽

10.1017/s0140525x09000326 ◽

2009 ◽

Vol 32 (1) ◽

pp. 87-88 ◽

Cited By ~ 4

Author(s):

Wim De Neys

Keyword(s):

Bayesian Model ◽

Response Selection ◽

Data Fitting ◽

Output Data ◽

The Core ◽

Response Output ◽

Exclusive Focus ◽

Better Than

AbstractOaksford & Chater (O&C) rely on a data fitting approach to show that a Bayesian model captures the core reasoning data better than its logicist rivals. The problem is that O&C's modeling has focused exclusively on response output data. I argue that this exclusive focus is biasing their conclusions. Recent studies that focused on the processes that resulted in the response selection are more positive for the role of logic.

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Dynamic Modeling of an Unmanned Helicopter From Flight Test Data

Volume 3: 2011 ASME/IEEE International Conference on Mechatronic and Embedded Systems and Applications, Parts A and B ◽

10.1115/detc2011-48836 ◽

2011 ◽

Author(s):

Guanlin Wang ◽

Jihong Zhu ◽

Hui Xia

Keyword(s):

Minimum Cost ◽

Flight Test ◽

Model Parameters ◽

Model Structure ◽

Unmanned Helicopter ◽

Final Model ◽

Identification Approach ◽

Model Set ◽

Partition Method ◽

Approach Time

Accurately modeling the dynamic characteristics of a helicopter is difficult and time-consuming. This paper presents a new identification approach which applies the modes partition method and structure traversal (MPM/ST) algorithm. The dynamic modes, instead of model parameters of each model structure, are sequentially identified through MPM. The model with the minimum cost function (CF) is chosen from the best model set and is defined as the final model. Real flight tests of an unmanned helicopter are carried out to verify the identification approach. Time- and frequency-domain results of the identified models clearly demonstrate the potential of MPM/ST in modeling such complex systems.

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Incremental model breakdown to assess the multi-hypotheses problem

Hydrology and Earth System Sciences ◽

10.5194/hess-22-4565-2018 ◽

2018 ◽

Vol 22 (8) ◽

pp. 4565-4581 ◽

Cited By ~ 3

Author(s):

Florian U. Jehn ◽

Lutz Breuer ◽

Tobias Houska ◽

Konrad Bestian ◽

Philipp Kraft

Keyword(s):

Hydrological Modeling ◽

Model Performance ◽

Complex Model ◽

Model Parameters ◽

Objective Functions ◽

Final Model ◽

Incremental Model ◽

Model Efficiency ◽

Improved Model ◽

Model Structures

Abstract. The ambiguous representation of hydrological processes has led to the formulation of the multiple hypotheses approach in hydrological modeling, which requires new ways of model construction. However, most recent studies focus only on the comparison of predefined model structures or building a model step by step. This study tackles the problem the other way around: we start with one complex model structure, which includes all processes deemed to be important for the catchment. Next, we create 13 additional simplified models, where some of the processes from the starting structure are disabled. The performance of those models is evaluated using three objective functions (logarithmic Nash–Sutcliffe; percentage bias, PBIAS; and the ratio between the root mean square error and the standard deviation of the measured data). Through this incremental breakdown, we identify the most important processes and detect the restraining ones. This procedure allows constructing a more streamlined, subsequent 15th model with improved model performance, less uncertainty and higher model efficiency. We benchmark the original Model 1 and the final Model 15 with HBV Light. The final model is not able to outperform HBV Light, but we find that the incremental model breakdown leads to a structure with good model performance, fewer but more relevant processes and fewer model parameters.

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Aerosol optical depth in the European Brewer Network

10.5194/acp-2017-1003 ◽

2017 ◽

Author(s):

Javier López-Solano ◽

Alberto Redondas ◽

Thomas Carlund ◽

Juan J. Rodriguez-Franco ◽

Henri Diémoz ◽

...

Keyword(s):

Real Time ◽

Aerosol Optical Depth ◽

Optical Depth ◽

Temporal Variability ◽

Input Data ◽

Spatial And Temporal Variability ◽

Ultraviolet Range ◽

Using Data ◽

Better Than

Abstract. The high spatial and temporal variability of aerosols make networks capable of measuring their properties in near real time of high scientific interest. In this work we present and discuss results of an aerosol optical depth algorithm to be used in the European Brewer Network, which provides data in near real time of more than 30 spectrophotometers located from Tamanrasset (Algeria) to Kangerlussuaq (Greenland). Using data from the Brewer Intercomparison Campaigns in the years 2013 and 2015, and the period in between, plus comparisons with Cimel sunphotometers and UVPFR instruments, we check the precision, stability, and uncertainty of the Brewer AOD in the ultraviolet range from 300 to 320 nm. Our results show a precision better than 0.01, an uncertainty of less than 0.05, and a stability similar to that of the ozone measurements for well-maintained instruments. We also discuss future improvements to our algorithm with respect to the input data, their processing, and the characterization of the Brewer instruments for the measurement of aerosols.

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Predictive Coding Models for Pain Perception

10.1101/843284 ◽

2019 ◽

Cited By ~ 2

Author(s):

Yuru Song ◽

Mingchen Yao ◽

Helen Kemprecos ◽

Áine Byrne ◽

Zhengdong Xiao ◽

...

Keyword(s):

Experimental Data ◽

Chronic Pain ◽

Pain Perception ◽

Predictive Coding ◽

Oscillatory Activity ◽

Top Down ◽

Perceptual Inference ◽

Bottom Up ◽

Temporal Coordination ◽

Mechanistic Insight

AbstractPain is a complex, multidimensional experience that involves dynamic interactions between sensory-discriminative and affective-emotional processes. Pain experiences have a high degree of variability depending on their context and prior anticipation. Viewing pain perception as a perceptual inference problem, we use a predictive coding paradigm to characterize both evoked and spontaneous pain. We record the local field potentials (LFPs) from the primary somatosensory cortex (S1) and the anterior cingulate cortex (ACC) of freely behaving rats—two regions known to encode the sensory-discriminative and affective-emotional aspects of pain, respectively. We further propose a framework of predictive coding to investigate the temporal coordination of oscillatory activity between the S1 and ACC. Specifically, we develop a high-level, empirical and phenomenological model to describe the macroscopic dynamics of bottom-up and top-down activity. Supported by recent experimental data, we also develop a mechanistic mean-field model to describe the mesoscopic population neuronal dynamics in the S1 and ACC populations, in both naive and chronic pain-treated animals. Our proposed predictive coding models not only replicate important experimental findings, but also provide new mechanistic insight into the uncertainty of expectation, placebo or nocebo effect, and chronic pain.Author SummaryPain perception in the mammalian brain is encoded through multiple brain circuits. The experience of pain is often associated with brain rhythms or neuronal oscillations at different frequencies. Understanding the temporal coordination of neural oscillatory activity from different brain regions is important for dissecting pain circuit mechanisms and revealing differences between distinct pain conditions. Predictive coding is a general computational framework to understand perceptual inference by integrating bottom-up sensory information and top-down expectation. Supported by experimental data, we propose a predictive coding framework for pain perception, and develop empirical and biologically-constrained computational models to characterize oscillatory dynamics of neuronal populations from two cortical circuits—one for the sensory-discriminative experience and the other for affective-emotional experience, and further characterize their temporal coordination under various pain conditions. Our computational study of biologically-constrained neuronal population model reveals important mechanistic insight on pain perception, placebo analgesia, and chronic pain.

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Inference and search on graph-structured spaces

10.1101/2020.03.06.981399 ◽

2020 ◽

Author(s):

Charley M. Wu ◽

Eric Schulz ◽

Samuel J Gershman

Keyword(s):

Human Behavior ◽

Bayesian Model ◽

Learning Curves ◽

Reward Learning ◽

Function Learning ◽

Sampling Behavior ◽

Complex Graph ◽

Human Function ◽

Better Than

How do people learn functions on structured spaces? And how do they use this knowledge to guide their search for rewards in situations where the number of options is large? We study human behavior on structures with graph-correlated values and propose a Bayesian model of function learning to describe and predict their behavior. Across two experiments, one assessing function learning and one assessing the search for rewards, we find that our model captures human predictions and sampling behavior better than several alternatives, generates human-like learning curves, and also captures participants’ confidence judgements. Our results extend past models of human function learning and reward learning to more complex, graph-structured domains.

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