Optimising a model-based approach to inferring fear learning from skin conductance responses

Staib, Matthias; Castegnetti, Giuseppe; Bach, Dominik R.

doi:10.1016/j.jneumeth.2015.08.009

Cited by 73 publications

(120 citation statements)

References 25 publications

Supporting

Mentioning

116

Contrasting

Order By: Relevance

“…SCR and ECG data from this experiment were published previously (Castegnetti et al, ; Staib et al, ). Two sets of CS+/CS‐ were used (simple stimuli: sine tones with a constant frequency of 400 or 800 Hz over the entire 4‐s interval; complex stimuli: a train of four frequency modulated sounds of 1 s each, which were rising from 400 to 800 Hz or falling from 800 to 400 Hz).…”

Section: Methodsmentioning

confidence: 99%

“…Overall, estimating neural processes via model‐based approaches tends to improve the signal‐to‐noise ratio (Bach & Friston, ). For example, model‐based analysis of SCR (Staib, Castegnetti, & Bach, ), heart period responses (Castegnetti et al, ), respiration responses (Castegnetti, Tzovara, Staib, Gerster, & Bach, in press), and possibly also startle eyeblink responses (Khemka, Tzovara, Gerster, Quednow, & Bach, ) provides better discrimination of CS+/CS‐ responses than peak scoring measures in fear conditioning paradigms.…”

mentioning

confidence: 99%

See 1 more Smart Citation

A pupil size response model to assess fear learning

et al. 2016

Self Cite

View full text Add to dashboard Cite

During fear conditioning, pupil size responses dissociate between conditioned stimuli that are contingently paired (CS+) with an aversive unconditioned stimulus, and those that are unpaired (CS‐). Current approaches to assess fear learning from pupil responses rely on ad hoc specifications. Here, we sought to develop a psychophysiological model (PsPM) in which pupil responses are characterized by response functions within the framework of a linear time‐invariant system. This PsPM can be written as a general linear model, which is inverted to yield amplitude estimates of the eliciting process in the central nervous system. We first characterized fear‐conditioned pupil size responses based on an experiment with auditory CS. PsPM‐based parameter estimates distinguished CS+/CS‐ better than, or on par with, two commonly used methods (peak scoring, area under the curve). We validated this PsPM in four independent experiments with auditory, visual, and somatosensory CS, as well as short (3.5 s) and medium (6 s) CS/US intervals. Overall, the new PsPM provided equal or decisively better differentiation of CS+/CS‐ than the two alternative methods and was never decisively worse. We further compared pupil responses with concurrently measured skin conductance and heart period responses. Finally, we used our previously developed luminance‐related pupil responses to infer the timing of the likely neural input into the pupillary system. Overall, we establish a new PsPM to assess fear conditioning based on pupil responses. The model has a potential to provide higher statistical sensitivity, can be applied to other conditioning paradigms in humans, and may be easily extended to nonhuman mammals.

show abstract

Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

A pupil size response model to assess fear learning

et al. 2016

Self Cite

View full text Add to dashboard Cite

show abstract

mentioning

confidence: 99%

“…Bach et al, 2010a;Staib, Castegnetti, & Bach, 2015) with PsPM 3.0 (http://pspm.sourceforge.net/;Bach et al, 2018), using the canonical skin conductance response function(Bach et al, 2010b). We used standard settings optimized for fear learning(Staib et al, 2015). Estimated sympathetic arousal for each participant was z-scored across all trials(Staib et al, 2015;Staib & Bach, 2018) 2.10 | Inference statistics For fMRI analysis, BOLD discriminability for each pair of CS or NS was computed and analyzed in a complexity × context factorial model.For behavioral analysis, levels of the stimulus factor were defined by the required response, and data were analyzed in a stimulus × complexity × context factorial model.Inference statistics were done in R 3.4.3 (www.r-project.org) using linear mixed-effects models (lme4; Pinheiro & Bates, 2006).…”

mentioning

confidence: 99%

Primary auditory cortex representation of fear‐conditioned musical sounds

Staib

Abivardi

Bach

2019

Human Brain Mapping

Self Cite

View full text Add to dashboard Cite

Auditory cortex is required for discriminative fear conditioning beyond the classical amygdala microcircuit, but its precise role is unknown. It has previously been suggested that Heschl's gyrus, which includes primary auditory cortex (A1), but also other auditory areas, encodes threat predictions during presentation of conditioned stimuli (CS) consisting of monophones, or frequency sweeps. The latter resemble natural prosody and contain discriminative spectro-temporal information. Here, we use functional magnetic resonance imaging (fMRI) in humans to address CS encoding in A1 for stimuli that contain only spectral but no temporal discriminative information.Two musical chords (complex) or two monophone tones (simple) were presented in a signaled reinforcement context (reinforced CS+ and nonreinforced CS−), or in a different context without reinforcement (neutral sounds, NS1 and NS2), with an incidental sound detection task. CS/US association encoding was quantified by the increased discriminability of BOLD patterns evoked by CS+/CS−, compared to NS pairs with similar physical stimulus differences and task demands. A1 was defined on a single-participant level and based on individual anatomy. We find that in A1, discriminability of CS+/CS− was higher than for NS1/NS2. This representation of unconditioned stimulus (US) prediction was of comparable magnitude for both types of sounds. We did not observe such encoding outside A1. Different from frequency sweeps investigated previously, musical chords did not share representations of US prediction with monophone sounds. To summarize, our findings suggest decodable representation of US predictions in A1, for various types of CS, including musical chords that contain no temporal discriminative information.associative learning, discriminative fear conditioning, emotional learning, multivariate pattern analysis, spectrotemporal information, threat conditioning, threat representation

show abstract

“…SCR was fitted with a canonical skin conductance response function (SCRF) embodied in a third‐order constant‐coefficient inhomogeneous linear ordinary differential equation (ODE), in line with our previous approach (Bach, Daunizeau et al, 2010; Bach et al, 2011; Staib et al, 2015):

normalS normalC normalR (|, normalt) = normalx

\overset{⃛}{x} + ϑ_{1} \ddot{x} + ϑ_{2} \dot{x} + ϑ_{3} normalx + normalu (|, normalt - ϑ_{4}) = 0 .

…”

Section: Methodsmentioning

confidence: 99%

Testing a linear time invariant model for skin conductance responses by intraneural recording and stimulation

et al. 2017

Self Cite

View full text Add to dashboard Cite

Skin conductance responses (SCR) are increasingly analyzed with model‐based approaches that assume a linear and time‐invariant (LTI) mapping from sudomotor nerve (SN) activity to observed SCR. These LTI assumptions have previously been validated indirectly, by quantifying how much variance in SCR elicited by sensory stimulation is explained under an LTI model. This approach, however, collapses sources of variability in the nervous and effector organ systems. Here, we directly focus on the SN/SCR mapping by harnessing two invasive methods. In an intraneural recording experiment, we simultaneously track SN activity and SCR. This allows assessing the SN/SCR relationship but possibly suffers from interfering activity of non‐SN sympathetic fibers. In an intraneural stimulation experiment under regional anesthesia, such influences are removed. In this stimulation experiment, about 95% of SCR variance is explained under LTI assumptions when stimulation frequency is below 0.6 Hz. At higher frequencies, nonlinearities occur. In the intraneural recording experiment, explained SCR variance is lower, possibly indicating interference from non‐SN fibers, but higher than in our previous indirect tests. We conclude that LTI systems may not only be a useful approximation but in fact a rather accurate description of biophysical reality in the SN/SCR system, under conditions of low baseline activity and sporadic external stimuli. Intraneural stimulation under regional anesthesia is the most sensitive method to address this question.

show abstract

Optimising a model-based approach to inferring fear learning from skin conductance responses

Abstract: HighlightsWe validate a Psychophysiological model (PsPM) to infer anticipatory sympathetic arousal from changes in skin conductance.We optimise the inversion of this PsPM in terms of a constrained non-linear dynamic causal model.This method allows a quantification of fear memory in humans.

Cited by 73 publications

References 25 publications

A pupil size response model to assess fear learning

A pupil size response model to assess fear learning

Primary auditory cortex representation of fear‐conditioned musical sounds

Testing a linear time invariant model for skin conductance responses by intraneural recording and stimulation

Contact Info

Product

Resources

About