A linear model for event-related respiration responses

Bach, Dominik R.; Gerster, Samuel; Tzovara, Athina; Castegnetti, Giuseppe

doi:10.1016/j.jneumeth.2016.06.001

Cited by 18 publications

(25 citation statements)

References 42 publications

(78 reference statements)

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“…The second PC resembled a time derivative of the first component. Rather than approximating the second component, we directly computed the time derivate of the gamma response function (SEBRF'), which was included together with the SEBRF into a model M2, analogous to previous models for SCR (Bach et al, ), HPR (Castegnetti et al, ; Paulus et al, ), respiration (Bach et al, ), and fMRI (Friston et al, ). Since the tail of the first PC component was not well fitted by M1 (Figure ), we created a third model M3 that combined the SEBRF with a Gaussian function to model the response tail.…”

Section: Methodsmentioning

confidence: 99%

“…We have previously demonstrated for SCR, HPR, respiratory measures, and pupil size responses that such implicit assumptions can be made explicit in a psychophysiological model (PsPM). This model specifies, in mathematical form, the expected shape of the response (Bach et al, ; Bach, Flandin, Friston, & Dolan, ; Bach, Gerster, Tzovara, & Castegnetti, ; Korn & Bach ; Paulus, Castegnetti, & Bach, ). The shared variance between expected response with unit amplitude and actual data, assessed, for example, in a regression model, can then be used to quantify response magnitude.…”

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Modeling startle eyeblink electromyogram to assess fear learning

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Pavlovian fear conditioning is widely used as a laboratory model of associative learning in human and nonhuman species. In this model, an organism is trained to predict an aversive unconditioned stimulus from initially neutral events (conditioned stimuli, CS). In humans, fear memory is typically measured via conditioned autonomic responses or fear‐potentiated startle. For the latter, various analysis approaches have been developed, but a systematic comparison of competing methodologies is lacking. Here, we investigate the suitability of a model‐based approach to startle eyeblink analysis for assessment of fear memory, and compare this to extant analysis strategies. First, we build a psychophysiological model (PsPM) on a generic startle response. Then, we optimize and validate this PsPM on three independent fear‐conditioning data sets. We demonstrate that our model can robustly distinguish aversive (CS+) from nonaversive stimuli (CS‐, i.e., has high predictive validity). Importantly, our model‐based approach captures fear‐potentiated startle during fear retention as well as fear acquisition. Our results establish a PsPM‐based approach to assessment of fear‐potentiated startle, and qualify previous peak‐scoring methods. Our proposed model represents a generic startle response and can potentially be used beyond fear conditioning, for example, to quantify affective startle modulation or prepulse inhibition of the acoustic startle response.

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Modeling startle eyeblink electromyogram to assess fear learning

et al. 2016

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“…For such a GLM, the design matrix is formed by convolving event onsets with the impulse response function with unit amplitude. This approach is commonplace in the analysis of neuroimaging data (Friston, ; Friston, Jezzard, & Turner, ), and has more recently been extended in the form of PsPM to skin conductance responses (SCR; Bach, Flandin, Friston, & Dolan, ), heart period responses (Paulus, Castegnetti, & Bach, ), and respiratory responses (Bach, Gerster, Tzovara, & Castegnetti, ). A similar approach has been used for pupil size measurements related to detecting auditory events (Knapen et al, ) and perceptual decision making (de Gee, Knapen, & Donner, ).…”

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A pupil size response model to assess fear learning

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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.

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“…Moreover, one study suggested changes in respiratory period and, relatedly, end‐tidal carbon dioxide pressure (PetCO 2 ), as a result of aversive conditioning (Van Diest, Bradley, Guerra, Van den Bergh, & Lang, ). However, we have recently shown that respiration amplitude responses (RAR) may be better suited to distinguish cognitive processes than respiration period (Bach et al, ). This motivates our current study in which we consider a possibility that conditioned changes in RAR may allow assessing fear memory.…”

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“…We capitalize on a previously developed psychophysiological model (PsPM) of event‐related RAR (Bach et al, ), which is formalized as a general linear model (GLM). We first analyze RAR to CS+ and CS− to establish an impulse response function for fear‐conditioned RAR.…”

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Assessing fear learning via conditioned respiratory amplitude responses

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Respiratory physiology is influenced by cognitive processes. It has been suggested that some cognitive states may be inferred from respiration amplitude responses (RAR) after external events. Here, we investigate whether RAR allow assessment of fear memory in cued fear conditioning, an experimental model of aversive learning. To this end, we built on a previously developed psychophysiological model (PsPM) of RAR, which regards interpolated RAR time series as the output of a linear time invariant system. We first establish that average RAR after CS+ and CS− are different. We then develop the response function of fear‐conditioned RAR, to be used in our PsPM. This PsPM is inverted to yield estimates of cognitive input into the respiratory system. We analyze five validation experiments involving fear acquisition and retention, delay and trace conditioning, short and medium CS‐US intervals, and data acquired with bellows and MRI‐compatible pressure chest belts. In all experiments, CS+ and CS− are distinguished by their estimated cognitive inputs, and the sensitivity of this distinction is higher for model‐based estimates than for peak scoring of RAR. Comparing these data with skin conductance responses (SCR) and heart period responses (HPR), we find that, on average, RAR performs similar to SCR in distinguishing CS+ and CS−, but is less sensitive than HPR. Overall, our work provides a novel and robust tool to investigate fear memory in humans that may allow wide and straightforward application to diverse experimental contexts.

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A linear model for event-related respiration responses

Cited by 18 publications

References 42 publications

Modeling startle eyeblink electromyogram to assess fear learning

Modeling startle eyeblink electromyogram to assess fear learning

A pupil size response model to assess fear learning

Assessing fear learning via conditioned respiratory amplitude responses

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