Modeling event‐related heart period responses

Abstract

Cardiac rhythm is generated locally in the sinoatrial node, but modulated by central neural input. This may provide a possibility to infer central processes from observed phasic heart period responses (HPR). Currently, operational methods are used for HPR analysis. These methods embody implicit assumptions on how central states influence heart period. Here, we build an explicit psychophysiological model (PsPM) for event‐related HPR. This phenomenological PsPM is based on three experiments involving white noise sounds, an auditory oddball task, and emotional picture viewing. The model is optimized with respect to predictive validity—the ability to separate experimental conditions from each other. To validate the PsPM, an independent sample of participants is presented with auditory stimuli of varying intensity and emotional pictures of negative and positive valence, at short intertrial intervals. Our model discriminates these experimental conditions from each other better than operational approaches. We conclude that our PsPM is more sensitive to distinguish experimental manipulations based on heart period data than operational methods, and furnishes a principled approach to analysis of HPR.

A pupil size response model to assess fear learning

“…Such correlations are interesting from the vantage point of the underlying neuroanatomy. SCR are solely determined by the sympathetic branch of the autonomous nervous system and heart period responses—at least, the early responses—are influenced by the parasympathetic branch (Paulus et al, ). In contrast, pupil size is antagonistically related to both sympathetic and parasympathetic branches for constriction and dilation, respectively (McDougal & Gamlin, ).…”

“…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, ).…”

Modeling startle eyeblink electromyogram to assess fear learning

“…QRS complexes were identified semiautomatically in the ECG data through a modified offline version (Paulus et al, ) of the Pan and Tompkins () real‐time QRS detection. Interbeat intervals were then linearly interpolated at 10 Hz to create a time series of equidistant time points (Paulus et al, ), which were band‐pass filtered with a bidirectional Butterworth filter (0.015–0.5 Hz). HPR were analyzed with a model‐based approach.…”

“…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.…”

A linear model for event-related respiration responses

“…In order to estimate the psychological input into this system, we used a general linear convolution model for each participant and experiment (GLM), in line with previous approaches to analysis of skin conductance responses (Bach et al, 2009), heart period responses (Castegnetti et al, 2016, Paulus et al, 2016), or functional magnetic resonance imaging (Friston et al, 1994). A GLM can be written as$Y=X\beta +\mathrm{ϵ}\text{,}$where X is design matrix in which each column is obtained by convolving impulse functions at event onset with each component of the RF.…”