Documentation

When you use PsPM code or concepts, we ask you to reference our papers - an annotated reference list is provided below. Some PsPM pre-processing algorithms are based on other people’s work. This is detailed in the help of the respective functions, and we ask you to reference these researchers’ work when you use them.

  • Bach DR & Friston KJ (2013). Model-based analysis of skin conductance responses: Towards causal models in psychophysiology. Psychophysiology, 50(1), 15-22. [PubMed]
  • Bach DR, Castegnetti G, Korn CW, Gerster S, Melinscak F, Moser T (2018). Psychophysiological modelling - current state and future directions. Psychophysiology, 55, e13209. [PubMed]

Models for skin conductance responses

General: the skin conductance response function

  • Bach DR, Flandin G, Friston KJ, Dolan RJ (2010). Modelling event-related skin conductance responses. International Journal of Psychophysiology, 75, 349-356. [PubMed] (Development of the SCRF, and an indirect test of the LTI assumptions)
  • Gerster S, Namer B, Elam M, Bach DR (2018). Testing a linear time invariant model for skin conductance responses by intraneural recording and stimulation. Psychophysiology, 55, e12986. [PubMed] (A direct test of the LTI assumptions, using intraneural recording and stimulation)

GLM for evoked skin conductance responses (eSCR)

This model assumes that sympathetic nerve (SN) responses follow a short stimulus with constant latency. The amplitude of these responses is estimated in the framework of a general linear convolution model, using a canonical skin conductance response function (SCRF), under linear time-invariance assumptions, and an informed, linear neural model.

  • Bach DR, Flandin G, Friston KJ, Dolan RJ (2009). Time-series analysis for rapid event-related skin conductance responses. Journal of Neuroscience Methods, 184, 224-234. [PubMed] (Proof-of-principle of the GLM for SCR) Bach DR,Friston KJ, Dolan RJ (2013). An improved algorithm for model-based analysis of evoked skin conductance responses. *Biological Psychology, 94, 490-497. [PubMed] (optimisation of the algorithm and latest recommendations)
  • Bach DR (2014). A head-to-head comparison of SCRalyze and Ledalab, two model-based methods for skin conductance analysis. Biological Psychology, 103, 63-88. [PubMed] (direct comparison with Ledalab)

This model is used, e.g. for conditioned (anticiaptory) responses in fear conditioning. It assumes that the onset of the neural response is not precisely known, and estimates onset, dispersion, and amplitude of the response. Non-linear model inversion is accomplished in the computational framework of the Variational Bayes approach.

  • Bach DR, Daunizeau J, Friston KJ, Dolan RJ (2010). Dynamic causal modelling of anticipatory skin conductance responses. Biological Psychology, 85, 163-70. [PubMed] (proof of principle of the non-linear model for SCR)
  • Staib M, Castegnetti G, Bach DR (2015). Optimising a model-based approach to inferring fear learning from skin conductance responses. Journal of Neuroscience Methods, 255, 131-138. [PubMed] (optimisation of the algorithm and latest recommendations)

Models for spontaneous skin conductance fluctuations (SF)

These models are entirely uninformed about the onset of neural responses. SF are often thought to index tonic arousal. A very simple model estimates the mean number of responses times mean amplitude per time unit, as area under the curve (AUC) of the signal. The number of responses is however more informative than their amplitude, such that a more sophisticated model was developed to estimate neural response onsets and amplitudes separately. This is similar to the approach for event-related SCR: it uses a slightly modified SCRF, an uninformed non-linear neural model and the VBA model inversion framework.

  • Bach DR, Friston KJ, Dolan RJ (2010). Analytic measures for quantification of arousal from spontaneous skin conductance fluctuations. International Journal of Psychophysiology, 76, 52-55. [PubMed] (test for LTI assumptions, a modified SCRF for SF, AUC method)
  • Bach DR, Daunizeau J, Kuelzow N, Friston KJ, Dolan RJ (2011). Dynamic causal modelling of spontaneous fluctuations in skin conductance. Psychophysiology, 48, 252-257. [PubMed] (Non-linear model for SF)
  • Bach DR, Staib M (2015). A matching pursuit algorithm for inferring tonic sympathetic arousal from spontaneous skin conductance fluctuations. Psychophysiology, 52, 1106-1112. [PubMed] (MP approximation for SF)

Models for pupil data

Models are available for illuminance-elicited changes, cognitive input, and specifically for fear conditioning. All model build on LTI systems and are inverted in the framework of GLM. PsPM contains many pre-processing and convenience functions for eye-tracking data.

  • Korn CK, Staib M, Tzovara A, Castegnetti G, Bach DR (2017). A pupil size response model to assess fear learning. Psychophysiology, 54, 330-343. [PubMed] (GLM for fear-conditioned pupil size responses)
  • Korn CW & Bach DR (2016). A solid frame for the window on cognition: Modeling event-related pupil responses. Journal of Vision, 16, 28. [PubMed] (illuminance model and its application to cognitive paradigms)
  • Abivardi A, Korn CW, Rojkov I, Gerster S, Hurlemann R, Bach DR (2023). Acceleration of inferred neural responses to oddball targets in an individual with bilateral amygdala lesion compared to healthy controls. Scientific Reports, 13, 41357. [doi] (application of the illuminance model to a lesion patient group)

Models for heart data

  • Castegnetti G, Tzovara A, Staib M, Paulus PC, Hofer N, & Bach DR (2016). Modelling fear-conditioned bradycardia in humans. Psychophysiology, 53, 930-939. [PubMed] (GLM for fear-conditioned bradycardia)
  • Paulus PC, Castegnetti G, & Bach DR (2016). Modeling event-related heart period responses. Psychophysiology, 53, 837-846. [PubMed] (GLM for evoked heart period responses)

Models for respiration data

Castegnetti G, Tzovara A, Staib M, Gerster S, Bach DR (2017). Assessing fear learning via conditioned respiratory amplitude responses. Psychophysiology, 54, 215-223. [PubMed] (GLM for fear-conditioned respiration amplitude)

  • Bach DR, Gerster S, Tzovara A, Castegnetti G (2016). A linear model for event-related respiration responses. Journal of Neuroscience Methods, 270, 174-155. [PubMed] (GLM for evoked respiratory responses)
  • Khemka S, Tzovara A, Gerster S, Quednow BB, Bach DR (2017). Modelling startle eye blink electromyogram to assess fear learning. Psychophysiology, 54, 202-214. [PubMed]

Recent examples for application studies using PsPM

  • Xia Y, Wehrli J, Abivardi A , Hostiuc M, Kleim B, Bach DR (2024). Attenuating human fear memory retention with minocycline: a randomized placebo-controlled trial. Translational Psychiatry, 14, 28.
  • Wehrli J, Xia Y, Abivardi A , Kleim B, Bach DR (2024). The impact of doxycycline on human contextual fear memory. Psychopharmacology, 241, 1065–1077.
  • Wehrli J, Xia Y, Offenhammer B, Kleim B, Müller D, Bach DR (2023). Effect of the matrix metalloproteinase inhibitor doxycycline on human trace fear memory. eNeuro, ENEURO.0243-22.2023.
  • Xia Y, Wehrli J, Gerster S, Kroes M, Houtekamer M, Bach DR (2023). Measuring human context fear conditioning and retention after consolidation. Learning & Memory, 30, 139–150.
  • Ojala KE, Staib M, Gerster S, Ruff CC, Bach DR (2022). Inhibiting human aversive memory by transcranial theta-burst stimulation to primary sensory cortex. Biological Psychiatry, 92, 149-157.
  • Wehrli JM, Xia Y, Gerster S, & Bach DR (2022). Measuring human trace fear conditioning. Psychophysiology, 59, e14119.
  • Homan P, Lau HL, Levy I, Raio CM, Bach DR, Carmel D, Schiller S (2021). Evidence for a minimal role of stimulus awareness in reversal of threat learning. Learning & Memory, 28, 95-103.

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