Biology subjects

Ehlis, A.-C.

Publications and source records attributed to Ehlis, A.-C..

3 recordsLinked to original sources

bioRxiv2026

Menstrual cycle phase and premenstrual symptoms in relation with the stress response: an fMRI study

Objective: Women with premenstrual syndrome (PMS) experience affective lability, irritability, depressed mood, and anxiety during the late luteal phase of the menstrual cycle. Stress reactivity may contribute to the development of these symptoms, yet neural correlates remain unexplored. We examined neural, behavioral and endocrine stress responses across menstrual cycle phases in women with and without PMS. Methods: 26 women with PMS and 24 women without PMS participated in two fMRI sessions, during the mid-follicular (days 5 to 11) and late-luteal phases (days -7 to -1). Participants completed the Montreal Imaging Stress Task (MIST). Subjective stress ratings and salivary cortisol were collected at six time points before and after stress induction. Stress-related brain activation was assessed using whole-brain and region-of-interest analyses. Results: Subjective stress ratings increased as expected from before to after the MIST, with no group or phase differences. Salivary cortisol was significantly higher during the mid-follicular phase, with no group effect or significant pre-post MIST difference. While the stress task elicited activation patterns in stress-related regions, whole-brain analysis revealed no significant effects of group (PMS vs. no PMS) or menstrual cycle phase (follicular vs. luteal) on the neural stress response. Considering premenstrual symptom severity as a predictor revealed significant group-by-symptom severity interactions in the right inferior frontal gyrus and the right superior temporal gyrus. Conclusion: Using a within-subject repeated-measures design, we found neural responses to psychosocial stress to be stable across menstrual cycle phases. Individual differences in symptom severity may nevertheless be associated with differences in stress-related brain activation.

neuroscience↗

bioRxiv2023

Computations and neural dynamics of audiovisual causal and perceptual inference in schizophrenia

Hallucinations and perceptual abnormalities in psychosis are thought to arise from imbalanced integration of prior information and sensory inputs during perceptual inference. In this study, we combined psychophysics, Bayesian modelling and electroencephalography (EEG) to investigate potential changes in perceptual and causal inference in medicated individuals with schizophrenia when exposed to audiovisual sequences with varying numbers of flashes and beeps from either common or independent sources. Our findings reveal that individuals with schizophrenia, like their healthy controls, balance sensory integration and segregation in line with Bayesian causal inference rather than resorting to simpler heuristics. Both groups showed comparable weighting of prior information regarding the signals causal structure, with the schizophrenia group slightly overweighting prior information about the number of flashes or beeps. At the neural level, both groups computed Bayesian causal inference through dynamic encoding of perceptual estimates that segregate and flexibly combine audiovisual inputs. In conclusion, our results demonstrate that the computational and neural mechanisms of multisensory perceptual and causal inference remain remarkably intact in medicated individuals with schizophrenia during flash-beep scenarios.

neuroscience↗

bioRxiv2018

The neural dynamics of hierarchical Bayesian inference in multisensory perception

Transforming the barrage of sensory signals into a coherent multisensory percept relies on solving the binding problem - deciding whether signals come from a common cause and should be integrated, or instead be segregated. Human observers typically arbitrate between integration and segregation consistent with Bayesian Causal Inference, but the neural mechanisms remain poorly understood. We presented observers with audiovisual sequences that varied in the number of flashes and beeps. Combining Bayesian modelling and EEG representational similarity analyses, we show that the brain initially represents the number of flashes and beeps and their numeric disparity mainly independently. Later, it computes them by averaging the forced-fusion and segregation estimates weighted by the probabilities of common and independent cause models (i.e. model averaging). Crucially, prestimulus oscillatory alpha power and phase correlate with observers prior beliefs about the worlds causal structure that guide their arbitration between sensory integration and segregation.

neuroscience↗