Boulton, B. | Search bioRxiv

bioRxiv2026

BeatRatePy - Automated Hight-Throughput Python-Based Beat Rate Quantification and Generation of Speed and Contraction Traces

Analysing cardiac motion from label free imaging can be difficult, especially when recordings vary in quality or show complex movement. We developed a Python-based pipeline that automatically measures beat rate, contraction, speed from transmitted light timelapse data. The workflow runs in a Jupyter notebook and uses straightforward steps that can be followed easily, making the analysis accessible including to users with limited coding experience. Using human iPSC-derived cardiomyocytes imaged on a Zeiss Cell Discoverer 7, the pipeline produced beat rates, contraction and contraction speed measurements that matched expected behaviour. To test wider use, we applied the same workflow to killifish larvae heart recordings and public datasets, changing only basic input parameters. The pipeline performed consistently across these different systems and did not require changes to the underlying code. By supporting clear parameter control and use across laboratories, microscopy platforms and repositories, this approach strengthens reproducible analysis of cardiac motion from label free imaging.

cell biology↗

bioRxiv2024

A hierarchical Bayesian model reveals increased precision weighting for afferent cardiac signals, and reduced anxiety, as a function of interoceptive training

Perceptual accuracy for interoceptive signals, such as heartbeats, varies in a trait-like manner across individuals and may influence the capacity for emotion regulation and vulnerability to affective symptoms, notably anxiety. Here, we demonstrate that an interoceptive training protocol improved perceptual accuracy in two tasks of heartbeat perception and reduced both state and trait anxiety in a subclinical sample, extending previous findings in autistic adults. Computational modelling indicated that accuracy improvement in the heartbeat discrimination task was associated with increases in the internal reliability estimate for interoceptive signals - their precision weighting - while a lower-level parameter representing noise in the interoceptive signal itself (which influences speed of learning) moderated this precision weighting improvement. Reductions in both state and trait anxiety in the training group were uniquely explained by computational parameter estimates, and not by conventional accuracy measures. These findings indicate that trait-like differences in interoceptive processing are modifiable and can be targeted to alleviate anxiety symptoms, and that interoceptive interventions may be best guided by a computational phenotyping approach.

neuroscience↗