Measuring Heart Rate in Freely Moving Mice.

Measuring autonomic parameters like heart rate in behaving mice is not only a standard procedure in cardiovascular research but is applied in many other interdisciplinary research fields. With an electrocardiogram (ECG), the heart rate can be measured by deriving the electrical potential between subcutaneously implanted wires across the chest. This is an inexpensive and easy-to-implement technique and particularly suited for repeated recordings of up to eight weeks. This protocol describes a step-by-step guide for manufacturing the needed equipment, performing the surgical procedure of electrode implantation, and processing of acquired data, yielding accurate and reliable detection of heartbeats and calculation of heart rate (HR). We provide MATLAB graphical user interface (GUI)-based tools to extract and start processing the acquired data without a lot of coding knowledge. Finally, based on an example of a data set acquired in the context of defensive reactions, we discuss the potential and pitfalls in analyzing HR data. Key features • Next to surgical steps, the protocol provides a detailed description of manufacturing custom-made ECG connectors and a shielded, light-weight patch cable. • Suitable for recordings in which signal quality is challenged by ambient noise or noise from other recording devices. • Described for 2-channel differential recording but easily expandable to record from more channels. • Includes a summary of potential analysis methods and a discussion on the interpretation of HR dynamics in the case study of fear states.

Deep learning-enabled segmentation of ambiguous bioimages with deepflash2.

Bioimages frequently exhibit low signal-to-noise ratios due to experimental conditions, specimen characteristics, and imaging trade-offs. Reliable segmentation of such ambiguous images is difficult and laborious. Here we introduce deepflash2, a deep learning-enabled segmentation tool for bioimage analysis. The tool addresses typical challenges that may arise during the training, evaluation, and application of deep learning models on ambiguous data. The tool's training and evaluation pipeline uses multiple expert annotations and deep model ensembles to achieve accurate results. The application pipeline supports various use-cases for expert annotations and includes a quality assurance mechanism in the form of uncertainty measures. Benchmarked against other tools, deepflash2 offers both high predictive accuracy and efficient computational resource usage. The tool is built upon established deep learning libraries and enables sharing of trained model ensembles with the research community. deepflash2 aims to simplify the integration of deep learning into bioimage analysis projects while improving accuracy and reliability.

Integrated cardio-behavioral responses to threat define defensive states.

Fear and anxiety are brain states that evolved to mediate defensive responses to threats. The defense reaction includes multiple interacting behavioral, autonomic and endocrine adjustments, but their integrative nature is poorly understood. In particular, although threat has been associated with various cardiac changes, there is no clear consensus regarding the relevance of these changes for the integrated defense reaction. Here we identify rapid microstates that are associated with specific behaviors and heart rate dynamics, which are affected by long-lasting macrostates and reflect context-dependent threat levels. In addition, we demonstrate that one of the most commonly used defensive behavioral responses-freezing as measured by immobility-is part of an integrated cardio-behavioral microstate mediated by Chx10+ neurons in the periaqueductal gray. Our framework for systematic integration of cardiac and behavioral readouts presents the basis for a better understanding of complex neural defensive states and their associated systemic functions.

Induction of BDNF Expression in Layer II/III and Layer V Neurons of the Motor Cortex Is Essential for Motor Learning.

Motor learning depends on synaptic plasticity between corticostriatal projections and striatal medium spiny neurons. Retrograde tracing from the dorsolateral striatum reveals that both layer II/III and V neurons in the motor cortex express BDNF as a potential regulator of plasticity in corticostriatal projections in male and female mice. The number of these BDNF-expressing cortical neurons and levels of BDNF protein are highest in juvenile mice when adult motor patterns are shaped, while BDNF levels in the adult are low. When mice are trained by physical exercise in the adult, BDNF expression in motor cortex is reinduced, especially in layer II/III projection neurons. Reduced expression of cortical BDNF in 3-month-old mice results in impaired motor learning while space memory is preserved. These findings suggest that activity regulates BDNF expression differentially in layers II/III and V striatal afferents from motor cortex and that cortical BDNF is essential for motor learning.SIGNIFICANCE STATEMENT Motor learning in mice depends on corticostriatal BDNF supply, and regulation of BDNF expression during motor learning is highest in corticostriatal projection neurons in cortical layer II/III.