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Drakopoulos, V.

Publications and source records attributed to Drakopoulos, V..

2 recordsLinked to original sources

Acute threats modulate hunger circuit dynamics to instruct avoidance behaviour

During foraging animals must balance food-seeking with predator avoidance, yet how the brain integrates sensory information relating to food and threat remains unclear. Using in vivo calcium imaging in mice, we show that hunger-sensitive AgRP neurons in the hypothalamus are rapidly inhibited by threats across a threat imminence continuum, from a low environmental risk to a high physical restraint threat, independent of fasting state. This suppression is driven by GABAergic inputs from dorsomedial hypothalamus (DMH) neurons, which increase activity during threat exposure. While AgRP population activity shows uniform inhibition, pathway-specific monitoring using axonal GCaMP reveals distinct projection patterns. For example, AgRP terminals in BNST and LH decrease activity to both threat and food, while threats increased AgRP axonal activity in the PVN. Furthermore, location-specific optogenetic inhibition of AgRP neurons conditions spatial avoidance, mimicking a threat-induced defensive behavioural responses. These findings reveal a hypothalamic circuit where DMH GABA neurons suppress AgRP activity in response to external threats, prioritising avoidance over food-seeking to optimise adaptive behavioural responses during foraging.

neuroscience↗

FiPhoPHA - A fiber photometry python package for post-hoc analysis

Fiber photometry is a neuroscience technique that can continuously monitor in vivo fluorescence to assess population neural activity or neuropeptide/transmitter release in freely behaving animals. Despite the widespread adoption of this technique, methods to statistically analyse data in an unbiased, objective, and easily adopted manner are lacking. Various pipelines for data analysis exist, but they are often system-specific, only for pre-processing data, and/or lack usability. Current post hoc statistical approaches involve inadvertently biased user-defined time-binned averages or area under the curve analysis. To date, no post-hoc user-friendly tool with few assumptions for a standardised unbiased analysis exists, yet such a tool would improve reproducibility and statistical reliability for all users. Hence, we have developed a user-friendly post hoc statistical analysis package in Python that is easily downloaded and applied to data from any fiber photometry system. This Fiber Photometry Post Hoc Analysis (FiPhoPHA) package incorporates a variety of tools, a downsampler, bootstrapped confidence intervals (CIs) for analyzing peri-event signals between groups and compared to baseline, and permutation tests for comparing peri-event signals across comparison periods. We also include the ability to quickly and efficiently sort the data into mean time bins, if desired. This provides an open-source, user-friendly python package for unbiased and standardised post-hoc statistical analysis to improve reproducibility using data from any fiber photometry system.

neuroscience↗