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Ben-Ami Bartal, I.

Publications and source records attributed to Ben-Ami Bartal, I..

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Brainways: An Open-Source AI-based Software For Registration and Analysis of Fluorescent Markers on Coronal Brain Slices

A central current trend in neuroscience involves the identification of brain-wide neural circuits associated with complex behavior. A major challenge for this approach involves the laborious process for registration and quantification of fluorescence on histological brain slices, as well as the difficulty of deriving functional insight from the complex resulting datasets. As a solution, we developed Brainways, a simple-to-use AI-based open-source software for the identification of neural networks involved in a specific behavior, from digital images to network analysis. Brainways offers automatic registration of coronal slices to any 3D brain atlas, and provides quantification of fluorescent markers (e.g. activity marker, tracer) per region, as well as statistical comparisons with visual mapping of contrasts between conditions. A built-in partial least squares task analysis provides the neural patterns associated with a specific contrast, as well as network graph analysis representing functional connectivity. Trained on atlases for rats and mice, Brainways currently provides above 80% atlas registration accuracy and allows the user to easily adjust the outputs for better fit. Below, a case study validation of Brainways is demonstrated on a previously published data set describing the neural correlates of empathic helping behavior in rats. The original results were successfully replicated and expanded upon, due to the exponentially larger sample size that covered over a 100 times more brain tissue compared to the original manual sampling. Brainways thus provides a fast, accurate solution for quantification of large-scale projects and facilitates novel neurobiological insights about the structural and functional neural networks involved in complex behavior. Brainways has a highly accessible GUI and is functionality exposed through a Python-based API, which can be enhanced for different applications.

neuroscience↗

Adolescent rats extend help to outgroup members, highlighting a neural network for group identity categorization.

Prosocial behavior, in particular helping others in need, occurs preferentially in response to the perceived distress of ones own group members, or ingroup. The development of neural mechanisms underlying social selectivity towards ingroup members are not well established. Here, we used a rat helping behavior test to explore the development and neural basis of ingroup bias for prosocial behavior in adolescent rats. We previously found that adult rats selectively help others from their own social group, and that this selectivity is associated with activation in reward and motivation circuits. Surprisingly, we found that adolescent rats helped both ingroup and outgroup members, evidence suggesting that ingroup bias emerges in adulthood. Analysis of brain-wide neural activity, indexed by expression of the early-immediate gene c-Fos, revealed increased activity for ingroup members across a broad set of regions, which was congruent for adults and adolescents. However, adolescents showed reduced hippocampal and insular activity, and increased orbitofrontal cortex activity compared to adults. Adolescent rats who did not help trapped others also demonstrated increased amygdala connectivity. Together, these findings demonstrate that biases for group-dependent prosocial behavior develop with age in rats and suggest that specific brain regions contribute to this prosocial selectivity, overall pointing to possible targets for the functional modulation of ingroup bias. One Sentence SummaryProsocial selectivity increases with age in parallel with hippocampal and insular activation, providing insight into the neural classification of group membership.

neuroscience↗