Being able to replicate real experiments with computational simulations is a unique opportunity to refine and validate models with experimental data and redesign the experiments based on simulations. However, since it is technically demanding to model all components of an experiment, traditional approaches to modeling reduce the experimental setups as much as possible. In this study, our goal is to replicate all the relevant features of an experiment on motor control and motor rehabilitation after stroke. To this aim, we propose an approach that allows continuous integration of new experimental data into a computational modeling framework. First, results show that we could reproduce experimental object displacement with high accuracy via the simulated embodiment in the virtual world by feeding a spinal cord model with experimental registration of the cortical activity. Second, by using computational models of multiple granularities, our preliminary results show the possibility of simulating several features of the brain after stroke, from the local alteration in neuronal activity to long-range connectivity remodeling. Finally, strategies are proposed to merge the two pipelines. We further suggest that additional models could be integrated into the framework thanks to the versatility of the proposed approach, thus allowing many researchers to achieve continuously improved experimental design.

In experimental neuroscientific research, anatomical location is a key attribute of experimental observations and critical for interpretation of results, replication of findings, and comparison of data across studies. With steadily rising numbers of publications reporting basic experimental results, there is an increasing need for integration and synthesis of data. Since comparison of data relies on consistently defined anatomical locations, it is a major concern that practices and precision in the reporting of location of observations from different types of experimental studies seem to vary considerably. To elucidate and possibly meet this challenge, we have evaluated and compared current practices for interpreting and documenting the anatomical location of measurements acquired from murine brains with different experimental methods. Our observations show substantial differences in approach, interpretation and reproducibility of anatomical locations among reports of different categories of experimental research, and strongly indicate that ambiguous reports of anatomical location can be attributed to missing descriptions. Based on these findings, we suggest a set of minimum requirements for documentation of anatomical location in experimental murine brain research. We furthermore demonstrate how these requirements have been applied in the EU Human Brain Project to optimize workflows for integration of heterogeneous data in common reference atlases. We propose broad adoption of some straightforward steps for improving the precision of location metadata and thereby facilitating interpretation, reuse and integration of data.

Brain atlases are widely used in neuroscience as resources for conducting experimental studies, and for integrating, analyzing, and reporting data from animal models. A variety of atlases are available, and it may be challenging to find the optimal atlas for a given purpose and to perform efficient atlas-based data analyses. Comparing findings reported using different atlases is also not trivial, and represents a barrier to reproducible science. With this perspective article, we provide a guide to how mouse and rat brain atlases can be used for analyzing and reporting data in accordance with the FAIR principles that advocate for data to be findable, accessible, interoperable, and re-usable. We first introduce how atlases can be interpreted and used for navigating to brain locations, before discussing how they can be used for different analytic purposes, including spatial registration and data visualization. We provide guidance on how neuroscientists can compare data mapped to different atlases and ensure transparent reporting of findings. Finally, we summarize key considerations when choosing an atlas and give an outlook on the relevance of increased uptake of atlas-based tools and workflows for FAIR data sharing.

The ability of Timm’s sulphide silver method to stain zincergic terminal fields has made it a useful neuromorphological marker. Beyond its roles in zinc-signalling and neuromodulation, zinc is involved in the pathophysiology of ischemic stroke, epilepsy, degenerative diseases and neuropsychiatric conditions. In addition to visualising zincergic terminal fields, the method also labels transition metals in neuronal perikarya and glial cells. To provide a benchmark reference for planning and interpretation of experimental investigations of zinc-related phenomena in rat brains, we have established a comprehensive repository of serial microscopic images from a historical collection of coronally, horizontally and sagittally oriented rat brain sections stained with Timm’s method. Adjacent Nissl-stained sections showing cytoarchitecture, and customised atlas overlays from a three-dimensional rat brain reference atlas registered to each section image are included for spatial reference and guiding identification of anatomical boundaries. The Timm-Nissl atlas, available from EBRAINS, enables experimental researchers to navigate normal rat brain material in three planes and investigate the spatial distribution and density of zincergic terminal fields across the entire brain.

2Being able to replicate real experiments with computational simulations is a unique opportunity 3 to refine and validate models with experimental data and redesign the experiments based on 4 simulations. However, since it is technically demanding to model all components of an experiment, 5 traditional approaches to modeling reduce the experimental setups as much as possible. In this 6 1 Allegra Mascaro, Falotico, Petkoski et al. Towards closed-loop experiments and simulationsstudy, our goal is to replicate all the relevant features of an experiment on motor control and 7 motor rehabilitation after stroke. To this aim, we propose an approach that allows continuous 8 integration of new experimental data into a computational modeling framework. First, results show 9 that we could reproduce experimental object displacement with high accuracy via the simulated 10 embodiment in the virtual world by feeding a spinal cord model with experimental registration of the 11 cortical activity. Second, by using computational models of multiple granularities, our preliminary 12 results show the possibility of simulating several features of the brain after stroke, from the 13 local alteration in neuronal activity to long-range connectivity remodeling. Finally, strategies 14 are proposed to merge the two pipelines. We further suggest that additional models could be 15 integrated into the framework thanks to the versatility of the proposed approach, thus allowing 16 many researchers to achieve continuously improved experimental design. 17 18 response of the environment itself, in that the output of the brain is relevant only if it has the ability to 19 impact the future and hence the input the brain receives. This "closed-loop" can be simulated in a virtual 20 world, where simulated experiments reproduce actions (output from the brain) that have consequences 21 (future input to the brain) (Zrenner et al., 2016). To the aim of reproducing in silico the complexity of real 22 experiments, different levels of modeling shall be integrated. However, since modeling all components of an 23 experiment is very difficult, traditional approaches of computational neuroscience reduce the experimental 24 setups as much as possible. An "Embodied brain" (or "task dynamics", see Zrenner et al. (2016)) approach Allegra Mascaro, Falotico, Petkoski et al. Towards closed-loop experiments and simulationscould overcome these limits by associating the modelled brain activity with the generation of behavior 25 within a virtual or real environment, i.e. an entailment between an output of the brain and a feedback 26 signal into the brain (Tessadori et al., 2012; DeMarse et al., 2001; Reger et al., 2000). The experimenter 27 can interfere with the flow of information between the neural system and environment on the one hand 28 and the state and transition dynamics of the environment on the other. Closing the loop can be performed 29 effectively by (i) validating the models on experimental data, and (ii) designing new experiments based on 30 the hypotheses f...