The study by Lin Tian, Matthew R. Banghart, and Michael R. Bruchas aims to enhance the reproducibility of their research published in the Nature Portfolio. The reporting summary emphasizes the importance of consistency and transparency in scientific reporting. It outlines the requirements for statistical analyses, including the sample size, statistical tests, assumptions, and effect sizes. The authors must provide detailed information on all statistical parameters, including central tendency, variation, and confidence intervals. For Bayesian analysis, details on priors and MCMC settings are required. For hierarchical and complex designs, the appropriate levels for tests and full reporting of outcomes are necessary. The study also includes information on software and code availability. The authors have used various software tools for data collection and analysis, including Fiber photometry, Video Freeze, MetaXpress, ScanImage, and others. Custom MATLAB codes are available on GitHub. The authors are required to make any custom algorithms or software available to editors and reviewers, preferably in a community repository. The study includes a data availability statement, providing access to publicly available datasets and source data. The authors have used the Allen Brain Atlas ISH data and made their source data available on GitHub. The study also mentions policies regarding research involving human participants, their data, or biological material, including the need for approval of the study protocol. The study adheres to field-specific reporting guidelines, including those for life sciences study design and specific materials, systems, and methods. The authors are required to indicate whether each material, system, or method listed is relevant to their study. The study also includes information on eukaryotic cell lines and animals and other research organisms, following relevant policies and guidelines.The study by Lin Tian, Matthew R. Banghart, and Michael R. Bruchas aims to enhance the reproducibility of their research published in the Nature Portfolio. The reporting summary emphasizes the importance of consistency and transparency in scientific reporting. It outlines the requirements for statistical analyses, including the sample size, statistical tests, assumptions, and effect sizes. The authors must provide detailed information on all statistical parameters, including central tendency, variation, and confidence intervals. For Bayesian analysis, details on priors and MCMC settings are required. For hierarchical and complex designs, the appropriate levels for tests and full reporting of outcomes are necessary. The study also includes information on software and code availability. The authors have used various software tools for data collection and analysis, including Fiber photometry, Video Freeze, MetaXpress, ScanImage, and others. Custom MATLAB codes are available on GitHub. The authors are required to make any custom algorithms or software available to editors and reviewers, preferably in a community repository. The study includes a data availability statement, providing access to publicly available datasets and source data. The authors have used the Allen Brain Atlas ISH data and made their source data available on GitHub. The study also mentions policies regarding research involving human participants, their data, or biological material, including the need for approval of the study protocol. The study adheres to field-specific reporting guidelines, including those for life sciences study design and specific materials, systems, and methods. The authors are required to indicate whether each material, system, or method listed is relevant to their study. The study also includes information on eukaryotic cell lines and animals and other research organisms, following relevant policies and guidelines.