The scripts are primarily written in Python and R and comprise four main packages: (1) a Python package for multi-echo fetal brain fMRI preprocessing, supplementing Ji (2024, PLOS Biology); (2) a Python package for T2*/R2* mapping from multi-echo fetal MRI data, supplementing Ji (2025, Human Brain Mapping) and Ji (2025, Communications Biology); (3) an R package for longitudinal modeling of brain network functional connectivity changes from the fetal to neonatal period, supplementing Ji (2024, PLOS Biology); and (4) an R package for statistical analysis and plot generation for Ji (2025, Communications Biology). Acknowledgments Codes for ICA denoising is developed based on AROMA source code downloaded from: https://github.com/maartenmennes/ICA-AROMA/tree/master. References: Pruim RHR, Mennes M, van Rooij D, Llera A, Buitelaar JK, Beckmann CF. ICA-AROMA: A robust ICA-based strategy for removing motion artifacts from fMRI data. Neuroimage. 2015 May 15;112:267-277. doi: 10.1016/j.neuroimage.2015.02.064. Epub 2015 Mar 11. PMID: 25770991. Code for CNN-based brain extraction is from Dr. Saige Rutherford's github, downloaded from: https://github.com/saigerutherford/fetal-code. Reference: Rutherford S, Sturmfels P, Angstadt M, Hect J, Wiens J, van den Heuvel MI, Scheinost D, Sripada C, Thomason M. Automated Brain Masking of Fetal Functional MRI with Open Data. Neuroinformatics. 2022 Jan;20(1):173-185. doi: 10.1007/s12021-021-09528-5. Epub 2021 Jun 15. PMID: 34129169; PMCID: PMC9437772. Primary contributors: Amyn Majbri, Lanxin Ji, Mark Duffy.PI: Moriah ThomasonFunding: This work is supported by NIH grants ES032294, MH122447, MH110793, ES026022 to Moriah Thomason. ### Dependencies for R2* estimation Python Version 3.10.8 PyCharm Community Edition 2022.3.3 - Build #PC-223.8836.34 Packages numpy 1.24.2 nibabel 5.0.1 matplotlib 3.7.1 scipy 1.10.1 pandas 1.5.3 ANTS Version 2.5.3.post16-g1892fef FSL Version 6.0.6.1 FSLeyes Version 1.10.3 MATLAB Version R2022b SPM Version SPM12