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Diffusion-weighted MRI (dMRI) enables non-invasive mapping of white matter pathways and construction of the brain\u2019s structural connectome, but its application to the fetal brain has been limited by data scarcity and technical difficulties in analyzing fetal dMRI data. Here, we present the largest study to date of in utero brain connectivity, analyzing high-quality dMRI data from 198 fetuses between 22 and 37 gestational weeks from the Developing Human Connectome Project. We employed advanced fetal-specific tools for brain segmentation and parcellation, and used ensemble tractography to encourage more complete reconstruction of various white matter tracts. For connection weighting, we relied on the notion of fiber bundle capacity. We reconstructed individual structural connectomes and characterized the developmental trajectories. Graph-theoretical analysis revealed consistent increases in integration and segregation metrics over gestation, while bootstrapping confirmed the robustness of nodal and edgewise developmental patterns. Furthermore, we proposed a novel method for constructing age\u2013specific connectome templates based on aggregation of individual subject connectomes. The new method follows an optimization-based approach to ensure that the connectome templates closely represent individual subject connectomes, are temporally consistent, and proportionally preserve short and long connections. Our analysis shows that this approach is superior to spatial alignment and averaging of the data in image space, with the resulting connectome templates supporting accurate prediction of the gestational age of individual fetuses (mean error= 0.90 \u00b1 0.81 weeks). In summary, this study provides robust normative benchmarks for fetal brain structural connectivity and demonstrates that connectome-based measures capture meaningful developmental signatures, offering a framework for future studies of early brain development.<\/jats:p>","DOI":"10.1101\/2025.09.14.676101","type":"posted-content","created":{"date-parts":[[2025,9,15]],"date-time":"2025-09-15T08:50:20Z","timestamp":1757926220000},"source":"Crossref","is-referenced-by-count":0,"title":["Charting the Normal Development of Structural Brain Connectivity in Utero using Diffusion MRI"],"prefix":"10.64898","author":[{"given":"Davood","family":"Karimi","sequence":"first","affiliation":[{"name":"Department of Radiology, Boston Children\u2019s Hospital, and Harvard Medical School, Boston, Massachusetts, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bo","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Radiology, Boston Children\u2019s Hospital, and Harvard Medical School, Boston, Massachusetts, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Athena","family":"Taymourtash","sequence":"additional","affiliation":[{"name":"Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Camilo","family":"Jaimes","sequence":"additional","affiliation":[{"name":"Department of Radiology, Massachusetts General Hospital, and Harvard Medical School, Boston, Massachusetts, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1005-4013","authenticated-orcid":false,"given":"Ellen P.","family":"Grant","sequence":"additional","affiliation":[{"name":"Department of Radiology, Boston Children\u2019s Hospital, and Harvard Medical School, Boston, Massachusetts, USA"},{"name":"Division of Neuroradiology, Boston Children\u2019s Hospital, Boston, Massachusetts, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Simon K.","family":"Warfield","sequence":"additional","affiliation":[{"name":"Department of Radiology, Boston Children\u2019s Hospital, and Harvard Medical School, Boston, Massachusetts, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"54368","reference":[{"key":"2026051309201211000_2025.09.14.676101v3.1","unstructured":"Seung S. 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