Nature Neuroscience
Resource https://doi.org/10.1038/s41593-024-01741-0
participant’s first two baseline scans (that is, preconception) to derive 
within-participant variability estimates.
Benchmarking our data in this way allows us to capture the degree 
of change expected due to factors such as image processing and 
instrumentation variability or other day-to-day changes that could 
potentially modulate brain size and shape (see ref. 80 for review). The 
percent change observed over pregnancy (baseline versus 36 weeks 
gestation) far exceeds the expected variability estimated using both 
the Day2Day dataset (Supplementary Fig. 11) and our within-participant 
control data. This was quantified by dividing the observed percent 
change in GMV metrics (baseline versus 36 weeks) by the global meas-
ure of GMV percent variability of each control group (that is, Day2Day, 
within-participant control), independently for cortex and subcortex.
Reporting summary
Further information on research design is available in the Nature 
Portfolio Reporting Summary linked to this article.
Data availability
The dataset consists of 26 MRI scans (T1w, T2w and diffusion scans) 
alongside state-dependent measures and serum assessments of ovar-
ian sex hormones for each session. The raw data is publicly available at 
https://openneuro.org/datasets/ds005299. Source data are provided 
with this paper.
Code availability
No custom code was used.
References
49. Karch, J. D. et al. Identifying predictors of within-person  
variance in MRI-based brain volume estimates. NeuroImage 200, 
575–589 (2019).
50. Cohen, S., Kamarck, T. & Mermelstein, R. A global measure of 
perceived stress. J. Health Soc. Behav. 24, 385–396 (1983).
51. Buysse, D. J., Reynolds, C. F. III, Monk, T. H., Berman, S. R. &  
Kupfer, D. J. The Pittsburgh sleep quality index: a new instrument 
for psychiatric practice and research. Psychiatry Res. 28, 193–213 
(1989).
52. Speilberger, C. D. & Vagg, P. R. Psychometric properties of the 
STAI: a reply to Ramanaiah, Franzen, and Schill. J. Pers. Assess. 48, 
95–97 (1984).
53. Pollock, V., Cho, D. W., Reker, D. & Volavka, J. Profile of mood 
states: the factors and their physiological correlates. J. Nerv. 
Ment. Dis. 167, 612–614 (1979).
54. Avants, B. B. et al. A reproducible evaluation of ANTs similarity 
metric performance in brain image registration. Neuroimage 54, 
2033–2044 (2011).
55. Tustison, N. J. et al. Large-scale evaluation of ANTs and  
FreeSurfer cortical thickness measurements. Neuroimage 99, 
166–179 (2014).
56. Das, S. R., Avants, B. B., Grossman, M. & Gee, J. C. Registration 
based cortical thickness measurement. Neuroimage 45,  
867–879 (2009).
57. Friston, K. J., Rotshtein, P., Geng, J. J., Sterzer, P. & Henson, R. N. 
A critique of functional localisers. Neuroimage 30, 1077–1087 
(2006).
58. Schaefer, A. et al. Local-global parcellation of the human cerebral 
cortex from intrinsic functional connectivity MRI. Cereb. Cortex 
28, 3095–3114 (2018).
59. Yeo, B. T. et al. The organization of the human cerebral cortex 
estimated by intrinsic functional connectivity. J. Neurophysiol. 
106, 1125–1165 (2011).
60. Dale, A. M., Fischl, B. & Sereno, M. I. Cortical surface-based 
analysis: I. Segmentation and surface reconstruction. Neuroimage 
9, 179–194 (1999).
61. Reuter, M., Schmansky, N. J., Rosas, H. D. & Fischl, B. 
Within-subject template estimation for unbiased longitudinal 
image analysis. Neuroimage 61, 1402–1418 (2012).
62. Desikan, R. S. et al. An automated labeling system for subdividing 
the human cerebral cortex on MRI scans into gyral based regions 
of interest. Neuroimage 31, 968–980 (2006).
63. Fischl, B. et al. Whole brain segmentation: automated labeling 
of neuroanatomical structures in the human brain. Neuron 33, 
341–355 (2002).
64. Esteban, O. et al. MRIQC: advancing the automatic prediction  
of image quality in MRI from unseen sites. PLoS ONE 12, e0184661 
(2017).