Mica

A detailed description of the subjects inclusions, image processing, and analysis methodology can be found in SI Appendix, Supplemental Materials and Methods. In brief, we selected healthy adults from the HCP S900 release for whom all four rs-fMRI and structural scans were available. We selected two cohorts without family relationships, both within and between cohorts, and acceptable image quality: discovery [n = 217 (122 women), mean ± SD age = 28.5 ± 3.7 y] and validation [n = 134 (77 women), age = 28.7 ± 3.8 y].
All MRI data used in this study were publicly available and anonymized. Participant recruitment procedures and informed consent forms, including consent to share deidentified data, were previously approved by the Washington University Institutional Review Board as part of the HCP.
Based on high-resolution T1-weighted images, we segmented CA1–3, CA4–DG, and subiculum using a patch-based algorithm in every subject (17 ). The algorithm employs a population-based patch normalization relative to a template library, which offers good time and space complexity. Notably, by operating on T1-weighted images only, the currently preferred anatomical contrast of many big data MRI initiatives, it avoids reliance on T2-weighted MRI data, a modality that may be prone to motion and flow artifacts, and that may be susceptible to intensity changes due to pathological changes in the hippocampal formation. In previous validations, this algorithm has shown high segmentation accuracy of hippocampal subfields (17 ). We then generated surfaces running through each subfield’s core (24 ), which allowed for the sampling of rs-fMRI time series and for hippocampal unfolding. We also sampled cortical time series using the surfaces provided by HCP and subcortical time series using segmentations from FSL FIRST (50 (link)). We correlated hippocampal and cortical time series, and used Fisher z transformations to render correlation coefficients more normally distributed. Subfield connectivity in Fig. 1B was mapped using linear and mixed-effects models in SurfStat [www.math.mcgill.ca/keith/surfstat/ (51 )]. Diffusion embedding (ref. 26 ; Matlab code: https://github.com/MICA-MNI/micaopen/) identified principal gradients in rs-fMRI connectivity along subfield surfaces, with the anterior/posterior gradient shown in Fig. 1C and the medial/lateral gradient shown in Fig. 3B. We repeated diffusion embedding based on metaanalytical coactivation maps derived from Neurosynth in Fig. 2 (28 (link)).
To assess the relation between functional organization, hippocampal anatomy, and microstructure, we related rs-fMRI gradients to manual segmentations of hippocampal head, body, and tail in Fig. 2 (27 (link)) and to surface-sampled T1w/T2w intensity in Fig. 3B, a proxy for myelin content (20 (link)) (see also comparison between HCP-derived T1w/T2w intensities and quantitative T1 relaxation times from ref. 27 (link)) (SI Appendix, Fig. S7). Findings were consistent in the left and right hippocampus (SI Appendix, Figs. S2–S6, for right hemisphere findings). We demonstrated test/retest stability in all individuals from the discovery cohort in Fig. 4A, by correlating connectivity and gradients maps between two scans within each subject to the other two. Furthermore, we assessed reproducibility, by correlating subfield connectivity and gradient maps between the discovery and validation dataset in Fig. 4B.