text: *White matter microstructure*. Diffusion scans were preprocessed using 
  the automation software QSIprep (version 0.16.1) compiled using a 
  singularity container73 and run primarily with the default parameters, 
  with the exceptions ‘–output resolution 1.8’, ‘–dwi denoise window 5′, 
  –force-spatial-normalization’, ‘–hmc model 3dSHORE’, ‘–hmc- 
  transform Rigid’ and ‘–shoreline iters 2’. Twenty-three sessions were 
  preprocessed and analyzed, with the remaining three scans excluded 
  due to missing DSI scans (sessions 9 and 15) or corresponding field map 
  for distortion correction (session 7). Despite passing QC assessments 
  during preprocessing, visual inspection of the field maps in session 10 
  revealed a slight artifact. However, removal of this session had minimal 
  impact on the overall results and remained in the final analyses. T1w 
  images were corrected for intensity nonuniformity (N4BiasFieldCorrec- 
  tion) and skull-stripped (antsBrainExtraction). The images underwent 
  spatial normalization and registration to the ICBM 152 Nonlinear Asym- 
  metric template. Finally, brain tissue segmentation of CSF, GM and WM 
  was performed on each brain-extracted T1w using FMRIB’s Automated 
  Segmentation Tool (FAST). Preprocessing of diffusion images began by 
  implementing MP-PCA denoising with a 5-voxel window using MRtrix3’s 
  dwidenoise function. B1 field inhomogeneity was corrected using dwibi- 
  ascorrect from MRtrix3 with the N4 algorithm. Motion was corrected 
  using the SHORELine method. Susceptibility distortion correction was 
  based on GRE field maps. Preprocessed Nifti scans were prepared for 
  tractography using DSI Studio via singularity container version Chen- 
  2022-07-31 (ref. 74). Diffusion images were converted to source code 
  files using the DSI Studio command line ‘--action=src’ and a custom 
  script to convert all images. The diffusion data were reconstructed in 
  MNI space using q-space diffeomorphic reconstruction75 with a diffu- 
  sion sampling of 1.25 and output resolution of 1.8 mm isotropic. The 
  following output metrics were specified to be included in the output 
  FIB file: QA and mean diffusivity (MD). The quality and integrity of 
  reconstructed images were assessed using ‘QC1: SRC Files Quality Con- 
  trol’. First, the consistency of image dimension, resolution, DWI count 
  and shell count was checked for each image. Second, each image was 
  assessed for the ‘neighboring DWI correlation’ which calculates the 
  correlation coefficient of low*b*DWI volumes that have similar gradi- 
  ent direction. Lower correlation values may indicate issues with the 
  diffusion signal due to artifacts or head motion. Finally, DSI Studio  *Hippocampal segmentation*. T1- and T2-weighted images (*n*= 25) were 
  submitted to the automatic segmentation of hippocampal subfields 
  package (ASHS67, version July 2018) for parcellation of seven MTL 
  subregions: CA1, CA2/CA3, dentate gyrus, subiculum, perirhinal cor- 
  tex, entorhinal cortex and PHC (Supplementary Fig. 6b). The ASHS 
  segmentation pipeline automatically segmented the hippocampus in 
  the T2w MRI scans using a segmented population atlas, the Princeton 
  Young Adult 3T ASHS Atlas template68 (*n*= 24, mean age = 22.5 years). 
  A rigid-body transformation aligned each T2w image to the respective 
  T1w scan for each day. Using ANTs deformable registration, the T1w 
  was registered to the population atlas. The resulting deformation 
  fields were used to resample the data into the space of the left and 
  right template MTL ROI. Within each template ROI, each of the T2w 
  scans of the atlas package was registered to that day’s T2w scan. The 
  manual atlas segmentations were then mapped into the space of the 
  T2w scan, with segmentation of the T2w scan computed using joint 