Fig. 1: Building a functional atlas of the cerebellum across datasets.

a, Parcellations (K = 68) derived from each single dataset (multi-domain task battery dataset, MDTB; high-resolution multi-domain task battery dataset, Highres-MDTB; Nishimoto dataset, individual brain charting dataset, IBC; working memory dataset, WM; demand dataset, Demand; somatotopic dataset, Somatotopic; Human Connectome Project dataset, HCP). The probabilistic parcellation is shown as a winner-take-all projection onto a flattened representation of the cerebellum³². Functionally similar regions are colored similarly within a parcellation (see methods: parcel similarity) and spatially similar parcels are assigned similar colors across parcellations. Dotted lines indicate lobular boundaries. b, Projection of the between-dataset adjusted Rand Index (ARI) of single-dataset parcellations into a 2d-space through multi-dimensional scaling (see methods: Single-dataset parcellations and similarity analysis of parcellations). c, Within-dataset reliability of parcellation, calculated as the mean ARI across the 5 levels of granularity (10, 20, 34, 40 and 68 regions). Errorbars indicate SE of the mean across the five granularity pairs. Dots show individual reliability values (n = 10). d, Reliability-adjusted ARI between each single-dataset parcellations and the multi-domain task battery (MDTB; task-based) and Human Connectome Project (HCP; resting parcellation) parcellation. Errorbars indicate standard error of the mean across the five levels of granularity. Dots show individual similarity values (n = 25). Paired two-tailed t-tests were calculated between the ARI of each single-dataset to the MDTB parcellation and to the HCP parcellation at each granularity: MDTB-Highres: \({t}_{24}=16.404,p=1.523\times {10}^{-14}\); IBC: \({t}_{24}=3.513,p=.0017\); WM: \({t}_{24}=4.727,p=8.318\times {10}^{-5}\); Demand: \({t}_{24}=3.262,p=.0033\); Somatotopic: \({t}_{24}=12.538,p=5.015\times {10}^{-12}\). ** \(p < 0.01\), *** \(p < 0.0001\). e, Distance-Controlled Boundary Coefficient (DCBC) evaluation of the symmetric and asymmetric atlas averaged across granularities evaluated on the group map (left) or on individual maps derived with that atlas (right). Errorbars indicate SE of the mean across subjects. Gray connecting lines show individual subjects (n = 111). For visualization purposes of the subject data, the subject mean was subtracted and the group mean added. f, DCBC evaluation of the symmetric group map and of individual maps derived from the model with 10, 20, 34, 40, and 68 regions. Shaded area indicates SE of the mean across subjects. Source data are provided as a Source Data file.