Fig 1.
Population template and volume-based brain parcellation atlas of the tree shrew brain.
(A) Multi-subject coronal (left) and sagittal (right) views of T2*-weighted MRI and FA-weighted DEC images. DEC, directionally encoded color; FA, fractional anisotropy. Female, N08 and N10; the remaining individuals are male. (B) Ultra-high-resolution T2*-weighted MRI (green box: 50 × 50 × 75 μm3) demonstrating fine-scale cerebellar lobular architecture. This contrasted to conventional-resolution data (black box: 100 × 100 × 200 μm3) with missing structural granularity. (C) Population-averaged volume templates of the tree shrew brain of multi-individual T2*-weighted MRI (top) and FA-weighted DEC (DEC*FA, bottom), displaying in triplanar views (coronal, sagittal, and horizontal). (D) Whole-brain parcellation in coronal (top) and sagittal (bottom) views, with slice positions numerically labeled. Anatomical regions are color-coded and abbreviated as follows: cerebral cortex (Cortex), cerebral white matter (Wm), amygdala (Amy), thalamus (Thal), medial geniculate nucleus (MGN), lateral geniculate nucleus (LGN), caudate (Cd), putamen (Pu), nucleus accumbens (Acb), claustrum/endopiriform claustrum (Cl), hypothalamus (Hypo), septum (Sep), globus pallidus (GP), inferior colliculus (IC), superior colliculus (SC), periaqueductal gray (PAG), substantia nigra (SNR), hippocampus (Hip), cerebellum (Ceb), and olfactory bulb (Olf). (E) Subregional parcellation of the hippocampus (left) and cerebellum (right), with slice positions indicated above each panel.
Fig 2.
Surface-based brain parcellation atlas of the tree shrew.
(A) Surface templates for the cerebellum (green), hippocampus (red), and cerebral cortex (gray). Anatomical orientations are labeled as left (L), right (R), anterior (A), posterior (P), medial (M), lateral (La), dorsal (D), and ventral (V). (B) Surface-based parcellation of the cerebellum (left) and hippocampus (right), generated by projecting volume-derived partitions onto their respective morphometric surfaces. (C–E) Surface area distributions across broad anatomical regions (C), hippocampal subregions (D), and cerebellar lobules (E). The data underlying this Figure can be found in S1 Data.
Fig 3.
Cross-species comparisons based on brain parcellation.
(A) Parcellation maps for brain, cortex, cerebellum (Ceb), and hippocampus (Hip) in the mouse, tree shrew, marmoset, and macaque. Each map has a scale bar reflecting species-specific brain dimensions. (B) Absolute volumes of brain, cortex, cerebellum (Ceb), and hippocampus (Hip) across the mouse, tree shrew, marmoset, and macaque. (C, D) Relative volumes (normalized to total brain volume) of cerebral cortex (C) and hippocampus (D) across species. (E) Relative volumes for the hippocampal subregions (normalized to hippocampal volume) across species. (F) Relative volumes (normalized to total brain volume) of cerebellum across species. (G) Relative volumes for the cerebellar lobules (normalized to cerebellar volume) across species. The abbreviations are defined in the Materials and Methods section. (H) Cerebellar gyrification index (GI) across species. GI quantifies folding complexity of the cerebellar cortex, calculated as the ratio of the pial surface area (yellow line) to the outer contour surface area (green line). Higher GI values indicate greater cortical folding. (I) Cerebellar-to-neocortical surface area ratio across species. Ratio was calculated as cerebellar pial surface area (Ceb; yellow line) divided by neocortical surface area (cortex; red line). The data underlying this Figure can be found in S1 Data.
Fig 4.
Structural connectivity gradients of the tree shrew cerebellum.
(A) Surface-based cerebellar atlas (Cerebellar regions; left) and structural gradients 1–4 (SG1–SG4). Gradient values (GVs) are color-mapped. (B) SG1 values across cerebellar lobules, displayed as a box plot (median ± interquartile range). (C) Spatial localization of extremal gradient values (top/last 10%) for SG1 and SG2, overlaid on the surface of cerebellar cortex. (D) Surface-based gradient mappings in cerebellums: FG1/SG1 in the macaque, marmoset, and mouse; SG1/SG4 in the tree shrew. (E) Absolute Pearson coefficient of correlation (| r |) comparison among the structural and functional gradients across species based on the regional-averaged-GV with hemispheric differentiation. The data underlying this Figure can be found in S1 Data.
Fig 5.
Structural connectivity gradients of the tree shrew hippocampus.
(A) Bilateral hippocampal surface atlas (hippocampal regions) and structural gradients 1–4 (SG1–SG4). Arrows denote longitudinal axis orientation (dorsoventral axis, DV) or short axis (medial to lateral, M-La), as indicated. Abbreviations: L, left; R, right; M, medial; La, lateral; D, dorsal; V, ventral. (B–F) Left hippocampus analyses. (B) Spatial alignment of SG1 and SG2 values along the longitudinal axis (DV-axis). (C) DV-axis progression of SG1 and SG2 values, color-mapped by DV-axis position. (D) Subregional heterogeneity of SG1 and SG2 across hippocampal subregions (color-coded). (E) Spatial localization of extremal gradient values (top/last 10%) for SG1 and SG2. (F) Spatial localization of extremal gradient values for SG1(top/last 10%) and SG2 (last 10%), overlaid on the surface in the left hippocampus. Results for right hippocampus of the tree shrew are presented in S7 Fig. The data underlying this Figure can be found in S1 Data.
Fig 6.
Structural connectivity gradients of the tree shrew left cerebral cortex and peak geometry–gradient coupling (GGC) across species.
(A) Cortical surface maps of the first four structural gradients (SG1–SG4) in the left cerebral cortex, with color-encoded by gradient value (GV). (B, C) Spatial localization of extremal gradient values (top/last 10%) for SG1 (B) and SG2 (C), overlaid on the surface in the left cerebral cortex. (D) Correlation spectra quantify absolute Pearson coefficients (| r |) between the first 100 geometric eigenmodes and SG1/SG2 in the tree shrew. Blue circles identify peak GGC values, defined as the maximal coupling strength between each gradient and its optimally correlated geometric eigenmode. (E) Surface mappings of the peak GGC relationships identified in (D) for SG1 (aligned with Mode 3) and SG2 (aligned with Mode 2) in the tree shrew cerebral cortex. (F) Scatterplots demonstrate a robust positive correlation (r = 0.847) between the peak GGC strength (| r | values from D) and gradient-specific explained variance. (G) Cross-domain quantification of peak GGC strength. Absolute Pearson correlations (| r |) represent peak GGC (maximal coupling strength between region-specific gradients (structural/functional) and their optimally correlated geometric eigenmodes). Values annotate the maximal | r | per gradient-domain combination with corresponding eigenmode indices (e.g., SG1-Mode3 for left cortex of the tree shrew: | r | = 0.78). (H) Domain-wise GGC-variance correlations. Each point represents the Pearson correlation coefficient (r) quantifying the association between peak GGC strength and gradient-explained variance within a specific species-brain region-imaging modality combination. The red dashed line marks the grand mean (r = 0.797) across 16 experimental units, demonstrating consistent geometry-driven constraints on neurobiologically critical gradients. (L, left hemisphere; R, right hemisphere). Results for right cerebral cortex of the tree shrew are presented in S8 Fig. The data underlying this Figure can be found in S1 Data.