Surface-Based Display of Volume-Averaged Cerebellar Imaging Data

Abstract

The paper presents a flat representation of the human cerebellum, useful for visualizing functional imaging data after volume-based normalization and averaging across subjects. Instead of reconstructing individual cerebellar surfaces, the method uses a white- and grey-matter surface defined on volume-averaged anatomical data. Functional data can be projected along the lines of corresponding vertices on the two surfaces. The flat representation is optimized to yield a roughly proportional relationship between the surface area of the 2D-representation and the volume of the underlying cerebellar grey matter. The map allows users to visualize the activation state of the complete cerebellar grey matter in one concise view, equally revealing both the anterior-posterior (lobular) and medial-lateral organization. As examples, published data on resting-state networks and task-related activity are presented on the flatmap. The software and maps are freely available and compatible with most major neuroimaging packages.

Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Fig 1. Representation of individual fingers in the human cerebellum.

Shown is the classification accuracy with which the moved finger can be determined from the local pattern of activity, with a threshold of z>1 [17]. (A) Data projected onto a surface based on a single anatomy [2] displays single closed activation clusters as a fractured series of blobs. (B) Projection to the new flatmap ensures that single clusters in the volume are also presented as such on the surface.

Fig 2. Surfaces defined on a group-averaged anatomical template image of the human cerebellum.

(A) The outer surface constitutes a hull around the average grey matter body. (B) The inner surface is placed on the boundary between average white and grey masses.

Fig 3. Distortion of the flatmap in representing cerebellar grey-matter volume as surface area.

(A) Flatmap with superimposed distortion factor (ratio of Area to Volume). Orange / red areas indicate regions that are disproportionally large on the flatmap, turquoise / blue areas indicate regions that are disproportionally small. Dotted lines indicate boundaries between lobules. Thick black lines on the perimeter indicate where cuts have been made to the map. The areas connected with dashed lines are immediately adjacent in the volume, but are unfolded in the flatmap to minimize distortion. (B) Volume of each lobule (in % of total grey-matter volume) plotted against the corresponding area on the flatmap (in % of total map area). Plotted are 28 compartments, hemisphere and vermis of each of the main lobules, as defined in the probabilistic atlas of the human cerebellum.

Fig 4. Surface-based mapping pipeline for cerebellar data.

Functional data is first normalized using standard volume-based methods and then projected onto the flat representations using corresponding vertices on outer and inner surface. For the process of surface projection, it is therefore important to take into account the type of volume-based normalization algorithm used.

Fig 5. Probabilistic atlas of the cerebellar lobules.

(A) The compartments of the cerebellar atlas [16] projected to the flatmap. Note that for lobule VI-X, a vermal and two hemispheric compartments (shown in slightly different colors) are defined. (B) The same data displayed on a posterior view of the outer surface.

Fig 6. Atlas of cerebellar-cortical connectivity.

(A) Cortical networks of resting-state connectivity [23]. 17 networks are shown on an inflated cortical surface of the left and right hemisphere—with both the lateral and medial surface shown. (B) Map showing the cortical resting-state network that correlated best with the activity in the corresponding cerebellar area [18]. Maps are based on N = 1000 subjects.

Fig 7. Functional activity maps from the Human Connectome Project.

(A) Sensorimotor topography of activation for hand, foot and tongue movements. (B) Working memory; contrast between a 2-back and 0-back condition. (C) Emotion processing; contrast between matching emotional faces vs. matching neutral shapes. (D) Social cognition; observing dot motion with intentional content vs. random dot motion. (E) Language vs. mathematical processing. Positive values indicate higher activity during processing of a story vs. arithmetic operations. Negative values represent the opposite contrast. All maps are based on N = 100 subjects. All colored areas in cognitive maps (B-E) exceed an FWE-corrected significance threshold of p<0.05.