Interacting with an estimated cognitive ontology: A visual extension of Yeo et al. (in press)

The basis of this estimated cognitive ontology is detailed in our paper Functional Specialization and Flexibility in Human Association Cortex. Please note that this version is slightly different from what was originally posted. (Updated 20 January 2015.)

The interactive image below allows you to investigate the probabilistic relationships among tasks, cognitive components, and brain regions. These relationships were estimated using a hierarchical Bayesian model. The model formalizes the intuition that performing a given task engages multiple cognitive components, which in turn recruit multiple brain areas (see Poldrack, 2006).

The figure at right illustrates these relationships. Each experiment in the BrainMap database is either associated or not associated with a given brain voxel, which allowed us to calculate the association between tasks and brain activation. For example, if 3 of the 4 experiments using the n-back task involved Voxel 1, that would yield Pr(Voxel 1 | n-back) = 0.75. Using these observed relationships, the unobserved cognitive components were found by estimating Pr(Component | Task) and Pr(Voxel | Component).

Mousing over the different tasks and components below highlights a subset of the relationships, whereas clicking on a task will bring up its description from the Cognitive Atlas. (Note that these task definitions may differ slightly from the BrainMap task definitions.)

Our interactive visualization was created using a combination of CIRCOS, Matlab, Adobe Illustrator, and JavaScript, with inspiration from Satyan Devadoss.

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(Caption for figure above). 12-component model estimates. * indicates components that are unified in 11-component model; + indicates component that splits in 13-component model.

(a) Probability of tasks recruiting different components Pr(Component | Task). The components, C1 to C12, lie on the top right. The 83 tasks lie in the remaining segments of the circle. For the purpose of visualization, tasks with similar Pr(Component | Task) are more closely positioned and have similar associated colors. Each line connects one task with one component. The thickness of the lines is proportional to the magnitude of Pr(Component | Task), with the ends flared for easier visual comparison. Note that these probabalistic associations sum to 1 for each task.

(b) Probability of components activating different brain voxels Pr(Voxel | Component). The cerebral hemisphere with the stronger activation is shown, although most components have high probabilities of bilateral activation. An exception is component C5, which has high probability of activating the left, but not the right, hemisphere. Many components, especially C11 and C12, also activate subcortical regions. Note that these probabilistic associations sum to 1 for each component.

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