Cohesive parcellation of the human brain using resting-state fMRI

The data burden for resting-state fMRI analysis rises with increasing resolutions available at ultrahigh fields. Therefore, a fundamental preprocessing step in brain network analysis is to reduce the data, usually by performing some kind of data parcellation. Most functional parcellations based on rsfMRI connectivity are synthesized from the dense connectome. In contrast, most network analyses begin by reducing each parcel to a single exemplar time series. This disconnect between parcel formation and usage assumes that parcel exemplars adequately represent their member voxels, which is not always the case for commonly used parcellations.

We propose to parcellate the brain based on parcel cohesion, a measure of similarity between a parcel's exemplar and its member voxels. A spatially constrained agglomerative hierarchical framework is used to synthesize parcels based on a minimum cohesion threshold, rather than a predetermined number of parcels.

Cohesive parcellation generally results in more parcels than existing approaches. The number of parcels scales with the amount of smoothing in preprocessing, yet retains adequate information to extract common intrinsic functional networks.

Cohesive parcellation performs better than several widely used anatomical, functional, and data-driven parcellations on the basis of parcel cohesion and comparably using several traditional measures of cluster validity.

Cohesive parcellation ensures that the way parcels are synthesized directly corresponds to the way they are used in subsequent analyses. The resulting parcels are straightforward to interpret and optimal for downstream analysis.