Thalamocortical Connection Topography Mapping in Human by Co-clustering

Abstract

Connection topography mapping is crucial for understanding how information is processed in the brain, which is an essential precursor for revealing principles of brain organization. However, existing connectopic mapping methods are dependent on prior knowledge, or not completely driven by data. Accordingly, the constructed connection topographies by these methods are biased towards hypotheses, or deviate from data. For these challenges, we propose a novel co-clustering based method for connection topography mapping in a fully data-driven manner. The proposed method aims to construct the connection topography between two ROIs of a certain neural circuit in consideration by leveraging the power of co-clustering. More precisely, the proposed method parcellates one ROI into subregions and identified their respective connected subregions from the other ROI simultaneously. The effectiveness of our method was validated on the mapping of the human thalamocortical system for 57 subjects based on their resting state fMRI data. The validation experiment results have demonstrated that our method can construct neurobiologically meaningful thalamocortical connection topography. Compared with existing methods, our method yields more meaningful and interpretable connection topography.