DICCCOL: Dense Individualized and Common Connectivity-Based Cortical Landmarks

Abstract

Is there a common structural and functional cortical architecture that can be quantitatively encoded and precisely reproduced across individuals and populations? This question is still largely unanswered due to the vast complexity, variability, and nonlinearity of the cerebral cortex. Here, we hypothesize that the common cortical architecture can be effectively represented by group-wise consistent structural fiber connections and take a novel data-driven approach to explore the cortical architecture. We report a dense and consistent map of 358 cortical landmarks, named Dense Individualized and Common Connectivity-based Cortical Landmarks (DICCCOLs). Each DICCCOL is defined by group-wise consistent white-matter fiber connection patterns derived from diffusion tensor imaging (DTI) data. Our results have shown that these 358 landmarks are remarkably reproducible over more than one hundred human brains and possess accurate intrinsically established structural and functional cross-subject correspondences validated by large-scale functional magnetic resonance imaging data. In particular, these 358 cortical landmarks can be accurately and efficiently predicted in a new single brain with DTI data. Thus, this set of 358 DICCCOL landmarks comprehensively encodes the common structural and functional cortical architectures, providing opportunities for many applications in brain science including mapping human brain connectomes, as demonstrated in this work.

Inferring Group-Wise Consistent Multimodal Brain Networks via Multi-view Spectral Clustering

“…Both structural and functional brain networks (Fig. 1a-b) were constructed from DTI and resting state fMRI (RfMIR) data of the same group of subjects based on our recently validated 358 cortical landmarks [5]. The joint connectivity matrix (Fig.…”

“…Recently we created and validated 358 cortical landmarks that have intrinsically established structural and functional correspondences in different brains [5], providing natural and ideal nodes for brain network construction. In brief, each of the 358 cortical landmarks was optimized to possess group-wise consistent white matter fiber connection patterns, which have been demonstrated to be predictive of functional localizations in the brain [5,7]. The neuroscience basis is that each brain's cytoarchitectonic region has a unique set of intrinsic axonal inputs and outputs, called the "connectional fingerprint" [8], which largely determines the functions that brain area can perform.…”

“…The neuroscience basis is that each brain's cytoarchitectonic region has a unique set of intrinsic axonal inputs and outputs, called the "connectional fingerprint" [8], which largely determines the functions that brain area can perform. In particular, the functional correspondences of these 358 cortical landmarks were extensively validated by functional brain networks derived from both task-based fMRI data and R-fMRI data [5]. Based on these 358 cortical landmarks/ROIs ( Fig.…”

“…In response to this issue, this paper presents a novel approach to infer group-wise consistent brain networks from multimodal DTI/fMRI datasets via multi-view spectral clustering of large-scale cortical landmarks and their connectivities. Based on our recently developed and validated large-scale connectivity-based cortical landmarks [5] as network nodes, we constructed both structural and functional brain networks from multimodal DTI/fMRI data of 40 healthy brains. Then, we applied an effective multi-view spectral clustering algorithm [6] on these 80 multimodal structural and functional brain networks to derive consistent multimodal brain sub-networks.…”

Manifold learning and unwrapping using density ridges

“…Unfortunately, the estimation of these functional connectivities from subject to subject can be difficult to do robustly and recent research has focused on imposing a prior to the sparse representation. One such example is the work of [107], which uses structurally-weighted least absolute shrinkage and selection operator (LASSO) regression, and models the directional functional interactions of resting state fMRI data based on structural connectivity constraints encoded by 358 cortical landmarks derived from DTI data [108].…”