Publications (2)9.52 Total impact
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Article: Fine-Granularity Functional Interaction Signatures for Characterization of Brain Conditions.
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ABSTRACT: In the human brain, functional activity occurs at multiple spatial scales. Current studies on functional brain networks and their alterations in brain diseases via resting-state functional magnetic resonance imaging (rs-fMRI) are generally either at local scale (regionally confined analysis and inter-regional functional connectivity analysis) or at global scale (graph theoretic analysis). In contrast, inferring functional interaction at fine-granularity sub-network scale has not been adequately explored yet. Here our hypothesis is that functional interaction measured at fine-granularity sub-network scale can provide new insight into the neural mechanisms of neurological and psychological conditions, thus offering complementary information for healthy and diseased population classification. In this paper, we derived fine-granularity functional interaction (FGFI) signatures in subjects with Mild Cognitive Impairment (MCI) and Schizophrenia by diffusion tensor imaging (DTI) and rs-fMRI, and used patient-control classification experiments to evaluate the distinctiveness of the derived FGFI features. Our experimental results have shown that the FGFI features alone can achieve comparable classification performance compared with the commonly used inter-regional connectivity features. However, the classification performance can be substantially improved when FGFI features and inter-regional connectivity features are integrated, suggesting the complementary information achieved from the FGFI signatures.Neuroinformatics 01/2013; · 2.97 Impact Factor -
Article: DICCCOL: Dense Individualized and Common Connectivity-based Cortical Landmarks
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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 (DICCCOL). 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 (fMRI) 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.Cerebral Cortex 04/2012; · 6.54 Impact Factor
