Connectomics signature for characterizaton of mild cognitive impairment and schizophrenia

Human connectomes constructed via neuroimaging data offer a comprehensive description of the macro-scale structural connectivity within the brain. Thus quantitative assessment of connectome-scale structural and functional connectivities will not only fundamentally advance our understanding of normal brain organization and function, but also have significant importance to systematically and comprehensively characterize many devastating brain conditions. In recognition of the importance of connectome and connectomics, in this paper, we develop and evaluate a novel computational framework to construct structural connectomes from diffusion tensor imaging (DTI) data and assess connectome-scale functional connectivity alterations in mild cognitive impairment (MCI) and schizophrenia (SZ) from concurrent resting state fMRI (R-fMRI) data, in comparison with their healthy controls. By applying effective feature selection approaches, we discovered informative and robust functional connectomics signatures that can distinctively characterize and successfully differentiate the two brain conditions of MCI and SZ from their healthy controls (classification accuracies are 96% and 100%, respectively). Our results suggest that connectomics signatures could be a general, powerful methodology for characterization and classification of many brain conditions in the future.

1. INTRODUCTION

Genomic technologies such as genome-scale gene expression analyses and their derived genomics signatures are transforming medicine in many facets including disease prevention, differential diagnosis, disease staging, disease sub-type identification, personalized treatment, follow-up and prognosis. In parallel, in the neuroscience field, quantitative mapping of human brain connectomes [1] aims to construct a comprehensive description of the macro-scale structural connectivity within the human brain via neuroimaging data. Considering the brain function is realized via large-scale structural and functional connectivities [2], mapping connectomes will fundamentally advance our understanding of brain structure and function. In particular, a variety of neurological or psychiatric conditions such as Alzheimer's disease and Schizophrenia exhibit widespread alterations in brain connectivities. Essentially, quantitative mapping of brain connectomes in healthy and disease populations and extraction of informative and robust connectomics signatures have significant importance to systematically and comprehensively understand, characterize, diagnose and treat those many devastating brain diseases. Simply, what connectomics is to brain connectivity in neuroscience resembles what genomics is to genetics.