title: Connectome-scale assessments of structural and functional connectivity in MCI.
abstract: Mild cognitive impairment (MCI) has received increasing attention not only because of its potential as a precursor for Alzheimer's disease but also as a predictor of conversion to other neurodegenerative diseases. Although MCI has been defined clinically, accurate and efficient diagnosis is still challenging. Although neuroimaging techniques hold promise, compared to commonly used biomarkers including amyloid plaques, tau protein levels and brain tissue atrophy, neuroimaging biomarkers are less well validated. In this article, we propose a connectomes-scale assessment of structural and functional connectivity in MCI via two independent multimodal DTI/fMRI datasets. We first used DTI-derived structural profiles to explore and tailor the most common and consistent landmarks, then applied them in a whole-brain functional connectivity analysis. The next step fused the results from two independent datasets together and resulted in a set of functional connectomes with the most differentiation power, hence named as "connectome signatures." Our results indicate that these "connectome signatures" have significantly high MCI-vs-controls classification accuracy, at more than 95%. Interestingly, through functional meta-analysis, we found that the majority of "connectome signatures" are mainly derived from the interactions among different functional networks, for example, cognition-perception and cognition-action domains, rather than from within a single network. Our work provides support for using functional "connectome signatures" as neuroimaging biomarkers of MCI.
authors.affiliation: Department of Computer Science, The University of Georgia, Georgia; Bioimaging Research Center, The University of Georgia, Georgia.
authors.forename: Dajiang
authors.initials: D
authors.lastname: Zhu
authors.forename: Kaiming
authors.initials: K
authors.lastname: Li
authors.forename: Douglas P
authors.initials: DP
authors.lastname: Terry
authors.forename: A Nicholas
authors.initials: AN
authors.lastname: Puente
authors.forename: Lihong
authors.initials: L
authors.lastname: Wang
authors.forename: Dinggang
authors.initials: D
authors.lastname: Shen
authors.forename: L Stephen
authors.initials: LS
authors.lastname: Miller
authors.forename: Tianming
authors.initials: T
authors.lastname: Liu
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D001921:Brain / Q000098:blood supply* / Q000473:pathology*; D060825:Cognitive Dysfunction / Q000473:pathology*; D063132:Connectome; D066264:Datasets as Topic; D038524:Diffusion Magnetic Resonance Imaging; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D009434:Neural Pathways / Q000098:blood supply* / Q000473:pathology*; D010100:Oxygen; D060388:Support Vector Machine
chemical_list: D010100:Oxygen
keywords: connectivity; diffusion tensor imaging; mild cognitive impairment (MCI); resting state fMRI