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 (AD), but also as a predictor of conversion to other neurodegenerative diseases. Although MCI has been defined clinically, accurate and efficient diagnosis is still challenging. While 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 the present paper, 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, e.g., 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.

Multimodal manifold-regularized transfer learning for MCI conversion prediction

“…Besides modalities used in this paper, i.e., MRI, PET, and CSF, there also exist other modalities (e.g., Diffusion Tensor Imaging (DTI) and Resting-state functional Magnetic Resonance Imaging (RS-fMRI)) which can be used for AD/MCI classification (Jie et al 2014; Supekar et al 2008; Wang et al 2007; Wee et al 2012, 2014; Zhu et al 2014). It will be interesting to further investigate the incorporation of these modalities into our proposed M2TL model, which will be one of our future works.…”

IC‐03‐03: Cognitive Complaints in Older Adults at Risk For Alzheimer’s Disease are Associated with Altered Resting State Networks

“…Grouping brain regions according to RSN allows for dimension reduction and provides psychophysiologically relevant results [14]. There is evidence that abnormalities in brain networks appear in early stages of AD [15][16][17]. Because current evidence indicates that neurodegenerative changes begin before clinical manifestations become apparent [18], it is crucial to identify the earliest changes during preclinical and prodromal stages of disease.…”

A novel framework for groupwise registration of fMRI images based on common functional networks

“…For each subject, there are 140 volumes (time points). Preprocessing steps of the rsfMRI data included brain skull removal, motion correction, spatial smoothing, temporal pre-whitening, slice time correction, global drift removal, and band pass filtering (0.01–0.1 Hz) [13]. …”