Identifying the Alteration Patterns of Brain Functional Connectivity in Progressive Mild Cognitive Impairment Patients: A Longitudinal Whole-Brain Voxel-Wise Degree Analysis

Abstract

Patients with mild cognitive impairment (MCI) are at high risk for developing Alzheimer’s disease (AD), while some of them may remain stable over decades. The underlying mechanism is still not fully understood. In this study, we aimed to explore the connectivity differences between progressive MCI (PMCI) and stable MCI (SMCI) individuals on a whole-brain scale and on a voxel-wise basis, and we also aimed to reveal the differential dynamic alteration patterns between these two disease subtypes. The resting-state functional magnetic resonance images of PMCI and SMCI patients at baseline and year-one were obtained from the Alzheimer’s Disease Neuroimaging Initiative dataset, and the progression was determined based on a 3-year follow-up. A whole-brain voxel-wise degree map that was calculated based on graph-theory was constructed for each subject, and then the cross-sectional and longitudinal analyses on the degree maps were performed between PMCI and SMCI patients. In longitudinal analyses, compared with SMCI group, PMCI group showed decreased long-range degree in the left middle occipital/supramarginal gyrus, while the short-range degree was increased in the left supplementary motor area and middle frontal gyrus and decreased in the right middle temporal pole. A significant longitudinal alteration of decreased short-range degree in the right middle occipital was found in PMCI group. Taken together with previous evidence, our current findings may suggest that PMCI, compared with SMCI, might be a “severe” presentation of disease along the AD continuum, and the rapidly reduced degree in the right middle occipital gyrus may have indicative value for the disease progression. Moreover, the cross-sectional comparison results and corresponding receiver-operator characteristic-curves analyses may indicate that the baseline degree difference is not a good predictor of disease progression in MCI patients. Overall, these findings may provide objective evidence and an indicator to characterize the progression-related brain connectivity changes in MCI patients.

Prediction of Conversion From Amnestic Mild Cognitive Impairment to Alzheimer's Disease Based on the Brain Structural Connectome

“…Before disease transition, more severe structural or functional connectivity alterations already existed in the aMCI converters compared with non-converters (37–41). From the current study, we found that the network measures from DTI data are sensitive enough to detect the topological differences even at baseline, and correlated with the disease severity evaluated by clinical scores (MMSE, MoCA, and AVLT-Immediate Recall) in aMCI patients.…”

“…Recently, machine learning, deep learning and complex brain networks have been recently applied to the early diagnosis of neurodegenerative diseases with interesting results (33–36). Specifically, functional MRI network studies have found more severe disruptions in MCI converters, which may distinguish converters from non-converters with high accuracy (37–39). Structural MRI studies have also found topological differences of brain connectome between the two groups (40–42).…”

Changes in Centrality Frequency of the Default Mode Network in Individuals With Subjective Cognitive Decline

“…Based on this method, researchers found that in MCI subjects, not only functional connectivity between the left thalamus and a set of regions was decreased (Wang et al, 2012) but also functional connectivity in cortical midline structures was decreased (Ries et al, 2010). Besides, the whole-brain voxel-wise degree map measured by static functional connectivity also showed the reduced degree in the right middle occipital gyrus in the progression from MCI to AD (Deng et al, 2016). In addition, Vega et al (2016) found that elderly SCD women who reported more severe cognitive decline showed weaker negative functional connectivity within the frontal cortex and stronger positive connectivity within the right middle temporal gyrus.…”

Effect-Size Estimation Using Semiparametric Hierarchical Mixture Models in Disease-Association Studies with Neuroimaging Data

“…See Appendix E for the differences in effect size estimates for top voxels within subregion between the proposed and naive methods. The top subregion, corresponding to the right middle temporal pole (TPOmid.R), has been reported by a connectivity analysis to be a region in which converters exhibited a decreased short-range degree of functional connectivity [ 23 ]. The other regions have already been associated with conversion to Alzheimer's disease.…”