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Whole-brain functional networks in cognitively normal, mild cognitive impairment, and Alzheimer's disease.

ABSTRACT: The conceptual significance of understanding functional brain alterations and cognitive deficits associated with Alzheimer's disease (AD) process has been widely established. However, the whole-brain functional networks of AD and its prodromal stage, mild cognitive impairment (MCI), are not well clarified yet. In this study, we compared the characteristics of the whole-brain functional networks among cognitively normal (CN), MCI, and AD individuals by applying graph theoretical analyses to [(18)F] fluorodeoxyglucose positron emission tomography (FDG-PET) data. Ninety-four CN elderly, 183 with MCI, and 216 with AD underwent clinical evaluation and FDG-PET scan. The overall small-world property as seen in the CN whole-brain network was preserved in MCI and AD. In contrast, individual parameters of the network were altered with the following patterns of changes: local clustering of networks was lower in both MCI and AD compared to CN, while path length was not different among the three groups. Then, MCI had a lower level of local clustering than AD. Subgroup analyses for AD also revealed that very mild AD had lower local clustering and shorter path length compared to mild AD. Regarding the local properties of the whole-brain networks, MCI and AD had significantly decreased normalized betweenness centrality in several hubs regionally associated with the default mode network compared to CN. Our results suggest that the functional integration in whole-brain network progressively declines due to the AD process. On the other hand, functional relatedness between neighboring brain regions may not gradually decrease, but be the most severely altered in MCI stage and gradually re-increase in clinical AD stages.

SUBMITTER: Seo EH

PROVIDER: S-EPMC3545923 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Mild cognitive impairment (MCI) represents a cognitive state intermediate between normal aging and early Alzheimer's disease (AD). To investigate if the molecular signature of MCI parallels the clinical picture, we use microarrays to extensively profile gene expression in 4 cortical brain regions (entorhinal cortex, hippocampus, superior frontal gyrus, post-central gyrus) using the postmortem tissue from cognitively normal aged controls, MCI, and AD cases. Our data reveal that gene expression patterns in MCI are not an extension of aging, and for the most part, are not intermediate between aged controls and AD. Functional enrichment analysis of significant genes revealed prominent upregulation in MCI brains of genes associated with anabolic and biosynthetic pathways (notably transcription, protein biosynthesis, protein trafficking, and turnover) as well as mitochondrial energy generation. In addition, many synaptic genes showed altered expression in MCI, predominantly upregulation, including genes for central components of the vesicle fusion machinery at the synapse, synaptic vesicle trafficking, neurotransmitter receptors, and synaptic structure and stabilization. These data suggest that there is a rebalancing of synaptic transmission in the MCI brain. To investigate if synaptic gene expression levels in MCI were related to cognitive function, Pearson correlation coefficient between the Mini Mental State Examination (MMSE) and region-specific messenger RNA expression were computed for MCI cases. A number of synaptic genes showed strong significant correlations (r > 0.8, p < 0.01) most notably in the entorhinal cortex, with fewer in the hippocampus, and very few in neocortical regions. The synaptic genes with highly significant correlations were predominantly related to synaptic transmission and plasticity, and myelin composition. Unexpectedly, we found that gene expression changes that facilitate synaptic excitability and plasticity were overwhelmingly associated with poorer MMSE, and conversely that gene expression changes that inhibit plasticity were positively associated with MMSE. These data suggest that there are excessive excitability and apparent plasticity in limbic brain regions in MCI, that is associated with impaired synaptic and cognitive function. Such changes would be predicted to contribute to increased excitability, in turn leading to greater metabolic demand and ultimately progressive degeneration and AD, if not controlled.

Project description:The oscillatory patterns of electroencephalography (EEG), during resting states, are informative and helpful in understanding the functional states of brain network and their contribution to behavioral performances. The aim of this study is to characterize the functional brain network alterations in patients with amnestic mild cognitive impairment (aMCI). To this end, rsEEG signals were recorded before and after a cognitive task. Functional connectivity metrics were calculated using debiased weighted phase lag index (DWPLI). Topological features of the functional connectivity network were analyzed using both the classical graph approach and minimum spanning tree (MST) algorithm. Subsequently, the network and connectivity values together with Mini-Mental State Examination cognitive test were used as features to classify the participants. Results showed that: (1) across the pre-task condition, in the theta band, the aMCI group had a significantly lower global mean DWPLI than the control group; the functional connectivity patterns were different in the left hemisphere between two groups; the aMCI group showed significantly higher average clustering coefficient and the remarkably lower global efficiency than the control. (2) Analysis of graph measures under post-task resting state, unveiled that for the percentage change of post-task vs. pre-task in beta EEG, a significant increase in tree hierarchy was observed in aMCI group (2.41%) than in normal control (-3.89%); (3) Furthermore, the classification analysis of combined measures of functional connectivity, brain topology, and MMSE test showed improved accuracy compared to the single method, for which the connectivity patterns and graph metrics were used as separate inputs. The classification accuracy obtained for the case of post-task resting state was 87.2%, while the one achieved under pre-task resting state was found to be 77.7%. Therefore, the functional network alterations in aMCI patients were more prominent during the post-task resting state. This study suggests that the disintegration observed in MCI functional network during the resting states, preceding and following a task, might be possible biomarkers of cognitive dysfunction in aMCI patients.

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Whole-brain functional networks in cognitively normal, mild cognitive impairment, and Alzheimer's disease.

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