BACKGROUND: Making use of multimodal data simultaneously to understand the neural mechanism of mild cognitive impairment (MCI) has been in the focus nowadays. The simultaneous use of multimodal data can take advantage of each modality which may only provide the view of one specific aspect of the brain. OBJECTIVE: To this end, the present study used structural magnetic resonance imaging (sMRI), fluorodeoxyglucose positron emission tomography (FDG-PET) and florbetapir PET to reveal the integrated brain network between MCI and normal controls (NCs). METHODS: In this study, 116 MCI, 116 NC and 116 Alzheimer disease (AD) subjects from the Alzheimer's Disease Neuroimaging Initiative were included for the evaluation of the brain covariance graphic model. Sparse inverse covariance estimation was utilized to get the graphic model. RESULTS: The connections among different brain regions were quite different between NC and MCI or between MCI and AD subjects (p < 0.01). The number of connections, which were represented by the covariance among different brain regions in the graphic model, decreased from NC to MCI and then AD, especially in the temporal lobe, occipital-parietal lobe and parietal-temporal lobe. CONCLUSION: These findings are good evidence to reveal the difference between MCI or AD and NC, and enhance the understanding of MCI.
BACKGROUND: Making use of multimodal data simultaneously to understand the neural mechanism of mild cognitive impairment (MCI) has been in the focus nowadays. The simultaneous use of multimodal data can take advantage of each modality which may only provide the view of one specific aspect of the brain. OBJECTIVE: To this end, the present study used structural magnetic resonance imaging (sMRI), fluorodeoxyglucose positron emission tomography (FDG-PET) and florbetapir PET to reveal the integrated brain network between MCI and normal controls (NCs). METHODS: In this study, 116 MCI, 116 NC and 116 Alzheimer disease (AD) subjects from the Alzheimer's Disease Neuroimaging Initiative were included for the evaluation of the brain covariance graphic model. Sparse inverse covariance estimation was utilized to get the graphic model. RESULTS: The connections among different brain regions were quite different between NC and MCI or between MCI and AD subjects (p < 0.01). The number of connections, which were represented by the covariance among different brain regions in the graphic model, decreased from NC to MCI and then AD, especially in the temporal lobe, occipital-parietal lobe and parietal-temporal lobe. CONCLUSION: These findings are good evidence to reveal the difference between MCI or AD and NC, and enhance the understanding of MCI.