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| Principal Investigator | |
| Principal Investigator's Name: | jun shu |
| Institution: | Fudan university |
| Department: | Department of Neurology, Huadong hospital |
| Country: | |
| Proposed Analysis: | Background: Mild behavioral impairment (MBI), characterized by the late-life onset of sustained and meaningful neuropsychiatric symptoms, is increasingly recognized as a prodromal stage of dementia. However, the underlying neural mechanisms of MBI remain unclear. Thus, I would like to investigate the topological abnormalities of covariance networks and functional connectivity based on fMRI in cognitively normal older adults(CN) and mild cognitive impairment (MCI)with MBI compared with those without MBI. Materials and methods Subjects Data were obtained from the ADNI database. I planned to identify non-demented subjects (characterized as either CN or MCI) from the ADNI GO/2/3 databases. All subjects had undergone rs‐fMRI scans and neuropsychological evaluations. Assessment of mild behavioral impairment MBI status and presence of MBI domains were assessed in accordance with the Alzheimer’s Association International Society to Advance Alzheimer’s Research and Treatment (ISTAART-AA) research diagnostic criteria for MBI using the NPI-Q. The 12 behaviors assessed by the NPI-Q were clustered into the following MBI domains based on the presence of an NPI-Q subitem: (1) decreased drive/motivation (apathy), (2) emotional dysregulation (depression, anxiety, and elation), (3) impulse dyscontrol (agitation/aggression, irritability, aberrant motor behavior), (4) social inappropriateness (disinhibition), and (5) psychosis (delusions, hallucinations). Participants were classified as MBI-positive (+) if they had one or more MBI domains present. MBI-negative (–) participants had no MBI domains present. MRI acquisition and preprocessing The rs‐fMRI imaging data were obtained from the ADNI database and were pre-processed with SPM12 on the MATLAB platform. I would discard the first 10 time points of the rs‐fMRI data because of the instability of the initial MRI signal and the subjects' adaptation to the scanning noise. Then the remaining images would be corrected for both timing differences between each slice and head motion. Subjects with more than 2.0 mm maximum displacement in the x, y, or z direction or 2.0° of angular motion during the whole scan would be excluded. Subsequently, the fMRI data would be warped to the MNI space using the EPI template and then resampled into 3×3×3mm3 cubic voxels. Finally, the fMRI data were smoothed using a 6mm full width at half maximum kernel. To minimize physiological noise, the Friston 24 head motion parameters, white matter (WM) signal, and cerebrospinal fluid (CSF) signal were corrected as nuisances. Brain network analysis The brain network nodes were defined by the parcellation of the whole brain into 90 distinct regions using the automated anatomical labeling (AAL) atlas. The time series of voxels within each region was averaged and the resulted signal was used as the representative signal of the node. Pearson’s correlation coefficients of signals of all pairs of AAL regions were employed to define the edges of the brain network. The network analysis of the brain functional connectomes was investigated using the GRETNA toolbox. The brain functional connectome was constructed over the whole range of the sparsity threshold. The global and nodal network metrics were used to derive the network association of the brain functional connectomes. The global network metrics were calculated using the following parameters such as clustering coefficient, characteristic path length, small-world index, global efficiency, and local efficiency, and nodal betweenness centrality are the nodal network parameters. An area under the curve (AUC) was calculated over sparsity ranges for each network metric. AUC provides summarized scalar information for the brain functional connectome topology. The significance level for the group differences in the global and regional network parameters were set at p < 0.05 and after false discovery rate correction was carried out for multiple comparisons. |
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