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Background: Early-onset Alzheimer's disease (EOAD) presents a different clinical profile than late-onset Alzheimer's disease (LOAD). Neuroimaging studies have demonstrated that patients with EOAD present more atrophy and functional disconnection than LOAD patients. However, it remains unknown whether the interhemispheric functional disconnection or its underlying structural impairment contributes to the different clinical profiles of EOAD and LOAD. Methods: According to the arbitrary cut-off age of 65, we included 22 EOAD patients, 27 LOAD patients and 38 healthy controls (further divided into 21 relatively young and 17 old controls). Participants underwent resting-state functional MRI, diffusion tensor imaging (DTI) and comprehensive neuropsychological assessments. We used voxel-mirrored homotopic connectivity (VMHC) to examine interhemispheric functional connectivity. Then, we calculated the diffusion index based on tract-based spatial statistics (TBSS). Two-sample t-tests were used to assess the interhemispheric connectivity differences between each patient group and its corresponding control group. Results: We found that the EOAD patients had lower VMHC in the hippocampus, parahippocampal gyrus (PHG), superior temporal gyrus (STG) and inferior parietal cortex (IPC) than did controls. Consistently, the EOAD patients exhibited white matter (WM) tract impairment in the posterior regions. On the other hand, the LOAD patients displayed increased VMHC and impaired WM tracts in the frontal region. Correlation analyses showed that VMHC in the IPC was related to executive function in the EOAD patients (r = -0.67, P < 0.05). Conclusion: In contrast to the LOAD patients, patients with EOAD exhibited more widely disrupted interhemispheric functional and structural connectivity, which overlapped well across brain regions. In addition, for the EOAD patients, decreased interhemispheric connectivity related to executive deficits. Our study suggested that different interhemispheric connectivity damage patterns may contribute to the distinct clinical profiles in EOAD and LOAD.
Pubmed ID: 30237764
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A MATLAB toolbox forpipeline data analysis of resting-state fMRI that is based on Statistical Parametric Mapping (SPM) and a plug-in software within DPABI. After the user arranges the Digital Imaging and Communications in Medicine (DICOM) files and click a few buttons to set parameters, DPARSF will then give all the preprocessed (slice timing, realign, normalize, smooth) data and results for functional connectivity, regional homogeneity, amplitude of low-frequency fluctuation (ALFF), fractional ALFF, degree centrality, voxel-mirrored homotopic connectivity (VMHC) results. DPARSF can also create a report for excluding subjects with excessive head motion and generate a set of pictures for easily checking the effect of normalization. In addition, users can also use DPARSF to extract time courses from regions of interest. DPARSF basic edition is very easy to use while DPARSF advanced edition (alias: DPARSFA) is much more flexible and powerful. DPARSFA can parallel the computation for each subject, and can be used to reorient images interactively or define regions of interest interactively. Users can skip or combine the processing steps in DPARSF advanced edition freely.
View all literature mentionsSoftware package for analysis of brain imaging data sequences. Sequences can be a series of images from different cohorts, or time-series from same subject. Current release is designed for analysis of fMRI, PET, SPECT, EEG and MEG.
View all literature mentionsA user-friendly convenient toolkit to calculate Functional Connectivity (FC), Regional Homogeneity (ReHo), Amplitude of Low-Frequency Fluctuation (ALFF), Fractional ALFF (fALFF), Gragner causality and perform statistical analysis. You also can use REST to view your data, perform Monte Carlo simulation similar to AlphaSim in AFNI, calculate your images, regress out covariates, extract Region of Interest (ROI) time courses, reslice images, and sort DICOM files.
View all literature mentionsMulti paradigm numerical computing environment and fourth generation programming language developed by MathWorks. Allows matrix manipulations, plotting of functions and data, implementation of algorithms, creation of user interfaces, and interfacing with programs written in other languages, including C, C++, Java, Fortran and Python. Used to explore and visualize ideas and collaborate across disciplines including signal and image processing, communications, control systems, and computational finance.
View all literature mentionsA matlab toolbox for pipeline processing of diffusion MRI images. For each subject, PANDA can provide outputs in 2 types: i) diffusion parameter data that is ready for statistical analysis; ii) brain anatomical networks constructed by using diffusion tractography. Particularly, there are 3 types of resultant diffusion parameter data: WM atlas-level, voxel-level and TBSS-level. The brain network generated by PANDA has various edge definitions, e.g. fiber number, length, or FA-weighted. The key advantages of PANDA are as follows: # fully-automatic processing from raw DICOM/NIFTI to final outputs; # Supporting both sequential and parallel computation. The parallel environment can be a single desktop with multiple-cores or a computing cluster with a SGE system; # A very friendly GUI (graphical user interface).
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