摘要

The Multimodal Imaging Brain Connectivity Analysis (MIBCA) toolbox is a fully automated all-in-one connectivity analysis toolbox that offers both pre-processing, connectivity, and graph theory analysis of multimodal images such as anatomical, diffusion, and functional MRI, and PET. In this work, the MIBCA functionalities were used to study Alzheimer's Disease (AD) in a multimodal MR/PET approach. Materials and Methods: Data from 12 healthy controls, and 36 patients with EMCI, LMCI and AD (12 patients for each group) were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (adni. loni. usc. edu), including T1-weighted (T1-w), Diffusion Tensor Imaging (DTI) data, and 18F-AV-45 (florbetapir) dynamic PET data from 40-60 min post injection (4x5 min). Both MR and PET data were automatically pre-processed for all subjects using MIBCA. T1-w data was parcellated into cortical and subcortical regions-of-interest (ROIs), and the corresponding thicknesses and volumes were calculated. DTI data was used to compute structural connectivity matrices based on fibers connecting pairs of ROIs. Lastly, dynamic PET images were summed, and the relative Standard Uptake Values calculated for each ROI. Results: An overall higher uptake of 18F-AV-45, consistent with an increased deposition of beta-amyloid, was observed for the AD group. Additionally, patients showed significant cortical atrophy (thickness and volume) especially in the entorhinal cortex and temporal areas, and a significant increase in Mean Diffusivity (MD) in the hippocampus, amygdala and temporal areas. Furthermore, patients showed a reduction of fiber connectivity with the progression of the disease, especially for intra-hemispherical connections. Conclusion: This work shows the potential of the MIBCA toolbox for the study of AD, as findings were shown to be in agreement with the literature. Here, only structural changes and beta-amyloid accumulation were considered. Yet, MIBCA is further able to process fMRI and different radiotracers, thus leading to integration of functional information, and supporting the research for new multimodal biomarkers for AD and other neurodegenerative diseases.

  • 出版日期2015-6