- Meeting abstract
- Open Access
Multimodal imaging brain connectivity analysis (MIBCA) toolbox: preliminary application to Alzheimer’s disease
© Ribeiro et al; licensee Springer 2014
- Published: 29 July 2014
- Diffusion Tensor Image
- Mean Diffusivity
- Temporal Area
- Diffusion Tensor Image Data
- Amyloid Accumulation
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 in a multimodal MR/PET approach.
Data from 11 healthy subjects and 10 AD patients were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu), including T1-weighted (T1w), Diffusion Tensor Imaging (DTI) data, and 18F-AV-45 (florbetapir) dynamic PET data from 40-60 min post injection (4x5min). Both MR and PET data were automatically pre-processed for all subjects using MIBCA. The T1w 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 Standard Uptake Values calculated for each ROI.
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 [2–4]. Here, only structural changes and beta amyloid accumulation were considered. Yet, MIBCA is further able to process fMRI and different radiotracers, and combine all the information in order to provide new insights into AD.
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