Volume 1 Supplement 1

Proceedings of the 3rd PSMR Conference on PET/MR and SPECT/MR

Open Access

Multimodal imaging brain connectivity analysis (MIBCA) toolbox: preliminary application to Alzheimer’s disease

  • Andre Santos Ribeiro1, 2,
  • Luís Miguel Lacerda2, 3,
  • Nuno André da Silva4 and
  • Hugo Alexandre Ferreira2
EJNMMI Physics20141(Suppl 1):A61

DOI: 10.1186/2197-7364-1-S1-A61

Published: 29 July 2014

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 [1]. 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.

An overall higher uptake of 18F-AV-45, consistent with an increased deposition of amyloid-Beta, was observed for the AD group. Additionally, patients showed significant cortical atrophy (thickness and volume) especially in the enthorhinal and temporal areas, and a significant increase in Mean Diffusivity (MD) in the hippocampus, amygdala and temporal areas, Figure 1. Furthermore, patients showed an overall decrease of both inter- and intra- hemispherical structural connections (tracts), Figure 2. Finally, the 3D-graph visualization showed that the structural loss was global and asymmetric, Figure 3.
Figure 1

Group difference Connectrogram. Significant differences between Healthy Subjects and AD patients are highlighted in color in the inner rings. From outside to inside rings: region-of-interest (ROI), Standard Uptake Value (18F-AV-45), mean diffusivity, ROI volume, cortical thickness, node degree computed from structural connectivity.

Figure 2

Group connectivity matrices for Healthy Subjects (upper row), and AD patients (lower row). From left to right are represented the structural, cortical thickness and cortical volume connectivity matrices.

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 [24]. 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.

Authors’ Affiliations

Center for Neuropsychopharmacology, Division of Brain Sciences, Department of Medicine, Imperial College London
Faculty of Sciences of the University of Lisbon, Institute of Biophysics and Biomedical Engineering
Centre for Neuroimaging Sciences, Institute of Psychiatry, King’s College London
Institute of Neuroscience and Medicine-4, Forschungszentrum Juelich GmbH


  1. Santos-Ribeiro A, Lacerda L, Rodrigues J, Sousa JM, Ferreira HA: Multimodal imaging brain connectivity analysis. In ESMRMB Congress 30th Annual Scientific Meeting of the European Society for Magnetic Resonance in Medicine and Biology. Toulouse, France; 2013.Google Scholar
  2. Johnson KA, Sperling RA, Gidicsin CM, Carmasin JS, Maye JE, Coleman RE, Reiman EM, Sabbagh MN, Sadowsky CH, Fleisher AS, Murali Doraiswamy P, Carpenter AP, Clark CM, Joshi AD, Lu M, Grundman M, Mintun MA, Pontecorvo MJ, Skovronsky DM: Florbetapir (F18-AV-45) PET to assess amyloid burden in Alzheimer's disease dementia, mild cognitive impairment, and normal aging. Alzheimers Dement 2013, 9: S72–83. 10.1016/j.jalz.2012.10.007PubMed CentralPubMedView ArticleGoogle Scholar
  3. Duara R, Loewenstein DA, Potter E, Appel J, Greig MT, Urs R, Shen Q, Raj A, Small B, Barker W, Schofield E, Wu Y, Potter H: Medial temporal lobe atrophy on MRI scans and the diagnosis of Alzheimer disease. Neurology 2008,71(24):1986–1992. 10.1212/01.wnl.0000336925.79704.9fPubMed CentralPubMedView ArticleGoogle Scholar
  4. Acosta-Cabronero J, Alley S, Williams GB, Pengas G, Nestor PJ: Diffusion Tensor Metrics as Biomarkers in Alzheimer's Disease. PLOS ONE 2012. 10.1371/journal.pone.0049072Google Scholar


© Ribeiro et al; licensee Springer 2014

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.