Abstract

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.

Highlights

  • N EUROIMAGING techniques have long been used to unfold the complexity of human brain anatomy and function

  • Magnetic Resonance Imaging (MRI) has been extensively used in the context of structural connectivity, where the measurement of the random displacement of water molecules using diffusion MRI allows the tracing of three-dimensional paths between different brain regions via tractography [1], [2]

  • The potential of the Multimodal Imaging Brain Connectivity Analysis (MIBCA) toolbox for the automatic pre-processing, analysis, visualization and integration, of neuroimaging data applied to the study of Alzheimer’s Disease (AD) was presented

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Summary

Introduction

N EUROIMAGING techniques have long been used to unfold the complexity of human brain anatomy and function. MRI allows measuring in vivo and non-invasively the human morphology, structure and dynamics with high resolution and soft tissue contrast. MRI has been extensively used in the context of structural connectivity, where the measurement of the random displacement of water molecules using diffusion MRI (dMRI) allows the tracing of three-dimensional paths between different brain regions via tractography [1], [2]. Functional connectivity, on the other hand, typically uses functional MRI (fMRI), which has helped undercover concepts about the basal level of activations in the brain (resting state networks) [3], [4]. The combination of complementary information from multimodal data is highly desired.

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