Multimodal Microstructure-Informed Connectivity: Acquisition, Reconstruction, Analysis and Validation


The Multimodal Microstructure-Informed Connectivity: Acquisition, Reconstruction, Analysis and Validation (MMINCARAV) project is an associate team between Empenn (Inria Rennes Bretagne Atlantique) and LTS5 (École Polytechnique Fédérale de Lausanne — EPFL) started in 2019. The main objective of this associate team is to develop novel methods for the quantification of microstructure properties in brain tissues using diffusion MRI and relaxometry, in relation with the macrostructure organization of the brain connectome. The project is organized into 4 work packages:

  1. Acquisition design in microstructure-enabled diffusion MRI using compressive sensing
  2. Combining relaxometry and diffusion
  3. Analyzing the microstructure-informed brain connectome
  4. Validating microstructure and connectivity reconstruction using simulations and reproducibility analysis

Team members

Context and motivations of the project

The ability to measure microscopic scale phenomena using magnetic resonance imaging (MRI) has triggered a revolution in neuroimaging, and brought hope for a better understanding of the brain’s structure and function. In particular, the recent developments of diffusion MRI and relaxometry have shown the tight link between tissue micro-organization and the measured signal. Due to their relatively large volume (~1mm³), there is a mix of tissue properties in every voxel in the image, and several bio-physical models have been proposed to encompass this heterogeneity. Besides, using this local information on microstructure, it is not only possible to better estimate the trajectory of major fiber pathways, but also to have a more relevant, quantitative description of the brain connectivity.

There is a large number of open problems to this end; first, recovering tissue properties from the measured signal requires solving an inverse problem, which is in general ill-posed due to the relatively small number of signal samples we can acquire during an in vivo scan (10 minutes up to one hour). A proper design of the acquisition sequence is of utmost importance in this respect, to make sure we take the best advantage of every single measurement. In addition, relaxometry and diffusion MRI provide complementary information; despite the growing interest, realistic models accounting for both relaxometry and diffusion remain to be developed to better characterize the brain microstructure. Progress in the microstructure modelling paves the way for quantitative connectivity measures, directly related to the actual number of axons connecting two regions. Computing and analyzing the microstructure-informed brain connectome, using methods of graph theory, opens new challenges since the statistical properties of this connectome are very different from current connectivity matrices, for which weights are computed counting streamlines of a tractogram. Last, recent contests organized in international conferences have shown the limits of state-of-the-art techniques in controlling the number of false positives when estimating the connectome, and there is a pressing demand in validating the reconstruction from the micro- to the macroscopic scale.

Objectives of the MMINCARAV associate team

We propose to address these challenges with the following work packages (WP).

WP1: Acquisition design

(key investigators: E. Caruyer, M. Pizzolato, R. Truffet)
In diffusion MRI, the use of generalized diffusion-weighting gradient waveforms was shown to provide higher sensitivity to microscopic tissue properties. While in practice most sampling schemes still use rectangular pulsed gradients due to their simplicity, we will exploit the larger number of degrees of freedom to design a family of gradient waveforms adapted to microstructure reconstruction. We will use the theory of compressive sensing to select gradients which are best adapted to reconstruction using sparse priors.

WP2: Combining relaxometry and diffusion

(key investigators: C. Barillot, O. Commowick, M. Pizzolato, T. Yu)
Diffusion MRI allows disentangling the signal contributions coming from the intracellular and interstitial water molecular dynamics, but is generally blind to water trapped in myelin sheets. Conversely, T2-relaxometry is blind to the separation of intracellular and interstitial water but it allows disentangling their joint contribution from that of myelin. We will exploit the complementarity of these two modalities by proposing a unified model of extended microstructure that includes these three major compartments. The pertinence of this model will be evaluated in healthy controls and patients.

WP3: Analyzing the microstructure-informed brain connectome

(key investigators: J. Coloigner, C. Barillot, G. Girard, J.-Ph. Thiran)
Graph theory provides powerful methods for evaluating structural brain networks.. This technique models the brain as a complex network where nodes are associated with regions of interest and an edge strength represents the degree of structural connection between a pair of regions. Conventional approaches use connectivity measures derived from the number of streamlines estimated by tractography algorithms. However, false positive connections arise frequently and can directly bias the inferred topology measures. Microstructure features, such as the mean axon diameter, have been shown to reduce false positive connections and biases in the estimated structural connectivity. We will develop new network analysis methods based on the measure of the microstructure-informed connectivity, and evaluate how this naturally reduces the biases.

WP4: Validating microstructure and connectivity reconstruction

(key investigators: E. Caruyer, G. Girard, J. Rafael Patiño, J.-Ph. Thiran)
The diffusion MRI community is in a pressing need for validation methods for sampling efficiency, microstructure model accuracy, and novel connectivity measures. We will build upon the respective expertise of both LTS5 and VisAGeS in simulating diffusion MRI at the mesoscopic scale and the macroscopic scale to create a macroscopic simulator using locally realistic microstructure modelling. We will also implement the designed acquisition sequences on a small animal MRI scanner and a human MRI scanner, and acquire a test-retest dataset to perform a reproducibility study. This will set the basis for the organization of a reconstruction challenge on connectivity using realistic data simulations.

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