#	Pagina
attuale pagina	/open-h2020/projects/204841/results.html

Report

Teaser, summary, work performed and final results

Periodic Reporting for period 2 - CoBCoM (Computational Brain Connectivity Mapping)

Teaser

Summary

One third of the burden of all the diseases in Europe is due to problems caused by diseases affecting the brain. Although exceptional progress has been obtained for exploring it during the past decades, The brain is still terra-incognita and calls for specific research
efforts to better understand its architecture and functioning.

CoBCoM is primarily devoted to develop new generation of computational models and methodological breakthroughs for brain connectivity mapping. To solve the limited view of the brain provided just by one imaging modality, our models are solidly grounded on advanced and complementary integrated non invasive imaging modalities: diffusion
Magnetic Resonance Imaging (dMRI) and Electro & Magneto-Encephalography (EEG & MEG).

To take up this immense challenge, CoBCoM has the overall objectives to develop advanced dMRI and MEEG source reconstruction methods for structural and functional brain connectivity mapping and build integrated dynamical brain networks from dMRI & M/EEG. This will help to push far forward the state-of-the-art in these modalities, to
better understand and reconstruct the structural and functional brain connectivity and to provide a clinical added value to better identify and characterize abnormalities in brain connectivity. Clearly, this represents a fantastic scientific challenge as well as a pressing clinical need that, when solved, will greatly open new perspectives in
neuroimaging and positively impact the unacceptable burden of brain diseases.

Work performed

We worked towards our objectives and harvested numerous and important results in the 4 main focus areas listed in our research program.

1] In Focus Area 1, we aimed to develop advanced dMRI methods for structural connectivity mapping. We developed generative and ground-breaking models for advanced acquisition and processing of dMRI data [Refs 4, 7]. Within the Microstructure Imaging framework developed in [Ref. 7], we contributed to the challenge to recover microstructure tissue parameters by developing the Diffusion Microstructure Imaging in Python (Dmipy) toolbox [Refs 25, 15] and we assesed the applicability of microstructure imaging in diseases [Refs.13,14,24,40]. To open the way to new dMRI biomarkers, far beyond from the classical second order tensor invariants, we investigated the use of high order diffusion models and developed new invariants from High Order Diffusion Models [ Refs. 9, 31]. We applied them to mild Traumatic Brain Injury data and verified that they are indeed sensitive to microstructural changes induced by the pathology [Ref. 31]. We have also developed new concepts and approaches grounded on microstructure from dMRI and applied them to Microstructure based tractography [Refs. 3, 50]. Finally, for validation of tractography and dMRI results, we contributed to bridge the gap between Polarized Light Imaging and dMRI demonstrating a great promise to validate diffusion MRI tractography thanks to multi-scale fiber tracking based on 3D-PLI [Refs. 22,23,27,28].

2] In Focus Area 2, we aimed to develop advanced M/EEG source reconstruction methods using spatial and temporal constraints and started to explore in more depth how to use diffusion MRI data for spatially regularizing the M/EEG inverse problem [Refs. 2,5,8,10,12,36,44]. we proposed groupwise parcellation of the whole cortex based on structural connectivity [Refs. 2,5,35,43] and we contributed to the development of our OpenMEEG software, a C++q opensource software available on GitHub at http://openmeeg.github.io/ for quasistatic electromagnetics, solving forward problems of EEG, MEG, ECoG, intracerebral EEG, and integrated it into several software suites for MEG/EEG analysis and processing (Brainstorm, Fieldtrip, SPM). OpenMEEG 2.4 has been released in August 2018 (http://openmeeg.gforge.inria.fr/download/) with notable new features.

3] In Focus Area 3, we started to develop joint structure-function models from which dynamical structural-functional brain connectivity networks can be extracted. To unravel dynamical brain networks using both dMRI and M/EEG data and build large brain effective network from EEG/MEG data and dMRI information, we contributed to reconstruct the information flow in the brain for a given task. Integrating the spatial information of dMRI and the temporal one coming from M/EEG is done in several tasks and steps of increasing complexity [Refs 6,33,48,49]. Our contributions based on a Bayesian dynamical model of WM information flow clearly shows that information flow visualization along white matter pathways has a great potential to explore the brain dynamics in novel ways. In addition, we started also to be interested by integrating the spatial information of dMRI and the temporal one coming from fMRI [Refs. 44,47,53,52,51].

4] In Focus Area 4, we applied our new high order invariants [Ref, 31] to mild traumatic brain injury data and verified that they are indeed sensitive to microstructural changes induced by the pathology. In addition, we also investigated the impact of tractography filtering [Ref. 16] on the structural networks of mild traumatic brain injury subjects. Our result, in [Ref. 21], highlight the existence of important differences between the brain connectivity obtained with distinct techniques that should be taken into account at the stage of interpretation and diagnosis.

Overall, and at mid-term of this project, we published 60 papers in the most selective journal and conferences of the domain.

Final results

We contributed to advance dMRI signal modeling with an efficient dMRI spatio-temporal representation and developed optimized acquisition designs that take, for the first time, into account time-dependence in dMRI [Refs. 4,7,19,35,40,55].

We opened the way to new dMRI biomarkers based on high order rotational invariants [Refs 9,18,31,32] and new concepts and approaches grounded on microstructure from dMRI [9] have also been developed and applied to Microstructure based tractography [3] to facilitate the recovery of the brain structural connectivity. We also contributed to the challenge to recover microstructure tissue parameters by developing the Diffusion Microstructure Imaging in Python (Dmipy) software (https://github.com/AthenaEPI/dmipy) [Refs 25,15].

We developed advanced M/EEG source reconstruction methods with dMRI based spatial and M/EEG temporal constraints [Refs.2,5,8,10,12,36,44] and developed parcellation techniques to help reconstructing the information flow in the brain from EEG/MEG data and dMRI information [2,5,35,43]. We contributed to the development of OpenMEEG, the state of the art for forward computations with a new release OpenMEEG 2.4 in Aug 2018 (http://openmeeg.gforge.inria.fr/download/) to make it handy to use by the bioelectromagnetic research community and to integrate with other packages.

We started to develop a large brain effective network from EEG/MEG data and dMRI and contributed to the inference and visualization of information flow using dMRI and EEG with the development of dynamical model of white matter information flow which relies on a Bayesian network built from diffusion MRI tractography and M/EEG functional data used as evidence into this network [6,33,48,49].

We applied our new high order rotation invariant dMRI features to mild traumatic brain injury data and verified that they are indeed sensitive to microstructural changes induced by the pathology [Ref. 31]. We also investigated the impact of tractography filtering on the structural networks of mild TBI subjects [16,21] and developed a tractography algorithm through peritumoral edema to be used for pre-operative surgical planning [50].

Overall, we have been able to set-up and recruit the research team and we advanced in the period as planned, or even ahead of schedule thanks to the contributions of all the team members. We harvested numerous and important results in the 4 main focus areas listed in our research program and we succeeded to publish 60 papers in the most selective and impacted journal and conferences of the domain. We continue to advance as planned towards the objectives of the project.