Periodic Reporting for period 4 - CoBCoM (Computational Brain Connectivity Mapping)
Okres sprawozdawczy: 2021-03-01 do 2021-08-31
CoBCoM is primarily devoted to develop new generation of computational
models and methodological breakthroughs to better understand the
structural and functional connectivity of the brain, To solve the
limited view of the brain provided just by one imaging modality, our
brain connectivity mapping models are solidly grounded on advanced and
complementary integrated non invasive structural and functional
imaging modalities: diffusion Magnetic Resonance Imaging (dMRI),
Electro & Magneto-Encephalography (MEEG for 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, infer the white
matter information flow mapping from joint diffusion MRI and EEG
measurements, explore the structure-function relationship and build
integrated dynamical brain networks.
This will help to push far forward the state-of-the-art in these
complementary imaging modalities and should open up new perspectives
for neuroimaging to better understand the structural and functional
brain connectivity and to provide a clinical added value to identify
and characterize abnormalities in brain connectivity.
models and methodological breakthroughs to better understand the
structural and functional connectivity of the brain, To solve the
limited view of the brain provided just by one imaging modality, our
brain connectivity mapping models are solidly grounded on advanced and
complementary integrated non invasive structural and functional
imaging modalities: diffusion Magnetic Resonance Imaging (dMRI),
Electro & Magneto-Encephalography (MEEG for 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, infer the white
matter information flow mapping from joint diffusion MRI and EEG
measurements, explore the structure-function relationship and build
integrated dynamical brain networks.
This will help to push far forward the state-of-the-art in these
complementary imaging modalities and should open up new perspectives
for neuroimaging to better understand the structural and functional
brain connectivity and to provide a clinical added value to identify
and characterize abnormalities in brain connectivity.
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 4,7,19,34,39,54]. We contributed to the challenge to recover
microstructure tissue parameters by developing the Diffusion
Microstructure Imaging in Python (Dmipy) toolbox [Refs 15, 25, 84] and
assessed some of its applicability [Refs.13,14,94]. To open the way to
new dMRI biomarkers, we investigated the use of high order diffusion
models and developed new high order invariants [Refs. 9,18,31,85]
applied to characterize some diseases [Ref. 31,95,76,80]. We have also
developed new concepts using spherical convolutional neural network
for dMRI fiber ODF estimation [Refs. 69,75], and new approaches
grounded on microstructure from dMRI applied them to Microstructure
based tractography [Refs. 3,50]. For validation of tractography and
dMRI results, we bridged 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. 22,23,27,28,73,82,88].
2] In Focus Area 2, we aimed to develop advanced M/EEG source
reconstruction methods using spatial and temporal constraints and
explored in more depth how to use diffusion MRI data for spatially
regularizing the M/EEG forward and inverse problems
[Refs. 8,10,12,29,45,55,57,77,78,79,97]. We proposed groupwise
parcellation of the whole cortex based on structural connectivity
[Refs. 2,5,35,38,43], solved the cross-subject parcel matching problem
using Optimal Transport [Refs. 59,60], developed a novel atlas of
human cerebral cortex based on extrinsic connectivity [Refs 41, 43],
and assessed how robustly an atlas captures the network topology
across different subjects in a homogeneous cohort [Ref. 93].
] In Focus Area 3, we developed 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 visual and motor tasks [Refs. 6, 33] and to estimate axonal
conduction speed, conduction delay, and diameter using connectivity
informed maximum entropy on the mean [Refs, 48,49]. In addition, we
explored the structure-function relationship and developed a unified
framework for multimodal structure-function mapping based on
eigenmodes to predict the function given the structure
[Refs. 72,87]. Integrating the spatial information of dMRI and the
temporal one coming from fMRI has also been developed and validated
[Refs. 44,47,51,52,53,74,83]. Finally, we have succeeded to combine
functional MRI, diffusion MRI, and M/EEG to infer communication and
information flow between cortical regions, therefore opening the door
to the non-invasive exploration of information flow in the white
matter [Ref. 86].
4] In Focus Area 4, We assessed the applicability of microstructure
imaging [Refs 7, 15, 25, 84,.24,40] and the use of our new high order
invariants in some diseases such as mild Traumatic Brain Injury [56] and
Multiple Sclerosis [Refs. 95,96]. In addition, we also
investigated the impact of tractography filtering [Refs. 16.21,91] on
the structural networks of mTBI injury subjects.
Finally, we contributed to develop a set of publicly available software packages:
The Diffusion Microstructure Imaging in Python package (Refs 15, 25, 84],
The OpenMEEG package [Ref. 67], The Tractograms As Linear Operators
in Neuroimaging package (https://pypi.org/project/cobcom-talon/) and
the Python implementation of the graph alignment WL-align algorithm
[Ref. 93].
erall, and at the end of this project, we published over a hundred
papers in the most selective journal and conferences of the domain.
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 4,7,19,34,39,54]. We contributed to the challenge to recover
microstructure tissue parameters by developing the Diffusion
Microstructure Imaging in Python (Dmipy) toolbox [Refs 15, 25, 84] and
assessed some of its applicability [Refs.13,14,94]. To open the way to
new dMRI biomarkers, we investigated the use of high order diffusion
models and developed new high order invariants [Refs. 9,18,31,85]
applied to characterize some diseases [Ref. 31,95,76,80]. We have also
developed new concepts using spherical convolutional neural network
for dMRI fiber ODF estimation [Refs. 69,75], and new approaches
grounded on microstructure from dMRI applied them to Microstructure
based tractography [Refs. 3,50]. For validation of tractography and
dMRI results, we bridged 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. 22,23,27,28,73,82,88].
2] In Focus Area 2, we aimed to develop advanced M/EEG source
reconstruction methods using spatial and temporal constraints and
explored in more depth how to use diffusion MRI data for spatially
regularizing the M/EEG forward and inverse problems
[Refs. 8,10,12,29,45,55,57,77,78,79,97]. We proposed groupwise
parcellation of the whole cortex based on structural connectivity
[Refs. 2,5,35,38,43], solved the cross-subject parcel matching problem
using Optimal Transport [Refs. 59,60], developed a novel atlas of
human cerebral cortex based on extrinsic connectivity [Refs 41, 43],
and assessed how robustly an atlas captures the network topology
across different subjects in a homogeneous cohort [Ref. 93].
] In Focus Area 3, we developed 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 visual and motor tasks [Refs. 6, 33] and to estimate axonal
conduction speed, conduction delay, and diameter using connectivity
informed maximum entropy on the mean [Refs, 48,49]. In addition, we
explored the structure-function relationship and developed a unified
framework for multimodal structure-function mapping based on
eigenmodes to predict the function given the structure
[Refs. 72,87]. Integrating the spatial information of dMRI and the
temporal one coming from fMRI has also been developed and validated
[Refs. 44,47,51,52,53,74,83]. Finally, we have succeeded to combine
functional MRI, diffusion MRI, and M/EEG to infer communication and
information flow between cortical regions, therefore opening the door
to the non-invasive exploration of information flow in the white
matter [Ref. 86].
4] In Focus Area 4, We assessed the applicability of microstructure
imaging [Refs 7, 15, 25, 84,.24,40] and the use of our new high order
invariants in some diseases such as mild Traumatic Brain Injury [56] and
Multiple Sclerosis [Refs. 95,96]. In addition, we also
investigated the impact of tractography filtering [Refs. 16.21,91] on
the structural networks of mTBI injury subjects.
Finally, we contributed to develop a set of publicly available software packages:
The Diffusion Microstructure Imaging in Python package (Refs 15, 25, 84],
The OpenMEEG package [Ref. 67], The Tractograms As Linear Operators
in Neuroimaging package (https://pypi.org/project/cobcom-talon/) and
the Python implementation of the graph alignment WL-align algorithm
[Ref. 93].
erall, and at the end of this project, we published over a hundred
papers in the most selective journal and conferences of the domain.
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. 4,7,19,34,39,54].
We developed a new framework for analytically
generating a complete set of algebraically independent rotation
invariants of the dMRI signal [Refs. 9,10,31,85] and applied them to mTBI
[Ref. 56] and MS data [Refs 95,96], thus opening the way to new and
robust dMRI biomarkers of abnormalities of the brain connectivity, far
beyond the state-of-the-art 2nd order tensor invariants.
New concepts and approaches grounded on microstructure from dMRI
[Refs. 7,9] have been developed and applied to Microstructure based
tractography [Ref. 3] to facilitate the recovery of the brain structural
connectivity.
We developed a unified framework for multimodal structure-function
mapping based on eigenmodes to predict the function given the
structure [Refs.72,87] and advance the state-of-the-art of the
understanding of the connection between brain structure and function.
We developed advanced M/EEG source reconstruction methods and
parcellation techniques using dMRI spatial and M/EEG temporal
constraints [Refs. 8,10,12,29,45,55,57,77,78,97,,2,5,35,38,43] to
develop a large brain effective network from dMRI and EEG/MEG. We
contributed to the inference and visualization of information flow
using dMRI and M/EEG with the development of dynamical model of WM
information flow which relies on a Bayesian network
[6,33,89]. Integrating dMRI,fMRI and M/EEG has been addressed in [Ref. 86].
We developed a new technique for aligning connectomes obtained by
adapting the Weisfeiler-Leman grah-isomorphism test which outperform
the state-of-the-art in aligning brain networks [Ref. 93].
We developed the Dmipy [Refs. 25,15,84], OpenMEEG [Ref. 67] and Talon
(https://pypi.org/project/cobcom-talon/) software packages designed
to make them handy to use, to easily integrate with
other packages and to benefit to our research community and advance
the state-of-the-art of our field with a limited development effort.
spatio-temporal representation and developed optimized acquisition
designs that take, for the first time, into account time-dependence in
dMRI [Refs. 4,7,19,34,39,54].
We developed a new framework for analytically
generating a complete set of algebraically independent rotation
invariants of the dMRI signal [Refs. 9,10,31,85] and applied them to mTBI
[Ref. 56] and MS data [Refs 95,96], thus opening the way to new and
robust dMRI biomarkers of abnormalities of the brain connectivity, far
beyond the state-of-the-art 2nd order tensor invariants.
New concepts and approaches grounded on microstructure from dMRI
[Refs. 7,9] have been developed and applied to Microstructure based
tractography [Ref. 3] to facilitate the recovery of the brain structural
connectivity.
We developed a unified framework for multimodal structure-function
mapping based on eigenmodes to predict the function given the
structure [Refs.72,87] and advance the state-of-the-art of the
understanding of the connection between brain structure and function.
We developed advanced M/EEG source reconstruction methods and
parcellation techniques using dMRI spatial and M/EEG temporal
constraints [Refs. 8,10,12,29,45,55,57,77,78,97,,2,5,35,38,43] to
develop a large brain effective network from dMRI and EEG/MEG. We
contributed to the inference and visualization of information flow
using dMRI and M/EEG with the development of dynamical model of WM
information flow which relies on a Bayesian network
[6,33,89]. Integrating dMRI,fMRI and M/EEG has been addressed in [Ref. 86].
We developed a new technique for aligning connectomes obtained by
adapting the Weisfeiler-Leman grah-isomorphism test which outperform
the state-of-the-art in aligning brain networks [Ref. 93].
We developed the Dmipy [Refs. 25,15,84], OpenMEEG [Ref. 67] and Talon
(https://pypi.org/project/cobcom-talon/) software packages designed
to make them handy to use, to easily integrate with
other packages and to benefit to our research community and advance
the state-of-the-art of our field with a limited development effort.