publications

2021

1. Network alignment and similarity reveal atlas-based topological differences in structural connectomes

   Frigo, Matteo, Cruciani, Emilio, Coudert, David, Deriche, Rachid, Natale, Emanuele, and Deslauriers-Gauthier, Samuel

   Network Neuroscience 2021

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   The interactions between different brain regions can be modeled as a graph, called connectome, whose nodes correspond to parcels from a predefined brain atlas. The edges of the graph encode the strength of the axonal connectivity between regions of the atlas which can be estimated via diffusion Magnetic Resonance Imaging (MRI) tractography. Herein, we aim at providing a novel perspective on the problem of choosing a suitable atlas for structural connectivity studies by assessing how robustly an atlas captures the network topology across different subjects in a homogeneous cohort. We measure this robustness by assessing the alignability of the connectomes, namely the possibility to retrieve graph matchings that provide highly similar graphs. We introduce two novel concepts. First, the graph Jaccard index (GJI), a graph similarity measure based on the well-established Jaccard index between sets; the GJI exhibits natural mathematical properties that are not satisfied by previous approaches. Second, we devise WL-align, a new technique for aligning connectomes obtained by adapting the Weisfeiler-Lehman (WL) graph-isomorphism test. We validated the GJI and WL-align on data from the Human Connectome Project database, inferring a strategy for choosing a suitable parcellation for structural connectivity studies. Code and data are publicly available.

2. Multi-Tissue Multi-Compartment Models of Diffusion MRI

   Frigo, Matteo, Fick, Rutger H.J., Zucchelli, Mauro, Deslauriers-Gauthier, Samuel, and Deriche, Rachid

   bioRxiv 2021

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   State-of-the-art multi-compartment microstructural models of diffusion MRI (dMRI) in the human brain have limited capability to model multiple tissues at the same time. In particular, the available techniques that allow this multi-tissue modelling are based on multi-TE acquisitions. In this work we propose a novel multi-tissue formulation of classical multi-compartment models that relies on more common single-TE acquisitions and can be employed in the analysis of previously acquired datasets. We show how modelling multiple tissues provides a new interpretation of the concepts of signal fraction and volume fraction in the context of multi-compartment modelling. The software that allows to inspect single-TE diffusion MRI data with multi-tissue multi-compartment models is included in the publicly available Dmipy Python package.Competing Interest StatementThe authors have declared no competing interest.

3. TALON: Tractograms As Linear Operators in Neuroimaging

   Frigo, Matteo, Zucchelli, Mauro, Deriche, Rachid, and Deslauriers-Gauthier, Samuel

   2021

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   TALON is a pure Python package that implements Tractograms As Linear Operators in Neuroimaging. The software provides the talon Python module, which includes all the functions and tools that are necessary for filtering a tractogram. In particular, specific functions are devoted to transforming a tractogram into a linear operator, solving the inverse problem associated to the filtering of a tractogram and perform these operations on a GPU.

4. Centering inclusivity in the design of online conferences—An OHBM–Open Science perspective

   Levitis, Elizabeth, Van Praag, Cassandra D Gould, Gau, Remi, Heunis, Stephan, DuPre, Elizabeth, Kiar, Gregory, Bottenhorn, Katherine L, Glatard, Tristan, Nikolaidis, Aki, Whitaker, Kirstie Jane, and others,

   GigaScience 2021

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   As the global health crisis unfolded, many academic conferences moved online in 2020. This move has been hailed as a positive step towards inclusivity in its attenuation of economic, physical, and legal barriers and effectively enabled many individuals from groups that have traditionally been underrepresented to join and participate. A number of studies have outlined how moving online made it possible to gather a more global community and has increased opportunities for individuals with various constraints, e.g., caregiving responsibilities. Yet, the mere existence of online conferences is no guarantee that everyone can attend and participate meaningfully. In fact, many elements of an online conference are still significant barriers to truly diverse participation: the tools used can be inaccessible for some individuals; the scheduling choices can favour some geographical locations; the set-up of the conference can provide more visibility to well-established researchers and reduce opportunities for early-career researchers. While acknowledging the benefits of an online setting, especially for individuals who have traditionally been underrepresented or excluded, we recognize that fostering social justice requires inclusivity to actively be centered in every aspect of online conference design. Here, we draw from the literature and from our own experiences to identify practices that purposefully encourage a diverse community to attend, participate in, and lead online conferences. Reflecting on how to design more inclusive online events is especially important as multiple scientific organizations have announced that they will continue offering an online version of their event when in-person conferences can resume.

5. On the generalizability of diffusion MRI signal representations across acquisition parameters, sequences and tissue types: Chronicles of the MEMENTO challenge

   De Luca, Alberto, Ianus, Andrada, Leemans, Alexander, Palombo, Marco, Shemesh, Noam, Zhang, Hui, Alexander, Daniel C., Nilsson, Markus, Froeling, Martijn, Biessels, Geert-Jan, Zucchelli, Mauro, Frigo, Matteo, Albay, Enes, Sedlar, Sara, Alimi, Abib, Deslauriers-Gauthier, Samuel, Deriche, Rachid, Fick, Rutger, Afzali, Maryam, Pieciak, Tomasz, Bogusz, Fabian, Aja-Fernández, Santiago, Özarslan, Evren, Jones, Derek K., Chen, Haoze, Jin, Mingwu, Zhang, Zhijie, Wang, Fengxiang, Nath, Vishwesh, Parvathaneni, Prasanna, Morez, Jan, Sijbers, Jan, Jeurissen, Ben, Fadnavis, Shreyas, Endres, Stefan, Rokem, Ariel, Garyfallidis, Eleftherios, Sanchez, Irina, Prchkovska, Vesna, Rodrigues, Paulo, Landman, Bennet A., and Schilling, Kurt G.

   NeuroImage 2021

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   Diffusion MRI (dMRI) has become an invaluable tool to assess the microstructural organization of brain tissue. Depending on the specific acquisition settings, the dMRI signal encodes specific properties of the underlying diffusion process. In the last two decades, several signal representations have been proposed to fit the dMRI signal and decode such properties. Most methods, however, are tested and developed on a limited amount of data, and their applicability to other acquisition schemes remains unknown. With this work, we aimed to shed light on the generalizability of existing dMRI signal representations to different diffusion encoding parameters and brain tissue types. To this end, we organized a community challenge - named MEMENTO, making available the same datasets for fair comparisons across algorithms and techniques. We considered two state-of-the-art diffusion datasets, including single-diffusion-encoding (SDE) spin-echo data from a human brain with over 3820 unique diffusion weightings (the MASSIVE dataset), and double (oscillating) diffusion encoding data (DDE/DODE) of a mouse brain including over 2520 unique data points. A subset of the data sampled in 5 different voxels was openly distributed, and the challenge participants were asked to predict the remaining part of the data. After one year, eight participant teams submitted a total of 80 signal fits. For each submission, we evaluated the mean squared error, the variance of the prediction error and the Bayesian information criteria. The received submissions predicted either multi-shell SDE data (37%) or DODE data (22%), followed by cartesian SDE data (19%) and DDE (18%). Most submissions predicted the signals measured with SDE remarkably well, with the exception of low and very strong diffusion weightings. The prediction of DDE and DODE data seemed more challenging, likely because none of the submissions explicitly accounted for diffusion time and frequency. Next to the choice of the model, decisions on fit procedure and hyperparameters play a major role in the prediction performance, highlighting the importance of optimizing and reporting such choices. This work is a community effort to highlight strength and limitations of the field at representing dMRI acquired with trending encoding schemes, gaining insights into how different models generalize to different tissue types and fiber configurations over a large range of diffusion encodings.

6. Computational Brain Connectivity Mapping : From Multi-Compartment Modeling To Network Topology Via Tractography Filtering

   Frigo, Matteo

   2021

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   Mapping the human brain is one of the complex challenges of contemporary science. It is a task that concatenates several problems from acquisition design to preprocessing, modelling, analysis, visualisation and assessment of the coherence with the state-of-the-art knowledge on the architecture and functioning of the human brain. For each of these steps a plethora of solutions has been and is being developed. It is of fundamental importance that the assumptions made in each step align with each other, demanding extra care in the verification of the theoretical requirements of the employed tools. In this thesis we focus on three specific parts of the chain of problems that leads to a comprehensive view of the brain architecture, highlighting the theoretical aspects that characterise the posed challenges and providing experimental evidence of the soundness of the proposed solutions. We present four contributions on three topical research areas of diffusion MRI methods for human brain mapping: brain tissue microstructure, tractography filtering and topological analysis of brain networks. First, we propose a new method for the estimation of tissue-specific volume fractions by means of multi-compartment models of the single-TE diffusion MRI signal. Then, we review the state of the art of tractography filtering and unveil its effects on the graph-theoretical analysis of the structural connectomes of both healthy subjects and patients affected by traumatic brain injury. In addition, we propose a novel filtering technique that integrates structural and functional information in the process. Finally, we propose a new similarity measure between brain networks and a new graph alignment techniques, allowing to obtain original insights into the problem of selecting the suitable parcellation for brain connectivity studies.

2020

1. A unified framework for multimodal structure–function mapping based on eigenmodes

   Deslauriers-Gauthier, Samuel, Zucchelli, Mauro, Frigo, Matteo, and Deriche, Rachid

   Medical Image Analysis 2020

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   Characterizing the connection between brain structure and brain function is essential for understanding how behaviour emerges from the underlying anatomy. A number of studies have shown that the network structure of the white matter shapes functional connectivity. Therefore, it should be possible to predict, at least partially, functional connectivity given the structural network. Many structure–function mappings have been proposed in the literature, including several direct mappings between the structural and functional connectivity matrices. However, the current literature is fragmented and does not provide a uniform treatment of current methods based on eigendecompositions. In particular, existing methods have never been compared to each other and their relationship explicitly derived in the context of brain structure–function mapping. In this work, we propose a unified computational framework that generalizes recently proposed structure–function mappings based on eigenmodes. Using this unified framework, we highlight the link between existing models and show how they can be obtained by specific choices of the parameters of our framework. By applying our framework to 50 subjects of the Human Connectome Project, we reproduce 6 recently published results, devise two new models and provide a direct comparison between all mappings. Finally, we show that a glass ceiling on the performance of mappings based on eigenmodes seems to be reached and conclude with possible approaches to break this performance limit.

2. Diffusion MRI tractography filtering techniques change the topology of structural connectomes

   Frigo, Matteo, Deslauriers-Gauthier, Samuel, Parker, Drew, Ismail, Abdol Aziz Ould, Kim, Junghoon John, Verma, Ragini, and Deriche, Rachid

   Journal of Neural Engineering 2020

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   Objective. The use of non-invasive techniques for the estimation of structural brain networks (i.e. connectomes) opened the door to large-scale investigations on the functioning and the architecture of the brain, unveiling the link between neurological disorders and topological changes of the brain network. This study aims at assessing if and how the topology of structural connectomes estimated non-invasively with diffusion MRI is affected by the employment of tractography filtering techniques in structural connectomic pipelines. Additionally, this work investigates the robustness of topological descriptors of filtered connectomes to the common practice of density-based thresholding. Approach. We investigate the changes in global efficiency, characteristic path length, modularity and clustering coefficient on filtered connectomes obtained with the spherical deconvolution informed filtering of tractograms and using the convex optimization modelling for microstructure informed tractography. The analysis is performed on both healthy subjects and patients affected by traumatic brain injury and with an assessment of the robustness of the computed graph-theoretical measures with respect to density-based thresholding of the connectome. Main results. Our results demonstrate that tractography filtering techniques change the topology of brain networks, and thus alter network metrics both in the pathological and the healthy cases. Moreover, the measures are shown to be robust to density-based thresholding. Significance. The present work highlights how the inclusion of tractography filtering techniques in connectomic pipelines requires extra caution as they systematically change the network topology both in healthy subjects and patients affected by traumatic brain injury. Finally, the practice of low-to-moderate density-based thresholding of the connectomes is confirmed to have negligible effects on the topological analysis.

3. Multi-compartment modelling of diffusion MRI signal shows TE-based volume fraction bias

   Frigo, Matteo, Zucchelli, Mauro, Fick, Rutger H.J., Deslauriers-Gauthier, Samuel, and Deriche, Rachid

   In OHBM 2020 - 26th meeting of the Organization of Human Brain Mapping 2020

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   Diffusion MRI (dMRI) has been widely used to estimate brain tissue microstructure in-vivo.Two of the most widely used microstructural indices are the white matter (WM) andintra-cellular (IC) volume fractions (VF) [2012z,2019f]. In estimating these fractions, acommon assumption of dMRI-based signal modeling is to assume that the T2-relaxation foreach compartment is equal. However, it has been shown that this assumption is inaccurate[2018v]. Here, we characterize the bias introduced by this assumption using a generalmulti-compartmental model of the dMRI signal in three distinct scenarios:3-S0) the realistic-case, where each compartment has its T2-dependent signal at b-value 0(S0).2-S0) in which we consider only two separated S0, one for WM and one for CSF similarly to[2014j].1-S0) a single average S0 is considered for all the compartments, as commonly done indMRI.Our simulations and experiments on real data show fitting the WM and IC VF using the moresimplistic 2-S0 and 1-S0 model, a systematic bias appears that potentially alters theinterpretation of conclusions drawn from studies focusing on WM and IC VF.

4. Multi Tissue Modelling of Diffusion MRI Signal Reveals Volume Fraction Bias

   Frigo, Matteo, Fick, Rutger H.J., Zucchelli, Mauro, Deslauriers-Gauthier, Samuel, and Deriche, Rachid

   In ISBI 2020 - IEEE International Symposium on Biomedical Imaging 2020

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   This paper highlights a systematic bias in white matter tissue microstructure modelling via diffusion MRI that is due to the common, yet inaccurate, assumption that all brain tissues have a similar T2 response. We show that the concept of "signal fraction" is more appropriate to describe what have always been referred to as "volume fraction". This dichotomy is described from the theoretical point of view by analysing the mathematical formulation of the diffusion MRI signal. We propose a generalized multi tissue modelling framework that allows to compute the actual volume fractions. The Dmipy implementation of this framework is then used to verify the presence of this bias in four classical tissue microstructure models computed on two subjects from the Human Connectome Project database. The proposed paradigm shift exposes the research field of brain tissue microstructure estimation to the necessity of a systematic review of the results obtained in the past that takes into account the difference between the concept of volume fraction and tissue fraction.

5. Quantitative assessment of multi-scale tractography: bridging the resolution gap with 3D-PLI

   Alimi, Abib, Frigo, Matteo, Deslauriers-Gauthier, Samuel, and Deriche, Rachid

   In ISMRM & SMRT Virtual Conference & Exhibition 2020

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   The in vivo validation of diffusion MRI (dMRI)-based tractography has beenshown to be a challenging task [Maier-hein et al.]. Therefore, we have been investigating how 3D Polarized Light Imaging (3D-PLI) could be used as a validation tool for dMRI-based fiber orientation estimation and tractography. PLI is an optical imaging technique that provides us with high-resolution fiber orientation measurements at micrometer scale. For this reason, it has been presented as a good candidate for the afore mentioned validation tasks [Axer et al,2011, Alimi et al, 2019 submitted]. In some previous works [alimi2017,18isbi,18ismrm,19,19submitted] we introduced an approach to close the resolution gap between dMRI and 3D-PLI. The study of the brain network from the topological point of view has seen an increasing interest in the last years [Sizemore et al, 2018, Chung et al, 2017]. In this work, we show how tractograms obtained at different spatial scales using 3D-PLI human brain datasets can bein spected using homology theory to perform a quantitative comparison between them. In particular, we investigate the persistence of the number of connected components in brain networks estimated from data at different resolutions.

2019

1. Effects of tractography filtering on the topology and interpretability of connectomes

   Frigo, Matteo, Deslauriers-Gauthier, Samuel, Parker, Drew, Ould Ismail, Abdol Aziz, Kim, Junghoon John, Verma, Ragini, and Deriche, Rachid

   In OHBM 2019 - Organization for Human Brain Mapping 2019

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   The analysis of connectomes and their associated network metrics forms an important part of clinical studies. These connectomes are based on tractography algorithms to estimate the structural connectivity between brain regions. However, tractography algorithms, are prone to false positive connections and this affects the quality of the connectomes. Several tractography filtering techniques (TFTs) have been proposed to alleviate this issue in studies, but their effect on connectomic analyses of pathology has not been investigated. The aim of our work is to investigate how TFTs affect network metrics and their interpretation in the context of clinical studies.

2018

1. Reducing false positive connection in tractograms using joint structure-function filtering

   Frigo, Matteo, Gallardo, Guillermo, Costantini, Isa, Daducci, Alessandro, Wassermann, Demian, Deriche, Rachid, and Deslauriers-Gauthier, Samuel

   In OHBM 2018 - Organization for Human Brain Mapping 2018

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   Due to its ill-posed nature, tractography generates a significant number of false positive connections between brain regions [3]. To reduce the number of false positives, Daducci et al. [1] proposed the COMMIT framework, which has the goal of re-establishing the link between tractography and tissue microstructure. In this framework, the diffusion MRI signal is modeled as a linear combination of local models associated with streamlines where the weights are identified by solving a convex optimization problem. Streamlines with a weight of zero do not contribute to the diffusion MRI data and are assumed to be false positives. Removing these false positives yields a subset of streamlines supporting the anatomical data. However, COMMIT does not make use of the link between structure and function and thus weights all bundles equally. In this work, we propose a new strategy that enhances the COMMIT framework by injecting the functional information provided by functional MRI. The result is an enhanced tractogram filtering strategy that considers both functional and structural data.

2. Resolving the crossing/kissing fiber ambiguity using Functionally Informed COMMIT

   Frigo, Matteo, Costantini, Isa, Deriche, Rachid, and Deslauriers-Gauthier, Samuel

   In Computational Diffusion MRI 2018 2018

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   The architecture of the white matter is endowed with kissing and crossing bundles configurations. When these white matter tracts are reconstructed using diffusion MRI tractography, this systematically induces the reconstruction of many fiber tracts that are not coherent with the structure of the brain. The question on how to discriminate between true positive connections and false positive connections is the one addressed in this work. State-of-the-art techniques provide a partial solution to this problem by considering anatomical priors in the false pos-itives detection process. We propose a novel model that tackles the same issue but takes into account both structural and functional information by combining them in a convex optimization problem. We validate it on two toy phantoms that reproduce the kissing and the crossing bundles configurations, showing that through this approach we are able to correctly distinguish true positives and false positives.

2016

1. Gibbs sampling approach to regime switching analysis of financial time series

   Di Persio, Luca, and Frigo, Matteo

   Journal of Computational and Applied Mathematics 2016

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   We will introduce a Monte Carlo type inference in the framework of Markov Switching models to analyse financial time series, namely the Gibbs Sampling. In particular we generalize the results obtained in Albert and Chib (1993), Di Persio and Vettori (2014) and Kim and Nelson (1999) to take into account the switching mean as well as the switching variance case. In particular the volatility of the relevant time series will be treated as a state variable in order to describe the abrupt changes in the behaviour of financial time series which can be implied, e.g., by social, political or economic factors. The accuracy of the proposed analysis will be tested considering financial dataset related to the U.S. stock market in the period 2007–2014.

2015

1. Maximum likelihood approach to markov switching models

   Di Persio, Luca, and Frigo, Matteo

   Transactions on Business and Economics 2015