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Simona Schiavi

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11 papers

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YNIMG Journal 2023 Journal Article

Evaluation of tractogram filtering methods using human-like connectome phantoms

Tabinda Sarwar
Kotagiri Ramamohanarao
Alessandro Daducci
Simona Schiavi
Robert E. Smith
Andrew Zalesky

Tractography algorithms are prone to reconstructing spurious connections. The set of streamlines generated with tractography can be post-processed to retain the streamlines that are most biologically plausible. Several microstructure-informed filtering algorithms are available for this purpose, however, the comparative performance of these methods has not been extensively evaluated. In this study, we aim to evaluate streamline filtering and post-processing algorithms using simulated connectome phantoms. We first establish a framework for generating connectome phantoms featuring brain-like white matter fiber architectures. We then use our phantoms to systematically evaluate the performance of a range of streamline filtering algorithms, including SIFT, COMMIT, and LiFE. We find that all filtering methods successfully improve connectome accuracy, although filter performance depends on the complexity of the underlying white matter fiber architecture. Filtering algorithms can markedly improve tractography accuracy for simple tubular fiber bundles (F-measure deterministic- unfiltered: 0.49 and best filter: 0.72; F-measure probabilistic- unfiltered: 0.37 and best filter: 0.81), but for more complex brain-like fiber architectures, the improvement is modest (F-measure deterministic- unfiltered: 0.53 and best filter: 0.54; F-measure probabilistic- unfiltered: 0.46 and best filter: 0.50). Overall, filtering algorithms have the potential to improve the accuracy of connectome mapping pipelines, particularly for weighted connectomes and pipelines using probabilistic tractography methods. Our results highlight the need for further advances tractography and streamline filtering to improve the accuracy of connectome mapping.

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YNIMG Journal 2023 Journal Article

Tractography passes the test: Results from the diffusion-simulated connectivity (disco) challenge

Gabriel Girard
Jonathan Rafael-Patiño
Raphaël Truffet
Dogu Baran Aydogan
Nagesh Adluru
Veena A. Nair
Vivek Prabhakaran
Barbara B. Bendlin

Estimating structural connectivity from diffusion-weighted magnetic resonance imaging is a challenging task, partly due to the presence of false-positive connections and the misestimation of connection weights. Building on previous efforts, the MICCAI-CDMRI Diffusion-Simulated Connectivity (DiSCo) challenge was carried out to evaluate state-of-the-art connectivity methods using novel large-scale numerical phantoms. The diffusion signal for the phantoms was obtained from Monte Carlo simulations. The results of the challenge suggest that methods selected by the 14 teams participating in the challenge can provide high correlations between estimated and ground-truth connectivity weights, in complex numerical environments. Additionally, the methods used by the participating teams were able to accurately identify the binary connectivity of the numerical dataset. However, specific false positive and false negative connections were consistently estimated across all methods. Although the challenge dataset doesn't capture the complexity of a real brain, it provided unique data with known macrostructure and microstructure ground-truth properties to facilitate the development of connectivity estimation methods.

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YNIMG Journal 2022 Journal Article

Bundle myelin fraction (BMF) mapping of different white matter connections using microstructure informed tractography

Simona Schiavi
Po-Jui Lu
Matthias Weigel
Antoine Lutti
Derek K. Jones
Ludwig Kappos
Cristina Granziera
Alessandro Daducci

To date, we have scarce information about the relative myelination level of different fiber bundles in the human brain. Indirect evidence comes from postmortem histology data but histological stainings are unable to follow a specific bundle and determine its intrinsic myelination. In this context, quantitative MRI, and diffusion MRI tractography may offer a viable solution by providing, respectively, voxel-wise myelin sensitive maps and the pathways of the major tracts of the brain. Then, "tractometry" can be used to combine these two pieces of information by averaging tissue features (obtained from any voxel-wise map) along the streamlines recovered with diffusion tractography. Although this method has been widely used in the literature, in cases of voxels containing multiple fiber populations (each with different levels of myelination), tractometry provides biased results because the same value will be attributed to any bundle passing through the voxel. To overcome this bias, we propose a new method - named "myelin streamline decomposition" (MySD) - which extends convex optimization modeling for microstructure informed tractography (COMMIT) allowing the actual value measured by a microstructural map to be deconvolved on each individual streamline, thereby recovering unique bundle-specific myelin fractions (BMFs). We demonstrate the advantage of our method with respect to tractometry in well-studied bundles and compare the cortical projection of the obtained bundle-wise myelin values of both methods. We also prove the stability of our approach across different subjects and different MRI sensitive myelin mapping approaches. This work provides a proof-of-concept of in vivo investigations of entire neuronal pathways that, to date, are not possible.

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YNIMG Journal 2022 Journal Article

Bundle-o-graphy: improving structural connectivity estimation with adaptive microstructure-informed tractography

Matteo Battocchio
Simona Schiavi
Maxime Descoteaux
Alessandro Daducci

Tractography is a powerful tool for the investigation of the complex organization of the brain in vivo, as it allows inferring the macroscopic pathways of the major fiber bundles of the white matter based on non-invasive diffusion-weighted magnetic resonance imaging acquisitions. Despite this unique and compelling ability, some studies have exposed the poor anatomical accuracy of the reconstructions obtained with this technique and challenged its effectiveness for studying brain connectivity. In this work, we describe a novel method to readdress tractography reconstruction problem in a global manner by combining the strengths of so-called generative and discriminative strategies. Starting from an input tractogram, we parameterize the connections between brain regions following a bundle-based representation that allows to drastically reducing the number of parameters needed to model groups of fascicles. The parameters space is explored following an MCMC generative approach, while a discrimininative method is exploited to globally evaluate the set of connections which is updated according to Bayes' rule. Our results on both synthetic and real brain data show that the proposed solution, called bundle-o-graphy, allows improving the anatomical accuracy of the reconstructions while keeping the computational complexity similar to other state-of-the-art methods.

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YNICL Journal 2022 Journal Article

Diffusion-based microstructure models in brain tumours: Fitting in presence of a model-microstructure mismatch

Umberto Villani
Erica Silvestri
Marco Castellaro
Simona Schiavi
Mariagiulia Anglani
Silvia Facchini
Elena Monai
Domenico D'Avella

Diffusion-based biophysical models have been used in several recent works to study the microenvironment of brain tumours. While the pathophysiological interpretation of the parameters of these models remains unclear, their use as signal representations may yield useful biomarkers for monitoring the treatment and the progression of this complex and heterogeneous disease. Up to now, however, no study was devoted to assessing the mathematical stability of these approaches in cancerous brain regions. To this end, we analyzed in 11 brain tumour patients the fitting results of two microstructure models (Neurite Orientation Dispersion and Density Imaging and the Spherical Mean Technique) and of a signal representation (Diffusion Kurtosis Imaging) to compare the reliability of their parameter estimates in the healthy brain and in the tumoral lesion. The framework of our between-tissue analysis included the computation of 1) the residual sum of squares as a goodness-of-fit measure 2) the standard deviation of the models' derived metrics and 3) models' sensitivity functions to analyze the suitability of the employed protocol for parameter estimation in the different microenvironments. Our results revealed no issues concerning the fitting of the models in the tumoral lesion, with similar goodness of fit and parameter precisions occurring in normal appearing and pathological tissues. Lastly, with the aim of highlight possible biomarkers, in our analysis we briefly discuss the correlation between the metrics of the three techniques, identifying groups of indices which are significantly collinear in all tissues and thus provide no additional information when jointly used in data-driven analyses.

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YNIMG Journal 2022 Journal Article

Evaluating reproducibility and subject-specificity of microstructure-informed connectivity

Philipp J. Koch
Gabriel Girard
Julia Brügger
Andéol G. Cadic-Melchior
Elena Beanato
Chang-hyun Park
Takuya Morishita
Maximilian J. Wessel

Tractography enables identifying and evaluating the healthy and diseased brain's white matter pathways from diffusion-weighted magnetic resonance imaging data. As previous evaluation studies have reported significant false-positive estimation biases, recent microstructure-informed tractography algorithms have been introduced to improve the trade-off between specificity and sensitivity. However, a major limitation for characterizing the performance of these techniques is the lack of ground truth brain data. In this study, we compared the performance of two relevant microstructure-informed tractography methods, SIFT2 and COMMIT, by assessing the subject specificity and reproducibility of their derived white matter pathways. Specifically, twenty healthy young subjects were scanned at eight different time points at two different sites. Subject specificity and reproducibility were evaluated using the whole-brain connectomes and a subset of 29 white matter bundles. Our results indicate that although the raw tractograms are more vulnerable to the presence of false-positive connections, they are highly reproducible, suggesting that the estimation bias is subject-specific. This high reproducibility was preserved when microstructure-informed tractography algorithms were used to filter the raw tractograms. Moreover, the resulting track-density images depicted a more uniform coverage of streamlines throughout the white matter, suggesting that these techniques could increase the biological meaning of the estimated fascicles. Notably, we observed an increased subject specificity by employing connectivity pre-processing techniques to reduce the underlaying noise and the data dimensionality (using principal component analysis), highlighting the importance of these tools for future studies. Finally, no strong bias from the scanner site or time between measurements was found. The largest intraindividual variance originated from the sole repetition of data measurements (inter-run).

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YNIMG Journal 2022 Journal Article

Incorporating outlier information into diffusion-weighted MRI modeling for robust microstructural imaging and structural brain connectivity analyses

Viljami Sairanen
Mario Ocampo-Pineda
Cristina Granziera
Simona Schiavi
Alessandro Daducci

The white matter structures of the human brain can be represented using diffusion-weighted MRI tractography. Unfortunately, tractography is prone to find false-positive streamlines causing a severe decline in its specificity and limiting its feasibility in accurate structural brain connectivity analyses. Filtering algorithms have been proposed to reduce the number of invalid streamlines but the currently available filtering algorithms are not suitable to process data that contains motion artefacts which are typical in clinical research. We augmented the Convex Optimization Modelling for Microstructure Informed Tractography (COMMIT) algorithm to adjust for these signals drop-out motion artefacts. We demonstrate with comprehensive Monte-Carlo whole brain simulations and in vivo infant data that our robust algorithm is capable of properly filtering tractography reconstructions despite these artefacts. We evaluated the results using parametric and non-parametric statistics and our results demonstrate that if not accounted for, motion artefacts can have severe adverse effects in human brain structural connectivity analyses as well as in microstructural property mappings. In conclusion, the usage of robust filtering methods to mitigate motion related errors in tractogram filtering is highly beneficial, especially in clinical studies with uncooperative patient groups such as infants. With our presented robust augmentation and open-source implementation, robust tractogram filtering is readily available.

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YNIMG Journal 2022 Journal Article

Insights from the IronTract challenge: Optimal methods for mapping brain pathways from multi-shell diffusion MRI

Chiara Maffei
Gabriel Girard
Kurt G. Schilling
Dogu Baran Aydogan
Nagesh Adluru
Andrey Zhylka
Ye Wu
Matteo Mancini

Limitations in the accuracy of brain pathways reconstructed by diffusion MRI (dMRI) tractography have received considerable attention. While the technical advances spearheaded by the Human Connectome Project (HCP) led to significant improvements in dMRI data quality, it remains unclear how these data should be analyzed to maximize tractography accuracy. Over a period of two years, we have engaged the dMRI community in the IronTract Challenge, which aims to answer this question by leveraging a unique dataset. Macaque brains that have received both tracer injections and ex vivo dMRI at high spatial and angular resolution allow a comprehensive, quantitative assessment of tractography accuracy on state-of-the-art dMRI acquisition schemes. We find that, when analysis methods are carefully optimized, the HCP scheme can achieve similar accuracy as a more time-consuming, Cartesian-grid scheme. Importantly, we show that simple pre- and post-processing strategies can improve the accuracy and robustness of many tractography methods. Finally, we find that fiber configurations that go beyond crossing (e.g., fanning, branching) are the most challenging for tractography. The IronTract Challenge remains open and we hope that it can serve as a valuable validation tool for both users and developers of dMRI analysis methods.

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YNIMG Journal 2022 Journal Article

Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: A review

Fan Zhang
Alessandro Daducci
Yong He
Simona Schiavi
Caio Seguin
Robert E Smith
Chun-Hung Yeh
Tengda Zhao

Diffusion magnetic resonance imaging (dMRI) tractography is an advanced imaging technique that enables in vivo reconstruction of the brain's white matter connections at macro scale. It provides an important tool for quantitative mapping of the brain's structural connectivity using measures of connectivity or tissue microstructure. Over the last two decades, the study of brain connectivity using dMRI tractography has played a prominent role in the neuroimaging research landscape. In this paper, we provide a high-level overview of how tractography is used to enable quantitative analysis of the brain's structural connectivity in health and disease. We focus on two types of quantitative analyses of tractography, including: 1) tract-specific analysis that refers to research that is typically hypothesis-driven and studies particular anatomical fiber tracts, and 2) connectome-based analysis that refers to research that is more data-driven and generally studies the structural connectivity of the entire brain. We first provide a review of methodology involved in three main processing steps that are common across most approaches for quantitative analysis of tractography, including methods for tractography correction, segmentation and quantification. For each step, we aim to describe methodological choices, their popularity, and potential pros and cons. We then review studies that have used quantitative tractography approaches to study the brain's white matter, focusing on applications in neurodevelopment, aging, neurological disorders, mental disorders, and neurosurgery. We conclude that, while there have been considerable advancements in methodological technologies and breadth of applications, there nevertheless remains no consensus about the "best" methodology in quantitative analysis of tractography, and researchers should remain cautious when interpreting results in research and clinical applications.

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YNIMG Journal 2021 Journal Article

Tractography dissection variability: What happens when 42 groups dissect 14 white matter bundles on the same dataset?

Kurt G. Schilling
François Rheault
Laurent Petit
Colin B. Hansen
Vishwesh Nath
Fang-Cheng Yeh
Gabriel Girard
Muhamed Barakovic

White matter bundle segmentation using diffusion MRI fiber tractography has become the method of choice to identify white matter fiber pathways in vivo in human brains. However, like other analyses of complex data, there is considerable variability in segmentation protocols and techniques. This can result in different reconstructions of the same intended white matter pathways, which directly affects tractography results, quantification, and interpretation. In this study, we aim to evaluate and quantify the variability that arises from different protocols for bundle segmentation. Through an open call to users of fiber tractography, including anatomists, clinicians, and algorithm developers, 42 independent teams were given processed sets of human whole-brain streamlines and asked to segment 14 white matter fascicles on six subjects. In total, we received 57 different bundle segmentation protocols, which enabled detailed volume-based and streamline-based analyses of agreement and disagreement among protocols for each fiber pathway. Results show that even when given the exact same sets of underlying streamlines, the variability across protocols for bundle segmentation is greater than all other sources of variability in the virtual dissection process, including variability within protocols and variability across subjects. In order to foster the use of tractography bundle dissection in routine clinical settings, and as a fundamental analytical tool, future endeavors must aim to resolve and reduce this heterogeneity. Although external validation is needed to verify the anatomical accuracy of bundle dissections, reducing heterogeneity is a step towards reproducible research and may be achieved through the use of standard nomenclature and definitions of white matter bundles and well-chosen constraints and decisions in the dissection process.

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YNIMG Journal 2019 Journal Article

Limits to anatomical accuracy of diffusion tractography using modern approaches

Kurt G. Schilling
Vishwesh Nath
Colin Hansen
Prasanna Parvathaneni
Justin Blaber
Yurui Gao
Peter Neher
Dogu Baran Aydogan

Diffusion MRI fiber tractography is widely used to probe the structural connectivity of the brain, with a range of applications in both clinical and basic neuroscience. Despite widespread use, tractography has well-known pitfalls that limits the anatomical accuracy of this technique. Numerous modern methods have been developed to address these shortcomings through advances in acquisition, modeling, and computation. To test whether these advances improve tractography accuracy, we organized the 3-D Validation of Tractography with Experimental MRI (3D-VoTEM) challenge at the ISBI 2018 conference. We made available three unique independent tractography validation datasets – a physical phantom and two ex vivo brain specimens - resulting in 176 distinct submissions from 9 research groups. By comparing results over a wide range of fiber complexities and algorithmic strategies, this challenge provides a more comprehensive assessment of tractography's inherent limitations than has been reported previously. The central results were consistent across all sub-challenges in that, despite advances in tractography methods, the anatomical accuracy of tractography has not dramatically improved in recent years. Taken together, our results independently confirm findings from decades of tractography validation studies, demonstrate inherent limitations in reconstructing white matter pathways using diffusion MRI data alone, and highlight the need for alternative or combinatorial strategies to accurately map the fiber pathways of the brain.

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