Tractography reproducibility challenge with empirical data (TraCED): The 2017 ISMRM diffusion study group challenge

Nath, Vishwesh; Schilling, Kurt G.; Parvathaneni, Prasanna; Huo, Yuankai; Blaber, Justin A.; Hainline, Allison E.; Baraković, Muhamed; Romascano, David; Rafael-Patiño, Jonathan; Frigo, Matteo; Girard, Gabriel; Thiran, Jean‐Philippe; Daducci, Alessandro; Rowe, Matt; Rodrigues, Paulo; Prčkovska, V.; Aydogan, Dogu Baran; Sun, Wei; Shi, Yonggang; Parker, William; Ismail, Abdol Aziz Ould; Verma, Ragini; Cabeen, Ryan P.; Toga, Arthur W.; Newton, Allen T.; Wasserthal, Jakob; Neher, Peter; Maier‐Hein, Klaus H.; Savini, Giovanni; Palesi, Fulvia; Kaden, Enrico; Wu, Ye; He, Jianzhong; Feng, Yuanjing; Paquette, Michael; Rheault, François; Sidhu, Jasmeen; Lebel, Catherine; Leemans, Alexander; Descoteaux, Maxime; Dyrby, Tim B.; Kang, Hakmook; Landman, Bennett A.

doi:10.1002/jmri.26794

Cited by 39 publications

(27 citation statements)

References 54 publications

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“…Third, we find that the variability across protocols is greater than the variability within protocols, and more importantly, similar to (or greater than) the variability across subjects. These results are in agreement with previous studies showing high overlap, high density correlations, and low disagreements within a protocol [30][31][32]. Most importantly, in our study, this represents a worst-case intra-protocol measure, because it includes sources of variability related to acquisition (and associated noise and artifacts), registration, reconstruction, and streamline generation -sources of variation which are shown to be still smaller than that across protocols.…”

Section: Discussionsupporting

confidence: 93%

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

Schilling¹,

Rheault²,

Petit

et al. 2020

Preprint

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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 wholebrain 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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Section: Discussionsupporting

confidence: 93%

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

Schilling¹,

Rheault²,

Petit

et al. 2020

Preprint

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“…Another issue that was opened by our work is how to define the best long-range brain connectivity to be used in TVB. In the MRI field, a relevant limitation is indeed the difficulty to reconstruct crossing and polysynaptic tracts and to identify the direction of axonal propagation (Hein et al, 2016;Nath et al, 2020). We have shown here that connectome corrections integrating physiological and anatomical constraints provide an effective solution that could be extended to an increasing number of long-range brain connections.…”

Section: Discussionmentioning

confidence: 90%

The Importance of Cerebellar Connectivity on Simulated Brain Dynamics

Palesi

Lorenzi

Casellato

et al. 2020

Front. Cell. Neurosci.

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“…While very high reproducibility is a very important feature of any tracking algorithm [40], its reliance as key quality measure (particularly, when this is at the expense of other important qualities, such as accuracy) when studying connectivity is considered here the second ‘sin’ in the list.…”

Section: Seven Deadly Sinsmentioning

confidence: 99%

The Seven Deadly Sins of Measuring Brain Structural Connectivity Using Diffusion MRI Streamlines Fibre-Tracking

Calamante

2019

Diagnostics

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There is great interest in the study of brain structural connectivity, as white matter abnormalities have been implicated in many disease states. Diffusion magnetic resonance imaging (MRI) provides a powerful means to characterise structural connectivity non-invasively, by using a fibre-tracking algorithm. The most widely used fibre-tracking strategy is based on the step-wise generation of streamlines. Despite their popularity and widespread use, there are a number of practical considerations that must be taken into account in order to increase the robustness of streamlines tracking results, particularly when these methods are used to study brain structural connectivity, and the connectome. This review article describes what we consider the ‘seven deadly sins’ of mapping structural connections using diffusion MRI streamlines fibre-tracking, with particular emphasis on ‘sins’ that can be practically avoided and they can have an important impact in the results. It is shown that there are important ‘deadly sins’ to be avoided at every step of the pipeline, such as during data acquisition, during data modelling to estimate local fibre architecture, during the fibre-tracking process itself, and during quantification of the tracking results. The recommendations here are intended to inform users on potential important shortcomings of their current tracking protocols, as well as to guide future users on some of the key issues and decisions that must be faced when designing their processing pipelines.

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Tractography reproducibility challenge with empirical data (TraCED): The 2017 ISMRM diffusion study group challenge

Cited by 39 publications

References 54 publications

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

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

The Importance of Cerebellar Connectivity on Simulated Brain Dynamics

The Seven Deadly Sins of Measuring Brain Structural Connectivity Using Diffusion MRI Streamlines Fibre-Tracking

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