Evaluating the reliability of human brain white matter tractometry

Kruper, John; Yeatman, Jason D.; Richie-Halford, Adam; Bloom, David C.; Grotheer, Mareike; Caffarra, Sendy; Kiar, Gregory; Karipidis, Iliana I.; Roy, Ethan; Rokem, Ariel

doi:10.1101/2021.02.24.432740

Cited by 26 publications

(38 citation statements)

References 90 publications

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“…A comparison of the DIPY implementation to the pyDesigner implementation provided a very close match ( Figure 6 ). Given that the implementations were done completely independently, this agreement provides a sign of the robustness of DKI across different software (Kruper et al, 2021 ). Finally, the recent version of the mrtrix software (Tournier et al, 2019 ) also includes an implementation of DKI estimation.…”

Section: Discussionmentioning

confidence: 99%

Diffusional Kurtosis Imaging in the Diffusion Imaging in Python Project

Henriques

Correia

Marrale

et al. 2021

Front. Hum. Neurosci.

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Diffusion-weighted magnetic resonance imaging (dMRI) measurements and models provide information about brain connectivity and are sensitive to the physical properties of tissue microstructure. Diffusional Kurtosis Imaging (DKI) quantifies the degree of non-Gaussian diffusion in biological tissue from dMRI. These estimates are of interest because they were shown to be more sensitive to microstructural alterations in health and diseases than measures based on the total anisotropy of diffusion which are highly confounded by tissue dispersion and fiber crossings. In this work, we implemented DKI in the Diffusion in Python (DIPY) project—a large collaborative open-source project which aims to provide well-tested, well-documented and comprehensive implementation of different dMRI techniques. We demonstrate the functionality of our methods in numerical simulations with known ground truth parameters and in openly available datasets. A particular strength of our DKI implementations is that it pursues several extensions of the model that connect it explicitly with microstructural models and the reconstruction of 3D white matter fiber bundles (tractography). For instance, our implementations include DKI-based microstructural models that allow the estimation of biophysical parameters, such as axonal water fraction. Moreover, we illustrate how DKI provides more general characterization of non-Gaussian diffusion compatible with complex white matter fiber architectures and gray matter, and we include a novel mean kurtosis index that is invariant to the confounding effects due to tissue dispersion. In summary, DKI in DIPY provides a well-tested, well-documented and comprehensive reference implementation for DKI. It provides a platform for wider use of DKI in research on brain disorders and in cognitive neuroscience.

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Section: Discussionmentioning

confidence: 99%

Diffusional Kurtosis Imaging in the Diffusion Imaging in Python Project

Henriques

Correia

Marrale

et al. 2021

Front. Hum. Neurosci.

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“…The method is packaged as open-source software called AFQ-Insight that is openly available at https://github.com/richford/AFQ-Insight , and provides a clear API to allow for extensions of the method. The sofware integrates within a broader automated fiber quantification software ecosystem: AFQ [ 5 ] and pyAFQ [ 57 ], which extract tract profile data from raw and processed dMRI datasets, as well as AFQ-Browser, which visualizes tract profiles data and facilitates sharing of the results of dMRI studies [ 58 ]. To facilitate reproducibility and ease use of the software, the results presented in this paper are also provided in https://github.com/richford/afq-insight-paper as a series of Jupyter notebooks [ 59 ].…”

Section: Discussionmentioning

confidence: 99%

“…These processes create bundle profiles, in which diffusion measures are quantified and averaged along eighteen major fiber tracts, which are enumerated in S1 Fig . See S3 Fig of [ 57 ] for a depiction of these white matter bundles. Here, we use only the mean diffusivity (MD) and the fractional anisotropy (FA) of the diffusion tensor, but additional dMRI-based maps or maps based on other quantitative MRI measurements can also be used.…”

Section: Methodsmentioning

confidence: 99%

“…Data from the HBN and Cam-CAN studies were processed using the updated Python Automated Fiber Quantification toolbox (pyAFQ; [57]). In addition to demonstrating the our analysis pipeline is robust to changes in tractometry software, the use of the updated pyAFQ capitalized upon the following improvements over the legacy Matlab version: (i) the ability to ingest data provided in the BIDS format [84], and (ii) the calculation of diffusion kurtosis imaging (DKI [43]) metrics We will refer to the mAFQ and pyAFQ pipeline collectively as AFQ.…”

Section: Plos Computational Biologymentioning

confidence: 99%

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Multidimensional analysis and detection of informative features in human brain white matter

et al. 2021

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The white matter contains long-range connections between different brain regions and the organization of these connections holds important implications for brain function in health and disease. Tractometry uses diffusion-weighted magnetic resonance imaging (dMRI) to quantify tissue properties along the trajectories of these connections. Statistical inference from tractometry usually either averages these quantities along the length of each fiber bundle or computes regression models separately for each point along every one of the bundles. These approaches are limited in their sensitivity, in the former case, or in their statistical power, in the latter. We developed a method based on the sparse group lasso (SGL) that takes into account tissue properties along all of the bundles and selects informative features by enforcing both global and bundle-level sparsity. We demonstrate the performance of the method in two settings: i) in a classification setting, patients with amyotrophic lateral sclerosis (ALS) are accurately distinguished from matched controls. Furthermore, SGL identifies the corticospinal tract as important for this classification, correctly finding the parts of the white matter known to be affected by the disease. ii) In a regression setting, SGL accurately predicts “brain age.” In this case, the weights are distributed throughout the white matter indicating that many different regions of the white matter change over the lifespan. Thus, SGL leverages the multivariate relationships between diffusion properties in multiple bundles to make accurate phenotypic predictions while simultaneously discovering the most relevant features of the white matter.

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“…In addition to the quantitative evaluation, we examined all bundles delineated using babyAFQ and AFQ qualitatively at all time-points (Supplementary Data 5), by evaluating how well they match the typical spatial extent and trajectory. We also provide an interactive 3D visualization of an example infant's bundles (created with pyAFQ 62 ).…”

Section: Babyafq Quality Assurancementioning

confidence: 99%

White matter myelination during early infancy is explained by spatial gradients and myelin content at birth

Grotheer

Rosenke

et al. 2021

Preprint

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Development of myelin, a fatty sheath that insulates nerve fibers, is critical for brain function. Myelination during infancy has been studied in postmortem histology, but such data cannot evaluate the developmental trajectory of the white matter bundles of the brain. To address this gap in knowledge, we (i) obtained longitudinal diffusion MRI measures and quantitative MRI measures of T1, which is sensitive to myelin, from newborns to 6-months-old infants, and (ii) developed an automated fiber quantification method that identifies bundles from dMRI and quantifies their T1 development in infants. Here we show that both along the length of each bundle and across bundles, T1 decreases from newborns to 6 months-old's and the rate of T1 decrease is inversely correlated with T1 at birth. As lower T1 indicates more myelin, these data suggest that in early infancy white matter bundles myelinate at different rates such that less mature bundles at birth develop faster to catch-up with the other bundles. We hypothesize that this development reflects experience-dependent myelination, which may promote efficient and coordinated neural communication. These findings open new avenues to measure typical and atypical white matter development in early infancy, which has important implications for early identification of neurodevelopmental disorders.

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Evaluating the reliability of human brain white matter tractometry

Cited by 26 publications

References 90 publications

Diffusional Kurtosis Imaging in the Diffusion Imaging in Python Project

Diffusional Kurtosis Imaging in the Diffusion Imaging in Python Project

Multidimensional analysis and detection of informative features in human brain white matter

White matter myelination during early infancy is explained by spatial gradients and myelin content at birth

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