scholarly journals TractoFlow: A robust, efficient and reproducible diffusion MRI pipeline leveraging Nextflow & Singularity

2019 ◽  
Author(s):  
Guillaume Theaud ◽  
Jean-Christophe Houde ◽  
Arnaud Boré ◽  
François Rheault ◽  
Felix Morency ◽  
...  
Keyword(s):  
Diffusion Mri ◽  
Diffusion Tensor ◽  
Brain Connectivity ◽  
Reversed Phase ◽  
Fiber Density ◽  
Processing Pipeline ◽  
Diffusion Weighted Images ◽  
Brain Extraction ◽  
Human Connectome Project ◽  
Over Time

AbstractA diffusion MRI (dMRI) tractography processing pipeline should be: i) reproducible in immediate test-test, ii) reproducible in time, iii) efficient and iv) easy to use. Two runs of the same processing pipeline with the same input data should give the same output today, tomorrow and in 2 years. However, processing dMRI data requires a large number of steps (20+ steps) that, at this time, may not be reproducible between runs or over time. If parameters such as the number of threads or the random number generator are not carefully set in the brain extraction, registration and fiber tracking steps, the end tractography results obtained can be far from reproducible and limit brain connectivity studies. Moreover, processing can take several hours to days of computation for a large database, even more so if the steps are running sequentially.To handle these issues, we present TractoFlow, a fully automated pipeline that processes datasets from the raw diffusion weighted images (DWI) to tractography. It also outputs classical diffusion tensor imaging measures (fractional anisotropy (FA) and diffu-sivities) and several HARDI measures (Number of Fiber Orientation (NuFO), Apparent Fiber Density (AFD)). The pipeline requires a DWI and T1-weighted image as NIfTI files and b-values/b-vectors in FSL format. An optional reversed phase encoded b=0 image can also be used. This pipeline is based on two technologies: Nextflow and Singularity, as well as recommended pre-processing and processing steps from the dMRI community. In this work, the TractoFlow pipeline is evaluated on three databases and shown to be efficient and reproducible from 98% to 100% depending on parameter choices. For example, 105 subjects from the Human Connectome Project (HCP) were fully ran in twenty-five (25) hours to produce, for each subject, a whole-brain tractogram with 4 million streamlines. The contribution of this paper is to introduce the importance of a robust pipeline in terms of runtime and reproducibility over time. In the era of open data and open science, efficiency and reproducibility is critical in neuroimaging projects. Our TractoFlow pipeline is publicly available for academic research and is an important step forward for better structural brain connectivity mapping.

Human Connectome-Based Tractographic Atlas of the Brainstem Connections and Surgical Approaches

Neurosurgery ◽  
2016 ◽  
Vol 79 (3) ◽  
pp. 437-455 ◽  
Author(s):  
Antonio Meola ◽  
Fang-Cheng Yeh ◽  
Wendy Fellows-Mayle ◽  
Jared Weed ◽  
Juan C. Fernandez-Miranda
Keyword(s):  
Spatial Resolution ◽  
Diffusion Mri ◽  
Diffusion Tensor ◽  
Population Based ◽  
Medial Longitudinal Fasciculus ◽  
Surgical Approaches ◽  
Medial Lemniscus ◽  
Spinothalamic Tract ◽  
Rubrospinal Tract ◽  
Human Connectome Project

Abstract BACKGROUND The brainstem is one of the most challenging areas for the neurosurgeon because of the limited space between gray matter nuclei and white matter pathways. Diffusion tensor imaging-based tractography has been used to study the brainstem structure, but the angular and spatial resolution could be improved further with advanced diffusion magnetic resonance imaging (MRI). OBJECTIVE To construct a high-angular/spatial resolution, wide-population-based, comprehensive tractography atlas that presents an anatomical review of the surgical approaches to the brainstem. METHODS We applied advanced diffusion MRI fiber tractography to a population-based atlas constructed with data from a total of 488 subjects from the Human Connectome Project-488. Five formalin-fixed brains were studied for surgical landmarks. Luxol Fast Blue-stained histological sections were used to validate the results of tractography RESULTS We acquired the tractography of the major brainstem pathways and validated them with histological analysis. The pathways included the cerebellar peduncles, corticospinal tract, corticopontine tracts, medial lemniscus, lateral lemniscus, spinothalamic tract, rubrospinal tract, central tegmental tract, medial longitudinal fasciculus, and dorsal longitudinal fasciculus. Then, the reconstructed 3-dimensional brainstem structure was sectioned at the level of classic surgical approaches, namely supracollicular, infracollicular, lateral mesencephalic, perioculomotor, peritrigeminal, anterolateral (to the medulla), and retro-olivary approaches. CONCLUSION The advanced diffusion MRI fiber tracking is a powerful tool to explore the brainstem neuroanatomy and to achieve a better understanding of surgical approaches.

scholarly journals Local connectome phenotypes predict social, health, and cognitive factors

2018 ◽  
Vol 2 (1) ◽  
pp. 86-105 ◽  
Author(s):  
Michael A. Powell ◽  
Javier O. Garcia ◽  
Fang-Cheng Yeh ◽  
Jean M. Vettel ◽  
Timothy Verstynen
Keyword(s):  
White Matter ◽  
Diffusion Mri ◽  
Social Health ◽  
Cognitive Factors ◽  
High Dimensional ◽  
Cognitive Measures ◽  
Human Connectome Project ◽  
Low Dimensional ◽  
The Brain ◽  
Over Time

The unique architecture of the human connectome is defined initially by genetics and subsequently sculpted over time with experience. Thus, similarities in predisposition and experience that lead to similarities in social, biological, and cognitive attributes should also be reflected in the local architecture of white matter fascicles. Here we employ a method known as local connectome fingerprinting that uses diffusion MRI to measure the fiber-wise characteristics of macroscopic white matter pathways throughout the brain. This fingerprinting approach was applied to a large sample ( N = 841) of subjects from the Human Connectome Project, revealing a reliable degree of between-subject correlation in the local connectome fingerprints, with a relatively complex, low-dimensional substructure. Using a cross-validated, high-dimensional regression analysis approach, we derived local connectome phenotype (LCP) maps that could reliably predict a subset of subject attributes measured, including demographic, health, and cognitive measures. These LCP maps were highly specific to the attribute being predicted but also sensitive to correlations between attributes. Collectively, these results indicate that the local architecture of white matter fascicles reflects a meaningful portion of the variability shared between subjects along several dimensions.

scholarly journals Predicting age and clinical risk from the neonatal connectome

2020 ◽  
Author(s):  
Yassine Taoudi-Benchekroun ◽  
Daan Christiaens ◽  
Irina Grigorescu ◽  
Andreas Schuh ◽  
Maximilian Pietsch ◽  
...  
Keyword(s):  
Diffusion Mri ◽  
Brain Connectivity ◽  
Later Life ◽  
Absolute Error ◽  
Brain Maturation ◽  
Preterm Neonates ◽  
Efficient Treatment ◽  
Human Connectome Project ◽  
Maturation Index ◽  
Atypical Development

AbstractThe development of perinatal brain connectivity underpins motor, cognitive and behavioural abilities in later life. With the rise of advanced imaging methods such as diffusion MRI, the study of brain connectivity has emerged as an important tool to understand subtle alterations associated with neurodevelopmental conditions. Brain connectivity derived from diffusion MRI is complex, multi-dimensional and noisy, and hence it can be challenging to interpret on an individual basis. Machine learning methods have proven to be a powerful tool to uncover hidden patterns in such data, thus opening an opportunity for early identification of atypical development and potentially more efficient treatment.In this work, we used Deep Neural Networks and Random Forests to predict neurodevelopmental characteristics from neonatal structural connectomes, in a large sample of neonates (N = 524) derived from the developing Human Connectome Project. We achieved a highly accurate prediction of post menstrual age (PMA) at scan on term-born infants (Mean absolute error (MAE) = 0.72 weeks, r = 0.83, p<<0.001). We also achieved good accuracy when predicting gestational age at birth on a cohort of term and preterm babies scanned at term equivalent age (MAE = 2.21 weeks, r = 0.82, p<<0.001). From our models of PMA at scan for infants born at term, we computed the brain maturation index (i.e. predicted minus actual age) of individual preterm neonates and found significant correlation of this index with motor outcome at 18 months corrected age. Our results suggest that the neural substrate for later neurological functioning is detectable within a few weeks after birth in the structural connectome.

scholarly journals DACO: Distortion/artefact correction for diffusion MRI data in an integrated framework

2021 ◽  
Author(s):  
Yung-Chin Hsu ◽  
Wen-Yih Isaac Tseng
Keyword(s):  
Diffusion Mri ◽  
Diffusion Tensor ◽  
Mathematical Formulation ◽  
Magnetic Resonance Images ◽  
Model Parameters ◽  
Weighted Image ◽  
The Real ◽  
Integrated Framework ◽  
Human Connectome Project ◽  
Anatomical Images

In this paper we propose a registration-based algorithm to correct various distortions or artefacts (DACO) commonly observed in diffusion weighted (DW) magnetic resonance images (MRI). The registration in DACO is proceeded on the basis of a pseudo b_0 image, which is synthesized from the anatomical images such as T1-weighted image or T2-weighted image, and a pseudo diffusion MRI (dMRI) data, which is derived from the Gaussian model of diffusion tensor imaging (DTI) or the Hermite model of MAP-MRI. DACO corrects (1) the susceptibility-induced distortions, (2) the intensity inhomogeneity, and (3) the misalignment between the dMRI data and anatomical images by registering the real b_0 image to the pseudo b_0 image, and corrects (4) the eddy current (EC)-induced distortions and (5) the head motions by registering each of the DW images in the real dMRI data to the corresponding image in the pseudo dMRI data. As the above artefacts interact with each other, DACO models each type of artefact in an integrated framework and estimates these models in an interleaved and iterative manner. The mathematical formulation of the models and the comprehensive estimation procedures are detailed in this paper. The evaluation using the human connectome project data shows that DACO could estimate the model parameters accurately. Furthermore, the evaluation conducted on the real human data acquired from clinical MRI scanners reveals that the method could reduce the artefacts effectively. The DACO method leverages the anatomical image, which is routinely acquired in clinical practice, to correct the artefacts, minimizing the additional acquisitions needed to conduct the algorithm. Therefore, our method is beneficial to most dMRI data, particularly to those without acquiring the field map or blip-up and blip-down images.

scholarly journals An atlas of white matter anatomy, its variability, and reproducibility based on Constrained Spherical Deconvolution of diffusion MRI

2021 ◽  
Author(s):  
Ahmed M. Radwan ◽  
Stefan Sunaert ◽  
Kurt G. Schilling ◽  
Maxime Descoteaux ◽  
Bennett A. Landman ◽  
...  
Keyword(s):  
White Matter ◽  
Diffusion Mri ◽  
Diffusion Tensor ◽  
Single Subject ◽  
Constrained Spherical Deconvolution ◽  
Spherical Deconvolution ◽  
Pyramidal Tracts ◽  
Human Connectome Project ◽  
Subject Specific ◽  
Virtual Dissection

Virtual dissection of white matter (WM) using diffusion MRI tractography is confounded by its poor reproducibility. Despite the increased adoption of advanced reconstruction models, early region-of-interest driven protocols based on diffusion tensor imaging (DTI) remain the dominant reference for virtual dissection protocols. Here we bridge this gap by providing a comprehensive description of typical WM anatomy reconstructed using a reproducible automated subject-specific parcellation-based approach based on probabilistic constrained-spherical deconvolution (CSD) tractography. We complement this with a WM template in MNI space comprising 68 bundles, including all associated anatomical tract selection labels and associated automated workflows. Additionally, we demonstrate bundle inter- and intra-subject variability using 40 (20 test-retest) datasets from the human connectome project (HCP) and 5 sessions with varying b-values and number of b-shells from the single-subject Multiple Acquisitions for Standardization of Structural Imaging Validation and Evaluation (MASSIVE) dataset. The most reliably reconstructed bundles were the whole pyramidal tracts, primary corticospinal tracts, whole superior longitudinal fasciculi, frontal, parietal and occipital segments of the corpus callosum and middle cerebellar peduncles. More variability was found in less dense bundles, e.g., the first segment of the superior longitudinal fasciculus, fornix, dentato-rubro-thalamic tract (DRTT), and premotor pyramidal tract. Using the DRTT as an example, we show that this variability can be reduced by using a higher number of seeding attempts. Overall inter-session similarity was high for HCP test-retest data (median weighted-dice = 0.963, stdev = 0.201 and IQR = 0.099). Compared to the HCP-template bundles there was a high level of agreement for the HCP test-retest data (median weighted-dice = 0.747, stdev = 0.220 and IQR = 0.277) and for the MASSIVE data (median weighted-dice = 0.767, stdev = 0.255 and IQR = 0.338). In summary, this WM atlas provides an overview of the capabilities and limitations of automated subject-specific probabilistic CSD tractography for mapping white matter fasciculi in healthy adults. It will be most useful in applications requiring a highly reproducible parcellation-based dissection protocol, as well as being an educational resource for applied neuroimaging and clinical professionals.

scholarly journals Automated processing pipeline for neonatal diffusion MRI in the developing Human Connectome Project

NeuroImage ◽  
2019 ◽  
Vol 185 ◽  
pp. 750-763 ◽  
Author(s):  
Matteo Bastiani ◽  
Jesper L.R. Andersson ◽  
Lucilio Cordero-Grande ◽  
Maria Murgasova ◽  
Jana Hutter ◽  
...  
Keyword(s):  
Diffusion Mri ◽  
Processing Pipeline ◽  
Human Connectome Project ◽  
Automated Processing

scholarly journals Age-related differences in white matter: Comparing fixel based and tensor based analyses

2019 ◽  
Author(s):  
Shannon Kelley ◽  
John Plass ◽  
Andrew R. Bender ◽  
Thad A. Polk
Keyword(s):  
White Matter ◽  
Cross Section ◽  
Healthy Aging ◽  
Diffusion Tensor ◽  
Internal Capsule ◽  
Fiber Density ◽  
Younger Adults ◽  
Diffusion Weighted Images ◽  
Age Related ◽  
Anterior Posterior

AbstractOlder adults tend to perform worse on cognitive, behavioral, motor, and sensory tasks compared to younger adults, and differences in white matter that may be associated with this phenomenon are being actively investigated. Most prior studies of white matter differences between older and younger adults have analyzed diffusion weighted images using diffusion tensor imaging (DTI) analysis. But DTI results can be affected by many different factors (e.g., fiber density, fiber cross-section, crossing fibers) that are difficult to distinguish, making the interpretation of these results challenging. Recently, new fixel-based analysis (FBA) techniques have been developed that address some of these concerns, but these techniques have not yet been applied in the domain of aging. In this study, we used both DTI and FBA to analyze differences in white matter in a large sample of older and younger healthy adults. Both analysis methods identified age differences in forceps minor, fornix, bilateral internal capsule, and bilateral inferior fronto-occipital fasciculi, but the FBA results provided novel insights into the underlying structural differences. Furthermore, DTI analysis identified differences in superior longitudinal fasciculus that are not reflected in fiber density or cross-section and may instead be due to differences in crossing fiber geometry. Finally, the FBA results identified clearer differences in limbic white matter than did the DTI analysis. It also provided stronger evidence of an anterior-posterior asymmetry and segment-specific variations in white matter differences between older and younger adults. These results demonstrate the power of fixel-based analysis and provide novel insights into some of the major white matter differences associated with healthy aging.

scholarly journals The Developing Human Connectome Project: a Minimal Processing Pipeline for Neonatal Cortical Surface Reconstruction

2017 ◽  
Author(s):  
Antonios Makropoulos ◽  
Emma C. Robinson ◽  
Andreas Schuh ◽  
Robert Wright ◽  
Sean Fitzgibbon ◽  
...  
Keyword(s):  
Brain Connectivity ◽  
Cortical Surface ◽  
Tissue Segmentation ◽  
Minimal Processing ◽  
Processing Pipeline ◽  
Human Connectome Project ◽  
Surface Models ◽  
Automated Processing ◽  
Magnetic Resonance Imaging Mri ◽  
Segmentation Models

AbstractThe Developing Human Connectome Project (dHCP) seeks to create the first 4-dimensional connectome of early life. Understanding this connectome in detail may provide insights into normal as well as abnormal patterns of brain development. Following established best practices adopted by the WU-MINN Human Connectome Project (HCP), and pioneered by FreeSurfer, the project utilises cortical surface-based processing pipelines. In this paper, we propose a fully automated processing pipeline for the structural Magnetic Resonance Imaging (MRI) of the developing neonatal brain. This proposed pipeline consists of a refined framework for cortical and sub-cortical volume segmentation, cortical surface extraction, and cortical surface inflation, which has been specifically designed to address considerable differences between adult and neonatal brains, as imaged using MRI. Using the proposed pipeline our results demonstrate that images collected from 465 subjects ranging from 28 to 45 weeks post-menstrual age (PMA) can be processed fully automatically; generating cortical surface models that are topologically correct, and correspond well with manual evaluations of tissue boundaries in 85% of cases. Results improve on state-of-the-art neonatal tissue segmentation models and significant errors were found in only 2% of cases, where these corresponded to subjects with high motion. Downstream, these surfaces will enhance comparisons of functional and diffusion MRI datasets, supporting the modelling of emerging patterns of brain connectivity.

376 Human Connectome-Based Tractographic Atlas of the Brainstem Connections and Surgical Approaches

Neurosurgery ◽  
2017 ◽  
Vol 64 (CN_suppl_1) ◽  
pp. 289-289
Author(s):  
Antonio Meola ◽  
Fang-Cheng Yeh ◽  
Wendy Fellows-Mayle ◽  
Jared Weed ◽  
Juan Carlos Fernandez-Miranda
Keyword(s):  
Spatial Resolution ◽  
Diffusion Mri ◽  
Diffusion Tensor ◽  
Population Based ◽  
Medial Longitudinal Fasciculus ◽  
Surgical Approaches ◽  
Medial Lemniscus ◽  
Rubrospinal Tract ◽  
Human Connectome Project ◽  
Magnetic Resonance Imaging Mri

Abstract INTRODUCTION The brainstem is one of the most challenging areas for the neuro- surgeon because of the limited space between gray matter nuclei and white matter pathways. Diffusion tensor imaging based tractography has been used to study the brainstem structure, but the angular and spatial resolution could be improved further with advanced diffusion magnetic resonance imaging (MRI). Objective: To construct a high angular/spatial resolution, wide-population based, comprehensive tractography atlas that presents an anatomical review of the surgical approaches to the brainstem. METHODS We applied advanced diffusion MRI finer tractography to a population-based atlas constructed with data from a total of 488 subjects from the Human Connectome Project-488. Five formalin-fixed brains were studied for surgical landmarks. Luxol Fast Blue stained histological sections were used to validate the results of tractography. RESULTS >We acquired the tractography of the major brainstem pathways and vali- dated them with histological analysis. The pathways included the cerebellar peduncles, corticospinal tract, corticopontine tracts, medial lemniscus, lateral lemniscus, spino- thalamic tract, rubrospinal tract, central tegmental tract, medial longitudinal fasciculus, and dorsal longitudinal fasciculus. Then, the reconstructed 3-dimensional brainstem structure was sectioned at the level of classic surgical approaches, namely supra- collicular, infracollicular, lateral mesencephalic, perioculomotor, peritrigeminal, antero- lateral (to the medulla), and retro-olivary approaches. CONCLUSION The advanced diffusion MRI fiber tracking is a powerful tool to explore the brainstem neuroanatomy and to achieve a better understanding of surgical approaches.

Segmentation of corpus callosum based on tensor fuzzy clustering algorithm

2021 ◽  
pp. 1-14
Author(s):  
Yujia Qu ◽  
Yuanjun Wang
Keyword(s):  
Corpus Callosum ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Diffusion Tensor ◽  
Region Of Interest ◽  
Midsagittal Plane ◽  
Fuzzy Clustering Algorithm ◽  
Human Connectome Project ◽  
Clustering Center ◽  
Fixed Region

BACKGROUND: The corpus callosum in the midsagittal plane plays a crucial role in the early diagnosis of diseases. When the anisotropy of the diffusion tensor in the midsagittal plane is calculated, the anisotropy of corpus callosum is close to that of the fornix, which leads to blurred boundary of the segmentation region. OBJECTIVE: To apply a fuzzy clustering algorithm combined with new spatial information to achieve accurate segmentation of the corpus callosum in the midsagittal plane in diffusion tensor images. METHODS: In this algorithm, a fixed region of interest is selected from the midsagittal plane, and the anisotropic filtering algorithm based on tensor is implemented by replacing the gradient direction of the structural tensor with an eigenvector, thus filtering the diffusion tensor of region of interest. Then, the iterative clustering center based on K-means clustering is used as the initial clustering center of tensor fuzzy clustering algorithm. Taking filtered diffusion tensor as input data and different metrics as similarity measures, the neighborhood diffusion tensor pixel calculation method of Log Euclidean framework is introduced in the membership function calculation, and tensor fuzzy clustering algorithm is proposed. In this study, MGH35 data from the Human Connectome Project (HCP) are tested and the variance, accuracy and specificity of the experimental results are discussed. RESULTS: Segmentation results of three groups of subjects in MGH35 data are reported. The average segmentation accuracy is 97.34%, and the average specificity is 98.43%. CONCLUSIONS: When segmenting the corpus callosum of diffusion tensor imaging, our method cannot only effective denoise images, but also achieve high accuracy and specificity.

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