Characterizing the canine and feline optic pathways in vivo with diffusion MRI

2021 ◽  
Author(s):  
Erica F. Andrews ◽  
Olivier Jacqmot ◽  
Filipe N. C. M. Espinheira Gomes ◽  
Megan F. Sha ◽  
Sumit N. Niogi ◽  
...  
Keyword(s):  
Diffusion Mri ◽  
Optic Pathways

scholarly journals Diffusion MRI microstructure models with in vivo human brain Connectome data: results from a multi-group comparison

2017 ◽  
Vol 30 (9) ◽  
pp. e3734 ◽  
Author(s):  
Uran Ferizi ◽  
Benoit Scherrer ◽  
Torben Schneider ◽  
Mohammad Alipoor ◽  
Odin Eufracio ◽  
...  
Keyword(s):  
Human Brain ◽  
Diffusion Mri ◽  
Group Comparison ◽  
Brain Connectome

scholarly journals Using diffusion MRI data acquired with ultra-high gradients to improve tractography in routine-quality data

2021 ◽  
Author(s):  
Chiara Maffei ◽  
Christine Lee ◽  
Michael Planich ◽  
Manisha Ramprasad ◽  
Nivedita Ravi ◽  
...  
Keyword(s):  
Diffusion Mri ◽  
Region Of Interest ◽  
Training Data ◽  
Quality Data ◽  
High Quality ◽  
Scanning Time ◽  
Human Connectome Project ◽  
High Gradients ◽  
Comprehensive Comparison

The development of scanners with ultra-high gradients, spearheaded by the Human Connectome Project, has led to dramatic improvements in the spatial, angular, and diffusion resolution that is feasible for in vivo diffusion MRI acquisitions. The improved quality of the data can be exploited to achieve higher accuracy in the inference of both microstructural and macrostructural anatomy. However, such high-quality data can only be acquired on a handful of Connectom MRI scanners worldwide, while remaining prohibitive in clinical settings because of the constraints imposed by hardware and scanning time. In this study, we first update the classical protocols for tractography-based, manual annotation of major white-matter pathways, to adapt them to the much greater volume and variability of the streamlines that can be produced from today's state-of-the-art diffusion MRI data. We then use these protocols to annotate 42 major pathways manually in data from a Connectom scanner. Finally, we show that, when we use these manually annotated pathways as training data for global probabilistic tractography with anatomical neighborhood priors, we can perform highly accurate, automated reconstruction of the same pathways in much lower-quality, more widely available diffusion MRI data. The outcomes of this work include both a new, comprehensive atlas of WM pathways from Connectom data, and an updated version of our tractography toolbox, TRActs Constrained by UnderLying Anatomy (TRACULA), which is trained on data from this atlas. Both the atlas and TRACULA are distributed publicly as part of FreeSurfer. We present the first comprehensive comparison of TRACULA to the more conventional, multi-region-of-interest approach to automated tractography, and the first demonstration of training TRACULA on high-quality, Connectom data to benefit studies that use more modest acquisition protocols.

scholarly journals Fixel-based Analysis of Diffusion MRI: Methods, Applications, Challenges and Opportunities

2020 ◽  
Author(s):  
Thijs Dhollander ◽  
Adam Clemente ◽  
Mervyn Singh ◽  
Frederique Boonstra ◽  
Oren Civier ◽  
...  
Keyword(s):  
Diffusion Mri ◽  
State Of The Art ◽  
Specific Information ◽  
Analysis Framework ◽  
Wide Range ◽  
Challenges And Opportunities ◽  
P Diffusion ◽  
Modelling Techniques ◽  
The Rich

Diffusion MRI has provided the neuroimaging community with a powerful tool to acquire in-vivo data sensitive to microstructural features of white matter, up to 3 orders of magnitude smaller than typical voxel sizes. The key to extracting such valuable information lies in complex modelling techniques, which form the link between the rich diffusion MRI data and various metrics related to the microstructural organisation. Over time, increasingly advanced techniques have been developed, up to the point where some diffusion MRI models can now provide access to properties specific to individual fibre populations in each voxel in the presence of multiple "crossing" fibre pathways. While highly valuable, such fibre-specific information poses unique challenges for typical image processing pipelines and statistical analysis. In this work, we review the "fixel-based analysis" (FBA) framework that implements bespoke solutions to this end, and has recently seen a stark increase in adoption for studies of both typical (healthy) populations as well as a wide range of clinical populations. We describe the main concepts related to fixel-based analyses, as well as the methods and specific steps involved in a state-of-the-art FBA pipeline, with a focus on providing researchers with practical advice on how to interpret results. We also include an overview of the scope of current fixel-based analysis studies (until August 2020), categorised across a broad range of neuroscientific domains, listing key design choices and summarising their main results and conclusions. Finally, we critically discuss several aspects and challenges involved with the fixel-based analysis framework, and outline some directions and future opportunities.

scholarly journals Quantifying nerve decussation abnormalities in the optic chiasm

2019 ◽  
Author(s):  
Robert J. Puzniak ◽  
Khazar Ahmadi ◽  
Jörn Kaufmann ◽  
Andre Gouws ◽  
Antony B. Morland ◽  
...  
Keyword(s):  
Diffusion Mri ◽  
Diffusion Tensor ◽  
Area Under The Curve ◽  
Optic Chiasm ◽  
List Type ◽  
Test Model ◽  
Discriminative Power ◽  
Significant Group ◽  
Structural Abnormalities

AbstractObjectiveThe human optic chiasm comprises partially crossing optic nerve fibres. Here we used diffusion MRI (dMRI) for the in-vivo identification of the abnormally high proportion of crossing fibres found in the optic chiasm of people with albinism.MethodsIn 9 individuals with albinism and 8 controls high-resolution 3T dMRI data was acquired and analyzed with a set of methods for signal modeling [Diffusion Tensor (DT) and Constrained Spherical Deconvolution (CSD)], tractography, and streamline filtering (LiFE, COMMIT, and SIFT2). The number of crossing and non-crossing streamlines and their weights after filtering entered ROC-analyses to compare the discriminative power of the methods based on the area under the curve (AUC). The dMRI results were cross-validated with fMRI estimates of misrouting in a subset of 6 albinotic individuals.ResultsWe detected significant group differences in chiasmal crossing for both unfiltered DT (p=0.014) and CSD tractograms (p=0.0009) also reflected by AUC measures (for DT and CSD: 0.61 and 0.75, respectively), underlining the discriminative power of the approach. Estimates of crossing strengths obtained with dMRI and fMRI were significantly correlated for CSD (R2=0.83, p=0.012). The results show that streamline filtering methods in combination with probabilistic tracking, both optimized for the data at hand, can improve the detection of crossing in the human optic chiasm.ConclusionsEspecially CSD-based tractography provides an efficient approach to detect structural abnormalities in the optic chiasm. The most realistic results were obtained with filtering methods with parameters optimized for the data at hand.SignificanceOur findings demonstrate a novel anatomy-driven approach for the individualized diagnostics of optic chiasm abnormalities.HighlightsDiffusion MRI is capable of detecting structural abnormalities of the optic chiasm.Quantification of crossing strength in optic chiasm is of promise for albinism diagnostics.Optic chiasm is a powerful test model for neuroimaging methods resolving crossing fibers.

scholarly journals Different patterns of cortical maturation before and after 38 weeks gestational age demonstrated by diffusion MRI in vivo

NeuroImage ◽  
2019 ◽  
Vol 185 ◽  
pp. 764-775 ◽  
Author(s):  
Dafnis Batalle ◽  
Jonathan O'Muircheartaigh ◽  
Antonios Makropoulos ◽  
Christopher J. Kelly ◽  
Ralica Dimitrova ◽  
...  
Keyword(s):  
Gestational Age ◽  
Diffusion Mri ◽  
Before And After ◽  
Cortical Maturation

In vivo evaluation of human patellar tendon microstructure and microcirculation with diffusion MRI

2019 ◽  
Vol 51 (3) ◽  
pp. 780-790
Author(s):  
Kenneth Wengler ◽  
Takeshi Fukuda ◽  
Dharmesh Tank ◽  
David E. Komatsu ◽  
Megan Paulus ◽  
...  
Keyword(s):  
Patellar Tendon ◽  
Diffusion Mri ◽  
In Vivo Evaluation

scholarly journals In vivo lung morphometry with accelerated hyperpolarized 3 He diffusion MRI: A preliminary study

2014 ◽  
Vol 73 (4) ◽  
pp. 1609-1614 ◽  
Author(s):  
Yulin V. Chang ◽  
James D. Quirk ◽  
Dmitriy A. Yablonskiy
Keyword(s):  
Diffusion Mri ◽  
Preliminary Study ◽  
Lung Morphometry

scholarly journals Multimodal image registration and connectivity analysis for integration of connectomic data from microscopy to MRI

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Maged Goubran ◽  
Christoph Leuze ◽  
Brian Hsueh ◽  
Markus Aswendt ◽  
Li Ye ◽  
...  
Keyword(s):  
Neurological Disorders ◽  
Diffusion Mri ◽  
Multimodal Integration ◽  
Connectivity Analysis ◽  
Histological Changes ◽  
Cellular Effects ◽  
Multimodal Image Registration ◽  
Fiber Tracts ◽  
And Diffusion

Abstract3D histology, slice-based connectivity atlases, and diffusion MRI are common techniques to map brain wiring. While there are many modality-specific tools to process these data, there is a lack of integration across modalities. We develop an automated resource that combines histologically cleared volumes with connectivity atlases and MRI, enabling the analysis of histological features across multiple fiber tracts and networks, and their correlation with in-vivo biomarkers. We apply our pipeline in a murine stroke model, demonstrating not only strong correspondence between MRI abnormalities and CLARITY-tissue staining, but also uncovering acute cellular effects in areas connected to the ischemic core. We provide improved maps of connectivity by quantifying projection terminals from CLARITY viral injections, and integrate diffusion MRI with CLARITY viral tracing to compare connectivity maps across scales. Finally, we demonstrate tract-level histological changes of stroke through this multimodal integration. This resource can propel investigations of network alterations underlying neurological disorders.

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