Keywords: Tractography, White Matter, structure-function relationships, sub-millimetre resolution, topology, U-fibres

Motivation: High spatial resolution diffusion weighted imaging (DWI) tractography may enable mapping of cortical topological structure based on white matter connectivity organisation as an additional method to functional MRI techniques.

Goal(s): We investigated whether retinotopic mapping of primary visual cortex (V1) combined with short association fibres tractography could predict retinotopic organisation in the higher visual areas (V2, V3).

Approach: We used sub-millimetre resolution DWI tractography combined with functional retinotopic mapping in a group of participants in vivo. 

Results: Our results demonstrated high accuracy and precision, especially at group level. Prediction was less reliable in the anatomical direction with the most pronounced gyral bias effects. 

Impact: Sub-millimetre resolution diffusion weighted imaging tractography enabled mapping of cortical topology by exploiting the topological structure of underlying short association fibre connections, opening the door to applications exploring brain reorganisation in response to injury or pathology from a new perspective.

Keywords: Diffusion Acquisition, Diffusion/other diffusion imaging techniques, Anterior Visual Pathway

Motivation: Diffusion-weighted MRI of the anterior-visual-pathway (AVP) is challenging due to the small diameter of the optic nerve and susceptibility artifacts.

Goal(s): To compare the performance of diffusion MRI acquisition, tractography, reconstruction, and fiber-tracking methods in reconstructing the AVP.

Approach: The overlap between a mask based on track-density and the T1-weighted imaged AVP was used to evaluate the accuracy of the reconstruction.

Results: Readout segmented EPI (RESOLVE) and the combined use of Constant Solid Angle (CSA) reconstruction with Parallel Transport Tractography (PTT) scored best.

Impact: Our findings support an optimal selection of methods for imaging the AVP in clinical research and radiology clinics.

Keywords: Tractography, White Matter, diffusion MRI, fiber clustering, tractography parcellation

Motivation: Existing white matter atlases are usually created based on a certain population, which may omit subtle differences across populations from different cultures.

Goal(s): This study presents a fine-scale white matter atlas that is created concurrently using high-quality diffusion MRI data from both Eastern and Western populations.

Approach: The curated atlas includes a cluster-level parcellation of 800 fiber clusters from the entire brain and an anatomical tract parcellation of 53 major long-range white matter connections.

Results: Comparative assessment between the two populations within the atlas shows highly visually similar white matter geometry but significant differences when measuring streamline counts in the fiber parcels.

Impact: We propose a diffusion MRI tractography atlas that enables concurrent white matter parcellation across Eastern and Western populations. While the white matter geometry is visually similar, the number of streamlines in the fiber parcels differs significantly between the two populations.  

 

Keywords: Tractography, White Matter, Structural Connectivity, Fiber Pathways

Motivation: Obtaining accurate anatomical information of connectional neuroanatomy across scales is crucial to improve in-vivodiffusion MRI techniques and advance our understanding of the brain white matter circuitries.  

Goal(s): To reveal the mesoscopic organization of the SLF-I, a major cortico-cortical fiber association system of the human brain.

Approach: We combine multi-scale, multi-species, multi-modality connectional data from humans and macaques to investigate the structural connectivity of medial fronto-parietal cortical regions.

Results: We provide preliminary novel evidence that the SLF-I is composed of a succession of shorter relay fibers, which, in lower-resolution dMRI tractography result erroneously in a long, direct association bundle. 

Impact: The mesoscopic anatomical validation of major white matter pathways in the human brain will increase the accuracy of their reconstruction in vivo, and open new avenues for our understanding of the functional substrates of these different connections.

Keywords: Tractography, Tractography & Fibre Modelling, Point cloud, Deep learning

Motivation: The prediction of cognitive performance scores using diffusion MRI tractography enables the study of relationships between brain structure and function.

Goal(s): Our goal is to achieve accurate prediction of cognition and identify critical brain regions for prediction.

Approach: We propose a geometric deep-learning framework for language score prediction. It utilizes a point cloud representation of fiber tracts for detailed spatial and microstructure information and incorporates a novel regression loss to utilize the continuity of language scores.

Results: Our method outperforms comparison methods with state-of-the-art representations of fiber tracts and identifies predictive language-related brain regions.

Impact: Our proposed novel geometric deep learning framework using a point cloud representation of fiber tracts can be applied to various tractography-based prediction tasks to improve performance and provide a probe to explore relationships between brain structure and function.

Keywords: Tractography, Tractography & Fibre Modelling

Motivation: Conventional diffusion tractography relies on error-prone voxel-to-voxel tracing and typically demands diffusion MRI with high signal-to-noise ratio, spatial and angular resolution, which can be challenging to acquire.

Goal(s): To generate bundle-specific streamlines from anatomical MRI.

Approach: We present a deep learning framework for anatomy to tract mapping (ATM), allowing bundle-specific streamlines to be generated from anatomical MRI. ATM generates streamlines without resorting to voxel-to-voxel tracing, hence sidesteps challenges involved in tracing across complex configurations such as crossings, kissing, and bending and the bottlenecks where multiple bundles converge toward before re-emerging.

Results: ATM effectively captures bundle shapes and generates bundle-specific streamlines from T1-weighted MRI.

Impact: We demonstrate that tract streamlines can be estimated directly from anatomical MRI. This allows (1) tractography in the absence of diffusion MRI and (2) anatomy tractography to guide diffusion tractography.

Keywords: Tractography, Tractography & Fibre Modelling, Spine;Intervertebral disc

Motivation: Evaluation of the fiber microstructure is valuable for assessing IVD degeneration.

Goal(s): To investigate the three-dimensional fiber structure and orientation of the intact IVD.

Approach: A high resolution DTI protocol was used to reconstruct the 3D fiber structure of an IVD specimen at the microstructural level on a 3T MRI scanner.

Results: The concentric lamella structure and interlamellar fibers were observed in the AF. The AF fiber orientations exhibited circumferential variability around the AF ring. The nucleus pulposus fibers exhibited a tri-directional distribution with perpendicular orientations. The major fibers in the cartilaginous endplate were horizontally orientated in the anterior-posterior direction.

Impact: The primary fiber orientations within human IVD tissues, including those of the annulus fibrosus, nucleus pulposus, and cartilaginous endplate, were observed using high resolution DTT, which may be a promising tool for IVD degeneration and regeneration study.

Keywords: Tractography, Tractography & Fibre Modelling

Motivation: Whole brain tractography is highly challenging, and it is prone to false positive streamlines despite the use of Anatomically Constraint Tractography (ACT).

Goal(s): This study aims to reduce implausible streamlines in tractograms by introducing a novel refinement approach, thereby enhancing the accuracy of white matter connectivity analyses.

Approach: We developed Hierarchical Anatomical Refinement of Pathways (HARP), an advanced modification of ACT. HARP integrates increasingly detailed anatomical priors in a hierarchical fashion to improve accuracy of tractograms.

Results: Amount of implausible streamlines in ACT-based tractograms are relatively low but they are systematic. HARP is effective in further reducing false positive connections in tractograms.

Impact: Hierarchical Anatomical Refinement of Pathways (HARP) enhances tractography, offering neuroscientists and clinicians a new level of precision in brain connectivity analysis, which could impact our understanding of the brain and its disorders.

Keywords: Tractography, Surgery

Motivation: Intraoperative fiber tractography could help to elucidate the nature of cerebellar mutism syndrome after tumor resection in pediatric posterior fossa tumor (pPFT) patients.

Goal(s): To explore the feasibility of reconstructing eloquent fiber tracts with a short intraoperative diffusion MRI scan in pPFT patients. 

Approach: We reconstructed tracts intraoperatively in four pPFT patients and compared their structural properties to preoperative scans obtained with the same settings. 

Results: It is in general possible, but challenging, to reconstruct the eloquent tracts in pre- and intraoperative data of pPFT patients. However, further optimization of the fiber tractography model is required to increase their quality.

Impact: The impact of the mechanical manipulation of the surgical procedure on the eloquent pathways in pediatric posterior fossa tumor patients is investigated through the development of a reliable fiber tractography model for intraoperative diffusion MRI.

Keywords: Tractography, Low-Field MRI

Motivation: Facilitating white matter mapping in traditionally inaccessible low income settings.

Goal(s): Demonstration of a viable protocol to resolve complex white matter architecture at low field.

Approach: A multi-modality protocol was devised to allow feasible acquisition of multi-direction diffusion weighted imaging (DWI) datasets at 64mT. A modified DWI protocol is shown, employing voxel-wise encoding non-uniformity calibration, extended readout, and optimal sampling density. Machine learning based denoising was employed to significantly reduce scan duration. T1 weighted scans were super-resolved to guide tractography.

Results: Anatomically-constrained tractography was shown to be viable at low field, and fine white matter microstructure was successfully recovered.

Impact: This work demonstrates that a 64mT system is capable of resolving microstructural detail even with lowered SNR, and gradient nonuniformity. This may have relevance in studies examining white matter neurodevelopment in historically underrepresented populations in low-income settings.

Keywords: Tractography, Tractography & Fibre Modelling

Motivation: Pre-surgical tractography aids neurosurgical planning around key language tracts. Standard reconstruction approaches lead to a trade-off between sensitivity and specificity, and may not characterise all aspects of the target tract.

Goal(s): Increase sensitivity and specificity of reconstruction methods for the inferior fronto-occipital fasciculus and arcuate fasciculus and language tracts.

Approach: A new set of three spherical seed, end, and supplementary waypoint regions of interest (ROIs), defined in MNI space, were implemented using volunteer datasets, and results compared to a reference atlas.

Results: The new 3-ROI approach achieved greater overlap with the reference tracts (higher Dice coefficient), with increased sensitivity and/or specificity.

Impact: Improvements in sensitivity and specificity of reconstructed language tracts using 3-ROI based tractography optimise the characterisation of tracts in healthy volunteers. Further evaluation should now be done to allow extension to clinical cases.

Keywords: Tractography, Brain, Diffusion MRI, Spiking Neural Network, Superficial White Matter Classification, Tractography

Motivation: The investigation of superficial white matter (SWM) poses challenges due to its small size, variability, delicate structure, high curvature, and fiber crossings in diffusion MRI tractography.

Goal(s): Our goal is to develop an innovative methodology for classifying SWM streamline clusters using diffusion MRI tractography, leveraging brain-inspired learning-based techniques.

Approach: A dual-phase method with Spiking Neural Networks (SNNs) and leaky integrate and fire (LIF) neurons is developed for the classification of 199 SWM clusters.

Results: Experiments were conducted using two open-source datasets, and our method achieves accurate SWM classification results with an accuracy of 93.73%. 

Impact: The findings of this study on SWM classification hold great potential for facilitating analyses of SWM within neuroscientific research, contributing to understanding the complexities and alterations in SWM associated with various health conditions and neurological disorders.

Keywords: Diffusion Analysis & Visualization, Tractography & Fibre Modelling, Fiber Orientation Distribution, Brain Connectivity, Enhancement

Motivation: Learning-based methods effectively enhance fibre orientation distributions (FODs) derived from limited single-shell acquisitions. However, the enhancement capacity with different number of gradient directions is not fully characterised.

Goal(s): This study aims to explore the impact of initial gradient directions on FOD enhancement capacity of clinically accessible single-shell diffusion data.

Approach: We employ a FOD enhancement framework on single-shell (b=1000) data with different numbers of gradient directions. The enhanced FODs and derivatives are evaluated through FOD-based, fixel-based and connectome analysis metrics.

Results: The optimal trade-off between the learning-based FOD enhancement outcome and the choice of number of gradient directions is at around 24 directions.

Impact: This work provides guidelines for the optimal design of dMRI acquisition protocols meeting the expectations of clinical research on FOD enhancement according to the capability of learning-based frameworks, ensuring high-quality tractography and connectomes without the need for multi-shell HARDI protocols.

Keywords: Tractography, Diffusion/other diffusion imaging techniques, Denoising, tractography, CASA

Motivation: The diffusion MRI (dMRI) signal exhibits a low signal-to-noise ratio. 

Goal(s): This study endeavors to enrich the quality of dMRI data by employing a pioneering denoising technique, which combines Component Analysis with Standard-deviation Attenuation (CASA) and Spherical Harmonics (SH).

Approach: Comparative analysis is conducted between the denoising capabilities of SH-based decomposition and the original PCA-based CASA technique using synthetic and in-vivo data.

Results: The findings demonstrate that both denoising methods notably enhance image and tractography quality. In the case of synthetic data, SH-CASA displayed the most substantial improvement.

Impact: Our novel denoising technique, SH-CASA, significantly enhances both the quality of raw diffusion MRI data and the resulting tractography. This holds particular significance in clinical settings where rescanning the patient is not always feasible.

Keywords: Tractography, Gradients, Structural Connectivity

Motivation: There is a pressing need to move beyond the brain major white matter fasciculi in our understanding and characterization of human connectional neuroanatomy. 

Goal(s): To perform initial in vivo high-resolution diffusion MRI on the next-generation Connectome 2.0 scanner equipped with 500 mT/m gradient strength. 

Approach: We acquired 1-mm isotropic dMRI in one healthy subject on Connectome 2.0 and evaluated its capabilities in characterizing fine fiber bundles in comparison to Connectome 1.0. 

Results: The SNR boost enabled by the Connectome 2.0 stronger gradient system allows resolution of fine white matter structures in deep brain regions and near the gray-white interface.

Impact: The ability to characterize fine white matter circuitry in the living human brain within reasonable scan times opens new possibilities for investigating their role in psychiatric and neurological disorders and enables the application of clinical interventions that target these pathways.

Keywords: Tractography, Tractography & Fibre Modelling, diffusion, filtering, streamline, tractometry, tractography, brain

Motivation: Tract extraction from whole-brain tractograms requires either an extensive knowledge of inclusion and exclusion zones or manual efforts to obtain clean tracts.

Goal(s): Automated filtering of spurious streamlines can accelerate the tract extraction process. The algorithm should be versatile, while preserving tract shape and minimizing parameter adjustments.

Approach: We developed a filtering algorithm based on  streamline direction and density along an average trajectory. Our method was compared to four other filtering implementations.

Results: Our algorithm is applicable to a wide variety of tracts with a high and low streamline count. It offers efficient filtering and provides a conservative filtering preserving tract morphology.

Impact: We introduced an efficient filtering algorithm, removing spurious streamlines while preserving tract morphology across tracts of low and high density with default parameters. Additionally, the computed average trajectory enables the analysis of metrics in multi-fixel models along the tract pathway.