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The retinogeniculate visual pathway (RGVP) is responsible for carrying visual information from the retina to the lateral geniculate nucleus. Identification and visualization of the RGVP are important in studying the anatomy of the visual system and can inform the treatment of related brain diseases. Diffusion MRI (dMRI) tractography is an advanced imaging method that uniquely enables in vivo mapping of the 3D trajectory of the RGVP. Currently, identification of the RGVP from tractography data relies on expert (manual) selection of tractography streamlines, which is time-consuming, has high clinical and expert labor costs, and is affected by inter-observer variability. In this paper, we present a novel deep learning framework, DeepRGVP, to enable fast and accurate identification of the RGVP from dMRI tractography data. We design a novel microstructure-informed supervised contrastive learning method that leverages both streamline label and tissue microstructure information to determine positive and negative pairs. We propose a new streamline-level data augmentation method to address highly imbalanced training data, where the number of RGVP streamlines is much lower than that of non-RGVP streamlines. In the experiments, we perform comparisons with several state-of-the-art deep learning methods that were designed for tractography parcellation. Furthermore, to assess the generalizability of the proposed RGVP method, we apply our method to dMRI tractography data from neurosurgical patients with pituitary tumors. In comparison with the state-of-the-art methods, we show superior RGVP identification results using DeepRGVP with significantly higher accuracy and F1 scores. In the patient data experiment, we show DeepRGVP can successfully identify RGVPs despite the effect of lesions affecting the RGVPs. Overall, our study shows the high potential of using deep learning to automatically identify the RGVP., (© 2024 The Author(s). Human Brain Mapping published by Wiley Periodicals LLC.)

Streamline tractography locally traces peak directions extracted from fiber orientation distribution (FOD) functions, lacking global information about the trend of the whole fiber bundle. Therefore, it is prone to producing erroneous tracks while missing true positive connections. In this work, we propose a new bundle-specific tractography (BST) method based on a bundle-specific tractogram distribution (BTD) function, which directly reconstructs the fiber trajectory from the start region to the termination region by incorporating the global information in the fiber bundle mask. A unified framework for any higher-order streamline differential equation is presented to describe the fiber bundles with disjoint streamlines defined based on the diffusion vectorial field. At the global level, the tractography process is simplified as the estimation of BTD coefficients by minimizing the energy optimization model, and is used to characterize the relations between BTD and diffusion tensor vector under the prior guidance by introducing the tractogram bundle information to provide anatomic priors. Experiments are performed on simulated Hough, Sine, Circle data, ISMRM 2015 Tractography Challenge data, FiberCup data, and in vivo data from the Human Connectome Project (HCP) for qualitative and quantitative evaluation. Results demonstrate that our approach reconstructs complex fiber geometry more accurately. BTD reduces the error deviation and accumulation at the local level and shows better results in reconstructing long-range, twisting, and large fanning tracts., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Zhejiang University of Technology. Published by Elsevier Inc. All rights reserved.)

The corticospinal tract (CST) is a critically important white matter fiber tract in the human brain that enables control of voluntary movements of the body. The CST exhibits a somatotopic organization, which means that the motor neurons that control specific body parts are arranged in order within the CST. Diffusion magnetic resonance imaging (MRI) tractography is increasingly used to study the anatomy of the CST. However, despite many advances in tractography algorithms over the past decade, modern, state-of-the-art methods still face challenges. In this study, we compare the performance of six widely used tractography methods for reconstructing the CST and its somatotopic organization. These methods include constrained spherical deconvolution (CSD) based probabilistic (iFOD1) and deterministic (SD-Stream) methods, unscented Kalman filter (UKF) tractography methods including multi-fiber (UKF2T) and single-fiber (UKF1T) models, the generalized q-sampling imaging (GQI) based deterministic tractography method, and the TractSeg method. We investigate CST somatotopy by dividing the CST into four subdivisions per hemisphere that originate in the leg, trunk, hand, and face areas of the primary motor cortex. A quantitative and visual comparison is performed using diffusion MRI data (N = 100 subjects) from the Human Connectome Project. Quantitative evaluations include the reconstruction rate of the eight anatomical subdivisions, the percentage of streamlines in each subdivision, and the coverage of the white matter-gray matter (WM-GM) interface. CST somatotopy is further evaluated by comparing the percentage of streamlines in each subdivision to the cortical volumes for the leg, trunk, hand, and face areas. Overall, UKF2T has the highest reconstruction rate and cortical coverage. It is the only method with a significant positive correlation between the percentage of streamlines in each subdivision and the volume of the corresponding motor cortex. However, our experimental results show that all compared tractography methods are biased toward generating many trunk streamlines (ranging from 35.10% to 71.66% of total streamlines across methods). Furthermore, the coverage of the WM-GM interface in the largest motor area (face) is generally low (under 40%) for all compared tractography methods. Different tractography methods give conflicting results regarding the percentage of streamlines in each subdivision and the volume of the corresponding motor cortex, indicating that there is generally no clear relationship, and that reconstruction of CST somatotopy is still a large challenge. Overall, we conclude that while current tractography methods have made progress toward the well-known challenge of improving the reconstruction of the lateral projections of the CST, the overall problem of performing a comprehensive CST reconstruction, including clinically important projections in the lateral (hand and face areas) and medial portions (leg area), remains an important challenge for diffusion MRI tractography., (© 2023 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

Background: Diffusion magnetic resonance imaging white matter tractography, an increasingly popular preoperative planning modality used for pre-surgical planning in brain tumor patients, is employed with the goal of maximizing tumor resection while sparing postoperative neurological function. Clinical translation of white matter tractography has been limited by several shortcomings of standard diffusion tensor imaging (DTI), including poor modeling of fibers crossing through regions of peritumoral edema and low spatial resolution for typical clinical diffusion MRI (dMRI) sequences. Track density imaging (TDI) is a post-tractography technique that uses the number of tractography streamlines and their long-range continuity to map the white matter connections of the brain with enhanced image resolution relative to the acquired dMRI data, potentially offering improved white matter visualization in patients with brain tumors. The aim of this study was to assess the utility of TDI-based white matter maps in a neurosurgical planning context compared to the current clinical standard of DTI-based white matter maps., Methods: Fourteen consecutive brain tumor patients from a single institution were retrospectively selected for the study. Each patient underwent 3-Tesla dMRI scanning with 30 gradient directions and a b-value of 1000 s/mm 2 . For each patient, two directionally encoded color (DEC) maps were produced as follows. DTI-based DEC-fractional anisotropy maps (DEC-FA) were generated on the scanner, while DEC-track density images (DEC-TDI) were generated using constrained spherical deconvolution based tractography. The potential clinical utility of each map was assessed by five practicing neurosurgeons, who rated the maps according to four clinical utility statements regarding different clinical aspects of pre-surgical planning. The neurosurgeons rated each map according to their agreement with four clinical utility statements regarding if the map 1 identified clinically relevant tracts, (2) helped establish a goal resection margin, (3) influenced a planned surgical route, and (4) was useful overall. Cumulative link mixed effect modeling and analysis of variance were performed to test the primary effect of map type (DEC-TDI vs. DEC-FA) on rater score. Pairwise comparisons using estimated marginal means were then calculated to determine the magnitude and directionality of differences in rater scores by map type., Results: A majority of rater responses agreed with the four clinical utility statements, indicating that neurosurgeons found both DEC maps to be useful. Across all four investigated clinical utility statements, the DEC map type significantly influenced rater score. Rater scores were significantly higher for DEC-TDI maps compared to DEC-FA maps. The largest effect size in rater scores in favor of DEC-TDI maps was observed for clinical utility statement 2, which assessed establishing a goal resection margin., Conclusion: We observed a significant neurosurgeon preference for DEC-TDI maps, indicating their potential utility for neurosurgical planning., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

The oculomotor nerve (OCN) is the main motor nerve innervating eye muscles and can be involved in multiple flammatory, compressive, or pathologies. The diffusion magnetic resonance imaging (dMRI) tractography is now widely used to describe the trajectory of the OCN. However, the complex cranial structure leads to difficulties in fiber orientation distribution (FOD) modeling, fiber tracking, and region of interest (ROI) selection. Currently, the identification of OCN relies on expert manual operation, resulting in challenges, such as the carries high clinical, time-consuming, and labor costs. Thus, we propose a method that can automatically identify OCN from dMRI tractography. First, we choose the multi-shell multi-tissue constraint spherical deconvolution (MSMT-CSD) FOD estimation model and deterministic tractography to describe the 3D trajectory of the OCN. Then, we rely on the well-established computational pipeline and anatomical expertise to create a data-driven OCN tractography atlas from 40 HCP data. We identify six clusters belonging to the OCN from the atlas, including the structures of three kinds of positional relationships (pass between, pass through, and go around) with the red nuclei and two kinds of positional relationships with medial longitudinal fasciculus. Finally, we apply the proposed OCN atlas to identify the OCN automatically from 40 new HCP subjects and two patients with brainstem cavernous malformation. In terms of spatial overlap and visualization, experiment results show that the automatically and manually identified OCN fibers are consistent. Our proposed OCN atlas provides an effective tool for identifying OCN by avoiding the traditional selection strategy of ROIs., (© 2022 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

Language and theory of mind (ToM) are the cognitive capacities that allow for the successful interpretation and expression of meaning. While functional MRI investigations are able to consistently localize language and ToM to specific cortical regions, diffusion MRI investigations point to an inconsistent and sometimes overlapping set of white matter tracts associated with these two cognitive domains. To further examine the white matter tracts that may underlie these domains, we use a two-tensor tractography method to investigate the white matter microstructure of 809 participants from the Human Connectome Project. 20 association white matter tracts (10 in each hemisphere) are uniquely identified by leveraging a neuroanatomist-curated automated white matter tract atlas. The fractional anisotropy (FA), mean diffusivity (MD), and number of streamlines (NoS) are measured for each white matter tract. Performance on neuropsychological assessments of semantic memory (NIH Toolbox Picture Vocabulary Test, TPVT) and emotion perception (Penn Emotion Recognition Test, PERT) are used to measure critical subcomponents of the language and ToM networks, respectively. Regression models are constructed to examine how structural measurements of left and right white matter tracts influence performance across these two assessments. We find that semantic memory performance is influenced by the number of streamlines of the left superior longitudinal fasciculus III (SLF-III), and emotion perception performance is influenced by the number of streamlines of the right SLF-III. Additionally, we find that performance on both semantic memory & emotion perception is influenced by the FA of the left arcuate fasciculus (AF). The results point to multiple, overlapping white matter tracts that underlie the cognitive domains of language and ToM. Results are discussed in terms of hemispheric dominance and concordance with prior investigations., Competing Interests: Declaration of Competing Interest None., (Copyright © 2021. Published by Elsevier Inc.)

Small size and intricate anatomical environment are the main difficulties facing tractography of the facial-vestibulocochlear nerve complex (FVN), and lead to challenges in fiber orientation distribution (FOD) modeling, fiber tracking, region-of-interest selection, and fiber filtering. Experts need rich experience in anatomy and tractography, as well as substantial labor costs, to identify the FVN. Thus, we present a pipeline to identify the FVN automatically, in what we believe is the first study of the automated identification of the FVN. First, we created an FVN template. Forty high-resolution multishell data were used to perform data-driven fiber clustering based on the multishell multitissue constraint spherical deconvolution FOD model and deterministic tractography. We selected the brainstem and cerebellum (BS-CB) region as the seed region and removed the fibers that reach other brain regions. We then performed spectral fiber clustering twice. The first clustering was to create a BS-CB atlas and separate the fibers that pass through the cerebellopontine angle, and the other one was to extract the FVN. Second, we registered the subject-specific fibers in the space of the FVN template and assigned each fiber to the closest cluster to identify the FVN automatically by spectral embedding. We applied the proposed method to different acquirement sites, including two different healthy datasets and two tumor patient datasets. Experimental results showed that our automatic identification results have ideal colocalization with expert manual identification in terms of spatial overlap and visualization. Importantly, we successfully applied our method to tumor patient data. The FVNs identified by the proposed method were in agreement with intraoperative findings., (© 2021 John Wiley & Sons, Ltd.)

The retinogeniculate visual pathway (RGVP) conveys visual information from the retina to the lateral geniculate nucleus. The RGVP has four subdivisions, including two decussating and two nondecussating pathways that cannot be identified on conventional structural magnetic resonance imaging (MRI). Diffusion MRI tractography has the potential to trace these subdivisions and is increasingly used to study the RGVP. However, it is not yet known which fiber tracking strategy is most suitable for RGVP reconstruction. In this study, four tractography methods are compared, including constrained spherical deconvolution (CSD) based probabilistic (iFOD1) and deterministic (SD-Stream) methods, and multi-fiber (UKF-2T) and single-fiber (UKF-1T) unscented Kalman filter (UKF) methods. Experiments use diffusion MRI data from 57 subjects in the Human Connectome Project. The RGVP is identified using regions of interest created by two clinical experts. Quantitative anatomical measurements and expert anatomical judgment are used to assess the advantages and limitations of the four tractography methods. Overall, we conclude that UKF-2T and iFOD1 produce the best RGVP reconstruction results. The iFOD1 method can better quantitatively estimate the percentage of decussating fibers, while the UKF-2T method produces reconstructed RGVPs that are judged to better correspond to the known anatomy and have the highest spatial overlap across subjects. Overall, we find that it is challenging for current tractography methods to both accurately track RGVP fibers that correspond to known anatomy and produce an approximately correct percentage of decussating fibers. We suggest that future algorithm development for RGVP tractography should take consideration of both of these two points., (© 2021 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

The deficits of white matter (WM) microstructure are involved during Parkinson's disease (PD) progression. Most current methods identify key WM tracts relying on cortical regions of interest (ROIs). However, such ROI methods can be challenged due to low diffusion anisotropy near the gray matter (GM), which could result in a low sensitivity of tract identification. This work proposes an automatic WM parcellation method to improve the accuracy of WM tract identification and locate abnormal tracts by using sensitive features. The proposed method consists of 1) whole brain WM parcellation using an established fiber clustering method, without using any ROIs, 2) features of fasciculus were calculated to quantify diffusion measures at each equal cross-section along the whole cluster. Then, we use the proposed features to investigate the WM difference in PD compared with healthy controls (HC). We also use these features to investigate the relationship of clinical symptoms and specific fiber tracts. The novelty of the proposed method is that it automatically identifies the abnormal WM fibers in cluster degree. Experiment results indicated that the proposed method had advantage in detecting the local WM abnormality by performing between-group statistical analysis in 30 patients with PD and 28 HC. We found 13 hemisphere clusters and 8 commissural clusters had significant group difference (p < 0.05, corrected by FDR method) in local regions, which belonged to multiple fiber tracts including cingulum bundle (CB), inferior occipito-frontal fasciculus (IoFF), corpus callosum (CC), external capsule (EC), uncinate fasciculus (UF), superior longitudinal fasciculus (SLF) and thalamo front (TF). We also found clusters that had relevance with clinical indices of cognitive function (2 clusters), athletic function (6 clusters), and depressive state (2 clusters) in these significant clusters. From the experiment results, it confirmed the ability of the proposed method to identify potential WM microstructure abnormality., Competing Interests: Declaration of Competing Interest The authors declare no conflict of interest in this work., (Copyright © 2020. Published by Elsevier B.V.)

Fiber orientation distribution estimation with diffusion magnetic resonance imaging (dMRI) is critical in white matter fiber tractography which is most commonly based on symmetric crossing structures. Ambiguous spatial correspondence between estimated diffusion directions and fiber geometry, such as asymmetric crossing, bending, fanning, or kissing, makes tractography challenging. Consequently, numerous tracts suggest intertwined connections in unexpected regions of the white matter or actually stop prematurely in the white matter. In this work, we propose a novel asymmetric fiber trajectory distribution (FTD) function defined on neighboring voxels based on a streamline differential equation from fluid mechanics. The spatial consistency with intra- and inter-voxel constraints is derived for FTD estimation by introducing the concept of divergence. At a local level, the FTD is a series of curve flows that minimize the energy function, characterizes the relations between fibers and joint fiber fragments within the same fiber bundle. Experiments are performed on FiberCup phantom, ISMRM 2015 Tractography challenge data, and in vivo brain dMRI data for qualitative and quantitative evaluations. Results show that our approach can reveal continuous asymmetric FTD details that are potentially useful for robust tractography., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Background: Fiber tracking with diffusion-weighted MRI has become an essential tool for estimating in vivo brain white matter architecture. Fiber tracking results are sensitive to the choice of processing method and tracking criteria., Purpose: To assess the variability for an algorithm in group studies reproducibility is of critical context. However, reproducibility does not assess the validity of the brain connections. Phantom studies provide concrete quantitative comparisons of methods relative to absolute ground truths, yet do no capture variabilities because of in vivo physiological factors. The ISMRM 2017 TraCED challenge was created to fulfill the gap., Study Type: A systematic review of algorithms and tract reproducibility studies., Subjects: Single healthy volunteers., Field Strength/sequence: 3.0T, two different scanners by the same manufacturer. The multishell acquisition included b-values of 1000, 2000, and 3000 s/mm 2 with 20, 45, and 64 diffusion gradient directions per shell, respectively., Assessment: Nine international groups submitted 46 tractography algorithm entries each consisting 16 tracts per scan. The algorithms were assessed using intraclass correlation (ICC) and the Dice similarity measure., Statistical Tests: Containment analysis was performed to assess if the submitted algorithms had containment within tracts of larger volume submissions. This also serves the purpose to detect if spurious submissions had been made., Results: The top five submissions had high ICC and Dice >0.88. Reproducibility was high within the top five submissions when assessed across sessions or across scanners: 0.87-0.97. Containment analysis shows that the top five submissions are contained within larger volume submissions. From the total of 16 tracts as an outcome relatively the number of tracts with high, moderate, and low reproducibility were 8, 4, and 4., Data Conclusion: The different methods clearly result in fundamentally different tract structures at the more conservative specificity choices. Data and challenge infrastructure remain available for continued analysis and provide a platform for comparison., Level of Evidence: 5 Technical Efficacy Stage: 1 J. Magn. Reson. Imaging 2020;51:234-249., (© 2019 International Society for Magnetic Resonance in Medicine.)

The human visual pathway starts from the retina, passes through the retinogeniculate visual pathway, the optic radiation, and finally connects to the primary visual cortex. Diffusion MRI tractography is the only technology that can noninvasively reconstruct the visual pathway. However, complete and accurate visual pathway reconstruction is challenging because of the skull base environment and complex fiber geometries. Specifically, the optic nerve within the complex skull base environment can cause abnormal diffusion signals. The crossing and fanning fibers at the optic chiasm, and a sharp turn of Meyer's loop at the optic radiation, contribute to complex fiber geometries of the visual pathway. A fiber trajectory distribution (FTD) function‐based tractography method of our previous work and several high sensitivity tractography methods can reveal these complex fiber geometries, but are accompanied by false‐positive fibers. Thus, the related studies of the visual pathway mostly applied the expert region of interest selection strategy. However, interobserver variability is an issue in reconstructing an accurate visual pathway. In this paper, we propose a unified global tractography framework to automatically reconstruct the visual pathway. We first extend the FTD function to a high‐order streamline differential equation for global trajectory estimation. At the global level, the tractography process is simplified as the estimation of global trajectory distribution coefficients by minimizing the cost between trajectory distribution and the selected directions under the prior guidance by introducing the tractography template as anatomic priors. Furthermore, we use a deep learning‐based method and tractography template prior information to automatically generate the mask for tractography. The experimental results demonstrate that our proposed method can successfully reconstruct the visual pathway with high accuracy. [ABSTRACT FROM AUTHOR]

Diffusion-weighted magnetic resonance imaging is a non-invasive imaging method that has been increasingly used in neuroscience imaging over the last decade. Partial volume effects (PVEs) exist in sampling signal for many physical and actual reasons, which lead to inaccurate fiber imaging. We overcome the influence of PVEs by separating isotropic signal from diffusion-weighted signal, which can provide more accurate estimation of fiber orientations. In this work, we use a novel response function (RF) and the correspondent fiber orientation distribution function (fODF) to construct different signal models, in which case the fODF is represented using dictionary basis function. We then put forward a new index Piso, which is a part of fODF to quantify white and gray matter. The classic Richardson-Lucy (RL) model is usually used in the field of digital image processing to solve the problem of spherical deconvolution caused by highly ill-posed least-squares algorithm. In this case, we propose an innovative model integrating RL model with spatial regularization to settle the suggested double-models, which improve noise resistance and accuracy of imaging. Experimental results of simulated and real data show that the proposal method, which we call iRL, can robustly reconstruct a more accurate fODF and the quantitative index Piso performs better than fractional anisotropy and general fractional anisotropy. [ABSTRACT FROM AUTHOR]

• Patients with Parkinson disease (PD) show sustained changes in white matter (WM) fibers during cognitive decline. • WM fibers alterations related to attention function, verbal memory and executive function in PD with cognitive decline. • Accurate WM localization may help physicians in clinical trials and making predictions. Cognitive impairment in Parkinson disease (PD) leads to substantial disability. Unlike external manifestations such as tremor, the decay of cognitive function is often an underlying process, and its neuroanatomic substrates are not yet fully elucidated. Knowledge regarding cognitive-related alterations in white matter (WM) pathways helps us understand the mechanisms of cognitive decline in patients with PD. Previous voxel-based analyses with Diffusion tensor imaging (DTI) metrics, such as fractional anisotropy (FA) and mean diffusivity (MD) have uncovered white matter differences in groupwise, but the conclusions were inconsistent. That was partially due to white matter fibers that are known to affect cognition, such as the corpus callosum (CC) and superior longitudinal fasciculus that cross in voxel, and are hard to interpreted by the abovementioned metrics. Furthermore, cognitive decay is a continuous process, it is difficult to reflect the continuous changes of white matter fibers between groups comparison. In the present work, we chose the constrained spherical deconvolution (CSD) and the fixel model, which avoided the effect of crossing fibers. To compare the white matter fiber in different cognitive stages of patients with PD, the results found that the CC, the cingulum bundle (CB), and the corticospinal tract (CST) showed the same trend in the decline of cognitive function, and this change may lead to the impairment of cognitive function. Our findings can help physicians determine the cognitive stage of PD from the perspective of white matter fiber and provide a reference for clinical trials and predictions. [ABSTRACT FROM AUTHOR]