Constrained Spherical Deconvolution Research Articles

Deep learning microstructure estimation of developing brains from diffusion MRI: A newborn and fetal study

Diffusion-weighted magnetic resonance imaging (dMRI) is widely used to assess the brain white matter. Fiber orientation distribution functions (FODs) are a common way of representing the orientation and density of white matter fibers. However, with standard FOD computation methods, accurate estimation requires a large number of measurements that usually cannot be acquired for newborns and fetuses. We propose to overcome this limitation by using a deep learning method to map as few as six diffusion-weighted measurements to the target FOD. To train the model, we use the FODs computed using multi-shell high angular resolution measurements as target. Extensive quantitative evaluations show that the new deep learning method, using significantly fewer measurements, achieves comparable or superior results than standard methods such as Constrained Spherical Deconvolution and two state-of-the-art deep learning methods. For voxels with one and two fibers, respectively, our method shows an agreement rate in terms of the number of fibers of 77.5% and 22.2%, which is 3% and 5.4% higher than other deep learning methods, and an angular error of 10° and 20°, which is 6° and 5° lower than other deep learning methods. To determine baselines for assessing the performance of our method, we compute agreement metrics using densely sampled newborn data. Moreover, we demonstrate the generalizability of the new deep learning method across scanners, acquisition protocols, and anatomy on two clinical external datasets of newborns and fetuses. We validate fetal FODs, successfully estimated for the first time with deep learning, using post-mortem histological data. Our results show the advantage of deep learning in computing the fiber orientation density for the developing brain from in-vivo dMRI measurements that are often very limited due to constrained acquisition times. Our findings also highlight the intrinsic limitations of dMRI for probing the developing brain microstructure.

Comparative validation of automated presurgical tractography based on constrained spherical deconvolution and diffusion tensor imaging with direct electrical stimulation.

Accurate presurgical brain mapping enables preoperative risk assessment and intraoperative guidance. This cross-sectional study investigated whether constrained spherical deconvolution (CSD) methods were more accurate than diffusion tensor imaging (DTI)-based methods for presurgical white matter mapping using intraoperative direct electrical stimulation (DES) as the ground truth. Five different tractography methods were compared (three DTI-based and two CSD-based) in 22 preoperative neurosurgical patients undergoing surgery with DES mapping. The corticospinal tract (CST, N = 20) and arcuate fasciculus (AF, N = 7) bundles were reconstructed, then minimum distances between tractograms and DES coordinates were compared between tractography methods. Receiver-operating characteristic (ROC) curves were used for both bundles. For the CST, binary agreement, linear modeling, and posthoc testing were used to compare tractography methods while correcting for relative lesion and bundle volumes. Distance measures between 154 positive (functional response, pDES) and negative (no response, nDES) coordinates, and 134 tractograms resulted in 860 data points. Higher agreement was found between pDES coordinates and CSD-based compared to DTI-based tractograms. ROC curves showed overall higher sensitivity at shorter distance cutoffs for CSD (8.5 mm) compared to DTI (14.5 mm). CSD-based CST tractograms showed significantly higher agreement with pDES, which was confirmed by linear modeling and posthoc tests (PFWE < .05). CSD-based CST tractograms were more accurate than DTI-based ones when validated using DES-based assessment of motor and sensory function. This demonstrates the potential benefits of structural mapping using CSD in clinical practice.

NVAM-Net: deep learning networks for reconstructing high-quality fiber orientation distributions.

Diffusion magnetic resonance imaging (dMRI) is a widely used non-invasive method for investigating brain anatomical structures. Conventional techniques for estimating fiber orientation distribution (FOD) from dMRI data often neglect voxel-level spatial relationships, leading to ambiguous associations between target voxels and their neighbors, which, in turn, adversely impacts FOD accuracy. This study aims to address this issue by introducing a novel neural network, the neighboring voxel attention mechanism network (NVAM-Net), designed to reconstruct high-quality FOD images. The NVAM-Net leverages a Transformer architecture and incorporates two innovative attention mechanisms: voxel attention and surface attention. These mechanisms are specifically designed to capture overlooked features among neighboring voxels. The processed features are subsequently passed through two fully connected layers, further enhancing FOD estimation accuracy by separately estimating spherical harmonics (SH) coefficients of varying orders. The experimental findings, based on the Human Connectome Project (HCP) dataset, reveal that the reconstructed super-resolution FOD images achieve results comparable to those obtained through more advanced dMRI acquisition protocols. These results underscore the NVAM-Net's robust performance in reconstructing multi-shell multi-tissue constrained spherical deconvolution (MSMT-CSD). In summary, this research underscores the NVAM-Net's advantages and practical feasibility in reconstructing high-quality FOD images. It provides a reliable reference point for clinical applications in the field of diffusion magnetic resonance imaging.

White matter associations with spelling performance.

Multiple neurocognitive processes are involved in the highly complex task of producing written words. Yet, little is known about the neural pathways that support spelling in healthy adults. We assessed the associations between performance on a difficult spelling-to-dictation task and microstructural properties of language-related white matter pathways, in a sample of 73 native English-speaking neurotypical adults. Participants completed a diffusion magnetic resonance imaging scan and a cognitive assessment battery. Using constrained spherical deconvolution modeling and probabilistic tractography, we reconstructed dorsal and ventral white matter tracts of interest, bilaterally, in individual participants. Spelling associations were found in both dorsal and ventral stream pathways. In high-performing spellers, spelling scores significantly correlated with fractional anisotropy (FA) within the left inferior longitudinal fasciculus, a ventral stream pathway. In low-performing spellers, spelling scores significantly correlated with FA within the third branch of the right superior longitudinal fasciculus, a dorsal pathway. An automated analysis of spelling errors revealed that high- and low- performing spellers also differed in their error patterns, diverging primarily in terms of the orthographic distance between their errors and the correct spelling, compared to the phonological plausibility of their spelling responses. The results demonstrate the complexity of the neurocognitive architecture of spelling. The distinct white matter associations and error patterns detected in low- and high- performing spellers suggest that they rely on different cognitive processes, such that high-performing spellers rely more on lexical-orthographic representations, while low-performing spellers rely more on phoneme-to-grapheme conversion.

Structural connectivity at term equivalent age and language in preterm children at 2 years corrected.

We previously reported interhemispheric structural hyperconnectivity bypassing the corpus callosum in children born extremely preterm (<28 weeks) versus term children. This increased connectivity was positively associated with language performance at 4-6 years of age in our prior work. In the present study, we aim to investigate whether this extracallosal connectivity develops in extremely preterm infants at term equivalent age by leveraging a prospective cohort study of 350 very and extremely preterm infants followed longitudinally in the Cincinnati Infant Neurodevelopment Early Prediction Study. For this secondary analysis, we included only children born extremely preterm and without significant brain injury (n = 95). We use higher-order diffusion modelling to assess the degree to which extracallosal pathways are present in extremely preterm infants and predictive of later language scores at 22-26 months corrected age. We compare results obtained from two higher-order diffusion models: generalized q-sampling imaging and constrained spherical deconvolution. Advanced MRI was obtained at term equivalent age (39-44 weeks post-menstrual age). For structural connectometry analysis, we assessed the level of correlation between white matter connectivity at the whole-brain level at term equivalent age and language scores at 2 years corrected age, controlling for post-menstrual age, sex, brain abnormality score and social risk. For our constrained spherical deconvolution analyses, we performed connectivity-based fixel enhancement, using probabilistic tractography to inform statistical testing of the hypothesis that fibre metrics at term equivalent age relate to language scores at 2 years corrected age after adjusting for covariates. Ninety-five infants were extremely preterm with no significant brain injury. Of these, 53 had complete neurodevelopmental and imaging data sets that passed quality control. In the connectometry analyses adjusted for covariates and multiple comparisons (P < 0.05), the following tracks were inversely correlated with language: bilateral cerebellar white matter and middle cerebellar peduncles, bilateral corticospinal tracks, posterior commissure and the posterior inferior fronto-occipital fasciculus. No tracks from the constrained spherical deconvolution/connectivity-based fixel enhancement analyses remained significant after correction for multiple comparisons. Our findings provide critical information about the ontogeny of structural brain networks supporting language in extremely preterm children. Greater connectivity in more posterior tracks that include the cerebellum and connections to the regions of the temporal lobes at term equivalent age appears to be disadvantageous for language development.

Constrained spherical deconvolution on diffusion-weighted images of dolphin brains

Invasive neuronal tract-tracing is not permitted in very large or endangered animals. This is especially the case in marine mammals like dolphins. Diffusion-weighted imaging of fiber tracts could be an alternative if feasible even in brains that have been fixed in formalin for a long time. This currently is a problem, especially for detecting crossing fibers. We applied a state-of-the-art algorithm of Diffusion-weighted imaging called Constrained Spherical Deconvolution on diffusion data of three fixed brains of bottlenose dolphins using clinical human MRI parameters and were able to identify complex fiber patterns within a voxel. Our findings indicate that in order to maintain the structural integrity of the tissue, short-term post-mortem fixation is necessary. Furthermore, pre-processing steps are essential to remove the classical Diffusion-weighted imaging artifacts from images: however, the algorithm is still able to resolve fiber tracking in regions with various signal intensities. The described imaging technique reveals complex fiber patterns in cetacean brains that have been preserved in formalin for extended periods of time and thus opens a new window into our understanding of cetacean neuroanatomy.

Abstract 70: Rich-Club Behaviour and Analyses in the Human Brain Connectome as Novel Early Biomarkers of Cognitive Decline

Introduction: Transient ischemic attack (TIA) patients have an increased risk of early dementia, but the mechanism is unclear. One potential mechanism is the damaging effects of microvascular disease on brain networks. Rich club is a phenomenon where highly connected nodes form a “rich club” amongst themselves, thus playing an important role in global brain network communication. In this study we aim to determine 1) whether the prominence of the rich club effect declines greater in TIA patients than controls over time, and 2) the relationship of rich club prominence with cognition. Methods: TIA patients (n=47) and controls (n=43) underwent DTI imaging and cognitive assessment at baseline, 1-year, 3-year, and 5-year. Whole-brain tractography utilizing constrained spherical deconvolution was performed to construct whole-brain connectomes for both groups at all available timepoints. The maximum normalized rich club coefficient (Φ max ) was used as an indicator of the prominence of the rich club effect. Independent-sample T-tests were used to compare Φ max cross-sectionally, while a linear mixed effect (LME) model was used to examine the longitudinal change in Φ max in TIA patients vs. controls, while controlling for covariates including age, sex, WMH volume, whole-brain volume, and peak width of skeletonized mean diffusivity. Results: TIA patients had a mean age of 63.94 ( SD =8.62) and 20 females. Controls had a mean age of 60.73 (8.16) and 29 females. The prominence of the rich club was not significantly different between TIA patients and controls at baseline or 1-year ( p &gt;0.05). However, TIA patients showed a significantly less prominent rich club at 3-year and 5-year compared to controls ( p =0.047 and 0.018, respectively). TIA patients exhibited a significantly greater decrease in rich club prominence over time, relative to controls ( p =0.023), after accounting for covariates. Longitudinal LME models showed no association between changes in rich club prominence and changes in cognition ( p &gt;0.05 for all cognitive domains). Conclusion: In this study, we show a decrease in rich club prominence in TIA patients compared to controls which might identify their increased risk of cognitive impairment, and the potential benefits of aggressive vascular risk reduction.

Robust fiber orientation distribution function estimation using deep constrained spherical deconvolution for diffusion-weighted magnetic resonance imaging.

Diffusion-weighted magnetic resonance imaging (DW-MRI) is a critical imaging method for capturing and modeling tissue microarchitecture at a millimeter scale. A common practice to model the measured DW-MRI signal is via fiber orientation distribution function (fODF). This function is the essential first step for the downstream tractography and connectivity analyses. With recent advantages in data sharing, large-scale multisite DW-MRI datasets are being made available for multisite studies. However, measurement variabilities (e.g., inter- and intrasite variability, hardware performance, and sequence design) are inevitable during the acquisition of DW-MRI. Most existing model-based methods [e.g., constrained spherical deconvolution (CSD)] and learning-based methods (e.g., deep learning) do not explicitly consider such variabilities in fODF modeling, which consequently leads to inferior performance on multisite and/or longitudinal diffusion studies. In this paper, we propose a data-driven deep CSD method to explicitly constrain the scan-rescan variabilities for a more reproducible and robust estimation of brain microstructure from repeated DW-MRI scans. Specifically, the proposed method introduces a three-dimensional volumetric scanner-invariant regularization scheme during the fODF estimation. We study the Human Connectome Project (HCP) young adults test-retest group as well as the MASiVar dataset (with inter- and intrasite scan/rescan data). The Baltimore Longitudinal Study of Aging dataset is employed for external validation. From the experimental results, the proposed data-driven framework outperforms the existing benchmarks in repeated fODF estimation. By introducing the contrastive loss with scan/rescan data, the proposed method achieved a higher consistency while maintaining higher angular correlation coefficients with the CSD modeling. The proposed method is assessing the downstream connectivity analysis and shows increased performance in distinguishing subjects with different biomarkers. We propose a deep CSD method to explicitly reduce the scan-rescan variabilities, so as to model a more reproducible and robust brain microstructure from repeated DW-MRI scans. The plug-and-play design of the proposed approach is potentially applicable to a wider range of data harmonization problems in neuroimaging.

Reconstructing the somatotopic organization of the corticospinal tract remains a challenge for modern tractography methods.

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.

An intracellular isotropic diffusion signal is positively associated with pubertal development in white matter

Puberty is a key event in adolescent development that involves significant, hormone-driven changes to many aspects of physiology including the brain. Understanding how the brain responds during this time period is important for evaluating neuronal developments that affect mental health throughout adolescence and the adult lifespan. This study examines diffusion MRI scans from the cross-sectional ABCD Study baseline cohort, a large multi-site study containing thousands of participants, to describe the relationship between pubertal development and brain microstructure. Using advanced, 3-tissue constrained spherical deconvolution methods, this study is able to describe multiple tissue compartments beyond only white matter (WM) axonal qualities. After controlling for age, sex, brain volume, subject handedness, scanning site, and sibling relationships, we observe a positive relationship between an isotropic, intracellular diffusion signal fraction and pubertal development across a majority of regions of interest (ROIs) in the WM skeleton. We also observe regional effects from an intracellular anisotropic signal fraction compartment and extracellular isotropic free water-like compartment in several ROIs. This cross-sectional work suggests that changes in pubertal status are associated with a complex response from brain tissue that cannot be completely described by traditional methods focusing only on WM axonal properties.

P01.07.A HUB-RELATED GRAPH METRICS AS MARKER FOR COGNITIVE OUTCOMES IN ADULT GLIOMA SURVIVORS AFTER MULTIMODAL THERAPY

Abstract BACKGROUND Although cognitive impairment is a serious and prevalent long-term side effect in adult glioma survivors after therapy, predictive imaging-based markers remain inconsistent to date. Structural brain network topology could be altered by both the tumor and its’ treatment through reorganization of hubs (i.e. highly interconnected nodes). Here, we explored the role of nodal hub-related graph metrics in higher cognitive functioning. MATERIAL AND METHODS 48 WHO grade 2-3 adult glioma survivors (&amp;gt;1 year post-therapy) and 42 matched controls, underwent a cognitive assessment, covering five cognitive domains. Cognitive scores were transformed into w-scores, corrected for age and education. In addition, weighted nodal graph metrics were calculated based on diffusion weighted imaging, using multi-shell multi-tissue constrained spherical deconvolution and anatomically-constrained tractography. Hubs (hub score≥2) were calculated based on 4 nodal metrics, in which the 20% nodes with highest nodal strength and betweenness centrality, and lowest clustering coefficient and characteristic path length received 1 point (score 0-4). Significant differences in nodal graph metrics between patients and controls, and hubs versus non-hubs were tested in a repeated measures ANOVA, using Bonferroni correction for multiple testing. Significant group differences in nodal graph metrics were correlated to cognitive scores per domain. RESULTS In healthy controls 12/78 nodes were defined as hubs, i.e. the bilateral postcentral, precentral, superior frontal, parietofrontal, superior parietal gyri, thalamus and putamen. In glioma survivors, the left superior-parietal region and left putamen had a significantly lower likelihood of being a hub (p&amp;lt;.001). These specific areas showed assortativity to be positively correlated with attention, language and executive function (r(90)&amp;gt;.51, p&amp;lt;.001) and characteristic path length to be negatively correlated with all cognitive domains (r(90)&amp;gt;.51, p&amp;lt;.001). Nodal metrics of clustering coefficient, characteristic path length, local efficiency and assortativity were significantly different between groups in both hub and non-hub regions (both p&amp;lt;.001). Except for betweenness centrality, all nodal metrics strongly correlated with cognitive outcomes, occurring more often in hubs (33-50%) than intermediate hubs (10-33%) or non-hubs (4-12%). CONCLUSION Reorganization of hubs in the brain network is frequently observed in this cohort of glioma survivors compared to healthy controls. Hub connectivity seems to be important in cognitive functioning, more than for non-hub regions. Therefore, structural nodal hub-related graph metrics might be important to consider in predicting cognitive outcomes in glioma survivors.

The prefrontal cortex of the bottlenose dolphin (Tursiops truncatus Montagu, 1821): a tractography study and comparison with the human.

Cetaceans are well known for their remarkable cognitive abilities including self-recognition, sound imitation and decision making. In other mammals, the prefrontal cortex (PFC) takes a key role in such cognitive feats. In cetaceans, however, a PFC could up to now not be discerned based on its usual topography. Classical in vivo methods like tract tracing are legally not possible to perform in Cetacea, leaving diffusion-weighted imaging (DWI) as the most viable alternative. This is the first investigation focussed on the identification of the cetacean PFC homologue. In our study, we applied the constrained spherical deconvolution (CSD) algorithm on 3T DWI scans of three formalin-fixed brains of bottlenose dolphins (Tursiops truncatus) and compared the obtained results to human brains, using the same methodology. We first identified fibres related to the medio-dorsal thalamic nuclei (MD) and then seeded the obtained putative PFC in the dolphin as well as the known PFC in humans. Our results outlined the dolphin PFC in areas not previously studied, in the cranio-lateral, ectolateral and opercular gyri, and furthermore demonstrated a similar connectivity pattern between the human and dolphin PFC. The antero-lateral rotation of the PFC, like in other areas, might be the result of the telescoping process which occurred in these animals during evolution.

VersaFlow: a versatile pipeline for resolution adapted diffusion MRI processing and its application to studying the variability of the PRIME-DE database.

The lack of "gold standards" in Diffusion Weighted Imaging (DWI) makes validation cumbersome. To tackle this task, studies use translational analysis where results in humans are benchmarked against findings in other species. Non-Human Primates (NHP) are particularly interesting for this, as their cytoarchitecture is closely related to humans. However, tools used for processing and analysis must be adapted and finely tuned to work well on NHP images. Here, we propose versaFlow, a modular pipeline implemented in Nextflow, designed for robustness and scalability. The pipeline is tailored to in vivo NHP DWI at any spatial resolution; it allows for maintainability and customization. Processes and workflows are implemented using cutting-edge and state-of-the-art Magnetic Resonance Imaging (MRI) processing technologies and diffusion modeling algorithms, namely Diffusion Tensor Imaging (DTI), Constrained Spherical Deconvolution (CSD), and DIstribution of Anisotropic MicrOstructural eNvironments in Diffusion-compartment imaging (DIAMOND). Using versaFlow, we provide an in-depth study of the variability of diffusion metrics computed on 32 subjects from 3 sites of the Primate Data Exchange (PRIME-DE), which contains anatomical T1-weighted (T1w) and T2-weighted (T2w) images, functional MRI (fMRI), and DWI of NHP brains. This dataset includes images acquired over a range of resolutions, using single and multi-shell gradient samplings, on multiple scanner vendors. We perform a reproducibility study of the processing of versaFlow using the Aix-Marseilles site's data, to ensure that our implementation has minimal impact on the variability observed in subsequent analyses. We report very high reproducibility for the majority of metrics; only gamma distribution parameters of DIAMOND display less reproducible behaviors, due to the absence of a mechanism to enforce a random number seed in the software we used. This should be taken into consideration when future applications are performed. We show that the PRIME-DE diffusion data exhibits a great level of variability, similar or greater than results obtained in human studies. Its usage should be done carefully to prevent instilling uncertainty in statistical analyses. This hints at a need for sufficient harmonization in acquisition protocols and for the development of robust algorithms capable of managing the variability induced in imaging due to differences in scanner models and/or vendors.

Hub architecture of the human structural connectome: Links to aging and processing speed

The human structural brain network, or connectome, has a rich-club organization with a small number of brain regions showing high network connectivity, called hubs. Hubs are centrally located in the network, energy costly, and critical for human cognition. Aging has been associated with changes in brain structure, function, and cognitive decline, such as processing speed. At a molecular level, the aging process is a progressive accumulation of oxidative damage, which leads to subsequent energy depletion in the neuron and causes cell death. However, it is still unclear how age affects hub connections in the human connectome. The current study aims to address this research gap by constructing structural connectome using fiber bundle capacity (FBC). FBC is derived from Constrained Spherical Deconvolution (CSD) modeling of white-matter fiber bundles, which represents the capacity of a fiber bundle to transfer information. Compared to the raw number of streamlines, FBC is less bias for quantifying connection strength within biological pathways. We found that hubs exhibit longer-distance connections and higher metabolic rates compared to peripheral brain regions, suggesting that hubs are biologically costly. Although the landscape of structural hubs was relatively age-invariant, there were wide-spread age effects on FBC in the connectome. Critically, these age effects were larger in connections within hub compared to peripheral brain connections. These findings were supported by both a cross-sectional sample with wide age-range (N = 137) and a longitudinal sample across 5 years (N = 83). Moreover, our results demonstrated that associations between FBC and processing speed were more concentrated in hub connections than chance level, and FBC in hub connections mediated the age-effects on processing speed. Overall, our findings indicate that structural connections of hubs, which demonstrate greater energy demands, are particular vulnerable to aging. The vulnerability may contribute to age-related impairments in processing speed among older adults.

Fiber Orientation Estimation from X-ray Dark Field Images of Fiber Reinforced Polymers Using Constrained Spherical Deconvolution

The properties of fiber reinforced polymers are strongly related to the length and orientation of the fibers within the polymer matrix, the latter of which can be studied using X-ray computed tomography (XCT). Unfortunately, resolving individual fibers is challenging because they are small compared to the XCT voxel resolution and because of the low attenuation contrast between the fibers and the surrounding resin. To alleviate both problems, anisotropic dark field tomography via grating based interferometry (GBI) has been proposed. Here, the fiber orientations are extracted by applying a Funk-Radon transform (FRT) to the local scatter function. However, the FRT suffers from a low angular resolution, which complicates estimating fiber orientations for small fiber crossing angles. We propose constrained spherical deconvolution (CSD) as an alternative to the FRT to resolve fiber orientations. Instead of GBI, edge illumination phase contrast imaging is used because estimating fiber orientations with this technique has not yet been explored. Dark field images are generated by a Monte Carlo simulation framework. It is shown that the FRT cannot estimate the fiber orientation accurately for crossing angles smaller than 70∘, while CSD performs well down to a crossing angle of 50∘. In general, CSD outperforms the FRT in estimating fiber orientations.

Multiparametric mapping of white matter reorganizations in patients with frontal glioma-related epilepsy.

Epilepsy is a common symptom in diffuse lower-grade glioma (DLGG). The specific role of white matter (WM) alteration in patients with glioma-related epilepsy (GRE) is largely unknown. This study aims to investigate the reorganization of WM tracts and changes in structural networks related to GRE. Diffusion-weighted images were collected from 70 patients with left frontal DLGG (GRE = 33, non-GRE = 37) and 41 healthy controls (HC). Tractometry with TractSeg was applied to segment tracts and quantify fractional anisotropy (FA) along each tract. Structural network was constructed using constrained spherical deconvolution and probabilistic tractography. FA and network properties were compared among three groups. Compared with HC, both GRE and non-GRE showed decreased FA in contralateral inferior fronto-occipital fasciculus, superior longitudinal fasciculus II and arcuate fasciculus, increased nodal efficiency in contralateral nodes of frontal-parietal and limbic networks, whereas decreased degree centrality and betweenness centrality in nodes of dorsal temporal lobe and rostral middle frontal gyrus (rMFG). Additionally, when compared GRE with non-GRE, increased FA in contralateral corticospinal tract (CST) and lower betweenness centrality in paracentral lobule (PCL) in GRE (all p < 0.05 after Bonferroni correction). This study indicates that patients with left frontal DLGG exhibit complex WM reorganization, and the altered regions mainly concentrated in the language, frontal-parietal and limbic networks. Moreover, the preserved integrity in contralateral CST and server decreased nodal betweenness in PCL may be potential neuroimaging markers underlying the occurrence of presurgical seizures of GRE.

A Pilot Study of Adolescents with Psychotic Experiences: Potential Cerebellar Circuitry Disruption Early Along the Psychosis Spectrum.

A berrant connectivity in the cerebellum has been found in psychotic conditions such as schizophrenia corresponding with cognitive and motor deficits found in these conditions. Diffusion differences in the superior cerebellar peduncles, the white matter connecting the cerebellar circuitry to the rest of the brain, have also been found in schizophrenia and high-risk states. However, white matter diffusivity in the peduncles in individuals with sub-threshold psychotic experiences (PEs) but not reaching the threshold for a definitive diagnosis remains unstudied. This study investigates the cerebellar peduncles in adolescents with PEs but no formal psychiatric diagnosis.Sixteen adolescents with PEs and 17 age-matched controls recruited from schools underwent High-Angular-Resolution-Diffusion neuroimaging. Following constrained spherical deconvolution whole-brain tractography, the superior, inferior and middle peduncles were isolated and virtually dissected out using ExploreDTI. Differences for macroscopic and microscopic tract metrics were calculated using one-way between-group analyses of covariance controlling for age, sex and estimated Total Intracranial Volume (eTIV). Multiple comparisons were corrected using Bonferroni correction.A decrease in fractional anisotropy was identified in the right (p = 0.045) and left (p = 0.058) superior cerebellar peduncle; however, this did not survive strict Bonferroni multiple comparison correction. There were no differences in volumes or other diffusion metrics in either the middle or inferior peduncles.Our trend level changes in the superior cerebellar peduncle in a non-clinical sample exhibiting psychotic experiences complement similar but more profound changes previously found in ultra-high-risk individuals and those with psychotic disorders. This suggests that superior cerebellar peduncle circuitry perturbations may occur early along in the psychosis spectrum.

Topological arrangement of coronal segments in human callosal fibers in vivo tractography.

The topography of human callosal fibers in the midsagittal corpus callosum (mid-CC), in terms of cortical termination, is inconsistent in the literature. Despite being a high-profile and controversial topic, heterotopic callosal bundles (HeCBs) have not been studied from a whole-brain perspective. Here, we used multi-modal magnetic resonance imaging data from Human Connectome Project Development to explore these two topographic aspects by combining whole-brain tractography based on multi-shell multi-tissue constrained spherical deconvolution, the post-tractography reducing-false-positive-streamline algorithm of Convex Optimization Modeling for Microstructure Informed Tractography 2, and the new cortex parcellation atlas of Human Connectome Project multi-modal parcellation, version 1.0. We proposed that the callosal streamlines would demonstrate a topological arrangement of coronal segments arranged from anterior to posterior, with each perpendicular to the long axis of the mid-CC following its natural curvature, and the adjacent segments overlapping one another owing to the existence of HeCBs. We found that the cortices connected by the coronal segments, from anterior to posterior, corresponded exactly to the cortices from anterior to posterior in the flattened cortical surfaces of this atlas, indicating the original relative positions of the neocortex before curling and flipping during brain evolution. For each cortical area defined by this atlas, the sum strength of the HeCBs was far greater than that of the homotopic callosal bundle. Our findings on the topography of the whole CC would help in further understanding the network between the bilateral hemispheres and preventing disconnection syndromes in clinical settings.

Directionally encoded color track density imaging in brain tumor patients: A potential application to neuro-oncology surgical planning

BackgroundDiffusion 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. MethodsFourteen 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/mm2. 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. ResultsA 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. ConclusionWe observed a significant neurosurgeon preference for DEC-TDI maps, indicating their potential utility for neurosurgical planning.

Applications of advanced diffusion MRI in early brain development: a comprehensive review.

Brain development follows a protracted developmental timeline with foundational processes of neurodevelopment occurring from the third trimester of gestation into the first decade of life. Defining structural maturational patterns of early brain development is a critical step in detecting divergent developmental trajectories associated with neurodevelopmental and psychiatric disorders that arise later in life. While considerable advancements have already been made in diffusion magnetic resonance imaging (dMRI) for pediatric research over the past three decades, the field of neurodevelopment is still in its infancy with remarkable scientific and clinical potential. This comprehensive review evaluates the application, findings, and limitations of advanced dMRI methods beyond diffusion tensor imaging, including diffusion kurtosis imaging (DKI), constrained spherical deconvolution (CSD), neurite orientation dispersion and density imaging (NODDI) and composite hindered and restricted model of diffusion (CHARMED) to quantify the rapid and dynamic changes supporting the underlying microstructural architectural foundations of the brain in early life.