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Traditional Diffusion Tensor Imaging (DTI) metrics are affected by crossing fibers and lesions. Most of the previous tractometry works use the single diffusion tensor, which leads to limited sensitivity and challenging interpretation of the results in crossing fiber regions. In this work, we propose a tractometry pipeline that combines white matter tractography with multi-tensor fixel-based metrics. These multi-tensors are estimated using the stable, accurate and robust to noise Multi-Resolution Discrete Search method (MRDS). The spatial coherence of the multi-tensor field estimated with MRDS, which includes up to three anisotropic and one isotropic tensors, is tractography-regularized using the Track Orientation Density Imaging method. Our end-to-end tractometry pipeline goes from raw data to track-specific multi-tensor-metrics tract profiles that are robust to noise and crossing fibers. A comprehensive evaluation conducted in a phantom simulating healthy and damaged tissue with the standard model, as well as in a healthy cohort of 20 individuals scanned along 5 time points, demonstrates the advantages of using multi-tensor metrics over traditional single-tensor metrics in tractometry. Qualitative assessment in a cohort of patients with relapsing-remitting multiple sclerosis reveals that the pipeline effectively detects white matter anomalies in the presence of crossing fibers and lesions.

Summary: Background: Research in healthy young adults shows that characteristic patterns of brain activity define individual “brain-fingerprints” that are unique to each person. However, variability in these brain-fingerprints increases in individuals with neurological conditions, challenging the clinical relevance and potential impact of the approach. Our study shows that brain-fingerprints derived from neurophysiological brain activity are associated with pathophysiological and clinical traits of individual patients with Parkinson’s disease (PD). Methods: We created brain-fingerprints from task-free brain activity recorded through magnetoencephalography in 79 PD patients and compared them with those from two independent samples of age-matched healthy controls (N = 424 total). We decomposed brain activity into arrhythmic and rhythmic components, defining distinct brain-fingerprints for each type from recording durations of up to 4 min and as short as 30 s. Findings: The arrhythmic spectral components of cortical activity in patients with Parkinson’s disease are more variable over short periods, challenging the definition of a reliable brain-fingerprint. However, by isolating the rhythmic components of cortical activity, we derived brain-fingerprints that distinguished between patients and healthy controls with about 90% accuracy. The most prominent cortical features of the resulting Parkinson’s brain-fingerprint are mapped to polyrhythmic activity in unimodal sensorimotor regions. Leveraging these features, we also demonstrate that Parkinson’s symptom laterality can be decoded directly from cortical neurophysiological activity. Furthermore, our study reveals that the cortical topography of the Parkinson’s brain-fingerprint aligns with that of neurotransmitter systems affected by the disease’s pathophysiology. Interpretation: The increased moment-to-moment variability of arrhythmic brain-fingerprints challenges patient differentiation and explains previously published results. We outline patient-specific rhythmic brain signaling features that provide insights into both the neurophysiological signature and symptom laterality of Parkinson’s disease. Thus, the proposed definition of a rhythmic brain-fingerprint of Parkinson’s disease may contribute to novel, refined approaches to patient stratification. Symmetrically, we discuss how rhythmic brain-fingerprints may contribute to the improved identification and testing of therapeutic neurostimulation targets. Funding: Data collection and sharing for this project was provided by the Quebec Parkinson Network (QPN), the Pre-symptomatic Evaluation of Novel or Experimental Treatments for Alzheimer’s Disease (PREVENT-AD; release 6.0) program, the Cambridge Centre for Aging Neuroscience (Cam-CAN), and the Open MEG Archives (OMEGA). The QPN is funded by a grant from Fonds de Recherche du Québec - Santé (FRQS). PREVENT-AD was launched in 2011 as a $13.5 million, 7-year public-private partnership using funds provided by McGill University, the FRQS, an unrestricted research grant from Pfizer Canada, the Levesque Foundation, the Douglas Hospital Research Centre and Foundation, the Government of Canada, and the Canada Fund for Innovation. The Brainstorm project is supported by funding to SB from the NIH (R01-EB026299-05). Further funding to SB for this study included a Discovery grant from the Natural Sciences and Engineering Research Council of Canada of Canada (436355-13), and the CIHR Canada research Chair in Neural Dynamics of Brain Systems (CRC-2017-00311).

IntroductionGirls and boys presenting disruptive behavior disorders (DBDs) display differences in white matter microstructure (WMM) relative to typically developing (TD) sex-matched peers. Boys with DBDs are at increased risk for traumatic brain injuries (TBIs), which are also known to impact WMM. This study aimed to disentangle associations of WMM with DBDs and TBIs.MethodsThe sample included 673 children with DBDs and 836 TD children, aged 9–10, from the Adolescent Brain Cognitive Development Study. Thirteen white matter bundles previously associated with DBDs were the focus of study. Analyses were undertaken separately by sex, adjusting for callous-unemotional traits (CU), attention-deficit hyperactivity disorder (ADHD), age, pubertal stage, IQ, ethnicity, and family income.ResultsAmong children without TBIs, those with DBDs showed sex-specific differences in WMM of several tracts relative to TD. Most differences were associated with ADHD, CU, or both. Greater proportions of girls and boys with DBDs than sex-matched TD children had sustained TBIs. Among girls and boys with DBDs, those who had sustained TBIs compared to those not injured, displayed WMM alterations that were robust to adjustment for all covariates. Across most DBD/TD comparisons, axonal density scores were higher among children presenting DBDs.DiscussionIn conclusion, in this community sample of children, those with DBDs were more likely to have sustained TBIs that were associated with additional, sex-specific, alterations of WMM. These additional alterations further compromise the future development of children with DBDs.

IntroductionOperculo-insular epilepsy (OIE) is a rare condition amenable to surgery in well-selected cases. Despite the high rate of neurological complications associated with OIE surgery, most postoperative deficits recover fully and rapidly. We provide insights into this peculiar pattern of functional recovery by investigating the longitudinal reorganization of structural networks after surgery for OIE in 10 patients.MethodsStructural T1 and diffusion-weighted MRIs were performed before surgery (t0) and at 6 months (t1) and 12 months (t2) postoperatively. These images were processed with an original, comprehensive structural connectivity pipeline. Using our method, we performed comparisons between the t0 and t1 timepoints and between the t1 and t2 timepoints to characterize the progressive structural remodeling.ResultsWe found a widespread pattern of postoperative changes primarily in the surgical hemisphere, most of which consisted of reductions in connectivity strength (CS) and regional graph theoretic measures (rGTM) that reflect local connectivity. We also observed increases in CS and rGTMs predominantly in regions located near the resection cavity and in the contralateral healthy hemisphere. Finally, most structural changes arose in the first six months following surgery (i.e., between t0 and t1).DiscussionTo our knowledge, this study provides the first description of postoperative structural connectivity changes following surgery for OIE. The ipsilateral reductions in connectivity unveiled by our analysis may result from the reversal of seizure-related structural alterations following postoperative seizure control. Moreover, the strengthening of connections in peri-resection areas and in the contralateral hemisphere may be compatible with compensatory structural plasticity, a process that could contribute to the recovery of functions seen following operculo-insular resections for focal epilepsy.

IntroductionMulti-shell diffusion Magnetic Resonance Imaging (dMRI) data has been widely used to characterise white matter microstructure in several neurodegenerative diseases. The lack of standardised dMRI protocols often implies the acquisition of redundant measurements, resulting in prolonged acquisition times. In this study, we investigate the impact of the number of gradient directions on Diffusion Tensor Imaging (DTI) and on Neurite Orientation Dispersion and Density Imaging (NODDI) metrics.MethodsData from 124 healthy controls collected in three different longitudinal studies were included. Using an in-house algorithm, we reduced the number of gradient directions in each data shell. We estimated DTI and NODDI measures on six white matter bundles clinically relevant for neurodegenerative diseases.ResultsFractional Anisotropy (FA) measures on bundles where data were sampled at the 30% rate, showed a median L1 distance of up to 3.92% and a 95% CI of (1.74, 8.97)% when compared to those obtained at reference sampling. Mean Diffusivity (MD) reached up to 4.31% and a 95% CI of (1.60, 16.98)% on the same premises. At a sampling rate of 50%, we obtained a median of 3.90% and a 95% CI of (1.99, 16.65)% in FA, and 5.49% with a 95% CI of (2.14, 21.68)% in MD. The Intra-Cellular volume fraction (ICvf) median L1 distance was up to 2.83% with a 95% CI of (1.98, 4.82)% at a 30% sampling rate and 3.95% with a 95% CI of (2.39, 7.81)% at a 50% sampling rate. The volume difference of the reconstructed white matter at reference and 50% sampling reached a maximum of (2.09 ± 0.81)%.DiscussionIn conclusion, DTI and NODDI measures reported at reference sampling were comparable to those obtained when the number of dMRI volumes was reduced by up to 30%. Close to reference DTI and NODDI metrics were estimated with a significant reduction in acquisition time using three shells, respectively with: 4 directions at a b value of 700 s/mm2, 14 at 1000 s/mm2, and 32 at 2000 s/mm2. The study revealed aspects that can be important for large-scale clinical studies on bundle-specific diffusion MRI.

Abstract We aimed to investigate changes in olfactory bulb volume and brain network in the white matter (WM) in patients with persistent olfactory disfunction (OD) following COVID-19. A cross-sectional study evaluated 38 participants with OD after mild COVID-19 and 24 controls, including Sniffin' Sticks identification test (SS-16), MoCA, and brain magnetic resonance imaging. Network-Based Statistics (NBS) and graph theoretical analysis were used to explore the WM. The COVID-19 group had reduced olfactory bulb volume compared to controls. In NBS, COVID-19 patients showed increased structural connectivity in a subnetwork comprising parietal brain regions. Regarding global network topological properties, patients exhibited lower global and local efficiency and higher assortativity than controls. Concerning local network topological properties, patients had reduced local efficiency (left lateral orbital gyrus and pallidum), increased clustering (left lateral orbital gyrus), increased nodal strength (right anterior orbital gyrus), and reduced nodal strength (left amygdala). SS-16 test score was negatively correlated with clustering of whole-brain WM in the COVID-19 group. Thus, patients with OD after COVID-19 had relevant WM network dysfunction with increased connectivity in the parietal sensory cortex. Reduced integration and increased segregation are observed within olfactory-related brain areas might be due to compensatory plasticity mechanisms devoted to recovering olfactory function.

Numerous filtering methods have been proposed for estimating asymmetric orientation distribution functions (ODFs) for diffusion magnetic resonance imaging (dMRI). It can be hard to make sense of all these different methods, which share similar features and result in similar outputs. In this work, we disentangle these many filtering methods proposed in the past and combine them into a novel, unified filtering equation. We also propose a self-supervised data-driven approach for calibrating the filtering parameter values. Our equation is implemented in an open-source GPU-accelerated python software to facilitate its integration into any existing dMRI processing pipeline. Our method is applied on multi-shell multi-tissue fiber ODFs from the Human Connectome Project dataset (1.25 mm3 native resolution) and on single-shell single-tissue fiber ODFs from the Bilingualism and the Brain dataset (2.0 mm3 isotropic resolution) to evaluate the occurrence of asymmetric patterns on different spatial resolutions, representing cutting-edge and “clinical” research data. Asymmetry measures such as the asymmetric index (ASI) and our novel number of fiber directions (NuFiD) are then used to explain the behaviour of our method in these images. The contributions of this work are: (i) the disentanglement and unification of filtering methods for estimating asymmetric ODFs; (ii) a calibration method for automatically fixing the parameters governing the filtering; (iii) an open-source, efficient implementation of our unified filtering method for estimating asymmetric ODFs; (iv) a novel number of fiber directions (NuFiD) index for explaining asymmetric fiber configurations; and (v) a novel template of asymmetries, revealing that our filtering method estimates asymmetric configurations in at least 50% of the brain voxels (∼31% of the white matter and ∼63% of the gray matter).

Abstract Introduction Executive function deficits and adverse psychological outcomes are common in youth with congenital heart disease (CHD) or born preterm. Association white matter bundles play a critical role in higher order cognitive and emotional functions and alterations to their microstructural organization may result in adverse neuropsychological functioning. This study aimed to examine the relationship of myelination and axon density and orientation alterations within association bundles with executive functioning, psychosocial well‐being, and resilience in youth with CHD or born preterm. Methods Youth aged 16 to 26 years born with complex CHD or preterm at ≤33 weeks of gestational age and healthy controls completed a brain MRI and self‐report assessments of executive functioning, psychosocial well‐being, and resilience. Multicomponent driven equilibrium single‐pulse observation of T1 and T2 and neurite orientation dispersion and density imaging were used to calculate average myelin water fraction (MWF), neurite density index (NDI), and orientation dispersion index values for eight bilateral association bundles. The relationships of bundle‐average metrics with neuropsychological outcomes were explored with linear regression and mediation analyses. Results In the CHD group, lower MWF in several bundles was associated with poorer working memory and behavioral self‐monitoring and mediated self‐monitoring deficits relative to controls. In the preterm group, lower NDI in several bundles was associated with poorer emotional control and lower MWF in the left superior longitudinal fasciculus III mediated planning/organizing deficits relative to controls. No significant relationships were observed for psychosocial well‐being or resilience. Conclusion The findings of this study suggest that microstructural alterations to association bundles, including lower myelination and axon density, have different relationships with executive functioning in youth with CHD and youth born preterm. Future studies should aim to characterize other neurobiological, social, and environmental influences that may interact with white matter microstructure and neuropsychological functioning in these at‐risk individuals.

Abstract Since 2015, research groups have sought to produce the ne plus ultra of tractography algorithms using the ISMRM 2015 Tractography Challenge as evaluation. In particular, since 2017, machine learning has made its entrance into the tractography world. The ISMRM 2015 Tractography Challenge is the most used phantom during tractography validation, although it contains limitations. Here, we offer a new scoring system for this phantom, where segmentation of the bundles is now based on manually defined regions of interest rather than on bundle recognition. Bundles are now more reliably segmented, offering more representative metrics for future users. New code is available online. Scores of the initial 96 submissions to the challenge are updated. Overall, conclusions from the 2015 challenge are confirmed with the new scoring, but individual tractogram scores have changed, and the data is much improved at the bundle- and streamline-level. This work also led to the production of a ground truth tractogram with less broken or looping streamlines and of an example of processed data, all available on the Tractometer website. This enhanced scoring system and new data should continue helping researchers develop and evaluate the next generation of tractography techniques.

White matter bundle segmentation is a cornerstone of modern tractography to study the brain’s structural connectivity in domains such as neurological disorders, neurosurgery, and aging. In this study, we present FIESTA (FIbEr Segmentation in Tractography using Autoencoders), a reliable and robust, fully automated, and easily semi-automatically calibrated pipeline based on deep autoencoders that can dissect and fully populate white matter bundles. This pipeline is built upon previous works that demonstrated how autoencoders can be used successfully for streamline filtering, bundle segmentation, and streamline generation in tractography. Our proposed method improves bundle segmentation coverage by recovering hard-to-track bundles with generative sampling through the latent space seeding of the subject bundle and the atlas bundle. A latent space of streamlines is learned using autoencoder-based modeling combined with contrastive learning. Using an atlas of bundles in standard space (MNI), our proposed method segments new tractograms using the autoencoder latent distance between each tractogram streamline and its closest neighbor bundle in the atlas of bundles. Intra-subject bundle reliability is improved by recovering hard-to-track streamlines, using the autoencoder to generate new streamlines that increase the spatial coverage of each bundle while remaining anatomically correct. Results show that our method is more reliable than state-of-the-art automated virtual dissection methods such as RecoBundles, RecoBundlesX, TractSeg, White Matter Analysis and XTRACT. Our framework allows for the transition from one anatomical bundle definition to another with marginal calibration efforts. Overall, these results show that our framework improves the practicality and usability of current state-of-the-art bundle segmentation framework

Measurement(s) Diffusion Weighted Imaging • Magnetic Resonance Imaging of the Brain without Contrast • Diffusion Tensor Imaging Technology Type(s) 3 T MRI scanner Factor Type(s) Age • Gender Sample Characteristic - Organism Homo sapiens

Estimating structural connectivity from diffusion-weighted magnetic resonance imaging is a challenging task, partly due to the presence of false-positive connections and the misestimation of connection weights. Building on previous efforts, the MICCAI-CDMRI Diffusion-Simulated Connectivity (DiSCo) challenge was carried out to evaluate state-of-the-art connectivity methods using novel large-scale numerical phantoms. The diffusion signal for the phantoms was obtained from Monte Carlo simulations. The results of the challenge suggest that methods selected by the 14 teams participating in the challenge can provide high correlations between estimated and ground-truth connectivity weights, in complex numerical environments. Additionally, the methods used by the participating teams were able to accurately identify the binary connectivity of the numerical dataset. However, specific false positive and false negative connections were consistently estimated across all methods. Although the challenge dataset doesn’t capture the complexity of a real brain, it provided unique data with known macrostructure and microstructure ground-truth properties to facilitate the development of connectivity estimation methods.

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.

Measurement(s) brain connectivity measurement Technology Type(s) Fiber tracking

IntroductionAlterations to white matter microstructure as detected by diffusion tensor imaging have been documented in both individuals born with congenital heart disease (CHD) and individuals born preterm. However, it remains unclear if these disturbances are the consequence of similar underlying microstructural disruptions. This study used multicomponent driven equilibrium single pulse observation of T1 and T2 (mcDESPOT) and neurite orientation dispersion and density imaging (NODDI) to characterize and compare alterations to three specific microstructural elements of white matter – myelination, axon density, and axon orientation – in youth born with CHD or born preterm.MethodsParticipants aged 16 to 26 years with operated CHD or born ≤33 weeks gestational age and a group of healthy peers of the same age underwent a brain MRI including mcDESPOT and high angular resolution diffusion imaging acquisitions. Using tractometry, average values of myelin water fraction (MWF), neurite density index (NDI), and orientation dispersion index (ODI) were first calculated and compared between groups for 30 white matter bundles. Afterwards, bundle profiling was performed to further characterize the topology of the detected microstructural alterations.ResultsThe CHD and preterm groups both presented with widespread bundles and bundle segments with lower MWF, accompanied by some occurrences of lower NDI, relative to controls. While there were no differences in ODI between the CHD and control groups, the preterm group presented with both higher and lower ODI compared to the control group and lower ODI compared to the CHD group.DiscussionWhile youth born with CHD or born preterm both presented with apparent deficits in white matter myelination and axon density, youth born preterm presented with a unique profile of altered axonal organization. Future longitudinal studies should aim to better understand the emergence of these common and distinct microstructural alterations, which could orient the development of novel therapeutic approaches.

It is currently unknown how quantitative diffusion and myelin MRI designs affect the results of a longitudinal study. We used two independent datasets containing 6 monthly MRI measurements from 20 healthy controls and 20 relapsing-remitting multiple sclerosis (RR-MS) patients. Six designs were tested, including 3 MRI acquisitions, either over 6 months or over a shorter study duration, with balanced (same interval) or unbalanced (different interval) time intervals between MRI acquisitions. First, we show that in RR-MS patients, the brain changes over time obtained with 3 MRI acquisitions were similar to those observed with 5 MRI acquisitions and that designs with an unbalanced time interval showed the highest similarity, regardless of study duration. No significant brain changes were found in the healthy controls over the same periods. Second, the study duration affects the sample size in the RR-MS dataset; a longer study requires more subjects and vice versa. Third, the number of follow-up acquisitions and study duration affect the sensitivity and specificity of the associations with clinical parameters, and these depend on the white matter bundle and MRI measure considered. Together, this suggests that the optimal design depends on the assumption of the dynamics of change in the target population and the accuracy required to capture these dynamics. Thus, this work provides a better understanding of key factors to consider in a longitudinal study and provides clues for better strategies in clinical trial design.

Cerebral palsy (CP), a neuromotor disorder characterized by prenatal brain lesions, leads to white matter alterations and sensorimotor deficits. However, the CP-related diffusion neuroimaging literature lacks rigorous and consensual methodology for preprocessing and analyzing data due to methodological challenges caused by the lesion extent. Advanced methods are available to reconstruct diffusion signals and can update current advances in CP. Our study demonstrates the feasibility of analyzing diffusion CP data using a standardized and open-source pipeline. Eight children with CP (8–12 years old) underwent a single diffusion magnetic resonance imaging (MRI) session on a 3T scanner (Achieva 3.0T (TX), Philips Healthcare Medical Systems, Best, The Netherlands). Exclusion criteria were contraindication to MRI and claustrophobia. Anatomical and diffusion images were acquired. Data were corrected and analyzed using Tractoflow 2.3.0 version, an open-source and robust tool. The tracts were extracted with customized procedures based on existing atlases and freely accessed standardized libraries (ANTs, Scilpy). DTI, CSD, and NODDI metrics were computed for each tract. Despite lesion heterogeneity and size, we successfully reconstructed major pathways, except for a participant with a larger lesion. Our results highlight the feasibility of identifying and quantifying subtle white matter pathways. Ultimately, this will increase our understanding of the clinical symptoms to provide precision medicine and optimize rehabilitation.

Tractography is a powerful tool for the investigation of the complex organization of the brain in vivo, as it allows inferring the macroscopic pathways of the major fiber bundles of the white matter based on non-invasive diffusion-weighted magnetic resonance imaging acquisitions. Despite this unique and compelling ability, some studies have exposed the poor anatomical accuracy of the reconstructions obtained with this technique and challenged its effectiveness for studying brain connectivity. In this work, we describe a novel method to readdress tractography reconstruction problem in a global manner by combining the strengths of so-called generative and discriminative strategies. Starting from an input tractogram, we parameterize the connections between brain regions following a bundle-based representation that allows to drastically reducing the number of parameters needed to model groups of fascicles. The parameters space is explored following an MCMC generative approach, while a discrimininative method is exploited to globally evaluate the set of connections which is updated according to Bayes’ rule. Our results on both synthetic and real brain data show that the proposed solution, called bundle-o-graphy, allows improving the anatomical accuracy of the reconstructions while keeping the computational complexity similar to other state-of-the-art methods.

BackgroundFatigue and cognitive complaints are the most frequent persistent symptoms in patients after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. This study aimed to assess fatigue and neuropsychological performance and investigate changes in the thickness and volume of gray matter (GM) and microstructural abnormalities in the white matter (WM) in a group of patients with mild-to-moderate coronavirus disease 2019 (COVID-19).MethodsWe studied 56 COVID-19 patients and 37 matched controls using magnetic resonance imaging (MRI). Cognition was assessed using Montreal Cognitive Assessment and Cambridge Neuropsychological Test Automated Battery, and fatigue was assessed using Chalder Fatigue Scale (CFQ-11). T1-weighted MRI was used to assess GM thickness and volume. Fiber-specific apparent fiber density (FD), free water index, and diffusion tensor imaging data were extracted using diffusion-weighted MRI (d-MRI). d-MRI data were correlated with clinical and cognitive measures using partial correlations and general linear modeling.ResultsCOVID-19 patients had mild-to-moderate acute illness (95% non-hospitalized). The average period between real-time quantitative reverse transcription polymerase chain reaction-based diagnosis and clinical/MRI assessments was 93.3 (±26.4) days. The COVID-19 group had higher total CFQ-11 scores than the control group (p < 0.001). There were no differences in neuropsychological performance between groups. The COVID-19 group had lower FD in the association, projection, and commissural tracts, but no change in GM. The corona radiata, corticospinal tract, corpus callosum, arcuate fasciculus, cingulate, fornix, inferior fronto-occipital fasciculus, inferior longitudinal fasciculus, superior longitudinal fasciculus, and uncinate fasciculus were involved. CFQ-11 scores, performance in reaction time, and visual memory tests correlated with microstructural changes in patients with COVID-19.ConclusionsQuantitative d-MRI detected changes in the WM microstructure of patients recovering from COVID-19. This study suggests a possible brain substrate underlying the symptoms caused by SARS-CoV-2 during medium- to long-term recovery.

Measurement(s) spinal cord Technology Type(s) magnetic resonance imaging Factor Type(s) manufacturer • site Sample Characteristic - Organism Homo sapiens Sample Characteristic - Location Canada • Switzerland • Australia • United States of America • United Kingdom • Germany • French Republic • Czech Republic • Italy • Japan • Kingdom of Spain • China Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.14052269

Abstract Background The visualization and analysis of brain data such as white matter diffusion tractography and magnetic resonance imaging (MRI) volumes is commonly used by neuro-specialist and researchers to help the understanding of brain structure, functionality and connectivity. As mobile devices are widely used among users and their technology shows a continuous improvement in performance, different types of applications have been designed to help users in different work areas. Results We present, ABrainVis, an Android mobile tool that allows users to visualize different types of brain images, such as white matter diffusion tractographies, represented as fibers in 3D, segmented fiber bundles, MRI 3D images as rendered volumes and slices, and meshes. The tool enables users to choose and combine different types of brain imaging data to provide visual anatomical context for specific visualization needs. ABrainVis provides high performance over a wide range of Android devices, including tablets and cell phones using medium and large tractography datasets. Interesting visualizations including brain tumors and arteries, along with fiber, are given as examples of case studies using ABrainVis. Conclusions The functionality, flexibility and performance of ABrainVis tool introduce an improvement in user experience enabling neurophysicians and neuroscientists fast visualization of large tractography datasets, as well as the ability to incorporate other brain imaging data such as MRI volumes and meshes, adding anatomical contextual information.

Abstract Initiation of combination antiretroviral therapy (cART) reduces inflammation in HIV-infected (HIV+) individuals. Recent studies demonstrated that diffusion MRI based extracellular free water (FW) modeling can be sensitive to neuroinflammation. Here, we investigate the FW in HIV-infection, its temporal evolution, and its association with blood markers, and cognitive scores. Using 96 age-matched participants, we found that FW was significantly elevated in grey and white matter in cART-naïve HIV+ compared to HIV-uninfected (HIV−) individuals at baseline. These increased FW values positively correlated with neurofilament light chain (NfL) and negatively correlated with CD4 counts. FW in grey and white matter, as well as NfL decreased in the HIV+ after 12 weeks of cART treatment. No significant FW differences were noted between the HIV+ and HIV− cohorts at 1 and 2-year follow-up. Results suggest that FW elevation in cART-naïve HIV+ participants is likely due to neuroinflammation. The correlation between FW and NfL, and the improvement in both FW and NfL after 12 weeks of cART treatment further reinforces this conclusion. The longer follow-up at 1 and 2 years suggests that cART helped control neuroinflammation as inferred by FW. Therefore, FW could be used as a biomarker to monitor HIV-associated neuroinflammation.

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

Background: The heterogeneity of white matter damage and symptoms in concussion has been identified as a major obstacle to therapeutic innovation. In contrast, most diffusion MRI (dMRI) studies on concussion have traditionally relied on group-comparison approaches that average out heterogeneity. To leverage, rather than average out, concussion heterogeneity, we combined dMRI and multivariate statistics to characterize multi-tract multi-symptom relationships. Methods: Using cross-sectional data from 306 previously concussed children aged 9–10 from the Adolescent Brain Cognitive Development Study, we built connectomes weighted by classical and emerging diffusion measures. These measures were combined into two informative indices, the first representing microstructural complexity, the second representing axonal density. We deployed pattern-learning algorithms to jointly decompose these connectivity features and 19 symptom measures. Results: Early multi-tract multi-symptom pairs explained the most covariance and represented broad symptom categories, such as a general problems pair, or a pair representing all cognitive symptoms, and implicated more distributed networks of white matter tracts. Further pairs represented more specific symptom combinations, such as a pair representing attention problems exclusively, and were associated with more localized white matter abnormalities. Symptom representation was not systematically related to tract representation across pairs. Sleep problems were implicated across most pairs, but were related to different connections across these pairs. Expression of multi-tract features was not driven by sociodemographic and injury-related variables, as well as by clinical subgroups defined by the presence of ADHD. Analyses performed on a replication dataset showed consistent results. Conclusions: Using a double-multivariate approach, we identified clinically-informative, cross-demographic multi-tract multi-symptom relationships. These results suggest that rather than clear one-to-one symptom-connectivity disturbances, concussions may be characterized by subtypes of symptom/connectivity relationships. The symptom/connectivity relationships identified in multi-tract multi-symptom pairs were not apparent in single-tract/single-symptom analyses. Future studies aiming to better understand connectivity/symptom relationships should take into account multi-tract multi-symptom heterogeneity. Funding: Financial support for this work came from a Vanier Canada Graduate Scholarship from the Canadian Institutes of Health Research (G.I.G.), an Ontario Graduate Scholarship (S.S.), a Restracomp Research Fellowship provided by the Hospital for Sick Children (S.S.), an Institutional Research Chair in Neuroinformatics (M.D.), as well as a Natural Sciences and Engineering Research Council CREATE grant (M.D.).

Medial temporal structures, namely the hippocampus, the entorhinal cortex and the parahippocampal gyrus, are particularly vulnerable to Alzheimer’s disease and hypoxemia. Here, we tested the associations between obstructive sleep apnea (OSA) severity and medial temporal lobe volumes in 114 participants aged 55–86 years (35 % women). We also investigated the impact of sex, age, cognitive status, and free-water fraction correction on these associations. Increased OSA severity was associated with larger hippocampal and entorhinal cortex volumes in women, but not in men. Greater OSA severity also correlated with increased hippocampal volumes in participants with amnestic mild cognitive impairment, but not in cognitively unimpaired participants, regardless of sex. Using free-water corrected volumes eliminated all significant associations with OSA severity. Therefore, the increase in medial temporal subregion volumes may possibly be due to edema. Whether these structural manifestations further progress to neuronal death in non-treated OSA patients should be investigated.

White matter bundle segmentation using diffusion MRI fiber tractography has become the method of choice to identify white matter fiber pathways in vivo in human brains. However, like other analyses of complex data, there is considerable variability in segmentation protocols and techniques. This can result in different reconstructions of the same intended white matter pathways, which directly affects tractography results, quantification, and interpretation. In this study, we aim to evaluate and quantify the variability that arises from different protocols for bundle segmentation. Through an open call to users of fiber tractography, including anatomists, clinicians, and algorithm developers, 42 independent teams were given processed sets of human whole-brain streamlines and asked to segment 14 white matter fascicles on six subjects. In total, we received 57 different bundle segmentation protocols, which enabled detailed volume-based and streamline-based analyses of agreement and disagreement among protocols for each fiber pathway. Results show that even when given the exact same sets of underlying streamlines, the variability across protocols for bundle segmentation is greater than all other sources of variability in the virtual dissection process, including variability within protocols and variability across subjects. In order to foster the use of tractography bundle dissection in routine clinical settings, and as a fundamental analytical tool, future endeavors must aim to resolve and reduce this heterogeneity. Although external validation is needed to verify the anatomical accuracy of bundle dissections, reducing heterogeneity is a step towards reproducible research and may be achieved through the use of standard nomenclature and definitions of white matter bundles and well-chosen constraints and decisions in the dissection process.

ObjectiveTo characterize microstructural white matter changes related to relapsing-remitting multiple sclerosis using advanced diffusion MRI modeling and tractography. The association between imaging data and patient’s cognitive performance, fatigue severity and depressive symptoms is also explored.MethodsIn this cross-sectional study, 24 relapsing-remitting multiple sclerosis patients and 11 healthy controls were compared using high angular resolution diffusion imaging (HARDI). The imaging method includes a multi-shell scheme, free water correction to obtain tissue-specific measurements, probabilistic tracking algorithm robust to crossing fibers and white matter lesions, automatic streamlines and bundle dissection and tract-profiling with tractometry. The neuropsychological evaluation included the Symbol Digit Modalities Test, Paced Auditory Serial Addition Test, Modified Fatigue Impact Scale and Beck Depression Inventory-II.ResultsBundle-wise analysis by tractometry revealed a difference between patients and controls for 11 of the 14 preselected white matter bundles. In patients, free water corrected fractional anisotropy was significantly reduced while radial and mean diffusivities were increased, consistent with diffuse demyelination. The fornix and left inferior fronto-occipital fasciculus exhibited a higher free water fraction. Eight bundles showed an increase in total apparent fiber density and four bundles had a higher number of fiber orientations, suggesting axonal swelling and increased organization complexity, respectively. In the association study, depressive symptoms were associated with diffusion abnormalities in the right superior longitudinal fasciculus.ConclusionTissue-specific diffusion measures showed abnormalities along multiple cerebral white matter bundles in patients with relapsing-remitting multiple sclerosis. The proposed methodology combines free-water imaging, advanced bundle dissection and tractometry, which is a novel approach to investigate cerebral pathology in multiple sclerosis. It opens a new window of use for HARDI-derived measures and free water corrected diffusion measures. Advanced diffusion MRI provides a better insight into cerebral white matter changes in relapsing-remitting multiple sclerosis, namely diffuse demyelination, edema and increased fiber density and complexity.

In the central nervous system of primates, several pathways are characterized by different spectra of axon diameters. In vivo methods, based on diffusion-weighted magnetic resonance imaging, can provide axon diameter index estimates non-invasively. However, such methods report voxel-wise estimates, which vary from voxel-to-voxel for the same white matter bundle due to partial volume contributions from other pathways having different microstructure properties. Here, we propose a novel microstructure-informed tractography approach, COMMITAxSize, to resolve axon diameter index estimates at the streamline level, thus making the estimates invariant along trajectories. Compared to previously proposed voxel-wise methods, our formulation allows the estimation of a distinct axon diameter index value for each streamline, directly, furnishing a complementary measure to the existing calculation of the mean value along the bundle. We demonstrate the favourable performance of our approach comparing our estimates with existing histologically-derived measurements performed in the corpus callosum and the posterior limb of the internal capsule. Overall, our method provides a more robust estimation of the axon diameter index of pathways by jointly estimating the microstructure properties of the tissue and the macroscopic organisation of the white matter connectivity.

Beta-amyloid (Aβ) and tau proteins, the pathological hallmarks of Alzheimer’s disease (AD), are believed to spread through connected regions of the brain. Combining diffusion imaging and positron emission tomography, we investigated associations between white matter microstructure specifically in bundles connecting regions where Aβ or tau accumulates and pathology. We focused on free-water-corrected diffusion measures in the anterior cingulum, posterior cingulum, and uncinate fasciculus in cognitively normal older adults at risk of sporadic AD and presymptomatic mutation carriers of autosomal dominant AD. In Aβ-positive or tau-positive groups, lower tissue fractional anisotropy and higher mean diffusivity related to greater Aβ and tau burden in both cohorts. Associations were found in the posterior cingulum and uncinate fasciculus in preclinical sporadic AD, and in the anterior and posterior cingulum in presymptomatic mutation carriers. These results suggest that microstructural alterations accompany pathological accumulation as early as the preclinical stage of both sporadic and autosomal dominant AD.

Abstract Introduction White matter (WM) energy supply is crucial for axonal function and myelin maintenance. An exogenous source of ketones, the brain's alternative fuel to glucose, bypasses the brain's glucose‐specific energy deficit and improves cognitive outcomes in mild cognitive impairment (MCI). How an additional supply of ketones affects glucose or ketone uptake in specific WM fascicles in MCI has not previously been reported. Methods This 6‐month interventional study included MCI participants randomized to a placebo (n = 16) or ketogenic medium chain triglyceride (kMCT; n = 17) drink. A neurocognitive battery and brain imaging were performed pre‐ and post‐intervention. WM fascicle uptake of ketone and glucose and structural properties were assessed using positron emission tomography and diffusion imaging, respectively. Results Ketone uptake was increased in the kMCT group by 2.5‐ to 3.2‐fold in all nine WM fascicles of interest (P

Diffusion-weighted magnetic resonance imaging (DW-MRI) tractography is a non-invasive tool to probe neural connections and the structure of the white matter. It has been applied successfully in studies of neurological disorders and normal connectivity. Recent work has revealed that tractography produces a high incidence of false-positive connections, often from “bottleneck” white matter configurations. The rich literature in histological connectivity analysis studies in the macaque monkey enables quantitative evaluation of the performance of tractography algorithms. In this study, we use the intricate connections of frontal, cingulate, and parietal areas, well established by the anatomical literature, to derive a symmetrical histological connectivity matrix composed of 59 cortical areas. We evaluate the performance of fifteen diffusion tractography algorithms, including global, deterministic, and probabilistic state-of-the-art methods for the connectivity predictions of 1711 distinct pairs of areas, among which 680 are reported connected by the literature. The diffusion connectivity analysis was performed on a different ex-vivo macaque brain, acquired using multi-shell DW-MRI protocol, at high spatial and angular resolutions. Across all tested algorithms, the true-positive and true-negative connections were dominant over false-positive and false-negative connections, respectively. Moreover, three-quarters of streamlines had endpoints location in agreement with histological data, on average. Furthermore, probabilistic streamline tractography algorithms show the best performances in predicting which areas are connected. Altogether, we propose a method for quantitative evaluation of tractography algorithms, which aims at improving the sensitivity and the specificity of diffusion-based connectivity analysis. Overall, those results confirm the usefulness of tractography in predicting connectivity, although errors are produced. Many of the errors result from bottleneck white matter configurations near the cortical grey matter and should be the target of future implementation of methods.

Diffusion MRI tractography processing pipeline requires a large number of steps (typically 20+ steps). If parameters of these steps, number of threads, and random seed generators are not carefully controlled, the resulting tractography can easily be non-reproducible and non-replicable, even in test-test experiments. To handle these issues, we developed TractoFlow. TractoFlow is fully automatic from raw diffusion weighted images to tractography. The pipeline also outputs classical diffusion tensor imaging measures and several fiber orientation distribution function measures. TractoFlow supports the recent Brain Imaging Data Structure (BIDS) format as input and is based on two engines: Nextflow and Singularity. In this work, the TractoFlow pipeline is evaluated on three databases and shown to be efficient and reproducible from 98% to 100%, depending on parameter choices. Moreover, it is easy to use for non-technical users, with little to no installation requirements. TractoFlow is publicly available for academic research and is an important step forward for better structural brain connectivity mapping.

Background: In temporal lobe epilepsy (TLE), advanced neuroimaging techniques reveal anomalies extending beyond the temporal lobe such as thinning of fronto-central cortices. Operculo-insular epilepsy (OIE) is an under-recognized and poorly characterized condition with the potential of mimicking TLE. In this work, we investigated insular and extra-insular cortical thickness (CT) changes in OIE. Methods: All participants (14 patients with refractory OIE, 9 age- and sex-matched patients with refractory TLE and 26 healthy controls) underwent a T1-weighted acquisition on a 3 T MRI. Anatomical images were processed with Advanced Normalization Tools. Between-group analysis of CT was performed using a two-sided t-test (threshold of p

Abstract Background We investigate the use of different denoising filters on low signal-to-noise ratio cardiac images of the rat heart acquired with a birdcage volume coil at 7T. Accuracy and variability of cardiac function parameters were measured from manual segmentation of rat heart images with and without filtering. Methods Ten rats were studied using a 7T Varian system. End-diastolic and end-systolic volumes, ejection fraction and left ventricle mass (LVM) were calculated from manual segmentation by two experts on cine-FLASH short-axis slices covering the left ventricle. Series were denoised with an anisotropic diffusion filter, a whole variation regularization or an optimized Rician non-local means (ORNLM) filtering technique. The effect of the different filters was evaluated by the calculation of signal-to-noise (SNR) and contrast-to-noise (CNR) ratios, followed by a study of intra- and inter-expert variability of the measurement of physiological parameters. The calculated LVM was compared to the LVM obtained by weighing the heart ex vivo. Results The SNR and the CNR increased after application of the different filters. The performance of the ORNLM filter was superior for all the parameters of the cardiac function, as judged from the inter- and intra-observer variabilities. Moreover, this filtering technique resulted in the lowest variability in the LVM evaluation. Conclusions In cardiac MRI of rats, filtering is an interesting alternative that yields better contrast between myocardium and surrounding tissues and the ORNLM filter provided the largest improvements.

Though tractography is widely used, it has not been systematically validated. Here, authors report results from 20 groups showing that many tractography algorithms produce both valid and invalid bundles.