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Significance Diffusion-weighted MRI (DWI) tractography is widely used to map structural connections of the human brain in vivo and has been adopted by large-scale initiatives such as the human connectome project. Our results indicate that, even with high-quality data, DWI tractography alone is unlikely to provide an anatomically accurate map of the brain connectome. It is crucial to complement tractography results with a combination of histological or neurophysiological methods to map structural connectivity accurately. Our findings, however, do not diminish the importance of diffusion MRI as a noninvasive tool that offers important quantitative measures related to brain tissue microstructure and white matter architecture. Tractography based on diffusion-weighted MRI (DWI) is widely used for mapping the structural connections of the human brain. Its accuracy is known to be limited by technical factors affecting in vivo data acquisition, such as noise, artifacts, and data undersampling resulting from scan time constraints. It generally is assumed that improvements in data quality and implementation of sophisticated tractography methods will lead to increasingly accurate maps of human anatomical connections. However, assessing the anatomical accuracy of DWI tractography is difficult because of the lack of independent knowledge of the true anatomical connections in humans. Here we investigate the future prospects of DWI-based connectional imaging by applying advanced tractography methods to an ex vivo DWI dataset of the macaque brain. The results of different tractography methods were compared with maps of known axonal projections from previous tracer studies in the macaque. Despite the exceptional quality of the DWI data, none of the methods demonstrated high anatomical accuracy. The methods that showed the highest sensitivity showed the lowest specificity, and vice versa. Additionally, anatomical accuracy was highly dependent upon parameters of the tractography algorithm, with different optimal values for mapping different pathways. These results suggest that there is an inherent limitation in determining long-range anatomical projections based on voxel-averaged estimates of local fiber orientation obtained from DWI data that is unlikely to be overcome by improvements in data acquisition and analysis alone.

There has been growing attention on the effect of COVID‐19 on white‐matter microstructure, especially among those that self‐isolated after being infected. There is also immense scientific interest and potential clinical utility to evaluate the sensitivity of single‐shell diffusion magnetic resonance imaging (MRI) methods for detecting such effects. In this work, the performances of three single‐shell‐compatible diffusion MRI modeling methods are compared for detecting the effect of COVID‐19, including diffusion‐tensor imaging, diffusion‐tensor decomposition of orthogonal moments and correlated diffusion imaging. Imaging was performed on self‐isolated patients at the study initiation and 3‐month follow‐up, along with age‐ and sex‐matched controls. We demonstrate through simulations and experimental data that correlated diffusion imaging is associated with far greater sensitivity, being the only one of the three single‐shell methods to demonstrate COVID‐19‐related brain effects. Results suggest less restricted diffusion in the frontal lobe in COVID‐19 patients, but also more restricted diffusion in the cerebellar white matter, in agreement with several existing studies highlighting the vulnerability of the cerebellum to COVID‐19 infection. These results, taken together with the simulation results, suggest that a significant proportion of COVID‐19 related white‐matter microstructural pathology manifests as a change in tissue diffusivity. Interestingly, different b‐values also confer different sensitivities to the effects. No significant difference was observed in patients at the 3‐month follow‐up, likely due to the limited size of the follow‐up cohort. To summarize, correlated diffusion imaging is shown to be a viable single‐shell diffusion analysis approach that allows us to uncover opposing patterns of diffusion changes in the frontal and cerebellar regions of COVID‐19 patients, suggesting the two regions react differently to viral infection. We used simulations and experimental data to demonstrate the feasibility of the novel correlated diffusion imaging for detecting microstructural changes in human white matter. We demonstrate in the case of mild COVID‐19, correlated diffusion imaging is superior to diffusion tensor imaging when only single‐shell data are available. Moreover, correlated diffusion imaging may exhibit sensitivities to different pathologies at different b‐values.

Large-scale longitudinal neuroimaging studies with diffusion imaging techniques are necessary to test and validate models of white matter neurophysiological processes that change in time, both in healthy and diseased brains. The predictive power of such longitudinal models will always be limited by the reproducibility of repeated measures acquired during different sessions. At present, there is limited quantitative knowledge about the across-session reproducibility of standard diffusion metrics in 3T multi-centric studies on subjects in stable conditions, in particular when using tract based spatial statistics and with elderly people. In this study we implemented a multi-site brain diffusion protocol in 10 clinical 3T MRI sites distributed across 4 countries in Europe (Italy, Germany, France and Greece) using vendor provided sequences from Siemens (Allegra, Trio Tim, Verio, Skyra, Biograph mMR), Philips (Achieva) and GE (HDxt) scanners. We acquired DTI data (2×2×2mm3, b=700s/mm2, 5 b0 and 30 diffusion weighted volumes) of a group of healthy stable elderly subjects (5 subjects per site) in two separate sessions at least a week apart. For each subject and session four scalar diffusion metrics were considered: fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD) and axial (AD) diffusivity. The diffusion metrics from multiple subjects and sessions at each site were aligned to their common white matter skeleton using tract-based spatial statistics. The reproducibility at each MRI site was examined by looking at group averages of absolute changes relative to the mean (%) on various parameters: i) reproducibility of the signal-to-noise ratio (SNR) of the b0 images in centrum semiovale, ii) full brain test–retest differences of the diffusion metric maps on the white matter skeleton, iii) reproducibility of the diffusion metrics on atlas-based white matter ROIs on the white matter skeleton. Despite the differences of MRI scanner configurations across sites (vendors, models, RF coils and acquisition sequences) we found good and consistent test–retest reproducibility. White matter b0 SNR reproducibility was on average 7±1% with no significant MRI site effects. Whole brain analysis resulted in no significant test–retest differences at any of the sites with any of the DTI metrics. The atlas-based ROI analysis showed that the mean reproducibility errors largely remained in the 2–4% range for FA and AD and 2–6% for MD and RD, averaged across ROIs. Our results show reproducibility values comparable to those reported in studies using a smaller number of MRI scanners, slightly different DTI protocols and mostly younger populations. We therefore show that the acquisition and analysis protocols used are appropriate for multi-site experimental scenarios. •We implement a multi-site 3T MRI protocol for brain DTI on 10 EU sites.•We acquire across-session test–retest data on 50 healthy elderly subjects.•We use full brain TBSS and ROI analysis to calculate FA, MD, RD and AD.•Reproducibility errors are in the 2–6% range.•Reproducibility errors tended to be lower in sites with shorter acquisitions.

ABSTRACT The clinical translation of diffusion magnetic resonance imaging (dMRI)‐derived quantitative contrasts hinges on robust reproducibility, minimizing both same‐scanner and cross‐scanner variability. As multi‐site data sets, including multi‐shell dMRI, expand in scope, enhancing reproducibility across variable MRI systems and MRI protocols becomes crucial. This study evaluates the reproducibility of diffusion kurtosis imaging (DKI) metrics (beyond conventional diffusion tensor imaging (DTI)), at the voxel and region‐of‐interest (ROI) levels on magnitude and complex‐valued dMRI data, using denoising with and without harmonization. We compared same‐scanner, cross‐scanner, and cross‐protocol variability for a multi‐shell dMRI protocol (2‐mm isotropic resolution, b = 0, 1000, 2000 s/mm2) in 20 subjects. We first evaluated the effectiveness of Marchenko‐Pastur Principal Component Analysis (MPPCA) based denoising strategies for both magnitude and complex data to mitigate noise‐induced bias and variance, to improve dMRI parametric maps and reproducibility. Next, we examined the impact of denoising under different population analysis approaches, specifically comparing voxel‐wise versus region of interest (ROI)‐based methods. We also evaluated the role of denoising when harmonizing dMRI across scanners and protocols. The results indicate that DTI and DKI maps visually improve after MPPCA denoising, with noticeably fewer outliers in kurtosis maps. Denoising, either using magnitude or complex dMRI, enhances voxel‐wise reproducibility, with test–retest variability of kurtosis indices reduced from 15%–20% without denoising to 5%–10% after denoising. Complex dMRI denoising reduces the noise floor by up to 60%. Denoising not only reduced variability across scans and protocols, but also increased statistical power for low SNR voxel‐wise comparisons when comparing cross sectional groups. In conclusion, MPPCA denoising, either over magnitude or complex dMRI data, enhances the reproducibility and precision of higher‐order diffusion metrics across same‐scanner, cross‐scanner, and cross‐protocol assessments. The enhancement in data quality and precision facilitates the broader application and acceptance of these advanced imaging techniques in both clinical practice and large‐scale neuroimaging studies. MPPCA denoising enhances the reproducibility and precision of higher‐order diffusion metrics in dMRI, by reducing variability and noise across same‐scanner, cross‐scanner, and cross‐protocol assessments. This improvement supports broader clinical application and acceptance of advanced imaging techniques.

Objectives 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. Methods 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. Results 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). Conclusions 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. Presurgical white matter mapping using probabilistic CSD tractography is more accurate and sensitive than manual DTI FACT or automated probabilistic DTI tractography. This study included 22 patients with DES data, which was used as the ground truth. Distance in mm between tractograms and DES data resulted in 860 datapoints, 685 of which belonged to the CST and were used for linear modeling; AUC, area under the curve; CSD, constrained spherical deconvolution; DTI, diffusion tensor imaging; FWE, family‐wise error rate; TCK, tractogram/tractography.

ABSTRACT Accelerated imaging in diffusion MRI has been widely used to reduce scan time. This can be particularly important in reducing the burden in patients, such as those with mild cognitive impairment (MCI). However, the impact on reliability is not fully understood. Moreover, the impact on effect sizes in group comparisons has not been examined. We conducted a test–retest study of the impact of simultaneous multislice (SMS, also called multiband) and in‐plane acceleration (IPA, also called phase acceleration) on reliability and effect sizes in diffusion imaging in MCI, healthy older adults, and young adults. We evaluated diffusion tensor imaging measures (fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity) and neurite orientation and dispersion measures (orientation dispersion, isotropic volume fraction, intracellular volume fraction) under no acceleration (S1P1), SMS = 3 with no in‐plane acceleration (S3P1), SMS = 3 with IPA = 2 (S3P2), S6P1, and S6P2, with scan times varying from over 20 min in S1P1 to under 4 min in S6P2. In white matter voxels, the ranking of the accelerations with respect to intraclass correlations (ICCs) was S1P1 ≈$$ \\approx $$ S3P1 ≥$$ \\ge $$ S3P2 >$$ > $$ S6P1 >$$ > $$ S6P2, with ICCs in the good range across most DWI measures in S1P1, S3P1, and S3P2, moderate to good in S6P1, and poor to moderate in S6P2. In‐plane acceleration did not improve ICC in areas of high susceptibility distortion. Acceleration significantly impacted the values of white matter microstructure with an overall trend of increase in fractional anisotropy and decrease in orientation dispersion with increasing multiband acceleration. In group comparisons, effect sizes tended to be similar across S1P1, S3P1, S3P2, and S6P1, including medium effect sizes in MCI versus healthy older adults and large effect sizes in young versus healthy older adults. Our results provide guidance regarding the costs of acceleration (reduced ICC from high acceleration) while also characterizing the benefits (S3P1 has similar reliability as S1P1 while requiring one third of the acquisition time, ROI‐level group comparisons similar between S1P1, S3P1, S3P2, and S6P1). The overall high reliability and medium effect sizes of white matter microstructure measures with a moderate SMS factor indicates accelerated DWI can be used in developing biomarkers of neurological decline. Comparing multiband factors (S = 1, 3, 6) and in‐plane acceleration factors (P = 1, 2) in diffusion‐weighted imaging, S3P1 has good reliability, similar to no acceleration. S3P2 is competitive. Acceleration alters diffusion metrics. Effect sizes comparing mild cognitive impairment, older adults, and young adults in S1P1, S3P1, S3P2, and S6P1 were generally similar.

The prenatal period represents a critical time for brain growth and development. These rapid neurological advances render the fetus susceptible to various influences with life-long implications for mental health. Maternal distress signals are a dominant early life influence, contributing to birth outcomes and risk for offspring psychopathology. This prospective longitudinal study evaluated the association between prenatal maternal distress and infant white matter microstructure. Participants included a racially and socioeconomically diverse sample of 85 mother–infant dyads. Prenatal distress was assessed at 17 and 29 weeks’ gestational age (GA). Infant structural data were collected via diffusion tensor imaging (DTI) at 42–45 weeks’ postconceptional age. Findings demonstrated that higher prenatal maternal distress at 29 weeks’ GA was associated with increased fractional anisotropy, b = .283, t (64) = 2.319, p = .024, and with increased axial diffusivity, b = .254, t (64) = 2.067, p = .043, within the right anterior cingulate white matter tract. No other significant associations were found with prenatal distress exposure and tract fractional anisotropy or axial diffusivity at 29 weeks’ GA, or earlier in gestation.

ABSTRACT The Adolescent Brain and Cognitive Development (ABCD) project is the largest study of adolescent brain development. ABCD longitudinally tracks 11,868 participants aged 9–10 years from 21 sites using standardized protocols for multi‐site MRI data collection and analysis. While the multi‐site and multi‐scanner study design enhances the robustness and generalizability of analysis results, it may also introduce nonbiological variances including scanner‐related variations, subject motion, and deviations from protocols. ABCD imaging data were collected biennially within a period of ongoing maturation in cortical thickness and integrity of cerebral white matter. These changes can bias the classical test–retest methodologies, such as intraclass correlation coefficients (ICC). We developed a site‐wise adaptive ICC (AICC) to evaluate the reliability of imaging‐derived phenotypes while accounting for ongoing brain development. AICC iteratively estimates the population‐level age‐related brain development trajectory using a weighted mixed model and updates age‐corrected site‐wise reliability until convergence. We evaluated the test–retest reliability of regional fractional anisotropy (FA) measures from diffusion tensor imaging and cortical thickness (CT) from structural MRI data for each site. The mean AICC for 20 FA tracts across sites was 0.61 ± 0.19, lower than the mean AICC for CT in 34 regions across sites, 0.76 ± 0.12. Remarkably, sites using Siemens scanners consistently showed significantly higher AICC values compared with those using GE/Philips scanners for both FA (AICC = 0.71 ± 0.12 vs. 0.46 ± 0.17, p < 0.001) and CT (AICC = 0.80 ± 0.10 vs. 0.69 ± 0.11, p < 0.001). These findings demonstrate site‐and‐scanner related variations in data quality and underscore the necessity for meticulous data curation in subsequent association analyses. The test–retest reliability of structural neuroimaging data in the Adolescent Brain and Cognitive Development (ABCD) study varies by scanner manufacturers. A novel adaptive interclass correlation coefficient was introduced to assess the reliability of longitudinal imaging data of developing brains.

In this work we investigate the use of sum of squares constraints for various diffusion-weighted MRI models, with a goal of enforcing strict, global non-negativity of the diffusion propagator. We formulate such constraints for the mean apparent propagator model and for spherical deconvolution, guaranteeing strict non-negativity of the corresponding diffusion propagators. For the cumulant expansion similar constraints cannot exist, and we instead derive a set of auxiliary constraints that are necessary but not sufficient to guarantee non-negativity. These constraints can all be verified and enforced at reasonable computational costs using semidefinite programming. By verifying our constraints on standard reconstructions of the different models, we show that currently used weak constraints are largely ineffective at ensuring non-negativity. We further show that if strict non-negativity is not enforced then estimated model parameters may suffer from significant errors, leading to serious inaccuracies in important derived quantities such as the main fiber orientations, mean kurtosis, etc. Finally, our experiments confirm that the observed constraint violations are mostly due to measurement noise, which is difficult to mitigate and suggests that properly constrained optimization should currently be considered the norm in many cases.

Diffusion tractography offers enormous potential for the study of human brain anatomy. However, as a method to study brain connectivity, tractography suffers from limitations, as it is indirect, inaccurate, and difficult to quantify. Despite these limitations, appropriate use of tractography can be a powerful means to address certain questions. In addition, while some of tractography's limitations are fundamental, others could be alleviated by methodological and technological advances. This article provides an overview of diffusion magnetic resonance tractography methods with a focus on how future advances might address challenges in measuring brain connectivity. Parts of this review are somewhat provocative, in the hope that they may trigger discussions possibly lacking in a field where the apparent simplicity of the methods (compared to their functional magnetic resonance imaging counterparts) can hide some fundamental issues that ultimately hinder the interpretation of findings, and cast doubt as to what tractography can really teach us about human brain anatomy.