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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.

Sleep deprivation influences several critical functions, yet how it affects human brain white matter (WM) is not well understood. The aim of the present work was to investigate the effect of 32 hours of sleep deprivation on WM microstructure compared to changes observed in a normal sleep-wake cycle (SWC). To this end, we utilised diffusion weighted imaging (DWI) including the diffusion tensor model, diffusion kurtosis imaging and the spherical mean technique, a novel biophysical diffusion model. 46 healthy adults (23 sleep deprived vs 23 with normal SWC) underwent DWI across four time points (morning, evening, next day morning and next day afternoon, after a total of 32 hours). Linear mixed models revealed significant group × time interaction effects, indicating that sleep deprivation and normal SWC differentially affect WM microstructure. Voxel-wise comparisons showed that these effects spanned large, bilateral WM regions. These findings provide important insight into how sleep deprivation affects the human brain.

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.

Diffusion tensor imaging (DTI) corrected for the free‐water (FW) enables the separation of a hindered Gaussian‐like profile from an isotropic component, which represents diffusion found in cerebrospinal and interstitial fluids within the extracellular space of grey and white matter. The assessment of the reproducibility and reliability properties of FW‐corrected DTI is a crucial factor in demonstrating the potential clinical utility of this refinement, particularly considering the examinations across multiple medical centres. This paper explores the variability, reliability, and separability properties of free‐water volume fraction (FWVF) and FW‐corrected DTI‐based measures in healthy human brain white matter using publicly available test–retest databases acquired in (1) intra‐scanner, (2) intra‐scanner longitudinal and (3) inter‐scanner settings under varying acquisition schemes. Three different estimation techniques to retrieve the FW‐corrected DTI parameters tailored to single‐ or multiple‐shell diffusion‐sensitising magnetic resonance (MR) acquisitions are investigated: (i) a direct optimization of bi‐tensor signal representation in the variational framework, (ii) the region contraction‐based approach and (iii) the spherical means technique combined with a correction of diffusion‐weighted MR signal prior to DTI estimation. We found the previous suggestion that the FW correction to DTI in a single‐shell diffusion‐weighted MR acquisition improves the repeatability of DTI‐based measures may be data‐ and methodology‐dependent, and does not generalise to multiple‐shell scenarios. The study also confirms that the single‐shell variational FW‐correction method fails to retrieve meaningful information from the mean diffusivity (MD) parameter. In contrast, the combined FW‐correction scheme reduces the biological variability of MD, regardless of whether DTI is estimated from single‐ or multiple‐shell data, given that the FWVF used for the correction in both cases is derived from multiple‐shell acquisitions. Our experiments have shown that the most reliable and repeatable/reproducible measures, while preserving a moderate separability property, are fractional anisotropy and axial diffusivity estimated in a multiple‐shell variant under a combined FW‐correction scheme. On the contrary, our results show evidence that the least reliable measures are the mean diffusivity estimated using any FW‐correction procedure, as well as the FWVF parameter itself. These results can be used to establish the direction for selecting the most attractive FW‐correction DTI scheme for clinical applications in terms of the variability‐reliability‐separability criterion.

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.

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.

Mindfulness meditation training (MMT) reliably reduces stress and anxiety while also improving attention. The primary aim of this study was to investigate the relationship between MMT, stress and anxiety reduction, and its impact upon improvements in attention on the behavioral and neuronal levels. As a second aim, we sought to explore any relationship between MMT, attention, and modified states of mind such as flow. 118 healthy, meditation-naïve, participants were either assigned to a 31-day, web-based, MMT or an active control, health training (HT). Participants underwent functional magnetic resonance imaging while performing the attention network test (ANT) to assess functional and behavioural attentional changes, diffusion tensor imaging (DTI) to assess microstructural neuronal changes and completed relevant questionnaires to explore changes in psychological outcomes. Results confirmed a reduction in perceived stress and anxiety levels in the MMT group and significant improvements in the overall reaction time during the ANT, albeit no specific effects on the attentional components were observed. No statistically significant changes were found in the HT group. Interestingly, a significant group-by-time interaction was seen in flow experience. Functional data exhibited an increased activity in the superior frontal gyrus, posterior cingulate cortex, and right hippocampus during the alerting condition of the ANT after the MMT; decreased stress and trait anxiety were significantly correlated with the activation in the right hippocampus, and increased flow was also significantly correlated with all the aforementioned areas. DTI data showed increased fractional anisotropy values in the right uncinate fasciculus indicating white matter microarchitecture improvement between the right hippocampus and frontal areas of the brain. This study, therefore, demonstrates the effectiveness of web-based MMT on overall well-being and attentional performance, while also providing insight into the relationship between psychological outcomes, attention, and neuroplastic changes.

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.