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Recent work has shown rapid microstructural brain changes in response to learning new tasks. These cognitive tasks tend to draw on multiple brain regions connected by white matter (WM) tracts. Therefore, behavioural performance change is likely to be the result of microstructural, functional activation, and connectivity changes in extended neural networks. Here we show for the first time that learning-induced microstructural change in WM tracts, quantified with diffusion tensor and kurtosis imaging (DTI, DKI) is linked to functional connectivity changes in brain areas that use these tracts to communicate. Twenty healthy participants engaged in a month of virtual reality (VR) systematic audiovisual (AV) training. DTI analysis using repeated-measures ANOVA unveiled a decrease in mean diffusivity (MD) in the SLF II, alongside a significant increase in fractional anisotropy (FA) in optic radiations post-training, persisting in the follow-up (FU) assessment (post: MD t(76) = 6.13, p < 0.001, FA t(76) = 3.68, p < 0.01, FU: MD t(76) = 4.51, p < 0.001, FA t(76) = 2.989, p < 0.05). The MD reduction across participants was significantly correlated with the observed behavioural performance gains. A functional connectivity (FC) analysis showed significantly enhanced functional activity correlation between primary visual and auditory cortices post-training, which was evident by the DKI microstructural changes found within these two regions as well as in the sagittal stratum including WM tracts connecting occipital and temporal lobes (mean kurtosis (MK): cuneus t(19)=2.3 p < 0.05, transverse temporal t(19)=2.6 p < 0.05, radial kurtosis (RK): sagittal stratum t(19)=2.3 p < 0.05). DTI and DKI show complementary data, both of which are consistent with the task-relevant brain networks. The results demonstrate the utility of multimodal imaging analysis to provide complementary evidence for brain changes at the level of networks. In summary, our study shows the complex relationship between microstructural adaptations and functional connectivity, unveiling the potential of multisensory integration within immersive VR training. These findings have implications for learning and rehabilitation strategies, facilitating more effective interventions within virtual environments., Competing Interests: Declaration of competing interest The authors report no relevant financial or non-financial conflicts in relation to this study., (Copyright © 2025 The Authors. Published by Elsevier Inc. All rights reserved.)

The neural pathways that carry information from the foveal, macular, and peripheral visual fields have distinct biological properties. The optic radiations (OR) carry foveal and peripheral information from the thalamus to the primary visual cortex (V1) through adjacent but separate pathways in the white matter. Here, we perform white matter tractometry using pyAFQ on a large sample of diffusion MRI (dMRI) data from subjects with healthy vision in the U.K. Biobank dataset (UKBB; N = 5382; age 45-81). We use pyAFQ to characterize white matter tissue properties in parts of the OR that transmit information about the foveal, macular, and peripheral visual fields, and to characterize the changes in these tissue properties with age. We find that (1) independent of age there is higher fractional anisotropy, lower mean diffusivity, and higher mean kurtosis in the foveal and macular OR than in peripheral OR, consistent with denser, more organized nerve fiber populations in foveal/parafoveal pathways, and (2) age is associated with increased diffusivity and decreased anisotropy and kurtosis, consistent with decreased density and tissue organization with aging. However, anisotropy in foveal OR decreases faster with age than in peripheral OR, while diffusivity increases faster in peripheral OR, suggesting foveal/peri-foveal OR and peripheral OR differ in how they age., (© 2023 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

Brain development follows a protracted developmental timeline with foundational processes of neurodevelopment occurring from the third trimester of gestation into the first decade of life. Defining structural maturational patterns of early brain development is a critical step in detecting divergent developmental trajectories associated with neurodevelopmental and psychiatric disorders that arise later in life. While considerable advancements have already been made in diffusion magnetic resonance imaging (dMRI) for pediatric research over the past three decades, the field of neurodevelopment is still in its infancy with remarkable scientific and clinical potential. This comprehensive review evaluates the application, findings, and limitations of advanced dMRI methods beyond diffusion tensor imaging, including diffusion kurtosis imaging (DKI), constrained spherical deconvolution (CSD), neurite orientation dispersion and density imaging (NODDI) and composite hindered and restricted model of diffusion (CHARMED) to quantify the rapid and dynamic changes supporting the underlying microstructural architectural foundations of the brain in early life., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Objective: Comparative evaluation of diffusion kurtosis imaging (DKI) and diffusion tensor imaging (DTI) using a whole-brain atlas to comprehensively evaluate microstructural changes in the brain of Alzheimer disease (AzD) patients., Methods: Twenty-seven AzD patients and 25 age-matched controls were included. MRI data was analyzed using a whole-brain atlas with inclusion of 98 region of interests. White matter (WM) microstructural changes were assessed by Fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), radial diffusivity (RD), Kurtosis fractional anisotropy (KFA), mean kurtosis (MK), axial kurtosis (AK) and radial kurtosis (RK). Gray matter (GM) integrity was evaluated using KFA, MK, RK, AK and MD. Comparison of the DKI and DTI metrics were done using student t-test (p ≤ 0.001)., Results: In AzD patients widespread increase in MD, AD and RD were found in various WM and GM region of interests. The extent of abnormality for DKI parameters was more limited in both GM and WM regions and revealed reduced kurtosis values except in lentiform nuclei. Both DKI and DTI parameters were sensitive to detect abnormality in WM areas with coherent and complex fiber arrangement. Receiver operating characteristic curve analysis for hippocampal values revealed the highest specificity of 88% for AK <0.6965 and highest sensitivity of 95.2% for MD >1.2659., Conclusion: AzD patients have microstructural changes in both WM and GM and are well-depicted by both DKI and DTI. The alterations in kurtosis parameters, however, are more limited and correlate with areas in the brain primarily involved in cognition., (Copyright © 2021 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Objective: To evaluate: (a) the specific effect that the demyelination and axonal loss have on the DW signal, and (b) the impact of the sequence parameters on the sensitivity to damage of two clinically feasible DWI techniques, i.e. DKI and NODDI., Methods: We performed a Monte Carlo simulation of water diffusion inside a novel synthetic model of white matter in the presence of axonal loss and demyelination, with three compartments with permeable boundaries between them. We compared DKI and NODDI in their ability to detect and assess the damage, using several acquisition protocols. We used the F test statistic as an index of the sensitivity for each DWI parameter to axonal loss and demyelination, respectively., Results: DKI parameters significantly changed with increasing axonal loss, but, in most cases, not with demyelination; all the NODDI parameters showed sensitivity to both the damage processes (at p < 0.01). However, the acquisition protocol strongly affected the sensitivity to damage of both the DKI and NODDI parameters and, especially for NODDI, the parameter absolute values also., Discussion: This work is expected to impact future choices for investigating white matter microstructure in focusing on specific stages of the disease, and for selecting the appropriate experimental framework to obtain optimal data quality given the purpose of the experiment., (© 2021. European Society for Magnetic Resonance in Medicine and Biology (ESMRMB).)

Deriving reliable information about the structural and functional architecture of the brain in vivo is critical for the clinical and basic neurosciences. In the new era of large population-based datasets, when multiple brain imaging modalities and contrasts are combined in order to reveal latent brain structural patterns and associations with genetic, demographic and clinical information, automated and stringent quality control (QC) procedures are important. Diffusion magnetic resonance imaging (dMRI) is a fertile imaging technique for probing and visualising brain tissue microstructure in vivo, and has been included in most standard imaging protocols in large-scale studies. Due to its sensitivity to subject motion and technical artefacts, automated QC procedures prior to scalar diffusion metrics estimation are required in order to minimise the influence of noise and artefacts. However, the QC procedures performed on raw diffusion data cannot guarantee an absence of distorted maps among the derived diffusion metrics. Thus, robust and efficient QC methods for diffusion scalar metrics are needed. Here, we introduce Fast qualitY conTrol meThod foR derIved diffUsion Metrics (YTTRIUM), a computationally efficient QC method utilising structural similarity to evaluate diffusion map quality and mean diffusion metrics. As an example, we applied YTTRIUM in the context of tract-based spatial statistics to assess associations between age and kurtosis imaging and white matter tract integrity maps in U.K. Biobank data (n = 18,608). To assess the influence of outliers on results obtained using machine learning (ML) approaches, we tested the effects of applying YTTRIUM on brain age prediction. We demonstrated that the proposed QC pipeline represents an efficient approach for identifying poor quality datasets and artefacts and increases the accuracy of ML based brain age prediction., (© 2021 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

Objective: Phenylketonuria (PKU) is an autosomal recessive disorder whereby deficiencies in phenylalanine metabolism cause progressive neurological dysfunction. Managing PKU is challenging, with disease monitoring focussed on short-term phenylalanine control rather than measures of neuronal damage. Conventional imaging lacks sensitivity, however diffusion kurtosis imaging (DKI), a new MRI method may reveal subclinical white matter structural changes in PKU., Methods: This cohort study involved adults with PKU recruited during routine clinical care. MRI, neurocognitive assessment and historical phenylalanine (Phe) levels were collected. A hypothesis-generating case study comparing diet-compliant and non-compliant siblings confirmed that DKI metrics are sensitive to dietary adherence and prompted a candidate metric (K rad /K FA ratio). We then tested this metric in a Replication cohort (PKU = 20; controls = 43)., Results: Both siblings scored outside the range of controls for all DKI-based metrics, with severe changes in the periventricular white matter and a gradient of severity toward the cortex. K rad /K FA provided clear separation by diagnosis in the Replication cohort (p < 0.001 in periventricular, deep and pericortical compartments). The ratio also correlated negatively with attention (r = -0.51 & -0.50, p < 0.05) and positively with 3-year mean Phe (r = 0.45 & 0.58, p < 0.01)., Conclusion: DKI reveals regionally-specific, progressive abnormalities of brain diffusion characteristics in PKU, even in the absence of conspicuous clinical signs or abnormalities on conventional MRI. A DKI-based marker derived from these scores (K rad /K FA ratio) was sensitive to cognitive impairment and PKU control over the medium term and may provide a meaningful subclinical biomarker of end-organ damage., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

Diffusion kurtosis imaging (DKI) is an imaging modality that yields novel disease biomarkers and in combination with nervous tissue modeling, provides access to microstructural parameters. Recently, DKI and subsequent estimation of microstructural model parameters has been used for assessment of tissue changes in neurodegenerative diseases and associated animal models. In this study, mouse spinal cords from the experimental autoimmune encephalomyelitis (EAE) model of multiple sclerosis (MS) were investigated for the first time using DKI in combination with biophysical modeling to study the relationship between microstructural metrics and degree of animal dysfunction. Thirteen spinal cords were extracted from animals with varied grades of disability and scanned in a high-field MRI scanner along with five control specimen. Diffusion weighted data were acquired together with high resolution T 2 * images. Diffusion data were fit to estimate diffusion and kurtosis tensors and white matter modeling parameters, which were all used for subsequent statistical analysis using a linear mixed effects model. T 2 * images were used to delineate focal demyelination/inflammation. Our results reveal a strong relationship between disability and measured microstructural parameters in normal appearing white matter and gray matter. Relationships between disability and mean of the kurtosis tensor, radial kurtosis, radial diffusivity were similar to what has been found in other hypomyelinating MS models, and in patients. However, the changes in biophysical modeling parameters and in particular in extra-axonal axial diffusivity were clearly different from previous studies employing other animal models of MS. In conclusion, our data suggest that DKI and microstructural modeling can provide a unique contrast capable of detecting EAE-specific changes correlating with clinical disability., (Copyright © 2019 The Author(s). Published by Elsevier Inc. All rights reserved.)

Diffuse white matter (WM) response to traumatic brain injury (TBI) and transplantation of human bone marrow stromal cells (hMSCs) after the injury were non-invasively and dynamically investigated. Male Wistar rats (300-350 g) subjected to TBI were intravenously injected with 1 ml of saline (n = 10) or with hMSCs in suspension (∼3 × 10 6  hMSCs, n = 10) 1-week post-TBI. MRI measurements of T2-weighted imaging and diffusional kurtosis imaging (DKI) were acquired on all animals at multiple time points up to 3-months post-injury. Functional outcome was assessed using the Morris water maze test. DKI-derived metrics of fractional anisotropy (FA), axonal water fraction (AWF) and radial kurtosis (RK) longitudinally reveal an evolving pattern of structural alteration post-TBI occurring in the brain region remote from primary impact site. The progressive structural change is characterized by gradual disruption of WM integrity at an early stage (weeks post-TBI), followed by spontaneous recovery at a later stage (months post-TBI). Transplantation of hMSCs post-TBI promotes this structural plasticity as indicated by significantly increased FA and AWF in conjunction with substantially elevated RK at the later stage. Our long-term imaging data demonstrate that hMSC therapy leads to modified temporal profiles of these metrics, inducing an earlier presence of enhanced structural remodeling, which may contribute to improved functional recovery., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Nowadays, increasing longevity associated with declining cerebral nervous system functions, suggests the need for continued development of new imaging contrast mechanisms to support the differential diagnosis of age-related decline. In our previous papers, we developed a new imaging contrast metrics derived from anomalous diffusion signal representation and obtained from diffusion-weighted (DW) data collected by varying diffusion gradient strengths. Recently, we highlighted that the new metrics, named γ-metrics, depended on the local inhomogeneity due to differences in magnetic susceptibility between tissues and diffusion compartments in young healthy subjects, thus providing information about myelin orientation and iron content within cerebral regions. The major structural modifications occurring in brain aging are myelinated fibers damage in nerve fibers and iron accumulation in gray matter nuclei. Therefore, we investigated the potential of γ-metrics in relation to other conventional diffusion metrics such as DTI, DKI and NODDI in detecting age-related structural changes in white matter (WM) and subcortical gray matter (scGM). DW-images were acquired in 32 healthy subjects, adults and elderly (age range 20-77 years) using 3.0T and 12 b-values up to 5000 s/mm 2 . Association between diffusion metrics and subjects' age was assessed using linear regression. A decline in mean γ (Mγ) in the scGM and a complementary increase in radial γ (γ ⊥ ) in frontal WM, genu of corpus callosum and anterior corona radiata with advancing age were found. We suggested that the increase in γ⊥ might reflect declined myelin density, and Mγ decrease might mirror iron accumulation. An increase in D // and a decrease in the orientation dispersion index (ODI) were associated with axonal loss in the pyramidal tracts, while their inverted trends within the thalamus were thought to be linked to reduced architectural complexity of nerve fibers. γ-metrics together with conventional diffusion-metrics can more comprehensively characterize the complex mechanisms underlining age-related changes than conventional diffusion techniques alone., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Exposure to chronic hypercortisolism has multiple adverse effects on brain biology in humans. Cushing's disease (CD) represents a unique and natural human model for examining the effects of hypercortisolism on the brain. This cross-sectional study used Diffusional Kurtosis Imaging (DKI) to investigate the microstructure alterations in both white matter (WM) and gray matter (GM) of CD patients and to determine the relationship of these changes with clinical characteristics. DKI images were obtained from 15 active CD patients. DKI parametric maps were estimated through voxel-based analyses (VBA) and compared with 15 healthy controls matched for age, sex and education. In addition, correlations were analyzed between the altered DKI parameters and clinical characteristics. Compared with healthy controls, CD patients mainly exhibited significantly altered diffuse parameters in the GM and WM of the left medial temporal lobe (MTL). The mean values of increased radial diffusivity (RD) of CD patients in GM of the left hippocampus/parahippocampal gyrus correlated positively with the clinical severity of CD. Additionally, we also found altered kurtosis parameters in the cerebellum and frontal lobe. DKI imaging of CD patients could represent complementary information in both white matter and gray matter. The impairment of the left MTL might explain some part of the memory and cognition impairments in CD patients., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Objective: The objective was to investigate the capabilities of diffusional kurtosis imaging (DKI) in detection of age-related white matter (WM) changes in elderly patients with leukoaraiosis., Material and Methods: Fractional anisotropy (FA), kurtosis, and diffusion parameters in the frontal lobe and parietal lobe were compared between 14 patients at Fazekas scale 0 and 1, and 15 patients at Fazekas scale 2 and 3., Results: FA and DKI parameters were significantly altered in the ischemic lesions vs normal regions of WM in the severe patients., Conclusion: DKI can provide sensitive imaging biomarkers for assessing the severity of leukoaraiosis in reference to Fazekas score., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Our aim was to characterize the nature and extent of pathological changes in the normal-appearing white matter (NAWM) of patients with multiple sclerosis (MS) using novel diffusion kurtosis imaging-derived white matter tract integrity (WMTI) metrics and to investigate the association between these WMTI metrics and clinical parameters. Thirty-two patients with relapsing-remitting MS and 19 age- and gender-matched healthy controls underwent MRI and neurological examination. Maps of mean diffusivity, fractional anisotropy and WMTI metrics (intra-axonal diffusivity, axonal water fraction, tortuosity and axial and radial extra-axonal diffusivity) were created. Tract-based spatial statistics analysis was performed to assess for differences in the NAWM between patients and controls. A region of interest analysis of the corpus callosum was also performed to assess for group differences and to evaluate correlations between WMTI metrics and measures of disease severity. Mean diffusivity and radial extra-axonal diffusivity were significantly increased while fractional anisotropy, axonal water fraction, intra-axonal diffusivity and tortuosity were decreased in MS patients compared with controls (p values ranging from <0.001 to <0.05). Axonal water fraction in the corpus callosum was significantly associated with the expanded disability status scale score (ρ = -0.39, p = 0.035). With the exception of the axial extra-axonal diffusivity, all metrics were correlated with the symbol digits modality test score (p values ranging from 0.001 to <0.05). WMTI metrics are thus sensitive to changes in the NAWM of MS patients and might provide a more pathologically specific, clinically meaningful and practical complement to standard diffusion tensor imaging-derived metrics.

Diffusional kurtosis imaging (DKI) is a clinically feasible diffusion MRI technique for white matter (WM) fiber tractography (FT) with the ability to directly resolve intra-voxel crossing fibers by means of the kurtosis diffusion orientation distribution function (dODF). Here we expand on previous work by exploring properties of the kurtosis dODF and their subsequent effects on WM FT for in vivo human data. For comparison, the results are contrasted with fiber bundle orientation estimates provided by the diffusion tensor, which is the primary quantity obtained from diffusion tensor imaging. We also outline an efficient method for performing DKI-based WM FT that can substantially decrease the computational requirements. The recommended method for implementing the kurtosis ODF is demonstrated to optimize the reproducibility and sensitivity of DKI for detecting crossing fibers while reducing the occurrence of non-physically-meaningful, negative values in the kurtosis dODF approximation. In addition, DKI-based WM FT is illustrated for different protocols differing in image acquisition times from 48 to 5.3 min., (Copyright © 2015 John Wiley & Sons, Ltd.)

Purpose: In this MRI study, diffusional kurtosis imaging (DKI) and T2 * multiecho relaxometry were measured from the white matter (WM) of human brains and correlated with each other, with the aim of investigating the influence of magnetic-susceptibility (Δχ (H2O-TISSUE) ) on the contrast., Methods: We focused our in vivo analysis on assessing the dependence of mean, axial, and radial kurtosis (MK, K‖ , K⊥ ), as well as DTI indices on Δχ (H2O-TISSUE) (quantified by T2 *) between extracellular water and WM tissue molecules. Moreover, Monte Carlo (MC) simulations were used to elucidate experimental data., Results: A significant positive correlation was observed between K⊥ , MK and R2 * = 1/T2 *, suggesting that Δχ (H2O-TISSUE) could be a source of DKI contrast. In this view, K⊥ and MK-map contrasts in human WM would not just be due to different restricted diffusion processes of compartmentalized water but also to local Δχ (H2O-TISSUE) . However, MC simulations show a strong dependence on microstructure rearrangement and a feeble dependence on Δχ (H2O-TISSUE) of DKI signal., Conclusion: Our results suggests a concomitant and complementary existence of multi-compartmentalized diffusion process and Δχ (H2O-TISSUE) in DKI contrast that might explain why kurtosis contrast is more sensitive than DTI in discriminating between different tissues. However, more realistic numerical simulations are needed to confirm this statement., (© 2014 Wiley Periodicals, Inc.)

Diffusional kurtosis imaging (DKI) measures the diffusion and kurtosis tensors to quantify restricted, non-Gaussian diffusion that occurs in biological tissue. By estimating the kurtosis tensor, DKI accounts for higher order diffusion dynamics, when compared with diffusion tensor imaging (DTI), and consequently can describe more complex diffusion profiles. Here, we compare several measures of diffusional anisotropy which incorporate information from the kurtosis tensor, including kurtosis fractional anisotropy (KFA) and generalized fractional anisotropy (GFA), with the diffusion tensor-derived fractional anisotropy (FA). KFA and GFA demonstrate a net enhancement relative to FA when multiple white matter fiber bundle orientations are present in both simulated and human data. In addition, KFA shows net enhancement in deep brain structures, such as the thalamus and the lenticular nucleus, where FA indicates low anisotropy. Thus, KFA and GFA provide additional information relative to FA with regard to diffusional anisotropy, and may be particularly advantageous for the assessment of diffusion in complex tissue environments., (Copyright © 2015 John Wiley & Sons, Ltd.)

White matter microstructural changes during the first three years of healthy brain development are characterized using two different models developed for limited clinical diffusion data: White Matter Tract Integrity (WMTI) metrics from Diffusional Kurtosis Imaging (DKI) and Neurite Orientation Dispersion and Density Imaging (NODDI). Both models reveal a non-linear increase in intra-axonal water fraction and in tortuosity of the extra-axonal space as a function of age, in the genu and splenium of the corpus callosum and the posterior limb of the internal capsule. The changes are consistent with expected behavior related to myelination and asynchrony of fiber development. The intra- and extracellular axial diffusivities as estimated with WMTI do not change appreciably in normal brain development. The quantitative differences in parameter estimates between models are examined and explained in the light of each model's assumptions and consequent biases, as highlighted in simulations. Finally, we discuss the feasibility of a model with fewer assumptions., (Copyright © 2013 Elsevier Inc. All rights reserved.)

The cuprizone mouse model is well established for studying the processes of both demyelination and remyelination in the corpus callosum, and it has been utilized together with diffusion tensor imaging (DTI) to investigate myelin and axonal pathology. Although some underlying morphological mechanisms contributing to the changes in diffusion tensor (DT) metrics have been identified, the understanding of specific associations between histology and diffusion measures remains limited. Diffusional kurtosis imaging (DKI) is an extension of DTI that provides metrics of diffusional non-Gaussianity, for which an associated white matter modeling (WMM) method has been developed. The main goal of the present study was to quantitatively assess the relationships between diffusion measures and histological measures in the mouse model of cuprizone-induced corpus callosum demyelination. The diffusional kurtosis (DK) and WMM metrics were found to provide additional information that enhances the sensitivity to detect the morphological heterogeneity in the chronic phase of the disease process in the rostral segment of the corpus callosum. Specifically, in the rostral segment, axonal water fraction (d = 2.6; p < 0.0001), radial kurtosis (d = 2.0; p = 0.001) and mean kurtosis (d = 1.5; p = 0.005) showed the most sensitivity between groups with respect to yielding statistically significant p values and high Cohen's d values. These results demonstrate the ability of DK and WMM metrics to detect white mater changes and inflammatory processes associated with cuprizone-induced demyelination. They also validate, in part, the application of these new WMM metrics for studying neurological diseases, as well as helping to elucidate their biophysical meaning., (Copyright © 2014 John Wiley & Sons, Ltd.)

Introduction: Post-acute COVID syndrome (PACS) is a growing concern, given its impact on mental health and quality of life. However, its effects on cerebral white matter remain poorly understood, particularly in non-hospitalized cohorts. The goals of this cross-sectional, observational study were to examine (1) whether PACS was associated with distinct alterations in white matter microstructure, compared to symptom-matched non-COVID viral infection; and (2) whether microstructural alterations correlated with indices of post-COVID emotional health. Methods: Data were collected for 54 symptomatic individuals who tested positive for COVID-19 (mean age 41 ± 12 yrs., 36 female) and 14 controls who tested negative for COVID-19 (mean age 41 ± 14 yrs., 8 female), with both groups assessed an average of 4-5 months after COVID testing. Diffusion magnetic resonance imaging data were collected, and emotional health was assessed via the NIH emotion toolbox, with summary scores indexing social satisfaction, well-being and negative affect. Results: Despite similar symptoms, the COVID-19 group had reduced mean and axial diffusivity, along with increased mean kurtosis and neurite dispersion, in deep white matter. After adjusting for social satisfaction, higher levels of negative affect in the COVID-19 group were also correlated with increased mean kurtosis and reduced free water in white matter. Discussion: These results provide preliminary evidence that indices of white matter microstructure distinguish PACS from symptomatic non-COVID infection. Moreover, white matter effects seen in PACS correlate with the severity of emotional sequelae, providing novel insights into this highly prevalent disorder. [ABSTRACT FROM AUTHOR]

IntroductionPost-acute COVID syndrome (PACS) is a growing concern, given its impact on mental health and quality of life. However, its effects on cerebral white matter remain poorly understood, particularly in non-hospitalized cohorts. The goals of this cross-sectional, observational study were to examine (1) whether PACS was associated with distinct alterations in white matter microstructure, compared to symptom-matched non-COVID viral infection; and (2) whether microstructural alterations correlated with indices of post-COVID emotional health.MethodsData were collected for 54 symptomatic individuals who tested positive for COVID-19 (mean age 41 ± 12 yrs., 36 female) and 14 controls who tested negative for COVID-19 (mean age 41 ± 14 yrs., 8 female), with both groups assessed an average of 4–5 months after COVID testing. Diffusion magnetic resonance imaging data were collected, and emotional health was assessed via the NIH emotion toolbox, with summary scores indexing social satisfaction, well-being and negative affect.ResultsDespite similar symptoms, the COVID-19 group had reduced mean and axial diffusivity, along with increased mean kurtosis and neurite dispersion, in deep white matter. After adjusting for social satisfaction, higher levels of negative affect in the COVID-19 group were also correlated with increased mean kurtosis and reduced free water in white matter.DiscussionThese results provide preliminary evidence that indices of white matter microstructure distinguish PACS from symptomatic non-COVID infection. Moreover, white matter effects seen in PACS correlate with the severity of emotional sequelae, providing novel insights into this highly prevalent disorder.

Background: Previous studies have identified alterations in structural connectivity of patients with glioma. However, white matter (WM) integrity measured by diffusion kurtosis imaging (DKI) in pediatric patients with brainstem glioma (BSG) was lack of study. Here, the alterations in WM of patients with BSG were assessed through DKI analyses. Materials and methods: This study involved 100 patients with BSG from the National Brain Tumor Registry of China (NBTRC) and 50 age- and sexmatched healthy controls from social recruitment. WM tracts were segmented and reconstructed using U-Net and probabilistic bundle-specific tracking. Next, automatic fiber quantitative (AFQ) analyses of WM tracts were performed using tractometry module embedded in TractSeg. Results: WM quantitative analysis identified alterations in DKI-derived values in patients with BSG compared with healthy controls. WM abnormalities were detected in the projection fibers involved in the brainstem, including corticospinal tract (CST), superior cerebellar peduncle (SCP), middle cerebellar peduncle (MCP) and inferior cerebellar peduncle (ICP). Significant WM alterations were also identified in commissural fibers and association fibers, which were away from tumor location. Statistical analyses indicated the severity of WM abnormality was statistically correlated with the preoperative Karnofsky Performance Scale (KPS) and symptom duration of patients respectively. Conclusion: The results of this study indicated the widely distributed WM alterations in patients with BSG. DKI-derived quantitative assessment may provide additional information and insight into comprehensively understanding the neuropathological mechanisms of brainstem glioma. [ABSTRACT FROM AUTHOR]

Purpose: To quantitatively compare diffusion metrics for human brain estimated with diffusional kurtosis imaging (DKI) at applied field strengths of 1.5 and 3T.Materials and Methods: DKI data for brain were acquired at both 1.5 and 3T from each of six healthy volunteers using a twice-refocused diffusion-weighted imaging sequence. From these data, parametric maps of mean diffusivity (MD), axial diffusivity (D‖ ), radial diffusivity (D⊥ ), fractional anisotropy (FA), mean diffusional kurtosis (MK), axial kurtosis (K‖ ), radial kurtosis (K⊥ ), and kurtosis fractional anisotropy (KFA) were estimated. Comparisons of the results from the two field strengths were made for each metric using both Bland-Altman plots and linear regression to calculate coefficients of determination (R2 ) and best fit lines.Results: Diffusion metrics measured at 1.5 and 3T were observed to be similar. Linear regression of the full datasets had coefficients of determination varying from a low of R2 = 0.86 for KFA to a high of R2 = 0.97 for FA. The slopes of the 3T vs. 1.5T best linear fits varied from 0.881 ± 0.009 for KFA to 1.038 ± 0.010 for D‖ . From a Bland-Altman analysis of selected regions of interest, the mean differences of the metrics for the two field strengths were all found to be less than 6%, except for KFA, which showed the largest relative discrepancy of 10%.Conclusion: Diffusion metrics measured with DKI at 1.5 and 3T are strongly correlated and typically differ by only a few percent. The somewhat higher discrepancy for the KFA is argued to mainly reflect the effects of signal noise. This supports the robustness DKI results with respect to field strength.Level Of Evidence: 3 J. Magn. Reson. Imaging 2017;45:673-680. [ABSTRACT FROM AUTHOR]