Diffusion Tensor Metrics Research Articles

In vivo cardiac diffusion tensor imaging on an MR system featuring ultrahigh performance gradients with 200 mT/m maximum gradient strength.

Our aim is to assess the potential of an MR system with ultrahigh performance gradients (200 mT/m maximum gradient strength) to address two interrelated challenges in cardiac DTI: low SNR and sensitivity to bulk motion. Imaging was performed in 20 healthy volunteers, two patients, and one swine post-myocardial infarction. The impact of maximum gradient strength was assessed with spin echo cardiac DTI featuring second-order motion compensation and varying maximum system gradient strengths (40, 80, 200 mT/m). Motion compensation requirements at 200 mT/m were assessed with sequences featuring zeroth-, first-, and second-order motion compensation. SNR, mean diffusivity, fractional anisotropy, helix angle transmurality, and secondary eigenvector angle in the left ventricle were compared. Increasing maximum system gradient strength from 40 and 80 mT/m to 200 mT/m increased SNR of b = 500 s/mm2 images by 150% and 40% due to reductions in TE. Observed improvements in DTI metrics included reduction in variance in mean diffusivity and helix angle transmurality across healthy volunteers, improved visualization of myocardial borders and delineation of suspected scar. Whereas second-order motion compensation acquisitions were robust to motion-induced signal dropout, zeroth- and first-order motion compensation acquisitions suffered from severe signal loss and localized signal voids, respectively. Ultrahigh performance gradients (200 mT/m) enable high SNR DWIs of the heart and resultant improvements in diffusion tensor metrics. Despite reduced diffusion-encoding duration, second-order motion compensation is required to overcome sensitivity to cardiac motion.

Accurate low and high grade glioma classification using free water eliminated diffusion tensor metrics and ensemble machine learning

Glioma, a predominant type of brain tumor, can be fatal. This necessitates an early diagnosis and effective treatment strategies. Current diagnosis is based on biopsy, prompting the need for non invasive neuroimaging alternatives. Diffusion tensor imaging (DTI) is a promising method for studying the pathophysiological impact of tumors on white matter (WM) tissue. Single-shell DTI studies in brain glioma patients have not accounted for free water (FW) contamination due to tumors. This study aimed to (a) assess the efficacy of a two-compartment DTI model that accounts for FW contamination and (b) identify DTI-based biomarkers to classify low-grade glioma (LGG) and high-grade glioma (HGG) patients. DTI data from 86 patients (LGG n = 39, HGG n = 47) were obtained using a routine clinical imaging protocol. DTI metrics of tumorous regions and normal-appearing white matter (NAWM) were evaluated. Advanced stacked-based ensemble learning was employed to classify LGG and HGG patients using both single- and two-compartment DTI model measures. The DTI metrics of the two-compartment model outperformed those of the standard single-compartment DTI model in terms of sensitivity, specificity, and area under the curve of receiver operating characteristic (AUC-ROC) score in classifying LGG and HGG patients. Four features (out of 16 features), namely fractional anisotropy (FA) of the edema and core region and FA and mean diffusivity of the NAWM region, showed superior performance (sensitivity = 92%, specificity = 90%, and AUC-ROC = 90%) in classifying LGG and HGG. This demonstrates that both tumorous and NAWM regions may be differentially affected in LGG and HGG patients. Our results demonstrate the significance of using a two-compartment DTI model that accounts for FW contamination by improving diagnostic accuracy. This improvement may eventually aid in planning treatment strategies for glioma patients.

Diffusion tensor metrics, motor and non-motor symptoms in de novo Parkinson's disease.

Parkinson's disease (PD) is a neurodegenerative disorder characterized by dopaminergic neurons' degeneration of the substantia nigra, presenting with motor and non-motor symptoms. We hypothesized that altered diffusion metrics are associated with clinical symptoms in de novo PD patients. Fractional Anisotropy (FA) and Mean (MD), Axial (AD), and Radial Diffusivity (RD) were assessed in 55 de novo PD patients (58.62 ± 9.85 years, 37 men) and 55 age-matched healthy controls (59.92 ± 11.25 years, 34 men). Diffusion-weighted images and clinical variables were collected from the Parkinson's Progression Markers Initiative study. Tract-based spatial statistics were used to identify white matter (WM) changes, and fiber tracts were localized using the JHU-WM tractography atlas. Motor and non-motor symptoms were evaluated in patients. We observed higher FA values and lower RD values in patients than controls in various fiber tracts (p-TFCE < 0.05). No significant MD or AD difference was observed between groups. Diffusion metrics of several regions significantly correlated with non-motor (state and trait anxiety and daytime sleepiness) and axial motor symptoms in the de novo PD group. No correlations were observed between diffusion metrics and other clinical symptoms evaluated. Our findings suggest microstructural changes in de novo PD fiber tracts; however, limited associations with clinical symptoms reveal the complexity of PD pathology. They may contribute to understanding the neurobiological changes underlying PD and have implications for developing targeted interventions. However, further longitudinal research with larger cohorts and consideration of confounding factors are necessary to elucidate the underlying mechanisms of these diffusion alterations in de novo PD.

Anisotropy component of DTI reveals long-term neuroinflammation following repetitive mild traumatic brain injury in rats

BackgroundThis study aimed to investigate the long-term effects of repetitive mild traumatic brain injury (rmTBI) with varying inter-injury intervals by measuring diffusion tensor metrics, including mean diffusivity (MD), fractional anisotropy (FA), and diffusion magnitude (L) and pure anisotropy (q).MethodsEighteen rats were randomly divided into three groups: short-interval rmTBI (n = 6), long-interval rmTBI (n = 6), and sham controls (n = 6). MD, FA, L, and q values were analyzed from longitudinal diffusion tensor imaging at days 50 and 90 after rmTBI. Immunohistochemical staining against neurons, astrocytes, microglia, and myelin was performed. Analysis of variance, Pearson correlation coefficient, and simple linear regression model were used.ResultsAt day 50 post-rmTBI, lower cortical FA and q values were shown in the short-interval group (p ≤ 0.038). In contrast, higher FA and q values were shown for the long-interval group (p ≤ 0.039) in the corpus callosum. In the ipsilesional external capsule and internal capsule, no significant changes were found in FA, while lower L and q values were shown in the short-interval group (p ≤ 0.028) at day 90. The q values in the external capsule and internal capsule were negatively correlated with the number of microglial cells and the total number of astroglial cells (p ≤ 0.035).ConclusionTensor scalar measurements, such as L and q values, are sensitive to exacerbated chronic injury induced by rmTBI with shorter inter-injury intervals and reflect long-term astrogliosis induced by the cumulative injury.Relevance statementTensor scalar measurements, including L and q values, are potential DTI metrics for detecting long-term and subtle injury following rmTBI; in particular, q values may be used for quantifying remote white matter (WM) changes following rmTBI.Key PointsThe alteration of L and q values was demonstrated after chronic repetitive mild traumatic brain injury.Changing q values were observed in the impact site and remote WM.The lower q values in the remote WM were associated with astrogliosis.Graphical

Harmonization of Longitudinal Diffusion Tensor Imaging Data of the Pediatric Cervical and Thoracic Spinal Cord at 3T Using Longitudinal ComBat.

Diffusion tensor imaging (DTI) of the spinal cord has been extensively used to identify biomarkers for spinal cord pathology. Previously, the longitudinal ComBat (longComBat) technique was examined to reduce scanner effects in multi-site, multi-scanner spinal cord DTI data. This study aimed to assess its effectiveness on longitudinal scans using a single-scanner pediatric dataset, including healthy and spinal cord injury (SCI) subjects. Two identical datasets were collected from 42 healthy and 27 SCI subjects with a 2-hour interval between scans on a 3T Siemens MRI scanner. Axial DTI images of the entire cervical and thoracic spinal cord were obtained, and various average diffusion tensor metrics (FA, MD, RD, & AD) were measured at each vertebral level. Pearson correlation and intraclass correlation coefficients were used to evaluate inter- and intra-subject agreement pre- and post-harmonization. Minimal improvement in agreement was observed with the mean square residual (MSR) model, while the restricted maximum likelihood estimator (REML) model showed reduced intra-subject agreement in all the tensor metrics. The significant variability between longitudinal DTI scans within a single scanner was likely due to physiological motion rather than scanner effects. Post-harmonization using the longComBat MSR model showed limited improvement in agreement.

Accelerating cardiac diffusion tensor imaging using Swin transformer with octave convolution

AbstractCardiac diffusion tensor imaging (cDTI) is a promising and noninvasive magnetic resonance imaging (MRI) technique for assessing myocardial fiber microstructure. However, its clinical application is hampered by lengthy scanning and susceptibility to motion artifacts. This study proposes a cDTI reconstruction model (OSDTI), which leverages octave convolution (OctConv) and Swin Transformer to reconstruct diffusion‐weighted (DW) images from undersampled k‐space data. Our model capitalizes on the strengths of both OctConv and Swin Transformers. Specifically, the OctConv module excels at capturing intricate details and texture information within DW images, while the residual‐connected Swin Transformer module facilitates information retention across various feature extraction stages, thereby enhancing overall image comprehension and reconstruction accuracy. The evaluation of OSDTI using a human ex vivo heart dataset robustly validated the generalization ability of the model. Comparative analyze against existing deep‐learning methods demonstrated OSDTI's superior performance in both DW image quality and diffusion tensor metrics.

Anat-SFSeg: Anatomically-guided superficial fiber segmentation with point-cloud deep learning

Diffusion magnetic resonance imaging (dMRI) tractography is a critical technique to map the brain's structural connectivity. Accurate segmentation of white matter, particularly the superficial white matter (SWM), is essential for neuroscience and clinical research. However, it is challenging to segment SWM due to the short adjacent gyri connection in a U-shaped pattern. In this work, we propose an Anatomically-guided Superficial Fiber Segmentation (Anat-SFSeg) framework to improve the performance on SWM segmentation. The framework consists of a unique fiber anatomical descriptor (named FiberAnatMap) and a deep learning network based on point-cloud data. The spatial coordinates of fibers represented as point clouds, as well as the anatomical features at both the individual and group levels, are fed into a neural network. The network is trained on Human Connectome Project (HCP) datasets and tested on the subjects with a range of cognitive impairment levels. One new metric named fiber anatomical region proportion (FARP), quantifies the ratio of fibers in the defined brain regions and enables the comparison with other methods. Another metric named anatomical region fiber count (ARFC), represents the average fiber number in each cluster for the assessment of inter-subject differences. The experimental results demonstrate that Anat-SFSeg achieves the highest accuracy on HCP datasets and exhibits great generalization on clinical datasets. Diffusion tensor metrics and ARFC show disorder severity associated alterations in patients with Alzheimer's disease (AD) and mild cognitive impairments (MCI). Correlations with cognitive grades show that these metrics are potential neuroimaging biomarkers for AD. Furthermore, Anat-SFSeg could be utilized to explore other neurodegenerative, neurodevelopmental or psychiatric disorders.

Nonlinear Gradient Field Estimation in Diffusion MRI Tensor Simulation.

Gradient nonlinearities not only induce spatial distortion in magnetic resonance imaging (MRI), but also introduce discrepancies between intended and acquired diffusion sensitization in diffusion weighted (DW) MRI. Advances in scanner performance have increased the importance of correcting gradient nonlinearities. The most common approaches for gradient nonlinear field estimations rely on phantom calibration field maps which are not always feasible, especially on retrospective data. Here, we derive a quadratic minimization problem for the complete gradient nonlinear field (L(r)). This approach starts with corrupt diffusion signal and estimates the L(r) in two scenarios: (1) the true diffusion tensor known and (2) the true diffusion tensor unknown (i.e., diffusion tensor is estimated). We show the validity of this mathematical approach, both theoretically and through tensor simulation. The estimated field is assessed through diffusion tensor metrics: mean diffusivity (MD), fractional anisotropy (FA), and principal eigenvector (V1). In simulation with 300 diffusion tensors, the study shows the mathematical model is not ill-posed and remains stable. We find when the true diffusion tensor is known (1) the change in determinant of the estimated L(r) field and the true field is near zero and (2) the median difference in estimated L(r) corrected diffusion metrics to true values is near zero. We find the results of L(r) estimation are dependent on the level of L(r) corruption. This work provides an approach to estimate gradient field without the need for additional calibration scans. To the best of our knowledge, the mathematical derivation presented here is novel.

Diffusion tensor imaging metrics as natural markers of multiple sclerosis-induced brain disorders with a low Expanded Disability Status Scale score

Non-invasive and effective differentiation along with determining the degree of deviations compared to the healthy cohort is important in the case of various brain disorders, including multiple sclerosis (MS). Evaluation of the effectiveness of diffusion tensor metrics (DTM) in 3T DTI for recording MS-related deviations was performed using a time-acceptable MRI protocol with unique comprehensive detection of systematic errors related to spatial heterogeneity of magnetic field gradients. In a clinical study, DTMs were acquired in segmented regions of interest (ROIs) for 50 randomly selected healthy controls (HC) and 50 multiple sclerosis patients. Identical phantom imaging was performed for each clinical measurement to estimate and remove the influence of systematic errors using the b-matrix spatial distribution in the DTI (BSD-DTI) technique. In the absence of statistically significant differences due to age in healthy volunteers and patients with multiple sclerosis, the existence of significant differences between groups was proven using DTM. Moreover, a statistically significant impact of spatial systematic errors occurs for all ROIs and DTMs in the phantom and for approximately 90 % in the HC and MS groups. In the case of a single patient measurement, this appears for all the examined ROIs and DTMs. The obtained DTMs effectively discriminate healthy volunteers from multiple sclerosis patients with a low mean score on the Expanded Disability Status Scale. The magnitude of the group differences is typically significant, with an effect size of approximately 0.5, and similar in both the standard approach and after elimination of systematic errors. Differences were also observed between metrics obtained using these two approaches. Despite a small alterations in mean DTMs values for groups and ROIs (1–3 %), these differences were characterized by a huge effect (effect size ∼0.8 or more). These findings indicate the importance of determining the spatial distribution of systematic errors specific to each MR scanner and DTI acquisition protocol in order to assess their impact on DTM in the ROIs examined. This is crucial to establish accurate DTM values for both individual patients and mean values for a healthy population as a reference. This approach allows for an initial reliable diagnosis based on DTI metrics.

Integration of diffusion tensor imaging parameters with mesh morphing for in-depth analysis of brain white matter fibre tracts.

Averaging is commonly used for data reduction/aggregation to analyse high-dimensional MRI data, but this often leads to information loss. To address this issue, we developed a novel technique that integrates diffusion tensor metrics along the whole volume of the fibre bundle using a 3D mesh-morphing technique coupled with principal component analysis for delineating case and control groups. Brain diffusion tensor MRI scans of high school rugby union players (n = 30, age 16-18) were acquired on a 3 T MRI before and after the sports season. A non-contact sport athlete cohort with matching demographics (n = 12) was also scanned. The utility of the new method in detecting differences in diffusion tensor metrics of the right corticospinal tract between contact and non-contact sport athletes was explored. The first step was to run automated tractography on each subject's native space. A template model of the right corticospinal tract was generated and morphed into each subject's native shape and space, matching individual geometry and diffusion metric distributions with minimal information loss. The common dimension of the 20 480 diffusion metrics allowed further data aggregation using principal component analysis to cluster the case and control groups as well as visualization of diffusion metric statistics (mean, ±2 SD). Our approach of analysing the whole volume of white matter tracts led to a clear delineation between the rugby and control cohort, which was not possible with the traditional averaging method. Moreover, our approach accounts for the individual subject's variations in diffusion tensor metrics to visualize group differences in quantitative MR data. This approach may benefit future prediction models based on other quantitative MRI methods.

Microstructural Changes in the Brainstem Auditory Pathway in Children With Hearing Loss.

To assess the utility of diffusion tensor imaging of the auditory pathway in children with sensorineural hearing loss (SNHL). Retrospective cohort study. A single academic tertiary children's hospital. Sixteen pediatric patients with bilateral SNHL of at least moderate severity in the poorer ear (eight male; mean age, 5.3 ± 4.9 yrs). Controls consisted of age- and sex-matched children with normal hearing who were imaged for nonotologic, non-neurologic medical concerns and found to have normal magnetic resonance imaging (MRI). Three Tesla MRI scanners were used for diffusion tensor imaging. Quantitative diffusion tensor metrics were extracted from the superior olivary nucleus (SON), inferior colliculus (IC), and ipsilateral fiber tracts between the SON and IC delineated by tractography. We identified differences in fractional anisotropy of the SON between the SNHL cohort and controls (0.377 ± 0.056 vs. 0.422 ± 0.052; p = 0.009), but not in the IC. There were no differences in the mean diffusivity (MD) values in the IC and SON. Among younger children (≤5 yrs), MD was decreased in the SNHL cohort compared with controls in the IC (0.918 ± 0.051 vs. 1.120 ± 0.142; p < 0.001). However, among older children (>5 yrs), there were no differences in MD (1.124 ± 0.198 vs. 0.997 ± 0.103; p = 0.119). There were no differences in MD or fractional anisotropy in the white matter fibers of the IC-SON tract. Our results suggest abnormal neural tracts along the central auditory pathway among children with SNHL. Longitudinal studies should assess the prognostic value of these MRI-based findings for assessing long-term outcomes and determining intervention efficacy.

White and Gray Matter Abnormality in Burning Mouth Syndrome Evaluated with Diffusion Tensor Imaging and Neurite Orientation Dispersion and Density Imaging.

Burning mouth syndrome (BMS) is defined by a burning sensation or pain in the tongue or other oral sites despite the presence of normal mucosa on inspection. Both psychiatric and neuroimaging investigations have examined BMS; however, there have been no analyses using the neurite orientation dispersion and density imaging (NODDI) model, which provides detailed information of intra- and extracellular microstructures. Therefore, we performed voxel-wise analyses using both NODDI and diffusion tensor imaging (DTI) models and compared the results to better comprehend the pathology of BMS. Fourteen patients with BMS and 11 age- and sex-matched healthy control subjects were prospectively scanned using a 3T-MRI machine using 2-shell diffusion imaging. Diffusion tensor metrics (fractional anisotropy [FA], mean diffusivity [MD], axial diffusivity [AD], and radial diffusivity [RD]) and neurite orientation and dispersion index metrics (intracellular volume fraction [ICVF], isotropic volume fraction [ISO], and orientation dispersion index [ODI]) were retrieved from diffusion MRI data. These data were analyzed using tract-based spatial statistics (TBSS) and gray matter-based spatial statistics (GBSS). TBSS analysis showed that patients with BMS had significantly higher FA and ICVF and lower MD and RD than the healthy control subjects (family-wise error [FWE] corrected P < 0.05). Changes in ICVF, MD, and RD were observed in widespread white matter areas. Fairly small areas with different FA were included. GBSS analysis showed that patients with BMS had significantly higher ISO and lower MD and RD than the healthy control subjects (FWE-corrected P < 0.05), mainly limited to the amygdala. The increased ICVF in the BMS group may represent myelination and/or astrocytic hypertrophy, and microstructural changes in the amygdala in GBSS analysis indicate the emotional-affective profile of BMS.

Medial Temporal Lobe White Matter Integrity is Related to Memory and Amyloid‐Beta Pathology in Aging

AbstractBackgroundDamage to white matter (WM) within the medial temporal lobe (MTL) may underlie memory decline in aging and Alzheimer’s disease (AD). We measured MTL WM tract integrity through diffusion tensor imaging and Neurite Orientation Dispersion and Density Imaging (NODDI). We tested WM integrity associations with memory performance and amyloid‐ß pathology in cognitively normal older adults.Method105 cognitively normal older adults (70.8 years old, 65% female) with diffusion MRI were processed with MRtrix3. Standard diffusion metrics of fractional anisotropy (FA) and mean diffusivity (MD) were calculated. NODDI MDT was used to generate the neurite density index (NDI), representing number of voxels with unhindered diffusion, and the orientation dispersion index (ODI), representing the variability of neurite orientation. We calculated these measures for MTL tracts from the JHU WM Atlas: cingulum‐hippocampus, fornix column/body, and fornix/stria terminalis. Participants received 18F‐florbetapir (FBP) to measure Aß pathology (n = 94), and global FBP SUVR was calculated. Memory was measured with the Rey Auditory Verbal Learning Test delayed recall score (RAVLT, n = 81). Analyses covaried for age and/or education.ResultBetter memory was associated with lower ODI in the fornix column/body (r = ‐0.27, p = 0.018), with trends for higher FA (r = 0.021, p = 0.06) and lower NDI (r = ‐0.19, p = 0.09). Better memory was also associated with higher MD in the cingulum‐hippocampus (right: r = 0.23, p = 0.04; left: r = 0.25, p = 0.03), with trends for lower ODI (right: r = ‐.20, p = 0.08; left: r = ‐0.20, p = 0.09). Higher FBP SUVR was associated with increased FA in the fornix column/body (r = 0.31, p = 0.003) and a trend in the left cingulum hippocampus (r = 0.20, p = 0.054). Memory or FBP SUVR was not significantly related to integrity of the fornix/stria terminalis.ConclusionIntegrity of fornix column/body and cingulum‐hippocampus were related to both memory performance and Aß pathology. Our results suggest that using a combination of NODDI as well as diffusion tensor metrics provided a more robust understanding of memory impacts in aging.

Diurnal oscillations of MRI metrics in the brains of male participants

Regulation of biological processes according to a 24-hr rhythm is essential for the normal functioning of an organism. Temporal variation in brain MRI data has often been attributed to circadian or diurnal oscillations; however, it is not clear if such oscillations exist. Here, we provide evidence that diurnal oscillations indeed govern multiple MRI metrics. We recorded cerebral blood flow, diffusion-tensor metrics, T1 relaxation, and cortical structural features every three hours over a 24-hr period in each of 16 adult male controls and eight adult male participants with bipolar disorder. Diurnal oscillations are detected in numerous MRI metrics at the whole-brain level, and regionally. Rhythmicity parameters in the participants with bipolar disorder are similar to the controls for most metrics, except for a larger phase variation in cerebral blood flow. The ubiquitous nature of diurnal oscillations has broad implications for neuroimaging studies and furthers our understanding of the dynamic nature of the human brain.

Dystonia is Associated with Macro and Microstructural Abnormalities of the Cerebellum: Analysis of Volumetric and Diffusion-Weighted MRI Data in the UK Biobank (P11-11.004)

<h3>Objective:</h3> To determine whether subjects with dystonia in the UK Biobank exhibit MRI-based cerebellar pathology. <h3>Background:</h3> Dystonia is a movement disorder characterized by sustained or intermittent muscle contractions causing abnormal movements and/or postures. Although classically considered a basal-ganglia disorder, neuroimaging studies of dystonia have revealed physiologic, structural, and functional changes in both the (1) pallido-thalamic and (2) cerebello-thalamic networks. Diffusion-weighted-MRI (DWI) analyses of both networks in dystonia have revealed differences in non-specific Diffusion-Tensor metrics such as Fractional Anisotropy. However newer DWI models, such as Neurite Orientation Dispersion and Density Imaging (NODDI), assess more specific and biologically relevant white-matter properties. <h3>Design/Methods:</h3> Using the UK Biobank, 23 subjects with isolated dystonia and diffusion-MRI data were identified, along with 69 control subjects without neurologic (ICD10 G-coded) diagnoses, matched (1:3) on age, sex, imaging site, and medical comorbidities relevant to white-matter integrity (hypertension, hyperlipidemia, and diabetes). Two DTI metrics (mean diffusivity and fractional anisotropy) and three NODDI metrics (intracellular volume fraction, isotropic volume fraction, and orientation dispersion) were extracted from each of four tracts (inferior, middle, and superior cerebellar peduncles, and superior thalamic radiations). Volumetric data consisted of cerebellar (15 regions), basal ganglia (3 regions), and whole-brain grey and white-matter volumes. <h3>Results:</h3> After correcting for multiple-comparisons (40 volumetric and 35 diffusion-related) using the Benjamini-Hochberg procedure (FDR = 0.05), intracellular-volume-fraction (ICVF) of the middle- and superior-cerebellar peduncles was significantly lower in subjects with dystonia, suggesting reduced axon density; volumetric analysis showed significantly reduced volumes of the motor cerebellum (lobules VI and VIII). There were no differences in basal ganglia, cortical, or whole-brain volumes. <h3>Conclusions:</h3> These findings support the hypothesis that abnormalities of cerebellar networks contribute to the pathogenesis of dystonia. <b>Disclosure:</b> Dr. Mason has nothing to disclose. Miss Haddad has nothing to disclose. The institution of Neda Jahanshad has received research support from Biogen Inc.

Integrated diffusion image operator (iDIO): A pipeline for automated configuration and processing of diffusion MRI data.

The preprocessing of diffusion magnetic resonance imaging (dMRI) data involve numerous steps, including the corrections for head motion, susceptibility distortion, low signal-to-noise ratio, and signal drifting. Researchers or clinical practitioners often need to configure different preprocessing steps depending on disparate image acquisition schemes, which increases the technical threshold for dMRI analysis for nonexpert users. This could cause disparities in data processing approaches and thus hinder the comparability between studies. To make the dMRI data processing steps transparent and adapt to various dMRI acquisition schemes for researchers, we propose a semi-automated pipeline tool for dMRI named integrated diffusion image operator or iDIO. This pipeline integrates features from a wide range of advanced dMRI software tools and targets at providing a one-click solution for dMRI data analysis, via adaptive configuration for a set of suggested processing steps based on the image header of the input data. Additionally, the pipeline provides options for post-processing, such as estimation of diffusion tensor metrics and whole-brain tractography-based connectomes reconstruction using common brain atlases. The iDIO pipeline also outputs an easy-to-interpret quality control report to facilitate users to assess the data quality. To keep the transparency of data processing, the execution log and all the intermediate images produced in the iDIO's workflow are accessible. The goal of iDIO is to reduce the barriers for clinical or nonspecialist users to adopt the state-of-art dMRI processing steps.

White matter microstructure and receptive vocabulary in children with cerebral palsy: The role of interhemispheric connectivity.

Communication and cognitive impairments are common impediments to participation and social functioning in children with cerebral palsy (CP). Bilateral language networks underlie the function of some high-level language-related cognitive functions. To explore the association between receptive vocabulary and white-matter microstructure in the temporal lobes and the central part of the temporo-temporal bundles in children with CP. 37 children with spastic motor type CP (mean age 9.6 years, 25 male) underwent a receptive vocabulary test (Peabody Picture Vocabulary Test, PPVT-IV) and 3T MRI. Mean fractional anisotropy (FA) and mean diffusivity (MD) were calculated for the temporal lobes and the interhemispheric bundles traversing the splenium of the corpus callosum and the anterior commissure. Associations between microstructure and receptive vocabulary function were explored using univariable linear regression. PPVT-IV scores were significantly associated with mean white matter MD in the left temporal lobe, but not the right temporal lobe. There was no association between PPVT-IV and mean white matter FA in the temporal lobes. PPVT-IV scores were not significantly associated with the laterality of these diffusion tensor metrics. Within the corpus callosum, FA, but not MD of the temporo-temporal bundles was significantly associated with the PPVT-IV scores. Within the anterior commissure no equivalent relationship between diffusion metrics and PPVT-IV was found. Our findings add further understanding to the pathophysiological basis underlying receptive vocabulary skills in children with CP that could extend to other patients with early brain damage. This study highlights the importance of interhemispheric connections for receptive vocabulary.

Aberrant frontal lobe “U”-shaped association fibers in first-episode schizophrenia: A 7-Tesla Diffusion Imaging Study

Schizophrenia is believed to be a developmental disorder with one hypothesis suggesting that symptoms arise due to abnormal interactions (or disconnectivity) between different brain regions. While some major deep white matter pathways have been extensively studied (e.g. arcuate fasciculus), studies of short-ranged, “U”-shaped tracts have been limited in patients with schizophrenia, in part due to the sheer abundance of tracts present and due to the spatial variations across individuals that defy probabilistic characterization in the absence of reliable templates. In this study, we use diffusion magnetic resonance imaging (dMRI) to investigate frontal lobe superficial white matter that are present in the majority of study participants, comparing healthy controls and minimally treated patients with first-episode schizophrenia (<3 median days of lifetime treatment). Through group comparisons, 3 out of 63 frontal lobe “U”-shaped tracts were found to demonstrate localized aberrations affecting the microstructural tissue properties (via diffusion tensor metrics) in this early stage of disease. No associations were found in patients between aberrant segments of affected tracts and clinical or cognitive variables. Aberrations in the frontal lobe “U”-shaped tracts in early untreated stages of psychosis occur irrespective of symptom burden, and are distributed across critical functional networks associated with executive function and salience processing. While we limited the investigation to the frontal lobe, a framework has been developed to study such connections in other brain regions, enabling further extensive investigations jointly with the major deep white matter pathways.

Microstructural Alterations in Projection and Association Fibers in Neonatal Hypoxia-Ischemia.

Diffusion MRI (dMRI) is known to be sensitive to hypoxic-ischemic encephalopathy (HIE). However, existing dMRI studies used simple diffusion tensor metrics and focused only on a few selected cerebral regions, which cannot provide a comprehensive picture of microstructural injury. To systematically characterize the microstructural alterations in mild, moderate, and severe HIE neonates compared to healthy neonates with advanced dMRI using region of interest (ROI), tract, and fixel-based analyses. Prospective. A total of 42 neonates (24 males and 18 females). 3-T, diffusion-weighted echo-planar imaging. Fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), axial diffusivity (AD), fiber density (FD), fiber cross-section (FC), and fiber density and cross-section (FDC) were calculated in 40 ROIs and 6 tracts. Fixel-based analysis was performed to assess group differences in individual fiber components within a voxel (fixel). One-way analysis of covariance (ANCOVA) to compare dMRI metrics among severe/moderate/mild HIE and control groups and general linear model for fixel-wise group differences (age, sex, and body weight as covariates). Adjusted P value < 0.05 was considered statistically significant. For severe HIE, ROI-based analysis revealed widespread regions, including the deep nuclei and white matter with reduced FA, while in moderate injury, only FC was decreased around the posterior watershed zones. Tract-based analysis demonstrated significantly reduced FA, FD, and FC in the right inferior fronto-occipital fasciculus (IFOF), right inferior longitudinal fasciculus (ILF), and splenium of corpus callosum (SCC) in moderate HIE, and in right IFOF and left anterior thalamic radiation (ATR) in mild HIE. Correspondingly, we found altered fixels in the right middle-posterior IFOF and ILF, and in the central-to-right part of SCC in moderate HIE. For severe HIE, extensive microstructural injury was identified. For moderate-mild HIE, association fiber injury in posterior watershed area with a rightward lateralization was found. 1 TECHNICAL EFFICACY: Stage 3.

A groupwise registration and tractography framework for cardiac myofiber architecture description by diffusion MRI: An application to the ventricular junctions.

BackgroundKnowledge of the normal myocardial–myocyte orientation could theoretically allow the definition of relevant quantitative biomarkers in clinical routine to diagnose heart pathologies. A whole heart diffusion tensor template representative of the global myofiber organization over species is therefore crucial for comparisons across populations. In this study, we developed a groupwise registration and tractography framework to resolve the global myofiber arrangement of large mammalian sheep hearts. To demonstrate the potential application of the proposed method, a novel description of sub-regions in the intraventricular septum is presented.MethodsThree explanted sheep (ovine) hearts (size ~12×8×6 cm3, heart weight ~ 150 g) were perfused with contrast agent and fixative and imaged in a 9.4T magnet. A group-wise registration of high-resolution anatomical and diffusion-weighted images were performed to generate anatomical and diffusion tensor templates. Diffusion tensor metrics (eigenvalues, eigenvectors, fractional anisotropy …) were computed to provide a quantitative and spatially-resolved analysis of cardiac microstructure. Then tractography was performed using deterministic and probabilistic algorithms and used for different purposes: i) Visualization of myofiber architecture, ii) Segmentation of sub-area depicting the same fiber organization, iii) Seeding and Tract Editing. Finally, dissection was performed to confirm the existence of macroscopic structures identified in the diffusion tensor template.ResultsThe template creation takes advantage of high-resolution anatomical and diffusion-weighted images obtained at an isotropic resolution of 150 μm and 600 μm respectively, covering ventricles and atria and providing information on the normal myocardial architecture. The diffusion metric distributions from the template were found close to the one of the individual samples validating the registration procedure. Small new sub-regions exhibiting spatially sharp variations in fiber orientation close to the junctions of the septum and ventricles were identified. Each substructure was defined and represented using streamlines. The existence of a fiber-bundles in the posterior junction was validated by anatomical dissection. A complex structural organization of the anterior junction in comparison to the posterior junction was evidenced by the high-resolution acquisition.ConclusionsA new framework combining cardiac template generation and tractography was applied on the whole sheep heart. The framework can be used for anatomical investigation, characterization of microstructure and visualization of myofiber orientation across samples. Finally, a novel description of the ventricular junction in large mammalian sheep hearts was proposed.