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.

Value of Spinal Cord Diffusion Imaging and Tractography in Providing Predictive Factors for Tumor Resection in Patients with Intramedullary Tumors: A Pilot Study.

This pilot study aimed to investigate the interest of high angular resolution diffusion imaging (HARDI) and tractography of the spinal cord (SC) in the management of patients with intramedullary tumors by providing predictive elements for tumor resection. Eight patients were included in a prospective study. HARDI images of the SC were acquired using a 3T MRI scanner with a reduced field of view. Opposed phase-encoding directions allowed distortion corrections. SC fiber tracking was performed using a deterministic approach, with extraction of tensor metrics. Then, regions of interest were drawn to track the spinal pathways of interest. HARDI and tractography added value by providing characteristics about the microstructural organization of the spinal white fibers. In patients with SC tumors, tensor metrics demonstrated significant changes in microstructural architecture, axonal density, and myelinated fibers (all, p < 0.0001) of the spinal white matter. Tractography aided in the differentiation of tumor histological types (SC-invaded vs. pushed back by the tumor), and differentiation of the spinal tracts enabled the determination of precise anatomical relationships between the tumor and the SC, defining the tumor resectability. This study underlines the value of using HARDI and tractography in patients with intramedullary tumors, to show alterations in SC microarchitecture and to differentiate spinal tracts to establish predictive factors for tumor resectability.

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.

Investigating apparent differences between standard DKI and axisymmetric DKI and its consequences for biophysical parameter estimates.

The purpose of the study is to identify differences between axisymmetric diffusion kurtosis imaging (DKI) and standard DKI, their consequences for biophysical parameter estimates, and the protocol choice influence on parameter estimation. Noise-free and noisy, synthetic diffusion MRI human brain data is simulated using standard DKI for a standard and the fast "199" acquisition protocol. First the noise-free "baseline" difference between both DKI models is estimated and the influence of fiber complexity is investigated. Noisy data is used to establish the signal-to-noise ratio at which the baseline difference exceeds noise variability. The influence of protocol choices and denoising is investigated. The five axisymmetric DKI tensor metrics (AxTM), the parallel and perpendicular diffusivity and kurtosis and mean of the kurtosis tensor are used to compare both DKI models. Additionally, the baseline difference is also estimated for the five parameters of the WMTI-Watson model. The parallel and perpendicular kurtosis and all of the WMTI-Watson parameters had large baseline differences. Using a Westin or FA mask reduced the number of voxels with large baseline difference, that is, by selecting voxels with less complex fibers. For the noisy data, precision was worsened by the fast "199" protocol but adaptive denoising can help counteract these effects. For the diffusivities and mean of the kurtosis tensor, axisymmetric DKI with a standard protocol delivers similar results as standard DKI. Fiber complexity is one main driver of the baseline differences. Using the "199" protocol worsens precision in noisy data but adaptive denoising mitigates these effects.

Evaluation of vertical alignment in carbon nanotubes: A quantitative approach

An automated quantitative study of the alignment of vertically aligned carbon nanotubes (VA-CNTs) from scanning electron microscopy (SEM) images has been demonstrated. It is based on the fact that the image gradient (directional change in intensity or color) at every pixel in the image contains the information about the anisotropy at that particular pixel i.e., the local alignment is maintained in the orthogonal direction to the gradient. Structure tensor metrics are formulated demonstrating to be able to summarize the distribution of gradient directions within the neighborhood of any pixel within a two-dimensional domain (surface). An image analysis is presented that evaluates the alignment in desired regions of interest (ROIs) in SEM images of VA-CNTs. This method has been exploited to study the alignment of two different kinds of VA-CNTs: one grown via the thermal chemical vapor deposition (T-CVD) method and the second synthesized via the plasma enhanced chemical vapor deposition (PE-CVD) method.

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.

Body Mass Index, but not Visceral Abdominal Obesity and Insulin Resistance, Negatively Impacts White Matter Microstructure in Midlife

AbstractBackgroundMidlife obesity, as evidenced by high body mass index (BMI), is increasingly understood as a risk factor for development of Alzheimer’s disease. Importantly, visceral fat, by producing adipokines, contributes to inflammation as well as insulin resistance, mechanisms involved in development of Alzheimer’s disease. Herein, we aimed to assess association of white matter microstructure, measured by Diffusion tensor imaging (DTI), with obesity and abdominal adipose tissue composition at midlife.MethodA total of 34 middle‐aged (age: 51.27 ± 6.12 years, BMI: 32.28 ± 6.39 kg/m2), cognitively normal participants, underwent bloodwork, brain and abdominal MRI. Insulin resistance status was assessed using Homeostatic Model Assessment for Insulin Resistance. Abdominal visceral and subcutaneous adipose tissue (VAT, SAT) were automatically segmented using VOXel Analysis Suite (Voxa) and VAT/SAT ratio was calculated. A diffusion spectrum imaging scheme with a total of 98 diffusion samplings and a voxel size of 2 mm was acquired. Using DSI studio (https://dsi‐studio.labsolver.org/), the diffusion data were reconstructed using generalized q‐sampling imaging and the tensor metrics were calculated. Fractional anisotropy (FA), mean, axial, and radial diffusivity (MD, AD, RD, respectively) for white matter tracts were compared across study groups using one‐way ANOVA. Multiple regression models with sex, age, and education as covariates were used to assess the association of BMI, VAT/SAT ratio, and HOMA‐IR with DTI metrics.ResultOur data showed that BMI was negatively associated with AD values in right inferior fronto‐occipital fasciculus, tapetum and forceps major of Corpus Callosum (CC), superior and middle cerebellar peduncle, and bilateral reticular tract. The negative association of BMI with AD values in tapetum of CC was significant only in women. Right reticular tract MD and superior cerebellar peduncle MD and RD values were also significantly associated with BMI. There were no significant associations between neither of diffusion metrics and VAT/SAT ratio or HOMA‐IR.ConclusionAssociation of midlife obesity with lower axonal diffusivity, reflective of lower white matter integrity, with differential patterns in men and women especially in CC, is in line with previous findings in healthy individuals. Reduced axonal diffusivity in reticular and brain stem pathways could be indicative of early dysregulations observed in Alzheimer’s disease.

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.