Diffusion Tensor Imaging Data Research Articles

Integration of whole-exome sequencing and structural neuroimaging analysis in major depressive disorder: a joint study

Major depressive disorder (MDD) is a common mental illness worldwide and is triggered by an intricate interplay between environmental and genetic factors. Although there are several studies on common variants in MDD, studies on rare variants are relatively limited. In addition, few studies have examined the genetic contributions to neurostructural alterations in MDD using whole-exome sequencing (WES). We performed WES in 367 patients with MDD and 161 healthy controls (HCs) to detect germline and copy number variations in the Korean population. Gene-based rare variants were analyzed to investigate the association between the genes and individuals, followed by neuroimaging-genetic analysis to explore the neural mechanisms underlying the genetic impact in 234 patients with MDD and 135 HCs using diffusion tensor imaging data. We identified 40 MDD-related genes and observed 95 recurrent regions of copy number variations. We also discovered a novel gene, FRMPD3, carrying rare variants that influence MDD. In addition, the single nucleotide polymorphism rs771995197 in the MUC6 gene was significantly associated with the integrity of widespread white matter tracts. Moreover, we identified 918 rare exonic missense variants in genes associated with MDD susceptibility. We postulate that rare variants of FRMPD3 may contribute significantly to MDD, with a mild penetration effect.

Empirical assessment of the assumptions of ComBat with diffusion tensor imaging.

Diffusion tensor imaging (DTI) is a magnetic resonance imaging technique that provides unique information about white matter microstructure in the brain but is susceptible to confounding effects introduced by scanner or acquisition differences. ComBat is a leading approach for addressing these site biases. However, despite its frequent use for harmonization, ComBat's robustness toward site dissimilarities and overall cohort size have not yet been evaluated in terms of DTI. As a baseline, we match participants from two sites to create a "silver standard" that simulates a cohort for multi-site harmonization. Across sites, we harmonize mean fractional anisotropy and mean diffusivity, calculated using participant DTI data, for the regions of interest defined by the JHU EVE-Type III atlas. We bootstrap 10 iterations at 19 levels of total sample size, 10 levels of sample size imbalance between sites, and 6 levels of mean age difference between sites to quantify (i), the linear regression coefficient of the relationship between FA and age; (ii), the ComBat-estimated site-shift; and (iii), the ComBat-estimated site-scaling. We characterize the reliability of ComBat by evaluating the root mean squared error in these three metrics and examine if there is a correlation between the reliability of ComBat and a violation of assumptions. ComBat remains well behaved for when and when the mean age difference is less than 4 years. The assumptions of the ComBat model regarding the normality of residual distributions are not violated as the model becomes unstable. Prior to harmonization of DTI data with ComBat, the input cohort should be examined for size and covariate distributions of each site. Direct assessment of residual distributions is less informative on stability than bootstrap analysis. We caution use ComBat of in situations that do not conform to the above thresholds.

Exploiting macro- and micro-structural brain changes for improved Parkinson’s disease classification from MRI data

Parkinson’s disease (PD) is the second most common neurodegenerative disease. Accurate PD diagnosis is crucial for effective treatment and prognosis but can be challenging, especially at early disease stages. This study aimed to develop and evaluate an explainable deep learning model for PD classification from multimodal neuroimaging data. The model was trained using one of the largest collections of T1-weighted and diffusion-tensor magnetic resonance imaging (MRI) datasets. A total of 1264 datasets from eight different studies were collected, including 611 PD patients and 653 healthy controls (HC). These datasets were pre-processed and non-linearly registered to the MNI PD25 atlas. Six imaging maps describing the macro- and micro-structural integrity of brain tissues complemented with age and sex parameters were used to train a convolutional neural network (CNN) to classify PD/HC subjects. Explainability of the model’s decision-making was achieved using SmoothGrad saliency maps, highlighting important brain regions. The CNN was trained using a 75%/10%/15% train/validation/test split stratified by diagnosis, sex, age, and study, achieving a ROC-AUC of 0.89, accuracy of 80.8%, specificity of 82.4%, and sensitivity of 79.1% on the test set. Saliency maps revealed that diffusion tensor imaging data, especially fractional anisotropy, was more important for the classification than T1-weighted data, highlighting subcortical regions such as the brainstem, thalamus, amygdala, hippocampus, and cortical areas. The proposed model, trained on a large multimodal MRI database, can classify PD patients and HC subjects with high accuracy and clinically reasonable explanations, suggesting that micro-structural brain changes play an essential role in the disease course.

Structural changes in spinal cord following optic neuritis: Insights from quantitative spinal MRI

ObjectivesPrevious studies have demonstrated that optic neuritis (ON) affects brain plasticity. However, whether ON affects the spinal cord remains unclear. We aimed to investigate the spinal cord changes in ON and their associations with disability. MethodsA total of 101 ON patients, and 41 healthy controls (HC) were retrospectively recruited. High-resolution imaging was conducted using a Magnetization Prepared Rapid Acquisition Gradient-Echo (MP-RAGE) sequence for T1-weighted images and an echo planar imaging (EPI) sequence for Diffusion Tensor Imaging (DTI) data collection. Additionally, patients' disability and cognitive impairment were evaluated using the Expanded Disability Status Scale (EDSS) and the Paced Auditory Serial Addition Test (PASAT), respectively. The quantitative spinal MRI was employed to examine the cross-sectional area (CSA) and diffusion indicators, with a specific focus on calculating the average values across the C2-C7 cervical spinal cord segments. CSA, fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were compared between groups. Correlation analyses were performed between CSA, diffusion indicators, and clinical variables. ResultsNo significant differences were found in CSA between ON patients and HCs. MD (p = 0.007) and RD (p = 0.018) were increased in ON patients compared with HCs, and AD was decreased in ON (p = 0.013). The AD values of the ON patients were significantly positively correlated with PASAT scores (r = 0.37, p < 0.001). ConclusionsThis study provided imaging evidence for DTI abnormalities in patients with ON. Spinal cord DTI can improve our knowledge of the path physiology of ON, and clinical progression.

Changes in Structural Neural Networks in the Recovery Process of Motor Paralysis after Stroke.

In recent years, neurorehabilitation has been actively used to treat motor paralysis after stroke. However, the impacts of rehabilitation on neural networks in the brain remain largely unknown. Therefore, we investigated changes in structural neural networks after rehabilitation therapy in patients who received a combination of low-frequency repetitive transcranial magnetic stimulation (LF-rTMS) and intensive occupational therapy (intensive-OT) as neurorehabilitation. Fugl-Meyer assessment (FMA) for upper extremity (FMA-UE) and Action Research Arm Test (ARAT), both of which reflected upper limb motor function, were conducted before and after rehabilitation therapy. At the same time, diffusion tensor imaging (DTI) and three-dimensional T1-weighted imaging (3D T1WI) were performed. After analyzing the structural connectome based on DTI data, measures related to connectivity in neural networks were calculated using graph theory. Rehabilitation therapy prompted a significant increase in connectivity with the isthmus of the cingulate gyrus in the ipsilesional hemisphere (p < 0.05) in patients with left-sided paralysis, as well as a significant decrease in connectivity with the ipsilesional postcentral gyrus (p < 0.05). These results indicate that LF-rTMS combined with intensive-OT may facilitate motor function recovery by enhancing the functional roles of networks in motor-related areas of the ipsilesional cerebral hemisphere.

White matter development and language abilities during infancy in autism spectrum disorder.

White matter (WM) fiber tract differences are present in autism spectrum disorder (ASD) and could be important markers of behavior. One of the earliest phenotypic differences in ASD are language atypicalities. Although language has been linked to WM in typical development, no work has evaluated this association in early ASD. Participants came from the Infant Brain Imaging Study and included 321 infant siblings of children with ASD at high likelihood (HL) for developing ASD; 70 HL infants were later diagnosed with ASD (HL-ASD), and 251 HL infants were not diagnosed with ASD (HL-Neg). A control sample of 140 low likelihood infants not diagnosed with ASD (LL-Neg) were also included. Infants contributed expressive language, receptive language, and diffusion tensor imaging data at 6-, 12-, and 24 months. Mixed effects regression models were conducted to evaluate associations between WM and language trajectories. Trajectories of microstructural changes in the right arcuate fasciculus were associated with expressive language development. HL-ASD infants demonstrated a different developmental pattern compared to the HL-Neg and LL-Neg groups, wherein the HL-ASD group exhibited a positive association between WM fractional anisotropy and language whereas HL-Neg and LL-Neg groups showed weak or no association. No other fiber tracts demonstrated significant associations with language. In conclusion, results indicated arcuate fasciculus WM is linked to language in early toddlerhood for autistic toddlers, with the strongest associations emerging around 24 months. To our knowledge, this is the first study to evaluate associations between language and WM development during the pre-symptomatic period in ASD.

Urban environment during pregnancy and childhood and white matter microstructure in preadolescence in two European birth cohorts

Growing evidence suggests that urban environment may influence cognition and behavior in children, but the underlying pollutant and neurobiological mechanisms are unclear. We evaluated the association of built environment and urban natural space indicators during pregnancy and childhood with brain white matter microstructure in preadolescents, and examined the potential mediating role of air pollution and road-traffic noise. We used data of the Generation R Study, a population-based birth cohort in Rotterdam, the Netherlands (n = 2725; 2002–2006) for the primary analyses. Replication of the main findings was attempted on an independent neuroimaging dataset from the PELAGIE birth cohort, France (n = 95; 2002–2006). We assessed exposures to 12 built environment and 4 urban natural spaces indicators from conception up to 9 years of age. We computed 2 white matter microstructure outcomes (i.e., average of fractional anisotropy (FA) and mean diffusivity (MD) from 12 white matte tracts) from diffusion tensor imaging data. Greater distance to the nearest major green space during pregnancy was associated with higher whole-brain FA (0.001 (95%CI 0.000; 0.002) per 7 m increase), and higher land use diversity during childhood was associated with lower whole-brain MD (−0.001 (95%CI -0.002; −0.000) per 0.12-point increase), with no evidence of mediation by air pollution nor road-traffic noise. Higher percentage of transport and lower surrounding greenness during pregnancy were associated with lower whole-brain FA, and road-traffic noise mediated 19% and 52% of these associations, respectively. We found estimates in the same direction in the PELAGIE cohort, although confidence intervals were larger and included the null. This study suggests an association between urban environment and white matter microstructure, mainly through road-traffic noise, indicating that greater access to green space nearby might promote white matter development.

Distinguishing schizophrenia and bipolar disorder through a Multiclass Classification model based on multimodal neuroimaging data

This study aimed to identify neural biomarkers for schizophrenia (SZ) and bipolar disorder (BP) by analyzing multimodal neuroimaging. Utilizing data from structural magnetic resonance imaging (sMRI), diffusion tensor imaging (DTI), and resting-state functional magnetic resonance imaging (rs-fMRI), multiclass classification models were created for SZ, BP, and healthy controls (HC). A total of 113 participants (BP: 31, SZ: 39, and HC: 43) were recruited under strict enrollment control, from which 272, 200, and 1875 features were extracted from sMRI, DTI, and rs-fMRI data, respectively. A support vector machine (SVM) with recursive feature elimination (RFE) was employed to build the models using a one-against-one approach and leave-one-out cross-validation, achieving a classification accuracy of 70.8%. The most discriminative features were primarily from rs-fMRI, along with significant findings in sMRI and DTI. Key biomarkers identified included the increased thickness of the left cuneus cortex and decreased regional functional connectivity strength (rFCS) in the left supramarginal gyrus as shared indicators for BP and SZ. Additionally, decreased fractional anisotropy in the left superior fronto-occipital fasciculus was suggested as specific to BP, while decreased rFCS in the left inferior parietal area might serve as a specific biomarker for SZ. These findings underscore the potential of multimodal neuroimaging in distinguishing between BP and SZ and contribute to the understanding of their neural underpinnings.

A structural equation modeling approach using behavioral and neuroimaging markers in major depressive disorder

Major depressive disorder (MDD) has consistently proven to be a multifactorial and highly comorbid disease. Despite recent depression-related research demonstrating causalities between MDD-related factors and a small number of variables, including brain structural changes, a high-statistical power analysis of the various factors is yet to be conducted. We retrospectively analyzed data from 155 participants (84 healthy controls and 71 patients with MDD). We used magnetic resonance imaging and diffusion tensor imaging data, scales assessing childhood trauma, depression severity, cognitive dysfunction, impulsivity, and suicidal ideation. To simultaneously evaluate the causalities between multivariable, we implemented two types of MDD-specified structural equation models (SEM), the behavioral and neurobehavioral models. Behavioral SEM showed significant results in the MDD group: Comparative Fit Index [CFI] = 1.000, Root Mean Square Error of Approximation [RMSEA]) = 0.000), with a strong correlation in the scales for childhood trauma, depression severity, suicidal ideation, impulsivity, and cognitive dysfunction. Based on behavioral SEM, we established neurobehavioral models showing the best-fit in MDD, especially including the right cingulate cortex, central to the posterior corpus callosum, right putamen, pallidum, whole brainstem, and ventral diencephalon, including the thalamus (CFI >0.96, RMSEA <0.05). Our MDD-specific model revealed that the limbic-associated regions are strongly connected with childhood trauma rather than depression severity, and that they independently affect suicidal ideation and cognitive dysfunction. Furthermore, cognitive dysfunction could affect impulsivity.

Positive Effects of Uric Acid on White Matter Microstructures and Treatment Response in Patients With Schizophrenia.

Schizophrenia involves microstructural changes in white matter (WM) tracts. Oxidative stress is a key factor causing WM damage by hindering oligodendrocyte development and myelin maturation. Uric acid (UA), an endogenous antioxidant, may protect against oxidative stress. We investigated the effect of UA on WM connectivity in antipsychotic-naive or -free patients with early- or chronic-stage schizophrenia. A total of 192 patients with schizophrenia (122 recent-onset [ROS] and 70 chronic [CS]) and 107 healthy controls (HCs) participated in this study. Diffusion tensor imaging data and serum UA levels at baseline were obtained. Fractional anisotropy was lower in the widespread WM regions across the whole brain, and diffusivity measures were higher in both schizophrenia groups than in HCs. The CS group showed lower diffusivity in some WM tracts than the ROS or HC groups. The linear relationship of serum UA levels with axial and mean diffusivity in the right frontal region was significantly different between schizophrenia stages, which was driven by a negative association in the CS group. WM diffusivity associated with serum UA levels correlated with 8-week treatment responses only in patients with CS, suggesting UA to be protective against long-term schizophrenia. UA may protect against the WM damage associated with the progression of schizophrenia by reducing oxidative stress and supporting WM repair against oxidative damage. These results provide insights into the positive role of UA and may facilitate the development of novel disease-modifying therapies.

Alterations in white matter fiber tracts and their correlation with flying cadet behavior.

An increasing number of studies have shown that flight training alters the human brain structure; however, most studies have focused on gray matter, and the exploration of white matter structure has been largely neglected. This study aimed to investigate the changes in white matter structure induced by flight training and estimate the correlation between such changes and psychomotor and flight performance. Diffusion tensor imaging data were obtained from 25 flying cadets and 24 general college students. Data were collected in 2019 and 2022 and analyzed using automated fiber quantification. This study found no significant changes in the flight group in 2019. However, in 2022, the flight group exhibited significant alterations in the diffusion tensor imaging of the right anterior thalamic radiation, left cingulum cingulate, bilateral superior longitudinal fasciculus, and left arcuate fasciculus. These changes occurred within local nodes of the fiber tracts. In addition, we found that changes in fiber tracts in the 2022 flight group were correlated with the reaction time of the psychomotor test task and flight duration. These findings may help improve flight training programs and provide new ideas for the selection of excellent pilots.

Microstructural white matter changes underlying speech deficits in Parkinson’s disease

Speech impairments are one of the common symptoms of individuals with Parkinson’s disease (PD). However, little is known about the underlying neuroanatomical structural deficits specifically in the basal ganglia-thalamocortical (BGTC) loop in the speech deficits of PD. Here we investigated white matter differences in PD using probabilistic tractography. Diffusion tensor imaging data were downloaded from the Parkinson’s Progression Markers Initiative database. We included three groups of participants: 20 PD individuals with speech deficits, 20 PD individuals without speech deficits, and 20 age- and gender-matched control participants. Overall, PD individuals with speech deficits had higher mean diffusivity in the BGTC pathway in the left hemisphere compared with PD individuals without speech deficits. The present study exhibits that there may be a distinct pathophysiological profile of white matter for speech deficits in PD.

Predictions of neural control deficits in elders with subjective memory complaints and Alzheimer’s disease

Alzheimer’s disease (AD) is a common disease, affecting over 6 million elders in the U.S in 2024. However, AD remains untreatable due to the absence of an effective biomarker to assess the underlying deficits in cognitive control. Disruptions in the brain's control systems are key factors in the learning and memory impairments that define AD. In this study, we hypothesized that we could predict neural control deficits in elders with subjective memory complaints (SMC) and AD patients using Diffusion Tensor Imaging (DTI) data and brain controllability analysis. DTI is a non-invasive neuroimaging tool that detects how fluid travels along the white matter tracts inside the brain. To test our hypothesis, we used the DTI data of 12 elders with SMC, 12 AD patients, and 12 healthy subjects from the open-source dataset Alzheimer's Disease Neuroimaging Initiative. First, we constructed individual brain connectivity networks. Using graph theory and brain controllability analysis, we assessed node degree and controllability. We then averaged these measures across brain areas to calculate global metrics for each subject. Results illustrated that the node degree could not identify SMC and AD from healthy subjects, while the controllability measure could differentiate SMC and AD from healthy subjects and distinguish between SMC and AD. In conclusion, this study provides a promising biomarker for detecting neural control deficits in elders with SMC and AD patients for future clinical application.

Multi-Dimensional Clustering Based on Restricted Distance-Dependent Mixture Dirichlet Process for Diffusion Tensor Imaging

Brain imaging research poses challenges due to the intricate structure of the brain and the absence of clearly discernible features in the images. In this study, we propose a technique for analyzing brain image data identifying crucial regions relevant to patients’ conditions, specifically focusing on Diffusion Tensor Imaging data. Our method utilizes the Bayesian Dirichlet process prior incorporating generalized linear models, that enhances clustering performance while it benefits from the flexibility of accommodating varying numbers of clusters. Our approach improves the performance of identifying potential classes utilizing locational information by considering the proximity between locations as clustering constraints. We apply our technique to a dataset from Transforming Research and Clinical Knowledge in Traumatic Brain Injury study, aiming to identify important regions in the brain’s gray matter, white matter, and overall brain tissue that differentiate between young and old age groups. Additionally, we explore a link between our discoveries and the existing outcomes in the field of brain network research.

Alteration of white matter microstructure in patients with sleep disorders after COVID-19 infection

BackgroundThe pathophysiology of coronasomnia remains unclear. This study aimed to investigate changes in white matter (WM) microstructure and inflammatory factors in patients with sleep disorders (SD) characterized by poor sleep quantity, quality, or timing following coronavirus disease 2019 (COVID-19) infection in the acute phase (within one month) and whether these changes could be recovered at 3-month follow-up. Methods29 acute COVID-19 patients with SD (COVID_SD) and 27 acute COVID-19 patients without SD (COVID_NonSD) underwent diffusion tensor imaging (DTI), tested peripheral blood inflammatory cytokines level, and measured Pittsburgh Sleep Quality Index (PSQI), and matched 30 uninfected healthy controls. Analyzed WM abnormalities between groups in acute phase and explored its changes in COVID_SD at 3-month follow-up by using tract-based spatial statistics (TBSS). Correlations between DTI and clinical data were examined using Spearman partial correlation analysis. ResultsBoth COVID_SD and COVID_NonSD exhibited widespread WM microstructure abnormalities. The COVID_SD group showed specific WM microstructure changes in right inferior fronto-occipital fasciculus (IFOF) (lower fractional anisotropy [FA]/axial diffusivity [AD] and higher radial diffusivity [RD]) and left corticospinal tract (CST) (higher FA and lower RD) and higher interleukin-1β (IL-1β) compared with COVID_NonSD group. These WM abnormalities and IL-1β levels were correlated PSQI score. After 3 months, the IFOF integrity and IL-1β levels tended to return to normal accompanied by symptom improvement in the COVID_SD relative to baseline. ConclusionAbnormalities in right IFOF and left CST and elevated IL-1β levels were important neurophenotypes correlated with COVID_SD, which might provide new insights into the pathogenesis of neuroinflammation in SD patients induced by COVID-19.

A machine learning pipeline for efficient differentiation between bipolar and major depressive disorder based on multimodal structural neuroimaging

Due to the overlapping depressive symptomatology with major depressive disorder (MDD), 60% of patients with bipolar disorder (BD) are initially misdiagnosed, calling for the definition of reliable biomarkers that can support the diagnostic process. Here, we optimized a machine learning pipeline for the differentiation between depressed BD and MDD patients based on multimodal structural neuroimaging features. Diffusion tensor imaging (DTI) and T1-weighted magnetic resonance imaging (MRI) data were acquired for 282 depressed BD (n = 180) and MDD (n = 102) patients. Images were preprocessed to obtain axial (AD), radial (RD), mean (MD) diffusivity, fractional anisotropy (FA), and voxel-based morphometry (VBM) maps. Each feature was entered separately into a 5-fold nested cross-validated predictive pipeline differentiating between BD and MDD patients, comprising: confound regression for nuisance variables removal, feature standardization, principal component analysis for feature reduction, and an elastic-net penalized regression. The DTI-based models reached accuracies ranging from 75% to 78%, whereas the VBM model reached 61% of accuracy. All the models were significantly different from a null model distribution at a 5000-permutation test. A 5000 bootstrap procedure revealed that widespread differences drove the classification, with BD patients associated to overall higher values of AD and FA, and grey matter volumes. Our results suggest that structural neuroimaging, in particular white matter microstructure and grey matter volumes, may be able to differentiate between MDD and BD patients with good predictive accuracy, being significantly higher than chance-level.

Effects of web-based mindfulness training on psychological outcomes, attention, and neuroplasticity

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

Quantification of perineural satellitosis in pretreatment glioblastoma with structural MRI and a diffusion tensor imaging template.

Survival outcomes for glioblastoma (GBM) patients remain unfavorable, and tumor recurrence is often observed. Understanding the radiological growth patterns of GBM could aid in improving outcomes. This study aimed to examine the relationship between contrast-enhancing tumor growth direction and white matter, using an image registration and deformation strategy. In GBM patients 2 pretreatment scans (diagnostic and neuronavigation) were gathered retrospectively, and coregistered to a template and diffusion tensor imaging (DTI) atlas. The GBM lesions were segmented and coregistered to the same space. Growth vectors were derived and divided into vector populations parallel (Φ = 0-20°) and perpendicular (Φ = 70-90°) to white matter. To test for statistical significance between parallel and perpendicular groups, a paired samples Student's t-test was performed. O6-methylguanine-DNA methyltransferase (MGMT) methylation status and its correlation to growth rate were also tested using a one-way ANOVA test. For 78 GBM patients (mean age 61 years ± 13 SD, 32 men), the included GBM lesions showed a predominant preference for perineural satellitosis (P < .001), with a mean percentile growth of 30.8% (95% CI: 29.6-32.0%) parallel (0° < |Φ| < 20°) to white matter. Perpendicular tumor growth with respect to white matter microstructure (70° < |Φ| < 90°) showed to be 22.7% (95% CI: 21.3-24.1%) of total tumor growth direction. The presented strategy showed that tumor growth direction in pretreatment GBM patients correlated with white matter architecture. Future studies with patient-specific DTI data are required to verify the accuracy of this method prospectively to identify its usefulness as a clinical metric in pre and posttreatment settings.

Facilitating diffusion tensor imaging of the brain during continuous gross head motion with first and second order motion compensating diffusion gradients.

To demonstrate the feasibility of motion compensating diffusion gradient schemes in the acquisition of quality diffusion tensor images (DTI) of the brain during continuous gross head motion. Five healthy subjects were scanned using a clinical 3 T MRI with and without continuous head motion. For one volunteer, DTI data was acquired using standard (M0) diffusion-weighted (DW) gradients, and first (M1) and second (M2) order gradient schemes that were previously developed for use in cardiac DTI. In four additional volunteers, DTI data was acquired with M0 and M2 gradients. DTI parameters were calculated and compared with established retrospective motion corrections. In the absence of motion, DTI parameters calculated from M0, M1, and M2 data were consistent. In the presence of motion, up to 44% of DW images acquired with M0 gradients were corrupted by signal dropout, compared to 0% of the M2 images. In voxelwise comparisons, DTI parameters calculated using motion-M0 data were elevated compared to reference data. Retrospective corrections for extreme motion applied to motion-M0 data did not improve consistency with reference data in cases where motion corrupted >15% of DW images. In contrast, DTI parameters calculated with motion-M2 data were consistent with reference data. This proof-of-principle study demonstrates that motion compensating diffusion gradients can mitigate artifacts because of continuous motion in DTI of the brain and offers promise for improved DTI accessibility. Further study will be necessary to determine the robustness of the approach in patient populations with high susceptibility to head motion.

Diffusion tensor imaging reveals distributed white matter abnormalities in primary trigeminal neuralgia: Tract-based spatial statistics study

BackgroundPrimary trigeminal neuralgia (PTN) is a prevalent chronic pain disorder whose pathogenesis is not limited to the trigeminal system. Despite the significant advances in uncovering underlying mechanisms, there is a paucity of comprehensive and consistent data regarding the role of white matter throughout the entire brain in PTN. MethodsWe performed a prospective case-control study. Sixty patients with PTN and 28 age- and sex-matched healthy controls were evaluated using diffusion tensor imaging (DTI). A tract-based spatial statistical approach was performed to investigate white matter impairment in patients with PTN with several metrics, including fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD) and axial diffusivity (AD). Additionally, ROI-based analysis was performed for each white matter tract to compare FA values between groups with correction for patient age and sex. Correlations between DTI data and nerve root compression severity, as well as pain severity, were also evaluated in patients with PTN. ResultsOur analysis demonstrated a widespread and symmetrical reduction in FA values among TN patients when compared to the control group (p < 0.05). Specifically, this FA decrease was predominantly observed in regions such as the corona radiata, internal capsule, optic radiation, and thalami, as well as structures within the posterior fossa, notably the cerebellar peduncles. No statistically significant differences were found between patients and the control group during the MD, AD and RD map analyses. ROI-based analysis did not reveal statistically significant changes in FA values in white matter tracts (p > 0.05 in all comparisons, FDR-corrected); however, there were trends towards FA value decreases in the internal capsule (p = 0.08, FDR-corrected) and inferior fronto-occipital fasciculus (p = 0.09, FDR-corrected). ConclusionsOur findings indicate the presence of microstructural abnormalities in white matter among individuals with primary trigeminal neuralgia, which may potentially play a role in the development and progression of the condition.