Radial Diffusion Research Articles

Visual outcomes and their association with grey and white matter microstructure in adults born preterm with very low birth weight

Individuals born with very low birth weight (VLBW; < 1500 g) have a higher risk of reduced visual function and brain alterations. In a longitudinal cohort study, we assessed differences in visual outcomes and diffusion metrics from diffusion tensor imaging (DTI) at 3 tesla in the visual white matter pathway and primary visual cortex at age 26 in VLBW adults versus controls and explored whether DTI metrics at 26 years was associated with visual outcomes at 32 years. Thirty-three VLBW adults and 50 term-born controls was included in the study. Visual outcomes included best corrected visual acuity, contrast sensitivity, P100 latency, and retinal nerve fibre layer thickness. Mean diffusivity, axial diffusivity, radial diffusivity, and fractional anisotropy was extracted from seven regions of interest in the visual pathway: splenium, genu, and body of corpus callosum, optic radiations, lateral geniculate nucleus, inferior-fronto occipital fasciculus, and primary visual cortex. On average the VLBW group had lower contrast sensitivity, a thicker retinal nerve fibre layer and higher axial diffusivity and radial diffusivity in genu of corpus callosum and higher radial diffusivity in optic radiations than the control group. Higher fractional anisotropy in corpus callosum areas were associated with better visual function in the VLBW group but not the control group.

Diffusion Tensor and Kurtosis MRI-Based Radiomics Analysis of Kidney Injury in Type 2 Diabetes.

Diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) can provide quantitative parameters that show promise for evaluation of diabetic kidney disease (DKD). The combination of radiomics with DTI and DKI may hold potential clinical value in detecting DKD. To investigate radiomics models of DKI and DTI for predicting DKD in type 2 diabetes mellitus (T2DM) and evaluate their performance in automated renal parenchyma segmentation. Prospective. One hundred and sixty-three T2DM patients (87 DKD; 63 females; 27-80 years), randomly divided into training cohort (N = 114) and validation cohort (N = 49). 1.5-T, diffusion spectrum imaging (DSI) with 9 different b-values. The images of DSI were processed to generate DKI and DTI parameter maps, including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD). The Swin UNETR model was trained with 5-fold cross-validation using 100 samples for renal parenchyma segmentation. Subsequently, radiomics features were automatically extracted from each parameter map. The performance of the radiomics models on the validation cohort was evaluated by utilizing the receiver operating characteristic (ROC) curve. Mann-Whitney U test, Chi-squared test, Pearson correlation coefficient, least absolute shrinkage and selection operator (LASSO), dice similarity coefficient (DSC), decision curve analysis (DCA), area under the curve (AUC), and DeLong's test. The threshold for statistical significance was set at P < 0.05. The DKI_MD achieved the best segmentation performance (DSC, 0.925 ± 0.011). A combined radiomics model (DTI_FA, DTI_MD, DKI_FA, DKI_MD, and DKI_RD) showed the best performance (AUC, 0.918; 95% confidence interval [CI]: 0.820-0.991). When the threshold probability was greater than 20%, the combined model provided the greatest net benefit. Among the single parameter maps, the DTI_FA exhibited superior diagnostic performance (AUC, 887; 95% CI: 0.779-0.972). The radiomics signature constructed based on DKI and DTI may be used as an accurate and non-invasive tool to identify T2DM and DKD. 2 TECHNICAL EFFICACY: Stage 2.

Simulating Long‐Term Dynamics of Radiation Belt Electrons Using DREAM3D Model

AbstractWe compared the performance of DREAM3D simulations in reproducing the long‐term radiation belt dynamics observed by Van Allen Probes over the entire year of 2017 with various boundary conditions (BCs) and model inputs. Specifically, we investigated the effects of three different outer boundary conditions, two different low‐energy boundary conditions for seed electrons, four different radial diffusion (RD) coefficients (DLL), four hiss wave models, and two chorus wave models from the literature. Using the outer boundary condition driven by GOES data, our benchmark simulation generally well reproduces the observed radiation belt dynamics inside L* = 6, with a better model performance at lower μ than higher μ, where μ is the first adiabatic invariant. By varying the boundary conditions and inputs, we find that: (a) The data‐driven outer boundary condition is critical to the model performance, while adding in the data‐driven seed population doesn't further improve the performance. (b) The model shows comparable performance with DLL from Brautigam and Albert (2000, https://doi.org/10.1029/1999ja900344), Ozeke et al. (2014, https://doi.org/10.1002/2013ja019204), and Liu et al. (2016, https://doi.org/10.1002/2015gl067398), while with DLL from Ali et al. (2016, https://doi.org/10.1002/2016ja023002) the model shows less RD compared to data. (c) The model performance is similar with data‐based hiss models, but the results show faster loss is still needed inside the plasmasphere. (d) The model performs similarly with the two different chorus models, but better capturing the electron enhancement at higher μ using the Wang et al. (2019, https://doi.org/10.1029/2018ja026183) model due to its stronger wave power, since local heating for higher energy electrons is under‐reproduced in the current model.

Restricted diffusion characteristics in oscillating gradient spin echo with mesoscopic phantom

In diffusion magnetic resonance imaging, oscillating gradient spin echo (OGSE) has an extremely short diffusion time if motion probing gradient (MPG) is applied to the waveform. Further, it can detect microstructural specificity. OGSE changes sensitivity to spin displacement velocity based on the MPG phase. The current study aimed to investigate the restricted diffusion characteristics of each OGSE waveform using the capillary phantom with various b-values, frequencies, and MPG phases. We performed OGSE (b-value = 300, 500, 800, 1200, 1600, and 2000 s/mm2) for the sine and cosine waveforms using the capillary phantom (6, 12, 25, 50, and 100 μm and free water) with a 9.4-T experimental magnetic resonance imaging system and a solenoid coil. We evaluated the axial and radial diffusivity (AD, RD) of each structure size. The output current of the MPG was assessed with an oscilloscope and analyzed with the gradient modulation power spectra by fast Fourier transform.In sine, the sidelobe spectrum was enhanced with increasing frequency, and the central spectrum slightly increased. The difference in RD was detected at 6 and 12 μm; however, it did not depend on the structure scale at 50 or 100 μm and free water. In cosine, the diffusion spectrum was enhanced, whereas the central spectrum decreased with increasing frequency. Both AD and RD in cosine had a frequency dependence, and AD and RD increased with a higher frequency regardless of structure size. AD and RD in either sine or cosine had no evident b-value dependence. We evaluated the OGSE-restricted diffusion characteristics. The measurements obtained diffusion information similar to the pulsed gradient spin echo. Hence, the cosine measurements indicated that a higher frequency could capture faster diffusion within the diffusion phenomena.

Decreased uncinate fasciculus integrity in functional seizures following traumatic brain injury.

The uncinate fasciculus (UF) has been implicated previously in contributing to the pathophysiology of functional (nonepileptic) seizures (FS). FS are frequently preceded by adverse life events (ALEs) and present with comorbid psychiatric symptoms, yet neurobiological correlates of these factors remain unclear. To address this gap, using advanced diffusion magnetic resonance imaging (dMRI), UF tracts in a large cohort of patients with FS and pre-existing traumatic brain injury (TBI + FS) were compared to those in patients with TBI without FS (TBI-only). We hypothesized that dMRI measures in UF structural connectivity would reveal UF differences when controlling for TBI status. Partial correlation tests assessed the potential relationships with psychiatric symptom severity measures. Participants with TBI-only (N = 46) and TBI + FS (N = 55) completed a series of symptom questionnaires and MRI scanning. Deterministic tractography via diffusion spectrum imaging (DSI) was implemented in DSI studio (https://dsi-studio.labsolver.org) with q-space diffeomorphic reconstruction (QSDR), streamline production, and manual segmentation to assess bilateral UF integrity. Fractional anisotropy (FA), radial diffusivity (RD), streamline counts, and their respective asymmetry indices (AIs) served as estimates of white matter integrity. Compared to TBI-only, TBI + FS participants demonstrated decreased left hemisphere FA and RD asymmetry index (AI) for UF tracts (both p < .05, false discovery rate [FDR] corrected). Additionally, TBI + FS reported higher symptom severity in depression, anxiety, and PTSD measures (all p < .01). Correlation tests comparing UF white matter integrity differences to psychiatric symptom severity failed to reach criteria for significance (all p > .05, FDR corrected). In a large, well-characterized sample, participants with FS had decreased white matter health after controlling for the history of TBI. Planned follow-up analysis found no evidence to suggest that UF connectivity measures are a feature of group differences in mood or anxiety comorbidities for FS. These findings suggest that frontolimbic structural connectivity may play a role in FS symptomology, after accounting for prior ALEs and comorbid psychopathology severity.

Amelioration of the brain structural connectivity is accompanied with changes of gut microbiota in a tuberous sclerosis complex mouse model

Tuberous sclerosis complex (TSC) is a genetic disease that causes benign tumors and dysfunctions in many organs, including the brain. Aside from the brain malformations, many individuals with TSC exhibit neuropsychiatric symptoms. Among these symptoms, autism spectrum disorder (ASD) is one of the most common co-morbidities, affecting up to 60% of the population. Past neuroimaging studies strongly suggested that the impairments in brain connectivity contribute to ASD, whether or not TSC-related. Specifically, the tract-based diffusion tensor imaging (DTI) analysis provides information on the fiber integrity and has been used to study the neuropathological changes in the white matter of TSC patients with ASD symptoms. In our previous study, curcumin, a diet-derived mTOR inhibitor has been shown to effectively mitigate learning and memory deficits and anxiety-like behavior in Tsc2+/− mice via inhibiting astroglial proliferation. Recently, gut microbiota, which is greatly influenced by the diet, has been considered to play an important role in regulating several components of the central nervous system, including glial functions. In this study, we showed that the abnormal social behavior in the Tsc2+/− mice can be ameliorated by the dietary curcumin treatment. Second, using tract-based DTI analysis, we found that the Tsc2+/− mice exhibited altered fractional anisotropy, axial and radial diffusivities of axonal bundles connecting the prefrontal cortex, nucleus accumbens, hypothalamus, and amygdala, indicating a decreased brain network. Third, the dietary curcumin treatment improved the DTI metrics, in accordance with changes in the gut microbiota composition. At the bacterial phylum level, we showed that the abundances of Actinobacteria, Verrucomicrobia, and Tenericutes were significantly correlated with the DTI metrics FA, AD, and RD, respectively. Finally, we revealed that the expression of myelin-associated proteins, myelin bassic protein (MBP) and proteolipid protein (PLP) was increased after the treatment. Overall, we showed a strong correlation between structural connectivity alterations and social behavioral deficits, as well as the diet-dependent changes in gut microbiota composition.

In-vitro gadolinium retro-microdialysis in agarose gel-a human brain phantom study.

Cerebral microdialysis is a technique that enables monitoring of the neurochemistry of patients with significant acquired brain injury, such as traumatic brain injury (TBI) and subarachnoid haemorrhage (SAH). Cerebral microdialysis can also be used to characterise the neuro-pharmacokinetics of small-molecule study substrates using retrodialysis/retromicrodialysis. However, challenges remain: (i) lack of a simple, stable, and inexpensive brain tissue model for the study of drug neuropharmacology; and (ii) it is unclear how far small study-molecules administered via retrodialysis diffuse within the human brain. Here, we studied the radial diffusion distance of small-molecule gadolinium-DTPA from microdialysis catheters in a newly developed, simple, stable, inexpensive brain tissue model as a precursor for in-vivo studies. Brain tissue models consisting of 0.65% weight/volume agarose gel in two kinds of buffers were created. The distribution of a paramagnetic contrast agent gadolinium-DTPA (Gd-DTPA) perfusion from microdialysis catheters using magnetic resonance imaging (MRI) was characterized as a surrogate for other small-molecule study substrates. We found the mean radial diffusion distance of Gd-DTPA to be 18.5 mm after 24 h (p < 0.0001). Our brain tissue model provides avenues for further tests and research into infusion studies using cerebral microdialysis, and consequently effective focal drug delivery for patients with TBI and other brain disorders.

Correlation between postoperative microstructural changes in cerebral white matter and early postoperative cognitive function in patients undergoing meningioma resection

Objective: To analyze the correlation between microstructure changes in cerebral white matter before and after surgery and early postoperative cognitive function in patients undergoing meningioma resection. Methods: A total of 17 patients who underwent their first meningioma resection at Xuanwu Hospital of Capital Medical University from April 2022 to April 2023 were prospectively included as observation group, with 5 males and 12 females, aged (56.4±7.3) years. Another 15 age- and education-matched patients with cerebral benign tumor were recruited as control group during the same period, with 5 males and 10 females, aged (55.2±8.0) years. Neuropsychological tests (NST), mainly including auditory verbal learning test of Huashan version (AVLT-H), the Montreal cognitive assessment-basic (MoCA-B), clock drawing task-30 (CDT-30), shape trails test-B (STT-B) and animal fluence test (AFT), were conducted at 1 day before surgery, 1 day and within 3-4 days after surgery in the observation group. Simultaneously, magnetic resonance imaging (MRI) scans were performed to collect diffusion tensor imaging (DTI) images at 1 day before surgery and within 3-4 days after surgery. The same NST were conducted at 1 day, 3 days and 6 days after admission in the control group to adjust for learning effects from repeated tests. The microstructure changes of the whole brain white matter were evaluated at the group level by using tract-based spatial statistics (TBSS) technology, including changes of fractional anisotropy (FA), mean diffusion (MD), axial diffusion (AD), and radial diffusion (RD). Then, correlation was performed between DTI indicators with statistically significant and cognitive function. Results: After adjusting for the learning effects, the AVLT-H (R), MoCA-B, and CDT-30 scores decreased, and the evaluation time of STT-B prolonged after surgery in patients with meningioma. And their perioperative decreased values were -0.78 (95%CI:-3.28--0.28) points, -2.22 (95%CI:-4.22--0.72) points, -2.74 (95%CI:-5.29--0.19) points, and 61.49 (95%CI: 5.71-117.27) seconds, respectively, with statistically significant differences (all P<0.05). Group level analysis of TBSS based on DTI images showed decreased FA mainly in the right superior cerebellar peduncle, left posterior limb of internal capsule and genu of corpus callosum, and increased RD mainly in the left anterior corona radiata in patients undergoing meningioma resection, with statistically significant differences (all PFWE<0.05). Linear correlation showed that the perioperative decreased values of FA in genu of corpus callosum and right superior cerebellar peduncle were positively correlated with the perioperative decreased values of AVLT-H (L) after adjusting for learning effects (r=0.72, 0.52, all PFWE<0.05). Conclusions: Patients undergoing meningioma resection are at risk of postoperative cognitive decline. Perioperative decreased values of FA in genu of corpus callosum and right superior cerebellar peduncle based on DTI images are positively correlated with the perioperative decreased values of AVLT-H (L) after adjusting for learning effects.

"Pscore": A Novel Percentile-Based Metric to Accurately Assess Individual Deviations in Non-Gaussian Distributions of Quantitative MRI Metrics.

Quantitative magnetic resonance imaging (MRI) metrics could be used in personalized medicine to assess individuals against normative distributions. Conventional Zscore analysis is inadequate in the presence of non-Gaussian distributions. Therefore, if quantitative MRI metrics deviate from normality, an alternative is needed. To confirm non-Gaussianity of diffusion MRI (dMRI) metrics on a publicly available dataset, and to propose a novel percentile-based method, "Pscore" to address this issue. Retrospective cohort. Nine hundred and sixty-one healthy young adults (age: 22-35 years, females: 53%) from the Human Connectome Project. 3-T, spin-echo diffusion echo-planar imaging, T1-weighted: MPRAGE. The dMRI data were preprocessed using the TORTOISE pipeline. Forty-eight regions of interest (ROIs) from the JHU atlas were redrawn on a study-specific diffusion tensor (DT) template and average values were computed from various DT and mean apparent propagator (MAP) metrics. For each ROI, percentile ranks across participants were computed to generate "Pscores"-which normalized the difference between the median and a participant's value with the corresponding difference between the median and the 5th/95th percentile values. ROI-wise distributions were assessed using log transformations, Zscore, and the "Pscore" methods. The percentages of extreme values above-95th and below-5th percentile boundaries (PEV>95(%), PEV<5(%)) were also assessed in the overall white matter. Bootstrapping was performed to test the reliability of Pscores in small samples (N = 100) using 100 iterations. The dMRI metric distributions were systematically non-Gaussian, including positively skewed (eg, mean and radial diffusivity) and negatively skewed (eg, fractional and propagator anisotropy) metrics. This resulted in unbalanced tails in Zscore distributions (PEV>95 ≠ 5%, PEV<5 ≠ 5%) whereas "Pscore" distributions were symmetric and balanced (PEV>95 = PEV<5 = 5%); even for small bootstrapped samples (average [SD]). The inherent skewness observed for dMRI metrics may preclude the use of conventional Zscore analysis. The proposed "Pscore" method may help estimating individual deviations more accurately in skewed normative data, even from small datasets. 1 TECHNICAL EFFICACY: Stage 1.

Diagnostic efficacy of tract-specific diffusion tensor imaging in cervical spondylotic myelopathy with electrophysiological examination validation.

This study aimed to investigate the effectiveness of tract-specific diffusion tensor imaging (DTI) metrics in identifying the responsible segments for neurological dysfunction in cervical spondylotic myelopathy (CSM). The study encompassed nineteen participants diagnosed with CSM, including 10 males and 9 females. Additionally, a control group consisting of ten healthy caregivers (5 males and 5 females) were recruited with no symptoms and no compressions on magnetic resonance imaging (MRI). All participants underwent a comprehensive physical examination, MRI assessment, and DTI examination conducted by a senior chief physician. Several parameters were collected from the MR images, including the aspect ratio (defined as the anteroposterior diameter / the transverse diameter of the corresponding segment's spinal cord), transverse ratio (defined as the transverse diameter of the corresponding segment's spinal cord / the transverse diameter of the spinal cord at C2/3), and T2 high signal of the spinal cord. Furthermore, quantitative DTI metrics, such as axial diffusivity (AD), mean diffusivity (MD), radial diffusivity (RD), and fractional anisotropy (FA), were calculated using automatic region-of-interest (ROI) analysis for both whole spinal cord column and dorsal column. Receiver operating characteristic (ROC) curves were constructed to evaluate the diagnostic efficacy of the aspect ratio, transverse ratio, and DTI parameters. The area under the curve (AUC), sensitivity, and specificity were calculated. Intraoperative spinal cord electrophysiological examination was performed as the objective measure of spinal cord function during surgery. As determined by electrophysiological examination, neurological dysfunction was found in 2 patients due to C3/4 compression, in 10 patients due to C4/5 compression, in 6 patients due to C5/6 compression, and in 1 patient due to C6/7 compression. The modified Japanese Orthopedic Association scale (mJOA) was 12.71 ± 1.55 in the CSM group, with 4.87 ± 0.72 for sensory nerve function and 5.05 ± 1.35 for motor nerve function. For the control group, none of the volunteers had neurological dysfunction. T2 high signal was found at the most stenotic segment in 13 patients of the CSM group. Considering all the cervical segments, the aspect ratio (AUC = 0.823, P = 0.001, Sensitivity = 68.42%, Specificity = 82.47%) was more capable of determining the responsible segment than transverse ratio (AUC = 0.661, P = 0.027, Sensitivity = 68.42%, Specificity = 67.01%). AD, MD, and RD were significantly higher while FA was significantly lower in the responsible segment than in the irresponsible segment (P < 0.05). The AUC of DTI-Dorsal column parameters (AD, MD, RD, FA) was larger than the corresponding parameters of the DTI (Whole spinal cord). AD of DTI-Dorsal Column possessed the greatest efficacy (AUC = 0.823, sensitivity = 84.21%, specificity = 77.32%) to determine the responsible segment, larger than AD of DTI-Whole spinal cord (AUC = 0.822, P = 0.001, Sensitivity = 89.47%, Specificity = 77.32%), aspect ratio (AUC = 0.823, P = 0.001, Sensitivity = 68.42%, Specificity = 82.47%) and transverse ratio (AUC = 0.661, P = 0.027, Sensitivity = 68.42%, Specificity = 67.01%). Subgroup analysis revealed that the diagnostic efficacy of DTI and MRI parameters was influenced by cervical spine segment. When considering all cervical segments, AD from the DTI-Dorsal Column exhibited the most significant potential in identifying responsible segments. This potential was found to be superior to that of DTI-Whole spinal cord, aspect ratio, the most stenotic segment, T2 high signals, transverse ratio, motor nerve dysfunction, and sensory nerve dysfunction. The diagnostic effectiveness of both DTI and MRI parameters was notably influenced by the specific cervical spine segment.

Diffusion-kurtosis magnetic resonance imaging of the brain in the differential diagnostics of metastases of tumors of various primary localization

Background: Metastatic brain lesions lead to the most unfavorable prognosis for the course of an oncological disease. Most often, brain metastases arise from primary tumors such as lung cancer, breast cancer, and melanoma. Of particular interest are groups of secondary intracranial tumors without an identified primary focus. Methods of non-invasive differential diagnosis based on a possible histological affiliation, including diffusion-kurtosis magnetic resonance imaging, can improve the diagnostic search for the primary tumor. Aim: The aim of this study is to improve the quality of differential diagnosis for brain metastases of tumors of different primary localization by introducing the diffusion-kurtosis magnetic resonance imaging technique into the magnetic resonance scanning protocol. Methods: Our work included studies of 60 patients who underwent examination and treatment at the N.N. Blokhin National Research Medical Center of Oncology of the Ministry of Health of Russia from October 2019 to March 2022. According to magnetic resonance imaging, metastatic formations were detected in the brain of the patients, with different localizations of the primary tumor. 20 patients were diagnosed with lung cancer (33.3%), 20 patients with breast cancer (33.3%) and 20 patients with melanomas (33.3%). We evaluated the tumor size, diffusion and kurtosis parameters, such as the mean kurtosis, axial kurtosis, radial kurtosis, kurtosis anisotropy, radial diffusion, and fractional anisotropy, and relative anisotropy, axial diffusion of the extra-axonal fluid, radial diffusion of the extra-axonal fluid, axonal fluid fraction, and tortuosity of the diffusion trajectory. Results: Statistically significantly (p 0.05) differing parameters of diffusion and kurtosis in the comparative evaluation of the above indicators were identified in the structure associated with melanoma and lung cancer axial diffusion, fractional anisotropy, relative anisotropy, radial kurtosis and tortuosity of the diffusion trajectory, as well as in the structure of melanoma and breast cancer — axial diffusion, axonal fluid fraction, fractional anisotropy, axial diffusion of extra-axonal fluid, mean kurtosis, relative anisotropy, radial kurtosis and tortuosity of the diffusion trajectory. Conclusion: Diffusion-kurtosis magnetic resonance imaging is a promising technique that allows obtaining additional differential information in the case of metastatic lesions of the brain matter, especially those from an undetected primary focus.

A matched case-control study of early cervical spondylotic myelopathy based on diffusion magnetic resonance imaging

BackgroundEarly cervical spondylotic myelopathy (CSM) is challenging to diagnose and easily missed. Diffusion MRI (dMRI) has the potential to identify early CSM.MethodsUsing diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), and neurite orientation dispersion and density imaging (NODDI), a 1:1 matched case-control study was conducted to evaluate the potential of dMRI in identifying early CSM and assessing uncompressed segments of CSM patients. CSM patients and volunteers were matched by age and spinal location. The differences in dMRI parameters between groups were assessed by the paired t-test, the multicollinearity of the dMRI parameters was evaluated by the variance inflation factor (VIF), and the value of dMRI parameters in distinguishing controls from CSM patients was determined by logistic regression. The univariate t-test was used to analyse differences between CSM patients and volunteers in adjacent uncompressed areas.ResultsIn total, 56 CSM patients and 56 control volunteers were included. Paired t-tests revealed significant differences in nine dMRI parameters between groups. Multicollinearity calculated through VIF and combined with logistic regression showed that the orientation division index (ODI) was significantly positively correlated (r = 2.12, p = 0.035), and the anisotropic water fraction (AWF) was significantly negatively correlated (r = −0.98, p = 0.015). The fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), isotropic volume fraction (ISOVF), ODI, and AWF were significantly different in the upper and lower uncompressed areas at all ages.ConclusiondMRI can noninvasively identify early CSM patients and potentially identify the extent of CSM lesions involving the cervical spinal cord.Critical relevance statementDiffusion MRI (dMRI) can identify early cervical spondylotic myelopathy (CSM) and has the potential to help determine the extent of CSM involvement. The application of dMRI can help screen for early CSM and develop clinical surgical and rehabilitation treatment plans.Key points• Diffusion MRI can differentiate between normal and early-stage cervical spondylotic myelopathy patients.• Diffusion MRI has the ability to identify the extent of spinal cord involvement in cervical spondylotic myelopathy.• Diffusion MRI enables the early screening of cervical spondylotic myelopathy and helps guide clinical treatment.Graphical

Multimodal imaging with magnetization transfer and diffusion tensor imaging reveals evidence of myelin damage in children and youth treated for a brain tumor.

The microstructural damage underlying compromise of white matter following treatment for pediatric brain tumors is unclear. We use multimodal imaging employing advanced diffusion tensor imaging (DTI) and magnetization transfer imaging (MTI) MRI methods to examine chronic microstructural damage to white matter in children and adolescents treated for pediatric brain tumor. Notably, MTI may be more sensitive to macromolecular content, including myelin, than DTI. Fifty patients treated for brain tumors (18 treated with surgery ± chemotherapy and 32 treated with surgery followed by cranial-spinal radiation; time from diagnosis to scan ~6 years) and 45 matched healthy children completed both MTI and DTI scans. Voxelwise and region-of-interest approaches were employed to compare white matter microstructure metrics (magnetization transfer ratio (MTR); DTI- fractional anisotropy [FA], radial diffusivity [RD], axial diffusivity [AD], mean diffusivity [MD]) between patients and healthy controls. MTR was decreased across multiple white matter tracts in patients when compared to healthy children, P < .001. These differences were observed for both patients treated with radiation and those treated with only surgery, P < .001. We also found that children and adolescents treated for brain tumors exhibit decreased FA and increased RD/AD/MD compared to their healthy counterparts in several white matter regions, Ps < .02. Finally, we observed that MTR and DTI metrics were related to multiple white matter tracts in patients, Ps < .01, but not healthy control children. Our findings provide evidence that the white matter damage observed in patients years after treatment of pediatric posterior fossa tumors, likely reflects myelin disruption.

Brain microstructure mapping in major depressive disorder: a pilot MR study

INTRODUCTION: Major depressive disorder is a common mental health disorder. Alterations in cortical structures have been identified in this disease, but findings have been variable and inconsistent. Previous studies have illustrated that the cingulate and prefrontal gyrus, along with the amygdala, are involved in emotional processing and the development of abnormal emotional responses in depression.OBJECTIVE: Our research aims to investigate the neurological structural differences and alterations in ACC, bilateral amygdala, and dmPFC regions in patients with MDD using quantitative MRI (MPF and Diffusion parameters mapping (DPM), including diffusion kurtosis).MATERIALS AND METHODS: In this study, we utilized advanced quantitative MRI techniques, specifically Diffusion Tensor Imaging, Diffusion Kurtosis Imaging, and Мacromolecular Proton Fraction Mapping, to investigate microstructural differences and alterations in the specific regions in patients diagnosed with major depressive disorder. RESULTS: Our findings revealed no significant interaction between Мacromolecular proton fraction Mapping with depressive disorder. However, patients with major depressive disorder exhibited a statistically significant increase in apparent mean, axial and radial diffusivity (F=6.3, p=0.01, F=5.0, p=0.03, F=7.08, p=0.01, respectively) in the bilateral amygdala compared to healthy controls, as well as in mean and radial diffusivity in the anterior cingulate cortex (F=5.61, p=0.02, F=7.08, p=0.01, respectively).DISCUSSION: These findings suggest that altered molecular diffusion characteristics in the amygdala and the anterior cingulate cortex may be specifically associated with major depressive disorder.CONCLUSIONS: The importance of using new quantitative MRI methods to assess structural changes at the molecular level in the brain is shown, which, ultimately, expands the fundamental understanding of the pathophysiology of depression.

Modeling the Simultaneous Dropout of Energetic Electrons and Protons by Magnetopause Shadowing

AbstractMagnetopause shadowing (MPS) effect could drive a concurrent dropout of radiation belt electrons and ring current protons. However, its relative role in the dropout of both plasma populations has not been well quantified. In this work, we study the simultaneous dropout of MeV electrons and 100s keV protons during an intense geomagnetic storm in May 2017. A radial diffusion model with an event‐specific last closed drift shell is used to simulate the MPS loss of both populations. The model well captures the fast shadowing loss of both populations at L* &gt; 4.6, while the loss at L* &lt; 4.6, possibly due to the electromagnetic ion cyclotron wave scattering, is not captured. The observed butterfly pitch angle distributions of electron fluxes in the initial loss phase are well reproduced by the model. The initial proton losses at low pitch angles are underestimated, potentially also contributed by other mechanisms such as field line curvature scattering.

More Accurate Measurement of Return Peak Current in Cyclic Voltammetry Using Diffusional Baseline Fitting.

The difficulty associated with accurately measuring the height of the back peak (Ipb) in cyclic voltammetry (CV) has long plagued electrochemists. Most commonly, Ipb is measured by extrapolating a linear fit from a selected region of a voltammogram after the switching potential (Eλ), but without substantial separation between the peak potential (Ep) and Eλ, this approach always overestimates the background current and so underestimates Ipb. Moreover, experimental conditions can present challenges for this method as an appropriate region for linear fitting is often lacking due to neighboring peaks or solvent electrolysis current. Here, we present a new method for finding the baseline current for the back peak in CV experiments. By examining the CV data as a function of time rather than potential, it is possible to fit a generalized Cottrell or Shoup-Szabo equation to the current decay of the forward peak and extrapolate this function as a baseline for the return peak. This approach was tested by using simulated and experimental data in a variety of conditions, including data demonstrating linear and radial diffusional control. We found that the method allows for more accurate determination of back peak currents, especially when linear fits are complicated by narrow electrochemical windows or radial diffusion. A user-friendly Python program was written to automatically find an appropriate fitting range for this analysis and measure peak currents. We have made this program available to the electrochemical community at large.

Age of onset of obsessive-compulsive disorder differentially affects white matter microstructure

Previous diffusion MRI studies have reported mixed findings on white matter microstructure alterations in obsessive-compulsive disorder (OCD), likely due to variation in demographic and clinical characteristics, scanning methods, and underpowered samples. The OCD global study was created across five international sites to overcome these challenges by harmonizing data collection to identify consistent brain signatures of OCD that are reproducible and generalizable. Single-shell diffusion measures (e.g., fractional anisotropy), multi-shell Neurite Orientation Dispersion and Density Imaging (NODDI) and fixel-based measures, were extracted from skeletonized white matter tracts in 260 medication-free adults with OCD and 252 healthy controls. We additionally performed structural connectome analysis. We compared cases with controls and cases with early (<18) versus late (18+) OCD onset using mixed-model and Bayesian multilevel analysis. Compared with healthy controls, adult OCD individuals showed higher fiber density in the sagittal stratum (B[SE] = 0.10[0.05], P = 0.04) and credible evidence for higher fiber density in several other tracts. When comparing early (n = 145) and late-onset (n = 114) cases, converging evidence showed lower integrity of the posterior thalamic radiation —particularly radial diffusivity (B[SE] = 0.28[0.12], P = 0.03)—and lower global efficiency of the structural connectome (B[SE] = 15.3[6.6], P = 0.03) in late-onset cases. Post-hoc analyses indicated divergent direction of effects of the two OCD groups compared to healthy controls. Age of OCD onset differentially affects the integrity of thalamo-parietal/occipital tracts and the efficiency of the structural brain network. These results lend further support for the role of the thalamus and its afferent fibers and visual attentional processes in the pathophysiology of OCD.

Microstructural white matter abnormalities in overactive bladder syndrome evaluation with diffusion kurtosis imaging tract-based spatial statistics analysis.

This prospective study aimed to explore the microstructural alterations of the white matter in overactive bladder syndrome (OAB) using the Tract-based Spatial Statistics (TBSS) method of diffusion kurtosis imaging (DKI). A total of 30 patients were enrolled and compared with 30 controls. White matter (WM) status was assessed using tract-based spatial statistics for DKI. The differences in DKI-derived parameters, including kurtosis fractional anisotropy (KFA), fractional anisotropy (FA), mean kurtosis (MK), mean diffusivity (MD), radial kurtosis (RK), axial kurtosis (AK), axial diffusivity (AD), and radial diffusivity (RD), were compared between the two groups using the TBSS method. The correlation between the altered DKI-derived parameters and the (OABSS) scores was analyzed. A receiver operating characteristic curve (ROC) was used to evaluate the diagnostic performance of different white matter parameters. As a result, compared with the HC group, the KFA, and FA values decreased significantly in the OAB group. Compared with the HC group, the MK and MD values increased significantly in the OAB group. The KFA values of the genu of corpus callosum (GCC) were significantly correlated with the OABSS scores (r = -0.509; p = 0.004). The FA values of anterior corona radiata (ACR) were significantly correlated with OABSS scores (r = -0.447; p = 0.013). The area under the ROC curve (AUC) for the genu of corpus callosum KFA values was higher than FA for the diagnosis of OAB patients. DKI is a promising approach to the investigation of the pathophysiology of OAB and a potential biomarker for clinical diagnosis of OAB.

Understanding Wellbeing Profiles According to White Matter Structural Connectivity Sub-types in Early Adolescents: The First Hundred Brains Cohort from the Longitudinal Adolescent Brain Study

Wellbeing is protective against the emergence of psychopathology. Neurobiological markers associated with mental wellbeing during adolescence are important to understand. Limited research has examined neural networks (white matter tracts) and mental wellbeing in early adolescence specifically. A cross-sectional diffusion tensor imaging analysis approach was conducted, from the Longitudinal Adolescent Brain study, First Hundred Brains cohort (N = 99; 46.5% female; Mage = 13.01, SD = 0.55). Participants completed self-report measures including wellbeing, quality-of-life, and psychological distress. Potential neurobiological profiles using fractional anisotropy, axial, and radial diffusivity were determined via a whole brain voxel-wise approach, and hierarchical cluster analysis of fractional anisotropy values, obtained from 21 major white matter tracts. Three cluster groups with significantly different neurobiological profiles were distinguished. No significant differences were found between the three cluster groups and measures of wellbeing, but two left lateralized significant associations between white matter tracts and wellbeing measures were found. These results provide preliminary evidence for potential neurobiological markers of mental health and wellbeing in early adolescence and should be tracked longitudinally to provide more detailed and robust findings.

Machine learning research based on diffusion tensor images to distinguish between anorexia nervosa and bulimia nervosa.

Anorexia nervosa (AN) and bulimia nervosa (BN), two subtypes of eating disorders, often present diagnostic challenges due to their overlapping symptoms. Machine learning has proven its capacity to improve group classification without requiring researchers to specify variables. The study aimed to distinguish between AN and BN using machine learning models based on diffusion tensor images (DTI). This is a cross-sectional study, drug-naive females diagnosed with anorexia nervosa (AN) and bulimia nervosa (BN) were included. Demographic data and DTI were collected for all patients. Features for machine learning included Fractional anisotropy (FA), axial diffusivity (AD), radial diffusivity (RD), and mean diffusivity (MD). Support vector machine was constructed by LIBSVM, MATLAB2013b, and FSL5.0.9 software. A total of 58 female patients (24 AN, 34 BN) were included in this study. Statistical analysis revealed no significant differences in age, years of education, or course of illness between the two groups. AN patients had significantly lower BMI than BN patients. The AD model exhibited an area under the curve was 0.793 (accuracy: 75.86%, sensitivity: 66.67%, specificity: 88.23%), highlighting the left middle temporal gyrus (MTG_L) and the left superior temporal gyrus (STG_L) as differentiating brain regions. AN patients exhibited lower AD features in the STG_L and MTG_L than BN. Machine learning analysis indicated no significant differences in FA, MD, and RD values between AN and BN groups (p > 0.001). Machine learning based on DTI could effectively distinguish between AN and BN, with MTG_L and STG_L potentially serving as neuroimaging biomarkers.