Diffusion Magnetic Resonance Imaging Research Articles

Early Detection of hyperemia with Magnetic Resonance Fluid Attenuation Inversion Recovery Imaging after Superficial Temporal Artery to Middle Cerebral Artery Anastomosis.

Cerebral hyperperfusion syndrome (CHS) manifests as a collection of symptoms brought on by heightened focal cerebral blood flow (CBF), afflicting nearly 30% of patients who have undergone superficial temporal artery (STA)-middle cerebral artery (MCA) anastomosis. The aim of this study was to investigate whether the amalgamation of magnetic resonance imaging (MRI) fluid-attenuated inversion recovery (FLAIR) and apparent diffusion coefficient (ADC) imaging via MRI can discern cerebral hyperemia after STA-MCA anastomosis surgery. A retrospective study was performed of patients who underwent STA-MCA anastomosis due to Moyamoya disease or atherosclerotic steno-occlusive disease. A protocol aimed at preventing CHS was instituted, leveraging the use of MRI FLAIR. Patients underwent MRI diffusion with FLAIR imaging 24 hours after STA-MCA anastomosis. A high signal on FLAIR images signified the presence of hyperemia at the bypass site, triggering a protocol of hyperemia care. All patients underwent hemodynamic evaluations, including perfusion MRI, single-photon emission computed tomography (SPECT), and digital subtraction angiography, both before and after the surgery. If a high signal intensity is observed on MRI FLAIR within 24 hours of the surgery, a repeat MRI is performed to confirm the presence of hyperemia. Patients with confirmed hyperemia are managed according to a protocol aimed at preventing further progression. Out of a total of 162 patients, 24 individuals (comprising 16 women and 8 men) exhibited hyperemia on their MRI FLAIR scans following the procedure. SPECT was conducted on 23 patients, and 11 of them yielded positive results. All 24 patients underwent perfusion MRI, but nine of them showed no significant findings. Among the patients, 10 displayed elevations in both CBF and cerebral blood volume (CBV), three only showed elevation in CBF, and two only showed elevation in CBV. Follow-up MRI FLAIR scans conducted 6 months later on these patients revealed complete normalization of the previously observed high signal intensity, with no evidence of ischemic injury. The study determined that the use of MRI FLAIR and ADC mapping is a competent means of early detection of hyperemia after STA-MCA anastomosis surgery. The protocol established can be adopted by other neurosurgical institutions to enhance patient outcomes and mitigate the hazard of permanent cerebral injury caused by cerebral hyperemia.

Study of the Potential of MRI (Magnetic Resonance Imaging) in Detecting Epileptogenic Zones in the Brain

The epileptogenic zone is an area of the brain that is a source of uncontrolled epileptic activity. MRI can produce images of the brain with high spatial resolution, allowing detailed visualization of brain structure. This allows identification of pathological changes, such as lesions, hemorrhages, or vascular malformations, that may be a cause or indicator of an epileptogenic zone. MRI offers a variety of scanning techniques, including structural MRI (T1-weighted and T2-weighted), diffusion MRI, functional MRI (fMRI), and proton spectroscopy MRI. The literature search process was carried out on various databases (PubMed, Web of Sciences, EMBASE, Cochrane Libraries, and Google Scholar) regarding the potential of MRI (Magnetic Resonance Imaging) in detecting epileptogenic zones in the brain. This study follows the preferred reporting items for systematic reviews and meta-analysis (PRISMA) recommendations. MRI can produce images of the brain with high spatial resolution, allowing detailed identification of brain structures that may be associated with the epileptogenic zone. Lesions such as tumors, blood clots, or scar tissue that are the focus of epileptic activity can be clearly seen on MRI. Various MRI techniques, such as structural MRI, diffusion MRI, functional MRI, and proton spectroscopy MRI, can be used together to provide comprehensive information about the epileptogenic zone. MRI allows monitoring of brain structural changes over time, which is useful in determining the evolution of the epileptogenic zone. fMRI techniques help understand brain activity during epileptic attacks or as part of the epileptogenic zone.

Whole tumor based texture analysis of magnetic resonance diffusion imaging for colorectal liver metastases: A prospective study for diffusion model comparison and early response biomarker

PurposeTo evaluate and compare the diagnostic value of diffusion-related texture analysis parameters obtained from various magnetic resonance diffusion models as early predictors of the clinical response to chemotherapy in patients with colorectal liver metastases (CRLM). MethodsPatients (n = 145) with CRLM were prospectively and consecutively enrolled and scanned using diffusion-weighted imaging (DWI)-magnetic resonance imaging (MRI)/intravoxel incoherent motion (IVIM)/diffusion kurtosis imaging (DKI) before (baseline) and two–three weeks after (follow-up) commencing chemotherapy. Therapy response was evaluated based on the Response Evaluation Criteria in Solid Tumors (RECIST, version 1.1). The histogram and texture parameters of each diffusion-related parametric map were analysed between the responding and non-responding groups, screened using LASSO, and fitted with binary logistic regression models. The diagnostic efficacy of each model in the early prediction of CRLM was analysed, and the corresponding receiver operating characteristic (ROC) curve was drawn. The area under the curve (AUC) and 95% confidence intervals (CI) were calculated. ResultsOf the 145 analysed patients, 69 were in the responding group and 76 were in the non-responding group. Among all models, the difference value based on the histogram and texture features of the DKI-derived parameters performed best for the early prediction of CRLM treatment efficacy. The AUC of the DKI model in the validation set reached 0.795 (95% CI 0.652–0.938). Among the IVIM-derived parameters, the difference model based on D and D* performed best, and the AUC in the validation set reached 0.737 (95% CI 0.586–0.889). Finally, in the DWI sequence, the model comprising baseline features performed the best, with an AUC of 0.699 (95% CI 0.537–0.86) in the validation set. ConclusionsBaseline DWI parameters and follow-up changes in IVIM and DKI parameters predicted the chemotherapeutic response in patients with CRLM. In addition, as very early predictors, DKI-derived parameters were more effective than DWI- and IVIM-related parameters, in which changes in D-parameters performed best.

Extended Window Thrombolytics for Ischemic Stroke: A Telestroke versus In-person Experience.

Telestroke evaluation of patients with acute ischemic stroke is supported by American Heart and Stroke Association Guidelines. However, there is no data on outcomes or safety of administering IV thrombolytic stroke therapy using extended window criteria (>4.5h since onset of symptoms with a hyperacute MRI diffusion T2/FLAIR mismatch) via telestroke. Here, we report adverse events and outcomes of extended-window thrombolysis by telestroke vs in-person care. We performed a retrospective cohort review from 2020 to 2022 of prospectively collected multinstitutional databases from a large, not-for-profit health system with both in-person stroke and telestroke care. The primary outcome was frequency of symptomatic intracranial hemorrhage (sICH). Secondary outcomes were favorable functional outcome at hospital discharge (modified Rankin Scale, mRS, 0-3) and discharge disposition. A total of 33 patients were treated with extended-window thrombolysis (n = 20 in-person, n = 13 telestroke). The median NIH stroke scale was 6, and time since last known normal was similar (median [95% CI]: in-person 13h [11-15h] vs telestroke 12h [9-16h], P = .33). The sICH frequency was low and occurred in one patient (4.8% in-person vs 0% by telestroke). Favorable outcome at discharge was not different between in-person and telestroke care (median mRS [95% CI]: 2 [1-3] vs 1 [0-2], OR .0 [.0-1.8], P = .27), and discharge deposition was also similar. In patients eligible for extended window acute stroke treatment with thrombolytics, there was no difference in adverse events between telestroke and in-person care.

Brains of endurance athletes differ in the association areas but not in the primary areas.

Regular participation in sports results in a series of physiological adaptations. However, little is known about the brain adaptations to physical activity. Here we aimed to investigate whether young endurance athletes and non-athletes differ in the gray and white matter of the brain and whether cardiorespiratory fitness (CRF) is associated with these differences. We assessed the CRF, volumes of the gray and white matter of the brain using structural magnetic resonance imaging (sMRI), and brain white matter connections using diffusion magnetic resonance imaging (dMRI) in 20 young male endurance athletes and 21 healthy non-athletes. While total brain volume was similar in both groups, the white matter volume was larger and the gray matter volume was smaller in the athletes compared to non-athletes. The reduction of gray matter was located in the association areas of the brain that are specialized in processing of sensory stimuli. In the microstructure analysis, significant group differences were found only in the association tracts, for example, the inferior occipito-frontal fascicle (IOFF) showing higher fractional anisotropy and lower radial diffusivity, indicating stronger myelination in this tract. Additionally, gray and white matter brain volumes, as well as association tracts correlated with CRF. No changes were observed in other brain areas or tracts. In summary, the brain signature of the endurance athlete is characterized by changes in the integration of sensory and motor information in the association areas.

Value of radiomics models based on MRI diffusion weighted imaging and apparent diffusion coefficient in differentiating benign and malignant thyroid nodules

Objective: To investigate the value of radiomics models based on magnetic resonance imaging (MRI) diffusion weighted imaging (DWI) and apparent diffusion coefficient (ADC) maps in distinguishing benign and malignant thyroid nodules. Methods: A cross-sectional study. Clinical data of 148 thyroid nodules (50 benign, 98 malignant) from 140 patients who underwent thyroid MRI examination in Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences between January 2019 and December 2022 were retrospectively analyzed. The nodules were used as the study units, and a leave-one-out method was used to randomly divide the nodules into a training set and a test set at a 7∶3 ratio. Region of interest was segmented and radiomics features were extracted from the DWI and ADC images. In the training set, feature selection was performed using inter-observer agreement analysis, U-test, least absolute shrinkage and selection operator algorithm, and correlation analysis. Four classifiers, including support vector machine (SVM), random forest (RF), k-nearest neighbors (KNN) and logistic regression (LR) were used to build models with the selected features, including the DWI models, ADC models, and combined models. The models were independently tested in the test set. The performance of the radiomics models in distinguishing benign and malignant thyroid nodules was evaluated using the receiver operating characteristic (ROC) curve, with pathological results as the gold standard. Results: Of the 140 patients, there were 40 males and 100 females, with a mean age of (38.4±12.2) years. After feature selection, 11 DWI features and 11 ADC features were used to build the models. In the training set, the AUC values of the combined models were higher than those of the corresponding DWI and ADC models. In the test set, the SVM combined model showed the best predictive performance, with an AUC of 0.873 (95%CI:0.740-0.954), accuracy of 75.6%, sensitivity of 46.7%, specificity of 90.0%, positive predictive value (PPV) of 70.0% and negative predictive value (NPV) of 77.1%, while the RF combined model had an AUC of 0.836 (95%CI:0.695-0.929), accuracy of 77.8%, sensitivity of 40.0%, specificity of 96.7%, PPV of 85.7% and NPV of 76.3%, the KNN combined model had an AUC of 0.832 (95%CI:0.691-0.927), accuracy of 77.8%, sensitivity of 33.3%, specificity of 100%, PPV of 100% and NPV of 75.0%, the LR combined model had an AUC of 0.813 (95%CI:0.669-0.914), accuracy of 77.8%, sensitivity of 60.0%, specificity of 86.7%, PPV of 69.2% and NPV of 81.3%. Conclusions: Radiomics models based on DWI and ADC image features can effectively distinguish benign and malignant thyroid nodules. The SVM combined model had the best prediction performance.

Vascular risk, gait, behavioral, and plasma indicators of VCID.

Cost-effective screening tools for vascular contributions to cognitive impairment and dementia (VCID) has significant implications. We evaluated non-imaging indicators of VCID using magnetic resonance imaging (MRI)-measured white matter (WM) damage and hypothesized that these indicators differ based on age. In 745 participants from the Mayo Clinic Study of Aging (≥50 years of age) with serial WM assessments from diffusion MRI and fluid-attenuated inversion recovery (FLAIR)-MRI, we examined associations between baseline non-imaging indicators (demographics, vascular risk factors [VRFs], gait, behavioral, plasma glial fibrillary acidic protein [GFAP], and plasma neurofilament light chain [NfL]) and WM damage across three age tertiles. VRFs and gait were associated with diffusion changes even in low age strata. All measures (VRFs, gait, behavioral, plasma GFAP, plasma NfL) were associated with white matter hyperintensities (WMHs) but mainly in intermediate and high age strata. Non-imaging indicators of VCID were related to WM damage and may aid in screening participants and assessing outcomes for VCID. Non-imaging indicators of VCID can aid in prediction of MRI-measured WM damage but their importance differed by age. Vascular risk and gait measures were associated with early VCID changes measured using diffusion MRI. Plasma markers explained variability in WMH across age strata. Most non-imaging measures explained variability in WMH and vascular WM scores in intermediate and older age groups. The framework developed here can be used to evaluate new non-imaging VCID indicators proposed in the future.

Free-Water Imaging in Friedreich Ataxia Using Multi-Compartment Models.

The neurological phenotype of Friedreich ataxia (FRDA) is characterized by neurodegeneration and neuroinflammation in the cerebellum and brainstem. Novel neuroimaging approaches quantifying brain free-water using diffusion magnetic resonance imaging (dMRI) are potentially more sensitive to these processes than standard imaging markers. To quantify the extent of free-water and microstructural change in FRDA-relevant brain regions using neurite orientation dispersion and density imaging (NODDI), and bitensor diffusion tensor imaging (btDTI). Multi-shell dMRI was acquired from 14 individuals with FRDA and 14 controls. Free-water measures from NODDI (FISO) and btDTI (FW) were compared between groups in the cerebellar cortex, dentate nuclei, cerebellar peduncles, and brainstem. The relative sensitivity of the free-water measures to group differences was compared to microstructural measures of NODDI intracellular volume, free-water corrected fractional anisotropy, and conventional uncorrected fractional anisotropy. In individuals with FRDA, FW was elevated in the cerebellar cortex, peduncles (excluding middle), dentate, and brainstem (P < 0.005). FISO was elevated primarily in the cerebellar lobules (P < 0.001). On average, FW effect sizes were larger than all other markers (mean ηρ 2 = 0.43), although microstructural measures also had very large effects in the superior and inferior cerebellar peduncles and brainstem (ηρ 2 > 0.37). Across all regions and metrics, effect sizes were largest in the superior cerebellar peduncles (ηρ 2 > 0.46). Multi-compartment diffusion measures of free-water and neurite integrity distinguish FRDA from controls with large effects. Free-water magnitude in the brainstem and cerebellum provided the greatest distinction between groups. This study supports further applications of multi-compartment diffusion modeling, and investigations of free-water as a measure of disease expression and progression in FRDA. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

16 Longitudinal Cognitive Functioning in Gulf War veterans with and without Gulf War Illness: Data Mining from the BBRAIN Repository

Objective:Veterans from the 1991 Gulf War (GW) experienced several neurotoxicant exposures, including chemical weapons, pesticide sprays and creams, oil well fires and pyridostigmine bromide anti-nerve gas pills during the war. Research has shown these exposures to affect cognition and mood. Moreover, MR diffusion imaging has shown microstructural changes in the white matter that may be related to psychomotor slowing. Over a third of all GW veterans suffer from a chronic multi-symptom disorder called Gulf War Illness (GWI). The Kansas Criteria for GWI consists of six distinct criteria including symptoms of fatigue/sleep problems, pain symptoms, neurologic/cognitive/mood symptoms, gastrointestinal symptoms, respiratory symptoms, and skin symptoms. The Boston Gulf War Illness Consortium (GWIC) was a multi-site study designed to assess symptoms of GWI. After the conclusion of the GWIC study, the Boston Biorepository Recruitment and Integrative Network for Gulf War Illness (BBRAIN) was developed to harmonize retrospectively collected GW Veteran data while simultaneously collecting Time 2 data and samples from GW veterans who participated in the original study. This analysis includes the first 58 participants who have completed the GWIC study and the BBRAIN study.Participants and Methods:We conducted a longitudinal analysis of cognitive outcomes from the BBRAIN data repository. Verbal learning, memory, attention, and executive functioning were assessed using neuropsychological tests including the Continuous Performance Test (CPT3), Trail Making Test A, Delis-Kaplan Executive Function System (DKEFS), California Verbal Learning Test (CVLT-II). A total of 58 participants were re-evaluated from the original GWIC cohort with a total of 47 cases and 11 controls. Paired t-tests for the cognitive measures were completed separately for GWI cases and healthy GW veteran controls for each of the neuropsychological test measures. Average time between assessments was four years.Results:The overall sample was on average 56 years old, 84% male and 75% White. The average level of education was 15 years. GWI cases showed significantly more commission errors and slower reaction times on the CPT3 at Time 2 compared to Time 1 (p &lt; 0.05). Cases also showed a slowing in time to completion on Trails A at the second time point (p&lt;0.05). On the other hand, controls only showed significantly slower reaction times on the CPT3 at Time 2 (p&lt;0.05).Conclusions:These results showed that veterans with GWI are showing more decline over time in cognitive functioning particularly on psychomotor slowing and impulsivity than control veterans. It is important to document illness trajectories for veterans with GWI in order to devise strategies for interventions and treatments. The importance of studying longitudinal cohorts is to document changes in the same individuals over time. The next steps are to assess if this accelerated aging develops into neurodegenerative conditions by using brain imaging and other biomarkers in addition to cognitive evaluations. This could identify individuals who should be the focus of targeted treatment strategies while there is still time to intervene.

Longitudinal evolution of diffusion metrics after left hemisphere ischaemic stroke

Abstract White matter is often severely affected after human ischaemic stroke. While animal studies have suggested that various factors may contribute to white matter structural damage after ischaemic stroke, the characterization of damaging processes to the affected hemisphere after human stroke remains poorly understood. Thus, the present study aims to thoroughly describe the longitudinal pattern of evolution of diffusion magnetic resonance imaging metrics in different parts of the ipsilesional white matter after stroke. We acquired diffusion and anatomical images in 17 patients who had suffered from a single left hemisphere ischaemic stroke, at 24–72 h, 8–14 days and 6 months post-stroke. For each patient, we created three regions of interest: (i) the white matter lesion; (ii) the perilesional white matter; and (iii) the remaining white matter of the left hemisphere. We extracted diffusion metrics (fractional anisotropy, mean, axial and radial diffusivities) for each region and conducted two-way repeated measures ANOVAs with stage post-stroke (acute, subacute and chronic) × regions of interest (white matter lesion, perilesional white matter and remaining white matter). Fractional anisotropy values stayed consistent across time-points, with significantly lower values in the white matter lesion compared to the perilesional white matter and remaining white matter tissue. Fractional anisotropy values of the perilesional white matter were also significantly lower than that of the remaining white matter. Mean, axial and radial diffusivities in the white matter lesion were all decreased in the acute stage compared to perilesional white matter and remaining white matter, but significantly increased in both the subacute and chronic stages. Significant increases in mean and radial diffusivities in the perilesional white matter were seen in the later stages of stroke. Our findings suggest that various physiological processes are at play in the acute, subacute and chronic stages following ischaemic stroke, with the infarct territory and perilesional white matter affected by ischaemia at different rates and to different extents throughout the stroke recovery stages. The examination of multiple diffusivity metrics may inform us about the mechanisms occurring at different time-points, i.e. focal swelling, axonal damage or myelin loss.

HCTMFS: A multi-modal feature selection framework with higher-order correlated topological manifold for ESRDaMCI

Background and ObjectiveThe diagnosis of end-stage renal disease associated with mild cognitive impairment (ESRDaMCI) mainly relies on objective cognitive assessment, clinical observation, and neuro-psychological evaluation, while only adopting clinical tools often limits the diagnosis accuracy. MethodsWe proposed a multi-modal feature selection framework with higher-order correlated topological manifold (HCTMFS) to classify ESRDaMCI patients and identify the discriminative brain regions. It constructed brain structural and functional networks with diffuse kurtosis imaging (DKI) and functional magnetic resonance imaging (fMRI) data, and extracted node efficiency and clustering coefficient from the brain networks to construct multi-modal feature matrices. The topological relationship matrices were constructed to measure the lower-order topological correlation between features. Then the consensus matrices were learned to approximate the topological relationship matrices at different confidence levels and eliminate the noise influence of individual matrices. ResultsThe higher-order topological correlation between features was explored by the Laplacian matrix of the hypergraph, which was calculated through the consensus matrix. The new framework achieved an accuracy rate of 93.56 % for classifying ESRDaMCI patients, and outperformed the existing state-of-the-art methods in terms of sensitivity, specificity, and area under the curve. ConclusionsThis study contributes to effectively reflect the functional neural degradation of ESRDaMCI and provide a reference for the diagnosis of ESRDaMCI by selecting discriminative brain regions.

SUPER-RESOLUTION OF DIFFUSION MRI VIA JOINT CONSIDERATION OF SPATIAL AND ANGULAR SPACES USING GRAPH CNN

Diffusion magnetic resonance imaging (dMRI) is an essential technique for studying brain white matter. However, its application suffers from the tradeoff between resolution, signal-to-noise ratio (SNR), and scanning time. Under a given setting, post-acquisition super-resolution (SR) has displayed the exciting performance of increasing the spatial or angular resolution of dMRI without prolonging the acquisition time. dMRI is acquired from a heterogeneous space composed of the spatial domain (x-space) and the diffusion wave vector domain (q-space). We introduced a graph convolution neural network (GCNN) to fit the mapping from low-resolution (LR) dMRI to high-resolution (HR) counterpart by jointly considering x-space and q-space to make an effective learning in this non-Euclidean space to increase the spatial resolution. Real brain dMRI data were downloaded from the Human Connectome Project (HCP). Comparisons were made between different spaces, including x-space, q-space, and xq-space. The results indicated that the best performance was obtained when two spaces were considered jointly using GCNN. Compared with the traditional methods, our network fully used spatio-angular information to improve the quality of dMRI SR. Furthermore, some feature metrics, including fractional anisotropic mapping, fiber orientation distribution (FOD), and fiber tractography, were presented to support the improvement. The cross-phantom test on synthetic data generated by the Phantomas tool was performed to further evaluate our model. Extended experiments revealed that GCNN might serve as a new way of enhancing dMRI.

Deep Learning-Driven Transformation: A Novel Approach for Mitigating Batch Effects in Diffusion MRI Beyond Traditional Harmonization.

"Batch effect" in MR images, due to vendor-specific features, MR machine generations, and imaging parameters, challenges image quality and hinders deep learning (DL) model generalizability. We aim to develop a DL model using contrast adjustment and super-resolution to reduce diffusion-weighted images (DWIs) diversity across magnetic field strengths and imaging parameters. Retrospective. The DL model was built using an open dataset from one individual. The MR machine identification model was trained and validated on a dataset of 1134 adults (54% females, 46% males), with 1050 subjects showing no DWI abnormalities and 84 with conditions like stroke and tumors. The 21,000 images were divided into 80% for training, 20% for validation, and 3500 for testing. Seven MR scanners from four manufacturers with 1.5 T and 3 T magnetic field strengths. DWIs were acquired using spin-echo sequences and high-resolution T2WIs using the T2-SPACE sequence. An experienced, board-certified radiologist evaluated the effectiveness of restoring high-resolution T2WI and harmonizing diverse DWI with metrics such as PSNR and SSIM, and the texture and frequency attributes were further analyzed using gray-level co-occurrence matrix and 1-dimensional power spectral density. The model's impact on machine-specific characteristics was gauged through the performance metrics of a ResNet-50 model. Comprehensive statistical tests were employed for statistical robustness, including McNemar's test and the Dice index. Our DL protocol reduced DWI contrast and resolution variation. ResNet-50 model's accuracy decreased from 0.9443 to 0.5786, precision from 0.9442 to 0.6494, recall from 0.9443 to 0.5786, and F1 score from 0.9438 to 0.5587. The t-SNE visualization indicated more consistent image features across multiple MR devices. Autoencoder halved learning iterations; Dice coefficient >0.74 confirmed signal reproducibility in 84 lesions. This study presents a DL strategy to mitigate batch effects in diffusion MR images, improving their quality and generalizability. 3 TECHNICAL EFFICACY: Stage 1.

Role of diffusion tensor imaging in stenotic and non-stenotic spinal canal

Background and purposeCervical Spondylotic Myelopathy (CSM) is a gradually escalating spinal cord disturbance set in motion by the degenerative narrowing of the vertebral canal. Routine MRI may fail to detect the subtle early alterations of the cord. MRI Diffusion Tensor Imaging (DTI) possesses the potential to detect these changes. This study intends to estimate the potential of the DTI technique in non-stenotic & stenotic spinal canals in individuals affected with CSM. MethodologySixty-four subjects who met the requirements of the inclusion criteria were incorporated into the investigation. All subjects underwent routine MRI sequences in addition to DTI of the cervical spine region. Scalars such as Fractional Anisotropy (FA), besides Apparent Diffusion Coefficient (ADC), were computed at each cervical intervertebral fibrocartilaginous disc level for all subjects. DTI fiber tractography was then performed to qualitatively assess the microstructural integrity of the tracts. ResultsA noteworthy difference (p<0.05) was seen in the FA parameter and ADC parameter values between the stenotic and non-stenotic groups, with the non-stenotic group having a higher mean FA and a lower ADC than the stenotic group (at the level of stenosis). A significant difference in age was seen between both groups, with most of the patients in the stenotic group belonging to 40 years and above. Tractography helped in demonstrating the morphology of the fiber tracts. ConclusionDTI parameters, namely FA and ADC, are sensitive to damage to the white matter and can be used to detect microstructural changes in the cord. However, standardization of the protocol is necessary when imaging the spinal canal.

Changes in Cortical Microstructure of the Human Brain Resulting from Long-Term Motor Learning.

The mechanisms subserving motor skill acquisition and learning in the intact human brain are not fully understood. Previous studies in animals have demonstrated a causal relationship between motor learning and structural rearrangements of synaptic connections, raising the question of whether neurite-specific changes are also observable in humans. Here, we use advanced diffusion magnetic resonance imaging (MRI), sensitive to dendritic and axonal processes, to investigate neuroplasticity in response to long-term motor learning. We recruited healthy male and female human participants (age range 19-29) who learned a challenging dynamic balancing task (DBT) over four consecutive weeks. Diffusion MRI signals were fitted using Neurite Orientation Dispersion and Density Imaging (NODDI), a theory-driven biophysical model of diffusion, yielding measures of tissue volume, neurite density and the organizational complexity of neurites. While NODDI indices were unchanged and reliable during the control period, neurite orientation dispersion increased significantly during the learning period mainly in primary sensorimotor, prefrontal, premotor, supplementary, and cingulate motor areas. Importantly, reorganization of cortical microstructure during the learning phase predicted concurrent behavioral changes, whereas there was no relationship between microstructural changes during the control phase and learning. Changes in neurite complexity were independent of alterations in tissue density, cortical thickness, and intracortical myelin. Our results are in line with the notion that structural modulation of neurites is a key mechanism supporting complex motor learning in humans.SIGNIFICANCE STATEMENT The structural correlates of motor learning in the human brain are not fully understood. Results from animal studies suggest that synaptic remodeling (e.g., reorganization of dendritic spines) in sensorimotor-related brain areas is a crucial mechanism for the formation of motor memory. Using state-of-the-art diffusion magnetic resonance imaging (MRI), we found a behaviorally relevant increase in the organizational complexity of neocortical microstructure, mainly in primary sensorimotor, prefrontal, premotor, supplementary, and cingulate motor regions, following training of a challenging dynamic balancing task (DBT). Follow-up analyses suggested structural modulation of synapses as a plausible mechanism driving this increase, while colocalized changes in cortical thickness, tissue density, and intracortical myelin could not be detected. These results advance our knowledge about the neurobiological basis of motor learning in humans.

Ratio of Incidentally Detected Brain Tumors in Diffusion Magnetic Resonance Imaging Scans Performed in the Emergency Department on the Suspicion of Acute Stroke

Ratio of Incidentally Detected Brain Tumors in Diffusion Magnetic Resonance Imaging Scans Performed in the Emergency Department on the Suspicion of Acute Stroke

Corticolimbic structural connectivity encapsulates real-world emotional reactivity andhappiness.

Emotional reactivity to everyday events predicts happiness, but the neural circuits underlying this relationship remain incompletely understood. Here, we combined experience sampling methods and diffusion magnetic resonance imaging to examine the association among corticolimbic structural connectivity, real-world emotional reactivity and daily experiences of happiness from 79 young adults (35 females). Participants recorded momentary assessments of emotional and happiness experiences five times a day for a week, approximately 2weeks after brain scanning. Model-based emotional reactivity scores, which index the degree to which moment-to-moment affective state varies with the occurrence of positive or negative events, were computed. Results showed that stronger microstructural integrity of the uncinate fasciculus and the external capsule was associated with both greater positive and negative emotional reactivity scores. The relationship between these fiber tracts and experienced happiness was explained by emotional reactivity. Importantly, this indirect effect was observed for emotional reactivity to positive but not negative real-world events. Our findings suggest that the corticolimbic circuits supporting socioemotional functions are associated with emotional reactivity and happiness in the real world.

Multimodel habitats constructed by perfusion and/or diffusion MRI predict isocitrate dehydrogenase mutation status and prognosis in high-grade gliomas

To determine whether tumour vascular and cellular heterogeneity of high-grade glioma (HGG) is predictive of isocitrate dehydrogenase (IDH) mutation status and overall survival (OS) by using tumour habitat-based analysis constructed by perfusion and/or diffusion magnetic resonance imaging (MRI). Seventy-eight HGG patients that met the 2021 World Health Organization WHO Classification of Tumors of the Central Nervous System, 5th edition (WHO CNS5), were enrolled to predict IDH mutation status, of which 32 grade 4 patients with unmethylated O6-methylguanine-DNA methyltransferase (MGMT) promoter were enrolled for prognostic analysis. The deep-learning-based model nnU-Net and K-means clustering algorithm were applied to construct the Traditional Habitat, Vascular Habitat (VH), Cellular Density Habitat (DH), and their Combined Habitat (CH). Quantitative parameters were extracted and compared between IDH-mutant and IDH-wild-type patients, respectively, and the prediction potential was evaluated by receiver operating characteristic (ROC) curve analysis. OS was analysed using Kaplan-Meier survival analysis and the log-rank test. Compared with IDH-mutants, median relative cerebral blood volume (rCBVmedian) values in the whole enhancing tumour (WET), VH1, VH3, CH1-4 habitats were significantly increased in IDH-wild-type HGGs (all p<0.05). Additionally, the accuracy of rCBVmedian values in CH1 outperformed other habitats in identifying IDH mutation status (p<0.001) at a cut-off value of 4.83 with AUC of 0.815. Kaplan-Meier survival analysis highlighted significant differences in OS between the populations dichotomised by the median of rCBVmedian in WET, VH1, CH1-3 habitats (all p<0.05). The habitat imaging technique may improve the accuracy of predicting IDH mutation status and prognosis, and even provide a new direction for subsequent personalised precision treatment.

MRI features for predicting the pathological grade of HCC in patients undergoing liver transplantation

BackgroundContrast-enhanced magnetic resonance imaging (MRI) plays a crucial role in the diagnosis of hepatocellular carcinoma (HCC). This study aims to assess the performance of MRI features for evaluating hepatocellular carcinoma (HCC) aggressiveness in living liver transplantation in patients.Material and methodsThis retrospective study included patients who underwent liver transplantation in our hospital between 2015 and 2020. Abdominal contrast-enhanced MRIs of these patients were reviewed, and clinical, radiological, and histopathological findings of HCCs were recorded. The prognostic features of HCCs as determined by MRI were compared with Edmondson-Steiner (E-S) grades. Liver parenchyma fibrosis based on an apparent diffusion coefficient (ADC) map was correlated with histological subclassification of cirrhosis using the Laennec staging system.ResultsThe study subjects included 37 men and 8 women with a mean age of 59.56 ± 7.81 (range: 25–72). The mean tumour size was 37.33 ± 22.27 mm (range: 10–118 mm), and nine tumours (23.1%) involved portal vein tumour thrombosis. There was a significant correlation between tumour grade and size (p = 0.007) and intratumoral fat (p = 0.014) even though no significant correlations between grade and mean ADC value, capsule appearance, presence of satellite lesions, smooth margin, imaging of the tumour feeding artery, and corona enhancement of HCC (p > 0.05) were found. There was a statistically significant correlation between mild (stage 4A) and moderate (stage 4B) fibrosis of non-tumorous liver parenchyma and ADC value (p < 0.001).ConclusionOur study found that ADC values can be used to distinguish mild cirrhotic livers from moderate cirrhotic livers. Diffusion MRI might be used to diagnose the degree of liver fibrosis without histopathological analysis. According to our results, only intralesional fat and tumour size correlated with tumour grade, and as such, these parameters could be used as prognostic MRI biomarkers for HCC.

Reconstructing the somatotopic organization of the corticospinal tract remains a challenge for modern tractography methods.

The corticospinal tract (CST) is a critically important white matter fiber tract in the human brain that enables control of voluntary movements of the body. The CST exhibits a somatotopic organization, which means that the motor neurons that control specific body parts are arranged in order within the CST. Diffusion magnetic resonance imaging (MRI) tractography is increasingly used to study the anatomy of the CST. However, despite many advances in tractography algorithms over the past decade, modern, state-of-the-art methods still face challenges. In this study, we compare the performance of six widely used tractography methods for reconstructing the CST and its somatotopic organization. These methods include constrained spherical deconvolution (CSD) based probabilistic (iFOD1) and deterministic (SD-Stream) methods, unscented Kalman filter (UKF) tractography methods including multi-fiber (UKF2T) and single-fiber (UKF1T) models, the generalized q-sampling imaging (GQI) based deterministic tractography method, and the TractSeg method. We investigate CST somatotopy by dividing the CST into four subdivisions per hemisphere that originate in the leg, trunk, hand, and face areas of the primary motor cortex. A quantitative and visual comparison is performed using diffusion MRI data (N = 100 subjects) from the Human Connectome Project. Quantitative evaluations include the reconstruction rate of the eight anatomical subdivisions, the percentage of streamlines in each subdivision, and the coverage of the white matter-gray matter (WM-GM) interface. CST somatotopy is further evaluated by comparing the percentage of streamlines in each subdivision to the cortical volumes for the leg, trunk, hand, and face areas. Overall, UKF2T has the highest reconstruction rate and cortical coverage. It is the only method with a significant positive correlation between the percentage of streamlines in each subdivision and the volume of the corresponding motor cortex. However, our experimental results show that all compared tractography methods are biased toward generating many trunk streamlines (ranging from 35.10% to 71.66% of total streamlines across methods). Furthermore, the coverage of the WM-GM interface in the largest motor area (face) is generally low (under 40%) for all compared tractography methods. Different tractography methods give conflicting results regarding the percentage of streamlines in each subdivision and the volume of the corresponding motor cortex, indicating that there is generally no clear relationship, and that reconstruction of CST somatotopy is still a large challenge. Overall, we conclude that while current tractography methods have made progress toward the well-known challenge of improving the reconstruction of the lateral projections of the CST, the overall problem of performing a comprehensive CST reconstruction, including clinically important projections in the lateral (hand and face areas) and medial portions (leg area), remains an important challenge for diffusion MRI tractography.