Selective vulnerability of brainstem and cervical spinal cord regions in people with non-progressive multiple sclerosis of Black or African American and European ancestry.

BACKGROUND: We evaluated imaging features suggestive of neurodegeneration within the brainstem and upper cervical spinal cord (UCSC) in non-progressive multiple sclerosis (MS). METHODS: Standardized 3-Tesla three-dimensional brain magnetic resonance imaging (MRI) studies were prospectively acquired. Rates of change in volume, surface texture, curvature were quantified at the pons and medulla-UCSC. Whole and regional brain volumes and T2-weighted lesion volumes were also quantified. Independent regression models were constructed to evaluate differences between those of Black or African ancestry (B/AA) and European ancestry (EA) with non-progressive MS. RESULTS: 209 people with MS (pwMS) having at least two MRI studies, 29% possessing 3-6 timepoints, resulted in 487 scans for analysis. Median follow-up time between MRI timepoints was 1.33 (25th-75th percentile: 0.51-1.98) years. Of 183 non-progressive pwMS, 88 and 95 self-reported being B/AA and EA, respectively. Non-progressive pwMS demonstrated greater rates of decline in pontine volume (p < 0.0001) in B/AA and in medulla-UCSC volume (p < 0.0001) for EA pwMS. Longitudinal surface texture and curvature changes suggesting reduced tissue integrity were observed at the ventral medulla-UCSC (p < 0.001), dorsal pons (p < 0.0001) and dorsal medulla (p < 0.0001) but not the ventral pons (p = 0.92) between groups. CONCLUSIONS: Selectively vulnerable regions within the brainstem-UCSC may allow for more personalized approaches to disease surveillance and management.

Diagnostic Utility of Hippocampal Volumetric Data in a Memory Disorder Clinic Setting.

BACKGROUND: Hippocampal volumetric data are widely used in research but are rarely examined in clinical populations in regard to aiding diagnosis or correlating with objective memory test scores. OBJECTIVE: To replicate and expand on the few prior clinical examinations of the utility of hippocampal volumetric data. We evaluated MRI volumetric data to determine (a) the degree of hippocampal loss across diagnostic groups compared with a cognitively intact group, (b) if total or lateralized hippocampal volumes predict diagnostic group membership, and (c) how total and lateralized volumes correlate with memory tests. METHOD: We retrospectively examined hippocampal volumetric data and memory test scores for 294 individuals referred to a memory clinic. RESULTS: Individuals with mild cognitive impairment or Alzheimer disease had smaller hippocampal volumes compared with cognitively intact individuals. The raw and normalized total and lateralized hippocampal volumes were essentially equal for predicting diagnostic group membership, and notably low hippocampal volumes evidenced greater specificity than sensitivity. All of the volumetric data correlated with the memory test scores, with the total and left hippocampal volumes accounting for the slightly more variance in the diagnostic groups. CONCLUSION: The diagnostic groups exhibited hippocampal volume loss, which can be a potential biomarker for neurodegenerative disease in clinical practice. However, solely using hippocampal volumetric data to predict diagnostic group membership or memory test failure was not supported. While extreme hippocampal volume loss was rare in the cognitively intact group, the sensitivity of these volumetric data suggests a need for supplementation by other tools when making a diagnosis.

Disruption of the Atrophy-based Functional Network in Multiple Sclerosis Is Associated with Clinical Disability: Validation of a Meta-Analytic Model in Resting-State Functional MRI.

Background In multiple sclerosis (MS), gray matter (GM) atrophy exhibits a specific pattern, which correlates strongly with clinical disability. However, the mechanism of regional specificity in GM atrophy remains largely unknown. Recently, the network degeneration hypothesis (NDH) was quantitatively defined (using coordinate-based meta-analysis) as the atrophy-based functional network (AFN) model, which posits that localized GM atrophy in MS is mediated by functional networks. Purpose To test the NDH in MS in a data-driven manner using the AFN model to direct analyses in an independent test sample. Materials and Methods Model fit testing was conducted with structural equation modeling, which is based on the computation of semipartial correlations. Model verification was performed in coordinate-based data of healthy control participants from the BrainMap database (https://www.brainmap.org). Model validation was conducted in prospectively acquired resting-state functional MRI in participants with relapsing-remitting MS who were recruited between September 2018 and January 2019. Correlation analyses of model fit indices and volumetric measures with Expanded Disability Status Scale (EDSS) scores and disease duration were performed. Results Model verification of healthy control participants included 80 194 coordinates from 9035 experiments. Model verification in healthy control data resulted in excellent model fit (root mean square error of approximation, 0.037; 90% CI: 0.036, 0.039). Twenty participants (mean age, 36 years ± 9 [standard deviation]; 12 women) with relapsing-remitting MS were evaluated. Model validation in resting-state functional MRI in participants with MS resulted in deviation from optimal model fit (root mean square error of approximation, 0.071; 90% CI: 0.070, 0.072), which correlated with EDSS scores (r = 0.68; P = .002). Conclusion The atrophy-based functional network model predicts functional network disruption in multiple sclerosis (MS), thereby supporting the network degeneration hypothesis. On resting-state functional MRI scans, reduced functional network integrity in participants with MS had a strong positive correlation with clinical disability. © RSNA, 2021 Online supplemental material is available for this article.

Combining inhomogeneous magnetization transfer and multipoint Dixon acquisition: Potential utility and evaluation.

PURPOSE: The recently introduced inhomogeneous magnetization transfer (ihMT) method has predominantly been applied for imaging the central nervous system. Future applications of ihMT, such as in peripheral nerves and muscles, will involve imaging in the vicinity of adipose tissues. This work aims to systematically investigate the partial volume effect of fat on the ihMT signal and to propose an efficient fat-separation method that does not interfere with ihMT measurements. METHODS: First, the influence of fat on ihMT signal was studied using simulations. Next, the ihMT sequence was combined with a multi-echo Dixon acquisition for fat separation. The sequence was tested in 9 healthy volunteers using a 3T human scanner. The ihMT ratio (ihMTR) values were calculated in regions of interest in the brain and the spinal cord using standard acquisition (no fat saturation), water-only, in-phase, and out-of-phase reconstructions. The values obtained were compared with a standard fat suppression method, spectral presaturation with inversion recovery. RESULTS: Simulations showed variations in the ihMTR values in the presence of fat, depending on the TEs used. The IhMTR values in the brain and spinal cord derived from the water-only ihMT multi-echo Dixon images were in good agreement with values from the unsuppressed sequence. The ihMT-spectral presaturation with inversion recovery combination resulted in 24%-35% lower ihMTR values compared with the standard non-fat-suppressed acquisition. CONCLUSION: The presence of fat within a voxel affects the ihMTR calculations. The IhMT multi-echo Dixon method does not compromise the observable ihMT effect and can potentially be used to remove fat influence in ihMT.

Fundamentals of Radiation Oncology for Treatment of Vertebral Metastases.

The fields of both radiology and radiation oncology have evolved considerably in the past few decades, resulting in an increased ability to delineate between tumor and normal tissue to precisely target and treat vertebral metastases with radiation therapy. These scientific advances have also led to improvements in assessing treatment response and diagnosing toxic effects related to radiation treatment. However, despite technological innovations yielding greatly improved rates of palliative relief and local control of osseous spinal metastases, radiation therapy can still lead to a number of acute and delayed posttreatment complications. Treatment-related adverse effects may include pain flare, esophageal toxic effects, dermatitis, vertebral compression fracture, radiation myelopathy, and myositis, among others. The authors provide an overview of the multidisciplinary approach to the treatment of spinal metastases, indications for surgical management versus radiation therapy, various radiation technologies and techniques (along with their applications for spinal metastases), and current principles of treatment planning for conventional and stereotactic radiation treatment. Different radiologic criteria for assessment of treatment response, recent advances in radiologic imaging, and both common and rare complications related to spinal irradiation are also discussed, along with the imaging characteristics of various adverse effects. Familiarity with these topics will not only assist the diagnostic radiologist in assessing treatment response and diagnosing treatment-related complications but will also allow more effective collaboration between diagnostic radiologists and radiation oncologists to guide management decisions and ensure high-quality patient care. ©RSNA, 2021.

Fundamentals of Radiation Oncology for Neurologic Imaging.

Although the physical and biologic principles of radiation therapy have remained relatively unchanged, a technologic renaissance has led to continuous and ever-changing growth in the field of radiation oncology. As a result, medical devices, techniques, and indications have changed considerably during the past 20-30 years. For example, advances in CT and MRI have revolutionized the treatment planning process for a variety of central nervous system diseases, including primary and metastatic tumors, vascular malformations, and inflammatory diseases. The resultant improved ability to delineate normal from abnormal tissue has enabled radiation oncologists to achieve more precise targeting and helped to mitigate treatment-related complications. Nevertheless, posttreatment complications still occur and can pose a diagnostic challenge for radiologists. These complications can be divided into acute, early-delayed, and late-delayed complications on the basis of the time that they manifest after radiation therapy and include leukoencephalopathy, vascular complications, and secondary neoplasms. The different irradiation technologies and applications of these technologies in the brain, current concepts used in treatment planning, and essential roles of the radiation oncologist in the setting of brain disease are reviewed. In addition, relevant imaging findings that can be used to delineate the extent of disease before treatment, and the expected posttreatment imaging changes are described. Common and uncommon complications related to radiation therapy and the associated imaging manifestations also are discussed. Familiarity with these entities may aid the radiologist in making the diagnosis and help guide appropriate management. ©RSNA, 2020.

Characterization of normal-appearing white matter in multiple sclerosis using quantitative susceptibility mapping in conjunction with diffusion tensor imaging.

PURPOSE: Quantitative susceptibility mapping (QSM) is influenced by iron as well as myelin, which makes interpretation of pathologic changes challenging. Concurrent acquisition of MR sequences that are sensitive to axonal/myelin integrity, such as diffusion tensor imaging (DTI), may provide context for interpreting quantitative susceptibility (QS) signal. The purpose of our study was to investigate alterations in normal-appearing white matter (NAWM) in multiple sclerosis (MS) using QSM in conjunction with DTI. METHODS: Twenty relapsing-remitting MS patients and 20 age-matched healthy controls (HC) were recruited for this prospective study. QS, radial diffusivity (RD), fractional anisotropy (FA), and R2* maps within the whole brain as well as individual tracts were generated for comparison between NAWM and HC white matter (HCWM). RESULTS: MS lesions demonstrated significant differences in QS, FA, RD, and R2* compared to HCWM (p < 0.03). These metrics did not show a significant difference between whole-brain NAWM and HCWM. Among NAWM tracts, the cingulate gyri demonstrated significantly decreased QS compared to HCWM (p = 0.004). The forceps major showed significant differences in FA and RD without corresponding changes in QS (p < 0.01). CONCLUSION: We found discordant changes in QSM and DTI metrics within the cingulate gyri and forceps major. This may potentially reflect the influence of paramagnetic substrates such as iron, which could be decreased along these NAWM tracts. Our results point to the potential role of QSM as a unique biomarker, although additional validation studies are needed.

The relationship between alcohol consumption and amygdala volume in a community-based sample.

Most prior studies have reported decreased amygdala volume in those with a history of alcohol use disorder. Decreased amygdala volume associated with alcohol use disorder may be related to an increased risk of addiction and relapse. However, the relationship between amygdala volume and a broad range of alcohol consumption is largely unexplored. The present cross-sectional analysis investigates the relationship between amygdala volume and self-reported alcohol consumption in participants of the Dallas Heart Study, a community-based study of Dallas County, Texas residents. Brain imaging and survey data from participants (n = 2023) were obtained, and multiple linear regressions were performed with the average amygdala volume as the dependent variable and drinking status, drinking risk, drinks per week, and binge drinking as independent variables. Drinking risk was categorized such that low-risk constituted ≤ 14 drinks per week in men and ≤ 7 drinks per week in women, while > 14 drinks per week in men and > 7 drinks per week in women constituted high-risk. Age, sex, intracranial volume, body mass index, education, and Quick Inventory of Depressive Symptomatology-Self Report score were included in all models as covariates. No statistically significant (p ≤ .05) associations were observed between self-reported alcohol consumption and amygdala volume. The present study suggests non-significant relationships between self-reported alcohol consumption and amygdala volume when controlling for relevant demographic factors in a large, community-based sample.

Antidepressant-related microstructural changes in the external capsule.

Several magnetic resonance imaging (MRI) studies have reported that antidepressant medications are strongly linked to brain microstructural alterations. Notably, external capsule alterations have been reported to be a biological marker for therapeutic response. However, prior studies did not investigate whether a change in the neurite density or directional coherence of white matter (WM) fibers underlies the observed microstructural alterations. This MRI-based case-control study examined the relationship between patients' current use of antidepressant medications and advanced measurements of external capsule WM microstructure derived from multishell diffusion imaging using neurite orientation dispersion and density imaging (NODDI). The study compared a group of thirty-five participants who were taking antidepressant medications comprising selective serotonin reuptake inhibitors (SSRIs) (n = 25) and serotonin and norepinephrine reuptake inhibitors (SNRIs) with a control group of thirty-five individuals matched in terms of age, sex, race, and atherosclerotic cardiovascular risk factors. All participants were selected from the Dallas Heart Study phase 2, a multi-ethnic, population-based cohort study. A series of multiple linear regression analyses were conducted to predict microstructural characteristics of the bilateral external capsule using age, sex, and antidepressant medications as predictor variables. There was significantly reduced neurite density in the bilateral external capsules of patients taking SSRIs. Increased orientation dispersion in the external capsule was predominantly seen in patients taking SNRIs. Our findings suggest an association between specific external capsule microstructural changes and antidepressant medications, including reduced neurite density for SSRIs and increased orientation dispersion for SNRIs.

Synthesizing Contrast-Enhanced MR Images from Noncontrast MR Images Using Deep Learning.

BACKGROUND AND PURPOSE: Recent developments in deep learning methods offer a potential solution to the need for alternative imaging methods due to concerns about the toxicity of gadolinium-based contrast agents. The purpose of the study was to synthesize virtual gadolinium contrast-enhanced T1-weighted MR images from noncontrast multiparametric MR images in patients with primary brain tumors by using deep learning. MATERIALS AND METHODS: We trained and validated a deep learning network by using MR images from 335 subjects in the Brain Tumor Segmentation Challenge 2019 training data set. A held out set of 125 subjects from the Brain Tumor Segmentation Challenge 2019 validation data set was used to test the generalization of the model. A residual inception DenseNet network, called T1c-ET, was developed and trained to simultaneously synthesize virtual contrast-enhanced T1-weighted (vT1c) images and segment the enhancing portions of the tumor. Three expert neuroradiologists independently scored the synthesized vT1c images by using a 3-point Likert scale, evaluating image quality and contrast enhancement against ground truth T1c images (1 = poor, 2 = good, 3 = excellent). RESULTS: The synthesized vT1c images achieved structural similarity index, peak signal-to-noise ratio, and normalized mean square error scores of 0.91, 64.35, and 0.03, respectively. There was moderate interobserver agreement between the 3 raters, regarding the algorithm's performance in predicting contrast enhancement, with a Fleiss kappa value of 0.61. Our model was able to accurately predict contrast enhancement in 88.8% of the cases (scores of 2 to 3 on the 3-point scale). CONCLUSIONS: We developed a novel deep learning architecture to synthesize virtual postcontrast enhancement by using only conventional noncontrast brain MR images. Our results demonstrate the potential of deep learning methods to reduce the need for gadolinium contrast in the evaluation of primary brain tumors.

Axonal and myelin changes and their inter-relationship in the optic radiations in people with multiple sclerosis.

Background: The imaging g-ratio, estimated from axonal volume fraction (AVF) and myelin volume fraction (MVF), is a novel biomarker of microstructural tissue integrity in multiple sclerosis (MS). Objective: To assess axonal and myelin changes and their inter-relationship as measured by g-ratio in the optic radiations (OR) in people with MS (pwMS) with and without previous optic neuritis (ON) compared to healthy controls (HC). Methods: Thirty pwMS and 17 HCs were scanned on a 3Tesla Connectom scanner. AVF and MVF, derived from a multi-shell diffusion protocol and macromolecular tissue volume, respectively, were measured in normal-appearing white matter (NAWM) and lesions within the OR and used to calculate imaging g-ratio. Results: OR AVF and MVF were decreased in pwMS compared to HC, and in OR lesions compared to NAWM, whereas the g-ratio was not different. Compared to pwMS with previous ON, AVF and g-ratio tended to be higher in pwMS without prior ON. AVF and MVF, particularly in NAWM, were positively correlated with retinal thickness, which was more pronounced in pwMS with prior ON. Conclusion: Axonal measures reflect microstructural tissue damage in the OR, particularly in the setting of remote ON, and correlate with established metrics of visual health in MS.

Errata: Brain tumor IDH, 1p/19q, and MGMT molecular classification using MRI-based deep learning: an initial study on the effect of motion and motion correction.

[This corrects the article DOI: 10.1117/1.JMI.9.1.016001.].

Direction and magnitude of displacement differ between slowly expanding and non-expanding multiple sclerosis lesions as compared to small vessel disease.

BACKGROUND AND PURPOSE: Differentiating between multiple sclerosis (MS) and small vessel disease (SVD) lesions represents a key challenge in the day-to-day management of patients. We aimed to distinguish between MS and SVD by identifying the dynamics of lesion movement patterns between enlarging and contracting foci from two MRI time points. METHODS: Standardized 3-Tesla 3-dimensional brain magnetic resonance imaging (MRI) studies were performed at two time points on enrolled MS and SVD patients. Selected supratentorial lesions were segmented and longitudinal changes in the direction of lesion displacement and magnitude along with the evolution of contracting and expanding T1-weighted and T2-weighted MS lesions were quantified based on lesion centroid positioning. Bayesian linear mixed effects regression models were constructed to evaluate associations between changes in lesion transitions and disease state. RESULTS: A total of 420 lesions were analyzed from 35 MS (female (F):22 (62.9%); median age (range):38 years (y) (22-61), median disease duration:7.38y (0.38-20.99)) and 12 SVD patients (F:11 (100%); 54y (40-66)). MS T2-weighted lesions that increased in volume between MRI time points demonstrated movement toward the cortex (p = 0.01), whereas those that decreased in volume moved toward the center (p < 0.0001). Lesion volume changes related to SVD demonstrated no effect on movement direction over time. Both expanding (p = 0.03) and contracting (p = 0.01) MS lesions demonstrated greater distances between centroids when compared to SVD. CONCLUSION: Lesion dynamics may reveal distinct characteristics associated with the biology of disease while providing further insights into the behavior of inflammatory CNS disorders.

Brain tumor IDH, 1p/19q, and MGMT molecular classification using MRI-based deep learning: an initial study on the effect of motion and motion correction.

Purpose: Deep learning has shown promise for predicting the molecular profiles of gliomas using MR images. Prior to clinical implementation, ensuring robustness to real-world problems, such as patient motion, is crucial. The purpose of this study is to perform a preliminary evaluation on the effects of simulated motion artifact on glioma marker classifier performance and determine if motion correction can restore classification accuracies. Approach: T2w images and molecular information were retrieved from the TCIA and TCGA databases. Simulated motion was added in the k-space domain along the phase encoding direction. Classifier performance for IDH mutation, 1p/19q co-deletion, and MGMT methylation was assessed over the range of 0% to 100% corrupted k-space lines. Rudimentary motion correction networks were trained on the motion-corrupted images. The performance of the three glioma marker classifiers was then evaluated on the motion-corrected images. Results: Glioma marker classifier performance decreased markedly with increasing motion corruption. Applying motion correction effectively restored classification accuracy for even the most motion-corrupted images. For isocitrate dehydrogenase (IDH) classification, 99% accuracy was achieved, exceeding the original performance of the network and representing a new benchmark in non-invasive MRI-based IDH classification. Conclusions: Robust motion correction can facilitate highly accurate deep learning MRI-based molecular marker classification, rivaling invasive tissue-based characterization methods. Motion correction may be able to increase classification accuracy even in the absence of a visible artifact, representing a new strategy for boosting classifier performance.

QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results.

Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder translating DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties could enable clinical review of the most uncertain regions, thereby building trust and paving the way toward clinical translation. Several uncertainty estimation methods have recently been introduced for DL medical image segmentation tasks. Developing scores to evaluate and compare the performance of uncertainty measures will assist the end-user in making more informed decisions. In this study, we explore and evaluate a score developed during the BraTS 2019 and BraTS 2020 task on uncertainty quantification (QU-BraTS) and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation. This score (1) rewards uncertainty estimates that produce high confidence in correct assertions and those that assign low confidence levels at incorrect assertions, and (2) penalizes uncertainty measures that lead to a higher percentage of under-confident correct assertions. We further benchmark the segmentation uncertainties generated by 14 independent participating teams of QU-BraTS 2020, all of which also participated in the main BraTS segmentation task. Overall, our findings confirm the importance and complementary value that uncertainty estimates provide to segmentation algorithms, highlighting the need for uncertainty quantification in medical image analyses. Finally, in favor of transparency and reproducibility, our evaluation code is made publicly available at https://github.com/RagMeh11/QU-BraTS.

Anatomic survey of seeding in Alzheimer's disease brains reveals unexpected patterns.

Tauopathies are heterogeneous neurodegenerative diseases defined by progressive brain accumulation of tau aggregates. The most common tauopathy, sporadic Alzheimer's disease (AD), involves progressive tau deposition that can be divided into specific stages of neurofibrillary tangle pathology. This classification is consistent with experimental data which suggests that network-based propagation is mediated by cell-cell transfer of tau "seeds", or assemblies, that serve as templates for their own replication. Until now, seeding assays of AD brain have largely been limited to areas previously defined by NFT pathology. We now expand this work to additional regions. We selected 20 individuals with AD pathology of NFT stages I, III, and V. We stained and classified 25 brain regions in each using the anti-phospho-tau monoclonal antibody AT8. We measured tau seeding in each of the 500 samples using a cell-based tau "biosensor" assay in which induction of intracellular tau aggregation is mediated by exogenous tau assemblies. We observed a progressive increase in tau seeding according to NFT stage. Seeding frequently preceded NFT pathology, e.g., in the basolateral subnucleus of the amygdala and the substantia nigra, pars compacta. We observed seeding in brain regions not previously known to develop tau pathology, e.g., the globus pallidus and internal capsule, where AT8 staining revealed mainly axonal accumulation of tau. AT8 staining in brain regions identified because of tau seeding also revealed pathology in a previously undescribed cell type: Bergmann glia of the cerebellar cortex. We also detected tau seeding in brain regions not previously examined, e.g., the intermediate reticular zone, dorsal raphe nucleus, amygdala, basal nucleus of Meynert, and olfactory bulb. In conclusion, tau histopathology and seeding are complementary analytical tools. Tau seeding assays reveal pathology in the absence of AT8 signal in some instances, and previously unrecognized sites of tau deposition. The variation in sites of seeding between individuals could underlie differences in the clinical presentation and course of AD.

Regional White Matter Diffusion Changes Associated with the Cumulative Tensile Strain and Strain Rate in Nonconcussed Youth Football Players.

The purpose of this study is to assess the relationship between regional white matter diffusion imaging changes and finite element strain measures in nonconcussed youth football players. Pre- and post-season diffusion-weighted imaging was performed in 102 youth football subject-seasons, in which no concussions were diagnosed. The diffusion data were normalized to the IXI template. Percent change in fractional anisotropy (%ΔFA) images were generated. Using data from the head impact telemetry system, the cumulative maximum principal strain one times strain rate (CMPS1 × SR), a measure of the cumulative tensile brain strain and strain rate for one season, was calculated for each subject. Two linear regression analyses were performed to identify significant positive or inverse relationships between CMPS1 × SR and %ΔFA within the international consortium for brain mapping white matter mask. Age, body mass index, days between pre- and post-season imaging, previous brain injury, attention disorder diagnosis, and imaging protocol were included as covariates. False discovery rate correction was used with corrected alphas of 0.025 and voxel thresholds of zero. Controlling for all covariates, a significant, positive linear relationship between %ΔFA and CMPS1 × SR was identified in the bilateral cingulum, fornix, internal capsule, external capsule, corpus callosum, corona radiata, corticospinal tract, cerebral and middle cerebellar peduncle, superior longitudinal fasciculus, and right superior fronto-occipital fasciculus. Post hoc analyses further demonstrated significant %ΔFA differences between high-strain football subjects and noncollision control athletes, no significant %ΔFA differences between low-strain subjects and noncollision control athletes, and that CMPS1 × SR significantly explained more %ΔFA variance than number of head impacts alone.

Comparison of atherosclerotic plaque by computed tomography angiography in patients with and without diabetes mellitus and with known or suspected coronary artery disease.

The aim of this study was to compare coronary artery plaque burden, composition, distribution, and the degree of coronary artery stenosis in diabetic and nondiabetic patients with known or suspected coronary artery disease (CAD). The study group consisted of 594 patients with known or suspected CAD, including 122 diabetics, who underwent multidetector computed tomographic coronary angiography and traditional invasive coronary artery angiography. Coronary artery calcium scores were compared in different age subgroups. Noncalcified plaque, calcified plaque, and mixed plaque were analyzed by coronary segment on computed tomographic coronary angiography, as well as the degree of coronary stenosis on coronary artery angiography. Obstructive vessels were compared between the 2 groups. Total coronary artery calcium score was higher in patients with diabetes compared to those without (378.4 ± 613.0 vs 226.0 ± 408.4, p = 0.003). The percentage of patients with coronary artery calcium scores >400 among diabetics (22.1%) was higher than among nondiabetics (14.2%) (p = 0.032). Diabetics had a higher percentage of coronary segments with noncalcified plaque, calcified plaque, and mixed plaque than nondiabetics (35.3% vs 26.2%, p <0.001; 17.5% vs 11.6%, p = 0.017; and 9.8% vs 7.9%, p = 0.008). More diabetics had multivessel obstructive disease compared to nondiabetics (p <0.05). With longer duration of diabetes mellitus, the stenosed segments of coronary arteries increased accordingly. In conclusion, diabetics have more atherosclerotic plaque burden and more severe coronary atherosclerosis than nondiabetics. Most obstructive lesions were caused by mixed plaques in diabetics and nondiabetics.