Magnetic Resonance Imaging Biomarkers Research Articles

A New Method for Cartilage Evaluation in Femoroacetabular Impingement Using Quantitative T2 Magnetic Resonance Imaging: Preliminary Validation against Arthroscopic Findings.

The outcome of arthroscopic treatment for femoroacetabular impingement (FAI) depends on the preoperative status of the hip cartilage. Quantitative T2 can detect early biochemical cartilage changes, but its routine implementation is challenging. Furthermore, intrinsic T2 variability between patients makes it difficult to define a threshold to identify cartilage lesions. To address this, we propose a normalized T2-index as a new method to evaluate cartilage in FAI. We retrospectively analyzed magnetic resonance imaging (MRI) data of 18 FAI patients with arthroscopically confirmed cartilage defects. Cartilage T2 maps were reconstructed from multi-spin-echo 3-T data using the echo-modulation-curve (EMC) model-based technique. The central femoral cartilage, assumed healthy in early-stage FAI, was used as the normalization reference to define a T2-index. We investigated the ability of the T2-index to detect surgically confirmed cartilage lesions. The average T2-index was 1.14 ± 0.1 and 1.13 ± 0.1 for 2 separated segmentations. Using T2-index >1 as the threshold for damaged cartilage, accuracy was 88% and 100% for the 2 segmentations. We found moderate intraobserver repeatability, although separate segmentations yielded comparable accuracy. Damaged cartilage could not be identified using nonnormalized average T2 values. This preliminary study confirms the importance of normalizing T2 values to account for interpatient variability and suggests that the T2-index is a promising biomarker for the detection of cartilage lesions in FAI. Future work is needed to confirm that combining T2-index with morphologic MRI and other quantitative biomarkers could improve cartilage assessment in FAI.

Are Simple Magnetic Resonance Imaging Biomarkers Predictive of Neurodevelopmental Outcome at Two Years in Very Preterm Infants?

Background: Preterm infants are at increased risk of neurodevelopmental impairment due to the vulnerability of the immature brain. Early risk stratification is necessary for predicting outcome in the period of highest neuroplasticity. Several biomarkers in magnetic resonance imaging (MRI) at term equivalent age (TEA) have therefore been suggested. Objective: To assess the predictive value of simple brain metrics and the total abnormality score (TAS) – a modified score for brain injury and growth – in relation to neurodevelopmental outcome of very preterm infants in MRI at TEA. Methods: Single-centre cohort study including preterm infants with gestational age (GA) ≤32 weeks and birth weight ≤1,500 g. Biparietal width (BPW), interhemispheric distance, transcerebellar diameter (TCD) and TAS were assessed. To detect subtle haemorrhages, additional susceptibility-weighted imaging (SWI) was used in addition to conventional MRI to evaluate its clinical relevance. Neurodevelopment was tested by the Mental and Psychomotor Developmental Index (MDI/PDI) of the Bayley Scales of Infant Development II at a corrected age of 24 months. Results: One hundred twenty-nine children with median GA of 28.1 weeks and median birth weight of 980 g were included. BPW significantly correlated with PDI (p= 0.01, R² = 0.06) and TCD with MDI (p < 0.01, R² = 0.05) and PDI (p < 0.01, R² = 0.06) but explained variances were low. TAS was not predictive of neurodevelopmental outcome. By using SWI, additional 4 cases of low grade haemorrhages were identified compared to conventional sequences. In one case this additional information was clinically relevant (MDI/PDI below average). Conclusion: Simple brain metrics and TAS did not reliably predict neurodevelopmental outcome in a cohort with low prevalence of high grade brain injury. The additional value of SWI is yet to be determined in larger cohorts. The combination of imaging and functional biomarkers may be advisable for the prediction of neurodevelopmental outcome.

Valoración de la grasa hepática y pancreática como biomarcadores de imagen en el síndrome metabólico

ObjectiveTo objectively evaluate hepatic and pancreatic involvement in metabolic syndrome through magnetic resonance imaging (MRI) biomarkers. Material and methodsFrom an initial retrospective sample of 407 patients diagnosed with metabolic syndrome studied by MRI in a single center during a 2-year period, 154 were excluded because of a lack of clinical and/or laboratory data, pancreatic abnormalities, or inadequate quality of MRI studies. To measure hepatic and pancreatic fat, we used chemical shift imaging (in-phase and out-of-phase), measuring the fat fraction (%) in regions of interest in the pancreas and liver. Associations between the fat fraction and selected clinical and laboratory variables were assessed with beta regression models. ResultsIn the end, 253 patients were included. The hepatic fat fraction was 4.9% and the pancreatic fat fraction was 7.9%. We found no significant associations between the hepatic fat fraction and any of the clinical or laboratory variables. However, the pancreatic fat fraction was positively associated with age (OR=1.025, p<0.001) and baseline glucose (OR=1.005, p<0.001). Patients with diabetes had higher values of pancreatic fat fraction (OR=2.64, p=0.038). Pancreatic fat fraction and hepatic fat fraction were positively associated (OR=69.44, p<0.001). ConclusionsPancreatic steatosis can be considered a marker of metabolic syndrome and diabetes. Quantitative MRI enables the diagnosis and grading of fatty pancreas through simple chemical shift techniques.

Resveratrol Protects Against Post-Contrast Acute Kidney Injury in Rabbits With Diabetic Nephropathy.

Resveratrol (Res) is a multi-functional polyphenol compound that has protective functions in acute kidney diseases. Here, we examined whether the resveratrol could ameliorate post-contrast acute kidney injury (PC–AKI) following diabetic nephropathy (DN), and explored any underlying mechanism(s) in vivo and in vitro. Twenty-four rabbits with DN were randomly divided into four groups: control (Cont), resveratrol (Res), iohexol (PC–AKI), and resveratrol plus iohexol (Res+PC–AKI) groups. Functional magnetic resonance imaging, renal histology, blood and urinary biomarkers, silent information regulator l (SIRT1), peroxisome proliferator-activated receptor gamma coactivator-1 alpha (PGC-1α), hypoxia-inducible transcription factor-1α (HIF-1α), and apoptosis-associated protein expression were assessed ex vivo. For in vitro experiments, renal tubular epithelial (HK-2) cells subjected to high glucose conditions were treated with resveratrol, Ex527, an SIRT1 inhibitor, or 2-methoxyestradiol (2-MeOE2), HIF-1α inhibitor, before treatment with iohexol. With regard to the rabbit model of acute renal injury in DN, compared to the PC–AKI group, the Res+PC–AKI group showed decreased levels of cystatin C and urinary neutrophil gelatinase-associated lipocalin, increased pure molecular diffusion (D) and the fraction of water flowing in capillaries (f), a decreased apparent relaxation rate (R2*), renal injury score and apoptosis rate, increased protein expression levels of SIRT1 and PGC-1α, and decreased levels of HIF-1α and apoptosis-associated protein. In addition, iohexol decreased HK-2 cell survival and increased the cell apoptosis rate; results were reversed after treating cells with resveratrol. Resveratrol reduced renal hypoxia, mitochondrial dysfunction and renal tubular cell apoptosis by activating SIRT1–PGC–1α–HIF-1α signaling pathways in PC–AKI with DN.

SAT-292 MR IMAGING BIOMARKERS OF DIABETIC KIDNEY DISEASE

New drug development tools are needed for monitoring response to therapy in chronic kidney disease (CKD) with increased precision and repeatability. Magnetic resonance imaging (MRI) has great potential to non-invasively assess functional and morphologic changes in the kidney to improve patient selection and response monitoring in clinical trials for CKD. In this study, we have assessed multiple MRI variables in a diabetic nephropathy (DN) population. The study examined T2DM patients with an established diagnosis of DN and an eGFR between 15-60 ml/min/1.73 m2 as well as age- and gender-matched healthy controls. The analysis evaluated 20 CKD4 and 16 CKD3 patients and 20 healthy controls. Measured glomerular filtration rate (mGFR) was assessed using iohexol clearance. A wide range of non-contrast MRI techniques were assessed. MRI techniques included:R2* for assessment of renal hypoxiaApparent Diffusion Coefficient (ADC) as a potential measurement of renal fibrosisR1 for interstitial water balanceArterial Spin Labelling (ASL) for cortical perfusionRenal artery blood flow (peak systolic and end diastolic velocity, mean arterial flow, renal artery resistive index (RARI))Global perfusion (mean arterial flow/kidney volume)Kidney volume corrected for body surface area Tabled 1Healthy controlsCKD3CKD4correlation with mGFR (r2)p-value of correlationR2* cortex (s-1)17.3 (1.4)17.2 (1.6)17.0 (1.2)0.0060.59R2* medulla (s-1)26.0 (2.3)24.5 (3.7)22.8 (3.6)0.1240.008ADC cortex (mm2s-1 x 10-3)2.14 (0.24)1.96 (0.17)1.87 (0.32)0.1940.0007ADC medulla (mm2s-1 x 10-3)2.04 (0.27)1.86 (0.17)1.85 (0.23)0.1260.007R1 cortex (s-1)1.17 (0.10)1.02 (0.07)0.95 (0.12)0.493<0.0001R1 medulla (s-1)0.77 (0.05)0.78 (0.04)0.76 (0.07)0.0070.53ASL perfusion cortex (ml/100g/min)250 (61)144 (60)94 (51)0.519<0.0001Peak velocity max (cm/s)54.3 (8.3)59.8 (14.2)43.1 (11.1)0.1020.015Peak velocity min (cm/s)17.0 (3.9)11.3 (3.2)6.72 (2.45)0.618<0.0001RARI0.68 (0.06)0.81 (0.06)0.84 (0.06)0.576<0.0001Mean arterial flow (ml/s)9.43 (1.76)6.39 (1.46)4.30 (1.28)0.725<0.0001PC Perfusion (ml/100g/min)458 (54)339 (54)266 (86)0.616<0.0001Kidney volume/BSA (ml)63.0 (7.4)56.7 (14.8)49.9 (11.0)0.1790.0009 Open table in a new tab Several of the measured MRI variables correlate strongly with mGFR, indicating that these biomarkers are biologically relevant while providing additional information on pathophysiology. Given the known relationship between MRI biomarkers and mGFR plus the intra-subject CoV, the number of subjects needed to detect, for example, a 2 ml/min/1.73m2change in mGFR can be calculated.

Machine learning methods for MRI biomarkers analysis of pediatric posterior fossa tumors

Medical imaging technologies provide an increasing number of opportunities for disease prediction and prognosis. Specifically, imaging biomarkers can quantify the entire tumor phenotypes to enhance the prediction. Machine learning technology can be explored to mine and analyze these biomarkers and to establish predictive models for the clinical applications. Several studies have applied various machine learning methods to imaging biomarkers based clinical predictions of different diseases. Here we seek to evaluate different machine learning methods in pediatric posterior fossa tumor prediction. We present a machine learning based magnetic resonance imaging biomarkers analysis framework for two kinds of pediatric posterior fossa tumors. In details, three feature extraction methods are used to obtain 300 imaging biomarkers. 10 feature selection methods and 11 classifiers are evaluated by the quantified predictive performance and stability, and importance consistency of features and the influence of the experimental factors are also analyzed. Our results demonstrate that the CFS feature selection method (accuracy: 83.85±5.51%, stability: [0.84, 0.06]) and SVM classifier (accuracy: 85.38±3.47%, RSD: 4.77%) show relatively better performance than others and should be preferred. Among all the biomarkers, 17 texture features seem to be more important. Multifactor analysis results indicate the choice of classifier accounts for the most contribution to the variability in performance (37.25%). The machine learning based framework is efficient for pediatric posterior fossa tumors biomarkers analysis and could provide valuable references and decision support for assisted clinical diagnosis.

Deriving and validating biomarkers associated with autism spectrum disorders from a large-scale resting-state database

Resting-state functional magnetic resonance imaging (MRI) has been used to investigate the brain activity related to autism spectrum disorder (ASD). In this study, we applied information from a large-scale dataset, the Autism Brain Imaging Data Exchange (ABIDE), to clinical applications. We recruited 21 patients with ASD and 23 individuals with neurotypical development (TD). We applied ASD biomarkers derived from ABIDE datasets and subsequently investigated the relationship between the MRI biomarkers and indicators from clinical screening questionnaires, the social responsiveness scale (SRS), and the Swanson, Nolan, and Pelham Questionnaire IV. The results indicated that the biomarkers generated from the default mode and executive control networks significantly differed between the participants with ASD and TD. In particular, the biomarkers derived from the default mode network were negatively correlated with the raw scores and model factors of the SRS. In summary, this study transferred the efforts of the global autism research community to clinical applications and identified connectivity-based biomarkers in ASD.

Advanced Magnetic Resonance Imaging Techniques in Management of Brain Metastases.

Brain metastases are the most common intracranial tumors and occur in 20–40% of all cancer patients. Lung cancer, breast cancer, and melanoma are the most frequent primary cancers to develop brain metastases. Treatment options include surgical resection, whole brain radiotherapy, stereotactic radiosurgery, and systemic treatment such as targeted or immune therapy. Anatomical magnetic resonance imaging (MRI) of the tumor (in particular post-Gadolinium T1-weighted and T2-weighted FLAIR) provide information about lesion morphology and structure, and are routinely used in clinical practice for both detection and treatment response evaluation for brain metastases. Advanced MRI biomarkers that characterize the cellular, biophysical, micro-structural and metabolic features of tumors have the potential to improve the management of brain metastases from early detection and diagnosis, to evaluating treatment response. Magnetic resonance spectroscopy (MRS), chemical exchange saturation transfer (CEST), quantitative magnetization transfer (qMT), diffusion-based tissue microstructure imaging, trans-membrane water exchange mapping, and magnetic susceptibility weighted imaging (SWI) are advanced MRI techniques that will be reviewed in this article as they pertain to brain metastases.

The value of hippocampal volume, shape, and texture for 11-year prediction of dementia: a population-based study

Hippocampal volume and shape are known magnetic resonance imaging biomarkers of neurodegeneration. Recently, hippocampal texture has been shown to improve prediction of dementia in patients with mild cognitive impairment, but it is unknown whether texture adds prognostic information beyond volume and shape and whether the predictive value extends to cognitively healthy individuals. Using 510 subjects from the Rotterdam Study, a prospective, population-based cohort study, we investigated if hippocampal volume, shape, texture, and their combination were predictive of dementia and determined how predictive performance varied with time to diagnosis and presence of early clinical symptoms of dementia. All features showed significant predictive performance with the area under the receiver operating characteristic curve ranging from 0.700 for texture alone to 0.788 for the combination of volume and texture. Although predictive performance extended to those without objective cognitive complaints or mild cognitive impairment, performance decreased with increasing follow-up time. We conclude that a combination of multiple hippocampal features on magnetic resonance imaging performs better in predicting dementia in the general population than any feature by itself.

Early Detection of Cerebral Palsy Using Sensorimotor Tract Biomarkers in Very Preterm Infants

BackgroundOur objectives were to evaluate the brain's sensorimotor network microstructure using diffusion magnetic resonance imaging (MRI) at term-corrected age and test the ability of sensorimotor microstructural parameters to accurately predict cerebral palsy in extremely-low-birth-weight infants. MethodsWe enrolled a prospective pilot cohort of extremely-low-birth-weight preterm infants (birth weight ≤ 1000 g) before neonatal intensive care unit discharge and studied them with structural and diffusion MRI at term-corrected age. Six sensorimotor tracts were segmented, and microstructural parameters from these tracts were evaluated for their ability to predict later development of cerebral palsy, diagnosed at 18 to 22 months corrected age. ResultsWe found significant differences in multiple diffusion MRI parameters from five of the six sensorimotor tracts in infants who developed cerebral palsy (n = 5) versus those who did not (n = 36). When compared with structural MRI or individual diffusion MRI biomarkers, the combination of two individual biomarkers—fractional anisotropy of superior thalamic radiations (sensory component) and radial diffusivity of the corticospinal tract—exhibited the highest sensitivity (80%), specificity (97%), and positive likelihood ratio (28.0) for prediction of cerebral palsy. This combination of diffusion MRI biomarkers accurately classified 95% of the study infants. ConclusionsDevelopment of cerebral palsy in very preterm infants is preceded by early brain injury or immaturity to one or more sensorimotor tracts. A larger study is warranted to evaluate if a combination of sensorimotor microstructural biomarkers could accurately facilitate early diagnosis of cerebral palsy.

Magnetic resonance imaging T1 relaxation times for the liver, pancreas and spleen in healthy children at 1.5 and 3tesla.

T1 relaxation time is a potential magnetic resonance imaging (MRI) biomarker for fibrosis and inflammation of the solid abdominal organs. However, normal T1 relaxation times of the solid abdominal organs have not been defined for children. The purpose of this study was to measure T1 relaxation times of the liver, pancreas and spleen in healthy children. This was an institutional review board-approved study of a convenience sample of prospectively recruited, healthy children ages 7 to 17years undergoing research abdominal MRI (1.5 or 3T) as part of a larger research study between February 2018 and October 2018. For the current study, T1 mapping was performed with a Modified Look-Locker sequence covering the upper abdomen. A single reviewer placed freehand regions of interest on the T1 parametric maps in the liver, pancreas and spleen, inclusive of as much parenchyma as possible. Student's t-tests and linear regression were used to compare T1 values by age and gender. Thirty-two participants were included (16 female:16 male; mean age: 12.2±3.1years; n=16 at 1.5T). Median T1 relaxation times (ms) per organ were liver: 581±64 (1.5T), 783±88 (3T); pancreas: 576±55 (1.5T), 730±30 (3T), and spleen: 1,172±71 (1.5T), 1,356±87 (3T). T1 values were not statistically significantly different between males and females. At both 1.5 and 3 T field strengths, linear regression showed no significant association between age and T1 values for the liver, pancreas and spleen. We report normal T1 relaxation times for the liver, pancreas and spleen at 1.5 and 3T in a cohort of healthy children.

Personalized risk for clinical progression in cognitively normal subjects\u2014the ABIDE project

BackgroundBiomarkers such as cerebrospinal fluid (CSF) and magnetic resonance imaging (MRI) have predictive value for progression to dementia in patients with mild cognitive impairment (MCI). The pre-dementia stage takes far longer, and the interpretation of biomarker findings is particular relevant for individuals who present at a memory clinic, but are deemed cognitively normal. The objective of the current study is to construct biomarker-based prognostic models for personalized risk of clinical progression in cognitively normal individuals presenting at a memory clinic.MethodsWe included 481 individuals with subjective cognitive decline (SCD) from the Amsterdam Dementia Cohort. Prognostic models were developed by Cox regression with patient characteristics, MRI, and/or CSF biomarkers to predict clinical progression to MCI or dementia. We estimated 5- and 3-year individualized risks based on patient-specific values. External validation was performed on Alzheimer’s Disease Neuroimaging Initiative (ADNI) and an European dataset.ResultsBased on demographics only (Harrell’s C = 0.70), 5- and 3-year progression risks varied from 6% [3–11] and 4% [2–8] (age 55, MMSE 30) to 38% [29–49] and 28% [21–37] (age 70, MMSE 27). Normal CSF biomarkers strongly decreased progression probabilities (Harrell’s C = 0.82). By contrast, abnormal CSF markedly increased risk (5 years, 96% [56–100]; 3 years, 89% [44–99]). The CSF model could reclassify 58% of the individuals with an “intermediate” risk (35–65%) based on the demographic model. MRI measures were not retained in the models.ConclusionThe current study takes the first steps in a personalized approach for cognitively normal individuals by providing biomarker-based prognostic models.

Elevated Aortic Stiffness is Associated with Lower Brain pH and Executive Function Performance in Middle‐aged and Older Adults

IntroductionElevated aortic stiffness and pulse pressure (PP) are risk factors for development of mild cognitive impairment (MCI) and Alzheimer's disease (AD) mediated in part by the development of cerebrovascular dysfunction. Cerebrovascular dysfunction uncouples cerebral blood flow (CBF) supply from metabolic demand, contributing in part to cerebral hypoperfusion, acidosis and cognitive decline in AD. Brain T1rho is a novel pH‐sensitive magnetic resonance imaging (MRI) biomarker that is increased in patients with MCI and AD compared with healthy controls (HC). T1rho measures the change in T1 relaxation time in the rotating frame with greater T1rho values (i.e., longer relaxation time) associated with a more acidic pH. However, the degree to which higher T1rho signal (i.e., lower brain pH = more acidic) is associated with 1) lower cognitive performance; 2) higher brachial PP and 3) elevated aortic stiffness in middle‐aged/older (MA/O) adults is unknown.Methods/ResultsTwenty‐seven MA/O adults (mean ± SE: 71.8 ± 2.1 years, range: 50–90 years) were recruited to undergo clinic blood pressure (BP), neuropsychological, MRI (structural and T1rho) testing and vascular testing (subset only). Executive function and delayed memory performance were measured using the Trailmaking test (TMT) A &amp; B and Rey Auditory Verbal Learning Test, respectively. In agreement with prior studies, global T1rho signal was elevated in individuals with cognitive impairment (MCI or early AD, n=10) compared with HC (90.7 ± 3.1 vs. 81.8 ± 1.2 ms, p= 0.004). Consistent with our hypothesis, higher T1rho signal was associated with greater time to completion on the TMT B (r = 0.44, p = 0.026), TMT B‐A (r = 0.39, p = 0.047) and lower delayed memory performance (r = −0.48, p = 0.012, all adjusted for age) in the entire cohort. However, T1rho was not related to PP (r = 0.38, p= 0.069) and remained unassociated after adjusting for age (p=0.80). In the subset of MA/O adults with T1rho (n = 15; 70.0 ± 2.0 years, HC, n= 21, MCI, n=2), greater aortic stiffness (carotid‐femoral pulse wave velocity, cfPWV) was associated with higher T1rho signal (r= 0.58, p= 0.039, adjusted for mean arterial pressure). Furthermore, cfPWV was associated with slower TMT B (r = 0.45, p = 0.034) but not TMT B‐A (r = 0.46, p = 0.066) or delayed memory performance (p= 0.77, all adjusted for mean arterial pressure). Additional adjustment for age abolished (p &gt; 0.05) the relation between cfPWV and TMT performance and T1rho signal in the present cohort.ConclusionThese preliminary data indicate that the age‐related increase in aortic stiffness is associated with lower executive function performance on the TMT task and higher T1rho signal, consistent with lower brain pH, in MA/O adults. More studies are needed to determine the mechanism by which elevated aortic stiffness contributes in part to decreased brain pH and how brain pH may potentially modulate reduced executive function with aging.Support or Funding InformationBiological Sciences Funding Program, Office of Vice President for Research, University of Iowa and NIH 1R03AG047306This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

Imaging biomarker analysis of advanced multiparametric MRI for glioma grading

Aims and objectivesTo investigate the value of advanced multiparametric MR imaging biomarker analysis based on radiomic features and machine learning classification, in the non-invasive evaluation of tumor heterogeneity towards the differentiation of Low Grade vs. High Grade Gliomas. Methods and materialsForty histologically confirmed glioma patients (20 LGG and 20 HGG) who underwent a standard 3T-MRI tumor protocol with conventional (T1 pre/post-contrast, T2-FSE, T2-FLAIR) and advanced techniques (Diffusion Tensor and Perfusion Imaging, 1H-MR Spectroscopy), were included. A semi-automated segmentation technique, based on T1W-C and DTI, was used for tumor core delineation in all available parametric maps. 3D Texture analysis considered 12 Histogram, 11 Co-Occurrence Matrix (GLCM) and 5 Run Length Matrix (GLRLM) features, derived from p, q, MD, FA, T1W-C, T2W-FSE, T2W-FLAIR and raw DSCE data. Along with 1H-MRS metabolic ratios and mean rCBV values, a total of 581 attributes for each subject were obtained. A Support Vector Machine – Recursive Feature Elimination (SVM-RFE) algorithm and SVM classifier were utilized for feature selection and classification, respectively. ResultsThree different SVM classifiers were evaluated with consecutively SVM-RFE feature subsets. Linear SMO classifier demonstrated the highest performance for determining the optimal feature subset. Finally, 21 SVM-RFE top-ranked features were adopted, for training and testing the SMO classifier with leave-one-out cross-validation, achieving 95.5% Accuracy, 95% Sensitivity, 96% Specificity and 95.5% Area Under ROC Curve. ConclusionResults demonstrate that quantitative analysis of phenotypic characteristics, based on advanced multiparametric MR neuroimaging data and texture features, utilizing state-of-the-art radiomic analysis methods, can significantly contribute to the pre-treatment glioma grade differentiation.

Classification of radiologically isolated syndrome and clinically isolated syndrome with machine-learning techniques.

The unanticipated detection by magnetic resonance imaging (MRI) in the brain of asymptomatic subjects of white matter lesions suggestive of multiple sclerosis (MS) has been named radiologically isolated syndrome (RIS). As the difference between early MS [i.e. clinically isolated syndrome (CIS)] and RIS is the occurrence of a clinical event, it is logical to improve detection of the subclinical form without interfering with MRI as there are radiological diagnostic criteria for that. Our objective was to use machine-learning classification methods to identify morphometric measures that help to discriminate patients with RIS from those with CIS. We used a multimodal 3-T MRI approach by combining MRI biomarkers (cortical thickness, cortical and subcortical grey matter volume, and white matter integrity) of a cohort of 17 patients with RIS and 17 patients with CIS for single-subject level classification. The best proposed models to predict the diagnosis of CIS and RIS were based on the Naive Bayes, Bagging and Multilayer Perceptron classifiers using only three features: the left rostral middle frontal gyrus volume and the fractional anisotropy values in the right amygdala and right lingual gyrus. The Naive Bayes obtained the highest accuracy [overall classification, 0.765; area under the receiver operating characteristic (AUROC), 0.782]. A machine-learning approach applied to multimodal MRI data may differentiate between the earliest clinical expressions of MS (CIS and RIS) with an accuracy of 78%.

Task-dependence of articulator synergies.

In speech production, the motor system organizes articulators such as the jaw, tongue, and lips into synergies whose function is to produce speech sounds by forming constrictions at the phonetic places of articulation. The present study tests whether synergies for different constriction tasks differ in terms of inter-articulator coordination. The test is conducted on utterances [ɑpɑ], [ɑtɑ], [ɑiɑ], and [ɑkɑ] with a real-time magnetic resonance imaging biomarker that is computed using a statistical model of the forward kinematics of the vocal tract. The present study is the first to estimate the forward kinematics of the vocal tract from speech production data. Using the imaging biomarker, the study finds that the jaw contributes least to the velar stop for [k], more to pharyngeal approximation for [ɑ], still more to palatal approximation for [i], and most to the coronal stop for [t]. Additionally, the jaw contributes more to the coronal stop for [t] than to the bilabial stop for [p]. Finally, the study investigates how this pattern of results varies by participant. The study identifies differences in inter-articulator coordination by constriction task, which support the claim that inter-articulator coordination differs depending on the active articulator synergy.

Harmonizing brain magnetic resonance imaging methods for vascular contributions to neurodegeneration

IntroductionMany consequences of cerebrovascular disease are identifiable by magnetic resonance imaging (MRI), but variation in methods limits multicenter studies and pooling of data. The European Union Joint Program on Neurodegenerative Diseases (EU JPND) funded the HARmoNizing Brain Imaging MEthodS for VaScular Contributions to Neurodegeneration (HARNESS) initiative, with a focus on cerebral small vessel disease. MethodsSurveys, teleconferences, and an in-person workshop were used to identify gaps in knowledge and to develop tools for harmonizing imaging and analysis. ResultsA framework for neuroimaging biomarker development was developed based on validating repeatability and reproducibility, biological principles, and feasibility of implementation. The status of current MRI biomarkers was reviewed. A website was created at www.harness-neuroimaging.org with acquisition protocols, a software database, rating scales and case report forms, and a deidentified MRI repository. ConclusionsThe HARNESS initiative provides resources to reduce variability in measurement in MRI studies of cerebral small vessel disease.

Magnetic resonance imaging biomarkers in musculoskeletal soft tissue tumors: Review of conventional features and focus on nonmorphologic imaging.

The majority of peripheral musculoskeletal soft tissue masses encountered in routine clinical practice are benign and associated with an excellent prognosis, whereas a small proportion of soft tissue masses are malignant and are associated with higher morbidity and mortality. Owing to the excellent tissue contrast resolution provided, magnetic resonance imaging (MRI) is a primary modality for the diagnostic evaluation of soft tissue masses, particularly for defining the anatomic extent. MRI is also useful for the characterization of soft tissue masses; however, after the exclusion of cysts and lipomas, it is challenging to accurately categorize neoplastic lesions as benign or malignant with conventional anatomic MRI. Quantitative MRI biomarkers obtained with nonconventional pulse sequences are becoming increasingly available, and can improve the accuracy of determining the character of soft tissue masses. In this article we discuss established qualitative and emerging quantitative MRI-based biomarkers available with dynamic contrast-enhanced MRI, diffusion-weighted imaging, and MR spectroscopy and their utility for the characterization of untreated soft tissue masses. Level of Evidence: 5 Technical Efficacy Stage: 5 J. Magn. Reson. Imaging 2019;50:11-27.

Post-Mortem MRI and Histopathology in Neurologic Disease: A Translational Approach.

In this review, combined post-mortem brain magnetic resonance imaging (MRI) and histology studies are highlighted, illustrating the relevance of translational approaches to define novel MRI signatures of neuropathological lesions in neuroinflammatory and neurodegenerative disorders. Initial studies combining post-mortem MRI and histology have validated various MRI sequences, assessing their sensitivity and specificity as diagnostic biomarkers in neurologic disease. More recent studies have focused on defining new radiological (bio)markers and implementing them in the clinical (research) setting. By combining neurological and neuroanatomical expertise with radiological development and pathological validation, a cycle emerges that allows for the discovery of novel MRI biomarkers to be implemented in vivo. Examples of this cycle are presented for multiple sclerosis, Alzheimer's disease, Parkinson's disease, and traumatic brain injury. Some applications have been shown to be successful, while others require further validation. In conclusion, there is much to explore with post-mortem MRI and histology studies, which can eventually be of high relevance for clinical practice.

Brain Morphometry and Longitudinal Relaxation Time of Spontaneously Hypertensive Rats (SHRs) in Early and Intermediate Stages of Hypertension Investigated by 3D VFA-SPGR MRI

Cerebral small vessel disease(s) (SVD) results from pathological changes of the small blood vessels in the brain and is common in older people. The diagnostic features by which SVD manifests in brain includes white matter hyperintensities, lacunes, dilated perivascular spaces, microbleeds, and atrophy. In the present study, we use in vivo magnetic resonance imaging (MRI) to characterize brain morphometry and longitudinal relaxation time (T1) of spontaneously hypertensive rats (SHRs) to study the contribution of chronic hypertension to SVD relevant pathology. Male SHR and Wistar-Kyoto (WKY) rats underwent 3D variable flip angle spoiled gradient echo brain MRI at 9.4 T at early (seven weeks old) and established (19 weeks old) stages of hypertension. The derived proton density weighted and T1 images were utilized for morphometry and to characterize T1 properties in gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF). Custom tissue probability maps were constructed for accurate computerized whole brain tissue segmentations and voxel-wise analyses. Characteristic morphological differences between the two strains included enlarged ventricles, smaller corpus callosum (CC) volumes and general ‘thinning’ of CC in SHR compared to WKY rats at both age groups. While we did not observe parenchymal T1 differences, the T1 of CSF was elevated in SHR compared to controls. Collectively these findings indicate that SHRs develop WM atrophy which is a clinically robust MRI biomarker associated with WM degeneration.