Purpose Several recent studies have used a three‐tissue constrained spherical deconvolution pipeline to obtain quantitative metrics of brain tissue microstructure from diffusion‐weighted MRI data. The three tissue compartments, consisting of white matter, gray matter, and CSF‐like (free water) signals, are potentially useful in the evaluation of brain microstructure in a range of pathologies. However, the reliability and long‐term stability of these metrics have not yet been evaluated. Methods This study examined estimates of whole‐brain microstructure for the three tissue compartments, in three separate test–retest cohorts. Each cohort had different lengths of time between baseline and retest, ranging from within the same scanning session in the shortest interval to 3 months in the longest interval. Each cohort was also collected with different acquisition parameters. Results The CSF‐like compartment displayed the greatest reliability across all cohorts, with intraclass correlation coefficient (ICC) values being above 0.95 in each cohort. White matter–like and gray matter–like compartments both demonstrated very high reliability in the immediate cohort (both ICC > 0.90); however, this declined in the 3‐month interval cohort to both compartments having ICC > 0.80. Regional CSF‐like signal fraction was examined in bilateral hippocampus and had an ICC > 0.80 in each cohort. Conclusion The three‐tissue constrained spherical deconvolution techniques provide reliable and stable estimates of tissue‐microstructure composition, up to 3 months longitudinally in a control population. This forms an important basis for further investigations using three‐tissue constrained spherical deconvolution techniques to track changes in microstructure across a variety of brain pathologies.

Purpose To describe a nonlinear finite element analysis method by using magnetic resonance (MR) images for the assessment of the mechanical competence of the hip and to demonstrate the reproducibility of the tool. Materials and Methods This prospective study received institutional review board approval and fully complied with HIPAA regulations for patient data. Written informed consent was obtained from all subjects. A nonlinear finite element analysis method was developed to estimate mechanical parameters that relate to hip fracture resistance by using MR images. Twenty-three women (mean age ± standard deviation, 61.7 years ± 13.8) were recruited from a single osteoporosis center. To thoroughly assess the reproducibility of the finite element method, three separate analyses were performed: a test-retest reproducibility analysis, where each of the first 13 subjects underwent MR imaging on three separate occasions to determine longitudinal variability, and an intra- and interoperator reproducibility analysis, where a single examination was performed in each of the next 10 subjects and four operators independently performed the analysis two times in each of the subjects. Reproducibility of parameters that reflect fracture resistance was assessed by using the intraclass correlation coefficient and the coefficient of variation. Results For test-retest reproducibility analysis and inter- and intraoperator analyses for proximal femur stiffness, yield strain, yield load, ultimate strain, ultimate load, resilience, and toughness in both stance and sideways-fall loading configurations each had an individual median coefficient of variation of less than 10%. Additionally, all measures had an intraclass correlation coefficient higher than 0.99. Conclusion This experiment demonstrates that the finite element analysis model can consistently and reliably provide fracture risk information on correctly segmented bone images. RSNA, 2016 Online supplemental material is available for this article.

We created a set of resources to enable research based on openly-available diffusion MRI (dMRI) data from the Healthy Brain Network (HBN) study. First, we curated the HBN dMRI data (N = 2747) into the Brain Imaging Data Structure and preprocessed it according to best-practices, including denoising and correcting for motion effects, susceptibility-related distortions, and eddy currents. Preprocessed, analysis-ready data was made openly available. Data quality plays a key role in the analysis of dMRI. To optimize QC and scale it to this large dataset, we trained a neural network through the combination of a small data subset scored by experts and a larger set scored by community scientists. The network performs QC highly concordant with that of experts on a held out set (ROC-AUC = 0.947). A further analysis of the neural network demonstrates that it relies on image features with relevance to QC. Altogether, this work both delivers resources to advance transdiagnostic research in brain connectivity and pediatric mental health, and establishes a novel paradigm for automated QC of large datasets.

Rationale and Objectives Severe progressive multi-focal heterotopic ossification (HO) is a rare occurrence seen predominantly in patients who have fibrodysplasia ossificans progressiva (FOP) and is difficult to quantitate due to patient-, disease-, logistical-, and radiation-related issues. The purpose of this study was to develop and validate a scoring system based on plain radiographs for quantitative assessment of HO lesions in FOP patients. Materials and Methods IRB approval was obtained from the University of Pennsylvania and all data comply with HIPAA regulations. The University of Pennsylvania Institutional Animal Care and Use Committee (IACUC) approved the use of mice in this study. First, we used a mouse model of FOP-like HO to validate a semi-quantitative analogue scale for estimating relative heterotopic bone volume. Second, we used this validated scale to estimate the relative amount of HO from a retrospective analysis of plain radiographs from 63 patients with classic FOP. Finally, the scale was applied to a retrospective analysis of CT images from three FOP patients. Results In the FOP-mouse model, the observed rating on the analogue scale is highly correlated to heterotopic bone volumes measured by micro-computed tomography (R2 = 0.89). The scoring system that was applied to radiographs of FOP patients captured the clinical range of HO typically present at all axial and appendicular sites. Analysis of CT scans of FOP patients found that observed radiograph ratings were highly correlated with HO volume (R2 = 0.80). Conclusion The scoring system described here could enable practical, quantitative assessment of HO in clinical trials to evaluate new treatment modalities, especially for FOP. Clinical Relevance The development of the six-point analogue scale described here provides and validates a much-needed, reproducible and quantifiable method for describing and assessing heterotopic ossification in FOP patients. This scale has the potential to be a key descriptor that can inform FOP patients and clinicians about disease progression and response of heterotopic ossification lesions to interventions and treatments.

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IntroductionIn concussion, clinical and physiological recovery are increasingly recognized as diverging definitions. This study investigated whether central microglial activation persisted in participants with concussion after receiving an unrestricted return-to-play (uRTP) designation using [18F]DPA-714 PET, an in vivo marker of microglia activation.MethodsEight (5 M, 3 F) current athletes with concussion (Group 1) and 10 (5 M, 5 F) healthy collegiate students (Group 2) were enrolled. Group 1 completed a pre-injury (Visit1) screen, follow-up Visit2 within 24 h of a concussion diagnosis, and Visit3 at the time of uRTP. Healthy participants only completed assessments at Visit2 and Visit3. At Visit2, all participants completed a multidimensional battery of tests followed by a blood draw to determine genotype and study inclusion. At Visit3, participants completed a clinical battery of tests, brain MRI, and brain PET; no imaging tests were performed outside of Visit3.ResultsFor Group 1, significant differences were observed between Visits 1 and 2 (p < 0.05) in ImPACT, SCAT5 and SOT performance, but not between Visit1 and Visit3 for standard clinical measures (all p > 0.05), reflecting clinical recovery. Despite achieving clinical recovery, PET imaging at Visit3 revealed consistently higher [18F]DPA-714 tracer distribution volume (VT) of Group 1 compared to Group 2 in 10 brain regions (p < 0.001) analyzed from 164 regions of the whole brain, most notably within the limbic system, dorsal striatum, and medial temporal lobe. No notable differences were observed between clinical measures and VT between Group 1 and Group 2 at Visit3.DiscussionOur study is the first to demonstrate persisting microglial activation in active collegiate athletes who were diagnosed with a sport concussion and cleared for uRTP based on a clinical recovery.

Background: Cholinergic nucleus 4 (Ch4) degeneration is associated with cognitive impairment in Parkinson's disease and dementia with Lewy bodies, but it is unknown if Ch4 degeneration is also present in isolated rapid eye movement sleep behavior disorder (iRBD). Objective: The aim was to determine if there is evidence of Ch4 degeneration in patients with iRBD and if it is associated with cognitive impairment. Methods: We analyzed the clinical and neuropsychological data of 35 iRBD patients and 35 age-and sex-matched healthy controls. Regional gray matter density (GMD) was calculated for Ch4 using probabilistic maps applied to brain magnetic resonance imaging (MRI). Results: Ch4 GMD was significantly lower in the iRBD group compared to controls (0.417 vs. 0.441, P = 0.02). Ch4 GMD was also found to be a significant predictor of letter number sequencing (β-coefficient = 58.31, P = 0.026, 95% confidence interval [7.47, 109.15]), a measure of working memory. Conclusions: iRBD is associated with Ch4 degeneration, and Ch4 degeneration in iRBD is associated with impairment in working memory.

Epigenetic clocks provide powerful tools for estimating health and lifespan but their ability to predict brain degeneration and neuronal damage during the aging process is unknown. In this study, we use GrimAge, an epigenetic clock correlated to several blood plasma proteins, to longitudinally investigate brain cellular microstructure in axonal white matter from a cohort of healthy aging individuals. Given the blood plasma correlations used to develop GrimAge, a specific focus was made on white matter hyperintensities, a visible neurological manifestation of small vessel disease, and the axonal pathways throughout each individual’s brain affected by their unique white matter hyperintensity location and volume. 98 subjects over 55 years of age were scanned at baseline with 41 returning for a follow-up scan 2 years later. Using diffusion MRI lesionometry, we reconstructed subject-specific networks of affected axonal tracts and examined the diffusion cellular microstructure composition of these areas, both at baseline and longitudinally, for evidence of cellular degeneration. A chronological age-adjusted version of GrimAge was significantly correlated with baseline WMH volume and markers of neuronal decline, indicated by increased extracellular free water, increased intracellular signal, and decreased axonal signal within WMH. By isolating subject-specific axonal regions ‘lesioned’ by crossing through a WMH, age-adjusted GrimAge was also able to predict longitudinal development of similar patterns of neuronal decline throughout the brain. This study is the first to establish a relationship between accelerated epigenetic GrimAge and brain cellular microstructure in humans.