The long-term neurologic consequences of exposure to repetitive head impacts (RHI) are not well understood. This study used magnetic resonance spectroscopy (MRS) to examine later-life neurochemistry and its association with RHI and clinical function in former National Football League (NFL) players. The sample included 77 symptomatic former NFL players and 23 asymptomatic individuals without a head trauma history. Participants completed cognitive, behavior, and mood measures. N-acetyl aspartate, glutamate/glutamine, choline, myo-inositol, creatine, and glutathione were measured in the posterior (PCG) and anterior (ACG) cingulate gyrus, and parietal white matter (PWM). A cumulative head impact index (CHII) estimated RHI. In former NFL players, a higher CHII correlated with lower PWM creatine (r = -0.23, p = 0.02). Multivariate mixed-effect models examined neurochemical differences between the former NFL players and asymptomatic individuals without a history of head trauma. PWM N-acetyl aspartate was lower among the former NFL players (mean diff. = 1.02, p = 0.03). Between-group analyses are preliminary as groups were recruited based on symptomatic status. The ACG was the only region associated with clinical function, including positive correlations between glutamate (r = 0.32, p = 0.004), glutathione (r = 0.29, p = 0.02), and myo-inositol (r = 0.26, p = 0.01) with behavioral/mood symptoms. Other positive correlations between ACG neurochemistry and clinical function emerged (i.e., behavioral/mood symptoms, cognition), but the positive directionality was unexpected. All analyses controlled for age, body mass index, and education (for analyses examining clinical function). In this sample of symptomatic former NFL players, there was a direct effect between RHI and reduced cellular energy metabolism (i.e., lower creatine). MRS neurochemicals associated with neuroinflammation also correlated with behavioral/mood symptoms.
BACKGROUND: Cranial radiotherapy (CRT) is a known risk factor for neurocognitive impairment in survivors of childhood acute lymphoblastic leukemia (ALL). Diffusion tensor imaging (DTI) and diffusional kurtosis imaging (DKI) are MRI techniques that quantify microstructural changes in brain white matter (WM) and DKI is regarded as the more sensitive of them. Our aim was to more thoroughly understand the nature of cognitive deficits after cranial radiotherapy (CRT) in adulthood after childhood ALL.
MATERIAL AND METHODS: Thirty-eight (21 women) ALL survivors, median age 38 (27-46) years, were investigated at median 34 years after diagnosis. All had been treated with a CRT dose of 24 Gy and with 11 years of complete hormone supplementation. DTI and DKI parameters were determined and neurocognitive tests were performed in ALL survivors and 29 matched controls.
RESULTS: ALL survivors scored lower than controls in neurocognitive tests of vocabulary, memory, learning capacity, spatial ability, executive functions, and attention (p < .001). The survivors had altered DTI parameters in the fornix, uncinate fasciculus, and ventral cingulum (all p < .05) and altered DKI parameters in the fornix, uncinate fasciculus, and dorsal and ventral cingulum (p < .05). Altered DTI parameters in the fornix were associated with impaired episodic verbal memory (r = -0.40, p < .04). The left and right uncinate fasciculus (r = 0.6, p < .001), (r = -0.5, p < .02) as well as the right ventral cingulum (r = 0.5, p < .007) were associated with impaired episodic visual memory. Altered DKI parameters in the fornix, right uncinate fasciculus (r = 0.3, r = 0.05, p = .02), and ventral cingulum (r = 0.3, p = .02) were associated with impaired results of episodic visual memory.
CONCLUSION: ALL survivors with cognitive deficits demonstrated microstructural damage in several WM tracts that were more extensive with DKI as compared to DTI; this might be a marker of radiation and chemotherapy neurotoxicity underlying cognitive dysfunction.
Diffusion MRI yields parameters sensitive to brain tissue microstructure. A structurally important aspect of this microstructure is the myelin wrapping around the axons. This study investigated the forward problem concerning whether water exchange via the spiraling structure of the myelin can meaningfully contribute to the signal in dMRI. Monte Carlo simulations were performed of a system with intra-axonal, myelin and extra-axonal compartments. Diffusion in the myelin was simulated as a spiral wrapping the axon, with a custom number of wraps. Exchange (or intra-axonal residence) times, were analyzed for various number of wraps and axon diameters. Pulsed gradient sequences were employed to simulate the dMRI signal, which was analyzed using different methods. Diffusional kurtosis imaging analysis yielded the radial diffusivity (RD) and radial kurtosis (RK), while the two-compartment Kärger model yielded estimates of the intra-axonal volume fraction (νic) and exchange time (τ). Results showed that τ was on the sub-second level for geometries with axon diameters below 1.0 μm and less than eight wraps. Otherwise, exchange was negligible compared to typical experimental durations, with τ of seconds or longer. In situations where exchange influenced the signal, estimates of RK and νic increased with the number of wraps, while RD decreased. τ estimates from simulated signals were in agreement with predicted ones. In conclusion, exchange through spiraling myelin permits sub-second τ for small diameters and low number of wraps. Such conditions may arise in the developing brain or in neurodegenerative disease, and thus the results could aid the interpretation of dMRI studies.
BACKGROUND: Auditory verbal hallucinations (AVH) are a cardinal feature of schizophrenia, but they can also appear in otherwise healthy individuals. Imaging studies implicate language networks in the generation of AVH; however, it remains unclear if alterations reflect biologic substrates of AVH, irrespective of diagnostic status, age, or illness-related factors. We applied multimodal imaging to identify AVH-specific pathology, evidenced by overlapping gray or white matter deficits between schizophrenia patients and healthy voice-hearers.
METHODS: Diffusion-weighted and T1-weighted magnetic resonance images were acquired in 35 schizophrenia patients with AVH (SCZ-AVH), 32 healthy voice-hearers (H-AVH), and 40 age- and sex-matched controls without AVH. White matter fractional anisotropy (FA) and gray matter thickness (GMT) were computed for each region comprising ICBM-DTI and Desikan-Killiany atlases, respectively. Regions were tested for significant alterations affecting both SCZ-AVH and H-AVH groups, relative to controls.
RESULTS: Compared with controls, the SCZ-AVH showed widespread FA and GMT reductions; but no significant differences emerged between H-AVH and control groups. While no overlapping pathology appeared in the overall study groups, younger (<40 years) H-AVH and SCZ-AVH subjects displayed overlapping FA deficits across four regions (p < 0.05): the genu and splenium of the corpus callosum, as well as the anterior limbs of the internal capsule. Analyzing these regions with free-water imaging ascribed overlapping FA abnormalities to tissue-specific anisotropy changes.
CONCLUSIONS: We identified white matter pathology associated with the presence of AVH, independent of diagnostic status. However, commonalities were constrained to younger and more homogenous groups, after reducing pathologic variance associated with advancing age and chronicity effects.
One-sided t-tests are commonly used in the neuroimaging field, but two-sided tests should be the default unless a researcher has a strong reason for using a one-sided test. Here we extend our previous work on cluster false positive rates, which used one-sided tests, to two-sided tests. Briefly, we found that parametric methods perform worse for two-sided t-tests, and that nonparametric methods perform equally well for one-sided and two-sided tests.
In vivo mapping of the neurite density with diffusion MRI (dMRI) is a high but challenging aim. First, it is unknown whether all neurites exhibit completely anisotropic ("stick-like") diffusion. Second, the "density" of tissue components may be confounded by non-diffusion properties such as T2 relaxation. Third, the domain of validity for the estimated parameters to serve as indices of neurite density is incompletely explored. We investigated these challenges by acquiring data with "b-tensor encoding" and multiple echo times in brain regions with low orientation coherence and in white matter lesions. Results showed that microscopic anisotropy from b-tensor data is associated with myelinated axons but not with dendrites. Furthermore, b-tensor data together with data acquired for multiple echo times showed that unbiased density estimates in white matter lesions require data-driven estimates of compartment-specific T2 values. Finally, the "stick" fractions of different biophysical models could generally not serve as neurite density indices across the healthy brain and white matter lesions, where outcomes of comparisons depended on the choice of constraints. In particular, constraining compartment-specific T2 values was ambiguous in the healthy brain and had a large impact on estimated values. In summary, estimating neurite density generally requires accounting for different diffusion and/or T2 properties between axons and dendrites. Constrained "index" parameters could be valid within limited domains that should be delineated by future studies.
Importance: Spaceflight results in transient balance declines and brain morphologic changes; to our knowledge, the effect on brain white matter as measured by diffusion magnetic resonance imaging (dMRI), after correcting for extracellular fluid shifts, has not been examined.
Objective: To map spaceflight-induced intracranial extracellular free water (FW) shifts and to evaluate changes in brain white matter diffusion measures in astronauts.
Design, Setting and Participants: We performed retrospective, longitudinal analyses on dMRI data collected between 2010 and 2015. Of the 26 astronauts' dMRI scans released by the National Aeronautics and Space Administration Lifetime Surveillance of Astronaut Health, 15 had both preflight and postflight dMRI scans and were included in the final analyses. Data were analyzed between 2015 and 2018.
Interventions or Exposures: Seven astronauts completed a space shuttle mission (≤30 days) and 8 completed a long-duration International Space Station mission (≤200 days).
Main Outcomes and Measures: The dMRI scans were acquired for clinical monitoring; in this retrospective analysis, we analyzed brain FW and white matter diffusion metrics corrected for FW. We also obtained scores from computerized dynamic posturography tests of balance to assess brain-behavior associations.
Results: Of the 15 astronauts included, the median (SD) age was 47.2 (1.5) years; 12 were men, and 3 were women. We found a significant, widespread increase in FW volume in the frontal, temporal, and occipital lobes from before spaceflight to after spaceflight. There was also a significant decrease in FW in the posterior aspect of the vertex. All FW changes were significant and ranged from approximately 2.5% to 4.0% across brain regions. We observed white matter changes in the right superior and inferior longitudinal fasciculi, the corticospinal tract, and cerebellar peduncles. All white matter changes were significant and ranged from approximately 0.75% to 1.25%. Spaceflight mission duration was associated with cerebellar white matter change, and white matter changes in the superior longitudinal fasciculus were associated with the balance changes seen in the astronauts from before spaceflight to after spaceflight.
Conclusions and Relevance: Free water redistribution with spaceflight likely reflects headward fluid shifts occurring in microgravity as well as an upward shift of the brain within the skull. White matter changes were of a greater magnitude than those typically seen during the same period with healthy aging. Future, prospective assessments are required to better understand the recovery time and behavioral consequences of these brain changes.
PURPOSE: To analyze the impact on image quality and motion fidelity of a motion-weighted space-time variant regularization term in compressed sensing cardiac cine MRI.
METHODS: k-t SPARSE-SENSE with temporal total variation (tTV) is used as the base reconstruction algorithm. Motion in the dynamic image is estimated by means of a robust registration technique for non-rigid motion. The resulting deformation fields are used to leverage the regularization term. The results are compared with standard k-t SPARSE-SENSE with tTV regularization as well as with an improved version of this algorithm that makes use of tTV and temporal Fast Fourier Transform regularization in x-f domain.
RESULTS: The proposed method with space-time variant regularization provides higher motion fidelity and image quality than the two previously reported methods. Difference images between undersampled reconstruction and fully sampled reference images show less systematic errors with the proposed approach.
CONCLUSIONS: Usage of a space-time variant regularization offers reconstructions with better image quality than the state of the art approaches used for comparison.
There are two popular approaches for automated white matter parcellation using diffusion MRI tractography, including fiber clustering strategies that group white matter fibers according to their geometric trajectories and cortical-parcellation-based strategies that focus on the structural connectivity among different brain regions of interest. While multiple studies have assessed test-retest reproducibility of automated white matter parcellations using cortical-parcellation-based strategies, there are no existing studies of test-retest reproducibility of fiber clustering parcellation. In this work, we perform what we believe is the first study of fiber clustering white matter parcellation test-retest reproducibility. The assessment is performed on three test-retest diffusion MRI datasets including a total of 255 subjects across genders, a broad age range (5-82 years), health conditions (autism, Parkinson's disease and healthy subjects), and imaging acquisition protocols (three different sites). A comprehensive evaluation is conducted for a fiber clustering method that leverages an anatomically curated fiber clustering white matter atlas, with comparison to a popular cortical-parcellation-based method. The two methods are compared for the two main white matter parcellation applications of dividing the entire white matter into parcels (i.e., whole brain white matter parcellation) and identifying particular anatomical fiber tracts (i.e., anatomical fiber tract parcellation). Test-retest reproducibility is measured using both geometric and diffusion features, including volumetric overlap (wDice) and relative difference of fractional anisotropy. Our experimental results in general indicate that the fiber clustering method produced more reproducible white matter parcellations than the cortical-parcellation-based method.
A carotid artery pseudoaneurysm in an irradiated neck is a rare entity with possible devastating results and management should be multidisciplinary. We present a successful endovascular treatment of a late carotid artery pseudoaneurysm following patch endarterectomy and cervical radiotherapy.
The recent explosion of 'big data' has ushered in a new era of artificial intelligence (AI) algorithms in every sphere of technological activity, including medicine, and in particular radiology. However, the recent success of AI in certain flagship applications has, to some extent, masked decades-long advances in computational technology development for medical image analysis. In this article, we provide an overview of the history of AI methods for radiological image analysis in order to provide a context for the latest developments. We review the functioning, strengths and limitations of more classical methods as well as of the more recent deep learning techniques. We discuss the unique characteristics of medical data and medical science that set medicine apart from other technological domains in order to highlight not only the potential of AI in radiology but also the very real and often overlooked constraints that may limit the applicability of certain AI methods. Finally, we provide a comprehensive perspective on the potential impact of AI on radiology and on how to evaluate it not only from a technical point of view but also from a clinical one, so that patients can ultimately benefit from it. KEY POINTS: • Artificial intelligence (AI) research in medical imaging has a long history • The functioning, strengths and limitations of more classical AI methods is reviewed, together with that of more recent deep learning methods. • A perspective is provided on the potential impact of AI on radiology and on its evaluation from both technical and clinical points of view.
The Genetic Epidemiology of Chronic Obstructive Pulmonary Disease (COPDGene) study, which began in 2007, is an ongoing multicenter observational cohort study of more than 10,000 current and former smokers. The study is aimed at understanding the etiology, progression, and heterogeneity of chronic obstructive pulmonary disease (COPD). In addition to genetic analysis, the participants have been extensively characterized by clinical questionnaires, spirometry, volumetric inspiratory and expiratory computed tomography, and longitudinal follow-up, including follow-up computed tomography at 5 years after enrollment. The purpose of this state-of-the-art review is to summarize the major advances in our understanding of COPD resulting from the imaging findings in the COPDGene study. Imaging features that are associated with adverse clinical outcomes include early interstitial lung abnormalities, visual presence and pattern of emphysema, the ratio of pulmonary artery to ascending aortic diameter, quantitative evaluation of emphysema, airway wall thickness, and expiratory gas trapping. COPD is characterized by the early involvement of the small conducting airways, and the addition of expiratory scans has enabled measurement of small airway disease. Computational advances have enabled indirect measurement of nonemphysematous gas trapping. These metrics have provided insights into the pathogenesis and prognosis of COPD and have aided early identification of disease. Important quantifiable extrapulmonary findings include coronary artery calcification, cardiac morphology, intrathoracic and extrathoracic fat, and osteoporosis. Current active research includes identification of novel quantitative measures for emphysema and airway disease, evaluation of dose reduction techniques, and use of deep learning for phenotyping COPD.
BACKGROUND: Transcranial magnetic stimulation (TMS) is a noninvasive neuromodulation technique with therapeutic applications for the treatment of major depressive disorder (MDD). The standard protocol uses high frequency stimulation over the left dorsolateral prefrontal cortex (DLPFC) identified in a heuristic manner leading to moderate clinical efficacy. A proposed strategy to increase the anatomical precision in targeting, based on resting-state functional MRI (rsfMRI), identifies the subregion within the DLPFC having the strongest anticorrelated functional connectivity with the subgenual cortex (SGC) for each individual subject.
OBJECTIVE: In this work, we comprehensively test the reliability and reproducibility of this targeting method for different scan lengths on 100 subjects from the Human Connectome Project (HCP) where each subject had a four 15-min rsfMRI scan on 2 different days.
METHODS: We quantified the inter-scan and inter-day distance between the rsfMRI-guided DLPFC targets for each subject controlling for a number of expected sources of noise using volumetric as well as surface analyses.
RESULTS: Our results show that the average inter-day distance (with fMRI scans lasting 30 min on each day) is 25% less variable than the inter-scan distance, which uses 50% less data. Specifically, the inter-scan distance was more than 37 mm, while for the longer-scan, the inter-day distance had lower variability at 25 mm. Finally, we tested the same rsfMRI strategy using the nucleus accumbens (NAc) as a control region relevant to MDD but less susceptible to artifacts, using both volume and surface rsfMRI data. The results showed similar variability to the SGC-DLPFC functional connectivity. Moreover, our results suggest that a smoothing kernel with 12 mm full-width half maximum (FWHM) lead to more stable and reliable target estimates.
CONCLUSION: Our work provides a quantitative assessment of the topographic precision of this targeting method, describing an anatomical variability that may surpass the spatial resolution of some forms of focal TMS as it is commonly applied, and provides recommendations for improved accuracy.
Diffusion MRI fiber tractography is widely used to probe the structural connectivity of the brain, with a range of applications in both clinical and basic neuroscience. Despite widespread use, tractography has well-known pitfalls that limits the anatomical accuracy of this technique. Numerous modern methods have been developed to address these shortcomings through advances in acquisition, modeling, and computation. To test whether these advances improve tractography accuracy, we organized the 3-D Validation of Tractography with Experimental MRI (3D-VoTEM) challenge at the ISBI 2018 conference. We made available three unique independent tractography validation datasets - a physical phantom and two ex vivo brain specimens - resulting in 176 distinct submissions from 9 research groups. By comparing results over a wide range of fiber complexities and algorithmic strategies, this challenge provides a more comprehensive assessment of tractography's inherent limitations than has been reported previously. The central results were consistent across all sub-challenges in that, despite advances in tractography methods, the anatomical accuracy of tractography has not dramatically improved in recent years. Taken together, our results independently confirm findings from decades of tractography validation studies, demonstrate inherent limitations in reconstructing white matter pathways using diffusion MRI data alone, and highlight the need for alternative or combinatorial strategies to accurately map the fiber pathways of the brain.
The contrast in diffusion-weighted MR images is due to variations of diffusion properties within the examined specimen. Certain microstructural information on the underlying tissues can be inferred through quantitative analyses of the diffusion-sensitized MR signals. In the first part of the paper, we review two types of approach for characterizing diffusion MRI signals: Bloch's equations with diffusion terms, and statistical descriptions. Specifically, we discuss expansions in terms of cumulants and orthogonal basis functions, the confinement tensor formalism and tensor distribution models. Further insights into the tissue properties may be obtained by integrating diffusion MRI with other techniques, which is the subject of the second part of the paper. We review examples involving magnetic susceptibility, structural tensors, internal field gradients, transverse relaxation and functional MRI. Integrating information provided by other imaging modalities (MR based or otherwise) could be a key to improve our understanding of how diffusion MRI relates to physiology and biology.
PURPOSE: The purpose of this study was to examine the prevalence of deviating vital parameters in general ward patients using rapid response team (RRT) criteria and National Early Warning Score (NEWS), assess exam duration, correct calculation and classification of risk score as well as mortality and adverse events.
METHODS: Point prevalence study of vital parameters according to NEWS and RRT criteria of all adult patients admitted to general wards at a Scandinavian university hospital with a mature RRT.
PRIMARY OUTCOME: prevalence of at-risk patients fulfilling at least one RRT criteria, total NEWS of 7 or greater or a single NEWS parameter of 3 (red NEWS).
SECONDARY OUTCOMES: mortality in-hospital and within 30 days or adverse events within 24 hours.
RESULTS: We assessed 598 (75%) of 798 admitted patients and examiners captured a fulfilled RRT calling criterion in 50 patients (8.4%), 36 (6.0%) had NEWS ≥ 7, 34 with a red NEWS parameter. Red NEWS occurred in 112 patients (18.7%). Secondary outcomes were fulfilled in 49 patients (8.2%). Mortality overall was 6.5% within 30 days, 1.8% in hospital. In 134 patients (22.4%) the manual calculation of score for NEWS was incorrectly performed by examiner.
CONCLUSION: Even with a mature RRT in place, we captured patients with failing physiology in general wards reflecting afferent limb failure. Manual calculation of NEWS is frequently incorrect, possibly leading to misclassification of patients at risk.
A joint and integrated analysis of multi-site diffusion MRI (dMRI) datasets can dramatically increase the statistical power of neuroimaging studies and enable comparative studies pertaining to several brain disorders. However, dMRI data sets acquired on multiple scanners cannot be naively pooled for joint analysis due to scanner specific nonlinear effects as well as differences in acquisition parameters. Consequently, for joint analysis, the dMRI data has to be harmonized, which involves removing scanner-specific differences from the raw dMRI signal. In this work, we propose a dMRI harmonization method that is capable of removing scanner-specific effects, while accounting for minor differences in acquisition parameters such as b-value, spatial resolution and number of gradient directions. We validate our algorithm on dMRI data acquired from two sites: Philadelphia Neurodevelopmental Cohort (PNC) with 800 healthy adolescents (ages 8-22 years) and Brigham and Women's Hospital (BWH) with 70 healthy subjects (ages 14-54 years). In particular, we show that gender and age-related maturation differences in different age groups are preserved after harmonization, as measured using effect sizes (small, medium and large), irrespective of the test sample size. Since we use matched control subjects from different scanners to estimate scanner-specific effects, our goal in this work is also to determine the minimum number of well-matched subjects needed from each site to achieve best harmonization results. Our results indicate that at-least 16 to 18 well-matched healthy controls from each site are needed to reliably capture scanner related differences. The proposed method can thus be used for retrospective harmonization of raw dMRI data across sites despite differences in acquisition parameters, while preserving inter-subject anatomical variability.
A large proportion (range of 44-75%) of women who experience intimate-partner violence (IPV) have been shown to sustain repetitive mild traumatic brain injuries (mTBIs) from their abusers. Further, despite requests for research on TBI-related health outcomes, there are currently only a handful of studies addressing this issue and only one prior imaging study that has investigated the neural correlates of IPV-related TBIs. In response, we examined specific regions of white matter microstructure in 20 women with histories of IPV. Subjects were imaged on a 3-Tesla Siemens Magnetom TrioTim scanner using diffusion magnetic resonance imaging. We investigated the association between a score reflecting number and recency of IPV-related mTBIs and fractional anisotropy (FA) in the posterior and superior corona radiata as well as the posterior thalamic radiation, brain regions shown previously to be involved in mTBI. We also investigated the association between several cognitive measures, namely learning, memory, and cognitive flexibility, and FA in the white matter regions of interest. We report a negative correlation between the brain injury score and FA in regions of the posterior and superior corona radiata. We failed to find an association between our cognitive measures and FA in these regions, but the interpretation of these results remains inconclusive due to possible power issues. Overall, these data build upon the small but growing literature demonstrating potential consequences of mTBIs for women experiencing IPV, and further underscore the urgent need for larger and more comprehensive studies in this area.
Schizophrenia has been characterized as a neurodevelopmental disorder, with structural brain abnormalities reported at all stages. However, at present, it remains unclear whether gray and white matter abnormalities represent related or independent pathologies in schizophrenia. In this study, we present findings from an integrative analysis exploring the morphological relationship between gray and white matter in 45 schizophrenia participants and 49 healthy controls. We utilized mutual information (MI), a measure of how much information two variables share, to assess the morphological dependence between gray and white matter in three segments of the corpus callsoum, and the gray matter regions these segments connect: (1) the genu and the left and right rostral middle frontal gyrus (rMFG), (2) the isthmus and the left and right superior temporal gyrus (STG), (3) the splenium and the left and right lateral occipital gyrus (LOG). We report significantly reduced MI between white matter tract dispersion of the right hemispheric callosal connections to the STG and both cortical thickness and area in the right STG in schizophrenia patients, despite a lack of group differences in cortical thickness, surface area, or dispersion. We believe that this reduction in morphological dependence between gray and white matter may reflect a possible decoupling of the developmental processes that shape morphological features of white and gray matter early in life. The present study also demonstrates the importance of studying the relationship between gray and white matter measures, as opposed to restricting analyses to gray and white matter measures independently.