A guide to the BRAIN Initiative Cell Census Network data ecosystem.

Characterizing cellular diversity at different levels of biological organization and across data modalities is a prerequisite to understanding the function of cell types in the brain. Classification of neurons is also essential to manipulate cell types in controlled ways and to understand their variation and vulnerability in brain disorders. The BRAIN Initiative Cell Census Network (BICCN) is an integrated network of data-generating centers, data archives, and data standards developers, with the goal of systematic multimodal brain cell type profiling and characterization. Emphasis of the BICCN is on the whole mouse brain with demonstration of prototype feasibility for human and nonhuman primate (NHP) brains. Here, we provide a guide to the cellular and spatial approaches employed by the BICCN, and to accessing and using these data and extensive resources, including the BRAIN Cell Data Center (BCDC), which serves to manage and integrate data across the ecosystem. We illustrate the power of the BICCN data ecosystem through vignettes highlighting several BICCN analysis and visualization tools. Finally, we present emerging standards that have been developed or adopted toward Findable, Accessible, Interoperable, and Reusable (FAIR) neuroscience. The combined BICCN ecosystem provides a comprehensive resource for the exploration and analysis of cell types in the brain.

A comprehensive transcriptional map of primate brain development.

The transcriptional underpinnings of brain development remain poorly understood, particularly in humans and closely related non-human primates. We describe a high-resolution transcriptional atlas of rhesus monkey (Macaca mulatta) brain development that combines dense temporal sampling of prenatal and postnatal periods with fine anatomical division of cortical and subcortical regions associated with human neuropsychiatric disease. Gene expression changes more rapidly before birth, both in progenitor cells and maturing neurons. Cortical layers and areas acquire adult-like molecular profiles surprisingly late in postnatal development. Disparate cell populations exhibit distinct developmental timing of gene expression, but also unexpected synchrony of processes underlying neural circuit construction including cell projection and adhesion. Candidate risk genes for neurodevelopmental disorders including primary microcephaly, autism spectrum disorder, intellectual disability, and schizophrenia show disease-specific spatiotemporal enrichment within developing neocortex. Human developmental expression trajectories are more similar to monkey than rodent, although approximately 9% of genes show human-specific regulation with evidence for prolonged maturation or neoteny compared to monkey.

DicomAnnotator: a Configurable Open-Source Software Program for Efficient DICOM Image Annotation.

Modern, supervised machine learning approaches to medical image classification, image segmentation, and object detection usually require many annotated images. As manual annotation is usually labor-intensive and time-consuming, a well-designed software program can aid and expedite the annotation process. Ideally, this program should be configurable for various annotation tasks, enable efficient placement of several types of annotations on an image or a region of an image, attribute annotations to individual annotators, and be able to display Digital Imaging and Communications in Medicine (DICOM)-formatted images. No current open-source software program fulfills these requirements. To fill this gap, we developed DicomAnnotator, a configurable open-source software program for DICOM image annotation. This program fulfills the above requirements and provides user-friendly features to aid the annotation process. In this paper, we present the design and implementation of DicomAnnotator. Using spine image annotation as a test case, our evaluation showed that annotators with various backgrounds can use DicomAnnotator to annotate DICOM images efficiently. DicomAnnotator is freely available at https://github.com/UW-CLEAR-Center/DICOM-Annotator under the GPLv3 license.

An anatomically comprehensive atlas of the adult human brain transcriptome.

Neuroanatomically precise, genome-wide maps of transcript distributions are critical resources to complement genomic sequence data and to correlate functional and genetic brain architecture. Here we describe the generation and analysis of a transcriptional atlas of the adult human brain, comprising extensive histological analysis and comprehensive microarray profiling of ∼900 neuroanatomically precise subdivisions in two individuals. Transcriptional regulation varies enormously by anatomical location, with different regions and their constituent cell types displaying robust molecular signatures that are highly conserved between individuals. Analysis of differential gene expression and gene co-expression relationships demonstrates that brain-wide variation strongly reflects the distributions of major cell classes such as neurons, oligodendrocytes, astrocytes and microglia. Local neighbourhood relationships between fine anatomical subdivisions are associated with discrete neuronal subtypes and genes involved with synaptic transmission. The neocortex displays a relatively homogeneous transcriptional pattern, but with distinct features associated selectively with primary sensorimotor cortices and with enriched frontal lobe expression. Notably, the spatial topography of the neocortex is strongly reflected in its molecular topography-the closer two cortical regions, the more similar their transcriptomes. This freely accessible online data resource forms a high-resolution transcriptional baseline for neurogenetic studies of normal and abnormal human brain function.

Perimesencephalic Hemorrhage: Yield of Single versus Multiple DSA Examinations-A Single-Center Study and Meta-Analysis.

Purpose To quantify the rate of detection of aneurysms at follow-up digital subtraction angiography (DSA) after initial DSA with results negative for aneurysms in subjects with perimesencephalic (PM) nonaneurysmal subarachnoid hemorrhage. Materials and Methods This single-center retrospective study and meta-analysis was approved by the institutional review board. At a single institution from 2000 to 2013, 252 consecutive patients with subarachnoid hemorrhage at computed tomography (CT) and two DSA examinations negative for aneurysm within 10 days were evaluated for inclusion in the study, and 131 met CT criteria for PM nonaneurysmal subarachnoid hemorrhage (53 women; mean age, 53 years [range, 33-88 years]). DS angiographic reports were reviewed for causative abnormalities. Three reviewers searched MEDLINE and electronic databases for studies that reported detection of aneurysm in subjects with PM hemorrhage who had undergone multiple DSA examinations. Main inclusion criteria were PM hemorrhage at CT per van Gijn classification, head CT performed within 72 hours of symptom onset, initial DS angiographic results negative for aneurysm, and two DSA examinations within 10 days. Studies with fewer than 25 subjects were excluded. Methodology was assessed by using Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The summary rate of aneurysm detection for subsequent DSA was calculated by using a fixed-effects model. Results Six studies with 298 subjects and a single-institution study with 131 subjects were included. No aneurysms were seen at follow-up DSA in the single-center study (0.0%). Three aneurysms were detected at follow-up DSA in three of six studies from the literature (one of 29 [3.4%], one of 65 [1.5%], and one of 34 [2.9%] patients). Two occurred in cases that likely preceded the use of the current DSA technique. The summary aneurysm detection rate at subsequent DSA was 1.6% (95% confidence interval: 0.7%, 3.8%; range of individual study detection rate: 0.0%-3.4%). Conclusion In patients with PM nonaneurysmal subarachnoid hemorrhage and initial DSA negative for aneurysms, the yield of follow-up DSA for detection of causative aneurysms is very low. © RSNA, 2016 Online supplemental material is available for this article.

Transcranial magnetic stimulation in the treatment of chronic widespread pain: a randomized controlled study.

OBJECTIVE: Our objective was to assess transcranial magnetic stimulation (TMS) in the treatment of chronic widespread pain. METHODS: Nineteen participants were randomized into 2 groups: one group receiving active TMS (n = 7) and another group receiving sham stimulation (n = 11) applied to the left dorsolateral prefrontal cortex. During sham stimulation, subjects heard a sound similar to the sound heard by those receiving the active treatment and received an active electrical stimulus to the scalp. The stimulation protocol consisted of 15 sessions completed within a 4-week period. Blind assessments were done at baseline and after each 5 sessions followed by blind assessments at 1 week, 1 month, and 3 months after the last TMS sessions. The primary outcome variable was a pain measure, the Gracely Box Intensity Scale (BIRS). RESULTS: The percentage of subjects who guessed that they were receiving TMS was similar in the 2 groups. Both the TMS group and the sham group showed a statistically significant reduction in the BIRS scores from baseline during the acute phase of treatment and the follow-up phase. However, the TMS and sham groups did not differ in the change in the BIRS scores. DISCUSSION: Although some previous clinical studies and basic science studies of TMS in treating pain are promising, this study found no difference in the analgesic effect of TMS and sham stimulation. Future studies should use a sham condition that attempts to simulate the sound and sensation of the TMS stimulation. Stimulus location and other stimulus parameters should be explored in future studies.

No change in neuropsychological functioning after receiving repetitive transcranial magnetic stimulation treatment for major depression.

Early studies of transcranial magnetic stimulation (TMS) have shown no adverse effects on neuropsychological function. However, further research using higher TMS intensities as well as a greater number of TMS pulses and with larger sample sizes is needed. We studied 68 patients with major depressive disorder who were randomized to receive either 15 sessions of sham or real TMS at 110% of the estimated prefrontal cortex threshold to the left dorsolateral prefrontal cortex. Each session consisted of 32 5-second trains of 10-Hz repetitive TMS at 110% adjusted motor threshold. A total of 24,000 pulses were given. Neuropsychological function was assessed before and immediately after TMS treatment with a battery of 8 tests. Using a higher TMS intensity as well as a greater number of pulses and having a larger sample size compared with most previous studies, this study found no negative neuropsychological effects of TMS. Changes in neuropsychological function were unrelated to changes in depression.

Longitudinal Relationship Between Brain Atrophy Patterns, Cognitive Decline, and Cerebrospinal Fluid Biomarkers in Alzheimer's Disease Explored by Orthonormal Projective Non-Negative Matrix Factorization.

Background: Longitudinal magnetic resonance imaging (MRI) has been proposed for tracking the progression of Alzheimer's disease (AD) through the assessment of brain atrophy. Objective: Detection of brain atrophy patterns in patients with AD as the longitudinal disease tracker. Methods: We used a refined version of orthonormal projective non-negative matrix factorization (OPNMF) to identify six distinct spatial components of voxel-wise volume loss in the brains of 83 subjects with AD from the ADNI3 cohort relative to healthy young controls from the ABIDE study. We extracted non-negative coefficients representing subject-specific quantitative measures of regional atrophy. Coefficients of brain atrophy were compared to subjects with mild cognitive impairment and controls, to investigate the cross-sectional and longitudinal associations between AD biomarkers and regional atrophy severity in different groups. We further validated our results in an independent dataset from ADNI2. Results: The six non-overlapping atrophy components represent symmetric gray matter volume loss primarily in frontal, temporal, parietal and cerebellar regions. Atrophy in these regions was highly correlated with cognition both cross-sectionally and longitudinally, with medial temporal atrophy showing the strongest correlations. Subjects with elevated CSF levels of TAU and PTAU and lower baseline CSF Aß42 values, demonstrated a tendency toward a more rapid increase of atrophy. Conclusions: The present study has applied a transferable method to characterize the imaging changes associated with AD through six spatially distinct atrophy components and correlated these atrophy patterns with cognitive changes and CSF biomarkers cross-sectionally and longitudinally, which may help us better understand the underlying pathology of AD.

Subject-specific material properties of the heel pad: An inverse finite element analysis.

Individuals with diabetes are at a higher risk of developing foot ulcers. To better understand internal soft tissue loading and potential treatment options, subject-specific finite element (FE) foot models have been used. However, existing models typically lack subject-specific soft tissue material properties and only utilize subject-specific anatomy. Therefore, this study determined subject-specific hindfoot soft tissue material properties from one non-diabetic and one diabetic subject using inverse FE analysis. Each subject underwent cyclic MRI experiments to simulate physiological gait and to obtain compressive force and three-dimensional soft tissue imaging data at 16 phases along the loading-unloading cycles. The FE models consisted of rigid bones and nearly-incompressible first-order Ogden hyperelastic skin, fat, and muscle (resulting in six independent material parameters). Then, calcaneus and loading platen kinematics were computed from imaging data and prescribed to the FE model. Two analyses were performed for each subject. First, the skin, fat, and muscle layers were lumped into a single generic soft tissue material and optimized to the platen force. Second, the skin, fat, and muscle material properties were individually determined by simultaneously optimizing for platen force, muscle vertical displacement, and skin mediolateral bulging. Our results indicated that compared to the individual without diabetes, the individual with diabetes had stiffer generic soft tissue behavior at high strain and that the only substantially stiffer multi-material layer was fat tissue. Thus, we suggest that this protocol serves as a guideline for exploring differences in non-diabetic and diabetic soft tissue material properties in a larger population.

Generalizability of Deep Learning Classification of Spinal Osteoporotic Compression Fractures on Radiographs Using an Adaptation of the Modified-2 Algorithm-Based Qualitative Criteria.

RATIONALE AND OBJECTIVES: Spinal osteoporotic compression fractures (OCFs) can be an early biomarker for osteoporosis but are often subtle, incidental, and underreported. To ensure early diagnosis and treatment of osteoporosis, we aimed to build a deep learning vertebral body classifier for OCFs as a critical component of our future automated opportunistic screening tool. MATERIALS AND METHODS: We retrospectively assembled a local dataset, including 1790 subjects and 15,050 vertebral bodies (thoracic and lumbar). Each vertebral body was annotated using an adaption of the modified-2 algorithm-based qualitative criteria. The Osteoporotic Fractures in Men (MrOS) Study dataset provided thoracic and lumbar spine radiographs of 5994 men from six clinical centers. Using both datasets, five deep learning algorithms were trained to classify each individual vertebral body of the spine radiographs. Classification performance was compared for these models using multiple metrics, including the area under the receiver operating characteristic curve (AUC-ROC), sensitivity, specificity, and positive predictive value (PPV). RESULTS: Our best model, built with ensemble averaging, achieved an AUC-ROC of 0.948 and 0.936 on the local dataset's test set and the MrOS dataset's test set, respectively. After setting the cutoff threshold to prioritize PPV, this model achieved a sensitivity of 54.5% and 47.8%, a specificity of 99.7% and 99.6%, and a PPV of 89.8% and 94.8%. CONCLUSION: Our model achieved an AUC-ROC>0.90 on both datasets. This testing shows some generalizability to real-world clinical datasets and a suitable performance for a future opportunistic osteoporosis screening tool.

Comparison of 3 Tesla proton MR spectroscopy, MR perfusion and MR diffusion for distinguishing glioma recurrence from posttreatment effects.

PURPOSE: To compare 3 Tesla (3T) multi-voxel and single-voxel proton MR spectroscopy (MRS), dynamic susceptibility contrast perfusion MRI (DSC), and diffusion-weighted MRI (DWI) for distinguishing recurrent glioma from postradiation injury. MATERIALS AND METHODS: We reviewed all 3T MRS, DSC and DWI studies performed for suspicion of malignant glioma recurrence between October 2006 and December 2008. Maximum Cho/NAA and Cho/Cr peak-area and peak-height ratios were recorded for both multi-voxel and single-voxel MRS. Maximum cerebral blood volume (CBV) and minimum apparent diffusion coefficient (ADC) were normalized to white matter. Histopathology and clinical-radiologic follow-up served as reference standards. Receiver operating characteristic curves for each parameter were compared. RESULTS: Forty lesions were classified as glioma recurrence (n = 30) or posttreatment effect (n = 10). Diagnostic performance was similar for CBV ratio (AUC = 0.917, P < 0.001), multi-voxel Cho/Cr peak-area (AUC = 0.913, P = 0.002), and multi-voxel Cho/NAA peak-height (AUC = 0.913, P = 0.002), while ADC ratio (AUC = 0.726, P = 0.035) did not appear to perform as well. Single-voxel MRS parameters did not reliably distinguish tumor recurrence from posttreatment effects. CONCLUSION: A 3T DSC and multi-voxel MRS Cho/Cr peak-area and Cho/NAA peak-height appear to outperform DWI for distinguishing glioma recurrence from posttreatment effects. Single-voxel MRS parameters do not appear to distinguish glioma recurrence from posttreatment effects reliably, and therefore should not be used in place of multi-voxel MRS.

Deep Learning Classification of Spinal Osteoporotic Compression Fractures on Radiographs using an Adaptation of the Genant Semiquantitative Criteria.

RATIONALE AND OBJECTIVES: Osteoporosis affects 9% of individuals over 50 in the United States and 200 million women globally. Spinal osteoporotic compression fractures (OCFs), an osteoporosis biomarker, are often incidental and under-reported. Accurate automated opportunistic OCF screening can increase the diagnosis rate and ensure adequate treatment. We aimed to develop a deep learning classifier for OCFs, a critical component of our future automated opportunistic screening tool. MATERIALS AND METHODS: The dataset from the Osteoporotic Fractures in Men Study comprised 4461 subjects and 15,524 spine radiographs. This dataset was split by subject: 76.5% training, 8.5% validation, and 15% testing. From the radiographs, 100,409 vertebral bodies were extracted, each assigned one of two labels adapted from the Genant semiquantitative system: moderate to severe fracture vs. normal/trace/mild fracture. GoogLeNet, a deep learning model, was trained to classify the vertebral bodies. The classification threshold on the predicted probability of OCF outputted by GoogLeNet was set to prioritize the positive predictive value (PPV) while balancing it with the sensitivity. Vertebral bodies with the top 0.75% predicted probabilities were classified as moderate to severe fracture. RESULTS: Our model yielded a sensitivity of 59.8%, a PPV of 91.2%, and an F1 score of 0.72. The areas under the receiver operating characteristic curve (AUC-ROC) and the precision-recall curve were 0.99 and 0.82, respectively. CONCLUSION: Our model classified vertebral bodies with an AUC-ROC of 0.99, providing a critical component for our future automated opportunistic screening tool. This could lead to earlier detection and treatment of OCFs.

Multi-rigid image segmentation and registration for the analysis of joint motion from three-dimensional magnetic resonance imaging.

We report an image segmentation and registration method for studying joint morphology and kinematics from in vivo magnetic resonance imaging (MRI) scans and its application to the analysis of foot and ankle joint motion. Using an MRI-compatible positioning device, a foot was scanned in a single neutral and seven other positions ranging from maximum plantar flexion, inversion, and internal rotation to maximum dorsiflexion, eversion, and external rotation. A segmentation method combining graph cuts and level set was developed. In the subsequent registration step, a separate rigid body transformation for each bone was obtained by registering the neutral position dataset to each of the other ones, which produced an accurate description of the motion between them. The segmentation algorithm allowed a user to interactively delineate 14 foot bones in the neutral position volume in less than 30 min total (user and computer processing unit [CPU]) time. Registration to the seven other positions took approximately 10 additional minutes of user time and 5.25 h of CPU time. For validation, our results were compared with those obtained from 3DViewnix, a semiautomatic segmentation program. We achieved excellent agreement, with volume overlap ratios greater than 88% for all bones excluding the intermediate cuneiform and the lesser metatarsals. For the registration of the neutral scan to the seven other positions, the average overlap ratio is 94.25%, while the minimum overlap ratio is 89.49% for the tibia between the neutral position and position 1, which might be due to different fields of view (FOV). To process a single foot in eight positions, our tool requires only minimal user interaction time (less than 30 min total), a level of improvement that has the potential to make joint motion analysis from MRI practical in research and clinical applications.

Evaluating foot kinematics using magnetic resonance imaging: from maximum plantar flexion, inversion, and internal rotation to maximum dorsiflexion, eversion, and external rotation.

The foot consists of many small bones with complicated joints that guide and limit motion. A variety of invasive and noninvasive means [mechanical, X-ray stereophotogrammetry, electromagnetic sensors, retro-reflective motion analysis, computer tomography (CT), and magnetic resonance imaging (MRI)] have been used to quantify foot bone motion. In the current study we used a foot plate with an electromagnetic sensor to determine an individual subject's foot end range of motion (ROM) from maximum plantar flexion, internal rotation, and inversion to maximum plantar flexion, inversion, and internal rotation to maximum dorsiflexion, eversion, and external rotation. We then used a custom built MRI-compatible device to hold each subject's foot during scanning in eight unique positions determined from the end ROM data. The scan data were processed using software that allowed the bones to be segmented with the foot in the neutral position and the bones in the other seven positions to be registered to their base positions with minimal user intervention. Bone to bone motion was quantified using finite helical axes (FHA). FHA for the talocrural, talocalcaneal, and talonavicular joints compared well to published studies, which used a variety of technologies and input motions. This study describes a method for quantifying foot bone motion from maximum plantar flexion, inversion, and internal rotation to maximum dorsiflexion, eversion, and external rotation with relatively little user processing time.

Learning Cortical Parcellations Using Graph Neural Networks.

Deep learning has been applied to magnetic resonance imaging (MRI) for a variety of purposes, ranging from the acceleration of image acquisition and image denoising to tissue segmentation and disease diagnosis. Convolutional neural networks have been particularly useful for analyzing MRI data due to the regularly sampled spatial and temporal nature of the data. However, advances in the field of brain imaging have led to network- and surface-based analyses that are often better represented in the graph domain. In this analysis, we propose a general purpose cortical segmentation method that, given resting-state connectivity features readily computed during conventional MRI pre-processing and a set of corresponding training labels, can generate cortical parcellations for new MRI data. We applied recent advances in the field of graph neural networks to the problem of cortical surface segmentation, using resting-state connectivity to learn discrete maps of the human neocortex. We found that graph neural networks accurately learn low-dimensional representations of functional brain connectivity that can be naturally extended to map the cortices of new datasets. After optimizing over algorithm type, network architecture, and training features, our approach yielded mean classification accuracies of 79.91% relative to a previously published parcellation. We describe how some hyperparameter choices including training and testing data duration, network architecture, and algorithm choice affect model performance.

Radiogenomic modeling predicts survival-associated prognostic groups in glioblastoma.

BACKGROUND: Combined whole-exome sequencing (WES) and somatic copy number alteration (SCNA) information can separate isocitrate dehydrogenase (IDH)1/2-wildtype glioblastoma into two prognostic molecular subtypes, which cannot be distinguished by epigenetic or clinical features. The potential for radiographic features to discriminate between these molecular subtypes has yet to be established. METHODS: Radiologic features (n = 35 340) were extracted from 46 multisequence, pre-operative magnetic resonance imaging (MRI) scans of IDH1/2-wildtype glioblastoma patients from The Cancer Imaging Archive (TCIA), all of whom have corresponding WES/SCNA data. We developed a novel feature selection method that leverages the structure of extracted MRI features to mitigate the dimensionality challenge posed by the disparity between a large number of features and the limited patients in our cohort. Six traditional machine learning classifiers were trained to distinguish molecular subtypes using our feature selection method, which was compared to least absolute shrinkage and selection operator (LASSO) feature selection, recursive feature elimination, and variance thresholding. RESULTS: We were able to classify glioblastomas into two prognostic subgroups with a cross-validated area under the curve score of 0.80 (±0.03) using ridge logistic regression on the 15-dimensional principle component analysis (PCA) embedding of the features selected by our novel feature selection method. An interrogation of the selected features suggested that features describing contours in the T2 signal abnormality region on the T2-weighted fluid-attenuated inversion recovery (FLAIR) MRI sequence may best distinguish these two groups from one another. CONCLUSIONS: We successfully trained a machine learning model that allows for relevant targeted feature extraction from standard MRI to accurately predict molecularly-defined risk-stratifying IDH1/2-wildtype glioblastoma patient groups.

Applying Artificial Intelligence to Mitigate Effects of Patient Motion or Other Complicating Factors on Image Quality.

Artificial intelligence, particularly deep learning, offers several possibilities to improve the quality or speed of image acquisition in magnetic resonance imaging (MRI). In this article, we briefly review basic machine learning concepts and discuss commonly used neural network architectures for image-to-image translation. Recent examples in the literature describing application of machine learning techniques to clinical MR image acquisition or postprocessing are discussed. Machine learning can contribute to better image quality by improving spatial resolution, reducing image noise, and removing undesired motion or other artifacts. As patients occasionally are unable to tolerate lengthy acquisition times or gadolinium agents, machine learning can potentially assist MRI workflow and patient comfort by facilitating faster acquisitions or reducing exogenous contrast dosage. Although artificial intelligence approaches often have limitations, such as problems with generalizability or explainability, there is potential for these techniques to improve diagnostic utility, throughput, and patient experience in clinical MRI practice.

Making Magnets More Attractive: Physics and Engineering Contributions to Patient Comfort in MRI.

Patient comfort is an important factor of a successful magnetic resonance (MR) examination, and improvements in the patient's MR scanning experience can contribute to improved image quality, diagnostic accuracy, and efficiency in the radiology department, and therefore reduced cost. Magnet designs that are more open and accessible, reduced auditory noise of MR examinations, light and flexible radiofrequency (RF) coils, and faster motion-insensitive imaging techniques can all significantly improve the patient experience in MR imaging. In this work, we review the design, development, and implementation of these physics and engineering approaches to improve patient comfort.

Practical Considerations for Radiologists in Implementing a Patient-friendly MRI Experience.

For many patients, numerous unpleasant features of the magnetic resonance imaging (MRI) experience such as scan duration, auditory noise, spatial confinement, and motion restrictions can lead to premature termination or low diagnostic quality of imaging studies. This article discusses practical, patient-oriented considerations that are helpful for radiologists contemplating ways to improve the MRI experience for patients. Patient friendly scanner properties are discussed, with an emphasis on literature findings of effectiveness in mitigating patient claustrophobia, other anxiety, or motion and on reducing scan incompletion rates or need for sedation. As shorter scanning protocols designed to answer specific diagnostic questions may be more practical and tolerable to the patient than a full-length standard-of-care examination, a few select protocol adjustments potentially useful for specific clinical settings are discussed. In addition, adjunctive devices such as audiovisual or other sensory aides that can be useful distractive approaches to reduce patient discomfort are considered. These modifications to the MRI scanning process not only allow for a more pleasant experience for patients, but they may also increase patient compliance and decrease patient movement to allow more efficient acquisition of diagnostic-quality images.

Thrombosis and hemorrhage in the acute period following Gamma Knife surgery for arteriovenous malformation. Case report.

Bleeding of an arteriovenous malformation (AVM) following stereotactic radiosurgery (SRS) is a known risk during the latency interval, but hemorrhage in the 30-day period following radiosurgery rarely has been reported in the literature. The authors present the case of a 57-year-old man who underwent Gamma Knife surgery for a large AVM, and they provide radiographic documentation of a thrombus in the primary draining vein immediately preceding an AVM hemorrhage within 9 days after radiosurgery. They postulate that the pathophysiology of an AVM hemorrhage in the acute period following SRS is related to an association among tissue irradiation, acute inflammatory response, and vessel thrombosis. The authors also review the literature on risk factors for hemorrhage due to untreated and radiosurgically treated AVMs. Recent evidence on the role of inflammation in the pathogenesis of AVMs and the pathophysiology of AVM rupture is presented. Inflammatory markers have been demonstrated in brain AVM tissue, and the association between inflammation and AVM hemorrhage has been established. There is an acute inflammatory response following tissue irradiation, resulting in structural and functional vascular changes that can lead to vessel thrombosis. Early hemorrhage following radiosurgical treatment of AVMs may be related to the acute inflammatory response and associated vascular changes that occur in irradiated tissue. In the first stage of a planned 2-stage Gamma Knife treatment for a large AVM in the featured case, the superior posteromedial portion of the primary draining vein was included in the treatment field. The authors present the planning images and subsequent CT scans demonstrating a new venous thrombus in the primary draining vein. An acute inflammatory response following radiosurgery with resultant acute venous thrombus formation and venous obstruction is proposed as one mechanism of an AVM hemorrhage in this patient. Radiographic evidence of the time course of thrombosis and hemorrhage supports the hypothesis that acute venous obstruction is a cause of intracranial hemorrhage.