Subject-level spinal osteoporotic fracture prediction combining deep learning vertebral outputs and limited demographic data.

Automated screening for vertebral fractures could improve outcomes. We achieved an AUC-ROC = 0.968 for the prediction of moderate to severe fracture using a GAM with age and three maximal vertebral body scores of fracture from a convolutional neural network. Maximal fracture scores resulted in a performant model for subject-level fracture prediction. Combining individual deep learning vertebral body fracture scores and demographic covariates for subject-level classification of osteoporotic fracture achieved excellent performance (AUC-ROC of 0.968) on a large dataset of radiographs with basic demographic data. PURPOSE: Osteoporotic vertebral fractures are common and morbid. Automated opportunistic screening for incidental vertebral fractures from radiographs, the highest volume imaging modality, could improve osteoporosis detection and management. We consider how to form patient-level fracture predictions and summarization to guide management, using our previously developed vertebral fracture classifier on segmented radiographs from a prospective cohort study of US men (MrOS). We compare the performance of logistic regression (LR) and generalized additive models (GAM) with combinations of individual vertebral scores and basic demographic covariates. METHODS: Subject-level LR and GAM models were created retrospectively using all fracture predictions or summary variables such as order statistics, adjacent vertebral interactions, and demographic covariates (age, race/ethnicity). The classifier outputs for 8663 vertebrae from 1176 thoracic and lumbar radiographs in 669 subjects were divided by subject to perform stratified fivefold cross-validation. Models were assessed using multiple metrics, including receiver operating characteristic (ROC) and precision-recall (PR) curves. RESULTS: The best model (AUC-ROC = 0.968) was a GAM using the top three maximum vertebral fracture scores and age. Using top-ranked scores only, rather than all vertebral scores, improved performance for both model classes. Adding age, but not ethnicity, to the GAMs improved performance slightly. CONCLUSION: Maximal vertebral fracture scores resulted in the highest-performing models. While combining multiple vertebral body predictions risks decreasing specificity, our results demonstrate that subject-level models maintain good predictive performance. Thresholding strategies can be used to control sensitivity and specificity as clinically appropriate.

Using an Ensemble of Segmentation Methods to Detect Vertebral Bodies on Radiographs.

BACKGROUND AND PURPOSE: Vertebral compression fractures may indicate osteoporosis but are underdiagnosed and underreported by radiologists. We have developed an ensemble of vertebral body (VB) segmentation models for lateral radiographs as a critical component of an automated, opportunistic screening tool. Our goal is to detect the approximate location of thoracic and lumbar VBs, including fractured vertebra, on lateral radiographs. MATERIALS AND METHODS: The Osteoporotic Fractures in Men Study (MrOS) data set includes spine radiographs of 5994 men aged ≥65 years from 6 clinical centers. Two segmentation models, U-Net and Mask-RCNN (Region-based Convolutional Neural Network), were independently trained on the MrOS data set retrospectively, and an ensemble was created by combining them. Primary performance metrics for VB detection success included precision, recall, and F1 score for object detection on a held-out test set. Intersection over union (IoU) and Dice coefficient were also calculated as secondary metrics of performance for the test set. A separate external data set from a quaternary health care enterprise was acquired to test generalizability, comprising diagnostic clinical radiographs from men and women aged ≥65 years. RESULTS: The trained models achieved F1 score of U-Net = 83.42%, Mask-RCNN = 86.30%, and ensemble = 88.34% in detecting all VBs, and F1 score of U-Net = 87.88%, Mask-RCNN = 92.31%, and ensemble = 97.14% in detecting severely fractured vertebrae. The trained models achieved an average IoU per VB of 0.759 for U-Net and 0.709 for Mask-RCNN. The trained models achieved F1 score of U-Net = 81.11%, Mask-RCNN = 79.24%, and ensemble = 87.72% in detecting all VBs in the external data set. CONCLUSIONS: An ensemble model combining predictions from U-Net and Mask-RCNN resulted in the best performance in detecting VBs on lateral radiographs and generalized well to an external data set. This model could be a key component of a pipeline to detect fractures on all vertebrae in a radiograph in an automated, opportunistic screening tool under development.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Model-based tracking of the bones of the foot: A biplane fluoroscopy validation study.

Measuring foot kinematics using optical motion capture is technically challenging due to the depth of the talus, small bone size, and soft tissue artifact. We present a validation of our biplane X-ray system, demonstrating its accuracy in tracking the foot bones directly. Using an experimental linear/rotary stage we imaged pairs of tali, calcanei, and first metatarsals, with embedded beads, through 30 poses. Model- and bead-based algorithms were employed for semi-automatic tracking. Translational and rotational poses were compared to the experimental stage (a reference standard) to determine registration performance. For each bone, 10 frames per pose were analyzed. Model-based: The resulting overall translational bias of the six bones was 0.058 mm with a precision of ± 0.049 mm. The overall rotational bias of the six bones was 0.42° with a precision of ± 0.41°. Bead-based: the overall translational bias was 0.037 mm with a precision of ± 0.032 mm and for rotation was 0.29° with a precision of ± 0.26°. We validated the accuracy of our system to determine the spatial position and orientation of isolated foot bones, including the talus, calcaneus, and first metatarsal over a range of quasi-static poses. Although the accuracy of dynamic motion was not assessed, use of an experimental stage establishes a reference standard.

A preliminary study of patient-specific mechanical properties of diabetic and healthy plantar soft tissue from gated magnetic resonance imaging.

Foot loading rate, load magnitude, and the presence of diseases such as diabetes can all affect the mechanical properties of the plantar soft tissues of the human foot. The hydraulic plantar soft tissue reducer instrument was designed to gain insight into which variables are the most significant in determining these properties. It was used with gated magnetic resonance imaging to capture three-dimensional images of feet under dynamic loading conditions. Custom electronics controlled by LabVIEW software simultaneously recorded system pressure, which was then translated to applied force values based on calibration curves. Data were collected for two subjects, one without diabetes (Subject A) and one with diabetes (Subject B). For a 0.2-Hz loading rate, and strains 0.16, 0.18, 0.20, and 0.22, Subject A's average tangential heel pad stiffness was 10 N/mm and Subject B's was 24 N/mm. Maximum test loads were approximately 200 N. Loading rate and load magnitude limitations (both were lower than physiologic values) will continue to be addressed in the next version of the instrument. However, the current hydraulic plantar soft tissue reducer did produce a data set for healthy versus diabetic tissue stiffness that agrees with previous trends. These data are also being used to improve finite element analysis models of the foot as part of a related project.

Association of Collateral Blood Vessels Detected by Arterial Spin Labeling Magnetic Resonance Imaging With Neurological Outcome After Ischemic Stroke.

Importance: Robust collateral blood vessels have been associated with better neurologic outcome following acute ischemic stroke (AIS). The most commonly used methods for identifying collaterals are contrast-based angiographic imaging techniques, which are not possible in all patients after AIS. Objective: To assess the association between the presence of collateral vessels identified using arterial spin labeling (ASL) magnetic resonance imaging, a technique that does not require exogenous administration of contrast, and neurologic outcome in patients after AIS. Design, Setting, and Participants: This retrospective cohort study examined 38 patients after AIS admitted to a tertiary academic medical center between 2012 and 2014 who underwent MRI with ASL. Main Outcomes and Measures: According to a prespecified hypothesis, ASL images were graded for the presence of collaterals by 2 neuroradiologists. Modified Rankin Scale (mRS) scores at discharge and other composite data were abstracted from the medical record by a neurologist blinded to radiologic data. Results: Of the 38 patients, 19 (50.0%) were male, and the mean (SD) age was 61 (20) years. In 25 of 38 patients (65.8%), collaterals were detected using ASL, which were significantly associated with both a good outcome (mRS score of 0-2 at discharge; P = .02) and a 1-point decrease in mRS score at discharge (odds ratio, 6.4; 95% CI, 1.7-23.4; P = .005). In a multivariable ordinal logistic regression model, controlling for admission National Institutes of Health Stroke Scale score, history of atrial fibrillation, premorbid mRS score, and stroke parent artery status, there was a strong association between the presence of ASL collaterals and a 1-point decrease in the mRS score at discharge (odds ratio, 5.1; 95% CI, 1.2-22.1; P = .03). Conclusions and Relevance: Following AIS, the presence of ASL collaterals is strongly associated with better neurological outcome at hospital discharge. This novel association between ASL collaterals and improved neurologic outcome may help guide prognosis and management, particularly in patients who are unable to undergo contrast-based radiological studies.

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.

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.

Canonical genetic signatures of the adult human brain.

The structure and function of the human brain are highly stereotyped, implying a conserved molecular program responsible for its development, cellular structure and function. We applied a correlation-based metric called differential stability to assess reproducibility of gene expression patterning across 132 structures in six individual brains, revealing mesoscale genetic organization. The genes with the highest differential stability are highly biologically relevant, with enrichment for brain-related annotations, disease associations, drug targets and literature citations. Using genes with high differential stability, we identified 32 anatomically diverse and reproducible gene expression signatures, which represent distinct cell types, intracellular components and/or associations with neurodevelopmental and neurodegenerative disorders. Genes in neuron-associated compared to non-neuronal networks showed higher preservation between human and mouse; however, many diversely patterned genes displayed marked shifts in regulation between species. Finally, highly consistent transcriptional architecture in neocortex is correlated with resting state functional connectivity, suggesting a link between conserved gene expression and functionally relevant circuitry.