Multimodality annotated hepatocellular carcinoma data set including pre- and post-TACE with imaging segmentation.

Hepatocellular carcinoma (HCC) is the most common primary liver neoplasm, and its incidence has doubled over the past two decades owing to increasing risk factors. Despite surveillance, most HCC cases are diagnosed at advanced stages and can only be treated using transarterial chemo-embolization (TACE) or systemic therapy. TACE failure may occur with incidence reaching up to 60% of cases, leaving patients with a financial and emotional burden. Radiomics has emerged as a new tool capable of predicting tumor response to TACE from pre-procedural computed tomography (CT) studies. This data report defines the HCC-TACE data collection of confirmed HCC patients who underwent TACE and have pre- and post-procedure CT imaging studies and available treatment outcomes (time-to-progression and overall survival). Clinically curated segmentation of pre-procedural CT studies was done for the purpose of algorithm training for prediction and automatic liver tumor segmentation.

Semantic Integration of Multi-Modal Data and Derived Neuroimaging Results Using the Platform for Imaging in Precision Medicine (PRISM) in the Arkansas Imaging Enterprise System (ARIES).

Neuroimaging is among the most active research domains for the creation and management of open-access data repositories. Notably lacking from most data repositories are integrated capabilities for semantic representation. The Arkansas Imaging Enterprise System (ARIES) is a research data management system which features integrated capabilities to support semantic representations of multi-modal data from disparate sources (imaging, behavioral, or cognitive assessments), across common image-processing stages (preprocessing steps, segmentation schemes, analytic pipelines), as well as derived results (publishable findings). These unique capabilities ensure greater reproducibility of scientific findings across large-scale research projects. The current investigation was conducted with three collaborating teams who are using ARIES in a project focusing on neurodegeneration. Datasets included magnetic resonance imaging (MRI) data as well as non-imaging data obtained from a variety of assessments designed to measure neurocognitive functions (performance scores on neuropsychological tests). We integrate and manage these data with semantic representations based on axiomatically rich biomedical ontologies. These instantiate a knowledge graph that combines the data from the study cohorts into a shared semantic representation that explicitly accounts for relations among the entities that the data are about. This knowledge graph is stored in a triple-store database that supports reasoning over and querying these integrated data. Semantic integration of the non-imaging data using background information encoded in biomedical domain ontologies has served as a key feature-engineering step, allowing us to combine disparate data and apply analyses to explore associations, for instance, between hippocampal volumes and measures of cognitive functions derived from various assessment instruments.

Metadata Stewardship in Nanosafety Research: Community-Driven Organisation of Metadata Schemas to Support FAIR Nanoscience Data.

The emergence of nanoinformatics as a key component of nanotechnology and nanosafety assessment for the prediction of engineered nanomaterials (NMs) properties, interactions, and hazards, and for grouping and read-across to reduce reliance on animal testing, has put the spotlight firmly on the need for access to high-quality, curated datasets. To date, the focus has been around what constitutes data quality and completeness, on the development of minimum reporting standards, and on the FAIR (findable, accessible, interoperable, and reusable) data principles. However, moving from the theoretical realm to practical implementation requires human intervention, which will be facilitated by the definition of clear roles and responsibilities across the complete data lifecycle and a deeper appreciation of what metadata is, and how to capture and index it. Here, we demonstrate, using specific worked case studies, how to organise the nano-community efforts to define metadata schemas, by organising the data management cycle as a joint effort of all players (data creators, analysts, curators, managers, and customers) supervised by the newly defined role of data shepherd. We propose that once researchers understand their tasks and responsibilities, they will naturally apply the available tools. Two case studies are presented (modelling of particle agglomeration for dose metrics, and consensus for NM dissolution), along with a survey of the currently implemented metadata schema in existing nanosafety databases. We conclude by offering recommendations on the steps forward and the needed workflows for metadata capture to ensure FAIR nanosafety data.

Author Correction: Imaging and clinical data archive for head and neck squamous cell carcinoma patients treated with radiotherapy.

In the original version of the Data Descriptor the surname of author Hesham Elhalawani was misspelled. This has now been corrected in the HTML and PDF versions.

Imaging and clinical data archive for head and neck squamous cell carcinoma patients treated with radiotherapy.

Cross sectional imaging is essential for the patient-specific planning and delivery of radiotherapy, a primary determinant of head and neck cancer outcomes. Due to challenges ensuring data quality and patient de-identification, publicly available datasets including diagnostic and radiation treatment planning imaging are scarce. In this data descriptor, we detail the collection and processing of computed tomography based imaging in 215 patients with head and neck squamous cell carcinoma that were treated with radiotherapy. Using cross sectional imaging, we calculated total body skeletal muscle and adipose content before and after treatment. We detail techniques for validating the high quality of these data and describe the processes of data de-identification and transfer. All imaging data are subject- and date-matched to clinical data from each patient, including demographics, risk factors, grade, stage, recurrence, and survival. These data are a valuable resource for studying the association between patient-specific anatomic and metabolic features, treatment planning, and oncologic outcomes, and the first that allows for the integration of body composition as a risk factor or study outcome.

Automated measurement of skull circumference, cranial index, and braincase volume from pediatric computed tomography.

Normative values of pediatric skull circumference, cranial index, and braincase volume would inform multiple disciplines including neurosurgery, plastic surgery and anthropology. Semi-automated methods exist for obtaining these data but are time consuming and require expertise. We report on a new method for automated extraction of in vivo measures of pediatric crania based on x-ray computed tomography scans (CT). Data were obtained from a clinical image repository for pediatric populations in whom no pathology was noted. The automated process showed good agreement with semi-automated measures, although there was a small bias for both braincase volume and circumference. We developed an open source program to automatically extract measures of skull circumference, cranial index, and braincase volume that are likely to prove useful in multiple disciplines.

Characterization of scale-free properties of human electrocorticography in awake and slow wave sleep States.

Like many complex dynamic systems, the brain exhibits scale-free dynamics that follow power-law scaling. Broadband power spectral density (PSD) of brain electrical activity exhibits state-dependent power-law scaling with a log frequency exponent that varies across frequency ranges. Widely divergent naturally occurring neural states, awake and slow wave sleep (SWS), were used to evaluate the nature of changes in scale-free indices of brain electrical activity. We demonstrate two analytic approaches to characterizing electrocorticographic (ECoG) data obtained during awake and SWS states. A data-driven approach was used, characterizing all available frequency ranges. Using an equal error state discriminator (EESD), a single frequency range did not best characterize state across data from all six subjects, though the ability to distinguish awake and SWS ECoG data in individual subjects was excellent. Multi-segment piecewise linear fits were used to characterize scale-free slopes across the entire frequency range (0.2-200 Hz). These scale-free slopes differed between awake and SWS states across subjects, particularly at frequencies below 10 Hz and showed little difference at frequencies above 70 Hz. A multivariate maximum likelihood analysis (MMLA) method using the multi-segment slope indices successfully categorized ECoG data in most subjects, though individual variation was seen. In exploring the differences between awake and SWS ECoG data, these analytic techniques show that no change in a single frequency range best characterizes differences between these two divergent biological states. With increasing computational tractability, the use of scale-free slope values to characterize ECoG and EEG data will have practical value in clinical and research studies.

Automated measurement of pediatric cranial bone thickness and density from clinical computed tomography.

Skull thickness and density measures of normal pediatric crania would inform multiple disciplines including neurosurgery, optical and magnetoelectrophysiological imaging, and biomechanical modeling of head trauma. We report on a new method for automated extraction of in vivo skull thickness and density measures of pediatric crania based on x-ray computed tomography scans (CT). Data were obtained from a clinical image repository for pediatric populations in whom no pathology was noted. Skull thickness and density measures were systematically obtained across the calvarium. We find a set of measures that correlated with physiological age that are likely to prove useful in multiple disciplines.

Modulation of the brain's functional network architecture in the transition from wake to sleep.

The transition from quiet wakeful rest to sleep represents a period over which attention to the external environment fades. Neuroimaging methodologies have provided much information on the shift in neural activity patterns in sleep, but the dynamic restructuring of human brain networks in the transitional period from wake to sleep remains poorly understood. Analysis of electrophysiological measures and functional network connectivity of these early transitional states shows subtle shifts in network architecture that are consistent with reduced external attentiveness and increased internal and self-referential processing. Further, descent to sleep is accompanied by the loss of connectivity in anterior and posterior portions of the default-mode network and more locally organized global network architecture. These data clarify the complex and dynamic nature of the transitional period between wake and sleep and suggest the need for more studies investigating the dynamics of these processes.

Cortical network functional connectivity in the descent to sleep.

Descent into sleep is accompanied by disengagement of the conscious brain from the external world. It follows that this process should be associated with reduced neural activity in regions of the brain known to mediate interaction with the environment. We examined blood oxygen dependent (BOLD) signal functional connectivity using conventional seed-based analyses in 3 primary sensory and 3 association networks as normal young adults transitioned from wakefulness to light sleep while lying immobile in the bore of a magnetic resonance imaging scanner. Functional connectivity was maintained in each network throughout all examined states of arousal. Indeed, correlations within the dorsal attention network modestly but significantly increased during light sleep compared to wakefulness. Moreover, our data suggest that neuronally mediated BOLD signal variance generally increases in light sleep. These results do not support the view that ongoing BOLD fluctuations primarily reflect unconstrained cognition. Rather, accumulating evidence supports the hypothesis that spontaneous BOLD fluctuations reflect processes that maintain the integrity of functional systems in the brain.

Facial recognition from volume-rendered magnetic resonance imaging data.

Three-dimensional (3-D) reconstructions of computed tomography (CT) and magnetic resonance (MR) brain imaging studies are a routine component of both clinical practice and clinical and translational research. A side effect of such reconstructions is the creation of a potentially recognizable face. The Health Insurance Portability and Accountability Act of 1996 (HIPAA) Privacy Rule requires that individually identifiable health information may not be used for research unless identifiers that may be associated with the health information including "Full face photographic images and other comparable images ..." are removed (de-identification). Thus, a key question is: Are reconstructed facial images comparable to full-face photographs for the purpose of identification? To address this question, MR images were selected from existing research repositories and subjects were asked to pair an MR reconstruction with one of 40 photographs. The chance probability that an observer could match a photograph with its 3-D MR image was 1 in 40 (0.025), and we considered 4 successes out of 40 (4/40, 0.1) to indicate that a subject could identify persons' faces from their 3-D MR images. Forty percent of the subjects were able to successfully match photographs with MR images with success rates higher than the null hypothesis success rate. The Blyth-Still-Casella 95% confidence interval for the 40% success rate was 29%-52%, and the 40% success rate was significantly higher ( P < 0.001) than our null hypothesis success rate of 1 in 10 (0.10).

Resting states affect spontaneous BOLD oscillations in sensory and paralimbic cortex.

The brain exhibits spontaneous neural activity that depends on the behavioral state of the organism. We asked whether the blood oxygenation level-dependent (BOLD) signal reflects these modulations. BOLD was measured under three steady-state conditions: while subjects kept their eyes closed, kept their eyes open, or while fixating. The BOLD spectral density was calculated across brain voxels and subjects. Visual, sensory-motor, auditory, and retrosplenial cortex showed modulations of the BOLD spectral density by resting state type. All modulated regions showed greater spontaneous BOLD oscillations in the eyes closed than the eyes open or fixation conditions, suggesting that the differences were endogenously driven. Next, we examined the pattern of correlations between regions whose ongoing BOLD signal was modulated by resting state type. Regional neuronal correlations were estimated using an analytic procedure from the comparison of BOLD-BOLD covariances in the fixation and eyes closed conditions. Most regions were highly correlated with one another, with the exception of the primary visual cortices, which showed low correlations with the other regions. In conclusion, changes in resting state were associated with synchronous modulations of spontaneous BOLD oscillations in cortical sensory areas driven by two spatially overlapping, but temporally uncorrelated signals.