Evidence-Based Network Modelling to Simulate Nucleus Pulposus Multicellular Activity in Different Nutritional and Pro-Inflammatory Environments.

Initiation of intervertebral disc degeneration is thought to be biologically driven. This reflects a process, where biochemical and mechanical stimuli affect cell activity (CA) that compromise the tissue strength over time. Experimental research enhanced our understanding about the effect of such stimuli on different CA, such as protein synthesis or mRNA expression. However, it is still unclear how cells respond to their native environment that consists of a "cocktail" of different stimuli that might locally vary. This work presents an interdisciplinary approach of experimental and in silico research to approximate Nucleus Pulposus CA within multifactorial biochemical environments. Thereby, the biochemical key stimuli glucose, pH, and the proinflammatory cytokines TNF-α and IL1ß were considered that were experimentally shown to critically affect CA. To this end, a Nucleus Pulposus multicellular system was modelled. It integrated experimental findings from in vitro studies of human or bovine Nucleus Pulposus cells, to relate the individual effects of targeted stimuli to alterations in CA. Unknown stimulus-CA relationships were obtained through own experimental 3D cultures of bovine Nucleus Pulposus cells in alginate beads. Translation of experimental findings into suitable parameters for network modelling approaches was achieved thanks to a new numerical approach to estimate the individual sensitivity of a CA to each stimulus type. Hence, the effect of each stimulus type on a specific CA was assessed and integrated to approximate a multifactorial stimulus environment. Tackled CA were the mRNA expressions of Aggrecan, Collagen types I & II, MMP3, and ADAMTS4. CA was assessed for four different proinflammatory cell states; non-inflamed and inflamed for IL1ß, TNF-α or both IL1ß&TNF-α. Inflamed cell clusters were eventually predicted in a multicellular 3D agent-based model. Experimental results showed that glucose had no significant impact on proinflammatory cytokine or ADAMTS4 mRNA expression, whereas TNF-α caused a significant catabolic shift in most explored CA. In silico results showed that the presented methodology to estimate the sensitivity of a CA to a stimulus type importantly improved qualitative model predictions. However, more stimuli and/or further experimental knowledge need to be integrated, especially regarding predictions about the possible progression of inflammatory environments under adverse nutritional conditions. Tackling the multicellular level is a new and promising approach to estimate manifold responses of intervertebral disc cells. Such a top-down high-level network modelling approach allows to obtain information about relevant stimulus environments for a specific CA and could be shown to be suitable to tackle complex biological systems, including different proinflammatory cell states. The development of this methodology required a close interaction with experimental research. Thereby, specific experimental needs were derived from systematic in silico approaches and obtained results were directly used to enhance model predictions, which reflects a novelty in this research field. Eventually, the presented methodology provides modelling solutions suitable for multiscale approaches to contribute to a better understanding about dynamics over multiple spatial scales. Future work should focus on an amplification of the stimulus environment by integrating more key relevant stimuli, such as mechanical loading parameters, in order to better approximate native physiological environments.

Simulating intervertebral disc cell behaviour within 3D multifactorial environments.

MOTIVATION: Low back pain is responsible for more global disability than any other condition. Its incidence is closely related to intervertebral disc (IVD) failure, which is likely caused by an accumulation of microtrauma within the IVD. Crucial factors in microtrauma development are not entirely known yet, probably because their exploration in vivo or in vitro remains tremendously challenging. In silico modelling is, therefore, definitively appealing, and shall include approaches to integrate influences of multiple cell stimuli at the microscale. Accordingly, this study introduces a hybrid Agent-based (AB) model in IVD research and exploits network modelling solutions in systems biology to mimic the cellular behaviour of Nucleus Pulposus cells exposed to a 3D multifactorial biochemical environment, based on mathematical integrations of existing experimental knowledge. Cellular activity reflected by mRNA expression of Aggrecan, Collagen type I, Collagen type II, MMP-3 and ADAMTS were calculated for inflamed and non-inflamed cells. mRNA expression over long periods of time is additionally determined including cell viability estimations. Model predictions were eventually validated with independent experimental data. RESULTS: As it combines experimental data to simulate cell behaviour exposed to a multifactorial environment, the present methodology was able to reproduce cell death within 3 days under glucose deprivation and a 50% decrease in cell viability after 7 days in an acidic environment. Cellular mRNA expression under non-inflamed conditions simulated a quantifiable catabolic shift under an adverse cell environment, and model predictions of mRNA expression of inflamed cells provide new explanation possibilities for unexpected results achieved in experimental research. AVAILABILITYAND IMPLEMENTATION: The AB model as well as used mathematical functions were built with open source software. Final functions implemented in the AB model and complete AB model parameters are provided as Supplementary Material. Experimental input and validation data were provided through referenced, published papers. The code corresponding to the model can be shared upon request and shall be reused after proper training. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Three-dimensional regional bi-ventricular shape remodeling is associated with exercise capacity in endurance athletes.

AIMS: Endurance athletes develop cardiac remodeling to cope with increased cardiac output during exercise. This remodeling is both anatomical and functional and shows large interindividual variability. In this study, we quantify local geometric ventricular remodeling related to long-standing endurance training and assess its relationship with cardiovascular performance during exercise. METHODS: We extracted 3D models of the biventricular shape from end-diastolic cine magnetic resonance images acquired from a cohort of 89 triathlon athletes and 77 healthy sedentary subjects. Additionally, the athletes underwent cardio-pulmonary exercise testing, together with an echocardiographic study at baseline and few minutes after maximal exercise. We used statistical shape analysis to identify regional bi-ventricular shape differences between athletes and non-athletes. RESULTS: The ventricular shape was significantly different between athletes and controls (p < 1e-6). The observed regional remodeling in the right heart was mainly a shift of the right ventricle (RV) volume distribution towards the right ventricular infundibulum, increasing the overall right ventricular volume. In the left heart, there was an increment of left ventricular mass and a dilation of the left ventricle. Within athletes, the amount of such remodeling was independently associated to higher peak oxygen pulse (p < 0.001) and weakly with greater post-exercise RV free wall longitudinal strain (p = 0.03). CONCLUSIONS: We were able to identify specific bi-ventricular regional remodeling induced by long-lasting endurance training. The amount of remodeling was associated with better cardiopulmonary performance during an exercise test.

Switching Operation Modes in the Neocortex via Cholinergic Neuromodulation : A Computational Model of Uncertainty, Learning, and Inhibition.

In order to deal with the uncertainty in the world, our brains need to be able to flexibly switch between the exploration of new sensory representations and exploitation of previously acquired ones. This requires forming accurate estimations of what and how much something is expected. While modeling has allowed for the development of several ways to form predictions, how the brain could implement those is still under debate. Here, we recognize acetylcholine as one of the main neuromodulators driving learning based on uncertainty, promoting the exploration of new sensory representations. We identify its interactions with cortical inhibitory interneurons and derive a biophysically grounded computational model able to capture and learn from uncertainty. This model allows us to understand inhibition beyond gain control by suggesting that different interneuron subtypes either encode predictions or estimate their uncertainty, facilitating detection of unexpected cues. Moreover, we show how acetylcholine-like neuromodulation uniquely interacts with global and local sources of inhibition, disrupting perceptual certainty and promoting the rapid acquisition of new perceptual cues. Altogether, our model proposes that cortical acetylcholine favors sensory exploration over exploitation in a cortical microcircuit dedicated to estimating sensory uncertainty.

Global and Regional Changes in Cortical Development Assessed by MRI in Fetuses with Isolated Nonsevere Ventriculomegaly Correlate with Neonatal Neurobehavior.

BACKGROUND AND PURPOSE: Fetuses with isolated nonsevere ventriculomegaly (INSVM) are at risk of presenting neurodevelopmental delay. However, the currently used clinical parameters are insufficient to select cases with high risk and determine whether subtle changes in brain development are present and might be a risk factor. The aim of this study was to perform a comprehensive evaluation of cortical development in INSVM by magnetic resonance (MR) imaging and assess its association with neonatal neurobehavior. MATERIALS AND METHODS: Thirty-two INSVM fetuses and 29 healthy controls between 26-28 weeks of gestation were evaluated using MR imaging. We compared sulci and fissure depth, cortical maturation grading of specific areas and sulci and volumes of different brain regions obtained from 3D brain reconstruction of cases and controls. Neonatal outcome was assessed by using the Neonatal Behavioral Assessment Scale at a mean of 4 ± 2 weeks after birth. RESULTS: Fetuses with INSVM showed less profound and underdeveloped sulcation, including the Sylvian fissure (mean depth: controls 16.8 ± 1.9 mm, versus INSVM 16.0 ± 1.6 mm; P = .01), and reduced global cortical grading (mean score: controls 42.9 ± 10.2 mm, versus INSVM: 37.8 ± 9.9 mm; P = .01). Fetuses with isolated nonsevere ventriculomegaly showed a mean global increase of gray matter volume (controls, 276.8 ± 46.0 ×10 mm3, versus INSVM 277.5 ± 49.3 ×10 mm3, P = .01), but decreased mean cortical volume in the frontal lobe (left: controls, 53.2 ± 8.8 ×10 mm3, versus INSVM 52.4 ± 5.4 ×10 mm3; P = < .01). Sulcal depth and brain volumes were significantly associated with the Neonatal Behavioral Assessment Scale severity (P = .005, Nagelkerke R2 = 0.732). CONCLUSIONS: INSVM fetuses showed differences in cortical development, including regions far from the lateral ventricles, that are associated with neonatal neurobehavior. These results suggest the possible use of these parameters to identify cases at higher risk of altered neurodevelopment.

Discrimination of osteoporosis-related vertebral fractures by DXA-derived 3D measurements: a retrospective case-control study.

A retrospective case-control study assessing the association of DXA-derived 3D measurements with osteoporosis-related vertebral fractures was performed. Trabecular volumetric bone mineral density was the measurement that best discriminates between fracture and control groups. INTRODUCTION: The aim of the present study was to evaluate the association of DXA-derived 3D measurements at the lumbar spine with osteoporosis-related vertebral fractures. METHODS: We retrospectively analyzed a database of 74 postmenopausal women: 37 subjects with incident vertebral fractures and 37 age-matched controls without any type of fracture. DXA scans at the lumbar spine were acquired at baseline (i.e., before the fracture event for subjects in the fracture group), and areal bone mineral density (aBMD) was measured. DXA-derived 3D measurements, such as volumetric BMD (vBMD), were assessed using a DXA-based 3D modeling software (3D-SHAPER). vBMD was computed at the trabecular, cortical, and integral bone. Cortical thickness and cortical surface BMD were also measured. Differences in DXA-derived measurements between fracture and control groups were evaluated using unpaired t test. Odds ratio (OR) and area under the receiver operating curve (AUC) were also computed. Subgroup analyses according to fractured vertebra were performed. RESULTS: aBMD of fracture group was 9.3% lower compared with control group (p < 0.01); a higher difference was found for trabecular vBMD in the vertebral body (- 16.1%, p < 0.001). Trabecular vBMD was the measurement that best discriminates between fracture and control groups, with an AUC of 0.733, against 0.682 for aBMD. Overall, similar findings were observed within the subgroup analyses. The L1 vertebral fractures subgroup had the highest AUC at trabecular vBMD (0.827), against aBMD (0.758). CONCLUSION: This study showed the ability of cortical and trabecular measurements from DXA-derived 3D models to discriminate between fracture and control groups. Large cohorts need to be analyzed to determine if these measurements could improve fracture risk prediction in clinical practice.

Cholinergic Behavior State-Dependent Mechanisms of Neocortical Gain Control: a Neurocomputational Study.

The embodied mammalian brain evolved to adapt to an only partially known and knowable world. The adaptive labeling of the world is critically dependent on the neocortex which in turn is modulated by a range of subcortical systems such as the thalamus, ventral striatum, and the amygdala. A particular case in point is the learning paradigm of classical conditioning where acquired representations of states of the world such as sounds and visual features are associated with predefined discrete behavioral responses such as eye blinks and freezing. Learning progresses in a very specific order, where the animal first identifies the features of the task that are predictive of a motivational state and then forms the association of the current sensory state with a particular action and shapes this action to the specific contingency. This adaptive feature selection has both attentional and memory components, i.e., a behaviorally relevant state must be detected while its representation must be stabilized to allow its interfacing to output systems. Here, we present a computational model of the neocortical systems that underlie this feature detection process and its state-dependent modulation mediated by the amygdala and its downstream target the nucleus basalis of Meynert. In particular, we analyze the role of different populations of inhibitory interneurons in the regulation of cortical activity and their state-dependent gating of sensory signals. In our model, we show that the neuromodulator acetylcholine (ACh), which is in turn under control of the amygdala, plays a distinct role in the dynamics of each population and their associated gating function serving the detection of novel sensory features not captured in the state of the network, facilitating the adjustment of cortical sensory representations and regulating the switching between modes of attention and learning.

RADStation3G: a platform for cardiovascular image analysis integrating PACS, 3D+t visualization and grid computing.

RADStation3G is a software platform for cardiovascular image analysis and surgery planning. It provides image visualization and management in 2D, 3D and 3D+t; data storage (images or operational results) in a PACS (using DICOM); and exploitation of patients' data such as images and pathologies. Further, it provides support for computationally expensive processes with grid technology. In this article we first introduce the platform and present a comparison with existing systems, according to the platform's modules (for cardiology, angiology, PACS archived enriched searching and grid computing), and then RADStation3G is described in detail.

Tecnología de simulación tridimensional para la planificación de la implantación de endoprótesis traqueales / Three-dimensional simulation technology for planning tracheal stenting

Objetivo: Se ha realizado un estudio observacional sobre pacientes con estenosis de la vía aérea principal con el objetivo de demostrar la mejorar en los resultados del tratamiento mediante broncoscopia rígida al realizar una planificación preoperatoria (..) (AU)

Objective: An observational study was made of patients with stenosis of the main airway in order to demonstrate the improved results of treatment by rigid bronchoscopy to perform preoperative planning with computer simulation (..) (AU)

Model-based respiratory motion compensation for emission tomography image reconstruction.

In emission tomography imaging, respiratory motion causes artifacts in lungs and cardiac reconstructed images, which lead to misinterpretations, imprecise diagnosis, impairing of fusion with other modalities, etc. Solutions like respiratory gating, correlated dynamic PET techniques, list-mode data based techniques and others have been tested, which lead to improvements over the spatial activity distribution in lungs lesions, but which have the disadvantages of requiring additional instrumentation or the need of discarding part of the projection data used for reconstruction. The objective of this study is to incorporate respiratory motion compensation directly into the image reconstruction process, without any additional acquisition protocol consideration. To this end, we propose an extension to the maximum likelihood expectation maximization (MLEM) algorithm that includes a respiratory motion model, which takes into account the displacements and volume deformations produced by the respiratory motion during the data acquisition process. We present results from synthetic simulations incorporating real respiratory motion as well as from phantom and patient data.

Automatic labelling and BI-RADS characterisation of mammogram densities.

Intelligent management of medical data is an important field of research in clinical information and decision support systems. Such systems are finding increasing use in the management of patients known to have, or suspected of having, breast cancer. Different types of breast-tissue patterns convey semantic information which is reported by the radiologist when reading mammograms. In this paper, a novel method is presented for the automatic labelling and characterisation of mammographic densities. The presented method is first concerned with the identification of the prominent structures in each mammogram. Subsequently, "dense tissue" is labelled in a mammogram data set, and BI-RADS classification is performed based on a 2D pdf that is contracted from a "ground truth" data set as well as a shape analysis framework. The presented method can be used in large-scale epidemiological studies which involve mammographic measurements of tissue-pattern, especially since breast-tissue density has been linked to an increased risk of breast cancer.

Evaluation and initial validation studies of anatomical structure morphing.

Anatomical Structure Morphing is the process of estimating the patient-specific 3D shape of a given anatomy from a few digitized surface points. This provides an appropriate riate intra-operative 3D visualization without pre oroperativeintra- perative imaging. Our method fits a statistical deformable model to the digitized landmarks and bone surface points which are usually sparse. The statistical deformable model is constructed using Principal Component Analysis (PCA) from an appropriate training set of objects. Our proposed technique extrapolates the 3D shape by computing a Mahalanobis distance weighted least-squares fit of this model to the minimal sparse 3D data. In this paper we present evaluation and initial validation studies of our morphing technique on 9 dry cadaver femur bones. The influence of size of the initial training set on the morphing performance is also evaluated by repeating our experiments on two different training sets of varying sizes.

Computer-assisted ankle joint arthroplasty using bio-engineered autografts.

Bio-engineered cartilage has made substantial progress over the last years. Preciously few cases, however, are known where patients were actually able to benefit from these developments. In orthopaedic surgery, there are two major obstacles between in-vitro cartilage engineering and its clinical application: successful integration of an autologuous graft into a joint and the high cost of individually manufactured implants. Computer Assisted Surgery techniques can potentially address both issues at once by simplifying the therapy, allowing pre-fabrication of bone grafts according to a shape model, individual operation planning based on CT images and providing optimal accuracy during the intervention. A pilot study was conducted for the ankle joint, comprising a simplified rotational symmetric bone surface model, a dedicated planning software and a complete cycle of treatment on one cadaveric human foot. The outcome was analysed using CT and MRI images; the post-operative CT was further segmented and registered with the implant shape to prove the feasibility of computer assisted arthroplasty using bio-engineered autografts.

Segmentation and measurement of brain structures in MRI including confidence bounds.

The advent of new and improved imaging devices has allowed an impressive increase in the accuracy and precision of MRI acquisitions. However, the volumetric nature of the image formation process implies an inherent uncertainty, known as the partial volume effect, which can be further affected by artifacts such as magnetic inhomogeneities and noise. These degradations seriously challenge the application to MRI of any segmentation method, especially on data sets where the size of the object or effect to be studied is small relative to the voxel size, as is the case in multiple sclerosis and schizophrenia. We develop an approach to this problem by estimating a set of bounds on the spatial location of each organ to be segmented. First, we describe a method for 3D segmentation from voxel data which combines statistical classification and geometry-driven segmentation; then we discuss how the partial volume effect is estimated and object measurements are obtained. A comprehensive validation study and a set of results on clinical applications are also described.