Sulci 3D mapping from human cranial endocasts: A powerful tool to study hominin brain evolution.

Key questions in paleoneurology concern the timing and emergence of derived cerebral features within the human lineage. Endocasts are replicas of the internal table of the bony braincase that are widely used in paleoneurology as a proxy for reconstructing a timeline for hominin brain evolution in the fossil record. The accurate identification of cerebral sulci imprints in endocasts is critical for assessing the topographic extension and structural organisation of cortical regions in fossil hominins. High-resolution imaging techniques combined with established methods based on population-specific brain atlases offer new opportunities for tracking detailed endocranial characteristics. This study provides the first documentation of sulcal pattern imprints from the superolateral surface of the cerebrum using a population-based atlas technique on extant human endocasts. Human crania from the Pretoria Bone Collection (South Africa) were scanned using micro-CT. Endocasts were virtually extracted, and sulci were automatically detected and manually labelled. A density map method was applied to project all the labels onto an averaged endocast to visualise the mean distribution of each identified sulcal imprint. This method allowed for the visualisation of inter-individual variation of sulcal imprints, for example, frontal lobe sulci, correlating with previous brain-MRI studies and for the first time the extensive overlapping of imprints in historically debated areas of the endocast (e.g. occipital lobe). In providing an innovative, non-invasive, observer-independent method to investigate human endocranial structural organisation, our analytical protocol introduces a promising perspective for future research in paleoneurology and for discussing critical hypotheses on the evolution of cognitive abilities among hominins.

Marmoset brain segmentation from deconvolved magnetic resonance images and estimated label maps.

PURPOSE: The proposed method aims to create label maps that can be used for the segmentation of animal brain MR images without the need of a brain template. This is achieved by performing a joint deconvolution and segmentation of the brain MR images. METHODS: It is based on modeling locally the image statistics using a generalized Gaussian distribution (GGD) and couples the deconvolved image and its corresponding labels map using the GGD-Potts model. Because of the complexity of the resulting Bayesian estimators of the unknown model parameters, a Gibbs sampler is used to generate samples following the desired posterior probability. RESULTS: The performance of the proposed algorithm is assessed on simulated and real MR images by the segmentation of enhanced marmoset brain images into its main compartments using the corresponding label maps created. Quantitative assessment showed that this method presents results that are comparable to those obtained with the classical method-registering the volumes to a brain template. CONCLUSION: The proposed method of using labels as prior information for brain segmentation provides a similar or a slightly better performance compared with the classical reference method based on a dedicated template.

Sulcal pattern variation in extant human endocasts.

Our knowledge of human brain evolution primarily relies on the interpretation of palaeoneurological evidence. In this context, an endocast or replica of the inside of the bony braincase can be used to reconstruct a timeline of cerebral changes that occurred during human evolution, including changes in topographic extension and structural organisation of cortical areas. These changes can be tracked by identifying cerebral imprints, particularly cortical sulci. The description of these crucial landmarks in fossil endocasts is, however, challenging. High-resolution imaging techniques in palaeoneurology offer new opportunities for tracking detailed endocranial neural characteristics. In this study, we use high-resolution imaging techniques to document the variation in extant human endocranial sulcal patterns for subsequent use as a platform for comparison with the fossil record. We selected 20 extant human crania from the Pretoria Bone Collection (University of Pretoria, South Africa), which were detailed using X-ray microtomography at a spatial resolution ranging from 94 to 123 µm (isometric). We used Endex to extract, and Matlab to analyse the cortical imprints on the endocasts. We consistently identified superior, middle and inferior sulci on the frontal lobe; and superior and inferior sulci on the temporal lobe. We were able to label sulci bordering critical functional areas such as Broca's cap. Mapping the sulcal patterns on extant endocasts is a prerequisite for constructing an atlas which can be used for automatic sulci recognition.

In vivo localization of cortical areas using a 3D computerized atlas of the marmoset brain.

We created a volumetric template of the marmoset (Callithrix jacchus) brain, which enables localization of the cortical areas defined in the Paxinos et al. (The marmoset brain in stereotaxic coordinates. Elsevier Academic Press, Cambridge, 2012) marmoset brain atlas, as well as seven broader cortical regions (occipital, temporal, parietal, prefrontal, motor, limbic, insular), different brain compartments (white matter, gray matter, cerebro-spinal fluid including ventricular spaces), and various other structures (brain stem, cerebellum, olfactory bulb, hippocampus). The template was designed from T1-weighted MR images acquired using a 3 T MRI scanner. It was based on a single fully segmented marmoset brain image, which was transported onto the mean of 13 adult marmoset brain images using a diffeomorphic strategy that fully preserves the brain topology. In addition, we offer an automatic segmentation pipeline which fully exploits the proposed template. The segmentation pipeline was quantitatively assessed by comparing the results of manual and automated segmentations. An associated program, written in Python, can be used from a command-line interface, or used interactively as a module of the 3DSlicer software. This program can be applied to the analysis of multimodal images, to map specific cortical areas in lesions or to define the seeds for further tractography analyses.

On Single-Image Super-Resolution in 3D Brain Magnetic Resonance Imaging.

The objective of this work is to apply 3D super resolution (SR) techniques to brain magnetic resonance (MR) image restoration. Two 3D SR methods are considered following different trends: one recently proposed tensor-based approach and one inverse problem algorithm based on total variation and low rank regularization. The evaluation of their effectiveness is assessed through the segmentation of brain compartments: gray matter, white matter and cerebrospinal fluid. The two algorithms are qualitatively and quantitatively evaluated on simulated images with ground truth available and on experimental data. The originality of this work is to consider the SR methods as an initial step towards the final segmentation task. The results show the ability of both methods to overcome the loss of spatial resolution and to facilitate the segmentation of brain structures with improved accuracy compared to native low-resolution MR images. Both algorithms achieved almost equivalent results with a highly reduced computational time cost for the tensor-based approach.

Diffeomorphic registration with self-adaptive spatial regularization for the segmentation of non-human primate brains.

Cerebral aging has been linked to structural and functional changes in the brain throughout life. Here, we study the marmoset, a small non-human primate, in order to get insights into the mechanisms of brain aging in normal and pathological conditions. Imaging the brain of small animals with techniques such as MRI, quickly becomes a challenging task when compared with human brain imaging. Very often, a simple pre-processing step such as brain extraction cannot be achieved with classical tools. In this paper, we propose a diffeomorphic registration algorithm, which makes use of learned constraints to propagate the manual segmentation of a marmoset brain template to other MR images of marmoset brains. The main methological contribution of our paper is to explore a new strategy to automatically tune the spatial regularization of the deformations. Results show that we obtain a robust segmentation of the brain, even for images with a low contrast.

Mechanisms of pressure-diuresis and pressure-natriuresis in Dahl salt-resistant and Dahl salt-sensitive rats.

BACKGROUND: Data on blood flow regulation, renal filtration, and urine output in salt-sensitive Dahl S rats fed on high-salt (hypertensive) and low-salt (prehypertensive) diets and salt-resistant Dahl R rats fed on high-salt diets were analyzed using a mathematical model of renal blood flow regulation, glomerular filtration, and solute transport in a nephron. RESULTS: The mechanism of pressure-diuresis and pressure-natriuresis that emerges from simulation of the integrated systems is that relatively small increases in glomerular filtration that follow from increases in renal arterial pressure cause relatively large increases in urine and sodium output. Furthermore, analysis reveals the minimal differences between the experimental cases necessary to explain the observed data. It is determined that differences in renal afferent and efferent arterial resistances are able to explain all of the qualitative differences in observed flows, filtration rates, and glomerular pressure as well as the differences in the pressure-natriuresis and pressure-diuresis relationships in the three groups. The model is able to satisfactorily explain data from all three groups without varying parameters associated with glomerular filtration or solute transport in the nephron component of the model. CONCLUSIONS: Thus the differences between the experimental groups are explained solely in terms of difference in blood flow regulation. This finding is consistent with the hypothesis that, if a shift in the pressure-natriuresis relationship is the primary cause of elevated arterial pressure in the Dahl S rat, then alternation in how renal afferent and efferent arterial resistances are regulated represents the primary cause of chronic hypertension in the Dahl S rat.

Identification of the catalytic mechanism and estimation of kinetic parameters for fumarase.

The enzyme fumarase catalyzes the reversible hydration of fumarate to malate. The reaction catalyzed by fumarase is critical for cellular energetics as a part of the tricarboxylic acid cycle, which produces reducing equivalents to drive oxidative ATP synthesis. A catalytic mechanism for the fumarase reaction that can account for the kinetic behavior of the enzyme observed in both isotope exchange studies and initial velocity studies has not yet been identified. In the present study, we develop an 11-state kinetic model of the enzyme based on the current consensus on its catalytic mechanism and design a series of experiments to estimate the model parameters and identify the major flux routes through the mechanism. The 11-state mechanism accounts for competitive binding of inhibitors and activation by different anions, including phosphate and fumarate. The model is identified from experimental time courses of the hydration of fumarate to malate obtained over a wide range of buffer and substrate concentrations. Further, the 11-state model is found to effectively reduce to a five-state model by lumping certain successive steps together to yield a mathematically less complex representation that is able to match the data. Analysis suggests the primary reaction route of the catalytic mechanism, with fumarate binding to the free unprotonated enzyme and a proton addition prior to malate release in the fumarate hydration reaction. In the reverse direction (malate dehydration), malate binds the protonated form of the enzyme, and a proton is generated before fumarate is released from the active site.

Multiscale model of liver DCE-MRI towards a better understanding of tumor complexity.

The use of quantitative imaging for the characterization of hepatic tumors in magnetic resonance imaging (MRI) can improve the diagnosis and therefore the treatment of these life-threatening tumors. However, image parameters remain difficult to interpret because they result from a mixture of complex processes related to pathophysiology and to acquisition. These processes occur at variable spatial and temporal scales. We propose a multiscale model of liver dynamic contrast-enhanced (DCE) MRI in order to better understand the tumor complexity in images. Our design couples a model of the organ (tissue and vasculature) with a model of the image acquisition. At the macroscopic scale, vascular trees take a prominent place. Regarding the formation of MRI images, we propose a distributed model of parenchymal biodistribution of extracellular contrast agents. Model parameters can be adapted to simulate the tumor development. The sensitivity of the multiscale model of liver DCE-MRI was studied through observations of the influence of two physiological parameters involved in carcinogenesis (arterial flow and capillary permeability) on its outputs (MRI images at arterial and portal phases). Finally, images were simulated for a set of parameters corresponding to the five stages of hepatocarcinogenesis (from regenerative nodules to poorly differentiated HepatoCellular Carcinoma).

Coupling texture analysis and physiological modeling for liver dynamic MRI interpretation.

We coupled our physiological model of the liver, to a MRI simulator (SIMRI) in order to find image markers of the tumor growth. Some pathological modifications related to the development of Hepatocellular carcinoma are simulated (flows, permeability, vascular density). Corresponding images simulated at typical acquisition phases (arterial, portal) are compared to real images. The evolution of some textural features with arterial flow is also presented.

A physiologically based pharmacokinetic model of vascular-extravascular exchanges during liver carcinogenesis: application to MRI contrast agents.

The extraction of physiological parameters by non-invasive imaging techniques such as dynamic magnetic resonance imaging (MRI) or positron emission tomography requires a knowledge of molecular distribution and exchange between microvascularization and extravascular tissues. These phenomena not only depend on the physicochemical characteristics of the injected molecules but also the pathophysiological state of the targeted organ. We developed a five-compartment physiologically based pharmacokinetic model focused on hepatic carcinogenesis and MRI contrast agents. This model includes physical characteristics of the contrast agent, dual specific liver supply, microvessel wall properties and transport parameters that are compatible with hepatocarcinoma development. The evolution of concentrations in the five compartments showed significant differences in the distribution of three molecules (differentiated by their diameters and diffusion coefficients ranging, respectively, from 0.9 to 62 nm and from 68.10(-9) to 47.10(-7) cm(2) s(-1)) in simulated regeneration nodules and dysplastic nodules, as well as in medium- and poorly differentiated hepatocarcinoma. These results are in agreement with known vascular modifications such as arterialization that occur during hepatocarcinogenesis. This model can be used to study the pharmacokinetics of contrast agents and consequently to extract parameters that are characteristic of the tumor development (like permeability), after fitting simulated to in vivo data.