Anatomical correlates for category-specific naming of objects and actions: A brain stimulation mapping study.

The production of object and action words can be dissociated in aphasics, yet their anatomical correlates have been difficult to distinguish in functional imaging studies. To investigate the extent to which the cortical neural networks underlying object- and action-naming processing overlap, we performed electrostimulation mapping (ESM), which is a neurosurgical mapping technique routinely used to examine language function during brain-tumor resections. Forty-one right-handed patients who had surgery for a brain tumor were asked to perform overt naming of object and action pictures under stimulation. Overall, 73 out of the 633 stimulated cortical sites (11.5%) were associated with stimulation-induced language interferences. These interference sites were very much localized (<1 cm(2) ), and showed substantial variability across individuals in their exact localization. Stimulation interfered with both object and action naming over 44 sites, whereas it specifically interfered with object naming over 19 sites and with action naming over 10 sites. Specific object-naming sites were mainly identified in Broca's area (Brodmann area 44/45) and the temporal cortex, whereas action-naming specific sites were mainly identified in the posterior midfrontal gyrus (Brodmann area 6/9) and Broca's area (P = 0.003 by the Fisher's exact test). The anatomical loci we emphasized are in line with a cortical distinction between objects and actions based on conceptual/semantic features, so the prefrontal/premotor cortex would preferentially support sensorimotor contingencies associated with actions, whereas the temporal cortex would preferentially underpin (functional) properties of objects. Hum Brain Mapp 35:429-443, 2014. © 2012 Wiley Periodicals, Inc.

Genomic organization, fine-mapping, and expression of the human islet-brain 1 (IB1)/c-Jun-amino-terminal kinase interacting protein-1 (JIP-1) gene.

Islet-brain 1 (IB1), a regulator of the pancreatic beta-cell function in the rat, is homologous to JIP-1, a murine inhibitor of c-Jun amino-terminal kinase (JNK). Whether IB1 and JIP-1 are present in humans was not known. We report the sequence of the 2133-bp human IB1 cDNA, the expression, structure, and fine-mapping of the human IB1 gene, and the characterization of an IB1 pseudogene. Human IB1 is 94% identical to rat IB1. The tissue-specific expression of IB1 in human is similar to that observed in rodent. The IB1 gene contains 12 exons and maps to chromosome 11 (11p11.2-p12), a region that is deleted in DEFECT-11 syndrome. Apart from an IB1 pseudogene on chromosome 17 (17q21), no additional IB1-related gene was found in the human genome. Our data indicate that the sequence and expression pattern of IB1 are highly conserved between rodent and human and provide the necessary tools to investigate whether IB1 is involved in human diseases.

Free-breathing 3 T magnetic resonance t(2)-mapping of the heart.

OBJECTIVES: This study sought to establish an accurate and reproducible T(2)-mapping cardiac magnetic resonance (CMR) methodology at 3 T and to evaluate it in healthy volunteers and patients with myocardial infarct. BACKGROUND: Myocardial edema affects the T(2) relaxation time on CMR. Therefore, T(2)-mapping has been established to characterize edema at 1.5 T. A 3 T implementation designed for longitudinal studies and aimed at guiding and monitoring therapy remains to be implemented, thoroughly characterized, and evaluated in vivo. METHODS: A free-breathing navigator-gated radial CMR pulse sequence with an adiabatic T(2) preparation module and an empirical fitting equation for T(2) quantification was optimized using numerical simulations and was validated at 3 T in a phantom study. Its reproducibility for myocardial T(2) quantification was then ascertained in healthy volunteers and improved using an external reference phantom with known T(2). In a small cohort of patients with established myocardial infarction, the local T(2) value and extent of the edematous region were determined and compared with conventional T(2)-weighted CMR and x-ray coronary angiography, where available. RESULTS: The numerical simulations and phantom study demonstrated that the empirical fitting equation is significantly more accurate for T(2) quantification than that for the more conventional exponential decay. The volunteer study consistently demonstrated a reproducibility error as low as 2 ± 1% using the external reference phantom and an average myocardial T(2) of 38.5 ± 4.5 ms. Intraobserver and interobserver variability in the volunteers were -0.04 ± 0.89 ms (p = 0.86) and -0.23 ± 0.91 ms (p = 0.87), respectively. In the infarction patients, the T(2) in edema was 62.4 ± 9.2 ms and was consistent with the x-ray angiographic findings. Simultaneously, the extent of the edematous region by T(2)-mapping correlated well with that from the T(2)-weighted images (r = 0.91). CONCLUSIONS: The new, well-characterized 3 T methodology enables robust and accurate cardiac T(2)-mapping at 3 T with high spatial resolution, while the addition of a reference phantom improves reproducibility. This technique may be well suited for longitudinal studies in patients with suspected or established heart disease.

Disparate activation of the coronary sinus and inferior left atrium during atrial tachycardia after persistent atrial fibrillation ablation: prevalence, pitfalls, and impact on mapping.

INTRODUCTION: Persistent atrial fibrillation (AF) ablation may lead to partial disconnection of the coronary sinus (CS). As a result, disparate activation sequences of the local CS versus contiguous left atrium (LA) may be observed during atrial tachycardia (AT). We aimed to evaluate the prevalence of this phenomenon and its impact on activation mapping. METHODS: AT occurring after persistent AF ablation were investigated in 74 consecutive patients. Partial CS disconnection during AT was suspected when double potentials with disparate activation sequences were observed on the CS catheter. Endocardial mapping facing CS bipoles was performed to differentiate LA far-field from local CS potentials. RESULTS: A total of 149 ATs were observed. Disparate LA-CS activations were apparent in 20 ATs after magnifying the recording scale (13%). The most common pattern (90%) was distal to proximal endocardial LA activation against proximal to distal CS activation, the latter involving the whole CS or its distal part. Perimitral macroreentry was more common when disparate LA-CS activations were observed (67% vs 29%; P = 0.002). Partial CS disconnection also resulted in "pseudo" mitral isthmus (MI) block during LA appendage pacing in 20% of patients as local CS activation was proximal to distal despite distal to proximal activation of the contiguous LA. CONCLUSION: Careful analysis of CS recordings during AT following persistent AF ablation often reveals disparate patterns of activation. Recognizing when endocardial LA activation occurs in the opposite direction to the more obvious local CS signals is critical to avoid misleading interpretations during mapping of AT and evaluation of MI block.

Differential epipolar constraint in mobile robot egomotion estimation

The estimation of camera egomotion is a well established problem in computer vision. Many approaches have been proposed based on both the discrete and the differential epipolar constraint. The discrete case is mainly used in self-calibrated stereoscopic systems, whereas the differential case deals with a unique moving camera. The article surveys several methods for mobile robot egomotion estimation covering more than 0.5 million samples using synthetic data. Results from real data are also given

Efficient learning of reactive robot behaviors with a Neural-Q_learning approach

The purpose of this paper is to propose a Neural-Q_learning approach designed for online learning of simple and reactive robot behaviors. In this approach, the Q_function is generalized by a multi-layer neural network allowing the use of continuous states and actions. The algorithm uses a database of the most recent learning samples to accelerate and guarantee the convergence. Each Neural-Q_learning function represents an independent, reactive and adaptive behavior which maps sensorial states to robot control actions. A group of these behaviors constitutes a reactive control scheme designed to fulfill simple missions. The paper centers on the description of the Neural-Q_learning based behaviors showing their performance with an underwater robot in a target following task. Real experiments demonstrate the convergence and stability of the learning system, pointing out its suitability for online robot learning. Advantages and limitations are discussed

Semi-online neural-Q_leaming for real-time robot learning

Reinforcement learning (RL) is a very suitable technique for robot learning, as it can learn in unknown environments and in real-time computation. The main difficulties in adapting classic RL algorithms to robotic systems are the generalization problem and the correct observation of the Markovian state. This paper attempts to solve the generalization problem by proposing the semi-online neural-Q_learning algorithm (SONQL). The algorithm uses the classic Q_learning technique with two modifications. First, a neural network (NN) approximates the Q_function allowing the use of continuous states and actions. Second, a database of the most representative learning samples accelerates and stabilizes the convergence. The term semi-online is referred to the fact that the algorithm uses the current but also past learning samples. However, the algorithm is able to learn in real-time while the robot is interacting with the environment. The paper shows simulated results with the "mountain-car" benchmark and, also, real results with an underwater robot in a target following behavior

Vision-based localization of an underwater robot in a structured environment

This paper presents a vision-based localization approach for an underwater robot in a structured environment. The system is based on a coded pattern placed on the bottom of a water tank and an onboard down looking camera. Main features are, absolute and map-based localization, landmark detection and tracking, and real-time computation (12.5 Hz). The proposed system provides three-dimensional position and orientation of the vehicle along with its velocity. Accuracy of the drift-free estimates is very high, allowing them to be used as feedback measures of a velocity-based low-level controller. The paper details the localization algorithm, by showing some graphical results, and the accuracy of the system

Policy gradient based Reinforcement Learning for real autonomous underwater cable tracking

This paper proposes a field application of a high-level reinforcement learning (RL) control system for solving the action selection problem of an autonomous robot in cable tracking task. The learning system is characterized by using a direct policy search method for learning the internal state/action mapping. Policy only algorithms may suffer from long convergence times when dealing with real robotics. In order to speed up the process, the learning phase has been carried out in a simulated environment and, in a second step, the policy has been transferred and tested successfully on a real robot. Future steps plan to continue the learning process on-line while on the real robot while performing the mentioned task. We demonstrate its feasibility with real experiments on the underwater robot ICTINEU AUV

Autonomous underwater vehicle control using reinforcement learning policy search methods

Autonomous underwater vehicles (AUV) represent a challenging control problem with complex, noisy, dynamics. Nowadays, not only the continuous scientific advances in underwater robotics but the increasing number of subsea missions and its complexity ask for an automatization of submarine processes. This paper proposes a high-level control system for solving the action selection problem of an autonomous robot. The system is characterized by the use of reinforcement learning direct policy search methods (RLDPS) for learning the internal state/action mapping of some behaviors. We demonstrate its feasibility with simulated experiments using the model of our underwater robot URIS in a target following task

Estimating the motion of an underwater robot from a monocular image sequence

When underwater vehicles perform navigation close to the ocean floor, computer vision techniques can be applied to obtain quite accurate motion estimates. The most crucial step in the vision-based estimation of the vehicle motion consists on detecting matchings between image pairs. Here we propose the extensive use of texture analysis as a tool to ameliorate the correspondence problem in underwater images. Once a robust set of correspondences has been found, the three-dimensional motion of the vehicle can be computed with respect to the bed of the sea. Finally, motion estimates allow the construction of a map that could aid to the navigation of the robot

VLSI architecture for an Underwater Robot Vision System

It is well known that image processing requires a huge amount of computation, mainly at low level processing where the algorithms are dealing with a great number of data-pixel. One of the solutions to estimate motions involves detection of the correspondences between two images. For normalised correlation criteria, previous experiments shown that the result is not altered in presence of nonuniform illumination. Usually, hardware for motion estimation has been limited to simple correlation criteria. The main goal of this paper is to propose a VLSI architecture for motion estimation using a matching criteria more complex than Sum of Absolute Differences (SAD) criteria. Today hardware devices provide many facilities for the integration of more and more complex designs as well as the possibility to easily communicate with general purpose processors

Positive contrast visualization of nitinol devices using susceptibility gradient mapping.

MRI visualization of devices is traditionally based on signal loss due to T(2)* effects originating from local susceptibility differences. To visualize nitinol devices with positive contrast, a recently introduced postprocessing method is adapted to map the induced susceptibility gradients. This method operates on regular gradient-echo MR images and maps the shift in k-space in a (small) neighborhood of every voxel by Fourier analysis followed by a center-of-mass calculation. The quantitative map of the local shifts generates the positive contrast image of the devices, while areas without susceptibility gradients render a background with noise only. The positive signal response of this method depends only on the choice of the voxel neighborhood size. The properties of the method are explained and the visualizations of a nitinol wire and two stents are shown for illustration.

Online Robust 3D Mapping Using Structure from Motion Cues

This paper presents a complete solution for creating accurate 3D textured models from monocular video sequences. The methods are developed within the framework of sequential structure from motion, where a 3D model of the environment is maintained and updated as new visual information becomes available. The camera position is recovered by directly associating the 3D scene model with local image observations. Compared to standard structure from motion techniques, this approach decreases the error accumulation while increasing the robustness to scene occlusions and feature association failures. The obtained 3D information is used to generate high quality, composite visual maps of the scene (mosaics). The visual maps are used to create texture-mapped, realistic views of the scene

Experimental Modeling and Control Strategies on an Open Mobile Robot Platform PRIM

This research work deals with the problem of modeling and design of low level speed controller for the mobile robot PRIM. The main objective is to develop an effective educational tool. On one hand, the interests in using the open mobile platform PRIM consist in integrating several highly related subjects to the automatic control theory in an educational context, by embracing the subjects of communications, signal processing, sensor fusion and hardware design, amongst others. On the other hand, the idea is to implement useful navigation strategies such that the robot can be served as a mobile multimedia information point. It is in this context, when navigation strategies are oriented to goal achievement, that a local model predictive control is attained. Hence, such studies are presented as a very interesting control strategy in order to develop the future capabilities of the system