Social cognition and real-life functioning in patient samples with 22q11.2 deletion syndrome with or without psychosis, compared to a large sample of patients with schizophrenia only and healthy controls

Patients with the 22q11.2 deletion syndrome (DS) show an increased risk of developing a psychotic illness lifetime. 22q11.2DS may represent a reliable model for studying the neurobiological underpinnings of schizophrenia. The study of social inference abilities in a genetic condition at high risk for psychosis, like 22q11.2DS, may shed light on the relationships between neurocognitive processes and patients' daily general functioning. The study sample consisted of 1736 participants, divided into four groups: 22q11.2DS patients with diagnosis of psychotic disorder (DEL SCZ, N = 20); 22q11.2DS subjects with no diagnosis of psychosis (DEL, N = 43); patients diagnosed with schizophrenia without 22q11.2DS (SCZ, N = 893); and healthy controls (HC, N = 780). Social cognition was assessed through The Awareness of Social Inference Test (TASIT) and general functioning through the Specific Levels of Functioning (SLoF) scale. We analysed data through regression analysis. The SCZ and DEL groups had similar levels of global functioning; they both had significantly lower SLoF Total scores than HC (p < .001); the DEL SCZ group showed significantly lower scores compared to the other groups (SCZ, p = .004; DEL, p = .003; HC, p < .001). A significant deficit in social cognition was observed in the three clinical groups. In the DEL SCZ and SCZ groups, TASIT scores significantly predicted global functioning (p < .05). Our findings of social cognition deficit in psychosis-prone patients point to the possible future adoption of rehabilitation programmes, like Social Skills Training and Cognitive Remediation, during premorbid stages of psychosis.

Certifiably Optimal Outlier-Robust Geometric Perception: Semidefinite Relaxations and Scalable Global Optimization

We propose the first general and scalable framework to design certifiable algorithms for robust geometric perception in the presence of outliers. Our first contribution is to show that estimation using common robust costs, such as truncated least squares (TLS), maximum consensus, Geman-McClure, Tukey's biweight, among others, can be reformulated as polynomial optimization problems (POPs). By focusing on the TLS cost, our second contribution is to exploit sparsity in the POP and propose a sparse semidefinite programming (SDP) relaxation that is much smaller than the standard Lasserre's hierarchy while preserving empirical exactness, i.e., the SDP recovers the optimizer of the nonconvex POP with an optimality certificate. Our third contribution is to solve the SDP relaxations at an unprecedented scale and accuracy by presenting [Formula: see text], a solver that blends global descent on the convex SDP with fast local search on the nonconvex POP. Our fourth contribution is an evaluation of the proposed framework on six geometric perception problems including single and multiple rotation averaging, point cloud and mesh registration, absolute pose estimation, and category-level object pose and shape estimation. Our experiments demonstrate that (i) our sparse SDP relaxation is empirically exact with up to 60%- 90% outliers across applications; (ii) while still being far from real-time, [Formula: see text] is up to 100 times faster than existing SDP solvers on medium-scale problems, and is the only solver that can solve large-scale SDPs with hundreds of thousands of constraints to high accuracy; (iii) [Formula: see text] safeguards existing fast heuristics for robust estimation (e.g., [Formula: see text] or Graduated Non-Convexity), i.e., it certifies global optimality if the heuristic estimates are optimal, or detects and allows escaping local optima when the heuristic estimates are suboptimal.

Kimera: From SLAM to spatial perception with 3D dynamic scene graphs

Humans are able to form a complex mental model of the environment they move in. This mental model captures geometric and semantic aspects of the scene, describes the environment at multiple levels of abstractions (e.g., objects, rooms, buildings), includes static and dynamic entities and their relations (e.g., a person is in a room at a given time). In contrast, current robots’ internal representations still provide a partial and fragmented understanding of the environment, either in the form of a sparse or dense set of geometric primitives (e.g., points, lines, planes, and voxels), or as a collection of objects. This article attempts to reduce the gap between robot and human perception by introducing a novel representation, a 3D dynamic scene graph (DSG), that seamlessly captures metric and semantic aspects of a dynamic environment. A DSG is a layered graph where nodes represent spatial concepts at different levels of abstraction, and edges represent spatiotemporal relations among nodes. Our second contribution is Kimera, the first fully automatic method to build a DSG from visual–inertial data. Kimera includes accurate algorithms for visual–inertial simultaneous localization and mapping (SLAM), metric–semantic 3D reconstruction, object localization, human pose and shape estimation, and scene parsing. Our third contribution is a comprehensive evaluation of Kimera in real-life datasets and photo-realistic simulations, including a newly released dataset, uHumans2, which simulates a collection of crowded indoor and outdoor scenes. Our evaluation shows that Kimera achieves competitive performance in visual–inertial SLAM, estimates an accurate 3D metric–semantic mesh model in real-time, and builds a DSG of a complex indoor environment with tens of objects and humans in minutes. Our final contribution is to showcase how to use a DSG for real-time hierarchical semantic path-planning. The core modules in Kimera have been released open source.

Clozapine-induced gastroesophageal rumination in 22q11.2 Deletion Syndrome. A case report on gastroesophageal side effects management without renouncing clozapine's effectiveness

Despite entailing more severe and uncommon side effects in 22q11.2DS compared to idiopathic schizophrenia, we strongly believe that clozapine should continue to be considered the gold standard for all treatment-resistant schizophrenia, even in 22qDS.

The impact of social cognition deficits on real life functioning in 22q11.2 deletion syndrome: A comparative study with a large population of patients with schizophrenia

Introduction

22q11.2 Deletion Syndrome (22q11.2DS) represents a congenital syndrome with several clinical features. It entails a 25% risk of psychotic onset in lifespan. 22q11.2DS is a reliable model for biological vulnerability to schizophrenia.

Objectives

With the hypothesis of similar impairments in schizophrenia and 22q11DS, to investigate a possible correlation between Social Cognition (SC) and Interpersonal Functioning (FU).

Methods

Sample consists of 1735 adults: 893 schizophrenic subjects (SCZ); 18 with 22q11.2DS and psychosis (DEL_SCZ); 44 22q11.2DS individuals (DEL); 780 healthy controls (HC). SCZ and HC data come from a multicentric study by Network for Research on Psychoses. SC was assessed with The Awareness of Social Interference Test (TASIT, consisting of three sections: T1= Emotion Recognition; T2=Minimal Social Inference; T3=Social Inference Enriched). The Specific Levels of Functioning (SLOF) interview was employed.

Results

DEL_SCZ (p<0.001) and SCZ (p<0.001) showed impairments in each TASIT sections compared to HC. Significant deficits in interpersonal functioning area were found in SCZ (p<0.001) compared to HC. The interpersonal functioning domain showed a positive correlation with SC in HC (T1: r=0.097; p<0.001; T2: r=0.120; p=0.001; T3: r=0.121; p=0.001); DEL (T1: r=0.380; p=0.024; T2: r=0.466; p=0.005) and SCZ (T1: r=0.113, p=0.001; T2: r=0.110, p=0.001; T3: r=0.134; p<0.001).

Conclusions

SC deficits both in subjects with 22q11.2DS and in people with schizophrenia suggest a role of endophenotypes. SC is directly correlated to interpersonal functioning in 22q11.2DS without psychosis and people with schizophrenia. DEL_SCZ may suffer from deeper cognitive and symptomatic conditions that both impact differently on FU.

Social cognition deficit and genetic vulnerability to schizophrenia in 22q11 deletion syndrome

INTRODUCTION

22q11.2 microdeletion syndrome (22q11DS) is associated with a 25% risk of psychotic onset.

MATERIALS AND METHODS

The sample consist of 120 subjects: 39 schizophrenics (SCZ); 20 siblings of schizophrenic patients (SIB); 34 22q11DS non-psychotic patients (DEL); 17 22q11DS psychotic patients (DEL_scz); 30 control subjects (CS). Social cognition was evaluated with the awareness of social interference test. Intelligence Quotient (IQ) was calculated with Wechsler Adult Intelligence Scale. TASIT (Awareness of Social Inference Test) performance was analyzed via MANOVA, including IQ as covariate.

RESULTS

Group and IQ showed significant effect (p < 0.001; p = 0.037). The only TASIT variables where IQ showed no effect were paradoxical sarcasm; sincerity; lie. In sincerity, CS group shows a better performance than both 22q11DS groups (p < 0.05). In paradoxical sarcasm and lie, CS group performed better than each clinical group (p < 0.05). Regarding lie, DEL group was worst also respect to SCZ group (p = 0.029).

CONCLUSIONS

Our results show a specific social cognition deficit in 22q11DS and schizophrenia.

Sparse depth sensing for resource-constrained robots

We consider the case in which a robot has to navigate in an unknown environment, but does not have enough on-board power or payload to carry a traditional depth sensor (e.g., a 3D lidar) and thus can only acquire a few (point-wise) depth measurements. We address the following question: is it possible to reconstruct the geometry of an unknown environment using sparse and incomplete depth measurements? Reconstruction from incomplete data is not possible in general, but when the robot operates in man-made environments, the depth exhibits some regularity (e.g., many planar surfaces with only a few edges); we leverage this regularity to infer depth from a small number of measurements. Our first contribution is a formulation of the depth reconstruction problem that bridges robot perception with the compressive sensing literature in signal processing. The second contribution includes a set of formal results that ascertain the exactness and stability of the depth reconstruction in 2D and 3D problems, and completely characterize the geometry of the profiles that we can reconstruct. Our third contribution is a set of practical algorithms for depth reconstruction: our formulation directly translates into algorithms for depth estimation based on convex programming. In real-world problems, these convex programs are very large and general-purpose solvers are relatively slow. For this reason, we discuss ad-hoc solvers that enable fast depth reconstruction in real problems. The last contribution is an extensive experimental evaluation in 2D and 3D problems, including Monte Carlo runs on simulated instances and testing on multiple real datasets. Empirical results confirm that the proposed approach ensures accurate depth reconstruction, outperforms interpolation-based strategies, and performs well even when the assumption of a structured environment is violated.

SE-Sync: A certifiably correct algorithm for synchronization over the special Euclidean group

Many important geometric estimation problems naturally take the form of synchronization over the special Euclidean group: estimate the values of a set of unknown group elements [Formula: see text] given noisy measurements of a subset of their pairwise relative transforms [Formula: see text]. Examples of this class include the foundational problems of pose-graph simultaneous localization and mapping (SLAM) (in robotics), camera motion estimation (in computer vision), and sensor network localization (in distributed sensing), among others. This inference problem is typically formulated as a non-convex maximum-likelihood estimation that is computationally hard to solve in general. Nevertheless, in this paper we present an algorithm that is able to efficiently recover certifiably globally optimal solutions of the special Euclidean synchronization problem in a non-adversarial noise regime. The crux of our approach is the development of a semidefinite relaxation of the maximum-likelihood estimation (MLE) whose minimizer provides an exact maximum-likelihood estimate so long as the magnitude of the noise corrupting the available measurements falls below a certain critical threshold; furthermore, whenever exactness obtains, it is possible to verify this fact a posteriori, thereby certifying the optimality of the recovered estimate. We develop a specialized optimization scheme for solving large-scale instances of this semidefinite relaxation by exploiting its low-rank, geometric, and graph-theoretic structure to reduce it to an equivalent optimization problem defined on a low-dimensional Riemannian manifold, and then design a Riemannian truncated-Newton trust-region method to solve this reduction efficiently. Finally, we combine this fast optimization approach with a simple rounding procedure to produce our algorithm, SE-Sync. Experimental evaluation on a variety of simulated and real-world pose-graph SLAM datasets shows that SE-Sync is capable of recovering certifiably globally optimal solutions when the available measurements are corrupted by noise up to an order of magnitude greater than that typically encountered in robotics and computer vision applications, and does so significantly faster than the Gauss–Newton-based approach that forms the basis of current state-of-the-art techniques.

Attentional functioning in individuals with 22q11 deletion syndrome: insight from ERPs

The 22q11 deletion syndrome (22q11DS), or DiGeorge syndrome (DG), is one of the most common genetic deletion syndromes. DG also carries a high risk for psychiatric disorders, with learning disabilities frequently being reported. Impairments in specific cognitive domains, such as executive functioning and attention, have also been described. The aim of this study was to investigate attentional functioning in a group of subjects with DG using ERPs, and in particular the P300 and CNV components. We studied ten patients with DG and ten healthy subjects that performed a P300 Novelty task and a CNV motor task. P3b amplitude was significantly lower in patients than in controls, while P3b latency was comparable in patients and controls. The P3a parameters were similar in both groups. All CNV amplitudes were significantly lower in DG patients than in controls. DG patients displayed slower reaction times in the CNV motor task than healthy subjects. These results point to a cognitive dysfunction related above all to executive attentional processing in DG patients. In particular, a specific difficulty emerged in selective attention and in the ability to orient and to sustain the anticipatory attention required for an executive motor response.

Distributed mapping with privacy and communication constraints: Lightweight algorithms and object-based models

We consider the following problem: a team of robots is deployed in an unknown environment and it has to collaboratively build a map of the area without a reliable infrastructure for communication. The backbone for modern mapping techniques is pose graph optimization, which estimates the trajectory of the robots, from which the map can be easily built. The first contribution of this paper is a set of distributed algorithms for pose graph optimization: rather than sending all sensor data to a remote sensor fusion server, the robots exchange very partial and noisy information to reach an agreement on the pose graph configuration. Our approach can be considered as a distributed implementation of a two-stage approach that already exists, where we use the Successive Over-Relaxation and the Jacobi Over-Relaxation as workhorses to split the computation among the robots. We also provide conditions under which the proposed distributed protocols converge to the solution of the centralized two-stage approach. As a second contribution, we extend the proposed distributed algorithms to work with the object-based map models. The use of object-based models avoids the exchange of raw sensor measurements (e.g. point clouds or RGB-D data) further reducing the communication burden. Our third contribution is an extensive experimental evaluation of the proposed techniques, including tests in realistic Gazebo simulations and field experiments in a military test facility. Abundant experimental evidence suggests that one of the proposed algorithms (the Distributed Gauss–Seidel method) has excellent performance. The Distributed Gauss–Seidel method requires minimal information exchange, has an anytime flavor, scales well to large teams (we demonstrate mapping with a team of 50 robots), is robust to noise, and is easy to implement. Our field tests show that the combined use of our distributed algorithms and object-based models reduces the communication requirements by several orders of magnitude and enables distributed mapping with large teams of robots in real-world problems. The source code is available for download at https://cognitiverobotics.github.io/distributed-mapper/

Planning in the continuous domain: A generalized belief space approach for autonomous navigation in unknown environments

We investigate the problem of planning under uncertainty, with application to mobile robotics. We propose a probabilistic framework in which the robot bases its decisions on the generalized belief, which is a probabilistic description of its own state and of external variables of interest. The approach naturally leads to a dual-layer architecture: an inner estimation layer, which performs inference to predict the outcome of possible decisions; and an outer decisional layer which is in charge of deciding the best action to undertake. Decision making is entrusted to a model predictive control (MPC) scheme. The formulation is valid for general cost functions and does not discretize the state or control space, enabling planning in continuous domain. Moreover, it allows to relax the assumption of maximum likelihood observations: predicted measurements are treated as random variables, and binary random variables are used to model the event that a measurement is actually taken by the robot. We successfully apply our approach to the problem of uncertainty-constrained exploration, in which the robot has to perform tasks in an unknown environment, while maintaining localization uncertainty within given bounds. We present an extensive numerical analysis of the proposed approach and compare it against related work. In practice, our planning approach produces smooth and natural trajectories and is able to impose soft upper bounds on the uncertainty. Finally, we exploit the results of this analysis to identify current limitations and show that the proposed framework can accommodate several desirable extensions.

A fast and accurate approximation for planar pose graph optimization

This work investigates the pose graph optimization problem, which arises in maximum likelihood approaches to simultaneous localization and mapping (SLAM). State-of-the-art approaches have been demonstrated to be very efficient in medium- and large-sized scenarios; however, their convergence to the maximum likelihood estimate heavily relies on the quality of the initial guess. We show that, in planar scenarios, pose graph optimization has a very peculiar structure. The problem of estimating robot orientations from relative orientation measurements is a quadratic optimization problem (after computing suitable regularization terms); moreover, given robot orientations, the overall optimization problem becomes quadratic. We exploit these observations to design an approximation of the maximum likelihood estimate, which does not require the availability of an initial guess. The approximation, named LAGO (Linear Approximation for pose Graph Optimization), can be used as a stand-alone tool or can bootstrap state-of-the-art techniques, reducing the risk of being trapped in local minima. We provide analytical results on existence and sub-optimality of LAGO, and we discuss the factors influencing its quality. Experimental results demonstrate that LAGO is accurate in common SLAM problems. Moreover, it is remarkably faster than state-of-the-art techniques, and is able to solve very large-scale problems in a few seconds.