Emanuelle Reynaud

Neural responses to dynamic expressions of fear in schizophrenia.

Abnormalities in social functioning are a significant feature of schizophrenia. One critical aspect of these abnormalities is the difficulty these individuals have with the recognition of facial emotions, particularly negative expressions such as fear. The present work focuses on fear perception and its relationship to the paranoid symptoms of schizophrenia, specifically, how underlying limbic system structures (i.e. the amygdala) react when probed with dynamic fearful facial expressions. Seven paranoid and eight non-paranoid subjects (all males) with a diagnosis of schizophrenia took part in functional magnetic resonance imaging study (1.5T) examining neural responses to emerging fearful expressions contrasted with dissipating fearful expressions. Subjects viewed emerging and dissipating expressions while completing a gender discrimination task. Their brain activation was compared to that of 10 healthy male subjects. Increased hippocampal activation was seen in the non-paranoid group, while abnormalities in the bilateral amygdalae were observed only in the paranoid individuals. These patterns may represent trait-related hippocampal dysfunction, coupled with state (specifically paranoia) related amygdala abnormalities. The findings are discussed in light of models of paranoia in schizophrenia.

Do you see what I see? Interpretations of intentional movement in schizophrenia☆

OBJECTIVE Current literature exploring theory of mind (ToM) abilities in patients with schizophrenia has failed to take into account the dynamic nature of complex social interactions. The aim of this study was to explore symptom specific impairments in theory of mind using a novel, dynamic task. METHODS Subjects viewed short animations displaying three types of movement; random, goal directed, and socially complex (theory of mind). Verbal descriptions of the animations were obtained from 61 patients with schizophrenia (divided into symptom sub-groups) and 22 healthy comparison subjects and were scored for accuracy, type of response and use of target terms (terms most appropriate to each animation type). RESULTS Accuracy on all three conditions discriminated behavioural signs (BS), and (to a lesser degree) paranoid subjects, from the other schizophrenia sub-groups (those in remission and those with passivity features) and the controls. Paranoid and BS groups had difficulties with all the animations, yet all symptom sub-groups failed to use the appropriate mentalising language to describe the ToM animations. CONCLUSIONS In this first exploration of on-line mentalising abilities in schizophrenia, it is suggested that a failure to use appropriate mentalising language may be a trait marker for the disease. The nature of the type of tasks used to assess social cognitive processing in this group needs careful consideration, and tasks tapping into the fluidity of social interactions yield results that differ from previously reported studies.

On the neurocognitive origins of human tool use : A critical review of neuroimaging data

Since more than a century, neuropsychological models have assumed that the left inferior parietal cortex is central to tool use by storing manipulation knowledge (the manipulation-based approach). Interestingly, recent neuropsychological evidence indicates that the left inferior parietal cortex might rather support the ability to reason about physical object properties (the reasoning-based approach). Historically, these two approaches have been developed from data obtained in left brain-damaged patients. This review is the first one to (1) give an overview of the two aforementioned approaches and (2) reanalyze functional neuroimaging data of the past decade to examine their predictions. Globally, we demonstrate that the left inferior parietal cortex is involved in the understanding of tool-use actions, providing support for the reasoning-based approach. We also discuss the functional involvement of the different regions of the tool-use brain network (left supramarginal gyrus, left intraparietal sulcus, left posterior temporal cortex). Our findings open promising avenues for future research on the neurocognitive basis of human tool use.

Does age worsen sleep‐dependent memory consolidation?

Slow wave sleep (SWS) is known to favour episodic memory consolidation. Given that ageing is associated with a reduction in SWS and episodic memory impairment, our aim was to investigate whether memory continues to benefit from sleep in older adults. Episodic memory consolidation was tested in 20 young (22.1 ± 1.7 years) and 20 older volunteers (68.9 ± 5.3 years) who performed a visuospatial two‐dimensional object‐location task. Retention capacities were evaluated after 12 h of wakefulness or 12 h of sleep. Performances before and after the interval allowed us to calculate a forgetting rate. Sleep architecture was measured by polysomnography (older adults = 410 min; young adults: 467 min). Our results showed that the beneficial effect of sleep on memory consolidation was reduced in older adults compared to young adults. In older adults, sleep did not enhance memory consolidation significantly compared to wakefulness. Sleep prevented young adults from forgetting (−0.10% ± 2.1), while the forgetting rate in older adults was still important after a period of sleep (16.60% ± 4.2; P = 0.05). The sleep architecture of older adults was characterized by a decrease in sleep efficiency (−12%; P < 0.05), in total cycle time (−137 min; P < 0.05), in percentage of total cycle time (−21%; P < 0.05) and in rapid eye movement time (−41 min; P < 0.05) compared to young adults. However, no difference in slow wave sleep was observed (−1%; not significant) and no correlation was found with performance. Age‐related changes in sleep parameters may have a negative impact on memory consolidation in older adults.

A testing framework for background subtraction algorithms comparison in intrusion detection context

Identifying objects from a video stream is a fundamental and critical task in many computer-vision applications. A popular approach is the background subtraction, which consists in separating foreground (moving objects) from background. Many methodologies have been developed for automatic background segmentation but this fundamental task is still challenging. We focus here on a particular application of computer vision: intrusion detection in video surveillance. We propose in this paper a multi-level methodology for evaluating and comparing background subtraction algorithms. Three levels are studied: first, pixel level to evaluate the accuracy of the segmentation algorithm to attribute the right class to each pixel. Second, image level, measuring the rate of right decision on each frame (intrusion vs no intrusion) and finally sequence level, measuring the accordance with the time span where objects appear. Moreover, we also propose a new similarity measure, called D-Score, adapted to the context of intrusion detection.

Group analysis of self-organizing maps based on functional MRI using restricted Frechet means

Studies of functional MRI data are increasingly concerned with the estimation of differences in spatio-temporal networks across groups of subjects or experimental conditions. Unsupervised clustering and independent component analysis (ICA) have been used to identify such spatio-temporal networks. While these approaches have been useful for estimating these networks at the subject-level, comparisons over groups or experimental conditions require further methodological development. In this paper, we tackle this problem by showing how self-organizing maps (SOMs) can be compared within a Frechean inferential framework. Here, we summarize the mean SOM in each group as a Frechet mean with respect to a metric on the space of SOMs. The advantage of this approach is twofold. Firstly, it allows the visualization of the mean SOM in each experimental condition. Secondly, this Frechean approach permits one to draw inference on group differences, using permutation of the group labels. We consider the use of different distance functions, and introduce one extension of the classical sum of minimum distance (SMD) between two SOMs, which take into account the spatial pattern of the fMRI data. The validity of these methods is illustrated on synthetic data. Through these simulations, we show that the two distance functions of interest behave as expected, in the sense that the ones capturing temporal and spatial aspects of the SOMs are more likely to reach significance under simulated scenarios characterized by temporal, spatial [and spatio-temporal] differences, respectively. In addition, a re-analysis of a classical experiment on visually-triggered emotions demonstrates the usefulness of this methodology. In this study, the multivariate functional patterns typical of the subjects exposed to pleasant and unpleasant stimuli are found to be more similar than the ones of the subjects exposed to emotionally neutral stimuli. In this re-analysis, the group-level SOM output units with the smallest sample Jaccard indices were compared with standard GLM group-specific z-score maps, and provided considerable levels of agreement. Taken together, these results indicate that our proposed methods can cast new light on existing data by adopting a global analytical perspective on functional MRI paradigms.

Detecting functional nodes in large-scale cortical networks with functional magnetic resonance imaging: a principal component analysis of the human visual system.

This study aimed to demonstrate how a regional variant of principal component analysis (PCA) can be used to delineate the known functional subdivisions of the human visual system. Unlike conventional eigenimage analysis, PCA was carried out as a second‐level analysis subsequent to model‐based General Linear Model (GLM)‐type functional activation mapping. Functional homogeneity of the functional magnetic resonance imaging (fMRI) time series within and between clusters was examined on several levels of the visual network, starting from the level of individual clusters up to the network level comprising two or more distinct visual regions. On each level, the number of significant components was identified and compared with the number of clusters in the data set. Eigenimages were used to examine the regional distribution of the extracted components. It was shown that voxels within individual clusters and voxels located in bilateral homologue visual regions can be represented by a single component, constituting the characteristic functional specialization of the cluster(s). If, however, PCA was applied to time series of voxels located in functionally distinct visual regions, more than one component was observed with each component being dominated by voxels in one of the investigated regions. The model of functional connections derived by PCA was in accordance with the well‐known functional anatomy and anatomical connectivity of the visual system. PCA in combination with conventional activation mapping might therefore be used to identify the number of functionally distinct nodes in an fMRI data set in order to generate a model of functional connectivity within a neuroanatomical network. Hum Brain Mapp, 2006.

Involvement of the Left Supramarginal Gyrus in Manipulation Judgment Tasks: Contributions to Theories of Tool Use

OBJECTIVES Two theories of tool use, namely the gesture engram and the technical reasoning theories, make distinct predictions about the involvement of the left inferior parietal lobe (IPL) in manipulation judgement tasks. The objective here is to test these alternative predictions based on previous studies on manipulation judgment tasks using transcranial magnetic stimulations (TMS) targeting the left supramarginal gyrus (SMG). METHODS We review recent TMS studies on manipulation judgement tasks and confront these data with predictions made by both tool use theories. RESULTS The left SMG is a highly intertwined region, organized following several functionally distinct areas and TMS may have disrupted a cortical network involved in the ability to use tools rather than only one functional area supporting manipulation knowledge. Moreover, manipulation judgement tasks may be impaired following virtual lesions outside the IPL. CONCLUSIONS These data are more in line with the technical reasoning hypothesis, which assumes that the left IPL does not store manipulation knowledge per se. (JINS, 2017, 23, 685-691).

A multisensory identification system for robotics

We present a new multimodal object identification system for a robot. Humans constantly face complex multimodal identification tasks with success. This work is based on a cognitive theory of multisensory integration, suggesting convergence of specific unimodal sensory pathways into heteromodal areas, and feedback influences from multimodal to unimodal processing. To implement the multisensory identification system of the robot, two suitable connectionist networks have been linked into a two-level modular architecture. The first level is made of small and fast incremental neural classifiers that recognize independently modality-specific inputs. The second level is a recurrent neural network: a multiple bidirectional associative memory that integrates outputs of each first level sub-system. These two levels cooperate in both forward and backward ways to identify the object perceived. This model has been tested with a virtual robot navigating in a multi-modal environment, where objects are composed of images with sounds. The inputs of the robot are dynamically generated according to the position of the robot, its orientation, and its perceptive fields.

A multiple BAM for hetero-association and multisensory integration modelling

We present in this article a dynamic neural network that works as a memory for multiple associations. Heterogeneous pairs of patterns can be tied together through learning within this memory, and recalled easily. Starting from Koskos bidirectional associative memory, we modify some fundamental features of the network (topology and learning algorithm). We show empirically that this network has a high storage capacity and is only weakly dependent upon learning hyperparameters. We demonstrate its robustness to corrupted or missing data. We finally present results from experiments where this network is used as a multisensory associative memory.