3D model fitting for facial expression analysis under uncontrolled imaging conditions

International audienceThis paper addresses the recovering of 3D pose and animation of the human face in a monocular single image under uncontrolled imaging conditions. Our goal is to fit a 3D animated model in a face image with possibly large variations of head pose and facial expressions. Our data were acquired from filmed epileptic seizures of patients undergoing investigation in the videotelemetry 1unit, La Timone hospital, Marseille, France

Smart Phone, Smart Science: How the Use of Smartphones Can Revolutionize Research in Cognitive Science

Investigating human cognitive faculties such as language, attention, and memory most often relies on testing small and homogeneous groups of volunteers coming to research facilities where they are asked to participate in behavioral experiments. We show that this limitation and sampling bias can be overcome by using smartphone technology to collect data in cognitive science experiments from thousands of subjects from all over the world. This mass coordinated use of smartphones creates a novel and powerful scientific “instrument” that yields the data necessary to test universal theories of cognition. This increase in power represents a potential revolution in cognitive science

Self-control of epileptic seizures by nonpharmacological strategies

Despite the unpredictability of epileptic seizures, many patients report that they can anticipate seizure occurrence. Using certain alert symptoms (i.e., auras, prodromes, precipitant factors), patients can adopt behaviors to avoid injury during and after the seizure or may implement spontaneous cognitive and emotional strategies to try to control the seizure itself. From the patient's view point, potential means of enhancing seizure prediction and developing seizure control supports are seen as very important issues, especially when the epilepsy is drug-resistant. In this review, we first describe how some patients anticipate their seizures and whether this is effective in terms of seizure prediction. Secondly, we examine how these anticipatory elements might help patients to prevent or control their seizures and how the patient's neuropsychological profile, specifically parameters of perceived self-control (PSC) and locus of control (LOC), might impact these strategies and quality of life (QOL). Thirdly, we review the external supports that can help patients to better predict seizures. Finally, we look at nonpharmacological means of increasing perceived self-control and achieving potential reduction of seizure frequency (i.e., stress-based and arousal-based strategies). In the past few years, various approaches for detection and control of seizures have gained greater interest, but more research is needed to confirm a positive effect on seizure frequency as well as on QOL

Semiology and Epileptic Networks

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Contribution of seizure semiology to diagnosis and anatomo-electrical localisation of epilepsy

Epileptic seizures, characterised by paroxysmal disturbance of brain electrical activity, are recognisable by temporary change in clinical state (for example motor signs, behavioural modification or altered conscious level), temporally associated with the cerebral discharge. While analysis of such clinical seizure signs (“semiology”) formed the main basis of epilepsy study from the late 19th century onwards, understanding of the neural basis of semiological expression has advanced relatively little, in comparison to other aspects of epilepsy research. Analysis of ictal clinical signs is today considered essential for diagnosis of epilepsy, offering clues to underlying anatomical localisation and pathophysiology; however, paradoxically, the cerebral substrate of semiological signs remains incompletely understood in many cases and its localising value is therefore debated. Characterising the anatomo-pathophysiological basis of seizure semiology is especially important in the context of epilepsy pre-surgical evaluation, even more so when no radiologically visible lesion is present, since semiological analysis, if validated for a given seizure type, offers crucial localising information. For pharmacoresistant focal epilepsies in which surgical treatment might be possible, a number of cases require intracranial EEG recording. The method of stereoelectroencephalography (SEEG) is particularly useful as this allows simultaneous exploration of multiple, distant brain structures using stereotaxically placed multi-lead electrodes with concurrent video recording. The data thus acquired help form a three dimensional view of spatio-temporal seizure dynamics. Using SEEG it is therefore possible to undertake detailed analysis of semiological patterns and to study their temporal relation to the abnormal electrical cerebral activity occurring in brain networks during seizures. Epileptic seizures characterised clinically by transient cognitive dysfunction, behavioral change and complex motor signs are particularly challenging to analyse and categorise semiologically; indeed any paroxysmal behavioral disturbance must also be analysed with regards to whether it is actually caused by an epileptic discharge or not, since other forms of pathology, particularly psychogenic nonepileptic seizures (PNES), may be difficult to distinguish from epileptic seizures on a purely clinical basis, and require video-EEG recording for confirmation. This issue is particularly pertinent for prefrontal and parietal lobe seizures, which pose specific challenges for electroclinical analysis. PNES have a different and as yet poorly defined neurobiological basis compared to epileptic seizures. However growing understanding of the brain networks underlying emotional dysfunction, complex motor behaviour and altered consciousness, in particular data derived from intracranial studies of epileptic seizures, can help to further knowledge of how altered activity within these neural networks might interact with psychological and other factors in the pathophysiology of PNES. Through detailed observation of multiple epileptic seizures across a large population of patients, it can be appreciated that similarities exist in both clinical pattern and anatomical organisation of seizures. The existence of semiological patterns is in favour of the hypothesis that specific neural circuits underlie some forms of behavioural expression, and thus reinforces the validity of pursuing this line of investigation in epileptic seizures

Frontal lobe seizures: overview and update

International audienceFrontal lobe seizures (FLS) are debilitating for patients, highly diverse and often challenging for clinicians to evaluate. Frontal lobe epilepsy is the second most common localization for focal epilepsy, and if pharmacoresistant, can be amenable to resective surgery. Detailed study of frontal seizure semiology in conjunction with careful anatomical and electrophysiological correlation based on intracerebral recording with stereoelectroencephalography (SEEG) has allowed demonstration that ictal motor semiology reflects a hierarchical rostro-caudal axis of frontal lobe functional organization, thus helping with presurgical localization. Main semiological features allowing distinction between different frontal sublobar regions include motor signs and emotional signs. Frontal lobe seizure semiology also represents a valuable source of in vivo human behavioral data from a neuroscientific perspective. Advances in defining underlying etiologies of FLE are likely to be crucial for appropriate selection and exploration of potential surgical candidates, which could improve upon current surgical outcomes. Future research on investigating the genetic basis of epilepsies and relation to structural substrate (e.g. focal cortical dysplasia) and seizure organization and expression, could permit a "genotype-phenotype" approach that could be complementary to anatomical electroclinical correlations in better defining the spectrum of FLS. This could help with optimizing patient selection and prognostication with regards to therapeutic choices

On seizure semiology

International audienceThe clinical expression of seizures represents the main symptomatic burden of epilepsy. Neural mechanisms of semiological production in epilepsy, especially for complex behaviors, remain poorly known. In a framework of epilepsy as a network rather than a focal disorder, we can think of semiology as being dynamically produced by a set of interconnected structures, in which specific rhythmic interactions, and not just anatomical localization, are likely to play an important part in clinical expression. This requires a paradigm shift in how we think about seizure organization, including from a presurgical evaluation perspective. Semiology is a key data source, albeit with significant methodological challenges for its use in research, including observer bias and choice of semiologic categories. Better understanding of semiologic categorization and pathophysiological correlates is relevant to seizure classification systems. Advances in knowledge of neural mechanisms as well as anatomic correlates of different semiological patterns could help improve knowledge of epilepsy networks and potentially contribute to therapeutic innovations

A Self-Supervised Pre-Training Framework for Vision-Based Seizure Classification

International audienceSeizure events feature temporary abnormalities in muscle control or movements. They are usually caused by excessive neuronal activities in the brain, and are called epileptic seizures (ES). Nevertheless, not all seizures are epileptic in origin. Some are caused by psychological reasons, and such type of seizures are called psychogenic non-epileptic seizures (PNES). We propose a method to classify ES and PNES based on clinical signs in the seizure videos. In particular, inspired by BERT, we propose a Transformer-based framework that pre-trains on large unlabeled clinical videos, and then we fine-tune the pre-trained model for seizure classification with a minimum modification. We conduct a leave-one-subjectout (LOSO) validation on our dataset. The F1-score and accuracy are 0.82 and 0.75, respectively. To our knowledge, the proposed approach is the first attempt to use large unannotated data and learn useful representations for downstream tasks in the field of video based seizure analysis