Characterizing the State of Apathy with Facial Expression and Motion Analysis

International audienceReduced emotional response, lack of motivation, and limited social interaction comprise the major symptoms of apathy. Current methods for apathy diagnosis require the patient's presence in a clinic, and time consuming clinical interviews and questionnaires involving medical personnel, which are costly and logistically inconvenient for patients and clinical staff, hindering among other large scale diagnostics. In this paper we introduce a novel machine learning framework to classify apathetic and non-apathetic patients based on analysis of facial dynamics, entailing both emotion and facial movement. Our approach caters to the challenging setting of current apathy assessment interviews, which include short video clips with wide face pose variations, very low-intensity expressions, and insignificant inter-class variations. We test our algorithm on a dataset consisting of 90 video sequences acquired from 45 subjects and obtained an accuracy of 84% in apathy classification. Based on extensive experiments, we show that the fusion of emotion and facial local motion produces the best feature set for apathy classification. In addition, we train regression models to predict the clinical scores related to the mental state examination (MMSE) and the neuropsychiatric apathy inventory (NPI) using the motion and emotion features. Our results suggest that the performance can be further improved by appending the predicted clinical scores to the video-based feature representation

Apathy Classification by Exploiting Task Relatedness

International audienceApathy is characterized by symptoms such as reduced emotional response, lack of motivation, and limited social interaction. Current methods for apathy diagnosis require the pa-tient's presence in a clinic and time consuming clinical interviews, which are costly and inconvenient for both patients and clinical staff, hindering among others large-scale diagnostics. In this work we propose a multi-task learning (MTL) framework for apathy classification based on facial analysis, entailing both emotion and facial movements. In addition, it leverages information from other auxiliary tasks (i.e., clinical scores), which might be closely or distantly related to the main task of apathy classification. Our proposed MTL approach (termed MTL+) improves apathy classification by jointly learning model weights and the relatedness of the auxiliary tasks to the main task in an iterative manner. Our results on 90 video sequences acquired from 45 subjects obtained an apathy classification accuracy of up to 80%, using the concatenated emotion and motion features. Our results further demonstrate the improved performance of MTL+ over MTL

Prevalence of lack of interest and anhedonia in the general population of the UK Biobank

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Multilingual Learning for Mild Cognitive Impairment Screening from a Clinical Speech Task

The Semantic Verbal Fluency Task (SVF) is an efficient and minimally invasive speech-based screening tool for Mild Cognitive Impairment (MCI). In the SVF, testees have to produce as many words for a given semantic category as possible within 60 seconds. State-of-the-art approaches for automatic evaluation of the SVF employ word embeddings to analyze semantic similarities in these word sequences. While these approaches have proven promising in a variety of test languages, the small amount of data available for any given language limits the performance. In this paper, we for the first time investigate multilingual learning approaches for MCI classification from the SVF in order to combat data scarcity. To allow for cross-language generalisation, these approaches either rely on translation to a shared language, or make use of several distinct word embeddings. In evaluations on a multilingual corpus of older French, Dutch, and German participants (Controls=66, MCI=66), we show that our multilingual approaches clearly improve over single-language baselines

Detecting Apathy in Older Adults with Cognitive Disorders Using Automatic Speech Analysis

International audienceBackground: Apathy is present in several psychiatric and neurological conditions and has been found to have a severe negative effect on disease progression. In older people, it can be a predictor of increased dementia risk. Current assessment methods lack objectivity and sensitivity, thus new diagnostic tools and broad-scale screening technologies are needed. Objective: This study is the first of its kind aiming to investigate whether automatic speech analysis could be used for characterization and detection of apathy. Methods: A group of apathetic and non-apathetic patients (n = 60) with mild to moderate neurocognitive disorder were recorded while performing two short narrative speech tasks. Paralinguistic markers relating to prosodic, formant, source, and temporal qualities of speech were automatically extracted, examined between the groups and compared to baseline assessments. Machine learning experiments were carried out to validate the diagnostic power of extracted markers.Results: Correlations between apathy sub-scales and features revealed a relation between temporal aspects of speech and the subdomains of reduction in interest and initiative, as well as between prosody features and the affective domain. Group differences were found to vary for males and females, depending on the task. Differences in temporal aspects of speech were found to be the most consistent difference between apathetic and non-apathetic patients. Machine learning models trained on speech features achieved top performances of AUC = 0.88 for males and AUC = 0.77 for females. Conclusions: These findings reinforce the usability of speech as a reliable biomarker in the detection and assessment of apathy

Automatic Detection of Apathy using Acoustic Markers extracted from Free Emotional Speech

International audienceApathy is a frequent neuropsychiatric syndrome in people with dementia. It leads to diminished motivation for physical, cognitive and emotional activity. Apathy is highly underdiagnosed since its criteria have been only recently established and rely heavily on the subjective evaluation of human observers. In this paper we analyse speech samples from demented people with and without apathy. Speech was provoked by asking patients two emotional questions. Acoustic features were extracted and used in a classification task. The resulting models show performances of AUC = 0:71 and AUC = 0:63. This is a decent first step into the direction of automatic detection of apathy from speech. Usefulness of stimuli to elicit free speech is found to depend on patients gender

A 3-year retrospective study of 866 children and adolescent outpatients followed in the Nice Pediatric Psychotrauma Center created after the 2016 mass terror attack

BackgroundThe mass terrorist attack in Nice, France, in July 2016 caused deaths and injuries in a local population, including children and adolescents. The Nice Pediatric Psychotrauma Center (NPPC) was opened to provide mental health care to the pediatric population (0–18 years) who experienced traumatic events.ObjectivesThis study describes the specificity of the care pathway for young trauma victims, with an explanation of how the NPPC works during the first three years.MethodsIn this retrospective study, we conducted quantitative and qualitative data collection about new and follow-up consultations, primary and comorbid diagnoses, and the kind of trauma (terrorist attack versus other kinds of trauma). Ethics approval was obtained from the local Ethics committee.Results866 children and adolescents were followed in the NPPC. We found a high rate of Post-Traumatic Stress Disorder (PTSD; 71%) in this population with a high rate of comorbidities (67%), mainly sleep disorders (34.7%) and mood and anxiety disorders (16.2%). A high number of children and adolescents impacted by the terrorist attack required follow-up consultations after exposure to the mass terrorist attack, the first care-seeking requests continued to occur three years later, although at a slower rate than in the first and second years. New consultations for other kinds of trauma were observed over time.DiscussionThis study supports previous findings on the significant impact of mass trauma in the pediatric population showing even a higher level of PTSD and a high rate of comorbidities. This may be explained by the brutality of the traumatic event, particularly for this age group. The findings of this study have implications for early interventions and long-term care for children and adolescents to prevent the development of chronic PTSD into adulthood

Cutoff scores for the “Interest game”, an application for the assessment of diminished interest in neurocognitive disorders

Diminished interest is a core feature of apathy that shows high prevalence in people with Mild and Major Neurocognitive disorders (NCD). In the clinical setting, apathy is mainly assessed using clinical scales and questionnaires, but new technologies are starting to be employed to complement classical instruments. Here, we explored the performance of the “Interest game,” a ludic application that assesses personal interests, in discriminating between persons with and without diminished interest based on the Apathy Diagnostic Criteria. Two hundred and twenty-seven elderly participants (56 healthy controls, 118 persons with mild-NCD, and 53 with major-NCD) completed the Interest game and were assessed by clinicians concerning the presence and the severity of apathy. Results showed that the application scores varied with the presence of apathy, the type of disorder, and the education level. Cutoff scores calculated for persons with Mild-NCD resulted in a sensitivity of 0.68 and a specificity of 0.65 for the main score index, suggesting the interest of employing this application in the clinical setting to complement the classical assessment

Recommendations for the non-pharmacological treatment of apathy in brain disorders

Apathy is a common neuropsychiatric syndrome observed across many neurocognitive and psychiatric disorders. Although there are currently no definitive standard therapies for the treatment of apathy, non-pharmacological treatment (NPT) is often considered to be at the forefront of clinical management. However, guidelines on how to select, prescribe and administer NPT in clinical practice are lacking. Furthermore, although new Information and Communication Technologies (ICT) are beginning to be employed in NPT, their role is still unclear. The objective of the present work is to provide recommendations for the use of NPT for apathy, and to discuss the role of ICT in this domain, based on opinions gathered from experts in the field. The expert panel included 20 researchers and healthcare professionals working on brain disorders and apathy. Following a standard Delphi methodology, experts answered questions via several rounds of web-surveys, and then discussed the results in a plenary meeting. The experts suggested that NPT are useful to consider as therapy for people presenting with different neurocognitive and psychiatric diseases at all stages, with evidence of apathy across domains. The presence of a therapist and/or a caregiver is important in delivering NPT effectively, but parts of the treatment may be performed by the patient alone. NPT can be delivered both in clinical settings and at home. However, while remote treatment delivery may be cost and time-effective, it should be considered with caution, and tailored based on the patient's cognitive and physical profile and living conditions

Comment peut-on impliquer les nTIC dans l’évaluation objective de l’apathie

Apathy is one of the most common neuropsychiatric symptoms in neurocognitive disorders and can be characterized as a significant reduction of goal directed behaviors in three dimensions: Emotions, Cognitions/Behaviors and Social Interactions. Apathy is assessed during clinical interviews with clinical scales which induces biases in the assessment. Information and Communication Technologies (ICT) can provide new objective measures and new assessment methods such as digital phenotyping, which consists of extracting a digital signature from an individual’s digital and physiological measures (voice features, heart rate, electrodermal activity, etc.). These measures can be collected from sensors connected to an application on smartphones, for instance, that can also comprise cognitive tests and questionnaires. As part of this thesis work, we identified existing methods of assessment of apathy and explored new methods using ICT. The first section will define this disorder from a theoretical point of view. A first chapter will give a description of the clinical issues, the neural correlates and the processes involved, as well as the main current computational models of apathy. A second chapter will present technological advances in the assessment of apathy and digital biomarkers of apathy. Finally, the MoTap project will be described, which is the central project carried out with the aim of developing a multidimensional and multimodal assessment tool for apathy. In the second section, three studies will be presented followed by a general discussion. The first study explores the use of a serious game to assess loss of interest in apathy. Results showed that subjects suffering from apathy had significantly less interest in activities than non-apathetic subjects. In a second study, we explored how voice features can correlate to apathy symptoms and differentiate between apathetic and non-apathetic subjects. We asked subjects to recall emotional events (positive and negative) of their life and recorded them. We then extracted acoustic features using automatic speech analysis. Results showed that the more severe the apathy symptoms were, the less talkative were the subjects and the more monotonous was their voice. In the final study presented, we measured facial expressivity from the same sample as the previous study. We extracted automatically facial features, also called action units (AU). Results showed that overall, the more severe the affect symptoms were, the less intense was the facial expressivity. In the single AU comparison, results were different depending on gender and the task’s emotional valence (positive or negative). Several emotional and non-emotional AUs were linked to apathy in the upper region of the face (e.g. inner and outer brows movements) and lower region (e.g. lips part). These studies all aimed to explore new measures for apathy and need more validation with larger samples. Future studies should involve multiple types of assessment such as self-questionnaires and physiological measures, to ensure that the results are specific to apathy and no other confounding factors (cognitive decline, depression, stress etc.).L’apathie est un trouble neuropsychiatrique qui se caractérise par une réduction quantifiable et significative des comportements dirigés vers un but dans trois domaines : les cognitions/comportements, les émotions et les interactions sociales. Elle peut être présente dans les troubles neurocognitifs (TNC) tels que la maladie d’Alzheimer, psychiatriques (ex. schizophrénie, dépression, etc.) ou à la suite d’un trauma crânien. Les échelles d’évaluation de l’apathie sont l’unique méthode d’évaluation comme pour l’ensemble des troubles affectifs. Les échelles comportent un certain nombre de biais notamment au niveau de l’objectivité puisque le clinicien doit se baser sur les dires du patient hors de son contexte habituel. Or l’apathie est un facteur de risque de développer un TNC chez les sujets âgés et sa détection à des stades précoces est importante pour mettre en place une prise en charge. Les Technologies de l’Information et de la Communication (TIC) offrent la possibilité de développer de nouveaux outils d’évaluation notamment par le biais du phénotypage numérique ou digital phenotyping. Ce dernier consiste à extraire un profil ou une signature numérique à partir de données de mesures physiologiques et comportementales (appelés biomarqueurs numériques) enregistrées par des appareils employés par les individus au quotidien (smartphone ou tablette). Ces supports offrent la possibilité d’intégrer entre autres des questionnaires ou des tests cognitifs numérisés et de les associer aux données capteurs (ex. micro, caméra, cardiaque). Dans le cadre de ce travail de thèse, nous avons recensé les méthodes d’évaluation déjà existantes de l’apathie et exploré de nouvelles méthodes en employant les TIC. La première étude est centrée sur le développement d’une application évaluant les centres d’intérêts des patients par le biais d’un serious game (jeu ludique). Les résultats ont montré que les sujets apathiques avaient significativement moins de centres d’intérêts que les sujets non apathiques. Dans une seconde étude, nous avons exploré les marqueurs vocaux acoustiques à partir d’une tâche de discours émotionnel. Les résultats ont montré que les sujets apathiques avaient un discours significativement plus lent et ponctué de pauses comparativement aux non apathiques. D’autres paramètres, tels que la variation de la mélodie de la voix ou la qualité de la voix, étaient significativement différents entre les groupes. Enfin, dans la dernière étude, nous avons exploré les marqueurs faciaux impliqués dans l’apathie dans la même tâche que l’étude précédente. Nous avons pu extraire automatiquement l’intensité et la fréquence de 17 expressions faciales ou Action Units (AU). Les résultats ont montré que, globalement, plus les symptômes de réduction d’affect sont importants moins sont intenses les expressions faciales. En comparant les AUs un à un, les résultats sont différents en fonction de la valence de l’histoire (positive ou négative) et du sexe. Plusieurs AUs émotionnels et non émotionnels sont impliqués au niveau de la partie supérieure (ex. froncement des sourcils) et inférieure (ex. plissement des lèvres) du visage, certains sont également impliqués dans la dépression (le sourire). Ces études portent toutes les trois sur de nouvelles mesures et nécessitent des recherches supplémentaires avec de plus gros échantillons pour valider nos résultats. Elles devront impliquer également des méthodes d’évaluations différentes (auto-questionnaire, mesures physiologiques telles que les données d’électroencéphalogramme, imagerie cérébrale, conductance cutanée) afin de s’assurer que ces résultats sont spécifiques à l’apathie