Associations between transdiagnostic traits of psychopathology and hybrid posttraumatic stress disorder factors in a trauma-exposed community sample

Dimensional conceptualizations of psychopathology hold promise for understanding the high rates of comorbidity with posttraumatic stress disorder (PTSD). Linking PTSD symptoms to transdiagnostic dimensions of psychopathology may enable researchers and clinicians to understand the patterns and breadth of behavioral sequelae following traumatic experiences that may be shared with other psychiatric disorders. To explore this premise, we recruited a trauma-exposed online community sample (N = 462) and measured dimensional transdiagnostic traits of psychopathology using parceled facets derived from the Personality Inventory for DSM-5 Faceted-Short Form. PTSD symptom factors were measured using the PTSD Checklist for DSM-5 and derived using confirmatory factor analysis according to the seven-factor hybrid model (i.e., Intrusions, Avoidance, Negative Affect, Anhedonia, Externalizing Behaviors, Anxious Arousal, And Dysphoric Arousal). We observed hypothesized associations between PTSD factors and transdiagnostic traits indicating that some transdiagnostic dimensions were associated with nearly all PTSD symptom factors (e.g., emotional lability: rmean = .35), whereas others showed more unique relationships (e.g., hostility-Externalizing Behavior: r = .60; hostility with other PTSD factors: rs = .12-.31). All PTSD factors were correlated with traits beyond those that would appear to be construct-relevant, suggesting the possibility of indirect associations that should be explicated in future research. The results indicate the breadth of trait-like consequences associated with PTSD symptom exacerbation, with implications for case conceptualization and treatment planning. Although PTSD is not a personality disorder, the findings indicate that increased PTSD factor severity is moderately associated with different patterns of trait-like disruptions in many areas of functioning.

Human and machine recognition of dynamic and static facial expressions: prototypicality, ambiguity, and complexity

A growing body of research suggests that movement aids facial expression recognition. However, less is known about the conditions under which the dynamic advantage occurs. The aim of this research was to test emotion recognition in static and dynamic facial expressions, thereby exploring the role of three featural parameters (prototypicality, ambiguity, and complexity) in human and machine analysis. In two studies, facial expression videos and corresponding images depicting the peak of the target and non-target emotion were presented to human observers and the machine classifier (FACET). Results revealed higher recognition rates for dynamic stimuli compared to non-target images. Such benefit disappeared in the context of target-emotion images which were similarly well (or even better) recognised than videos, and more prototypical, less ambiguous, and more complex in appearance than non-target images. While prototypicality and ambiguity exerted more predictive power in machine performance, complexity was more indicative of human emotion recognition. Interestingly, recognition performance by the machine was found to be superior to humans for both target and non-target images. Together, the findings point towards a compensatory role of dynamic information, particularly when static-based stimuli lack relevant features of the target emotion. Implications for research using automatic facial expression analysis (AFEA) are discussed.

DynAMoS: The Dynamic Affective Movie Clip Database for Subjectivity Analysis

In this paper, we describe the design, collection, and validation of a new video database that includes holistic and dynamic emotion ratings from 83 participants watching 22 affective movie clips. In contrast to previous work in Affective Computing, which pursued a single "ground truth" label for the affective content of each moment of each video (e.g., by averaging the ratings of 2 to 7 trained participants), we embrace the subjectivity inherent to emotional experiences and provide the full distribution of all participants' ratings (with an average of 76.7 raters per video). We argue that this choice represents a paradigm shift with the potential to unlock new research directions, generate new hypotheses, and inspire novel methods in the Affective Computing community. We also describe several interdisciplinary use cases for the database: to provide dynamic norms for emotion elicitation studies (e.g., in psychology, medicine, and neuroscience), to train and test affective content analysis algorithms (e.g., for dynamic emotion recognition, video summarization, and movie recommendation), and to study subjectivity in emotional reactions (e.g., to identify moments of emotional ambiguity or ambivalence within movies, identify predictors of subjectivity, and develop personalized affective content analysis algorithms). The database is made freely available to researchers for noncommercial use at https://dynamos.mgb.org.

Randomized trial of brief interpersonal psychotherapy and cognitive behavioral therapy for depression delivered both in-person and by telehealth

BACKGROUND

Expert consensus guidelines recommend Cognitive Behavioral Therapy (CBT) and Interpersonal Psychotherapy (IPT), interventions that were historically delivered face-to-face, as first-line treatments for Major Depressive Disorder (MDD). Despite ubiquity of telehealth following the COVID-19 pandemic, little is known about differential outcomes with CBT versus IPT delivered in-person (IP) or via telehealth (TH) or whether working alliance is affected.

METHODS

Adults meeting DSM-5 criteria for MDD were randomly assigned to either 8 sessions of IPT or CBT (group). Mid-trial, COVID-19 forced a change of therapy delivery from IP to TH (study phase). We compared changes in Hamilton Rating Scale for Depression (HRSD-17) and Working Alliance Inventory (WAI) scores for individuals by group and phase: CBT-IP (n = 24), CBT-TH (n = 11), IPT-IP (n = 25) and IPT-TH (n = 17).

RESULTS

HRSD-17 scores declined significantly from pre to post treatment (pre: M = 17.7, SD = 4.4 vs. post: M = 11.7, SD = 5.9; p < .001; d = 1.45) without significant group or phase effects. WAI scores did not differ by group or phase. Number of completed therapy sessions was greater for TH (M = 7.8, SD = 1.2) relative to IP (M = 7.2, SD = 1.6) (Mann-Whitney U = 387.50, z = -2.24, p = .025).

LIMITATIONS

Participants were not randomly assigned to IP versus TH. Sample size is small.

CONCLUSIONS

This study provides preliminary evidence supporting the efficacy of both brief IPT and CBT, delivered by either TH or IP, for depression. It showed that working alliance is preserved in TH, and delivery via TH may improve therapy adherence. Prospective, randomized controlled trials are needed to definitively test efficacy of brief IPT and CBT delivered via TH versus IP.

Weighting schemes and incomplete data: A generalized Bayesian framework for chance-corrected interrater agreement

Van Oest (2019) developed a framework to assess interrater agreement for nominal categories and complete data. We generalize this framework to all four situations of nominal or ordinal categories and complete or incomplete data. The mathematical solution yields a chance-corrected agreement coefficient that accommodates any weighting scheme for penalizing rater disagreements and any number of raters and categories. By incorporating Bayesian estimates of the category proportions, the generalized coefficient also captures situations in which raters classify only subsets of items; that is, incomplete data. Furthermore, this coefficient encompasses existing chance-corrected agreement coefficients: the S-coefficient, Scott's pi, Fleiss' kappa, and Van Oest's uniform prior coefficient, all augmented with a weighting scheme and the option of incomplete data. We use simulation to compare these nested coefficients. The uniform prior coefficient tends to perform best, in particular, if one category has a much larger proportion than others. The gap with Scott's pi and Fleiss' kappa widens if the weighting scheme becomes more lenient to small disagreements and often if more item classifications are missing; missingness biases play a moderating role. The uniform prior coefficient often performs much better than the S-coefficient, but the S-coefficient sometimes performs best for small samples, missing data, and lenient weighting schemes. The generalized framework implies a new interpretation of chance-corrected weighted agreement coefficients: These coefficients estimate the probability that both raters in a pair assign an item to its correct category without guessing. Whereas Van Oest showed this interpretation for unweighted agreement, we generalize to weighted agreement. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Toward Causal Understanding of Therapist-Client Relationships: A Study of Language Modality and Social Entrainment

The relationship between a therapist and their client is one of the most critical determinants of successful therapy. The working alliance is a multifaceted concept capturing the collaborative aspect of the therapist-client relationship; a strong working alliance has been extensively linked to many positive therapeutic outcomes. Although therapy sessions are decidedly multimodal interactions, the language modality is of particular interest given its recognized relationship to similar dyadic concepts such as rapport, cooperation, and affiliation. Specifically, in this work we study language entrainment, which measures how much the therapist and client adapt toward each other's use of language over time. Despite the growing body of work in this area, however, relatively few studies examine causal relationships between human behavior and these relationship metrics: does an individual's perception of their partner affect how they speak, or does how they speak affect their perception? We explore these questions in this work through the use of structural equation modeling (SEM) techniques, which allow for both multilevel and temporal modeling of the relationship between the quality of the therapist-client working alliance and the participants' language entrainment. In our first experiment, we demonstrate that these techniques perform well in comparison to other common machine learning models, with the added benefits of interpretability and causal analysis. In our second analysis, we interpret the learned models to examine the relationship between working alliance and language entrainment and address our exploratory research questions. The results reveal that a therapist's language entrainment can have a significant impact on the client's perception of the working alliance, and that the client's language entrainment is a strong indicator of their perception of the working alliance. We discuss the implications of these results and consider several directions for future work in multimodality.

Computational analysis of spoken language in acute psychosis and mania

OBJECTIVES

This study aimed to (1) determine the feasibility of collecting behavioral data from participants hospitalized with acute psychosis and (2) begin to evaluate the clinical information that can be computationally derived from such data.

METHODS

Behavioral data was collected across 99 sessions from 38 participants recruited from an inpatient psychiatric unit. Each session started with a semi-structured interview modeled on a typical "clinical rounds" encounter and included administration of the Positive and Negative Syndrome Scale (PANSS).

ANALYSIS

We quantified aspects of participants' verbal behavior during the interview using lexical, coherence, and disfluency features. We then used two complementary approaches to explore our second objective. The first approach used predictive models to estimate participants' PANSS scores from their language features. Our second approach used inferential models to quantify the relationships between individual language features and symptom measures.

RESULTS

Our predictive models showed promise but lacked sufficient data to achieve clinically useful accuracy. Our inferential models identified statistically significant relationships between numerous language features and symptom domains.

CONCLUSION

Our interview recording procedures were well-tolerated and produced adequate data for transcription and analysis. The results of our inferential modeling suggest that automatic measurements of expressive language contain signals highly relevant to the assessment of psychosis. These findings establish the potential of measuring language during a clinical interview in a naturalistic setting and generate specific hypotheses that can be tested in future studies. This, in turn, will lead to more accurate modeling and better understanding of the relationships between expressive language and psychosis.

A Bayesian multilevel analysis of the longitudinal associations between relationship quality and suicidal ideation and attempts among youth with bipolar disorder

BACKGROUND

Youth with bipolar disorder (BD) are at high risk for suicidal thoughts and behaviors and frequently experience interpersonal impairment, which is a risk factor for suicide. Yet, no study to date has examined the longitudinal associations between relationship quality in family/peer domains and suicidal thoughts and behaviors among youth with BD. Thus, we investigated how between-person differences - reflecting the average relationship quality across time - and within-person changes, reflecting recent fluctuations in relationship quality, act as distal and/or proximal risk factors for suicidal ideation (SI) and suicide attempts.

METHODS

We used longitudinal data from the Course and Outcome of Bipolar Youth Study (N = 413). Relationship quality variables were decomposed into stable (i.e., average) and varying (i.e., recent) components and entered, along with major clinical covariates, into separate Bayesian multilevel models predicting SI and suicide attempt. We also examined how the relationship quality effects interacted with age and sex.

RESULTS

Poorer average relationship quality with parents (β = -.33, 95% Bayesian highest density interval (HDI) [-0.54, -0.11]) or friends (β = -.33, 95% HDI [-0.55, -0.11]) was longitudinally associated with increased risk of SI but not suicide attempt. Worsening recent relationship quality with parents (β = -.10, 95% HDI [-0.19, -0.03]) and, to a lesser extent, friends (β = -.06, 95% HDI [-0.15, 0.03]) was longitudinally associated with increased risk of SI, but only worsening recent relationship quality with parents was also associated with increased risk of suicide attempt (β = -.15, 95% HDI [-0.31, 0.01]). The effects of certain relationship quality variables were moderated by gender but not age.

CONCLUSIONS

Among youth with BD, having poorer average relationship quality with peers and/or parents represents a distal risk factor for SI but not suicide attempts. Additionally, worsening recent relationship quality with parents may be a time-sensitive indicator of increased risk for SI or suicide attempt.

Reconsidering the Duchenne Smile: Formalizing and Testing Hypotheses about Eye Constriction and Positive Emotion

The common view of emotional expressions is that certain configurations of facial-muscle movements reliably reveal certain categories of emotion. The principal exemplar of this view is the Duchenne smile, a configuration of facial-muscle movements (i.e., smiling with eye constriction) that has been argued to reliably reveal genuine positive emotion. In this paper, we formalized a list of hypotheses that have been proposed regarding the Duchenne smile, briefly reviewed the literature weighing on these hypotheses, identified limitations and unanswered questions, and conducted two empirical studies to begin addressing these limitations and answering these questions. Both studies analyzed a database of 751 smiles observed while 136 participants completed experimental tasks designed to elicit amusement, embarrassment, fear, and physical pain. Study 1 focused on participants' self-reported positive emotion and Study 2 focused on how third-party observers would perceive videos of these smiles. Most of the hypotheses that have been proposed about the Duchenne smile were either contradicted by or only weakly supported by our data. Eye constriction did provide some information about experienced positive emotion, but this information was lacking in specificity, already provided by other smile characteristics, and highly dependent on context. Eye constriction provided more information about perceived positive emotion, including some unique information over other smile characteristics, but context was also important here as well. Overall, our results suggest that accurately inferring positive emotion from a smile requires more sophisticated methods than simply looking for the presence/absence (or even the intensity) of eye constriction.

Depression Severity Assessment for Adolescents at High Risk of Mental Disorders

Recent progress in artificial intelligence has led to the development of automatic behavioral marker recognition, such as facial and vocal expressions. Those automatic tools have enormous potential to support mental health assessment, clinical decision making, and treatment planning. In this paper, we investigate nonverbal behavioral markers of depression severity assessed during semi-structured medical interviews of adolescent patients. The main goal of our research is two-fold: studying a unique population of adolescents at high risk of mental disorders and differentiating mild depression from moderate or severe depression. We aim to explore computationally inferred facial and vocal behavioral responses elicited by three segments of the semi-structured medical interviews: Distress Assessment Questions, Ubiquitous Questions, and Concept Questions. Our experimental methodology reflects best practise used for analyzing small sample size and unbalanced datasets of unique patients. Our results show a very interesting trend with strongly discriminative behavioral markers from both acoustic and visual modalities. These promising results are likely due to the unique classification task (mild depression vs. moderate and severe depression) and three types of probing questions.

Toward Multimodal Modeling of Emotional Expressiveness

Emotional expressiveness captures the extent to which a person tends to outwardly display their emotions through behavior. Due to the close relationship between emotional expressiveness and behavioral health, as well as the crucial role that it plays in social interaction, the ability to automatically predict emotional expressiveness stands to spur advances in science, medicine, and industry. In this paper, we explore three related research questions. First, how well can emotional expressiveness be predicted from visual, linguistic, and multimodal behavioral signals? Second, how important is each behavioral modality to the prediction of emotional expressiveness? Third, which behavioral signals are reliably related to emotional expressiveness? To answer these questions, we add highly reliable transcripts and human ratings of perceived emotional expressiveness to an existing video database and use this data to train, validate, and test predictive models. Our best model shows promising predictive performance on this dataset (RMSE = 0.65, R 2 = 0.45, r = 0.74). Multimodal models tend to perform best overall, and models trained on the linguistic modality tend to outperform models trained on the visual modality. Finally, examination of our interpretable models' coefficients reveals a number of visual and linguistic behavioral signals-such as facial action unit intensity, overall word count, and use of words related to social processes-that reliably predict emotional expressiveness.

Reconsidering the Duchenne Smile: Indicator of Positive Emotion or Artifact of Smile Intensity?

The Duchenne smile hypothesis is that smiles that include eye constriction (AU6) are the product of genuine positive emotion, whereas smiles that do not are either falsified or related to negative emotion. This hypothesis has become very influential and is often used in scientific and applied settings to justify the inference that a smile is either true or false. However, empirical support for this hypothesis has been equivocal and some researchers have proposed that, rather than being a reliable indicator of positive emotion, AU6 may just be an artifact produced by intense smiles. Initial support for this proposal has been found when comparing smiles related to genuine and feigned positive emotion; however, it has not yet been examined when comparing smiles related to genuine positive and negative emotion. The current study addressed this gap in the literature by examining spontaneous smiles from 136 participants during the elicitation of amusement, embarrassment, fear, and pain (from the BP4D+ dataset). Bayesian multilevel regression models were used to quantify the associations between AU6 and self-reported amusement while controlling for smile intensity. Models were estimated to infer amusement from AU6 and to explain the intensity of AU6 using amusement. In both cases, controlling for smile intensity substantially reduced the hypothesized association, whereas the effect of smile intensity itself was quite large and reliable. These results provide further evidence that the Duchenne smile is likely an artifact of smile intensity rather than a reliable and unique indicator of genuine positive emotion.

Narcissistic Admiration and Rivalry: An Interpersonal Approach to Construct Validation

The present study applied the interpersonal perspective in testing the narcissistic admiration and rivalry concept (NARC) and examining the construct validity of the corresponding Narcissistic Admiration and Rivalry Questionnaire (NARQ). Two undergraduate samples (Sample 1: N = 290; Sample 2: N = 188) completed self-report measures of interpersonal processes based in the interpersonal circumplex (IPC), as well as measures of related constructs. In examining IPC correlates, the authors used a novel bootstrapping approach to determine if admiration and rivalry related to differing interpersonal profiles. Consistent with the authors' hypotheses, admiration was distinctly related to generally agentic (i.e., dominant) interpersonal processes, whereas rivalry generally reflected (low) communal (i.e., hostile) interpersonal processes. Furthermore, NARQ-admiration and NARQ-rivalry related to generally adaptive and maladaptive aspects of status-related constructs, emotional, personality, and social adjustment, respectively. This research provides further support for the NARC, as well as construct validation for the NARQ.

Alexithymia - Not autism - is associated with frequency of social interactions in adults

OBJECTIVE

While much is known about the quality of social behavior among neurotypical individuals and those with autism spectrum disorder (ASD), little work has evaluated quantity of social interactions. This study used ecological momentary assessment (EMA) to quantify in vivo daily patterns of social interaction in adults as a function of demographic and clinical factors.

METHOD

Adults with and without ASD (N_ASD = 23, N_Neurotypical = 52) were trained in an EMA protocol to report their social interactions via smartphone over one week. Participants completed measures of IQ, ASD symptom severity and alexithymia symptom severity.

RESULTS

Cyclical multilevel models were used to account for nesting of observations. Results suggest a daily cyclical pattern of social interaction that was robust to ASD and alexithymia symptoms. Adults with ASD did not have fewer social interactions than neurotypical peers; however, severity of alexithymia symptoms predicted fewer social interactions regardless of ASD status.

CONCLUSIONS

These findings suggest that alexithymia, not ASD severity, may drive social isolation and highlight the need to reevaluate previously accepted notions regarding differences in social behavior utilizing modern methods.

Properties of the Continuous Assessment of Interpersonal Dynamics Across Sex, Level of Familiarity, and Interpersonal Conflict

The Continuous Assessment of Interpersonal Dynamics (CAID) is a method in which trained observers continuously code the dominance and warmth of individuals who interact with one another in dyads. This method has significant promise for assessing dynamic interpersonal processes. The purpose of this study was to examine the impact of individual sex, dyadic familiarity, and situational conflict on patterns of interpersonal warmth, dominance, and complementarity as assessed via CAID. We used six samples with 603 dyads, including two samples of unacquainted mixed-sex undergraduates interacting in a collaborative task, two samples of couples interacting in both collaborative and conflict tasks, and two samples of mothers and children interacting in both collaborative and conflict tasks. Complementarity effects were robust across all samples, and individuals tended to be relatively warm and dominant. Results from multilevel models indicated that women were slightly warmer than men, whereas there were no sex differences in dominance. Unfamiliar dyads and dyads interacting in more collaborative tasks were relatively warmer, more submissive, and more complementary on warmth but less complementary on dominance. These findings speak to the utility of the CAID method for assessing interpersonal dynamics and provide norms for researchers who use the method for different types of samples and applications.

DARMA: Software for dual axis rating and media annotation

Continuous measurement systems provide a means of measuring dynamic behavioral and experiential processes as they play out over time. DARMA is a modernized continuous measurement system that synchronizes media playback and the continuous recording of two-dimensional measurements. These measurements can be observational or self-reported and are provided in real-time through the manipulation of a computer joystick. DARMA also provides tools for reviewing and comparing collected measurements and for customizing various settings. DARMA is a domain-independent software tool that was designed to aid researchers who are interested in gaining a deeper understanding of behavior and experience. It is especially well-suited to the study of affective and interpersonal processes, such as the perception and expression of emotional states and the communication of social signals. DARMA is open-source using the GNU General Public License (GPL) and is available for free download from http://darma.jmgirard.com .

The Association between Daily Posttraumatic Stress Symptoms and Pain over the First 14-days after Injury: An Experience Sampling Study

OBJECTIVES

Psychosocial factors and responses to injury modify the transition from acute to chronic pain. Specifically, posttraumatic stress disorder symptoms (PTSS; reexperiencing, avoidance, and hyperarousal symptoms) exacerbate and co-occur with chronic pain. Yet no study has prospectively considered the associations among these psychological processes and pain reports using experience sampling methods (ESM) during the acute aftermath of injury. This study applied ESM via daily text messaging to monitor and detect relationships among psychosocial factors and post-injury pain across the first 14-days after emergency department (ED) discharge.

METHODS

We recruited 75 adults (59% male; M age = 33) who experienced a potentially traumatic injury (i.e., involving life threat or serious injury) in the past 24-hours from the EDs of two Level 1 trauma centers. Participants received 5 questions per day via text messaging from Day-1 to Day-14 post-ED discharge; three questions measured PTSS, one question measured perceived social support, and one question measured physical pain.

RESULTS

Sixty-seven participants provided sufficient data for inclusion in the final analyses, and the average response rate per subject was 86%. Pain severity score decreased from a mean of 7.2 to 4.4 over 14 days and 50% of the variance in daily pain scores was within-person. In multilevel structural equation models, pain scores decreased over time, and daily fluctuations of hyperarousal (b = 0.22, 95% CI [0.08, 0.36]) were uniquely associated with daily fluctuations in reported pain level within each person.

CONCLUSIONS

Daily hyperarousal symptoms predict same-day pain severity over the acute post-injury recovery period. We also demonstrated feasibility to screen and identify patients at risk for pain chronicity in the acute aftermath of injury. Early interventions aimed at addressing hyperarousal (e.g. anxiolytics) could potentially aid in reducing experience of pain. This article is protected by copyright. All rights reserved.

Interpersonal problems across levels of the psychopathology hierarchy

We examined the relationship between psychopathology and interpersonal problems in a sample of 825 clinical and community participants. Sixteen psychiatric diagnoses and five transdiagnostic dimensions were examined in relation to self-reported interpersonal problems. The structural summary method was used with the Inventory of Interpersonal Problems Circumplex Scales to examine interpersonal problem profiles for each diagnosis and dimension. We built a structural model of mental disorders including factors corresponding to detachment (avoidant personality, social phobia, major depression), internalizing (dependent personality, borderline personality, panic disorder, posttraumatic stress, major depression), disinhibition (antisocial personality, drug dependence, alcohol dependence, borderline personality), dominance (histrionic personality, narcissistic personality, paranoid personality), and compulsivity (obsessive-compulsive personality). All dimensions showed good interpersonal prototypicality (e.g., detachment was defined by a socially avoidant/nonassertive interpersonal profile) except for internalizing, which was diffusely associated with elevated interpersonal distress. The findings for individual disorders were largely consistent with the dimension that each disorder loaded on, with the exception of the internalizing and dominance disorders, which were interpersonally heterogeneous. These results replicate previous findings and provide novel insights into social dysfunction in psychopathology by wedding the power of hierarchical (i.e., dimensional) modeling and interpersonal circumplex assessment.

FERA 2017 - Addressing Head Pose in the Third Facial Expression Recognition and Analysis Challenge

The field of Automatic Facial Expression Analysis has grown rapidly in recent years. However, despite progress in new approaches as well as benchmarking efforts, most evaluations still focus on either posed expressions, near-frontal recordings, or both. This makes it hard to tell how existing expression recognition approaches perform under conditions where faces appear in a wide range of poses (or camera views), displaying ecologically valid expressions. The main obstacle for assessing this is the availability of suitable data, and the challenge proposed here addresses this limitation. The FG 2017 Facial Expression Recognition and Analysis challenge (FERA 2017) extends FERA 2015 to the estimation of Action Units occurrence and intensity under different camera views. In this paper we present the third challenge in automatic recognition of facial expressions, to be held in conjunction with the 12th IEEE conference on Face and Gesture Recognition, May 2017, in Washington, United States. Two sub-challenges are defined: the detection of AU occurrence, and the estimation of AU intensity. In this work we outline the evaluation protocol, the data used, and the results of a baseline method for both sub-challenges.

Sayette Group Formation Task (GFT) Spontaneous Facial Expression Database

Despite the important role that facial expressions play in interpersonal communication and our knowledge that interpersonal behavior is influenced by social context, no currently available facial expression database includes multiple interacting participants. The Sayette Group Formation Task (GFT) database addresses the need for well-annotated video of multiple participants during unscripted interactions. The database includes 172,800 video frames from 96 participants in 32 three-person groups. To aid in the development of automated facial expression analysis systems, GFT includes expert annotations of FACS occurrence and intensity, facial landmark tracking, and baseline results for linear SVM, deep learning, active patch learning, and personalized classification. Baseline performance is quantified and compared using identical partitioning and a variety of metrics (including means and confidence intervals). The highest performance scores were found for the deep learning and active patch learning methods. Learn more at http://osf.io/7wcyz.

A Primer on Observational Measurement

Observational measurement plays an integral role in a variety of scientific endeavors within biology, psychology, sociology, education, medicine, and marketing. The current article provides an interdisciplinary primer on observational measurement; in particular, it highlights recent advances in observational methodology and the challenges that accompany such growth. First, we detail the various types of instrument that can be used to standardize measurements across observers. Second, we argue for the importance of validity in observational measurement and provide several approaches to validation based on contemporary validity theory. Third, we outline the challenges currently faced by observational researchers pertaining to measurement drift, observer reactivity, reliability analysis, and time/expense. Fourth, we describe recent advances in computer-assisted measurement, fully automated measurement, and statistical data analysis. Finally, we identify several key directions for future observational research to explore.

Momentary patterns of covariation between specific affects and interpersonal behavior: Linking relationship science and personality assessment

Relationships are among the most salient factors affecting happiness and wellbeing for individuals and families. Relationship science has identified the study of dyadic behavioral patterns between couple members during conflict as an important window in to relational functioning with both short-term and long-term consequences. Several methods have been developed for the momentary assessment of behavior during interpersonal transactions. Among these, the most popular is the Specific Affect Coding System (SPAFF), which organizes social behavior into a set of discrete behavioral constructs. This study examines the interpersonal meaning of the SPAFF codes through the lens of interpersonal theory, which uses the fundamental dimensions of Dominance and Affiliation to organize interpersonal behavior. A sample of 67 couples completed a conflict task, which was video recorded and coded using SPAFF and a method for rating momentary interpersonal behavior, the Continuous Assessment of Interpersonal Dynamics (CAID). Actor partner interdependence models in a multilevel structural equation modeling framework were used to study the covariation of SPAFF codes and CAID ratings. Results showed that a number of SPAFF codes had clear interpersonal signatures, but many did not. Additionally, actor and partner effects for the same codes were strongly consistent with interpersonal theory's principle of complementarity. Thus, findings reveal points of convergence and divergence in the 2 systems and provide support for central tenets of interpersonal theory. Future directions based on these initial findings are discussed. (PsycINFO Database Record

A Case Report: Pseudoangiomatous Stromal Hyperplasia Tumor Presenting as a Palpable Mass

We report a case of woman with a palpable lump on her left breast. On mammography, a huge mass located between the inner and the outer inferior breast quadrants of the left breast was found. The ultrasound examination realized later revealed a heterogeneous mass with smooth and lobulated borders. An MRI was also performed, showing an oval mass with heterogeneous areas of enhancement. Finally, a core biopsy under sonographic guidance revealed a pseudoangiomatous stromal hyperplasia of the breast.

Spontaneous facial expression in unscripted social interactions can be measured automatically

Methods to assess individual facial actions have potential to shed light on important behavioral phenomena ranging from emotion and social interaction to psychological disorders and health. However, manual coding of such actions is labor intensive and requires extensive training. To date, establishing reliable automated coding of unscripted facial actions has been a daunting challenge impeding development of psychological theories and applications requiring facial expression assessment. It is therefore essential that automated coding systems be developed with enough precision and robustness to ease the burden of manual coding in challenging data involving variation in participant gender, ethnicity, head pose, speech, and occlusion. We report a major advance in automated coding of spontaneous facial actions during an unscripted social interaction involving three strangers. For each participant (n = 80, 47 % women, 15 % Nonwhite), 25 facial action units (AUs) were manually coded from video using the Facial Action Coding System. Twelve AUs occurred more than 3 % of the time and were processed using automated FACS coding. Automated coding showed very strong reliability for the proportion of time that each AU occurred (mean intraclass correlation = 0.89), and the more stringent criterion of frame-by-frame reliability was moderate to strong (mean Matthew's correlation = 0.61). With few exceptions, differences in AU detection related to gender, ethnicity, pose, and average pixel intensity were small. Fewer than 6 % of frames could be coded manually but not automatically. These findings suggest automated FACS coding has progressed sufficiently to be applied to observational research in emotion and related areas of study.

Estimating smile intensity: A better way

Both the occurrence and intensity of facial expressions are critical to what the face reveals. While much progress has been made towards the automatic detection of facial expression occurrence, controversy exists about how to estimate expression intensity. The most straight-forward approach is to train multiclass or regression models using intensity ground truth. However, collecting intensity ground truth is even more time consuming and expensive than collecting binary ground truth. As a shortcut, some researchers have proposed using the decision values of binary-trained maximum margin classifiers as a proxy for expression intensity. We provide empirical evidence that this heuristic is flawed in practice as well as in theory. Unfortunately, there are no shortcuts when it comes to estimating smile intensity: researchers must take the time to collect and train on intensity ground truth. However, if they do so, high reliability with expert human coders can be achieved. Intensity-trained multiclass and regression models outperformed binary-trained classifier decision values on smile intensity estimation across multiple databases and methods for feature extraction and dimensionality reduction. Multiclass models even outperformed binary-trained classifiers on smile occurrence detection.

Automated Audiovisual Depression Analysis

Analysis of observable behavior in depression primarily relies on subjective measures. New computational approaches make possible automated audiovisual measurement of behaviors that humans struggle to quantify (e.g., movement velocity and voice inflection). These tools have the potential to improve screening and diagnosis, identify new behavioral indicators of depression, measure response to clinical intervention, and test clinical theories about underlying mechanisms. Highlights include a study that measured the temporal coordination of vocal tract and facial movements, a study that predicted which adolescents would go on to develop depression based on their voice qualities, and a study that tested the behavioral predictions of clinical theories using automated measures of facial actions and head motion.

Speech volume indexes sex differences in the social-emotional effects of alcohol

Men and women differ dramatically in their rates of alcohol use disorder (AUD), and researchers have long been interested in identifying mechanisms underlying male vulnerability to problem drinking. Surveys suggest that social processes underlie sex differences in drinking patterns, with men reporting greater social enhancement from alcohol than women, and all-male social drinking contexts being associated with particularly high rates of hazardous drinking. But experimental evidence for sex differences in social-emotional response to alcohol has heretofore been lacking. Research using larger sample sizes, a social context, and more sensitive measures of alcohol's rewarding effects may be necessary to better understand sex differences in the etiology of AUD. This study explored the acute effects of alcohol during social exchange on speech volume--an objective measure of social-emotional experience that was reliably captured at the group level. Social drinkers (360 male; 360 female) consumed alcohol (.82 g/kg males; .74 g/kg females), placebo, or a no-alcohol control beverage in groups of 3 over 36-min. Within each of the 3 beverage conditions, equal numbers of groups consisted of all males, all females, 2 females and 1 male, and 1 female and 2 males. Speech volume was monitored continuously throughout the drink period, and group volume emerged as a robust correlate of self-report and facial indexes of social reward. Notably, alcohol-related increases in group volume were observed selectively in all-male groups but not in groups containing any females. Results point to social enhancement as a promising direction for research exploring factors underlying sex differences in problem drinking.

How much training data for facial action unit detection?

By systematically varying the number of subjects and the number of frames per subject, we explored the influence of training set size on appearance and shape-based approaches to facial action unit (AU) detection. Digital video and expert coding of spontaneous facial activity from 80 subjects (over 350,000 frames) were used to train and test support vector machine classifiers. Appearance features were shape-normalized SIFT descriptors and shape features were 66 facial landmarks. Ten-fold cross-validation was used in all evaluations. Number of subjects and number of frames per subject differentially affected appearance and shape-based classifiers. For appearance features, which are high-dimensional, increasing the number of training subjects from 8 to 64 incrementally improved performance, regardless of the number of frames taken from each subject (ranging from 450 through 3600). In contrast, for shape features, increases in the number of training subjects and frames were associated with mixed results. In summary, maximal performance was attained using appearance features from large numbers of subjects with as few as 450 frames per subject. These findings suggest that variation in the number of subjects rather than number of frames per subject yields most efficient performance.

Nonverbal Social Withdrawal in Depression: Evidence from manual and automatic analysis

The relationship between nonverbal behavior and severity of depression was investigated by following depressed participants over the course of treatment and video recording a series of clinical interviews. Facial expressions and head pose were analyzed from video using manual and automatic systems. Both systems were highly consistent for FACS action units (AUs) and showed similar effects for change over time in depression severity. When symptom severity was high, participants made fewer affiliative facial expressions (AUs 12 and 15) and more non-affiliative facial expressions (AU 14). Participants also exhibited diminished head motion (i.e., amplitude and velocity) when symptom severity was high. These results are consistent with the Social Withdrawal hypothesis: that depressed individuals use nonverbal behavior to maintain or increase interpersonal distance. As individuals recover, they send more signals indicating a willingness to affiliate. The finding that automatic facial expression analysis was both consistent with manual coding and revealed the same pattern of findings suggests that automatic facial expression analysis may be ready to relieve the burden of manual coding in behavioral and clinical science.

CARMA: Software for continuous affect rating and media annotation

CARMA is a media annotation program that collects continuous ratings while displaying audio and video files. It is designed to be highly user-friendly and easily customizable. Based on Gottman and Levenson's affect rating dial, CARMA enables researchers and study participants to provide moment-by-moment ratings of multimedia files using a computer mouse or keyboard. The rating scale can be configured on a number of parameters including the labels for its upper and lower bounds, its numerical range, and its visual representation. Annotations can be displayed alongside the multimedia file and saved for easy import into statistical analysis software. CARMA provides a tool for researchers in affective computing, human-computer interaction, and the social sciences who need to capture the unfolding of subjective experience and observable behavior over time.

Social Risk and Depression: Evidence from Manual and Automatic Facial Expression Analysis

Investigated the relationship between change over time in severity of depression symptoms and facial expression. Depressed participants were followed over the course of treatment and video recorded during a series of clinical interviews. Facial expressions were analyzed from the video using both manual and automatic systems. Automatic and manual coding were highly consistent for FACS action units, and showed similar effects for change over time in depression severity. For both systems, when symptom severity was high, participants made more facial expressions associated with contempt, smiled less, and those smiles that occurred were more likely to be accompanied by facial actions associated with contempt. These results are consistent with the "social risk hypothesis" of depression. According to this hypothesis, when symptoms are severe, depressed participants withdraw from other people in order to protect themselves from anticipated rejection, scorn, and social exclusion. As their symptoms fade, participants send more signals indicating a willingness to affiliate. The finding that automatic facial expression analysis was both consistent with manual coding and produced the same pattern of depression effects suggests that automatic facial expression analysis may be ready for use in behavioral and clinical science.

Traumatic neuroprotection with inhibitors of nitric oxide and ADP-ribosylation

N-Methyl-D-aspartate (NMDA) receptor activation is known to contribute to neuronal damage from head trauma. Additionally, NMDA neurotoxicity occurs in part through the generation of nitric oxide (NO), and injury from NO has been shown to be mediated by ADP-ribosylation. Therefore, we investigated whether inhibitors of NO and ADP-ribosylation would protect against acute CA1 traumatic neuronal injury in hippocampal slices subjected to fluid percussion. Treatment with the nitric oxide synthase (NOS) inhibitor, methyl-L-arginine 170 microM for 35 min after trauma injury, improved CA1 antidromic population spike (PS) recovery to 91 +/- 2%, compared to unmediated slices which recovered to only a mean of 20 +/- 4%, 90 min after trauma. Similarly, hemoglobin 50 microM, which directly binds NO, protected against traumatic neuronal injury and yielded a mean CA1 PS recovery of 92 +/- 1%. Treatment with inhibitors of poly-ADP-ribosylation was also strongly protective, with the vitamin nicotinamide 10 mM and 3-aminobenzamide 1 mM yielding PS recoveries of 98 +/- 2% and 90 +/- 3%, respectively. Protection was also seen with inhibitors of mono-ADP-ribosylation, including novobiocin 500 microM and meta-iodobenzylguanidine 20 microM which yielded recoveries of 89 +/- 6% and 96 +/- 26%. Novobiocin also protected against direct application of NO and NMDA. These findings suggest that NO and ADP-ribosylation are mediators of acute traumatic neuronal injury.

Selective enrichment of Th1 CD45RBlow CD4+ T cells in autoimmune infiltrates in experimental allergic encephalomyelitis

The cytokine effector status of CD4+ T cells from lymph nodes (LN) and the central nervous system (CNS) of SJL/J mice immunized with autoantigen in adjuvant for the induction of experimental allergic encephalomyelitis (EAE) was compared. CD4+ T cells were FACS sorted based on the levels of expression of the activation marker CD45RB. Low levels of expression of this surface marker are induced by antigen recognition and are associated with 'effector' T cell function. Reverse transcriptase polymerase chain reaction (PCR) was used to analyze the expression of different T cell cytokine genes in the sorted populations. CD45RBlow cells constituted a minority of CD4+ cells in the LN and expressed elevated levels of IL-2, IFN-gamma, and IL-4 mRNA, whereas the CD45RBlow CD4+ population did not express detectable message for these cytokines under linear PCR conditions. By contrast to the LN, CD4+ cells from the CNS were predominantly CD45RBlow and expressed readily detectable levels of IL-2 and IFN-gamma mRNA, but almost no IL-4 transcription could be detected. IL-4 mRNA levels in CNS were 100- to 250-fold lower than in LN. Also, IL-4 message could not be detected in the CNS 1 week after remission. A cytokine-specific immunocytochemical single cell staining technique was used to enumerate cytokine-producing cells in LN cell populations and in CNS infiltrates. Between 1 and 5% cells in isolated LN cells produced detectable IL-2 and IFN-gamma. By contrast, the frequency of cytokine-producing cells stained in perivascular infiltrates in frozen sections from the brains of animals with active EAE was 10-fold higher.(ABSTRACT TRUNCATED AT 250 WORDS)

[Value of gradient echo MRI in patellar chondromalacia]

The use of MRI in the evaluation of cartilaginous lesions is now well established. Thirty patients suspected of presenting such lesions were investigated by MRI with gradient echo sequences. Twelve patients had an arthroscopy either before or after the MRI. The sequences have allowed characterisation of the lesions of chondromalacia and have showed early lesions not visible at arthroscopy, without need for intraarticular contrast injection.

[Magnetic resonance imaging in fractures caused by fatigue and by insufficiency]

The only important complication of a stress fracture is the true fracture. For this reason, a diagnostic should be made as fast as possible. 30 patients with stress fractures, whose 10 with magnetic resonance imaging are presented and the different radiological technics are compared. The magnetic resonance imaging, showing the first modifications inside the medullary cavity, allows a quick diagnosis in very difficult cases, when the other technics have not given the answer.