Motion energy analysis reveals altered body movement in youth at risk for psychosis

RETRACTED: Left hemisphere lateralization of the limbic system and frontoparietal network (FPN) correlates with positive and negative symptom improvement following cannabidiol (CBD) administration in psychosis and ultra-high risk (UHR) populations: A voxel-wise meta-analysis

This article has been retracted: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/locate/withdrawalpolicy). This article has been retracted at the request of the authors when they discovered and reported to the editors that articles containing population samples drawn from similar cohorts of healthy participants without psychosis were erroneously included in the psychosis subgroup of the meta-analysis. This error in the systematic review processes ultimately affects the findings in the meta-analysis. The authors deeply apologize for this error.

Speech characteristics yield important clues about motor function: Speech variability in individuals at clinical high-risk for psychosis

BACKGROUND AND HYPOTHESIS

Motor abnormalities are predictive of psychosis onset in individuals at clinical high risk (CHR) for psychosis and are tied to its progression. We hypothesize that these motor abnormalities also disrupt their speech production (a highly complex motor behavior) and predict CHR individuals will produce more variable speech than healthy controls, and that this variability will relate to symptom severity, motor measures, and psychosis-risk calculator risk scores.

STUDY DESIGN

We measure variability in speech production (variability in consonants, vowels, speech rate, and pausing/timing) in N = 58 CHR participants and N = 67 healthy controls. Three different tasks are used to elicit speech: diadochokinetic speech (rapidly-repeated syllables e.g., papapa…, pataka…), read speech, and spontaneously-generated speech.

STUDY RESULTS

Individuals in the CHR group produced more variable consonants and exhibited greater speech rate variability than healthy controls in two of the three speech tasks (diadochokinetic and read speech). While there were no significant correlations between speech measures and remotely-obtained motor measures, symptom severity, or conversion risk scores, these comparisons may be under-powered (in part due to challenges of remote data collection during the COVID-19 pandemic).

CONCLUSION

This study provides a thorough and theory-driven first look at how speech production is affected in this at-risk population and speaks to the promise and challenges facing this approach moving forward.

Detecting at-risk mental states for psychosis (ARMS) using machine learning ensembles and facial features

AIMS

Our study aimed to develop a machine learning ensemble to distinguish "at-risk mental states for psychosis" (ARMS) subjects from control individuals from the general population based on facial data extracted from video-recordings.

METHODS

58 non-help-seeking medication-naïve ARMS and 70 healthy subjects were screened from a general population sample. At-risk status was assessed with the Structured Interview for Prodromal Syndromes (SIPS), and "Subject's Overview" section was filmed (5-10 min). Several features were extracted, e.g., eye and mouth aspect ratio, Euler angles, coordinates from 51 facial landmarks. This elicited 649 facial features, which were further selected using Gradient Boosting Machines (AdaBoost combined with Random Forests). Data was split in 70/30 for training, and Monte Carlo cross validation was used.

RESULTS

Final model reached 83 % of mean F1-score, and balanced accuracy of 85 %. Mean area under the curve for the receiver operator curve classifier was 93 %. Convergent validity testing showed that two features included in the model were significantly correlated with Avolition (SIPS N2 item) and expression of emotion (SIPS N3 item).

CONCLUSION

Our model capitalized on short video-recordings from individuals recruited from the general population, effectively distinguishing between ARMS and controls. Results are encouraging for large-screening purposes in low-resource settings.

Gesticulation in individuals with at risk mental states for psychosis

Nonverbal communication (NVC) is a complex behavior that involves different modalities that are impaired in the schizophrenia spectrum, including gesticulation. However, there are few studies that evaluate it in individuals with at-risk mental states (ARMS) for psychosis, mostly in developed countries. Given our prior findings of reduced movement during speech seen in Brazilian individuals with ARMS, we now aim to determine if this can be accounted for by reduced gesticulation behavior. Fifty-six medication-naïve ARMS and 64 healthy controls were filmed during speech tasks. The frequency of specifically coded gestures across four categories (and self-stimulatory behaviors) were compared between groups and tested for correlations with prodromal symptoms of the Structured Interview for Prodromal Syndromes (SIPS) and with the variables previously published. ARMS individuals showed a reduction in one gesture category, but it did not survive Bonferroni's correction. Gesture frequency was negatively correlated with prodromal symptoms and positively correlated with the variables of the amount of movement previously analyzed. The lack of significant differences between ARMS and control contradicts literature findings in other cultural context, in which a reduction is usually seen in at-risk individuals. However, gesture frequency might be a visual proxy of prodromal symptoms, and of other movement abnormalities. Results show the importance of analyzing NVC in ARMS and of considering different cultural and sociodemographic contexts in the search for markers of these states.

Time perception at resting state and during active motion: The role of anxiety and depression

BACKGROUND

Time perception and motion intensity are interrelated factors that may influence symptom expression and severity in case of various psychiatric conditions, including anxiety and depression.

AIMS

The present study aimed to 1) explore the associations between the intensity of physical activity, time perception, impulsivity, anxiety and depressive symptoms, and to 2) investigate the extent to which resting state motion intensity can be used to identify the assessed psychiatric conditions.

METHODS

20 healthy controls and 20 psychiatric patients (with either anxiety or depression-related diagnoses) were included in the study and filled out a questionnaire consisting of validated anxiety, depression and impulsivity measures. Time perception was measured by a computerized time production task, whereas motion intensity was analyzed by a motion capture and analysis software. Respondents were randomly assigned to an experimental (with active motion task) and non-experimental group (resting state conditions). Both subgroups were repeatedly assessed, in order to explore changes in motion intensity, time perception and psychiatric symptom levels.

RESULTS

Random forest regression analysis identified the level of impulsivity, depression and anxiety as the strongest predictors of resting state motion intensity, while a path analysis model indicated that controls and psychiatric patients show different pathways regarding the connection between motion intensity changes, time production ratio alterations and symptom reduction.

CONCLUSIONS

Our study implies the importance of distinguishing between clinical and subclinical severity of psychiatric symptoms when considering the association between motion intensity, time perception, anxiety and depression. Potential transdiagnostic relevance of resting state motion intensity is also addressed.

Motion energy analysis during speech tasks in medication-naïve individuals with at-risk mental states for psychosis

Movement abnormalities are commonly observed in schizophrenia and at-risk mental states (ARMS) for psychosis. They are usually detected with clinical interviews, such that automated analysis would enhance assessment. Our aim was to use motion energy analysis (MEA) to assess movement during free-speech videos in ARMS and control individuals, and to investigate associations between movement metrics and negative and positive symptoms. Thirty-two medication-naïve ARMS and forty-six healthy control individuals were filmed during speech tasks. Footages were analyzed using MEA software, which assesses movement by differences in pixels frame-by-frame. Two regions of interest were defined—head and torso—and mean amplitude, frequency, and coefficient of variability of movements for them were obtained. These metrics were correlated with the Structured Interview for Prodromal Syndromes (SIPS) symptoms, and with the risk of conversion to psychosis—inferred with the SIPS risk calculator. ARMS individuals had significantly lower mean amplitude of head movement and higher coefficients of movement variability for both head and torso, compared to controls. Higher coefficient of variability was related to higher risk of conversion. Negative correlations were seen between frequency of movement and most SIPS negative symptoms. All positive symptoms were correlated with at least one movement variable. Movement abnormalities could be automatically detected in medication-naïve ARMS subjects by means of a motion energy analysis software. Significant associations of movement metrics with symptoms were found, supporting the importance of movement analysis in ARMS. This could be a potentially important tool for early diagnosis, intervention, and outcome prediction.

Change Process in Coaching: Interplay of Nonverbal Synchrony, Working Alliance, Self-Regulation, and Goal Attainment

Background: Psychological literature emphasizes that self-regulation is important as goal intentions, goal setting, or implementation intention does not automatically result in effective results in coaching. The question which coaching strategies to apply to strengthening clients' self-regulatory capacities as prerequisites of effective change outcomes remains a black box in coaching.

Method: This quantitative study explored clients' self-regulatory mechanisms by addressing how nonverbal synchrony influences clients' cognitive and emotional self-regulation across sessions. One hundred eighty-four coach–client pairs and their evolving change process were observed over 8 months. Video-recorded sessions were assessed with motion energy analysis to automatically capture coach and client nonverbal behavior and quantify nonverbal synchrony at the level of the dyad.

Results: Synchrony was differentially associated with clients' post-session questionnaires on result-oriented problem-reflection and self-reflection, affect balance, and working alliance. Network analyses suggested that the association between synchrony and other process variables did not correspond to the previously found positive association between synchrony and positive aspects of alliance or outcome. Instead, this association depended on the level of perceived outcome.

Discussion: Coaching success may be predicted by process variables assessed after each session: goal reflection, alliance, and mood all predict successful coaching. The assessment of nonverbal synchrony suggests a state-dependent effect of embodied processes on a coaching outcome that warrants further inspection.

Movement and Emotional Facial Expressions during the Adult Attachment Interview: Interaction Effects of Attachment and Anxiety Disorder

INTRODUCTION

Adult attachment is commonly associated with emotion regulation. Less is known about the nonverbal embodiment of adult attachment.

OBJECTIVE

We hypothesized that dismissing attachment is related to less movement and fewer facial expressions of emotions, whereas preoccupied attachment is associated with more negative emotional facial expressions. Moreover, the interaction of attachment and the presence of an anxiety disorder (AD) was explored.

METHODS

The sample included 95 individuals, 21 with AD without comorbidity, 21 with AD and comorbid major depression (AD-CD), and 53 healthy controls. We analyzed nonverbal behavior during a part of the Adult Attachment Interview (AAI) asking about the family and parental figures. The movements of the interviewees were captured via Motion Energy Analysis. Facial expressions were coded according to the Facial Action Coding System using the OpenFace software. We compared individuals with secure, dismissing, and preoccupied states of mind (assessed with the AAI) with regard to the frequency and complexity of movements and the frequency of the facial expressions such as happy, sad, and contemptuous.

RESULTS

As expected, dismissingly attached individuals moved less often and with lower complexity than securely attached. For emotional facial expressions, a main effect of the disorder group and interaction effects of attachment by disorder were found. In the AD-CD group, dismissingly attached patients showed comparatively fewer happy facial expressions than securely attached individuals.

CONCLUSIONS

Reduced movement specifically seems to be related to dismissing attachment when interviewees talk about significant parental figures. Facial expressions of emotions related to attachment occurred when maladaptive emotion regulation strategies were intensified by a psychological disorder.

Alterations in facial expressions of emotion: Determining the promise of ultrathin slicing approaches and comparing human and automated coding methods in psychosis risk

Alterations in facial expressions of emotion are a hallmark of psychopathology and may be present before the onset of mental illness. Technological advances have spurred interest in examining alterations based on "thin slices" of behavior using automated approaches. However, questions remain. First, can alterations be detected in ultrathin slices of behavior? Second, how do automated approaches converge with human coding techniques? The present study examined ultrathin (i.e., 1-min) slices of video-recorded clinical interviews of 42 individuals at clinical high risk (CHR) for psychosis and 42 matched controls. Facial expressions of emotion (e.g., joy, anger) were examined using two automated facial analysis programs and coded by trained human raters (using the Expressive Emotional Behavior Coding System). Results showed that ultrathin (i.e., 1-min) slices of behavior were sufficient to reveal alterations in facial expressions of emotion, specifically blunted joy expressions in individuals at CHR (with supplementary analyses probing links with attenuated positive symptoms and functioning). Furthermore, both automated analysis programs converged in the ability to detect blunted joy expressions and were consistent with human coding at the level of both second-by-second and aggregate data. Finally, there were areas of divergence across approaches for other emotional expressions beyond joy. These data suggest that ultrathin slices of behavior can yield clues about emotional dysfunction. Further, automated approaches (which do not require lengthy training and coder time but do lend well to mobile assessment and computational modeling) show promise, but careful evaluation of convergence with human coding is needed. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

eHealth tools for assessing psychomotor activity in schizophrenia: a systematic review

Objective:

Psychomotor abnormalities are relevant symptoms in the clinical presentation of schizophrenia, and assessing them could facilitate monitoring. New technologies can measure psychomotor activity objectively and continuously, but evidence on the topic is scarce. Our aim is to systematically review the existing evidence about eHealth tools for assessing psychomotor activity in patients diagnosed with schizophrenia.

Method:

We performed a systematic search of the PubMed and Embase databases and identified 15 relevant articles on eHealth tools for assessing psychomotor activity in schizophrenia.

Results:

eHealth devices accurately assessed psychomotor activity and were well accepted. Abnormalities in psychomotor activity helped differentiate between different subtypes of schizophrenia. Abnormal increases in psychomotor activity were correlated with acute presentations, while lower activity was associated with relapses, deterioration, and negative symptoms.

Conclusion:

Actigraphy is still the preferred eHealth device in research settings, but mobile applications have great potential. Further studies are needed to explore the possibilities of psychomotor monitoring and mobile health applications for preventing relapses in schizophrenia. eHealth could be useful for monitoring psychomotor activity, which might help prevent relapses.

Quantification of nonverbal synchrony using linear time series analysis methods: Lack of convergent validity and evidence for facets of synchrony

Nonverbal synchrony describes coordination of the nonverbal behavior of two interacting partners. Additionally, it seems to be important in human interactions, such as during psychotherapy. Currently, there are several options for the automated determination of synchrony based on linear time series analysis methods (TSAMs). However, investigations into whether the different methods measure the same construct have been missing. In this study, N = 84 patient-therapist dyads were videotaped during psychotherapy sessions. Motion energy analysis was used to assess body movements. We applied seven different TSAMs and recorded multiple output scores (average synchrony, maximum synchrony, and frequency of synchrony; in total, N = 16 scores). Convergent validity was examined using correlations of the output scores and exploratory factor analysis. Additionally, two criterion-based validations were conducted: investigations of concordant validity with a more generalized nonlinear method, and of the predictive validity of the synchrony scores for improvement in interpersonal problems at the end of therapy. We found that the synchrony measures only partially correlated with each other. The factor analysis did not support a common-factor model. A three-factor model with a second-order synchrony variable showed the best fit for eight of the selected synchrony scores. Only some synchrony scores were able to predict improvement at the end of therapy. We concluded that the considered TSAMs do not measure the same synchrony construct, but different facets of synchrony: the strength of synchrony of the total interaction, the strength of synchrony during synchronization intervals, and the frequency of synchrony.

Prediction, Psychosis, and the Cerebellum

An increasingly influential hypothesis posits that many of the diverse symptoms of psychosis can be viewed as reflecting dysfunctional predictive mechanisms. Indeed, to perceive something is to take a sensory input and make a prediction of the external source of that signal; thus, prediction is perhaps the most fundamental neural computation. Given the ubiquity of prediction, a more challenging problem is to specify the unique predictive role or capability of a particular brain structure. This question is relevant when considering recent claims that one aspect of the predictive deficits observed in psychotic disorders might be related to cerebellar dysfunction, a subcortical structure known to play a critical role in predictive sensorimotor control and perhaps higher-level cognitive function. Here, we review evidence bearing on this question. We first focus on clinical, behavioral, and neuroimaging findings suggesting cerebellar involvement in psychosis and, specifically, schizophrenia. We then review a relatively novel line of research exploring whether computational models of cerebellar motor function can also account for cerebellar involvement in higher-order human cognition, and in particular, language function. We end the review by highlighting some key gaps in these literatures, limitations that currently preclude strong conclusions regarding cerebellar involvement in psychosis.

Objective and quantitative equilibriometric evaluation of individual locomotor behaviour in schizophrenia: Translational and clinical implications

Psychiatry is the only medical specialty that lacks clinically applicable biomarkers for objective evaluation of the existing pathology at a single-patient level. On the basis of an original translational equilibriometric method for evaluation of movement patterns, we have introduced in the everyday clinical practice of psychiatry an easy-to-perform computerized objective quantification of the individual locomotor behaviour during execution of the Unterberger stepping test. For the last 20 years, we have gradually collected a large database of more than 1000 schizophrenic patients, their relatives, and matched psychiatric, neurological, and healthy controls via cross-sectional and longitudinal investigations. Comparative analyses revealed transdiagnostic locomotor similarities among schizophrenic patients, high-risk schizotaxic individuals, and neurological patients with multiple sclerosis and cerebellar ataxia, thus suggesting common underlying brain mechanisms. In parallel, intradiagnostic dissimilarities were revealed, which allow to separate out subclinical locomotor subgroups within the diagnostic categories. Prototypical qualitative (dysmetric and ataxic) locomotor abnormalities in schizophrenic patients were differentiated from 2 atypical quantitative ones, manifested as either hypolocomotion or hyperlocomotion. Theoretical analyses suggested that these 3 subtypes of locomotor abnormalities could be conceived as objectively measurable biomarkers of 3 schizophrenic subgroups with dissimilar brain mechanisms, which require different treatment strategies. Analogies with the prominent role of locomotor measures in some well-known animal models of mental disorders advocate for a promising objective translational research in the so far over-subjective field of psychiatry. Distinctions among prototypical, atypical, and diagnostic biomarkers, as well as between neuromotor and psychomotor locomotor abnormalities, are discussed. Conclusions are drawn about the translational and clinical implications of the new approach and its future perspectives.

Motor Dysfunction as a Risk Factor for Conversion to Psychosis Independent of Medication Use in a Psychosis-Risk Cohort

The Structured Interview for Psychosis-Risk Syndromes (SIPS) contains criteria for the Attenuated Positive Symptom Syndrome (APSS), a period of subthreshold positive symptoms that predates full-blown psychosis. Motor abnormalities are often associated with these symptoms but have not been adequately studied. We assessed a diverse sample of 192 APSS participants (27.1% female; 47.9% white; mean age = 20.03 years) for motor dysfunction (SIPS G.3. score) at baseline and conversion to psychosis every 3 months for up to 2 years. Fifty-nine (30.7%) participants converted to psychosis. Baseline G.3. score was significantly higher among converters than nonconverters (mean difference = 0.66; t[95.929] = 2.579, p < 0.05). No significant differences in baseline G.3. were found between demographic groups or those with differential medication use. These results point to the use of G.3. as a potential predictor of psychosis among APSS individuals and potentially implicate the shared biological underpinnings of motor dysfunction in the APSS and full-blown psychotic illnesses.