Securing direct stakeholder feedback to inform clinical research in serious mental illness: Results of a patient and family perspectives survey.

Mental illness research routinely includes unfamiliar or potentially frightening procedures like lumbar puncture (LP), contributing to low enrollment and retention. Previous studies related to LP acceptance have focused on older individuals, and little information on participant preferences for educational materials is available. We developed an online survey assessing existing knowledge, comfort and concerns, and preferences for educational materials in the context of our clinical study on schizophrenia spectrum conditions (SSCs). We found that participants were generally knowledgeable and interested in engaging with clinical SSC research. Frequency of engagement with research publications differed significantly by participant groups and age. Comfort levels were consistently highest for study procedures other than LP, though surprisingly the average number of informational needs per procedure was not significantly different for LP compared to other procedures. Preferences for format and source of educational materials varied across participant groups and age. Our results suggest that younger individuals with an SSC diagnosis are likely to have limited exposure to information, and proactively providing accessible and accurate educational materials may improve positive perceptions of LP. Providing content in a range of formats and sources will ensure that participants and their support networks have access to their preferred resources.

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.

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.

Ethical Implications of the Use of Language Analysis Technologies for the Diagnosis and Prediction of Psychiatric Disorders.

Recent developments in artificial intelligence technologies have come to a point where machine learning algorithms can infer mental status based on someone's photos and texts posted on social media. More than that, these algorithms are able to predict, with a reasonable degree of accuracy, future mental illness. They potentially represent an important advance in mental health care for preventive and early diagnosis initiatives, and for aiding professionals in the follow-up and prognosis of their patients. However, important issues call for major caution in the use of such technologies, namely, privacy and the stigma related to mental disorders. In this paper, we discuss the bioethical implications of using such technologies to diagnose and predict future mental illness, given the current scenario of swiftly growing technologies that analyze human language and the online availability of personal information given by social media. We also suggest future directions to be taken to minimize the misuse of such important technologies.

Using Language Processing and Speech Analysis for the Identification of Psychosis and Other Disorders.

Increasingly, data-driven methods have been implemented to understand psychopathology. Language is the main source of information in psychiatry and represents "big data" at the level of the individual. Language and behavior are amenable to computational natural language processing (NLP) analytics, which may help operationalize the mental status examination. In this review, we highlight the application of NLP to schizophrenia and its risk states as an exemplar of its use, operationalizing tangential and concrete speech as reductions in semantic coherence and syntactic complexity, respectively. Other clinical applications are reviewed, including forecasting suicide risk and detecting intoxication. Challenges and future directions are discussed, including biomarker development, harmonization, and application of NLP more broadly to behavior, including intonation/prosody, facial expression and gesture, and the integration of these in dyads and during discourse. Similar NLP analytics can also be applied beyond humans to behavioral motifs across species, important for modeling psychopathology in animal models. Finally, clinical neuroscience can inform the development of artificial intelligence.

Smell identification in individuals at clinical high risk for schizophrenia.

Smell identification deficits exist in schizophrenia, and may be associated with its negative symptoms. Less is known about smell identification and its clinical correlates in individuals at clinical high risk (CHR) for schizophrenia and related psychotic disorders. We examined smell identification, symptoms and IQ in 71 clinical high-risk (CHR) subjects and 36 healthy controls. Smell identification was assessed using both the 40-item University of Pennsylvania Smell Identification Test (UPSIT; Doty, R.L., Shaman, P., Kimmelman, C.P., Dann, M.S., 1984. University of Pennsylvania Smell Identification Test: a rapid quantitative olfactory function test for the clinic. Laryngoscope 94, 176-178) and its extracted 12-item Brief Smell Identification Test (Goudsmit, N., Coleman, E., Seckinger, R.A., Wolitzky, R., Stanford, A.D., Corcoran, C., Goetz, R.R., Malaspina, D., 2003. A brief smell identification test discriminates between deficit and non-deficit schizophrenia. Psychiatry Research 120, 155-164). Smell identification did not significantly differ between CHR subjects and controls. Among CHR subjects, smell identification did not predict schizophrenia (N=19; 27%) within 2 years, nor was it associated with negative or positive symptoms. This is the third prospective cohort study to examine smell identification in CHR subjects, and overall, findings are inconclusive, similar to what is found for other disorders in adolescents, such as autism spectrum, attention deficit and anxiety disorders. Smell identification deficit may not have clear utility as a marker of emergent schizophrenia and related psychotic disorders.