Can the Acoustic Analysis of Expressive Prosody Discriminate Schizophrenia?

Detecting schizophrenia, bipolar disorder, psychosis vulnerability and major depressive disorder from 5 minutes of online-collected speech

Background

Psychosis poses substantial social and healthcare burdens. The analysis of speech is a promising approach for the diagnosis and monitoring of psychosis, capturing symptoms like thought disorder and flattened affect. Recent advancements in Natural Language Processing (NLP) methodologies enable the automated extraction of informative speech features, which has been leveraged for early psychosis detection and assessment of symptomology. However, critical gaps persist, including the absence of standardized sample collection protocols, small sample sizes, and a lack of multi-illness classification, limiting clinical applicability. Our study aimed to (1) identify an optimal assessment approach for the online and remote collection of speech, in the context of assessing the psychosis spectrum and evaluate whether a fully automated, speech-based machine learning (ML) pipeline can discriminate among different conditions on the schizophrenia-bipolar spectrum (SSD-BD-SPE), help-seeking comparison subjects (MDD), and healthy controls (HC) at varying layers of analysis and diagnostic complexity.

Methods

We adopted online data collection methods to collect 20 minutes of speech and demographic information from individuals. Participants were categorized as "healthy" help-seekers (HC), having a schizophrenia-spectrum disorder (SSD), bipolar disorder (BD), major depressive disorder (MDD), or being on the psychosis spectrum with sub-clinical psychotic experiences (SPE). SPE status was determined based on self-reported clinical diagnosis and responses to the PHQ-8 and PQ-16 screening questionnaires, while other diagnoses were determined based on self-report from participants. Linguistic and paralinguistic features were extracted and ensemble learning algorithms (e.g., XGBoost) were used to train models. A 70%-30% train-test split and 30-fold cross-validation was used to validate the model performance.

Results

The final analysis sample included 1140 individuals and 22,650 minutes of speech. Using 5-minutes of speech, our model could discriminate between HC and those with a serious mental illness (SSD or BD) with 86% accuracy (AUC = 0.91, Recall = 0.7, Precision = 0.98). Furthermore, our model could discern among HC, SPE, BD and SSD groups with 86% accuracy (F1 macro = 0.855, Recall Macro = 0.86, Precision Macro = 0.86). Finally, in a 5-class discrimination task including individuals with MDD, our model had 76% accuracy (F1 macro = 0.757, Recall Macro = 0.758, Precision Macro = 0.766).

Conclusion

Our ML pipeline demonstrated disorder-specific learning, achieving excellent or good accuracy across several classification tasks. We demonstrated that the screening of mental disorders is possible via a fully automated, remote speech assessment pipeline. We tested our model on relatively high number conditions (5 classes) in the literature and in a stratified sample of psychosis spectrum, including HC, SPE, SSD and BD (4 classes). We tested our model on a large sample (N = 1150) and demonstrated best-in-class accuracy with remotely collected speech data in the psychosis spectrum, however, further clinical validation is needed to test the reliability of model performance.

Task-voting for schizophrenia spectrum disorders prediction using machine learning across linguistic feature domains.. [DOI: 10.1101/2024.08.31.24312886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Background and Hypothesis

Identifying schizophrenia spectrum disorders (SSD) from spontaneous speech features is a key focus in computational psychiatry today.

Study Design

We present a task-voting procedure using different speech-elicitation tasks to predict SSD in Spanish, followed by ablation studies highlighting the roles of specific tasks and feature domains. Speech from five tasks was recorded from 92 subjects (49 with SSD and 41 controls). A total of 319 features were automatically extracted, from which 24 were pre-selected based on between-feature correlations and ANOVA F-values, covering acoustic-prosody, morphosyntax, and semantic similarity metrics.

Study Results

ExtraTrees-based classification using these features yielded an accuracy of 0.840 on hold-out data. Ablating picture descriptions impaired performance most, followed by story reading, retelling, and free speech. Removing morphosyntactic measures impaired performance most, followed by acoustic and semantic measures. Mixed-effect models suggested significant group differences on all 24 features. In SSD, speech patterns were slower and more variable temporally, while variations in pitch, amplitude, and sound intensity decreased. Semantic similarity between speech and prompts decreased, while minimal distances from embedding centroids to each word increased, and word-to-word similarity arrays became more predictable, all replicating patterns documented in other languages. Morphosyntactically, SSD patients used more first-person pronouns together with less third-person pronouns, and more punctuations and negations. Semantic metrics correlated with a range of positive symptoms, and multiple acoustic-prosodic features with negative symptoms.

Conclusions

This study highlights the importance of combining different speech tasks and features for SSD detection, and validates previously found patterns in psychosis for Spanish.

Language in interaction: turn-taking patterns in conversations involving individuals with schizophrenia

Individuals with schizophrenia generally show difficulties in interpersonal communication. Linguistic analyses shed new light on speech atypicalities in schizophrenia. However, very little is known about conversational interaction management by these individuals. Moreover, the relationship between linguistic features, psychopathology, and patients' subjectivity has received limited attention to date. We used a novel methodology to explore dyadic conversations involving 58 participants (29 individuals with schizophrenia and 29 control persons) and medical doctors. High-quality stereo recordings were obtained and used to quantify turn-taking patterns. We investigated psychopathological dimensions and subjective experiences using the Positive and Negative Syndrome Scale for Schizophrenia (PANSS), the Examination of Anomalous Self Experience scale (EASE), the Autism Rating Scale (ARS) and the Abnormal Bodily Phenomena questionnaire (ABPq). Different turn-taking patterns of both patients and interviewers characterised conversations involving individuals with schizophrenia. We observed higher levels of overlap and mutual silence in dialogues with the patients compared to dialogues with control persons. Mutual silence was associated with negative symptom severity; no dialogical feature was correlated with anomalous subjective experiences. Our findings suggest that individuals with schizophrenia display peculiar turn-taking behaviour, thereby enhancing our understanding of interactional coordination in schizophrenia.

The contribution of prosody to machine classification of schizophrenia

We show how acoustic prosodic features, such as pitch and gaps, can be used computationally for detecting symptoms of schizophrenia from a single spoken response. We compare the individual contributions of acoustic and previously-employed text modalities to the algorithmic determination whether the speaker has schizophrenia. Our classification results clearly show that we can extract relevant acoustic features better than those textual ones. We find that, when combined with those acoustic features, textual features improve classification only slightly.

Automated analysis of speech as a marker of sub-clinical psychotic experiences

Automated speech analysis techniques, when combined with artificial intelligence and machine learning, show potential in capturing and predicting a wide range of psychosis symptoms, garnering attention from researchers. These techniques hold promise in predicting the transition to clinical psychosis from at-risk states, as well as relapse or treatment response in individuals with clinical-level psychosis. However, challenges in scientific validation hinder the translation of these techniques into practical applications. Although sub-clinical research could aid to tackle most of these challenges, there have been only few studies conducted in speech and psychosis research in non-clinical populations. This work aims to facilitate this work by summarizing automated speech analytical concepts and the intersection of this field with psychosis research. We review psychosis continuum and sub-clinical psychotic experiences, and the benefits of researching them. Then, we discuss the connection between speech and psychotic symptoms. Thirdly, we overview current and state-of-the art approaches to the automated analysis of speech both in terms of language use (text-based analysis) and vocal features (audio-based analysis). Then, we review techniques applied in subclinical population and findings in these samples. Finally, we discuss research challenges in the field, recommend future research endeavors and outline how research in subclinical populations can tackle the listed challenges.

Prosody and schizophrenia. Objective acoustic measurements of monotonous and flat intonation in young Danish people with a schizophrenia diagnosis. A pilot study

PURPOSE

Patients with schizophrenia have a flat and monotonous intonation. The purpose of the study was to find the variables of flat speech that differed in patients from those in healthy controls in Danish.

MATERIALS AND METHODS

We compared drug-naïve schizophrenic patients 5 men, 13 women and 18 controls, aged 18-35 years, which had all grown up in Copenhagen speaking modern Danish standard (rigsdansk). We used two different tasks that lay different demands on the speaker to elicit spontaneous speech: a retelling of a film clip and telling a story from pictures in a book. A linguist used the computer program Praat to extract the phonetic linguistic parameters.

RESULTS

We found different results for the two elicitation tasks (Task 1: a retelling of a film clip, task 2: telling a story from pictures in a book). There was higher intensity variation in task one in controls and higher pitch variation in task two in controls. We found a difference in intensity with higher intensity variation in the stresses in the controls in task one and fewer syllables between each stress in the controls. We also found higher F1 variation in task one and two in the patient group and higher F2 variation in the control group in both tasks.

CONCLUSIONS

The results varied between patients and controls, but the demands also made a difference. Further research is needed to elucidate the possibilities of acoustic measures in diagnostics or linguistic treatment related to schizophrenia.

Markers of schizophrenia at the prosody/pragmatics interface. Evidence from corpora of spontaneous speech interactions

The speech of individuals with schizophrenia exhibits atypical prosody and pragmatic dysfunctions, producing monotony. The paper presents the outcomes of corpus-based research on the prosodic features of the pathology as they manifest in real-life spontaneous interactions. The research relies on a corpus of schizophrenic speech recorded during psychiatric interviews (CIPPS) compared to a sampling of non-pathological speech derived from the LABLITA corpus of spoken Italian, which has been selected according to comparability requirements. Corpora has been intensively analyzed in the Language into Act Theory (L-AcT) frame, which links prosodic cues and pragmatic values. A cluster of linguistic parameters marked by prosody has been considered: utterance boundaries, information structure, speech disfluency, and prosodic prominence. The speech flow of patients turns out to be organized into small chunks of information that are shorter and scarcely structured, with an atypical proportion of post-nuclear information units (Appendix). It is pervasively scattered with silences, especially with long pauses between utterances and long silences at turn-taking. Fluency is hindered by retracing phenomena that characterize complex information structures. The acoustic parameters that give rise to prosodic prominence (f0 mean, f0 standard deviation, spectral emphasis, and intensity variation) have been measured considering the pragmatic roles of the prosodic units, distinguishing prominences within the illocutionary units (Comment) from those characterizing Topic units. Patients show a flattening of the Comment-prominence, reflecting impairments in performing the illocutionary activity. Reduced values of spectral emphasis and intensity variation also suggest a lack of engagement in communication. Conversely, Topic-prominence shows higher values for f0 standard deviation and spectral emphasis, suggesting effort when defining the domain of relevance of the illocutionary force. When comparing Topic and Comment-prominences of patients, the former consistently exhibit higher values across all parameters. In contrast, the non-pathological group displays the opposite pattern.

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.

Online speech assessment of the psychotic spectrum: Exploring the relationship between overlapping acoustic markers of schizotypy, depression and anxiety

BACKGROUND

Remote assessment of acoustic alterations in speech holds promise to increase scalability and validity in research across the psychosis spectrum. A feasible first step in establishing a procedure for online assessments is to assess acoustic alterations in psychometric schizotypy. However, to date, the complex relationship between alterations in speech related to schizotypy and those related to comorbid conditions such as symptoms of depression and anxiety has not been investigated. This study tested whether (1) depression, generalized anxiety and high psychometric schizotypy have similar voice characteristics, (2) which acoustic markers of online collected speech are the strongest predictors of psychometric schizotypy, (3) whether including generalized anxiety and depression symptoms in the model can improve the prediction of schizotypy.

METHODS

We collected cross-sectional, online-recorded speech data from 441 participants, assessing demographics, symptoms of depression, generalized anxiety and psychometric schizotypy.

RESULTS

Speech samples collected online could predict psychometric schizotypy, depression, and anxiety symptoms with weak to moderate predictive power, and with moderate and good predictive power when basic demographic variables were added to the models. Most influential features of these models largely overlapped. The predictive power of speech marker-based models of schizotypy significantly improved after including symptom scores of depression and generalized anxiety in the models (from R2 = 0.296 to R2 = 0. 436).

CONCLUSIONS

Acoustic features of online collected speech are predictive of psychometric schizotypy as well as generalized anxiety and depression symptoms. The acoustic characteristics of schizotypy, depression and anxiety symptoms significantly overlap. Speech models that are designed to predict schizotypy or symptoms of the schizophrenia spectrum might therefore benefit from controlling for symptoms of depression and anxiety.

Reading and lexical-semantic retrieval tasks outperforms single task speech analysis in the screening of mild cognitive impairment and Alzheimer's disease

Age-related cognitive impairment have increased dramatically in recent years, which has risen the interes in developing screening tools for mild cognitive impairment and Alzheimer's disease. Speech analysis allows to exploit the behavioral consequences of cognitive deficits on the patient's vocal performance so that it is possible to identify pathologies affecting speech production such as dementia. Previous studies have further shown that the speech task used determines how the speech parameters are altered. We aim to combine the impairments in several speech production tasks in order to improve the accuracy of screening through speech analysis. The sample consists of 72 participants divided into three equal groups of healthy older adults, people with mild cognitive impairment, or Alzheimer's disease, matched by age and education. A complete neuropsychological assessment and two voice recordings were performed. The tasks required the participants to read a text, and complete a sentence with semantic information. A stepwise linear discriminant analysis was performed to select speech parameters with discriminative power. The discriminative functions obtained an accuracy of 83.3% in simultaneous classifications of several levels of cognitive impairment. It would therefore be a promising screening tool for dementia.

Combining automatic speech recognition with semantic natural language processing in schizophrenia

Natural language processing (NLP) tools are increasingly used to quantify semantic anomalies in schizophrenia. Automatic speech recognition (ASR) technology, if robust enough, could significantly speed up the NLP research process. In this study, we assessed the performance of a state-of-the-art ASR tool and its impact on diagnostic classification accuracy based on a NLP model. We compared ASR to human transcripts quantitatively (Word Error Rate (WER)) and qualitatively by analyzing error type and position. Subsequently, we evaluated the impact of ASR on classification accuracy using semantic similarity measures. Two random forest classifiers were trained with similarity measures derived from automatic and manual transcriptions, and their performance was compared. The ASR tool had a mean WER of 30.4%. Pronouns and words in sentence-final position had the highest WERs. The classification accuracy was 76.7% (sensitivity 70%; specificity 86%) using automated transcriptions and 79.8% (sensitivity 75%; specificity 86%) for manual transcriptions. The difference in performance between the models was not significant. These findings demonstrate that using ASR for semantic analysis is associated with only a small decrease in accuracy in classifying schizophrenia, compared to manual transcripts. Thus, combining ASR technology with semantic NLP models qualifies as a robust and efficient method for diagnosing schizophrenia.

Initial evidence of vowel space reduction in a subset of individuals with schizophrenia

OBJECTIVE

Acoustic phonetic measures have been found to correlate with negative symptoms of schizophrenia, thus offering a path toward quantitative measurement of such symptoms. These acoustic properties include F1 and F2 measurements (affected by tongue height and tongue forward/back position, respectively), which determine a general "vowel space." Among patients and controls, we consider two phonetic measures of vowel space: average Euclidean distance from a participant's mean F1 and mean F2, and density of vowels around one standard deviation of mean F1 and of F2.

METHODS

Structured and spontaneous speech of 148 participants (70 patients and 78 controls) was recorded and measured acoustically. We examined correlations between the phonetic measures of vowel space and ratings of aprosody obtained using two clinical research measures, the Scale for the Assessment of Negative Symptoms (SANS) and the Clinical Assessment Interview for Negative Symptoms (CAINS).

RESULTS

Vowel space measurements were significantly associated with patient/control status, attributed to a cluster of 13 patients whose phonetic values correspond to reduced vowel space as assessed by both phoenetic measures. No correlation was found between phonetic measures and relevant items and averages of ratings on the SANS and CAINS. Reduced vowel space appears to affect only a subset of patients with schizophrenia, potentially those on higher antipsychotic dosages.

CONCLUSIONS

Acoustic phonetic measures may be more sensitive measures of constricted vowel space than clinical research rating scales of aprosody or monotone speech. Replications are needed before further interpretation of this novel finding, including potential medication effects.

A Narrative Review of Speech and EEG Features for Schizophrenia Detection: Progress and Challenges

Schizophrenia is a mental illness that affects an estimated 21 million people worldwide. The literature establishes that electroencephalography (EEG) is a well-implemented means of studying and diagnosing mental disorders. However, it is known that speech and language provide unique and essential information about human thought. Semantic and emotional content, semantic coherence, syntactic structure, and complexity can thus be combined in a machine learning process to detect schizophrenia. Several studies show that early identification is crucial to prevent the onset of illness or mitigate possible complications. Therefore, it is necessary to identify disease-specific biomarkers for an early diagnosis support system. This work contributes to improving our knowledge about schizophrenia and the features that can identify this mental illness via speech and EEG. The emotional state is a specific characteristic of schizophrenia that can be identified with speech emotion analysis. The most used features of speech found in the literature review are fundamental frequency (F0), intensity/loudness (I), frequency formants (F1, F2, and F3), Mel-frequency cepstral coefficients (MFCC's), the duration of pauses and sentences (SD), and the duration of silence between words. Combining at least two feature categories achieved high accuracy in the schizophrenia classification. Prosodic and spectral or temporal features achieved the highest accuracy. The work with higher accuracy used the prosodic and spectral features QEVA, SDVV, and SSDL, which were derived from the F0 and spectrogram. The emotional state can be identified with most of the features previously mentioned (F0, I, F1, F2, F3, MFCCs, and SD), linear prediction cepstral coefficients (LPCC), linear spectral features (LSF), and the pause rate. Using the event-related potentials (ERP), the most promissory features found in the literature are mismatch negativity (MMN), P2, P3, P50, N1, and N2. The EEG features with higher accuracy in schizophrenia classification subjects are the nonlinear features, such as Cx, HFD, and Lya.

Acoustic speech markers for schizophrenia-spectrum disorders: a diagnostic and symptom-recognition tool

BACKGROUND

Clinicians routinely use impressions of speech as an element of mental status examination. In schizophrenia-spectrum disorders, descriptions of speech are used to assess the severity of psychotic symptoms. In the current study, we assessed the diagnostic value of acoustic speech parameters in schizophrenia-spectrum disorders, as well as its value in recognizing positive and negative symptoms.

METHODS

Speech was obtained from 142 patients with a schizophrenia-spectrum disorder and 142 matched controls during a semi-structured interview on neutral topics. Patients were categorized as having predominantly positive or negative symptoms using the Positive and Negative Syndrome Scale (PANSS). Acoustic parameters were extracted with OpenSMILE, employing the extended Geneva Acoustic Minimalistic Parameter Set, which includes standardized analyses of pitch (F0), speech quality and pauses. Speech parameters were fed into a random forest algorithm with leave-ten-out cross-validation to assess their value for a schizophrenia-spectrum diagnosis, and PANSS subtype recognition.

RESULTS

The machine-learning speech classifier attained an accuracy of 86.2% in classifying patients with a schizophrenia-spectrum disorder and controls on speech parameters alone. Patients with predominantly positive v. negative symptoms could be classified with an accuracy of 74.2%.

CONCLUSIONS

Our results show that automatically extracted speech parameters can be used to accurately classify patients with a schizophrenia-spectrum disorder and healthy controls, as well as differentiate between patients with predominantly positive v. negatives symptoms. Thus, the field of speech technology has provided a standardized, powerful tool that has high potential for clinical applications in diagnosis and differentiation, given its ease of comparison and replication across samples.

Speech biomarkers of risk factors for vascular dementia in people with mild cognitive impairment

Introduction

In this study we intend to use speech analysis to analyze the cognitive impairments caused by pathologies of vascular origin such as diabetes, hypertension, hypercholesterolemia and heart disease, predictors of the development of vascular dementia.

Methods

In this study, 40 participants with mild cognitive impairment were asked to read while being recorded and they were inquired about their history of the aforementioned conditions. Their speech was then analyzed.

Results

We found that some speech parameters of frequencies and syllabic rhythm vary due to these pathologies. In addition, we conducted a discriminant analysis in which we found that diabetes and hypertension can be predicted with an accuracy over 95% with few speech parameters, and hypercholesterolemia and heart disease with an accuracy over 80%.

Discussion

The predictor parameters found are heterogeneous, including voice quality, amplitude, frequency, and rhythm parameters. This result may lead to investigate why such important qualitative changes occur in the voice of older adults with these pathologies. Rather than trying to find a diagnostic procedure already existing in classical medicine, we expect this finding to contribute to explore the causes and concomitant pathologies of these diseases. We discuss the implications of behavioral traits, such as speech, as digital biomarkers.

Vocal acoustic features may be objective biomarkers of negative symptoms in schizophrenia: A cross-sectional study

BACKGROUND

There are currently no objective biomarkers that allow the quantification of negative symptoms of schizophrenia. This study therefore explored the use of acoustic features in identifying the severity of negative symptoms in patients with schizophrenia.

METHODS

We recruited 79 inpatients who were diagnosed with schizophrenia according to the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (the schizophrenia group) at the Huilongguan Hospital in Beijing, China, and 79 healthy controls from the surrounding community (the control group). We assessed the clinical symptoms of the patients with schizophrenia using the Positive and Negative Syndrome Scale (PANSS) and the Brief Negative Symptom Scale (BNSS) and recorded the voice of each participant as they read emotionally positive, neutral, and negative texts. The Praat software was used to analyse and extract acoustic characteristics from the recordings, such as jitter, shimmer, and pitch. The acoustic differences between the two groups of participants and the relationship between acoustic characteristics and clinical symptoms in the patient group were analysed.

RESULTS

There were significant differences between the schizophrenia and control groups in pitch, voice breaks, jitter, shimmer, and the mean harmonics-to-noise ratio (p < 0.05). Jitter was negatively correlated with the blunted affect and alogia subscale scores of the BNSS, both in the positive and neutral emotion conditions, but the correlation disappeared in the negative emotion condition. However, shimmer exhibited a stable negative correlation with the blunted affect and alogia subscale scores of the BNSS in all three emotion conditions. A linear regression analysis showed that pitch, jitter, shimmer, and age were statistically significant predictors of BNSS subscale scores.

CONCLUSIONS

Acoustic emotional expression differs between patients with schizophrenia and healthy controls. Some acoustic characteristics are related to the severity of negative symptoms, regardless of semantic emotions, and may therefore be objective biomarkers of negative symptoms. A systematic method for assessing vocal acoustic characteristics could provide an accurate and feasible means of assessing negative symptoms in schizophrenia.

TWEET

Acoustic emotional expression differs between patients with schizophrenia and healthy controls. A systematic method for assessing vocal acoustics could provide an accurate and feasible means of assessing negative symptoms in schizophrenia.

Conversational metrics, psychopathological dimensions and self-disturbances in patients with schizophrenia

Difficulties in interpersonal communication, including conversational skill impairments, are core features of schizophrenia. However, very few studies have performed conversation analyses in a clinical population of schizophrenia patients. Here we investigate the conversational patterns of dialogues in schizophrenia patients to assess possible associations with symptom dimensions, subjective self-disturbances and social functioning. Thirty-five schizophrenia patients were administered the Positive and Negative Syndrome Scale (PANSS), the Clinical Language Disorder Rating Scale (CLANG), the Scale for the Assessment of Thought, Language and Communication (TLC), the Examination of Anomalous Self-Experience Scale (EASE), and the Social and Occupational Functioning Assessment Scale (SOFAS). Moreover, participants underwent a recorded semi-structured interview, to extract conversational variables. Conversational data were associated with negative symptoms and social functioning, but not with positive or disorganization symptoms. A significant positive correlation was found between "pause duration" and the EASE item "Spatialization of thought". The present study suggests an association between conversational patterns and negative symptom dimension of schizophrenia. Moreover, our findings evoke a relationship between the natural fluidity of conversation and of the natural unraveling of thoughts.

Gesture-speech synchrony in schizophrenia: A pilot study using a kinematic-acoustic analysis

Severe impairment of social functioning is the core feature of schizophrenia that persists despite treatment, and contributes to chronic functional disability. Abnormal non-verbal behaviors have been reported during interpersonal interactions but the temporal coordination of co-speech gestures with language abilities have been poorly studied to date in this pathology. Using the dynamical systems framework, the goal of the current study was to investigate whether gestures and speech synchrony is impaired in schizophrenia, exploring a new approach to report communicational skill disorders. Performing the first continuous kinematic-acoustic analysis in individuals with schizophrenia, we examined gesture-speech synchrony in solo spontaneous speech and in sensorimotor synchronization task. The experimental group consisted of twenty-eight participants with a diagnosis of schizophrenia and the control group consisted of twenty-four healthy participants age-gender-education matched. The results showed that spontaneous gesture-speech synchrony was preserved while intentional finger tapping-speech synchrony was impaired. In sensorimotor synchronization task, the schizophrenia group displayed greater asynchronies between finger tapping and syllable uttering and lower stability of coordination patterns. These findings suggest a specific deficit in time delay of information circulation and processing, especially in explicit functions. Thus, investigating intrapersonal coordination in schizophrenia may constitute a promising window into brain/behavior dynamic relationship.

Automatic Schizophrenia Detection Using Multimodality Media via a Text Reading Task

Schizophrenia is a crippling chronic mental disease that affects people worldwide. In this work, an automatic schizophrenia detection algorithm is proposed based on the reading deficit of schizophrenic patients. From speech and video modalities, the automatic schizophrenia detection algorithm illustrates abnormal speech, head movement, and reading fluency during the reading task. In the speech modality, an acoustic model of speech emotional flatness in schizophrenia is established to reflect the emotional expression flatness of schizophrenic speech from the perspective of speech production and perception. In the video modality, the head-movement-related features are proposed to illustrate the spontaneous head movement caused by repeated reading and unconscious movement, and the reading-fluency-related features are proposed to convey the damaged degree of schizophrenic patients' reading fluency. The experimental data of this work are 160 segments of speech and video data recorded by 40 participants (20 schizophrenic patients and 20 normal controls). Combined with support vector machines and random forest, the accuracy of the proposed acoustic model, the head-movement-related features, and the reading-fluency-related features range from 94.38 to 96.50%, 73.38 to 83.38%, and 79.50 to 83.63%, respectively. The average accuracy of the proposed automatic schizophrenia detection algorithm reaches 97.50%. The experimental results indicate the effectiveness of the proposed automatic detection algorithm as an auxiliary diagnostic method for schizophrenia.

Schizophrenia identification for phonetic coherence using SVM and blur approaches

The diagnosis cycle of schizophrenia is long, there is no objective diagnostic basis. The over-energy entropy product of the speech fluency rectangular parameter is designed in the paper, the fuzzy clustering is used to double locate speech pause areas and to assist in the diagnosis of schizophrenia. The pause area of speech is located based on the low speech fluency and flat energy in schizophrenia patients, an extraction algorithm is given for speech fluency quantification parameters, support vector machine (SVM) classifier is used in the approach. The fluency acoustic features of speech are taken from 28 schizophrenia patients and 28 normal controls, these are used to verify the effect of the method in schizophrenia recognition, there is a correct rate of over 85% . The automatic schizophrenia identification based on energy entropy product and fuzzy clustering can provide objective, effective and non-invasive auxiliary for clinical diagnosis of schizophrenia.

Facial and Vocal Markers of Schizophrenia Measured Using Remote Smartphone Assessments: Observational Study

Background

Machine learning–based facial and vocal measurements have demonstrated relationships with schizophrenia diagnosis and severity. Demonstrating utility and validity of remote and automated assessments conducted outside of controlled experimental or clinical settings can facilitate scaling such measurement tools to aid in risk assessment and tracking of treatment response in populations that are difficult to engage.

Objective

This study aimed to determine the accuracy of machine learning–based facial and vocal measurements acquired through automated assessments conducted remotely through smartphones.

Methods

Measurements of facial and vocal characteristics including facial expressivity, vocal acoustics, and speech prevalence were assessed in 20 patients with schizophrenia over the course of 2 weeks in response to two classes of prompts previously utilized in experimental laboratory assessments: evoked prompts, where subjects are guided to produce specific facial expressions and speech; and spontaneous prompts, where subjects are presented stimuli in the form of emotionally evocative imagery and asked to freely respond. Facial and vocal measurements were assessed in relation to schizophrenia symptom severity using the Positive and Negative Syndrome Scale.

Results

Vocal markers including speech prevalence, vocal jitter, fundamental frequency, and vocal intensity demonstrated specificity as markers of negative symptom severity, while measurement of facial expressivity demonstrated itself as a robust marker of overall schizophrenia symptom severity.

Conclusions

Established facial and vocal measurements, collected remotely in schizophrenia patients via smartphones in response to automated task prompts, demonstrated accuracy as markers of schizophrenia symptom severity. Clinical implications are discussed.

Multimodal assessment of communicative-pragmatic features in schizophrenia: a machine learning approach

An impairment in pragmatic communication is a core feature of schizophrenia, often associated with difficulties in social interactions. The pragmatic deficits regard various pragmatic phenomena, e.g., direct and indirect communicative acts, deceit, irony, and include not only the use of language but also other expressive means such as non-verbal/extralinguistic modalities, e.g., gestures and body movements, and paralinguistic cues, e.g., prosody and tone of voice. The present paper focuses on the identification of those pragmatic features, i.e., communicative phenomena and expressive modalities, that more reliably discriminate between individuals with schizophrenia and healthy controls. We performed a multimodal assessment of communicative-pragmatic ability, and applied a machine learning approach, specifically a Decision Tree model, with the aim of identifying the pragmatic features that best separate the data into the two groups, i.e., individuals with schizophrenia and healthy controls, and represent their configuration. The results indicated good overall performance of the Decision Tree model, with mean Accuracy of 82%, Sensitivity of 76%, and Precision of 91%. Linguistic irony emerged as the most relevant pragmatic phenomenon in distinguishing between the two groups, followed by violation of the Gricean maxims, and then extralinguistic deceitful and sincere communicative acts. The results are discussed in light of the pragmatic theoretical literature, and their clinical relevance in terms of content and design of both assessment and rehabilitative training.

Speech aging: Production and perception

In this overview we describe literature on how speech production and speech perception change in healthy or normal aging across the adult lifespan. In the production section we review acoustic characteristics that have been investigated as potentially distinguishing younger and older adults. In the speech perception section studies concerning speaker age estimation and those investigating older listeners' perception are addressed. Our discussion focuses on major themes and other fruitful areas for future research. This article is categorized under: Linguistics > Language in Mind and Brain Linguistics > Linguistic Theory Psychology > Development and Aging.

Language Dysfunction in Schizophrenia: Assessing Neural Tracking to Characterize the Underlying Disorder(s)?

Deficits in language production and comprehension are characteristic of schizophrenia. To date, it remains unclear whether these deficits arise from dysfunctional linguistic knowledge, or dysfunctional predictions derived from the linguistic context. Alternatively, the deficits could be a result of dysfunctional neural tracking of auditory information resulting in decreased auditory information fidelity and even distorted information. Here, we discuss possible ways for clinical neuroscientists to employ neural tracking methodology to independently characterize deficiencies on the auditory-sensory and abstract linguistic levels. This might lead to a mechanistic understanding of the deficits underlying language related disorder(s) in schizophrenia. We propose to combine naturalistic stimulation, measures of speech-brain synchronization, and computational modeling of abstract linguistic knowledge and predictions. These independent but likely interacting assessments may be exploited for an objective and differential diagnosis of schizophrenia, as well as a better understanding of the disorder on the functional level-illustrating the potential of neural tracking methodology as translational tool in a range of psychotic populations.

Computer Vision-Based Assessment of Motor Functioning in Schizophrenia: Use of Smartphones for Remote Measurement of Schizophrenia Symptomatology

Introduction

Motor abnormalities have been shown to be a distinct component of schizophrenia symptomatology. However, objective and scalable methods for assessment of motor functioning in schizophrenia are lacking. Advancements in machine learning-based digital tools have allowed for automated and remote "digital phenotyping" of disease symptomatology. Here, we assess the performance of a computer vision-based assessment of motor functioning as a characteristic of schizophrenia using video data collected remotely through smartphones.

Methods

Eighteen patients with schizophrenia and 9 healthy controls were asked to remotely participate in smartphone-based assessments daily for 14 days. Video recorded from the smartphone front-facing camera during these assessments was used to quantify the Euclidean distance of head movement between frames through a pretrained computer vision model. The ability of head movement measurements to distinguish between patients and healthy controls as well as their relationship to schizophrenia symptom severity as measured through traditional clinical scores was assessed.

Results

The rate of head movement in participants with schizophrenia (1.48 mm/frame) and those without differed significantly (2.50 mm/frame; p = 0.01), and a logistic regression demonstrated that head movement was a significant predictor of schizophrenia diagnosis (p = 0.02). Linear regression between head movement and clinical scores of schizophrenia showed that head movement has a negative relationship with schizophrenia symptom severity (p = 0.04), primarily with negative symptoms of schizophrenia.

Conclusions

Remote, smartphone-based assessments were able to capture meaningful visual behavior for computer vision-based objective measurement of head movement. The measurements of head movement acquired were able to accurately classify schizophrenia diagnosis and quantify symptom severity in patients with schizophrenia.

Language as a biomarker for psychosis: A natural language processing approach

Human ratings of conceptual disorganization, poverty of content, referential cohesion and illogical thinking have been shown to predict psychosis onset in prospective clinical high risk (CHR) cohort studies. The potential value of linguistic biomarkers has been significantly magnified, however, by recent advances in natural language processing (NLP) and machine learning (ML). Such methodologies allow for the rapid and objective measurement of language features, many of which are not easily recognized by human raters. Here we review the key findings on language production disturbance in psychosis. We also describe recent advances in the computational methods used to analyze language data, including methods for the automatic measurement of discourse coherence, syntactic complexity, poverty of content, referential coherence, and metaphorical language. Linguistic biomarkers of psychosis risk are now undergoing cross-validation, with attention to harmonization of methods. Future directions in extended CHR networks include studies of sources of variance, and combination with other promising biomarkers of psychosis risk, such as cognitive and sensory processing impairments likely to be related to language. Implications for the broader study of social communication, including reciprocal prosody, face expression and gesture, are discussed.

Understanding Language Abnormalities and Associated Clinical Markers in Psychosis: The Promise of Computational Methods

The language and speech of individuals with psychosis reflect their impairments in cognition and motor processes. These language disturbances can be used to identify individuals with and at high risk for psychosis, as well as help track and predict symptom progression, allowing for early intervention and improved outcomes. However, current methods of language assessment-manual annotations and/or clinical rating scales-are time intensive, expensive, subject to bias, and difficult to administer on a wide scale, limiting this area from reaching its full potential. Computational methods that can automatically perform linguistic analysis have started to be applied to this problem and could drastically improve our ability to use linguistic information clinically. In this article, we first review how these automated, computational methods work and how they have been applied to the field of psychosis. We show that across domains, these methods have captured differences between individuals with psychosis and healthy controls and can classify individuals with high accuracies, demonstrating the promise of these methods. We then consider the obstacles that need to be overcome before these methods can play a significant role in the clinical process and provide suggestions for how the field should address them. In particular, while much of the work thus far has focused on demonstrating the successes of these methods, we argue that a better understanding of when and why these models fail will be crucial toward ensuring these methods reach their potential in the field of psychosis.

"How are you?" Estimation of anxiety, sleep quality, and mood using computational voice analysis

We developed a method of estimating impactors of cognitive function (ICF) - such as anxiety, sleep quality, and mood - using computational voice analysis. Clinically validated questionnaires (VQs) were used to score anxiety, sleep and mood while salient voice features were extracted to train regression models with deep neural networks. Experiments with 203 subjects showed promising results with significant concordance correlation coefficients (CCC) between actual VQ scores and the predicted scores (0.46 = anxiety, 0.50 = sleep quality, 0.45 = mood).

Anomalies in language as a biomarker for schizophrenia

PURPOSE OF REVIEW

After more than a century of neuroscience research, reproducible, clinically relevant biomarkers for schizophrenia have not yet been established. This article reviews current advances in evaluating the use of language as a diagnostic or prognostic tool in schizophrenia.

RECENT FINDINGS

The development of computational linguistic tools to quantify language disturbances is rapidly gaining ground in the field of schizophrenia research. Current applications are the use of semantic space models and acoustic analyses focused on phonetic markers. These features are used in machine learning models to distinguish patients with schizophrenia from healthy controls or to predict conversion to psychosis in high-risk groups, reaching accuracy scores (generally ranging from 80 to 90%) that exceed clinical raters. Other potential applications for a language biomarker in schizophrenia are monitoring of side effects, differential diagnostics and relapse prevention.

SUMMARY

Language disturbances are a key feature of schizophrenia. Although in its early stages, the emerging field of research focused on computational linguistics suggests an important role for language analyses in the diagnosis and prognosis of schizophrenia. Spoken language as a biomarker for schizophrenia has important advantages because it can be objectively and reproducibly quantified. Furthermore, language analyses are low-cost, time efficient and noninvasive in nature.

Voice patterns in schizophrenia: A systematic review and Bayesian meta-analysis

Voice atypicalities have been a characteristic feature of schizophrenia since its first definitions. They are often associated with core negative symptoms such as flat affect and alogia, and with the social impairments seen in the disorder. This suggests that voice atypicalities may represent a marker of clinical features and social functioning in schizophrenia. We systematically reviewed and meta-analyzed the evidence for distinctive acoustic patterns in schizophrenia, as well as their relation to clinical features. We identified 46 articles, including 55 studies with a total of 1254 patients with schizophrenia and 699 healthy controls. Summary effect sizes (Hedges'g and Pearson's r) estimates were calculated using multilevel Bayesian modeling. We identified weak atypicalities in pitch variability (g = -0.55) related to flat affect, and stronger atypicalities in proportion of spoken time, speech rate, and pauses (g's between -0.75 and -1.89) related to alogia and flat affect. However, the effects were mostly modest (with the important exception of pause duration) compared to perceptual and clinical judgments, and characterized by large heterogeneity between studies. Moderator analyses revealed that tasks with a more demanding cognitive and social component showed larger effects both in contrasting patients and controls and in assessing symptomatology. In conclusion, studies of acoustic patterns are a promising but, yet unsystematic avenue for establishing markers of schizophrenia. We outline recommendations towards more cumulative, open, and theory-driven research.

Speech Prosody as a Bridge Between Psychopathology and Linguistics: The Case of the Schizophrenia Spectrum

Patients with schizophrenia spectrum disorders experience severe difficulties in interpersonal communication, as described by traditional psychopathology and current research on social cognition. From a linguistic perspective, pragmatic abilities are crucial for successful communication. Empirical studies have shown that these abilities are significantly impaired in this group of patients. Prosody, the tone of voice with which words and sentences are pronounced, is one of the most important carriers of pragmatic meaning and can serve a range of functions from linguistic to emotional ones. Most of the existing literature on prosody of patients with schizophrenia spectrum disorders focuses on the expression of emotion, generally showing significant impairments. By contrast, the use of non-emotional prosody in these patients is scarcely investigated. In this paper, we first present a linguistic model to classify prosodic functions. Second, we discuss existing studies on the use of non-emotional prosody in these patients, providing an overview of the state of the art. Third, we delineate possible future lines of research in this field, also taking into account some classical psychopathological assumptions, for both diagnostic and therapeutic purposes.

Non-verbal speech cues as objective measures for negative symptoms in patients with schizophrenia

Negative symptoms in schizophrenia are associated with significant burden and possess little to no robust treatments in clinical practice today. One key obstacle impeding the development of better treatment methods is the lack of an objective measure. Since negative symptoms almost always adversely affect speech production in patients, speech dysfunction have been considered as a viable objective measure. However, researchers have mostly focused on the verbal aspects of speech, with scant attention to the non-verbal cues in speech. In this paper, we have explored non-verbal speech cues as objective measures of negative symptoms of schizophrenia. We collected an interview corpus of 54 subjects with schizophrenia and 26 healthy controls. In order to validate the non-verbal speech cues, we computed the correlation between these cues and the NSA-16 ratings assigned by expert clinicians. Significant correlations were obtained between these non-verbal speech cues and certain NSA indicators. For instance, the correlation between Turn Duration and Restricted Speech is -0.5, Response time and NSA Communication is 0.4, therefore indicating that poor communication is reflected in the objective measures, thus validating our claims. Moreover, certain NSA indices can be classified into observable and non-observable classes from the non-verbal speech cues by means of supervised classification methods. In particular the accuracy for Restricted speech quantity and Prolonged response time are 80% and 70% respectively. We were also able to classify healthy and patients using non-verbal speech features with 81.3% accuracy.

Improving emotion recognition in schizophrenia with "VOICES": An on-line prosodic self-training

Introduction

Emotion recognition (ER) deficits have been extensively demonstrated in schizophrenia. These deficiencies are not only restricted to facial emotion recognition but also include emotional prosody (tone of the voice) recognition deficits. They have been significantly associated with symptom severity and poor social functioning. The aim of this study was to test the efficacy, in real clinical conditions, of an online self-training prosodic game included in the Social Cognition rehabilitation program e-Motional Training.

Method

A randomized, single-blind multicenter clinical trial was conducted with 50 outpatients with schizophrenia or schizoaffective disorder. The control group was treated with Treatment-as-usual (TAU), based on drug therapy, case management and individual and group psychotherapy (not focused on Social Cognition). The intervention group was treated with TAU plus the employment of Voices, an online self-training program devised for prosodic rehabilitation.

Statistical analysis

Linear regression was used to evaluate the effectiveness of the intervention in emotion recognition measured with the Reading the Mind in the Voice–Spanish Version (RMV-SV).

Results

There were statistically significant differences between the intervention and control group measured with RMV-SV (β = 3,6[IC 95%], p<0.001), with a response variable in RMV post R² = 0,617.

Discussion

Voices, a prosodic rehabilitation game included in e-Motional Training, seems to be a promising tool for improving emotional voice recognition deficits in schizophrenia, filling the need for such interventions.

Prosody abilities in a large sample of affective and non-affective first episode psychosis patients

OBJECTIVE

Prosody comprehension deficits have been reported in major psychoses. It is still not clear whether these deficits occur at early psychosis stages. The aims of our study were to investigate a) linguistic and emotional prosody comprehension abilities in First Episode Psychosis (FEP) patients compared to healthy controls (HC); b) performance differences between non-affective (FEP-NA) and affective (FEP-A) patients, and c) association between symptoms severity and prosodic features.

METHODS

A total of 208 FEP (156 FEP-NA and 52 FEP-A) patients and 77 HC were enrolled and assessed with the Italian version of the "Protocole Montréal d'Evaluation de la Communication" to evaluate linguistic and emotional prosody comprehension. Clinical variables were assessed with a comprehensive set of standardized measures.

RESULTS

FEP patients displayed significant linguistic and emotional prosody deficits compared to HC, with FEP-NA showing greater impairment than FEP-A. Also, significant correlations between symptom severity and prosodic features in FEP patients were found.

CONCLUSIONS

Our results suggest that prosodic impairments occur at the onset of psychosis being more prominent in FEP-NA and in those with severe psychopathology. These findings further support the hypothesis that aprosodia is a core feature of psychosis.

Protocolo de evaluación de la prosodia emocional y la pragmática en personas con esquizofrenia

La esquizofrenia se caracteriza por una distorsión del pensamiento, las percepciones, las emociones, el lenguaje, la conciencia de sí mismo y la conducta. Afecta a más de 21 millones de personas en todo el mundo y es una de las 10 enfermedades más incapacitantes según la Organización Mundial de la Salud, pero existe escasa evidencia sobre el déficit lingüístico con el que cursa la enfermedad. Estudio transversal y cuasi-experimental donde se analizan los datos resultantes de la evaluación por medio de diferentes pruebas de las áreas de pragmática y prosodia en una muestra compuesta por 96 sujetos, de los cuales 48 (50%) no tienen enfermedad mental y componen el grupo control y 48 (50%) tienen enfermedad mental y componen el grupo experimental.De acuerdo con la clasificación de Crow (1978), el 56,7% de los sujetos del grupo experimental (27 sujetos) tiene sintomatología positiva y el 43,8% (21 sujetos) tiene sintomatología negativa. Se observan diferentes patrones de déficit según la sintomatología, así como una afectación de las áreas evaluadas en el estudio (pragmática y prosodia afectiva).La esquizofrenia cursa con déficit en las áreas analizadas, si bien existe una controversia acerca de su origen. En relación con la pragmática, encontramos interpretaciones literales en ambos tipos de pacientes, aunque son más frecuentes en los sujetos con sintomatología positiva. En cuanto a la prosodia, encontramos frecuencias fundamentales excesivas, monotonía en la expresión de las emociones y dificultades para comprender emociones a través de los aspectos prosódicos.

The aprosody of schizophrenia: Computationally derived acoustic phonetic underpinnings of monotone speech

OBJECTIVE

Acoustic phonetic methods are useful in examining some symptoms of schizophrenia; we used such methods to understand the underpinnings of aprosody. We hypothesized that, compared to controls and patients without clinically rated aprosody, patients with aprosody would exhibit reduced variability in: pitch (F0), jaw/mouth opening and tongue height (formant F1), tongue front/back position and/or lip rounding (formant F2), and intensity/loudness.

METHODS

Audiorecorded speech was obtained from 98 patients (including 25 with clinically rated aprosody and 29 without) and 102 unaffected controls using five tasks: one describing a drawing, two based on spontaneous speech elicited through a question (Tasks 2 and 3), and two based on reading prose excerpts (Tasks 4 and 5). We compared groups on variation in pitch (F0), formant F1 and F2, and intensity/loudness.

RESULTS

Regarding pitch variation, patients with aprosody differed significantly from controls in Task 5 in both unadjusted tests and those adjusted for sociodemographics. For the standard deviation (SD) of F1, no significant differences were found in adjusted tests. Regarding SD of F2, patients with aprosody had lower values than controls in Task 3, 4, and 5. For variation in intensity/loudness, patients with aprosody had lower values than patients without aprosody and controls across the five tasks.

CONCLUSIONS

Findings could represent a step toward developing new methods for measuring and tracking the severity of this specific negative symptom using acoustic phonetic parameters; such work is relevant to other psychiatric and neurological disorders.

Right Hemisphere Regions Critical for Expression of Emotion Through Prosody

Impaired expression of emotion through pitch, loudness, rate, and rhythm of speech (affective prosody) is common and disabling after right hemisphere (RH) stroke. These deficits impede all social interactions. Previous studies have identified cortical areas associated with impairments of expression, recognition, or repetition of affective prosody, but have not identified critical white matter tracts. We hypothesized that: (1) differences across patients in specific acoustic features correlate with listener judgment of affective prosody and (2) these differences are associated with infarcts of specific RH gray and white matter regions. To test these hypotheses, 41 acute ischemic RH stroke patients had MRI diffusion weighted imaging and described a picture. Affective prosody of picture descriptions was rated by 21 healthy volunteers. We identified percent damage (lesion load) to each of seven regions of interest previously associated with expression of affective prosody and two control areas that have been associated with recognition but not expression of prosody. We identified acoustic features that correlated with listener ratings of prosody (hereafter “prosody acoustic measures”) with Spearman correlations and linear regression. We then identified demographic variables and brain regions where lesion load independently predicted the lowest quartile of each of the “prosody acoustic measures” using logistic regression. We found that listener ratings of prosody positively correlated with four acoustic measures. Furthermore, the lowest quartile of each of these four “prosody acoustic measures” was predicted by sex, age, lesion volume, and percent damage to the seven regions of interest. Lesion load in pars opercularis, supramarginal gyrus, or associated white matter tracts (and not control regions) predicted lowest quartile of the four “prosody acoustic measures” in logistic regression. Results indicate that listener perception of reduced affective prosody after RH stroke is due to reduction in specific acoustic features caused by infarct in right pars opercularis or supramarginal gyrus, or associated white matter tracts.

Paradigms for Assessing Hedonic Processing and Motivation in Humans: Relevance to Understanding Negative Symptoms in Psychopathology

Clinicians and researchers have long known that one of the debilitating aspects of psychotic disorders is the presence of "negative symptoms," which involve impairments in hedonic and motivational function, and/or alterations in expressive affect. We have a number of excellent clinical tools available for assessing the presence and severity of negative symptoms. However, to better understand the mechanisms that may give rise to negative symptoms, we need tools and methods that can help distinguish among different potential contributing causes, as a means to develop more targeted intervention pathways. Using such paradigms is particularly important if we wish to understand whether the causes are the same or different across disorders that may share surface features of negative symptoms. This approach is in line with the goals of the Research Diagnostic Criteria Initiative, which advocates understanding the nature of core dimensions of brain-behavior relationships transdiagnostically. Here we highlight some of the emerging measures and paradigms that may help us to parse the nature and causes of negative symptoms, illustrating both the research approaches from which they emerge and the types of constructs that they can help elucidate.