Neural correlates of the relationship between discourse coherence and sensory monitoring in schizophrenia

Aberrant brain language network in schizophrenia spectrum disorder: a systematic review of its relation to language signs beyond symptoms

Background

Language disturbances are a core feature of schizophrenia, often studied as a formal thought disorder. The neurobiology of language in schizophrenia has been addressed within the same framework, that language and thought are equivalents considering symptoms and not signs. This review aims to systematically examine published peer-reviewed studies that employed neuroimaging techniques to investigate aberrant brain-language networks in individuals with schizophrenia in relation to linguistic signs.

Methods

We employed a language model for automatic data extraction. We selected our studies according to the PRISMA recommendations, and we conducted the quality assessment of the selected studies according to the STROBE guidance.

Results

We analyzed the findings from 37 studies, categorizing them based on patient characteristics, brain measures, and language task types. The inferior frontal gyrus (IFG) and superior temporal gyrus (STG) exhibited the most significant differences among these studies and paradigms.

Conclusions

We propose guidelines for future research in this field based on our analysis. It is crucial to investigate larger networks involved in language processing, and language models with brain metrics must be integrated to enhance our understanding of the relationship between language and brain abnormalities in schizophrenia.

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.

Reflections on the nature of measurement in language-based automated assessments of patients' mental state and cognitive function

Modern advances in computational language processing methods have enabled new approaches to the measurement of mental processes. However, the field has primarily focused on model accuracy in predicting performance on a task or a diagnostic category. Instead the field should be more focused on determining which computational analyses align best with the targeted neurocognitive/psychological functions that we want to assess. In this paper we reflect on two decades of experience with the application of language-based assessment to patients' mental state and cognitive function by addressing the questions of what we are measuring, how it should be measured and why we are measuring the phenomena. We address the questions by advocating for a principled framework for aligning computational models to the constructs being assessed and the tasks being used, as well as defining how those constructs relate to patient clinical states. We further examine the assumptions that go into the computational models and the effects that model design decisions may have on the accuracy, bias and generalizability of models for assessing clinical states. Finally, we describe how this principled approach can further the goal of transitioning language-based computational assessments to part of clinical practice while gaining the trust of critical stakeholders.

Progressive changes in descriptive discourse in First Episode Schizophrenia: a longitudinal computational semantics study

Computational semantics, a branch of computational linguistics, involves automated meaning analysis that relies on how words occur together in natural language. This offers a promising tool to study schizophrenia. At present, we do not know if these word-level choices in speech are sensitive to the illness stage (i.e., acute untreated vs. stable established state), track cognitive deficits in major domains (e.g., cognitive control, processing speed) or relate to established dimensions of formal thought disorder. In this study, we collected samples of descriptive discourse in patients experiencing an untreated first episode of schizophrenia and healthy control subjects (246 samples of 1-minute speech; n = 82, FES = 46, HC = 36) and used a co-occurrence based vector embedding of words to quantify semantic similarity in speech. We obtained six-month follow-up data in a subsample (99 speech samples, n = 33, FES = 20, HC = 13). At baseline, semantic similarity was evidently higher in patients compared to healthy individuals, especially when social functioning was impaired; but this was not related to the severity of clinically ascertained thought disorder in patients. Across the study sample, higher semantic similarity at baseline was related to poorer Stroop performance and processing speed. Over time, while semantic similarity was stable in healthy subjects, it increased in patients, especially when they had an increasing burden of negative symptoms. Disruptions in word-level choices made by patients with schizophrenia during short 1-min descriptions are sensitive to interindividual differences in cognitive and social functioning at first presentation and persist over the early course of the illness.

Brain functional correlates of formal thought disorder in schizophrenia: examining the frontal/dysexecutive hypothesis

BACKGROUND

One hypothesis proposed to underlie formal thought disorder (FTD), the incoherent speech is seen in some patients with schizophrenia, is that it reflects impairment in frontal/executive function. While this proposal has received support in neuropsychological studies, it has been relatively little tested using functional imaging. This study aimed to examine brain activations associated with FTD, and its two main factor-analytically derived subsyndromes, during the performance of a working memory task.

METHODS

Seventy patients with schizophrenia showing a full range of FTD scores and 70 matched healthy controls underwent fMRI during the performance of the 2-back version of the n-back task. Whole-brain corrected, voxel-based correlations with FTD scores were examined in the patient group.

RESULTS

During 2-back performance the patients showed clusters of significant inverse correlation with FTD scores in the inferior frontal cortex and dorsolateral prefrontal cortex bilaterally, the left temporal cortex and subcortically in the basal ganglia and thalamus. Further analysis revealed that these correlations reflected an association only with 'alogia' (poverty of speech, poverty of content of speech and perseveration) and not with the 'fluent disorganization' component of FTD.

CONCLUSIONS

This study provides functional imaging support for the view that FTD in schizophrenia may involve impaired executive/frontal function. However, the relationship appears to be exclusively with alogia and not with the variables contributing to fluent disorganization.

Quantified language connectedness in schizophrenia-spectrum disorders

Language abnormalities are a core symptom of schizophrenia-spectrum disorders and could serve as a potential diagnostic marker. Natural language processing enables quantification of language connectedness, which may be lower in schizophrenia-spectrum disorders. Here, we investigated connectedness of spontaneous speech in schizophrenia-spectrum patients and controls and determine its accuracy in classification. Using a semi-structured interview, speech of 50 patients with a schizophrenia-spectrum disorder and 50 controls was recorded. Language connectedness in a semantic word2vec model was calculated using consecutive word similarity in moving windows of increasing sizes (2-20 words). Mean, minimal and variance of similarity were calculated per window size and used in a random forest classifier to distinguish patients and healthy controls. Classification based on connectedness reached 85% cross-validated accuracy, with 84% specificity and 86% sensitivity. Features that best discriminated patients from controls were variance of similarity at window sizes between 5 and 10. We show impaired connectedness in spontaneous speech of patients with schizophrenia-spectrum disorders even in patients with low ratings of positive symptoms. Effects were most prominent at the level of sentence connectedness. The high sensitivity, specificity and tolerability of this method show that language analysis is an accurate and feasible digital assistant in diagnosing schizophrenia-spectrum disorders.

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.

Lowering costs for large-scale screening in psychosis: a systematic review and meta-analysis of performance and value of information for speech-based psychiatric evaluation

OBJECTIVE

Obstacles for computational tools in psychiatry include gathering robust evidence and keeping implementation costs reasonable. We report a systematic review of automated speech evaluation for the psychosis spectrum and analyze the value of information for a screening program in a healthcare system with a limited number of psychiatrists (Maputo, Mozambique).

METHODS

Original studies on speech analysis for forecasting of conversion in individuals at clinical high risk (CHR) for psychosis, diagnosis of manifested psychotic disorder, and first-episode psychosis (FEP) were included in this review. Studies addressing non-verbal components of speech (e.g., pitch, tone) were excluded.

RESULTS

Of 168 works identified, 28 original studies were included. Valuable speech features included direct measures (e.g., relative word counting) and mathematical embeddings (e.g.: word-to-vector, graphs). Accuracy estimates reported for schizophrenia diagnosis and CHR conversion ranged from 71 to 100% across studies. Studies used structured interviews, directed tasks, or prompted free speech. Directed-task protocols were faster while seemingly maintaining performance. The expected value of perfect information is USD 9.34 million. Imperfect tests would nevertheless yield high value.

CONCLUSION

Accuracy for screening and diagnosis was high. Larger studies are needed to enhance precision of classificatory estimates. Automated analysis presents itself as a feasible, low-cost method which should be especially useful for regions in which the physician pool is insufficient to meet demand.

The role of microstructural integrity of major language pathways in narrative speech in the first year after stroke

BACKGROUND AND PURPOSE

Left hemisphere stroke often results in a variety of language deficits due to varying patterns of damage to language networks. The Cookie Theft picture description task, a classic, quick bedside assessment, has been shown to quantify narrative speech reliably. In this study, we utilized diffusion tensor imaging (DTI) to assess language network white matter tract correlates of lexical-semantic and syntactic impairments longitudinally.

METHODS

Twenty-eight patients with mild to severe language impairments after left hemispheric lobar and/or subcortical ischemic stroke underwent the Cookie Theft picture description test and DTI up to three different time points: within the first three months, six months and twelve months after stroke. Dorsal and ventral stream language pathways were segmented to obtain DTI integrity metrics of both hemispheres. Multivariable regression models and partial correlation analyses adjusted for age, education, and lesion load were conducted to evaluate the temporal DTI profile of the white matter microstructural integrity of the language tracts as neural correlates of narrative speech within the first year after stroke.

RESULTS

Among all the major language white matter pathways, the integrity of the left arcuate (AF), inferior fronto-occipital, and inferior longitudinal fasciculi (ILF) were related to picture description performance. After FDR correction, left ILF fractional anisotropy correlated with syntactic cohesiveness (r=0.85,p=0.00087) within the first three months after stroke, whereas at one year post-stroke, the strongest correlations were found between lexical-semantic performance and left AF radial diffusivity (r = -0.71, p = 0.00065).

CONCLUSION

Our study provides a temporal profile of associations between the integrity of the main language pathways and lexical semantics and syntactic impairments in left hemispheric strokes.

Linking language features to clinical symptoms and multimodal imaging in individuals at clinical high risk for psychosis

BACKGROUND

Abnormalities in the semantic and syntactic organization of speech have been reported in individuals at clinical high-risk (CHR) for psychosis. The current study seeks to examine whether such abnormalities are associated with changes in brain structure and functional connectivity in CHR individuals.

METHODS

Automated natural language processing analysis was applied to speech samples obtained from 46 CHR and 22 healthy individuals. Brain structural and resting-state functional imaging data were also acquired from all participants. Sparse canonical correlation analysis (sCCA) was used to ascertain patterns of covariation between linguistic features, clinical symptoms, and measures of brain morphometry and functional connectivity related to the language network.

RESULTS

In CHR individuals, we found a significant mode of covariation between linguistic and clinical features (r = 0.73; p = 0.003), with negative symptoms and bizarre thinking covarying mostly with measures of syntactic complexity. In the entire sample, separate sCCAs identified a single mode of covariation linking linguistic features with brain morphometry (r = 0.65; p = 0.05) and resting-state network connectivity (r = 0.63; p = 0.01). In both models, semantic and syntactic features covaried with brain structural and functional connectivity measures of the language network. However, the contribution of diagnosis to both models was negligible.

CONCLUSIONS

Syntactic complexity appeared sensitive to prodromal symptoms in CHR individuals while the patterns of brain-language covariation seemed preserved. Further studies in larger samples are required to establish the reproducibility of these findings.

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.

A machine learning approach to predicting psychosis using semantic density and latent content analysis

Subtle features in people’s everyday language may harbor the signs of future mental illness. Machine learning offers an approach for the rapid and accurate extraction of these signs. Here we investigate two potential linguistic indicators of psychosis in 40 participants of the North American Prodrome Longitudinal Study. We demonstrate how the linguistic marker of semantic density can be obtained using the mathematical method of vector unpacking, a technique that decomposes the meaning of a sentence into its core ideas. We also demonstrate how the latent semantic content of an individual’s speech can be extracted by contrasting it with the contents of conversations generated on social media, here 30,000 contributors to Reddit. The results revealed that conversion to psychosis is signaled by low semantic density and talk about voices and sounds. When combined, these two variables were able to predict the conversion with 93% accuracy in the training and 90% accuracy in the holdout datasets. The results point to a larger project in which automated analyses of language are used to forecast a broad range of mental disorders well in advance of their emergence.

Reductions in prefrontal activation predict off-topic utterances during speech production

The ability to speak coherently is essential for effective communication but declines with age: older people more frequently produce tangential, off-topic speech. Little is known, however, about the neural systems that support coherence in speech production. Here, fMRI was used to investigate extended speech production in healthy older adults. Computational linguistic analyses were used to quantify the coherence of utterances produced in the scanner, allowing identification of the neural correlates of coherence for the first time. Highly coherent speech production was associated with increased activity in bilateral inferior prefrontal cortex (BA45), an area implicated in selection of task-relevant knowledge from semantic memory, and in bilateral rostrolateral prefrontal cortex (BA10), implicated more generally in planning of complex goal-directed behaviours. These findings demonstrate that neural activity during spontaneous speech production can be predicted from formal analysis of speech content, and that multiple prefrontal systems contribute to coherence in speech.

The ability to speak coherently is essential for effective communication, but little is known about the neural systems that support coherence. Here, the authors show that activity in two prefrontal cortex regions, BA10 and BA45, predicts the level of coherence in the speech of healthy older adults.

Is formal thought disorder in schizophrenia related to structural and functional aberrations in the language network? A systematic review of neuroimaging findings

Formal thought disorder (FTD) is a core feature of schizophrenia, a marker of illness severity and a predictor of outcome. The underlying neural mechanisms are still a matter of debate. This study aimed at 1) reviewing the literature on the neural correlates of FTD in schizophrenia, and 2) testing the hypothesis that FTD correlates with structural and functional aberrations in the language network. Medline, PsychInfo, and Embase were searched for neuroimaging studies, which applied a clinical measure to assess FTD in adults with schizophrenia and were published in English or German in peer-reviewed journals until December 2016. Of 412 articles identified, 61 studies were included in the review. Volumetric studies reported bilateral grey matter deficits (L > R) to be associated with FTD in the inferior frontal gyrus, the superior temporal gyrus and the inferior parietal lobe. The same regions showed hyperactivity in resting state functional magnetic resonance imaging (fMRI) studies and both hyper- and hypoactivity in fMRI studies that employed semantic processing or free speech production tasks. Diffusion tensor imaging studies demonstrated white matter aberrations in fibre tracts that connect the frontal and temporo-parietal regions. FTD in schizophrenia was found to be associated with structural and functional aberrations in the language network. However, there are studies that did not find an association between FTD and neural aberrations of the language network and regions not included in the language network have been associated with FTD. Thus, future research is needed to clarify the specificity of the language network for FTD in schizophrenia.

A systematic review of task-based functional neuroimaging studies investigating language, semantic and executive processes in thought disorder

The aim of the current systematic review was to synthesise the research that has investigated thought disorder (TD) using task-based functional neuroimaging techniques to target executive, language, or semantic functions. Thirty-five pertinent studies were identified from January 1990 to August 2016. Functional correlates of TD included the superior and middle temporal, fusiform, and inferior frontal gyri bilaterally, as well as the left and right cingulate cortex, the right caudate nucleus, and the cerebellum. TD-related increases and decreases in activation were both evident in most of these regions. However, the specificity of these correlates from general clinical and cognitive influences is unknown. The cortical regions implicated overlap with those thought to contribute to language and semantic systems. Cortico-striatal circuitry may also play a role in some aspects of TD through aberrant salience representation and inappropriate attentional prioritisation. To advance the field further, greater integration across structural, functional, and behavioural measures is required, in addition to non-unitary considerations of TD.

Formal thought disorders: from phenomenology to neurobiology

Formal thought disorder (FTD) is present in most psychiatric disorders and in some healthy individuals. In this Review, we present a comprehensive, integrative, and multilevel account of what is known about FTD, covering genetic, cellular, and neurotransmitter effects, environmental influences, experimental psychology and neuropsychology, brain imaging, phenomenology, linguistics, and treatment. FTD is a dimensional, phenomenologically defined construct, which can be clinically subdivided into positive versus negative and objective versus subjective symptom clusters. Because FTDs have been traditionally linked to schizophrenia, studies in other diagnoses are scarce. Aetiologically, FTD is the only symptom under genetic influence in schizophrenia as shown in linkage studies, but familial communication patterns (allusive thinking) have also been associated with the condition. Positive FTDs are related to synaptic rarefication in the glutamate system of the superior and middle lateral temporal cortices. Cortical volume of the left superior temporal gyrus is decreased in patients with schizophrenia who have positive FTD in structural MRI studies and shows reversed hemispheric (right more than left) activation in functional MRI experiments during speech production. Semantic network dysfunction in positive FTD has been demonstrated in experiments of indirect semantic hyperpriming (reaction time). In acute positive FTD, antipsychotics are effective, but a subgroup of patients have treatment-resistant, chronic, positive or negative FTD. Specific psychotherapy as treatment for FTD has not yet been developed. With this solid data on the pathogenesis of FTD, we can now implement clinical studies to treat this condition.

Neural correlates of formal thought disorder: An activation likelihood estimation meta-analysis

Formal thought disorder (FTD) refers to a psychopathological dimension characterized by disorganized and incoherent speech. Whether symptoms of FTD arise from aberrant processing in language-related regions or more general cognitive networks, however, remains debated. Here, we addressed this question by a quantitative meta-analysis of published functional neuroimaging studies on FTD. The revised Activation Likelihood Estimation (ALE) algorithm was used to test for convergent aberrant activation changes in 18 studies (30 experiments) investigating FTD, of which 17 studies comprised schizophrenia patients and one study healthy subjects administered to S-ketamine. Additionally, we analyzed task-dependent and task-independent (resting-state) functional connectivity (FC) of brain regions showing convergence in activation changes. Subsequent functional characterization was performed for the initial clusters and the delineated connectivity networks by reference to the BrainMap database. Consistent activation changes were found in the left superior temporal gyrus (STG) and two regions within the left posterior middle temporal gyrus (p-MTG), ventrally (vp-MTG) and dorsally (dp-MTG). Functional characterization revealed a prominent functional association of ensuing clusters from our ALE meta-analysis with language and speech processing, as well as auditory perception in STG and with social cognition in dp-MTG. FC analysis identified task-dependent and task-independent networks for all three seed regions, which were mainly related to language and speech processing, but showed additional involvement in higher order cognitive functions. Our findings suggest that FTD is mainly characterized by abnormal activation in brain regions of the left hemisphere that are associated with language and speech processing, but also extend to higher order cognitive functions. Hum Brain Mapp 38:4946-4965, 2017. © 2017 Wiley Periodicals, Inc.

Can RDoC Help Find Order in Thought Disorder?

Thought disorder is a pernicious and nonspecific aspect of numerous serious mental illnesses (SMIs) and related conditions. Despite decades of empirical research on thought disorder, our present understanding of it is poor, our clinical assessments focus on a limited set of extreme behaviors, and treatments are palliative at best. Applying a Research Domain Criteria (RDoC) framework to thought disorder research offers advantages to explicate its phenotype; isolate its mechanisms; and develop more effective assessments, treatments, and potential cures. In this commentary, we discuss ways in which thought disorder can be understood within the RDoC framework. We propose operationalizing thought disorder within the RDoC construct of language using psycholinguistic sciences, to help objectify and quantify language within individuals; technologically sophisticated paradigms, to allow naturalistic behavioral sampling techniques with unprecedented ecological validity; and computational modeling, to account for a network of interconnected and dynamic linguistic, cognitive, affective, and social functions. We also highlight challenges for understanding thought disorder within an RDoC framework. Thought disorder likely does not occur as an isomorphic dysfunction in a single RDoC construct, but rather, as multiple potential dysfunctions in a network of RDoC constructs. Moreover, thought disorder is dynamic over time and context within individuals. In sum, RDoC is a useful framework to integrate multidisciplinary research efforts aimed at operationalizing, understanding, and ameliorating thought disorder.

Aberrant Temporal Connectivity in Persons at Clinical High Risk for Psychosis

Background

Schizophrenia, a neurodevelopmental disorder, involves abnormalities in functional connectivity (FC) across distributed neural networks, which are thought to antedate the emergence of psychosis. In a cohort of adolescents and young adults at clinical high risk (CHR) for psychosis, we applied data-driven approaches to resting-state fMRI data so as to systematically characterize FC abnormalities during this period and determine whether these abnormalities are associated with psychosis risk and severity of psychotic symptoms.

Methods

Fifty-one CHR participants and 47 matched healthy controls (HCs) were included in our analyses. Twelve of these CHR participants developed psychosis within 3.9 years. We estimated one multivariate measure of FC and studied its relationship to CHR status, conversion to psychosis and positive symptom severity.

Results

Multivariate analyses revealed between-group differences in whole-brain connectivity patterns of bilateral temporal areas, mostly affecting their functional connections to the thalamus. Further, more severe positive symptoms were associated with greater connectivity abnormalities in the anterior cingulate and frontal cortex.

Conclusions

Our study demonstrates that the well-established FC abnormalities of the thalamus and temporal areas observed in schizophrenia are also present in the CHR period, with aberrant connectivity of the temporal cortex most associated with psychosis risk.

An examination of the language construct in NIMH's research domain criteria: Time for reconceptualization!

The National Institute of Mental Health's Research Domain Criteria (RDoC) Initiative "calls for the development of new ways of classifying psychopathology based on dimensions of observable behavior." As a result of this ambitious initiative, language has been identified as an independent construct in the RDoC matrix. In this article, we frame language within an evolutionary and neuropsychological context and discuss some of the limitations to the current measurements of language. Findings from genomics and the neuroimaging of performance during language tasks are discussed in relation to serious mental illness and within the context of caveats regarding measuring language. Indeed, the data collection and analysis methods employed to assay language have been both aided and constrained by the available technologies, methodologies, and conceptual definitions. Consequently, different fields of language research show inconsistent definitions of language that have become increasingly broad over time. Individually, they have also shown significant improvements in conceptual resolution, as well as in experimental and analytic techniques. More recently, language research has embraced collaborations across disciplines, notably neuroscience, cognitive science, and computational linguistics and has ultimately re-defined classical ideas of language. As we move forward, the new models of language with their remarkably multifaceted constructs force a re-examination of the NIMH RDoC conceptualization of language and thus the neuroscience and genetics underlying this concept. © 2016 The Authors. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics Published by Wiley Periodicals, Inc.

A window into the intoxicated mind? Speech as an index of psychoactive drug effects

Abused drugs can profoundly alter mental states in ways that may motivate drug use. These effects are usually assessed with self-report, an approach that is vulnerable to biases. Analyzing speech during intoxication may present a more direct, objective measure, offering a unique 'window' into the mind. Here, we employed computational analyses of speech semantic and topological structure after ±3,4-methylenedioxymethamphetamine (MDMA; 'ecstasy') and methamphetamine in 13 ecstasy users. In 4 sessions, participants completed a 10-min speech task after MDMA (0.75 and 1.5 mg/kg), methamphetamine (20 mg), or placebo. Latent Semantic Analyses identified the semantic proximity between speech content and concepts relevant to drug effects. Graph-based analyses identified topological speech characteristics. Group-level drug effects on semantic distances and topology were assessed. Machine-learning analyses (with leave-one-out cross-validation) assessed whether speech characteristics could predict drug condition in the individual subject. Speech after MDMA (1.5 mg/kg) had greater semantic proximity than placebo to the concepts friend, support, intimacy, and rapport. Speech on MDMA (0.75 mg/kg) had greater proximity to empathy than placebo. Conversely, speech on methamphetamine was further from compassion than placebo. Classifiers discriminated between MDMA (1.5 mg/kg) and placebo with 88% accuracy, and MDMA (1.5 mg/kg) and methamphetamine with 84% accuracy. For the two MDMA doses, the classifier performed at chance. These data suggest that automated semantic speech analyses can capture subtle alterations in mental state, accurately discriminating between drugs. The findings also illustrate the potential for automated speech-based approaches to characterize clinically relevant alterations to mental state, including those occurring in psychiatric illness.

Neural activation abnormalities during self-referential processing in schizophrenia: an fMRI study

Impairments in self-awareness contribute to disability in schizophrenia. Studies have revealed activation abnormalities in schizophrenia in cortical midline structures associated with self-reference. We used functional magnetic resonance imaging to compare activation throughout the brain in people with schizophrenia and healthy controls (Kelly et al., 2002) while they indicated whether trait adjectives described attributes of themselves, their mother or a former president of the United States. Blood oxygenation level dependent signal in each condition was compared to resting fixation. Patients were less likely and slower to endorse positive self-attributes, and more likely and quicker to endorse negative self-attributes than controls. Activation abnormalities reported previously in cortical midline structures were again noted. In addition, patients showed greater signal increases in frontal, temporal gyri and insula, and smaller signal decreases in posterior regions than healthy controls when thinking about themselves. Group differences were less evident when subjects were thinking about their mothers and tended to go in the opposite direction when thinking about a president. Many of the areas showing abnormality have been shown in other studies to differ between patients and controls in structure and with other activation paradigms. We suggest that general neuropathology in schizophrenia alters the neural system configurations associated with self-representation.

Automated computerized analysis of speech in psychiatric disorders

PURPOSE OF REVIEW

Disturbances in communication are a hallmark feature of severe mental illnesses. Recent technological advances have paved the way for objectifying communication using automated computerized semantic, linguistic and acoustic analyses. We review recent studies applying various computer-based assessments to the natural language produced by adult patients with severe mental illness.

RECENT FINDINGS

Automated computerized methods afford tools with which it is possible to objectively evaluate patients in a reliable, valid and efficient manner that complements human ratings. Crucially, these measures correlate with important clinical measures. The clinical relevance of these novel metrics has been demonstrated by showing their relationship to functional outcome measures, their in-vivo link to classic 'language' regions in the brain, and, in the case of linguistic analysis, their relationship to candidate genes for severe mental illness.

SUMMARY

Computer-based assessments of natural language afford a framework with which to measure communication disturbances in adults with severe mental illnesses. Emerging evidence suggests that they can be reliable and valid, and overcome many practical limitations of more traditional assessment methods. The advancement of these technologies offers unprecedented potential for measuring and understanding some of the most crippling symptoms of some of the most debilitating illnesses known to humankind.