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A Window into the Intoxicated Mind? Speech as an Index of Psychoactive Drug Effects

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Thank you for visiting nature. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser or turn off compatibility mode in Internet Explorer. In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. 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. In 4 sessions, participants completed a min speech task after MDMA 0. 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. Speech on MDMA 0. Conversely, speech on methamphetamine was further from compassion than placebo. Classifiers discriminated between MDMA 1. 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. A fundamental characteristic of abused drugs is that they alter mental states, sometimes profoundly. These consciousness- and mood-altering effects appear to be intimately involved in motivations to use drugs Sumnall et al, ; Fischman and Foltin, , suggesting they are critically important in addiction. Investigating pharmacologically induced mental-state alterations may also provide insights into the neurobiology of consciousness Coyle et al, As long as drugs have been used, people have attempted to communicate drug-related alterations to mental state through artistic and literary approaches see, eg, Huxley, From the scientific perspective, drug effects on human mental states have been studied using two main approaches. The first comprises retrospective descriptive reports Coyle et al, , which may be affected by inaccurate recall. In addition, descriptive reports are nonstandardized, and hence most readily analyzed qualitatively, an analytic approach that can be time intensive Coyle et al, and lacks generalizability Rice and Ezzy, Most available descriptive reports are also from instances of drug use that were not blinded, leaving them open to expectancy effects Mitchell et al, Conversely, such reports have the advantage of being open-ended, allowing exhaustive examination of different mental-state alterations. Momentary reporting resolves the issue of biased recall, and standardized instruments allow straightforward quantitative analyses. However, the sensitivity of standardized scales is limited by the mood descriptors included. Moreover, both descriptive reports and self-report assessments rely on access to introspective experiences, as well as motivation and capacity to accurately report them, factors that may vary systematically with drug effects. A potentially more direct alternative is to investigate speech during intoxication. Several studies have examined the effects of drugs on the quantity of speech emitted see, eg, Foltin and Fischman, ; Higgins and Stitzer, , ; Stitzer et al, , but these provide little information about mental-state alterations because they did not examine speech content. Here, we investigate the hypothesis that speech content can provide a unique window into thought, allowing more direct assessment of mental-state alterations due to abused drugs and bypassing issues of measurement and motivation affecting other methods. In early studies, Adler et al , manually coded the speech of healthy volunteers after administration of ketamine as a pharmacological model for psychosis. They found that the drug produced disordered speech as identified with clinical ratings. Recent advances in computational measurement allow quantitative speech analysis using automated methods. For example, these methods have been used to detect repetitiveness in speech during ketamine intoxication and in schizophrenia Covington et al, Moreover, automated algorithms were able to discriminate between psychedelic drugs at above chance levels based on the content of descriptive reports written after the drug experience Coyle et al, , raising the possibility that speech emitted during intoxication could be characterized, semantically as well as structurally, with similar methods. Assessing drug-related mental-state alterations with automated speech analysis could add to existing methods in several ways. It would be less time and labor intensive than qualitative analyses of narratives, and less vulnerable to expectancy and biased recall. Assessing drug-related alterations to mental state broadly, this approach could be used to characterize the effects of emerging, relative to known, drugs. These drugs provide a sensitive assessment of automated speech analyses to measure drug effects because they have both commonalities and differences: they are both psychostimulants, but only MDMA appears to produce unique socioemotional subjective effects related to empathy, friendliness, and interpersonal intimacy Dumont et al, Thus, we focused semantic analyses on several specific mental states that might differentiate the effects produced by the two drugs. We tested the hypothesis that the drugs would produce unique semantic as well as structural changes to speech, and that these speech characteristics would accurately discriminate the drugs tested. Structural aspects of speech eg, syntax can be readily measured quantitatively, whereas semantics ie, the meaning of words is more elusive. The holistic theory of meaning posits that the semantic content of a word is determined by its relationship to other words in a language Quine, , partially expressed in dictionaries, thesauri, and similar databases. Specifically, semantic similarity between words can be captured by the frequency of cooccurrence of the words in text corpora, as words with similar or overlapping meaning tend to appear frequently together in consistent discourse Miller and Charles, We chose here a well-established implementation of this idea, Latent Semantic Analysis LSA; Deerwester et al, , to measure how speech semantic content is affected by drug intoxication. This study thus extended previous work by using automated approaches to measure both semantic and structural aspects of speech and by applying these methods to the assessment of drug-induced mental-state changes. In addition, we employed multivariate machine-learning methods to assess whether speech characteristics identified could differentiate between drug conditions at the level of the individual. Candidates underwent comprehensive medical and psychiatric screening and were excluded for: psychiatric disorder DSM-IV current Axis 1 diagnosis ; medical illness; body mass index outside All participants provided written informed consent, and were debriefed at completion. The design was within subject, double blind, and randomized, with four 5-h sessions in which participants received MDMA 0. Sessions were conducted in the afternoon in a comfortable laboratory environment. At arrival, baseline cardiovascular and self-report subjective measurements were collected, after which participants ingested a size 00 gelatin capsule containing MDMA hydrochloride David Nichols, Purdue University or methamphetamine hydrochloride Desoxyn, Ovation Pharmaceuticals, Chicago, IL with lactose or dextrose. Placebo capsules contained filler. Tasks were scheduled during the expected period of peak drug effects Cami et al, The person of importance was selected randomly from a list of four people provided by the participant at the beginning of the study Wardle et al, A different person was discussed in each session. Research assistants trained in active listening applied skills such as paraphrasing and reflecting feelings to minimize their impact on speech content. The same assistant interviewed each participant across sessions. A professional transcriber blind to drug condition manually transcribed audio recordings. First, we identified individual words in the text, discarding punctuation marks, resulting in a list of words for each text, with repetitions. We then parsed each interview into sentences, and identified the parts of speech eg, nouns using the Treebank tagger supplied by NLTK. We then lemmatized each word using the WordNet lemmatizer from NLTK: this corresponds to converting words into the root from which they are inflected. We have previously found that word lemmatizing facilitates robust measurement of abstract concepts and topological features in texts Diuk et al, ; Mota et al, Preprocessing resulted in a list of lemmatized words, each one in a new line maintaining original order, in lowercase and without punctuation marks or symbols. The analytic strategy was as follows: 1 transcripts were assessed for semantic proximity to several relevant concepts, chosen to approximate the subjective effects produced by MDMA Bedi et al, , ; we assessed for group-level effects of drug condition on these semantic proximity values; 2 we employed a machine-learning approach to classify drug conditions to determine whether a combination of the semantic proximity values could predict drug condition in the individual subject; and 3 we used a graph-based approach to assess whether the drugs altered structural components of speech. Therefore, any attempt to identify the presence of a particular concept in a text requires considering its distributed semantic sense, as opposed to a simple word count. Several methods have been introduced to obtain a notion of semantic proximity Fellbaum, ; Lund and Burgess, ; Patwardhan et al, ; Pedersen et al, One of the more widely used resources is LSA Deerwester et al, LSA is a high-dimensional associative model that captures similarity between words by assuming that semantically related words will necessarily cooccur in texts with coherent topics. LSA generates a linear representation of the semantic content of words based on their cooccurrence with other words in a text corpus. If the corpus is sufficiently large and diverse, the frequency of cooccurrence of words across different documents represents the extent to which the words are semantically related Landauer and Dumais, The input to LSA is a word-by-document occurrence matrix X , with each row corresponding to a unique word in the corpus N total words and each column corresponding to a document M total documents. Using singular value decomposition SVD , the dimensionality of this matrix is reduced to a smaller number of columns, preserving as much as possible the similarity structure between rows. The similarity in meaning between two words or semantic proximity can be measured by calculating the cosine between the corresponding vectors. That is, similarity between two words is computed as the dot product , where the vectors are the SVD representation of the words a and b. TASA consists of general reading texts believed to be common in the US educational system up to college, including a wide variety of short documents from novels, newspapers, and other sources. As the LSA method proposes, the SVD matrix may be cropped—reducing dimensionality—while conserving the range of the original matrix. The choice of dimensionality is an important factor for success in measuring semantic distance. Landauer and Dumais studied the effect of the number of dimensions in LSA and obtained maximum performance by retaining around dimensions, the number we used here. No weights were used for terms in the SVD. Finally, a mean was computed for each interview. We selected the words affect , anxiety , compassion , confidence , emotion , empathy , fear , feeling , forgive , friend , happy, intimacy , love , pain , peace , rapport , sad , support , talk , and think to capture a broad range of subjective mood states that have been reported to occur during MDMA intoxication Dumont and Verkes, Because psychostimulants increase speech quantity Wardle et al, , we also computed the total number of words ie, tokens , or verbosity , in each interview as an additional feature. Group-level drug effects on the mean semantic proximity values for each concept selected were assessed using repeated-measures ANOVA followed by planned comparisons between placebo and active drug conditions, with a significance threshold of 0. The methods and results for the data-driven analysis are in Supplementary Information ; see also Supplementary Figure S1. Thus, a classification approach that operates based on overall patterns within the pooled data with stringent cross-validation may be more appropriate. We reduced the problem of binary classification to information provided by the semantic similarity to rapport , love , and support , with the addition of verbosity. More precisely, N discriminative models were computed by learning the parameters on N -1 subjects, and testing on the remaining subject all of the six possible binary classifications. Finally, we implemented a four-way classifier via an off-the-shelf linear discriminant analysis LDA , using the same leave-subject-out cross-validation scheme, but with rapport , support , intimacy , and friend as semantic similarity measures, plus verbosity. The feature combination in both binary and four-way classifications was obtained by systematic search for the best classification accuracy, among the features with lowest p -values. For the purposes of classification, we applied a standard normalization transformation: each feature was normalized to zero mean for each subject over the four interviews, as a means to control for individual baselines. As mentioned above, we use here a leave-subject-out validation scheme, hence assuming access to the four conditions when testing the predictive model. Recently, a graph-based approach for identifying psychosis from speech was introduced Mota et al, In brief, a graph can be thought of as a network comprising a series of nodes connected by edges. Applying this approach to speech involves considering individual words to be nodes in this network, whereas edges represent grammatical or semantic relationships linking nodes. In psychosis, the method aims to capture thought disorder in the formal structure of discourse, regardless of the specific meaning of the words. An initial study showed that the differential disorganization of thought in people with schizophrenia and mania can be characterized using topological features of graphs derived from transcribed interviews Mota et al, As depicted in Figure 1 , we applied this method as follows: the tokens words obtained in preprocessing were assigned to nodes in a graph, while a directed edge was assigned from node i to j whenever token i immediately preceded token j in each interview. To assess group-level differences between drug conditions in these structural speech dimensions, we used repeated-measures ANOVA with planned comparisons between placebo and active drugs. Participants were asked to speak about someone of importance in their life. Speech graphs were derived such that individual words were assigned to nodes in the graph, while a directed edge was assigned between two nodes word A and word B whenever word A immediately preceded word B in an interview. In the example shown, nodes words are represented with circles, with edges shown as arrows and sequentially numbered. PowerPoint slide. A total of 13 participants 4 females provided consent for speech recording. Mean age was Of these participants, 11 were Caucasian, 1 was Black, and 1 was of mixed race. Participants reported previous ecstasy use on As shown in Figure 2 , the drug conditions differed from placebo in semantic proximity to several concepts. Speech after MDMA1. Speech after MDMA0. There were no significant differences in semantic proximity to the other selected concepts. Effects of MDMA 0. Data are means and SD of semantic proximity values during the transcribed free speech task. We implemented a SVM classification to assess whether a combination of the proximity values could differentiate drug conditions. Table 1 shows the classification accuracy for the different groupings, with the expected baseline if assignment was random. The highest accuracy classifications were between MDMA1. No other differences in speech structure were observed. Using a novel automated approach, we found that MDMA 0. Although MDMA altered speech meaning, it did not change its topological structure. To our knowledge, this is the first study using semantic and topological speech characteristics to study drug-related mental-state alterations. Group-level effects of MDMA on speech content were broadly consistent with purported prosocial effects of the drug Bedi et al, , supporting the utility of speech analyses to measure mental-state changes caused by drugs. Moreover, most effects were dose dependent, emerging only at the higher MDMA dose. An exception is semantic proximity to empathy , which increased only on the lower dose. In combination with the present finding, this suggests that dose-related effects of MDMA on social processing should be the focus of future study. Of note, many previous studies on this question used only one MDMA dose see, eg, Dumont et al, ; Hysek et al, The neurobiological mechanisms underlying these mental-state alterations, as reflected in speech, are unknown. MDMA produces psychoactive effects primarily via transporter-mediated serotonin release, with euphorigenic effects mediated by interaction with dopamine type 2 receptors Liechti and Vollenweider, and a potential role for norepinephrine Hysek et al, Data in rodents Thompson et al, and humans Dumont et al, indicate that oxytocin release is implicated in the prosocial effects of MDMA, potentially through interaction with vasopressin 1A receptors Ramos et al, Thus, the effects revealed here may be partially subserved by oxytocinergic mechanisms. Future research could valuably address the psychopharmacological mechanisms of these drug effects on speech. Importantly, in this study we not only assessed group-level drug effects, but also showed that a multivariate combination of speech characteristics could predict drug condition in the individual subject. This approach, using machine-learning algorithms, also effectively differentiated manic and schizophrenic patients based on speech structure Mota et al, Moreover, machine-learning classification discriminated between different types of psychedelic drugs above chance levels based on retrospective drug experience narratives in another study Coyle et al, These earlier findings combined with our own support the utility of computational approaches like machine learning to quantitatively characterize complex human behaviors such as speech. Previous studies show that several drugs alter speech quantity see, eg, Foltin and Fischman, ; Haney et al, ; Higgins and Stitzer, ; Stitzer et al, Our finding that methamphetamine increased verbosity is consistent with these findings see, eg, Wardle et al, Direct comparison between our findings on speech structure and the earlier results is complicated by methodological differences eg, we did not quantify pauses, and the prior study did not assess topology. These differences notwithstanding, it is interesting to note that we did not observe disrupted speech structure on MDMA, which is implied by the earlier finding of reduced fluency. However, the graph-based approach we employed can detect altered speech structure related to thought disorder in psychosis Mota et al, These findings thus emphasize the need to assess drug effects on speech content to access mental-state changes, rather than measuring only behavioral speech characteristics. These findings have several potential implications. Given the limitations of existing methods, automated speech analysis could be used as an adjunct to other approaches to better characterize drug-related mental-state alterations. Despite the relatively high computational demands of the analyses, measurement itself is easy to implement. Given the high rates of accuracy discriminating between different drugs, an important potential use could be to characterize new drugs relative to known ones. As an initial investigation, this study had limitations. The study used two MDMA doses and one methamphetamine dose, and a broader dose-response function would further validate the measure. Second, the number of subjects was small and they were a homogenous group, and the method needs to be tested in a broader sample. Furthermore, we chose the concepts of interest based on the apparently unique effects of MDMA, rather than methamphetamine, which may have contributed to the higher accuracy of classifications including the high MDMA dose. The semantic analysis employed does not require preselection of concepts of interest, and future studies might use a less hypothesis-driven approach. Although a priori approaches are more commonly employed in psychiatry and psychopharmacology research, data-driven approaches may yield important insights in the future. To assess speech structure, we used a graph-based approach previously shown to be sensitive to mental-state alterations in psychosis Mota et al, Alternative methods of constructing graphs from speech may have revealed effects of MDMA on speech structure. However, in addition to being sensitive to psychosis Mota et al, , the method employed detected effects of methamphetamine see Supplementary Figure S2 , supporting its sensitivity. The best method for psychiatric and psychopharmacological applications of automated speech analysis remains an important empirical question for future study. A final limitation relates to inherent limits on the extent to which speech can be understood to comprehensively reflect altered thoughts or mental states. For instance, types of thought such as mental imagery, which may occur frequently during intoxication, are unlikely to be detected via speech analyses. To the extent that thoughts cannot be directly measured, we are also unable to unequivocally state that the changes in speech observed are a direct reflection of altered thoughts or mental states. However, the current data combined with previous work in psychosis Mota et al, provide strong support for use of this method to detect altered mental states arising because of drug intoxication or mental illness. An important question for future research will be the effects of the speech task selected. Here, we employed a task suited to the apparent prosocial effects of MDMA, asking subjects to speak about important people in their life. This task was also selected because of its similarity to psychotherapy, given the recent interest in psychotherapeutic MDMA use Mithoefer et al, Conversely, earlier studies asked subjects to describe a dream Mota et al, ; Mota et al, or a movie Marrone et al, Such differences may affect the information that can be drawn from analyses of the speech emitted. The task we employed, which was repeated across sessions albeit with different individuals as the topic may have resulted in practice effects, or in variability between sessions that was unrelated to drug effects, because language used to describe a person may vary with the relationship. An alternative approach would be the use of shorter, more constrained language tasks that could be counterbalanced across conditions. Studies addressing the question of which tasks best reveal drug effects on speech will be an important direction for future research. Another relevant factor may be whether the speech is collected during an interaction or a monolog. A further focus for future research will be the relationship between automated methods of speech analyses and more traditional, manual approaches to coding. Importantly, this approach could potentially assist mental health professionals by providing diagnostic or prognostic information about individual patients. In the earlier study of speech structure, automated analyses accurately discriminated bipolar disorder from schizophrenia Mota et al, The present study extends this earlier method with the inclusion of automated content analyses. Other studies support the potential for automated analysis of acoustic features of speech ie, prosody to characterize diminished expressivity in psychiatric disorders Cohen et al, , A combination of semantic and structural speech characteristics, perhaps including acoustic features Low et al, ; Ooi et al, , when synthesized computationally, could provide fine-grained, previously unavailable data for clinicians on which to base diagnostic, prognostic, and treatment-related decisions. Such possibilities notwithstanding, these data provide initial evidence for the use of automated semantic speech analysis to characterize alterations to mental state after drugs. MDMA changed the meaning of speech in ways that are consistent with its purported subjective effects. Automated speech analyses could therefore prove a useful addition to existing methods to characterize the wide-ranging, sometimes profound, alterations to consciousness that can be occasioned by drugs of abuse. 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Neural correlates of the relationship between discourse coherence and sensory monitoring in schizophrenia. Cortex in press. Neuroscience : — Amphetamine as a social drug: effects of d-amphetamine on social processing and behavior. Download references. We thank participants for their involvement and Lisa Jerome for comments on an earlier draft. You can also search for this author in PubMed Google Scholar. Correspondence to Gillinder Bedi. Supplementary Information accompanies the paper on the Neuropsychopharmacology website. Reprints and permissions. Bedi, G. A Window into the Intoxicated Mind? Speech as an Index of Psychoactive Drug Effects. Neuropsychopharmacol 39 , — Download citation. Received : 08 September Revised : 20 January Accepted : 24 March Published : 03 April Issue Date : September Anyone you share the following link with will be able to read this content:. Sorry, a shareable link is not currently available for this article. Provided by the Springer Nature SharedIt content-sharing initiative. Skip to main content Thank you for visiting nature. Download PDF. Subjects Language Pharmacology Psychiatric disorders Social neuroscience. Abstract Abused drugs can profoundly alter mental states in ways that may motivate drug use. Detection of acute 3,4-methylenedioxymethamphetamine MDMA effects across protocols using automated natural language processing Article Open access 24 January Towards a scalable approach to assess speech organization across the psychosis-spectrum -online assessment in conjunction with automated transcription and extraction of speech measures Article Open access 21 March Drug-induced social connection: both MDMA and methamphetamine increase feelings of connectedness during controlled dyadic conversations Article Open access 22 September Design and Protocol The design was within subject, double blind, and randomized, with four 5-h sessions in which participants received MDMA 0. Analytic Approach A professional transcriber blind to drug condition manually transcribed audio recordings. Graph-based analysis of speech structure Recently, a graph-based approach for identifying psychosis from speech was introduced Mota et al, Figure 1. Full size image. Drug Effects on Semantic Proximity to Concepts of Interest As shown in Figure 2 , the drug conditions differed from placebo in semantic proximity to several concepts. Figure 2. Article Google Scholar Fellbaum C Acknowledgements We thank participants for their involvement and Lisa Jerome for comments on an earlier draft. View author publications. Additional information Supplementary Information accompanies the paper on the Neuropsychopharmacology website. Supplementary information. Supplementary Figure 1 JPG 89 kb. Supplementary Figure 2 JPG kb. PowerPoint slides PowerPoint slide for Fig. PowerPoint slide for Fig. Rights and permissions Reprints and permissions. About this article Cite this article Bedi, G. Copy to clipboard. This article is cited by Analysis of recreational psychedelic substance use experiences classified by substance Adrian Hase Max Erdmann Gregor Hasler Psychopharmacology Detection of acute 3,4-methylenedioxymethamphetamine MDMA effects across protocols using automated natural language processing Carla Agurto Guillermo A. Search Search articles by subject, keyword or author. Show results from All journals This journal. Advanced search.

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