Participants

Healthy volunteers (18–38 years old) reporting ecstasy use ⩾ twice were recruited with advertisements. 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 18.5–30 kg/m2; first-degree relative cardiovascular illness; prior adverse ecstasy response; and pregnancy/lactation. All participants provided written informed consent, and were debriefed at completion. Procedures were approved by the University of Chicago Institutional Review Board.

Design and Protocol

The design was within subject, double blind, and randomized, with four 5-h sessions in which participants received MDMA (0.75 mg/kg (MDMA0.75) or 1.5 mg/kg (MDMA1.5)), methamphetamine (20 mg; METH), or placebo (PBO). Before sessions, participants abstained from: food consumption for 2 h; cannabis for 7 days; alcohol or medications for 24 h; and all other illicit drugs for 48 h. Recent drug use was verified with urine (QuickTox Drug Screen Dipcard, Branan Medical Corporation, Irvine, CA), saliva (Oratect III, Branan Medical Corporation), and breathalyzer (Alco-sensor III, Intoximeters, St Louis, MO) tests. Females were tested for pregnancy at each session (Aimstrip, Craig Medical, Vista, CA).

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. Cardiovascular and mood measurements were obtained repeatedly throughout the session, and subjects completed behavioral tasks beginning 65 min after the capsule (see Bedi et al, 2010). At 130 min after the capsule, subjects completed the free speech task (below), providing the data used here. Tasks were scheduled during the expected period of peak drug effects (Cami et al, 2000).

Assessment Measures

During the free speech task, participants spoke to a research assistant for 10 min (average words=784) about a person of importance in their life, and the speech was recorded. 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, 2012). 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.

Analytic Approach

A professional transcriber blind to drug condition manually transcribed audio recordings. We preprocessed each transcribed interview using the Natural Language Toolkit (NLTK; Bird et al, 2009). 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, 2012; Mota et al, 2012). 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. Each interview thus resulted in a string of N tokens {w i }={w 1 ,w 2 ,…,w N } to be later fed to the semantic and structural analyzers.

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, 2009, 2010); 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.

Semantic proximity to the concepts of interest

As noted above, meaning can be understood as arising from mutual dependencies of words within a language, as partially captured by dictionaries, thesauri, and similar databases (Ferrer i Cancho and Solé, 2001; Quine, 1951; Sigman and Cecchi, 2002). 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, 2010; Lund and Burgess, 1996; Patwardhan et al, 2003; Pedersen et al, 2004). One of the more widely used resources is LSA (Deerwester et al, 1990). 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, 1998). 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. Formally, using SVD, we obtain a decomposition (U, S, V) cropped to k dimensions. By reducing dimensionality, each word is projected into a space where semantic ‘meaning’ is just its corresponding vector. 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. As the vectors are normalized, the range of possible values for the similarity measure is (−1, 1).

For our text corpus, we used TASA, a collection of educational materials compiled by Touchstone Applied Science Associates. TASA includes 37 651 documents and 12 190 931 words, from a vocabulary of 77 998 distinct words. 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. We lemmatized the TASA corpus using the WordNet lemmatizer from NLTK. After generating the occurrence matrix for TASA, SVD was executed on the term-frequency matrix obtaining the decomposition. 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 (1998) studied the effect of the number of dimensions in LSA and obtained maximum performance by retaining around 300 dimensions, the number we used here. No weights were used for terms in the SVD. LSA analyses employed Text to Matrix Generator software (http://scgroup20.ceid.upatras.gr:8000/tmg/).

The semantic analysis was performed as follows: the proximity to a selection of words related to well-established effects of MDMA {m 1 ,m 2 ,…,m K } was measured for all words in each interview {{d 1i },{d 2i },…,{d Ki }}. The resulting traces were discretized to {0,1} for similarity above a universal threshold of 0.1, resulting in a binary trace {{θ 1i },{θ 2i },…,{θ Ki }}. 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, 2006). Because psychostimulants increase speech quantity (Wardle et al, 2012), 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 0.05. Effect sizes are presented as partial η2. Analyses were conducted using SPSS 20 (IBM, Armonk, NY).

Although the main analysis used this a priori approach, selecting words hypothesized to be affected by MDMA but not by prototypical psychostimulants, we also conducted a data-driven, ‘black-box’ analysis to demonstrate an alternative approach. The methods and results for the data-driven analysis are in Supplementary Information; see also Supplementary Figure S1.

Prediction of drug condition using pattern classification

Univariate approaches such as that described above carry the possibility of ‘overfitting,’ that is, fitting a model so closely to a specific data set that it cannot generalize to other data. Thus, a classification approach that operates based on overall patterns within the pooled data with stringent cross-validation may be more appropriate. Here, we used an off-the-shelf Support Vector Machine (SVM) classifier. We reduced the problem of binary classification to information provided by the semantic similarity to rapport, love, and support, with the addition of verbosity. We implemented leave-subject-out cross-validation on the data set consisting of N=13 subjects and 4 conditions. 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. Analyses were conducted using the classification package in Matlab (MathWorks, Natick, MA).

Graph-based analysis of speech structure

Recently, a graph-based approach for identifying psychosis from speech was introduced (Mota et al, 2012). 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, 2012).

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. The resulting graphs were analyzed for topological features including: the number of different tokens/words (Nodes), the number of unique transitions between different nodes (Edges), the number of times the speaker returned to a token/word, going through 0, 1, 2, or 3 other words (loops: L1, L2, L3, L4), the number of edges normalized to number of nodes (mean degree), and the size of largest connected component (a connected component is an ‘island’ such that there is a path that connects any two nodes). 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.