Prediction of the onset of schizophrenia based on a multimodal approach

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Cultural differences in vocal emotion recognition : a behavioural and skin conductance study in Portugal and Guinea-Bissau

Publication . Cosme, Gonçalo; Tavares, Vânia; Nobre, Guilherme; Lima, César; Sá, Rui; Rosa, Pedro; Prata, Diana

Cross-cultural studies of emotion recognition in nonverbal vocalizations not only support the universality hypothesis for its innate features, but also an in-group advantage for culture-dependent features. Nevertheless, in such studies, differences in socio-economic-educational status have not always been accounted for, with idiomatic translation of emotional concepts being a limitation, and the underlying psychophysiological mechanisms still un-researched. We set out to investigate whether native residents from Guinea-Bissau (West African culture) and Portugal (Western European culture)-matched for socio-economic-educational status, sex and language-varied in behavioural and autonomic system response during emotion recognition of nonverbal vocalizations from Portuguese individuals. Overall, Guinea-Bissauans (as out-group) responded significantly less accurately (corrected p < .05), slower, and showed a trend for higher concomitant skin conductance, compared to Portuguese (as in-group)-findings which may indicate a higher cognitive effort stemming from higher difficulty in discerning emotions from another culture. Specifically, accuracy differences were particularly found for pleasure, amusement, and anger, rather than for sadness, relief or fear. Nevertheless, both cultures recognized all emotions above-chance level. The perceived authenticity, measured for the first time in nonverbal cross-cultural research, in the same vocalizations, retrieved no difference between cultures in accuracy, but still a slower response from the out-group. Lastly, we provide-to our knowledge-a first account of how skin conductance response varies between nonverbally vocalized emotions, with significant differences (p < .05). In sum, we provide behavioural and psychophysiological data, demographically and language-matched, that supports cultural and emotion effects on vocal emotion recognition and perceived authenticity, as well as the universality hypothesis.

2021Journal article

Open access

Pupil dilation reflects the authenticity of received nonverbal vocalizations

Publication . Cosme, Gonçalo; Rosa, Pedro J.; Lima, César F.; Tavares, Vânia; Scott, Sophie; Chen, Sinead; Wilcockson, Thomas D. W.; Crawford, Trevor J.; Prata, Diana

The ability to infer the authenticity of other’s emotional expressions is a social cognitive process taking place in all human interactions. Although the neurocognitive correlates of authenticity recognition have been probed, its potential recruitment of the peripheral autonomic nervous system is not known. In this work, we asked participants to rate the authenticity of authentic and acted laughs and cries, while simultaneously recording their pupil size, taken as proxy of cognitive effort and arousal. We report, for the first time, that acted laughs elicited higher pupil dilation than authentic ones and, reversely, authentic cries elicited higher pupil dilation than acted ones. We tentatively suggest the lack of authenticity in others’ laughs elicits increased pupil dilation through demanding higher cognitive effort; and that, reversely, authenticity in cries increases pupil dilation, through eliciting higher emotional arousal. We also show authentic vocalizations and laughs (i.e. main effects of authenticity and emotion) to be perceived as more authentic, arousing and contagious than acted vocalizations and cries, respectively. In conclusion, we show new evidence that the recognition of emotional authenticity can be manifested at the level of the autonomic nervous system in humans. Notwithstanding, given its novelty, further independent research is warranted to ascertain its psychological meaning.

2021Journal article

Open access

Onset probability prediction of schizophrenia based on a multimodal approach

Publication . Tavares, Vânia; Prata, Diana Maria Pinto; Ferreira, Hugo Alexandre Teixeira Duarte

Psychosis is a severe mental condition characterized by a complex set of disturbances of thinking, perception, affect and social behaviour. It is usually preceded by a prodromal phase lasting months to years and in which patients are clinically identified has being ‘At Risk Mental State’ (ARMS). Retrospective studies have showed that ARMS individuals have a 30% risk of transition to psychosis within the first 2 years after presentation to clinical services. Moreover, several neuroimaging, genetic and environmental biomarkers have been independently associated with the onset of psychosis in the ARMS. However, at present there is yet no established method for predicting which individuals will develop the illness and which will not – which would allow cost-efficient targeting of early intervention therapies. Furthermore, a few studies have demonstrated the feasibility to predict psychosis transition from an ARMS using structural magnetic resonance imaging (sMRI) data and machine learning (ML) methods. However, the reliability of these findings is unclear due to possible sampling bias. Moreover, the value of genetic and environmental data in predicting transition to psychosis from an ARMS is yet to be explored. In this study I aimed at predicting transition to psychosis from an ARMS using ML and quantitative data – neuroimaging, genetics, and environment – as predictors. I used several samples (one for each modality – neuroimaging, genetics or environment) drawn from a pool of 246 subjects identified as being at an ARMS when they first sought clinical help (i.e. at baseline). Subjects were clinically identified as transitioned to psychosis (ARMS-T, 60 subjects) if they later presented a first episode of psychosis (FEP) or as nottransitioned to psychosis (ARMS-NT, 186 subjects) if they did not present a FEP within at least a period of 2 years. Structural magnetic resonance imaging, genome-wide genotypes and environmental risk assessment data was collected from the ARMS subjects at baseline. Then, the modality-specific value in predicting transition to psychosis was evaluated using a) several feature types [regional and voxel-based grey matter and white matter volumes, and regional cortical thickness, and brain gyrification, sulci depth and complexity indexes (neuroimaging); a polygenic risk score (PRS) for schizophrenia, a list of psychosisassociated single nucleotide polymorphisms (SNP), and a list of psychosis-associated genes for which several brain tissue-specific expression quantitative trait loci (eQTL) scores were extracted (genetics); and an environmental risk score (ERS) for schizophrenia, and a list of environmental risks factors (environment)], b) several feature manipulation strategies [feature dimensionality reduction through principal component analysis, no feature selection, and forward feature selection (neuroimaging), and embedded feature selection (genetics and environment)], c) several ML algorithms [linear support vector machines (neuroimaging), elastic-net and simple logistic regression (genetics and environment)], d) several cross-validation (CV) strategies [5-fold CV and leave-one scanning acquisition protocol-out (neuroimaging) and leave-one per group, i.e. 1 ARMS-T and 1 ARMS-NT,-out (neuroimaging, genetics and environment)], e) sample balancing, i.e. same number of ARMS-T and ARMS-NT subjects, and f) bootstrapping, i.e. 5 (neuroimaging) or 100 (genetics and environment) semi-random subsamples drawn from the original pool. Then, only the modalities whose classification models showed a balanced accuracy across bootstrapped samples statistically better than chance level were included in a multimodal classification model. Overall, this study’s results showed that only genetics, and when using a set of psychosisassociated SNPs, could predict the transition to psychosis from an ARMS marginally better than chance, albeit with no clinical significance, (balanced accuracy = 53%, diagnostic odds ratio = 3.3 – averaged across bootstrapped samples). Furthermore, the environmental and neuroimaging alone could not predict psychosis from an ARMS, statistically better than chance. Therefore, no multimodal classification model was trained/tested. Moreover, and unexpectedly, I could not replicate previous findings showing the usefulness of structural MRI in predicting transition to psychosis from an ARMS using ML. Therefore, my results suggest that: a) genetic data may be promising for predicting transition to psychosis from an ARMS; and b) the value of structural MRI data in predicting psychosis from an ARMS, as suggested by previous evidence, should be reconsidered. Finally, this study serves as a proofof-concept on how multimodal quantitative data can be used to predict psychosis development already from a prodromal stage and should be replicated in larger ARMs samples.

2022-12Doctoral thesis

Open access

Effects of psychosis-associated genetic markers on brain volumetry: a systematic review of replicated findings and an independent validation

Publication . Vouga Ribeiro, Nuno; Tavares, Vânia; Bramon, Elvira; Toulopoulou, Timothea; Valli, Isabel; Shergill, Sukhi; Murray, Robin; Prata, Diana

Background: Given psychotic illnesses' high heritability and associations with brain structure, numerous neuroimaging-genetics findings have been reported in the last two decades. However, few findings have been replicated. In the present independent sample we aimed to replicate any psychosis-implicated SNPs (single nucleotide polymorphisms), which had previously shown at least two main effects on brain volume. Methods: A systematic review for SNPs showing a replicated effect on brain volume yielded 25 studies implicating seven SNPs in five genes. Their effect was then tested in 113 subjects with either schizophrenia, bipolar disorder, 'at risk mental state' or healthy state, for whole-brain and region-of-interest (ROI) associations with grey and white matter volume changes, using voxel-based morphometry. Results: We found FWER-corrected (Family-wise error rate) (i.e. statistically significant) associations of: (1) CACNA1C-rs769087-A with larger bilateral hippocampus and thalamus white matter, across the whole brain; and (2) CACNA1C-rs769087-A with larger superior frontal gyrus, as ROI. Higher replication concordance with existing literature was found, in decreasing order, for: (1) CACNA1C-rs769087-A, with larger dorsolateral-prefrontal/superior frontal gyrus and hippocampi (both with anatomical and directional concordance); (2) ZNF804A-rs11681373-A, with smaller angular gyrus grey matter and rectus gyri white matter (both with anatomical and directional concordance); and (3) BDNF-rs6265-T with superior frontal and middle cingulate gyri volume change (with anatomical and allelic concordance). Conclusions: Most literature findings were not herein replicated. Nevertheless, high degree/likelihood of replication was found for two genome-wide association studies- and one candidate-implicated SNPs, supporting their involvement in psychosis and brain structure.

2022Journal article

Open access