No abstract

Media Education (ME) has come a long way. Today, it can no longer be considered a field of study reserved for semiotic and communication researchers. Nor can it be regarded as a privileged practice of those teachers, who for some reason consider media of fundamental importance. On one hand, ME is now part of the agenda of international organizations, which consider the development of media competences a necessary requisite to fully exercise citizenship in the current contemporary society. On the other, ME practices are becoming increasingly widespread in schools involving a growing number of teachers. Notwithstanding, teaching the media still seems to be a rather solipsistic task where «everything is fine». Indeed, in ME there is a tremendous lack of research concerning the educational practices' quality and effectiveness. This book tries to cope with these issues by providing a set of instruments to design, develop and evaluate ME activities in schools, and supporting the enhancement of media educators' knowledge and skills.

Postural instability as a symptom of progressing Parkinson's disease (PD) greatly reduces quality of life. Hence, early detection of postural impairments is crucial to facilitate interventions. Our aim was to use a convolutional neural network (CNN) to differentiate people with early to mid-stage PD from healthy age-matched individuals based on spectrogram images obtained from their body movement. We hypothesized the time-frequency content of body sway to be predictive of PD, even when impairments are not yet manifested in day-to-day postural control. We tracked their center of pressure (COP) using a Wii Balance Board and their full-body motion using a Microsoft Kinect, out of which we calculated the trajectory of their center of mass (COM). We used 30 s-snippets of motion data from which we acquired wavelet-based time-frequency spectrograms that were fed into a custom-built CNN as labeled images. We used binary classification to have the network differentiate between individuals with PD and controls (n=15, respectively). Classification performance was best when the medio-lateral motion of the COM was considered. Here, our network reached an average predictive accuracy of 98.45 % with a receiver operating characteristic area under the curve of 1.0. Moreover, an explainable AI approach revealed high frequencies in the postural sway data to be most distinct between both groups. Our findings suggest a CNN classifier based on cost-effective and conveniently obtainable posturographic data to be a promising approach to detect postural impairments in early to mid-stage PD and to gain novel insight into the subtle characteristics of impairments at this stage of the disease.

Introduction: Language disorders - disorganized and incoherent speech in particular - are distinctive features of schizophrenia. Natural language processing (NLP) offers automated measures of incoherent speech as promising markers for schizophrenia. However, the scientific and clinical impact of NLP markers depends on their generalizability across contexts, samples, and languages, which we systematically assessed in the present study relying on a large, novel, cross-linguistic corpus. Methods: We collected a Danish (DK), German (GE), and Chinese (CH) cross-linguistic dataset involving transcripts from 187 participants with schizophrenia (111DK, 25GE, 51CH) and 200 matched controls (129DK, 29GE, 42CH) performing the Animated Triangle task. Fourteen previously published NLP coherence measures were calculated, and between-groups differences and association with symptoms were tested for cross-linguistic generalizability. Results: One coherence measure robustly generalized across samples and languages. We found several language-specific effects, some of which partially replicated previous findings (lower coherence in German and Chinese patients), while others did not (higher coherence in Danish patients). We found several associations between symptoms and measures of coherence, but the effects were generally inconsistent across languages and rating scales. Conclusions: Using a cumulative approach, we have shown that NLP findings of reduced semantic coherence in schizophrenia have limited generalizability across different languages, samples, and measures. We argue that several factors such as sociodemographic and clinical heterogeneity, cross-linguistic variation, and the different NLP measures reflecting different clinical aspects may be responsible for this variability. Future studies should take this variability into account in order to develop effective clinical applications targeting different patient populations.

Background: Disruptions in language and speech are considered promising markers of affective and psychotic disorders. However, little is known about the mechanisms and confounders underlying such communicative atypicalities. Medications might have a crucial, relatively unknown role both as potential confounders and relatedly offering an insight about the mechanisms at work. The integration of regulatory documents with pharmacovigilance techniques could provide a more comprehensive picture to account for in future investigations of communication-related biomarkers. Objectives: Our aim was to identify a list of drugs potentially associated with speech and language atypicalities within psychotic and affective disorders. Methods: To structure a search for potential drug-induced communicative atypicalities on the FDA Adverse Event Reporting System (FAERS, updated June 2021), we developed a query using the Medical Dictionary for Regulatory Activities (MedDRA). We performed a Bonferroni corrected disproportionality analysis (Reporting Odds Ratio) on three separate populations: psychotic, affective, and non-neuropsychiatric disorders, to account for the confounding role of different underlying conditions. Unexpected drug adverse event associations, which were not already reported in the SIDER database of labeled adverse drug reactions, were subjected to further robustness analyzes to account for expected biases. Results: We identified a list of 291 expected and 91 unexpected potential confounding medications. We corroborated known/suspected associations: e.g., corticosteroids-related dysphonia and immunosuppressant-related stuttering. We also identified novel signals: e.g., domperidone-associated aphasia or VEGFR inhibitors-related dysphonia. Conclusions: We provide a list of medications to account for in future studies of communication-related biomarkers in affective and psychotic disorders. The methodological tools here implemented for large scale pharmacosurveillance investigations will facilitate future investigations of communication-related biomarkers in other conditions and provide a case study in more rigorous procedures for digital phenotyping in general. Objectives: Our aim was to identify a list of drugs potentially associated with speech and language atypicalities within psychotic and affective disorders, to account for in future investigations of communicative markers and to provide tools for similar future endeavors. Methods: We identified terms from the Medical Dictionary for Regulatory Activities (MedDRA) related to speech and language adverse drug reactions and clustered them by partial semantic overlap to structure a search on the FDA Adverse Event Reporting System (FAERS, updated June 2021). A Bonferroni corrected disproportionality analysis was applied to three separate populations in the FAERS: psychotic, affective, and non-neuropsychiatric disorders, to account for the confounding role of different underlying conditions. Unexpected drug adverse event associations, which were not already reported in the SIDER database of labeled adverse drug reactions, were subjected to further robustness analyzes to account for expected biases. Results: We identified a list of 291 expected and 91 unexpected potential confounding medications. We also developed methodological tools for large-scale pharmacosurveillance investigations: a MedDRA query proposal for speech and language impairments, formalization of possible biases, and related analyzes to account for them. Conclusions: We provide a list of medications to account for in future studies of communicative behavioral biomarkers in affective and psychotic disorders. The developed methodological tools will facilitate future investigations of communicative biomarkers in other conditions and provide a case study in more rigorous procedures for digital phenotyping in general.