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Monitoring symptoms of bipolar disorder (BD) is a challenge faced by mental health services. Speech patterns are crucial in assessing the current experiences, emotions, and thought patterns of people with BD. Natural language processing (NLP) and acoustic signal processing may support ongoing BD assessment within a mobile health (mHealth) framework. Using both acoustic and NLP-based features from the speech of people with BD, we built an app-based tool and tested its feasibility and performance to remotely assess the individual clinical status. We carried out a pilot, observational study, sampling adults diagnosed with BD from the caseload of the Nord Milano Mental Health Trust (Italy) to explore the relationship between selected speech features and symptom severity and to test their potential to remotely assess mental health status. Symptom severity assessment was based on clinician ratings, using the Young Mania Rating Scale (YMRS) and Montgomery-Åsberg Depression Rating Scale (MADRS) for manic and depressive symptoms, respectively. Leveraging a digital health tool embedded in a mobile app, which records and processes speech, participants self-administered verbal performance tasks. Both NLP-based and acoustic features were extracted, testing associations with mood states and exploiting machine learning approaches based on random forest models. We included 32 subjects (mean [SD] age 49.6 [14.3] years; 50% [16/32] females) with a MADRS median (IQR) score of 13 (21) and a YMRS median (IQR) score of 5 (16). Participants freely managed the digital environment of the app, without perceiving it as intrusive and reporting an acceptable system usability level (average score 73.5, SD 19.7). Small-to-moderate correlations between speech features and symptom severity were uncovered, with sex-based differences in predictive capability. Higher latency time (ρ=0.152), increased silences (ρ=0.416), and vocal perturbations correlated with depressive symptomatology. Pressure of speech based on the mean intraword time (ρ=-0.343) and lower voice instability based on jitter-related parameters (ρ ranging from -0.19 to -0.27) were detected for manic symptoms. However, a higher contribution of NLP-based and conversational features, rather than acoustic features, was uncovered, especially for predictive models for depressive symptom severity (NLP-based: R2=0.25, mean squared error [MSE]=110.07, mean absolute error [MAE]=8.17; acoustics: R2=0.11, MSE=133.75, MAE=8.86; combined: R2=0.16; MSE=118.53, MAE=8.68). Remotely collected speech patterns, including both linguistic and acoustic features, are associated with symptom severity levels and may help differentiate clinical conditions in individuals with BD during their mood state assessments. In the future, multimodal, smartphone-integrated digital ecological momentary assessments could serve as a powerful tool for clinical purposes, remotely complementing standard, in-person mental health evaluations.

Pragmatics is key to communicating effectively, and its assessment in vulnerable populations is of paramount importance. Although tools exist for this purpose, they are often effortful and time-consuming, with complex scoring procedures, which hampers their inclusion in clinical practice. To address these issues, we present the Brief Assessment of Pragmatic Abilities and Cognitive Substrates (APACS Brief), a rapid (10 min), easy-to-use and freely distributed tool for evaluating pragmatics in Italian, inspired by the existing APACS test and already validated in the remote version (APACS Brief Remote). The APACS Brief test measures–with a simplified scale–the domains of discourse production and figurative language understanding and is developed in two parallel forms, each including novel items differing from APACS. Psychometric properties, cut-off scores, and thresholds for change were computed on 287 adults. The analysis revealed satisfactory internal consistency, good test–retest reliability, and strong concurrent and construct validity. Moreover, APACS Brief showed excellent discriminant validity on a sample of 56 patients with schizophrenia, who were also cross-classified consistently by APACS Brief and APACS cut-off values. Overall, APACS Brief is a reliable tool for evaluating pragmatic skills and their breakdown, with brief administration time and simple scoring making it well-suited for screening in at-risk populations.

Physical Restraint (PR) is a coercive procedure used in emergency psychiatric care to ensure safety in life-threatening situations. Because of its traumatic nature, studies emphasize the importance of considering the patient’s subjective experience. We pursued this aim by overcoming classic qualitative approaches and innovatively applying a multilayered semiautomated language analysis to a corpus of narratives about PR collected from 99 individuals across seven mental health services in Italy. Compared to a reference corpus, PR narratives were characterized by reduced fluency and lexical density, yet a greater use of emotional and cognitive terms, verbs, and first-person singular pronouns. Sadness was the most represented emotion, followed by anger and fear. One-third of the PR narratives contained at least one metaphor, with Animals and War/Prison as the most distinctive source domains. The quality and length of the PR experience impacted both the structure and the sentiment of the narratives. Findings confirm the distressful nature of PR but also point to the use of various linguistic mechanisms which might serve as an early adaptive response toward healing from the traumatic experience. Overall, the study highlights the importance of Natural Language Processing as an unobtrusive window into subjective experience, offering insights for therapeutic choices.

People with schizophrenia spectrum disorders (SSD) show anomalies in language processing with respect to “who is doing what” in an action. This linguistic behavior is suggestive of an atypical representation of the formal concepts of “Agent” in the lexical representation of a verb, i.e., its thematic grid. To test this hypothesis, we administered a silent-reading task with sentences including a semantic violation of the animacy trait of the grammatical subject to 30 people with SSD and 30 healthy control participants (HCs). When the anomalous grammatical subject was the Agent of the event, a significant increase of Gaze Duration was observed in HCs, but not in SSDs. Conversely, when the anomalous subject was a Theme, SSDs displayed an increased probability of go-back movements, unlike HCs. These results are suggestive of a higher tolerability for anomalous Agents in SSD compared to the normal population. The fact that SSD participants did not show a similar tolerability for anomalous Themes rules out the issue of an attention deficit. We suggest that general communication abilities in SSD might benefit from explicit training on deep linguistic structures.

Mobile technology has the potential to provide accurate, impactful data on the symptoms of depression, which could improve health management or assist in early detection of relapse. However, for this potential to be achieved, it is essential that patients engage with the technology. Although many barriers to and facilitators of the use of this technology are common across therapeutic areas and technology types, many may be specific to cultural and health contexts. This study aimed to determine the potential barriers to and facilitators of engagement with mobile health (mHealth) technology for remote measurement and management of depression across three Western European countries. Participants (N=25; 4:1 ratio of women to men; age range, 25-73 years) who experienced depression participated in five focus groups held in three countries (two in the United Kingdom, two in Spain, and one in Italy). The focus groups investigated the potential barriers to and facilitators of the use of mHealth technology. A systematic thematic analysis was used to extract themes and subthemes. Facilitators and barriers were categorized as health-related factors, user-related factors, and technology-related factors. A total of 58 subthemes of specific barriers and facilitators or moderators emerged. A core group of themes including motivation, potential impact on mood and anxiety, aspects of inconvenience, and ease of use was noted across all countries. Similarities in the barriers to and facilitators of the use of mHealth technology have been observed across Spain, Italy, and the United Kingdom. These themes provide guidance on ways to promote the design of feasible and acceptable cross-cultural mHealth tools.

Neuroscience is increasingly making use of statistical and mathematical tools to extract information from images of biological tissues. Computational neuroimaging tools require substantial computational resources and the increasing availability of large image datasets will further enhance this need. Many efforts have been directed towards creating brain image repositories including the recent US Alzheimer Disease Neuroimaging Initiative. Multisite-distributed computing infrastructures have been launched with the goal of fostering shared resources and facilitating data analysis in the study of neurodegenerative diseases. Currently, some Grid- and non-Grid-based projects are aiming to establish distributed e-infrastructures, interconnecting compatible imaging datasets and to supply neuroscientists with the most advanced information and communication technologies tools to study markers of Alzheimer ’s and other brain diseases, but they have so far failed to make a difference in the larger neuroscience community. NeuGRID is an Europeon comission-funded effort arising from the needs of the Alzheimer ’s disease imaging community, which will allow the collection and archiving of large amounts of imaging data coupled with Grid-based algorithms and sufficiently powered computational resources. The major benefit will be the faster discovery of new disease markers that will be valuable for earlier diagnosis and development of innovative drugs. The initial setup of neuGRID will feature three nodes equipped with supercomputer capabilities and resources of more than 300 processor cores, 300 GB of RAM memory and approximately 20 TB of physical space. The scope of this article is highlights the new perspectives and potential for the study of the neurodegenerative disorders using the emerging Grid technology.