Cognitive triggers of auditory hallucinations: An experimental investigation

It has proved difficult to establish the internal process by which mental events are transformed into auditory hallucinations. The earlier stages of the generation of hallucinations may prove more accessible to research. Cognitions have been reported by patients as a trigger of auditory hallucinations, but the role of these preceding thoughts has not been causally determined. Therefore, the role of cognition in triggering auditory hallucinations was tested in an experimental study. Thirty individuals who experienced auditory hallucinations in social situations entered a neutral social situation presented using virtual reality. Participants randomised to the experimental condition were instructed to think their hallucination-preceding thoughts, and those randomised to the control condition were instructed to think neutral thoughts. Twenty-seven participants (93%) were able to spontaneously identify a cognition which preceded a hallucination. There was no difference between the experimental and control groups in the occurrence or severity of auditory hallucinations in virtual reality. Virtual reality did not lead to physical side effects or an increase in anxiety. The relationship between antecedent cognitions and auditory hallucinations is likely to be more complex than the one tested. It is argued that the effect of cognition on auditory hallucinations may be mediated by affect but this needs to be investigated through further experimental research

Additional file 4: of Ambulatory oxygen in fibrotic lung disease (AmbOx): study protocol for a randomised controlled trial

Informed Consent Form: Version 3.0, 1 December 2014. (DOC 32 kb

Analysis of Forced Vital Capacity (FVC) trajectories in Idiopathic Pulmonary Fibrosis (IPF) identifies four distinct clusters of disease behaviour

Background: Idiopathic Pulmonary Fibrosis (IPF) is a progressive fibrotic lung disease with a variable clinical trajectory. Decline in Forced Vital Capacity (FVC) is the main indicator of progression, however missingness prevents long-term analysis of lung function patterns. We used Machine Learning (ML) techniques to identify patterns of lung function trajectory. Methods: Longitudinal FVC data were collected from 415 participants with IPF. The imputation performance of conventional and ML techniques to impute missing data was evaluated, then the fully imputed dataset was analysed by unsupervised clustering using Self-Organizing Maps (SOM). Anthropometrics, genomic associations, blood biomarkers and clinical outcomes were compared between clusters. Replication was performed using an independent dataset. Results: An unsupervised ML algorithm had the lowest imputation error amongst tested methods, and SOM identified four distinct clusters (CL1 to CL4), confirmed by sensitivity analysis. CL1 (n=140): linear decline over three years; CL2 (n=100): initial improvement in FVC before declining; CL3 (n=113): initial FVC decline before stabilisation; CL4(n=62): stable lung function. Median survival was shortest in CL1 (2.87 - 95%CI: 2.29–3.40) and longest in CL4 (5.65 - 95%CI: 5.18–6.62). Baseline FEV1/FVC ratio and biomarker SPD levels were significantly higher among clusters CL1 and CL3. Similar lung function clusters with some shared anthropometric characteristics were identified in the replication dataset. Conclusions: Using a data-driven unsupervised approach, we identified four clusters of lung function trajectory with distinct clinical and biochemical features. Enriching or stratifying longitudinal spirometric data into clusters may optimise evaluation of intervention efficacy during clinical trials and patient managemen