When watching another person's actions, a network of sensorimotor brain regions, collectively termed the action observation network (AON), is engaged. Previous research suggests that the AON is more responsive when watching familiar compared with unfamiliar actions. However, most research into AON function is premised on comparisons of AON engagement during different types of task using univariate, magnitude-based approaches. To better understand the relationship between action familiarity and AON engagement, here we examine how observed movement familiarity modulates AON activity in humans using dynamic causal modeling, a type of effective connectivity analysis. Twenty-one subjects underwent fMRI scanning while viewing whole-body dance movements that varied in terms of their familiarity. Participants' task was to either predict the next posture the dancer's body would assume or to respond to a non–action-related attentional control question. To assess individuals' familiarity with each movement, participants rated each video on a measure of visual familiarity after being scanned. Parametric analyses showed more activity in left middle temporal gyrus, inferior parietal lobule, and inferior frontal gyrus as videos were rated as increasingly familiar. These clusters of activity formed the regions of interest for dynamic causal modeling analyses, which revealed attenuation of effective connectivity bidirectionally between parietal and temporal AON nodes when participants observed videos they rated as increasingly familiar. As such, the findings provide partial support for a predictive coding model of the AON, as well as illuminate how action familiarity manipulations can be used to explore simulation-based accounts of action understanding.

Patients undergoing MRI often experience anxiety prior and during scanning. The aim of this study was to explore two simple, cost-effective and easily implemented interventions to reduce anxiety pre MRI scanning. Seventy four patients attending first time for a MRI head, spine or cardiac scan were randomised into one of three interventions: video demonstration; telephone conversation with a radiographer; or routine MRI preparation (appointment letter). The State-Trait Anxiety Inventory (STAI) questionnaire was used to measure anxiety levels both pre and post intervention. Motion artefacts were visually assessed by 2 observers and a post scan survey was used to capture patient's satisfaction. ANCOVA revealed a significant reduction of anxiety in the video group (F = 13.664, p = 0.001), and also in the telephone group (F = 6.443, p = 0.015) compared to control patients. No significant difference was found between the two interventions (F = 0.665, p = 0.419). No difference was seen in motion artefacts between all three groups (Chi2 = 2.363 (p = 0.359) for observer 1 and Chi2 = 1.280 (p = 0.865) for observer 2). Fifty one percent (51.4%) of patients admitted to being anxious, with the possible outcome of the MRI results being the most common (18.9%) reason given for anxiety. This study has demonstrated that either of the interventions used can significantly reduce pre-MRI anxiety, with the video performing slightly better than the phone call intervention. Importantly, the routine appointment letter did not contain enough information to satisfy most patients, which argues strongly for a change in current practice.

Vulnerability to relapse persists after remission of an acute episode of major depressive disorder. This has been attributed to abnormal biases in the processing of emotional stimuli in limbic circuits. However, neuroimaging studies have not so far revealed consistent evidence of abnormal responses to emotional stimuli in limbic structures, such as the amygdala, in remitted depression. This suggests the problem might lie in the integrated functioning of emotion processing circuits. We recruited 22 unmedicated patients in remission from major depressive disorder (rMDD) and 21 age-matched healthy control subjects. Functional magnetic resonance imaging was performed during a face emotion processing task. Dynamic causal modeling was used with Bayesian model selection to determine the most likely brain networks and valence-specific modulation of connectivity in healthy control subjects and rMDD. In healthy volunteers, sad faces modulated bi-directional connections between amygdala and orbitofrontal cortex and between fusiform gyrus and orbitofrontal cortex. Happy faces modulated unidirectional connections from fusiform gyrus to orbitofrontal cortex. In rMDD, the opposite pattern was observed, with evidence of happy faces modulating bidirectional frontotemporal connections and sad faces modulating unidirectional fusiform-orbitofrontal connections. Participants with rMDD have abnormal modulation of frontotemporal effective connectivity in response to happy and sad face emotions, despite normal activations within each region. Specifically, processing of mood incongruent happy information was associated with a more richly modulated frontotemporal brain network, whereas mood congruent sad information was associated with less network modulation. This supports a hypothesis of dysfunction within cortico-limbic connections in individuals vulnerable to depression.

Functional magnetic resonance imaging (fMRI) has proved to be useful for analyzing the effects of illness and pharmacological agents on brain activation. Many fMRI studies now incorporate effective connectivity analyses on data to assess the networks recruited during task performance. The assessment of the sample size that is necessary for carrying out such calculations would be useful if these techniques are to be confidently applied. Here, we present a method of estimating the sample size that is required for a study to have sufficient power. Our approach uses Bayesian Model Selection to find a best fitting model and then uses a bootstrapping technique to provide an estimate of the parameter variance. As illustrative examples, we apply this technique to two different tasks and show that for our data, ∼20 volunteers per group is sufficient. Due to variability between task, volunteers, scanner, and acquisition parameters, this would need to be evaluated on individual datasets. This approach will be a useful guide for Dynamic Causal Modeling studies.

Introduction In the UK, National Health Service (NHS) guidelines recommend that informal carers of people living with dementia should be offered training to help them develop care skills and manage their own physical and mental health. The WHO recommends access to affordable, proven, well-designed, online technologies for education, skills training and support for dementia carers. In response to these recommendations, this multisite randomised controlled trial (RCT) is the first study in the UK to evaluate the clinical and cost-effectiveness of an online support programme developed by the WHO called ‘iSupport for dementia carers’. Methods and analysis 350 informal carers (age 18+ years) living in Britain who self-identify as experiencing stress and depression will be recruited. They will be randomised to receive ‘iSupport’, or standardised information about caring for someone with dementia (control–comparison). Data will be collected via videoconferencing (eg, Zoom) or telephone interview at baseline, 3 months and 6 months. Intention-to-treat analysis will ascertain effectiveness in the primary outcomes (distress and depression) and combined cost, and quality-adjusted life-year data will be used to assess cost-effectiveness compared with usual care from a public sector and wider societal perspective. A mixed-methods process evaluation with a subgroup of carers in the intervention (~N=50) will explore the barriers and facilitators to implementing ‘iSupport’. A non-randomised feasibility study will adapt ‘iSupport’ for young carers (n=38 participants, age 11–17 years). Ethics and dissemination The research plan was scrutinised by National Institute for Health Research reviewers ahead of funding being awarded. Ethical approval was granted by Bangor University’s School of Health and Medical Sciences Academic Ethics Committee, reference number 2021-16915. Dissemination plans include delivering events for stakeholders, social media, a project website, developing policy briefings, presenting at conferences and producing articles for open access publications. Trial registration number ISRCTN17420703.