With the advent of new analysis methods in neuroimaging that involve independent component analysis (ICA) and dynamic causal modelling (DCM), investigations have focused on measuring both the activity and connectivity of specific brain networks. In this study we combined DCM with spatial ICA to investigate network switching in the brain. Using time courses determined by ICA in our dynamic causal models, we focused on the dynamics of switching between the default mode network (DMN), the network which is active when the brain is not engaging in a specific task, and the central executive network (CEN), which is active when the brain is engaging in a task requiring attention. Previous work using Granger causality methods has shown that regions of the brain which respond to the degree of subjective salience of a stimulus, the salience network, are responsible for switching between the DMN and the CEN (Sridharan et al., 2008). In this work we apply DCM to ICA time courses representing these networks in resting state data. In order to test the repeatability of our work we applied this to two independent datasets. This work confirms that the salience network drives the switching between default mode and central executive networks and that our novel technique is repeatable.

Introduction To investigate student clinical placement concerns and opinions, during the initial COVID-19 pandemic outbreak and to inform educational institution support planning. Methods Between mid-June to mid-July 2020, educational institutions from 12 countries were invited to participate in an online survey designed to gain student radiographer opinion from a wide geographical spread and countries with varying levels of COVID-19 cases. Results 1277 respondents participated, of these 592 had completed clinical placements during January to June 2020. Accommodation and cohabiting risks were identified as challenging, as was isolation from family, travel to clinical placements, and to a lesser extent childcare. Students stated they had been affected by the feeling of isolation and concerns about the virus whilst on placement. Overall 35.4% of all respondents were ‘Not at all worried’ about being a radiographer, however, 64.6% expressed varying levels of concern and individual domestic or health situations significantly impacted responses (p ≤ 0.05). Year 4 students and recent graduates were significantly more likely to be ‘Not worried at all’ compared to Year 2 and 3 students (p ≤ 0.05). The need for improved communication regarding clinical placements scheduling was identified as almost 50% of students on clinical placements between January to June 2020 identified the completion of assessments as challenging. Furthermore, only 66% of respondents with COVID-19 imaging experience stated being confident with personal protective equipment (PPE) use. Conclusion Student radiographers identified key challenges which require consideration to ensure appropriate measures are in place to support their ongoing needs. Importantly PPE training is required before placement regardless of prior COVID-19 imaging experience. Implications for practice As the next academic year commences, the study findings identify important matters to be considered by education institutions with responsibility for Radiography training and as students commence clinical placements during the on-going global COVID-19 pandemic.

Background and Purpose— Plaque inflammation contributes to stroke and coronary events. 18 F-fluorodeoxyglucose (FDG) positron emission tomography (PET) identifies plaque inflammation-related metabolism. Almost no prospective data exist on the relationship of carotid 18 F-FDG uptake and early recurrent stroke. Methods— We did a multicenter prospective cohort study BIOVASC (Biomarkers/Imaging Vulnerable Atherosclerosis in Symptomatic Carotid disease) of patients with carotid stenosis and recent stroke/transient ischemic attack with 90-day follow-up. On coregistered carotid 18 F-FDG PET/computed tomography angiography, 18 F-FDG uptake was expressed as maximum standardized uptake value (SUV max ) in the axial single hottest slice. We then conducted a systematic review of similar studies and pooled unpublished individual-patient data with 2 highly similar independent studies (Dublin and Barcelona). We analyzed the association of SUV max with all recurrent nonprocedural stroke (before and after PET) and with recurrent stroke after PET only. Results— In BIOVASC (n=109, 14 recurrent strokes), after adjustment (for age, sex, stenosis severity, antiplatelets, statins, diabetes mellitus, hypertension, and smoking), the hazard ratio for recurrent stroke per 1 g/mL SUV max was 2.2 (CI, 1.1–4.5; P =0.025). Findings were consistent in the independent Dublin (n=52, hazard ratio, 2.2; CI, 1.1–4.3) and Barcelona studies (n=35, hazard ratio, 2.8; CI, 0.98–5.5). In the pooled cohort (n=196), 37 recurrent strokes occurred (29 before and 8 after PET). Plaque SUV max was higher in patients with all recurrence ( P <0.0001) and post-PET recurrence ( P =0.009). The fully adjusted hazard ratio of any recurrent stroke was 2.19 (CI, 1.41–3.39; P <0.001) and for post-PET recurrent stroke was 4.57 (CI, 1.5–13.96; P =0.008). Recurrent stroke risk increased across SUV max quartiles (log-rank P =0.003). The area under receiver operating curve for all recurrence was 0.70 (CI, 0.59–0.78) and for post-PET recurrence was 0.80 (CI, 0.64–0.96). Conclusions— Plaque inflammation-related 18 F-FDG uptake independently predicted future recurrent stroke post-PET. Although further studies are needed, 18 F-FDG PET may improve patient selection for carotid revascularization and suggest that anti-inflammatory agents may have benefit for poststroke vascular prevention.

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Background and Purpose— In randomized trials of symptomatic carotid endarterectomy, only modest benefit occurred in patients with moderate stenosis and important subgroups experienced no benefit. Carotid plaque 18 F-fluorodeoxyglucose uptake on positron emission tomography, reflecting inflammation, independently predicts recurrent stroke. We investigated if a risk score combining stenosis and plaque 18 F-fluorodeoxyglucose would improve the identification of early recurrent stroke. Methods— We derived the score in a prospective cohort study of recent (<30 days) non-severe (modified Rankin Scale score ≤3) stroke/transient ischemic attack. We derived the SCAIL (symptomatic carotid atheroma inflammation lumen-stenosis) score (range, 0–5) including 18 F-fluorodeoxyglucose standardized uptake values (SUV max <2 g/mL, 0 points; SUV max 2–2.99 g/mL, 1 point; SUV max 3–3.99 g/mL, 2 points; SUV max ≥4 g/mL, 3 points) and stenosis (<50%, 0 points; 50%–69%, 1 point; ≥70%, 2 points). We validated the score in an independent pooled cohort of 2 studies. In the pooled cohorts, we investigated the SCAIL score to discriminate recurrent stroke after the index stroke/transient ischemic attack, after positron emission tomography-imaging, and in mild or moderate stenosis. Results— In the derivation cohort (109 patients), recurrent stroke risk increased with increasing SCAIL score ( P =0.002, C statistic 0.71 [95% CI, 0.56–0.86]). The adjusted (age, sex, smoking, hypertension, diabetes mellitus, antiplatelets, and statins) hazard ratio per 1-point SCAIL increase was 2.4 (95% CI, 1.2–4.5, P =0.01). Findings were confirmed in the validation cohort (87 patients, adjusted hazard ratio, 2.9 [95% CI, 1.9–5], P <0.001; C statistic 0.77 [95% CI, 0.67–0.87]). The SCAIL score independently predicted recurrent stroke after positron emission tomography-imaging (adjusted hazard ratio, 4.52 [95% CI, 1.58–12.93], P =0.005). Compared with stenosis severity (C statistic, 0.63 [95% CI, 0.46–0.80]), prediction of post-positron emission tomography stroke recurrence was improved with the SCAIL score (C statistic, 0.82 [95% CI, 0.66–0.97], P =0.04). Findings were confirmed in mild or moderate stenosis (adjusted hazard ratio, 2.74 [95% CI, 1.39–5.39], P =0.004). Conclusions— The SCAIL score improved the identification of early recurrent stroke. Randomized trials are needed to test if a combined stenosis-inflammation strategy improves selection for carotid revascularization where benefit is currently uncertain.