Predicting Activation Across Individuals with Resting-State Functional Connectivity Based Multi-Atlas Label Fusion

The alignment of brain imaging data for functional neuroimaging studies is challenging due to the discrepancy between correspondence of morphology, and equivalence of functional role. In this paper we map functional activation areas across individuals by a multi-atlas label fusion algorithm in a functional space. We learn the manifold of resting-state fMRI signals in each individual, and perform manifold alignment in an embedding space. We then transfer activation predictions from a source population to a target subject via multi-atlas label fusion. The cost function is derived from the aligned manifolds, so that the resulting correspondences are derived based on the similarity of intrinsic connectivity architecture. Experiments show that the resulting label fusion predicts activation evoked by various experiment conditions with higher accuracy than relying on morphological alignment. Interestingly, the distribution of this gain is distributed heterogeneously across the cortex, and across tasks. This offers insights into the relationship between intrinsic connectivity, morphology and task activation. Practically, the mechanism can serve as prior, and provides an avenue to infer task-related activation in individuals for whom only resting data is available. Keywords: Functional Connectivity, Cortical Surface, Task Activation, Target Subject, Intrinsic ConnectivityCongressionally Directed Medical Research Programs (U.S.) (Grant PT100120)Eunice Kennedy Shriver National Institute of Child Health and Human Development (U.S.) (R01HD067312)Neuroimaging Analysis Center (U.S.) (P41EB015902)Oesterreichische Nationalbank (14812)Oesterreichische Nationalbank (15929)Seventh Framework Programme (European Commission) (FP7 2012-PIEF-GA-33003

Groupwise Multimodal Image Registration using Joint Total Variation

In medical imaging it is common practice to acquire a wide range of modalities (MRI, CT, PET, etc.), to highlight different structures or pathologies. As patient movement between scans or scanning session is unavoidable, registration is often an essential step before any subsequent image analysis. In this paper, we introduce a cost function based on joint total variation for such multimodal image registration. This cost function has the advantage of enabling principled, groupwise alignment of multiple images, whilst being insensitive to strong intensity non-uniformities. We evaluate our algorithm on rigidly aligning both simulated and real 3D brain scans. This validation shows robustness to strong intensity non-uniformities and low registration errors for CT/PET to MRI alignment. Our implementation is publicly available at https://github.com/brudfors/coregistration-njtv

Highly valued despite burdens: Qualitative implementation research on rapid tests for hospital-based SARS-CoV-2 screening.

Antigen-based rapid diagnostic tests (RDTs) for SARS-CoV-2 have good reliability and have been repeatedly implemented as part of pandemic response policies, especially for screening in high-risk settings (e.g., hospitals and care homes) where fast recognition of an infection is essential. However, evidence from actual implementation efforts and associated experiences is lacking. We conducted a qualitative study at a large tertiary care hospital in Germany to identify step-by-step processes when implementing RDTs for the screening of incoming patients, as well as stakeholders' implementation experiences. We relied on 30 in-depth interviews with hospital staff (members of the regulatory body, department heads, staff working on the wards, staff training providers on how to perform RDTs, and providers performing RDTs as part of the screening) and patients being screened with RDTs. Despite some initial reservations, RDTs were rapidly accepted and adopted as the best available tool for accessible and reliable screening. Decentralized implementation efforts resulted in different procedures being operationalized across departments. Procedures were continuously refined based on initial experiences (e.g., infrastructural or scheduling constraints), pandemic dynamics (growing infection rates), and changing regulations (e.g., screening of all external personnel). To reduce interdepartmental tension, stakeholders recommended high-level, consistently communicated and enforced regulations. Despite challenges, RDT-based screening for all incoming patients was observed to be feasible and acceptable among implementers and patients, and merits continued consideration in the context of high infection and stagnating vaccination rates

The potential of SARS-CoV-2 antigen-detection tests in the screening of asymptomatic persons.

OBJECTIVES: The aim was to assess the performance of antigen-based rapid diagnostic tests (Ag-RDTs) for SARS CoV-2 when implemented for large-scale universal screening of asymptomatic individuals. METHODS: This study was a pragmatic implementation study for universal Ag-RDT-based screening at a tertiary care hospital in Germany where patients presenting for elective procedures and selected personnel without symptoms suggestive of SARS-CoV-2 were screened with an Ag-RDT since October 2020. Test performance was calculated on an individual patient level. RESULTS: In total, 49 542 RDTs were performed in 27 421 asymptomatic individuals over a duration of 5 and a half months. Out of 222 positive results, 196 underwent in-house confirmatory testing with PCR, out of which 170 were confirmed positive, indicating a positive predictive value of 86.7% (95% CI 81.2-91.1%). Negative Ag-RDTs were not routinely tested with PCR, but a total of 94 cases of false negative Ag-RDTs were detected due to PCR tests being performed within the following 5 days with a median cycle threshold value of 33 (IQR 29-35). DISCUSSION: This study provides evidence that Ag-RDTs can have a high diagnostic yield for transmission relevant infections with limited false positives when utilized at the point of care on asymptomatic patients and thus can be a suitable public health test for universal screening

Randomised placebo-controlled multicentre effectiveness trial of adjunct betamethasone therapy in hospitalised children with community-acquired pneumonia: a trial protocol for the KIDS-STEP trial

IntroductionCommunity-acquired pneumonia (CAP) causes around 10 hospitalisations per 1000 child-years, each associated with an average 13 non-routine days experienced and more than 4 parent workdays lost. In adults, steroid treatment shortens time to clinical stabilisation without an increase in complications in patients with CAP. However, despite promising data from observational studies, there is a lack of high-quality evidence for the use of steroids.Methods and analysisThe KIDS-STEP trial is a multicentre, randomised, double-blind, placebo-controlled superiority trial of betamethasone treatment on outcome of hospitalised children with CAP. Children are enrolled in paediatric emergency departments of hospitals across Switzerland and randomised to adjunct oral betamethasone for 2 days or matching placebo in addition to standard of care treatment. The co-primary outcomes are the proportion of children clinically stable 48 hours after randomisation and the proportion of children with CAP-related readmission within 28 days after randomisation. Secondary outcomes include length of hospital stay, time away from routine childcare and healthcare utilisation and total antibiotic prescriptions within 28 days from randomisation.Each of the co-primary outcomes will be analysed separately. We will test clinical stability rates using a proportion test; to test non-inferiority in readmission rates, we will construct 1−α % CI of the estimated difference and test if it contains the pre-defined margin of 7%. Success is conditional on both tests. A simulation-based sample size estimation determined that recruiting 700 patients will ensure a power of 80% for the study.Ethics and disseminationThe trial protocol and materials were approved by ethics committees in Switzerland (lead: Ethikkommission Nordwest und Zentralschweiz) and the regulatory authority Swissmedic. Participants and caregivers provide informed consent prior to study procedures commencing. The trial results will be published in peer-reviewed journals and at national and international conferences. Key messages will also be disseminated via press and social media where appropriate.Trial registration numberNCT03474991 and SNCTP000002864

Image-level harmonization of multi-site data using image-and-spatial transformer networks

We investigate the use of image-and-spatial transformer networks (ISTNs) to tackle domain shift in multi-site medical imaging data. Commonly, domain adaptation (DA) is performed with little regard for explainability of the inter-domain transformation and is often conducted at the feature-level in the latent space. We employ ISTNs for DA at the image-level which constrains transformations to explainable appearance and shape changes. As proof-of-concept we demonstrate that ISTNs can be trained adversarially on a classification problem with simulated 2D data. For real-data validation, we construct two 3D brain MRI datasets from the Cam-CAN and UK Biobank studies to investigate domain shift due to acquisition and population differences. We show that age regression and sex classification models trained on ISTN output improve generalization when training on data from one and testing on the other site

Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: The CADDementia challenge

Algorithms for computer-aided diagnosis of dementia based on structural MRI have demonstrated high performance in the literature, but are difficult to compare as different data sets and methodology were used for evaluation. In addition, it is unclear how the algorithms would perform on previously unseen data, and thus, how they would perform in clinical practice when there is no real opportunity to adapt the algorithm to the data at hand. To address these comparability, generalizability and clinical applicability issues, we organized a grand challenge that aimed to objectively compare algorithms based on a clinically representative multi-center data set. Using clinical practice as the starting point, the goal was to reproduce the clinical diagnosis. Therefore, we evaluated algorithms for multi-class classification of three diagnostic groups: patients with probable Alzheimer's disease, patients with mild cognitive impairment and healthy controls. The diagnosis based on clinical criteria was used as reference standard, as it was the best available reference despite its known limitations. For evaluation, a previously unseen test set was used consisting of 354 T1-weighted MRI scans with the diagnoses blinded. Fifteen research teams participated with a total of 29 algorithms. The algorithms were trained on a small training set (n = 30) and optionally on data from other sources (e.g., the Alzheimer's Disease Neuroimaging Initiative, the Australian Imaging Biomarkers and Lifestyle flagship study of aging). The best performing algorithm yielded an accuracy of 63.0% and an area under the receiver-operating-characteristic curve (AUC) of 78.8%. In general, the best performances were achieved using feature extraction based on voxel-based morphometry or a combination of features that included volume, cortical thickness, shape and intensity. The challenge is open for new submissions via the web-based framework: http://caddementia.grand-challenge.org

The effectiveness of e-Learning on biosecurity practice to slow the spread of invasive alien species

Online e-Learning is increasingly being used to provide environmental training. Prevention measures including biosecurity are essential to reducing the introduction and spread of invasive alien species (IAS) and are central to international and national IAS policy. This paper is the first to evaluate the effectiveness of e-Learning as a tool to increase awareness, risk perception and biosecurity behaviour in relation to IAS among individuals conducting work activities or research (fieldwork) in the field. We surveyed participants (a mixture of students and professionals) before, and 6 months after undertaking an e-Learning course on IAS and biosecurity practices. Awareness of IAS and self-reported biosecurity behaviour increased after e-Learning among students and professionals. Students had a lower awareness of IAS than professionals before training (20% of students vs 60% of professionals), but after training students showed a greater increase in awareness which led to similar levels of awareness post-training (81%). Prior to training, risk perception was also lower amongst students than professionals (33% of students and 59% of professionals were aware of the risk that their activities posed to the accidental spread of IAS). There was no change in risk perception amongst professionals after training, however training led to a doubling of risk perception in students. E-Learning also led to an increase in reported biosecurity behaviour and cleaning practices and there were higher levels of biosecurity cleaning amongst professionals. The higher awareness and better biosecurity amongst professionals is likely to reflect their familiarity with the issues of IAS and day-to-day activities in the field. Our results suggest that e-Learning is an effective tool to raise awareness and encourage behaviour change among field workers and researchers in an attempt to reduce the risk of accidental introduction and spread of IAS