Quantifying the performance of MEG source reconstruction using resting state data.

In magnetoencephalography (MEG) research there are a variety of inversion methods to transform sensor data into estimates of brain activity. Each new inversion scheme is generally justified against a specific simulated or task scenario. The choice of this scenario will however have a large impact on how well the scheme performs. We describe a method with minimal selection bias to quantify algorithm performance using human resting state data. These recordings provide a generic, heterogeneous, and plentiful functional substrate against which to test different MEG recording and reconstruction approaches. We used a Hidden Markov model to spatio-temporally partition data into self-similar dynamic states. To test the anatomical precision that could be achieved, we then inverted these data onto libraries of systematically distorted subject-specific cortical meshes and compared the quality of the fit using cross validation and a Free energy metric. This revealed which inversion scheme was able to identify the least distorted (most accurate) anatomical models, and allowed us to quantify an upper bound on the mean anatomical distortion accordingly. We used two resting state datasets, one recorded with head-casts and one without. In the head-cast data, the Empirical Bayesian Beamformer (EBB) algorithm showed the best mean anatomical discrimination (3.7 mm) compared with Minimum Norm/LORETA (6.0 mm) and Multiple Sparse Priors (9.4 mm). This pattern was replicated in the second (conventional dataset) although with a marginally poorer (non-significant) prediction of the missing (cross-validated) data. Our findings suggest that the abundant resting state data now commonly available could be used to refine and validate MEG source reconstruction methods and/or recording paradigms

Interpreting and acting upon home blood pressure readings: A qualitative study

This article is made available through the Brunel Open Access Publishing Fund. Copyright @ 2013 Vasileiou et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Background: Recent guidelines recognize the importance of home blood pressure monitoring (HBPM) as an adjunct to clinical measurements. We explored how people who have purchased and use a home blood pressure (BP) monitor make sense of, and act upon, readings and how they communicate with their doctor about the practice of home monitoring. Methods: A qualitative study was designed and participants were purposively recruited from several areas in England, UK. Semi-structured in-depth interviews were conducted with 18 users of home BP monitors. The transcribed data were thematically analysed. Results: Interpretation of home BP readings is complex, and is often characterised by uncertainty. People seek to assess value normality using ‘rules of thumb’, and often aim to identify the potential causes of the readings. This is done by drawing on lay models of BP function and by contextualising the readings to personal circumstances. Based on the perceived causes of the problematic readings, actions are initiated, mostly relating to changes in daily routines. Contacting the doctor was more likely when the problematic readings persisted and could not be easily explained, or when participants did not succeed in regulating their BP through their other interventions. Most users had notified their doctor of the practice of home monitoring, but medical involvement varied, with some participants reporting disinterest or reservations by doctors. Conclusions: Involvement from doctors can help people overcome difficulties and resolve uncertainties around the interpretation of home readings, and ensure that the rules of thumb are appropriate. Home monitoring can be used to strengthen the patient-clinician relationship

A Kernel to Exploit Informative Missingness in Multivariate Time Series from EHRs

A large fraction of the electronic health records (EHRs) consists of clinical measurements collected over time, such as lab tests and vital signs, which provide important information about a patient's health status. These sequences of clinical measurements are naturally represented as time series, characterized by multiple variables and large amounts of missing data, which complicate the analysis. In this work, we propose a novel kernel which is capable of exploiting both the information from the observed values as well the information hidden in the missing patterns in multivariate time series (MTS) originating e.g. from EHRs. The kernel, called TCK$_{IM}$, is designed using an ensemble learning strategy in which the base models are novel mixed mode Bayesian mixture models which can effectively exploit informative missingness without having to resort to imputation methods. Moreover, the ensemble approach ensures robustness to hyperparameters and therefore TCK$_{IM}$ is particularly well suited if there is a lack of labels - a known challenge in medical applications. Experiments on three real-world clinical datasets demonstrate the effectiveness of the proposed kernel.Comment: 2020 International Workshop on Health Intelligence, AAAI-20. arXiv admin note: text overlap with arXiv:1907.0525

Comparing the response modulation hypothesis and the integrated emotions system theory: The role of top-down attention in psychopathy

ERC advanced grant; TUBITAK-BIDEB visiting scientist gran

Simple models of the chemical field around swimming plankton

Background. Cervical cancer is the fourth most common cancer in women, and we recently reported human leukocyte antigen (HLA) alleles showing strong associations with cervical neoplasia risk and protection. HLA ligands are recognized by killer immunoglobulin-like receptors (KIRs) expressed on a range of immune cell subsets, governing their proinflammatory activity. We hypothesized that the inheritance of particular HLA-KIR combinations would increase cervical neoplasia risk. Methods. Here, we used HLA and KIR dosages imputed from single-nucleotide polymorphism genotype data from 2143 cervical neoplasia cases and 13 858 healthy controls of European decent. Results. The following 4 novel HLA alleles were identified in association with cervical neoplasia, owing to their linkage disequilibrium with known cervical neoplasia-associated HLA-DRB1 alleles: HLA-DRB3*9901 (odds ratio [OR], 1.24; P = 2.49 × 10−9), HLA-DRB5*0101 (OR, 1.29; P = 2.26 × 10−8), HLA-DRB5*9901 (OR, 0.77; P = 1.90 × 10−9), and HLA-DRB3*0301 (OR, 0.63; P = 4.06 × 10−5). We also found that homozygosity of HLA-C1 group alleles is a protective factor for human papillomavirus type 16 (HPV16)-related cervical neoplasia (C1/C1; OR, 0.79; P = .005). This protective association was restricted to carriers of either KIR2DL2 (OR, 0.67; P = .00045) or KIR2DS2 (OR, 0.69; P = .0006). Conclusions. Our findings suggest that HLA-C1 group alleles play a role in protecting against HPV16-related cervical neoplasia, mainly through a KIR-mediated mechanism

Multiple Imputation Ensembles (MIE) for dealing with missing data

Missing data is a significant issue in many real-world datasets, yet there are no robust methods for dealing with it appropriately. In this paper, we propose a robust approach to dealing with missing data in classification problems: Multiple Imputation Ensembles (MIE). Our method integrates two approaches: multiple imputation and ensemble methods and compares two types of ensembles: bagging and stacking. We also propose a robust experimental set-up using 20 benchmark datasets from the UCI machine learning repository. For each dataset, we introduce increasing amounts of data Missing Completely at Random. Firstly, we use a number of single/multiple imputation methods to recover the missing values and then ensemble a number of different classifiers built on the imputed data. We assess the quality of the imputation by using dissimilarity measures. We also evaluate the MIE performance by comparing classification accuracy on the complete and imputed data. Furthermore, we use the accuracy of simple imputation as a benchmark for comparison. We find that our proposed approach combining multiple imputation with ensemble techniques outperform others, particularly as missing data increases

Reporting of Factorial Randomized Trials: Extension of the CONSORT 2010 Statement

Importance: Transparent reporting of randomized trials is essential to facilitate critical appraisal and interpretation of results. Factorial trials, in which 2 or more interventions are assessed in the same set of participants, have unique methodological considerations. However, reporting of factorial trials is suboptimal. Objective: To develop a consensus-based extension to the Consolidated Standards of Reporting Trials (CONSORT) 2010 Statement for factorial trials. Design: Using the Enhancing the Quality and Transparency of Health Research (EQUATOR) methodological framework, the CONSORT extension for factorial trials was developed by (1) generating a list of reporting recommendations for factorial trials using a scoping review of methodological articles identified using a MEDLINE search (from inception to May 2019) and supplemented with relevant articles from the personal collections of the authors; (2) a 3-round Delphi survey between January and June 2022 to identify additional items and assess the importance of each item, completed by 104 panelists from 14 countries; and (3) a hybrid consensus meeting attended by 15 panelists to finalize the selection and wording of items for the checklist. Findings: This CONSORT extension for factorial trials modifies 16 of the 37 items in the CONSORT 2010 checklist and adds 1 new item. The rationale for the importance of each item is provided. Key recommendations are (1) the reason for using a factorial design should be reported, including whether an interaction is hypothesized, (2) the treatment groups that form the main comparisons should be clearly identified, and (3) for each main comparison, the estimated interaction effect and its precision should be reported. Conclusions and Relevance: This extension of the CONSORT 2010 Statement provides guidance on the reporting of factorial randomized trials and should facilitate greater understanding of and transparency in their reporting.

Application of the speed-duration relationship to normalize the intensity of high-intensity interval training

The tolerable duration of continuous high-intensity exercise is determined by the hyperbolic Speed-tolerable duration (S-tLIM) relationship. However, application of the S-tLIM relationship to normalize the intensity of High-Intensity Interval Training (HIIT) has yet to be considered, with this the aim of present study. Subjects completed a ramp-incremental test, and series of 4 constant-speed tests to determine the S-tLIM relationship. A sub-group of subjects (n = 8) then repeated 4 min bouts of exercise at the speeds predicted to induce intolerance at 4 min (WR4), 6 min (WR6) and 8 min (WR8), interspersed with bouts of 4 min recovery, to the point of exercise intolerance (fixed WR HIIT) on different days, with the aim of establishing the work rate that could be sustained for 960 s (i.e. 4×4 min). A sub-group of subjects (n = 6) also completed 4 bouts of exercise interspersed with 4 min recovery, with each bout continued to the point of exercise intolerance (maximal HIIT) to determine the appropriate protocol for maximizing the amount of high-intensity work that can be completed during 4×4 min HIIT. For fixed WR HIIT tLIM of HIIT sessions was 399±81 s for WR4, 892±181 s for WR6 and 1517±346 s for WR8, with total exercise durations all significantly different from each other (P<0.050). For maximal HIIT, there was no difference in tLIM of each of the 4 bouts (Bout 1: 229±27 s; Bout 2: 262±37 s; Bout 3: 235±49 s; Bout 4: 235±53 s; P>0.050). However, there was significantly less high-intensity work completed during bouts 2 (153.5±40. 9 m), 3 (136.9±38.9 m), and 4 (136.7±39.3 m), compared with bout 1 (264.9±58.7 m; P>0.050). These data establish that WR6 provides the appropriate work rate to normalize the intensity of HIIT between subjects. Maximal HIIT provides a protocol which allows the relative contribution of the work rate profile to physiological adaptations to be considered during alternative intensity-matched HIIT protocols

Shallow water methane-derived authigenic carbonate mounds at the Codling Fault Zone, western Irish Sea

Methane-derived authigenic carbonate (MDAC) mound features at the Codling Fault Zone (CFZ), located in shallow waters (50–120 m) of the western Irish Sea were investigated and provide a comparison to deep sea MDAC settings. Carbonates consisted of aragonite as the major mineral phase, with δ13C depletion to −50‰ and δ18O enrichment to ~ 2‰. These isotope signatures, together with the co-precipitation of framboidal pyrite confirm that anaerobic oxidation of methane (AOM) is an important process mediating methane release to thewater column and the atmosphere in this region. 18O-enrichment could be a result ofMDAC precipitation with seawater in colder than present day conditions, or precipitation with 18O-enriched water transported from deep petroleum sources. The 13C depletion of bulk carbonate and sampled gas(−70‰) suggests a biogenic source, but significant mixing of thermogenic gas and depletion of the original isotope signature cannot be ruled out. Active seepage was recorded from one mound and together with extensive areas of reduced sediment, confirms that seepage is ongoing. The mounds appear to be composed of stacked pavements that are largely covered by sand and extensively eroded. The CFZ mounds are colonized by abundant Sabellaria polychaetes and possible Nemertesia hydroids, which benefit indirectly from available hard substrate. In contrast to deep sea MDAC settings where seep-related macrofauna are commonly reported, seep-specialist fauna appear to be lacking at the CFZ. In addition, unlike MDAC in deep waters where organic carbon input from photosynthesis is limited, lipid biomarkers and isotope signatures related to marine planktonic production (e.g. sterols, alkanols) were most abundant. Evidence for microbes involved in AOM was limited from samples taken; possibly due to this dilution effect from organic matter derived from the photic zone, and will require further investigation