Uptake, elimination, and biotransformation of the lampricide 3-trifluoromethyl-4-nitrophenol (TFM) by larvae of the aquatic midge Chironomus tentans.

Accumulation of [14C]3-trifluoromethyl-4-nitrophenol ([14C]TFM) by chironomid larvae from sublethal aqueous concentrations is rapid and dependent on hardness of exposure water. TFM is readily biotransformed to at least two more polar derivatives, including sulfated TFM and TFM-glucuronide or galacturonide. Some TFM is also reduced to the phenol amine. Chironomids can quickly eliminate all TFM derivatives as well as the parent [14C]TFM.     

Temporal Autocorrelation in Univariate Linear Modeling of FMRI Data

In functional magnetic resonance imaging statistical analysis there are problems with accounting for temporal autocorrelations when assessing change within voxels. Techniques to date have utilized temporal filtering strategies to either shape these autocorrelations or remove them. Shaping, or "coloring," attempts to negate the effects of not accurately knowing the intrinsic autocorrelations by imposing known autocorrelation via temporal filtering. Removing the autocorrelation, or "prewhitening," gives the best linear unbiased estimator, assuming that the autocorrelation is accurately known. For single-event designs, the efficiency of the estimator is considerably higher for prewhitening compared with coloring. However, it has been suggested that sufficiently accurate estimates of the autocorrelation are currently not available to give prewhitening acceptable bias. To overcome this, we consider different ways to estimate the autocorrelation for use in prewhitening. After high-pass filtering is performed, a Tukey taper (set to smooth the spectral density more than would normally be used in spectral density estimation) performs best. Importantly, estimation is further improved by using nonlinear spatial filtering to smooth the estimated autocorrelation, but only within tissue type. Using this approach when prewhitening reduced bias to close to zero at probability levels as low as 1 x 10(-5).     

Statistical improvements in functional magnetic resonance imaging analyses produced by censoring high‐motion data points

Subject motion degrades the quality of task functional magnetic resonance imaging (fMRI) data. Here, we test two classes of methods to counteract the effects of motion in task fMRI data: (1) a variety of motion regressions and (2) motion censoring (“motion scrubbing”). In motion regression, various regressors based on realignment estimates were included as nuisance regressors in general linear model (GLM) estimation. In motion censoring, volumes in which head motion exceeded a threshold were withheld from GLM estimation. The effects of each method were explored in several task fMRI data sets and compared using indicators of data quality and signal‐to‐noise ratio. Motion censoring decreased variance in parameter estimates within‐ and across‐subjects, reduced residual error in GLM estimation, and increased the magnitude of statistical effects. Motion censoring performed better than all forms of motion regression and also performed well across a variety of parameter spaces, in GLMs with assumed or unassumed response shapes. We conclude that motion censoring improves the quality of task fMRI data and can be a valuable processing step in studies involving populations with even mild amounts of head movement. Hum Brain Mapp 35:1981–1996, 2014. © 2013 Wiley Periodicals, Inc .     

Regression-based analysis of combined EEG and eye-tracking data: Theory and applications

Fixation-related potentials (FRPs), neural responses aligned to the end of saccades, are a promising tool for studying the dynamics of attention and cognition under natural viewing conditions. In the past, four methodological problems have complicated the analysis of such combined eye-tracking/electroencephalogram experiments: (1) the synchronization of data streams, (2) the removal of ocular artifacts, (3) the condition-specific temporal overlap between the brain responses evoked by consecutive fixations, and (4) the fact that numerous low-level stimulus and saccade properties also influence the postsaccadic neural responses. Although effective solutions exist for the first two problems, the latter two are only beginning to be addressed. In the current paper, we present and review a unified regression-based framework for FRP analysis that allows us to deconvolve overlapping potentials while also controlling for both linear and nonlinear confounds on the FRP waveform. An open software implementation is provided for all procedures. We then demonstrate the advantages of this proposed (non)linear deconvolution modeling approach for data from three commonly studied paradigms: face perception, scene viewing, and reading. First, for a traditional event-related potential (ERP) face recognition experiment, we show how this technique can separate stimulus ERPs from overlapping muscle and brain potentials produced by small (micro)saccades on the face. Second, in natural scene viewing, we model and isolate multiple nonlinear effects of saccade parameters on the FRP. Finally, for a natural sentence reading experiment using the boundary paradigm, we show how it is possible to study the neural correlates of parafoveal preview after removing spurious overlap effects caused by the associated difference in average fixation time. Our results suggest a principal way of measuring reliable eye movement-related brain activity during natural vision.     

Does pre-operative anxiety and/or depression affect patient outcome after primary knee replacement arthroplasty?

Background

Chronic pain is associated with psychological distress, most commonly manifested as anxiety and/or depression.

Constrained linear basis sets for HRF modelling using Variational Bayes

FMRI modelling requires flexible haemodynamic response function (HRF) modelling, with the HRF being allowed to vary spatially and between subjects. To achieve this flexibility, voxelwise parameterised HRFs have been proposed; however, inference on such models is very slow. An alternative approach is to use basis functions allowing inference to proceed in the more manageable General Linear Model (GLM) framework. However, a large amount of the subspace spanned by the basis functions produces nonsensical HRF shapes. In this work we propose a technique for choosing a basis set, and then the means to constrain the subspace spanned by the basis set to only include sensible HRF shapes. Penny et al. showed how Variational Bayes can be used to infer on the GLM for FMRI. Here we extend the work of Penny et al. to give inference on the GLM with constrained HRF basis functions and with spatial Markov Random Fields on the autoregressive noise parameters. Constraining the subspace spanned by the basis set allows for far superior separation of activating voxels from nonactivating voxels in FMRI data. We use spatial mixture modelling to produce final probabilities of activation and demonstrate increased sensitivity on an FMRI dataset.     

Activation detection in diffuse optical imaging by means of the general linear model

Due to its non-invasive nature and low cost, diffuse optical imaging (DOI) is becoming a commonly used technique to assess functional activation in the brain. When imaging with DOI, two major issues arise in the data analysis: (i) the separation of noise of physiological origin and the recovery of the functional response; (ii) the tomographic image reconstruction problem. This paper focuses on the first issue. Although the general linear model (GLM) has been extensively used in functional magnetic resonance imaging (fMRI), DOI has mostly relied on filtering and averaging of raw data to recover brain functional activation. This is mainly due to the high temporal resolution of DOI which implies a new design of the drift basis modelling physiology. In this paper, we provide (i) a filtering method based on cosine functions that is more adapted than standard averaging techniques for DOI specifically; (ii) a new mode-locking technique to recover small signals and locate them temporally with high precision (shift method). Results on real data show the capability of the shift method to retrieve HbR and HbO(2) peak locations.     

The inpatient costs and hospital service use associated with invasive meningococcal disease in South Australian children

Background

Invasive meningococcal disease (IMD) remains a serious public health concern due to a sustained high case fatality rate and morbidity in survivors. This study aimed to estimate the hospital service costs associated with IMD and variables associated with the highest costs in Australian children admitted to a tertiary paediatric hospital.

Methods

Clinical details were obtained from medical records and associated inpatient costs were collected and inflated to 2011 Australian dollars using the medical and hospital services component of the Australian Consumer Price Index. Both unadjusted and adjusted analyses were undertaken. Multivariate regression models were used to adjust for potential covariates and determine independent predictors of high costs and increased length of hospital stay.

Results

Of 109 children hospitalised with IMD between May 2000 and April 2011, the majority were caused by serogroup B (70.6%). Presence of sequelae, serogroup B infection, male gender, infants less than one year of age, and previous medical diagnosis were associated with higher inpatient costs and length of stay (LOS) in hospital (p < 0.001) during the acute admissions. Children diagnosed with septicaemia had a longer predicted LOS (p = 0.033) during the acute admissions compared to those diagnosed with meningitis alone or meningitis with septicaemia. Serogroup B cases incurred a significantly higher risk of IMD related readmissions (IRR: 21.1, p = 0.008) for patients with sequelae. Serogroup B infection, male gender, diagnosis of septicaemia, infants less than one year of age, and no previous medical diagnosis were more likely to have higher inpatient costs and LOS during the IMD related readmissions for patients with sequelae (p < 0.05).

Conclusion

Although IMD is uncommon, the disease severity and associated long-term sequelae result in high health care costs, which should be considered in meningococcal B vaccine funding considerations.