Three-factor models versus time series models:quantifying time-dependencies of interactions between stimuli in cell biology and psychobiology for short longitudinal data

Signal integration determines cell fate on the cellular level, affects cognitive processes and affective responses on the behavioural level, and is likely to be involved in psychoneurobiological processes underlying mood disorders. Interactions between stimuli may subjected to time effects. Time-dependencies of interactions between stimuli typically lead to complex cell responses and complex responses on the behavioural level. We show that both three-factor models and time series models can be used to uncover such time-dependencies. However, we argue that for short longitudinal data the three factor modelling approach is more suitable. In order to illustrate both approaches, we re-analysed previously published short longitudinal data sets. We found that in human embryonic kidney 293 cells cells the interaction effect in the regulation of extracellular signal-regulated kinase (ERK) 1 signalling activation by insulin and epidermal growth factor is subjected to a time effect and dramatically decays at peak values of ERK activation. In contrast, we found that the interaction effect induced by hypoxia and tumour necrosis factor-alpha for the transcriptional activity of the human cyclo-oxygenase-2 promoter in HEK293 cells is time invariant at least in the first 12-h time window after stimulation. Furthermore, we applied the three-factor model to previously reported animal studies. In these studies, memory storage was found to be subjected to an interaction effect of the beta-adrenoceptor agonist clenbuterol and certain antagonists acting on the alpha-1-adrenoceptor / glucocorticoid-receptor system. Our model-based analysis suggests that only if the antagonist drug is administer in a critical time window, then the interaction effect is relevant.

Optimal time following fluorescein instillation to evaluate rigid gas permeable contact lens fit

PURPOSE: To examine the optimum time at which fluorescein patterns of gas permeable lenses (GPs) should be evaluated. METHODS: Aligned, 0.2mm steep and 0.2mm flat GPs were fitted to 17 patients (aged 20.6±1.1 years, 10 male). Fluorescein was applied to their upper temporal bulbar conjunctiva with a moistened fluorescein strip. Digital slit lamp images (CSO, Italy) at 10× magnification of the fluorescein pattern viewed with blue light through a yellow filter were captured every 15s. Fluorescein intensity in central, mid peripheral and edge regions of the superior, inferior, temporal and nasal quadrants of the lens were graded subjectively using a +2 to -2 scale and using ImageJ software on the simultaneously captured images. RESULTS: Subjectively graded and objectively image analysed fluorescein intensity changed with time (p<0.001), lens region (centre, mid-periphery and edge: p<0.05) and there was interaction between lens region with lens fit (p<0.001). For edge band width, there was a significant effect of time (F=118.503, p<0.001) and lens fit (F=5.1249, p=0.012). The expected alignment, flat and steep fitting patterns could be seen from approximately after 30 to 180s subjectively and 15 to 105s in captured images. CONCLUSION: Although the stability of fluorescein intensity can start to decline in as little as 45s post fluorescein instillation, the diagnostic pattern of alignment, steep or flat fit is seen in each meridian by subjective observation from about 30s to 3min indicating this is the most appropriate time window to evaluate GP lenses in clinical practice.

Approximate Bayesian techniques for inference in stochastic dynamical systems

This thesis is concerned with approximate inference in dynamical systems, from a variational Bayesian perspective. When modelling real world dynamical systems, stochastic differential equations appear as a natural choice, mainly because of their ability to model the noise of the system by adding a variant of some stochastic process to the deterministic dynamics. Hence, inference in such processes has drawn much attention. Here two new extended frameworks are derived and presented that are based on basis function expansions and local polynomial approximations of a recently proposed variational Bayesian algorithm. It is shown that the new extensions converge to the original variational algorithm and can be used for state estimation (smoothing). However, the main focus is on estimating the (hyper-) parameters of these systems (i.e. drift parameters and diffusion coefficients). The new methods are numerically validated on a range of different systems which vary in dimensionality and non-linearity. These are the Ornstein-Uhlenbeck process, for which the exact likelihood can be computed analytically, the univariate and highly non-linear, stochastic double well and the multivariate chaotic stochastic Lorenz '63 (3-dimensional model). The algorithms are also applied to the 40 dimensional stochastic Lorenz '96 system. In this investigation these new approaches are compared with a variety of other well known methods such as the ensemble Kalman filter / smoother, a hybrid Monte Carlo sampler, the dual unscented Kalman filter (for jointly estimating the systems states and model parameters) and full weak-constraint 4D-Var. Empirical analysis of their asymptotic behaviour as a function of observation density or length of time window increases is provided.

Estimation of functional connectivity from electromagnetic signals and the amount of empirical data required

An increasing number of neuroimaging studies are concerned with the identification of interactions or statistical dependencies between brain areas. Dependencies between the activities of different brain regions can be quantified with functional connectivity measures such as the cross-correlation coefficient. An important factor limiting the accuracy of such measures is the amount of empirical data available. For event-related protocols, the amount of data also affects the temporal resolution of the analysis. We use analytical expressions to calculate the amount of empirical data needed to establish whether a certain level of dependency is significant when the time series are autocorrelated, as is the case for biological signals. These analytical results are then contrasted with estimates from simulations based on real data recorded with magnetoencephalography during a resting-state paradigm and during the presentation of visual stimuli. Results indicate that, for broadband signals, 50-100 s of data is required to detect a true underlying cross-correlations coefficient of 0.05. This corresponds to a resolution of a few hundred milliseconds for typical event-related recordings. The required time window increases for narrow band signals as frequency decreases. For instance, approximately 3 times as much data is necessary for signals in the alpha band. Important implications can be derived for the design and interpretation of experiments to characterize weak interactions, which are potentially important for brain processing.

Estimating parameters in stochastic systems:a variational Bayesian approach

This work is concerned with approximate inference in dynamical systems, from a variational Bayesian perspective. When modelling real world dynamical systems, stochastic differential equations appear as a natural choice, mainly because of their ability to model the noise of the system by adding a variation of some stochastic process to the deterministic dynamics. Hence, inference in such processes has drawn much attention. Here a new extended framework is derived that is based on a local polynomial approximation of a recently proposed variational Bayesian algorithm. The paper begins by showing that the new extension of this variational algorithm can be used for state estimation (smoothing) and converges to the original algorithm. However, the main focus is on estimating the (hyper-) parameters of these systems (i.e. drift parameters and diffusion coefficients). The new approach is validated on a range of different systems which vary in dimensionality and non-linearity. These are the Ornstein–Uhlenbeck process, the exact likelihood of which can be computed analytically, the univariate and highly non-linear, stochastic double well and the multivariate chaotic stochastic Lorenz ’63 (3D model). As a special case the algorithm is also applied to the 40 dimensional stochastic Lorenz ’96 system. In our investigation we compare this new approach with a variety of other well known methods, such as the hybrid Monte Carlo, dual unscented Kalman filter, full weak-constraint 4D-Var algorithm and analyse empirically their asymptotic behaviour as a function of observation density or length of time window increases. In particular we show that we are able to estimate parameters in both the drift (deterministic) and the diffusion (stochastic) part of the model evolution equations using our new methods.

A Bayesian state space modelling approach to probabilistic quantitative precipitation forecasting

The generation of very short range forecasts of precipitation in the 0-6 h time window is traditionally referred to as nowcasting. Most existing nowcasting systems essentially extrapolate radar observations in some manner, however, very few systems account for the uncertainties involved. Thus deterministic forecast are produced, which have a limited use when decisions must be made, since they have no measure of confidence or spread of the forecast. This paper develops a Bayesian state space modelling framework for quantitative precipitation nowcasting which is probabilistic from conception. The model treats the observations (radar) as noisy realisations of the underlying true precipitation process, recognising that this process can never be completely known, and thus must be represented probabilistically. In the model presented here the dynamics of the precipitation are dominated by advection, so this is a probabilistic extrapolation forecast. The model is designed in such a way as to minimise the computational burden, while maintaining a full, joint representation of the probability density function of the precipitation process. The update and evolution equations avoid the need to sample, thus only one model needs be run as opposed to the more traditional ensemble route. It is shown that the model works well on both simulated and real data, but that further work is required before the model can be used operationally. © 2004 Elsevier B.V. All rights reserved.

Investigation of neural correlates of faces and emotional expressions using magnetoencephalography

The recognition of faces and of facial expressions in an important evolutionary skill, and an integral part of social communication. It has been argued that the processing of faces is distinct from the processing of non-face stimuli and functional neuroimaging investigations have even found evidence of a distinction between the perception of faces and of emotional expressions. Structural and temporal correlates of face perception and facial affect have only been separately identified. Investigation neural dynamics of face perception per se as well as facial affect would allow the mapping of these in space, time and frequency specific domains. Participants were asked to perform face categorisation and emotional discrimination tasks and Magnetoencephalography (MEG) was used to measure the neurophysiology of face and facial emotion processing. SAM analysis techniques enable the investigation of spectral changes within specific time-windows and frequency bands, thus allowing the identification of stimulus specific regions of cortical power changes. Furthermore, MEG’s excellent temporal resolution allows for the detection of subtle changes associated with the processing of face and non-face stimuli and different emotional expressions. The data presented reveal that face perception is associated with spectral power changes within a distributed cortical network comprising occipito-temporal as well as parietal and frontal areas. For the perception of facial affect, spectral power changes were also observed within frontal and limbic areas including the parahippocampal gyrus and the amygdala. Analyses of temporal correlates also reveal a distinction between the processing of faces and facial affect. Face perception per se occurred at earlier latencies whereas the discrimination of facial expression occurred within a longer time-window. In addition, the processing of faces and facial affect was differentially associated with changes in cortical oscillatory power for alpha, beta and gamma frequencies. The perception of faces and facial affect is associated with distinct changes in cortical oscillatory activity that can be mapped to specific neural structures, specific time-windows and latencies as well as specific frequency bands. Therefore, the work presented in this thesis provides further insight into the sequential processing of faces and facial affect.

Combining temporal and spectral information with spatial mapping to identify differences between phonological and semantic networks:a magnetoencephalographic approach

Early, lesion-based models of language processing suggested that semantic and phonological processes are associated with distinct temporal and parietal regions respectively, with frontal areas more indirectly involved. Contemporary spatial brain mapping techniques have not supported such clear-cut segregation, with strong evidence of activation in left temporal areas by both processes and disputed evidence of involvement of frontal areas in both processes. We suggest that combining spatial information with temporal and spectral data may allow a closer scrutiny of the differential involvement of closely overlapping cortical areas in language processing. Using beamforming techniques to analyze magnetoencephalography data, we localized the neuronal substrates underlying primed responses to nouns requiring either phonological or semantic processing, and examined the associated measures of time and frequency in those areas where activation was common to both tasks. Power changes in the beta (14-30 Hz) and gamma (30-50 Hz) frequency bandswere analyzed in pre-selected time windows of 350-550 and 500-700ms In left temporal regions, both tasks elicited power changes in the same time window (350-550 ms), but with different spectral characteristics, low beta (14-20 Hz) for the phonological task and high beta (20-30 Hz) for the semantic task. In frontal areas (BA10), both tasks elicited power changes in the gamma band (30-50 Hz), but in different time windows, 500-700ms for the phonological task and 350-550ms for the semantic task. In the left inferior parietal area (BA40), both tasks elicited changes in the 20-30 Hz beta frequency band but in different time windows, 350-550ms for the phonological task and 500-700ms for the semantic task. Our findings suggest that, where spatial measures may indicate overlapping areas of involvement, additional beamforming techniques can demonstrate differential activation in time and frequency domains. © 2012 McNab, Hillebrand, Swithenby and Rippon.

Abnormality of mismatch negativity in response to tone omission in dyslexic adults

Evidence of abnormalities in the perception of rapidly presented sounds in dyslexia has been interpreted as evidence of a prolonged time window within which sounds can influence the perception of temporally surrounding sounds. We recorded the magnetic mismatch negativity (MMNm) to infrequent tone omissions in a group of six dyslexic adults and six IQ and age-matched controls. An MMNm is only elicited in response to a complete stimulus omission when successive inputs fall within the temporal window of integration (stimulus onset asynchrony (SOA) ∼160 ms). No MMNm responses were recorded in either experimental group when stimuli were presented at SOAs falling just outside the temporal window of integration (SOA = 175 ms). However, while presentation rates of 100 ms resulted in MMNm responses for all control participants, the same stimulus omissions elicited an MMNm response in only one of the six dyslexic participants. These results cannot support the hypothesis of a prolonged time window of integration, but rather indicate auditory grouping deficits in the dyslexic population. © 2006 Elsevier B.V. All rights reserved.

How the brain blinks:towards a neurocognitive model of the attentional blink

When people monitor a visual stream of rapidly presented stimuli for two targets (T1 and T2), they often miss T2 if it falls into a time window of about half a second after T1 onset—the attentional blink (AB). We provide an overview of recent neuroscientific studies devoted to analyze the neural processes underlying the AB and their temporal dynamics. The available evidence points to an attentional network involving temporal, right-parietal and frontal cortex, and suggests that the components of this neural network interact by means of synchronization and stimulus-induced desynchronization in the beta frequency range. We set up a neurocognitive scenario describing how the AB might emerge and why it depends on the presence of masks and the other event(s) the targets are embedded in. The scenario supports the idea that the AB arises from ‘‘biased competition’’, with the top–down bias being generated by parietal–frontal interactions and the competition taking place between stimulus codes in temporal cortex.

Modulation of long-range neural synchrony reflects temporal limitations of visual attention in humans

Because of attentional limitations, the human visual system can process for awareness and response only a fraction of the input received. Lesion and functional imaging studies have identified frontal, temporal, and parietal areas as playing a major role in the attentional control of visual processing, but very little is known about how these areas interact to form a dynamic attentional network. We hypothesized that the network communicates by means of neural phase synchronization, and we used magnetoencephalography to study transient long-range interarea phase coupling in a well studied attentionally taxing dual-target task (attentional blink). Our results reveal that communication within the fronto-parieto-temporal attentional network proceeds via transient long-range phase synchronization in the beta band. Changes in synchronization reflect changes in the attentional demands of the task and are directly related to behavioral performance. Thus, we show how attentional limitations arise from the way in which the subsystems of the attentional network interact. The human brain faces an inestimable task of reducing a potentially overloading amount of input into a manageable flow of information that reflects both the current needs of the organism and the external demands placed on it. This task is accomplished via a ubiquitous construct known as “attention,” whose mechanism, although well characterized behaviorally, is far from understood at the neurophysiological level. Whereas attempts to identify particular neural structures involved in the operation of attention have met with considerable success (1-5) and have resulted in the identification of frontal, parietal, and temporal regions, far less is known about the interaction among these structures in a way that can account for the task-dependent successes and failures of attention. The goal of the present research was, thus, to unravel the means by which the subsystems making up the human attentional network communicate and to relate the temporal dynamics of their communication to observed attentional limitations in humans. A prime candidate for communication among distributed systems in the human brain is neural synchronization (for review, see ref. 6). Indeed, a number of studies provide converging evidence that long-range interarea communication is related to synchronized oscillatory activity (refs. 7-14; for review, see ref. 15). To determine whether neural synchronization plays a role in attentional control, we placed humans in an attentionally demanding task and used magnetoencephalography (MEG) to track interarea communication by means of neural synchronization. In particular, we presented 10 healthy subjects with two visual target letters embedded in streams of 13 distractor letters, appearing at a rate of seven per second. The targets were separated in time by a single distractor. This condition leads to the “attentional blink” (AB), a well studied dual-task phenomenon showing the reduced ability to report the second of two targets when an interval <500 ms separates them (16-18). Importantly, the AB does not prevent perceptual processing of missed target stimuli but only their conscious report (19), demonstrating the attentional nature of this effect and making it a good candidate for the purpose of our investigation. Although numerous studies have investigated factors, e.g., stimulus and timing parameters, that manipulate the magnitude of a particular AB outcome, few have sought to characterize the neural state under which “standard” AB parameters produce an inability to report the second target on some trials but not others. We hypothesized that the different attentional states leading to different behavioral outcomes (second target reported correctly or not) are characterized by specific patterns of transient long-range synchronization between brain areas involved in target processing. Showing the hypothesized correspondence between states of neural synchronization and human behavior in an attentional task entails two demonstrations. First, it needs to be demonstrated that cortical areas that are suspected to be involved in visual-attention tasks, and the AB in particular, interact by means of neural synchronization. This demonstration is particularly important because previous brain-imaging studies (e.g., ref. 5) only showed that the respective areas are active within a rather large time window in the same task and not that they are concurrently active and actually create an interactive network. Second, it needs to be demonstrated that the pattern of neural synchronization is sensitive to the behavioral outcome; specifically, the ability to correctly identify the second of two rapidly succeeding visual targets

Case study: auditory brain responses in a minimally verbal child with autism and cerebral palsy

An estimated 30% of individuals with autism spectrum disorders (ASD) remain minimally verbal into late childhood, but research on cognition and brain function in ASD focuses almost exclusively on those with good or only moderately impaired language. Here we present a case study investigating auditory processing of GM, a nonverbal child with ASD and cerebral palsy. At the age of 8 years, GM was tested using magnetoencephalography (MEG) whilst passively listening to speech sounds and complex tones. Where typically developing children and verbal autistic children all demonstrated similar brain responses to speech and nonspeech sounds, GM produced much stronger responses to nonspeech than speech, particularly in the 65–165 ms (M50/M100) time window post-stimulus onset. GM was retested aged 10 years using electroencephalography (EEG) whilst passively listening to pure tone stimuli. Consistent with her MEG response to complex tones, GM showed an unusually early and strong response to pure tones in her EEG responses. The consistency of the MEG and EEG data in this single case study demonstrate both the potential and the feasibility of these methods in the study of minimally verbal children with ASD. Further research is required to determine whether GM's atypical auditory responses are characteristic of other minimally verbal children with ASD or of other individuals with cerebral palsy.

Receiver memory requirement in mode delay compensated few-mode fibre spans with intermediate coupling

The required receiver time window after propagation through few-mode fibre is studied for a broad range of coupling and mode delay span configurations. Under intermediate coupling, effective mode delay compensation is observed for a compensation period of 25km.

Placenta growth factor-1 exerts time-dependent stabilization of adherens junctions following VEGF-induced vascular permeability

Increased vascular permeability is an early event characteristic of tissue ischemia and angiogenesis. Although VEGF family members are potent promoters of endothelial permeability the role of placental growth factor (PlGF) is hotly debated. Here we investigated PlGF isoforms 1 and 2 and present in vitro and in vivo evidence that PlGF-1, but not PlGF-2, can inhibit VEGF-induced permeability but only during a critical window post-VEGF exposure. PlGF-1 promotes VE-cadherin expression via the trans-activating Sp1 and Sp3 interaction with the VE-cadherin promoter and subsequently stabilizes transendothelial junctions, but only after activation of endothelial cells by VEGF. PlGF-1 regulates vascular permeability associated with the rapid localization of VE-cadherin to the plasma membrane and dephosphorylation of tyrosine residues that precedes changes observed in claudin 5 tyrosine phosphorylation and membrane localization. The critical window during which PlGF-1 exerts its effect on VEGF-induced permeability highlights the importance of the translational significance of this work in that PLGF-1 likely serves as an endogenous anti-permeability factor whose effectiveness is limited to a precise time point following vascular injury. Clinical approaches that would pattern nature's approach would thus limit treatments to precise intervals following injury and bring attention to use of agents only during therapeutic windows.

Use of narrow mass-window, high-resolution extracted product ion chromatograms for the sensitive and selective identification of protein modifications

Protein modifications, including oxidative modifications, glycosylations, and oxidized lipid-protein adducts, are becoming increasingly important as biomarkers and in understanding disease etiology. There has been a great deal of interest in mapping these on Apo B100 from low density lipoprotein (LDL). We have used extracted ion chromatograms of product ions generated using a very narrow mass window from high-resolution tandem mass spectrometric data collected on a rapid scanning quadrupole time-of-flight (QTOF) instrument, to selectively and sensitively detect modified peptides and identify the site and nature of a number of protein modifications in parallel. We have demonstrated the utility of this method by characterizing for the first time oxidized phospholipid adducts to LDL and human serum albumin and for the detection of glycosylation and kynurenin formation from the oxidation of tryptophan residues in LDL. © 2013 American Chemical Society.