ABSTRACT: Modeling the Hemodynamic Response Function (HRF) is a critical step in fMRI studies of brain activity, and it is often desirable to estimate HRF parameters with physiological interpretability. A biophysically informed model of the HRF can be described by a non-linear time-invariant dynamic system. However, the identification of this dynamic system may leave much uncertainty on the exact values of the parameters. Moreover, the high noise levels in the data may hinder the model estimation task. In this context, the estimation of the HRF may be seen as a problem of model falsification or invalidation, where we are interested in distinguishing among a set of eligible models of dynamic systems. Here, we propose a systematic tool to determine the distinguishability among a set of physiologically plausible HRF models. The concept of absolutely input-distinguishable systems is introduced and applied to a biophysically informed HRF model, by exploiting the structure of the underlying non-linear dynamic system. A strategy to model uncertainty in the input time-delay and magnitude is developed and its impact on the distinguishability of two physiologically plausible HRF models is assessed, in terms of the maximum noise amplitude above which it is not possible to guarantee the falsification of one model in relation to another. Finally, a methodology is proposed for the choice of the input sequence, or experimental paradigm, that maximizes the distinguishability of the HRF models under investigation. The proposed approach may be used to evaluate the performance of HRF model estimation techniques from fMRI data.
Project description:In allergic disease, mast cell activation is conventionally triggered by allergen-mediated cross-linking of receptor-bound IgE on the cell surface. In addition to its diverse range of intracellular roles in apoptosis, cell proliferation and cancer, Histamine-Releasing Factor (HRF) also activates mast cells and basophils. A subset of IgE antibodies bind HRF through their Fab regions, and two IgE binding sites on HRF have been mapped. HRF can form dimers, and a disulphide-linked dimer is critical for activity. The current model for the activity of HRF in mast cell activation involves cross-linking of receptor-bound IgE by dimeric HRF, mediated by HRF/Fab interactions. HRF crystal and solution structures have provided little insight into either the formation of disulphide-linked HRF dimers or the ability of HRF to activate mast cells. We report the first crystal structure of murine HRF (mHRF) to 4.0Å resolution, revealing a conserved fold. We also solved the structure of human HRF (hHRF) in two new crystal forms, one at the highest resolution (1.4Å) yet reported. The high resolution hHRF structure reveals a disulphide-linked dimer, in which the two molecules are closely associated, and provides a model for the role of both human and murine HRF in mast cell activation.
Project description:Hydroxyl-radical footprinting (HRF) is a powerful method for probing structures of nucleic acid-protein complexes with single-nucleotide resolution in solution. To tap the full quantitative potential of HRF, we describe a protocol, hydroxyl-radical footprinting interpretation for DNA (HYDROID), to quantify HRF data and integrate them with atomistic structural models. The stages of the HYDROID protocol are extraction of the lane profiles from gel images, quantification of the DNA cleavage frequency at each nucleotide and theoretical estimation of the DNA cleavage frequency from atomistic structural models, followed by comparison of experimental and theoretical results. Example scripts for each step of HRF data analysis and interpretation are provided for several nucleosome systems; they can be easily adapted to analyze user data. As input, HYDROID requires polyacrylamide gel electrophoresis (PAGE) images of HRF products and optionally can use a molecular model of the DNA-protein complex. The HYDROID protocol can be used to quantify HRF over DNA regions of up to 100 nucleotides per gel image. In addition, it can be applied to the analysis of RNA-protein complexes and free RNA or DNA molecules in solution. Compared with other methods reported to date, HYDROID is unique in its ability to simultaneously integrate HRF data with the analysis of atomistic structural models. HYDROID is freely available. The complete protocol takes ~3 h. Users should be familiar with the command-line interface, the Python scripting language and Protein Data Bank (PDB) file formats. A graphical user interface (GUI) with basic functionality (HYDROID_GUI) is also available.
Project description:Although most vaccines against blood stage malaria in development today use subunit preparations, live attenuated parasites confer significantly broader and more lasting protection. In recent years, Plasmodium genetically attenuated parasites (GAPs) have been generated in rodent models that cause self-resolving blood stage infections and induce strong protection. All such GAPs generated so far bear mutations in housekeeping genes important for parasite development in red blood cells. In this study, using a Plasmodium berghei model compatible with tracking anti-blood stage immune responses over time, we report a novel blood stage GAP that lacks a secreted factor related to histamine-releasing factor (HRF). Lack of HRF causes an IL-6 increase, which boosts T and B cell responses to resolve infection and leave a cross-stage, cross-species, and lasting immunity. Mutant-induced protection involves a combination of antiparasite IgG2c antibodies and Fc?R(+) CD11b(+) cell phagocytes, especially neutrophils, which are sufficient to confer protection. This immune-boosting GAP highlights an important role of opsonized parasite-mediated phagocytosis, which may be central to protection induced by all self-resolving blood stage GAP infections.
Project description:The hemodynamic response function (HRF) describes the local response of brain vasculature to functional activation. Accurate HRF modeling enables the investigation of cerebral blood flow regulation and improves our ability to interpret fMRI results. Block designs have been used extensively as fMRI paradigms because detection power is maximized; however, block designs are not optimal for HRF parameter estimation. Here we assessed the utility of block design fMRI data for HRF modeling. The trueness (relative deviation), precision (relative uncertainty), and identifiability (goodness-of-fit) of different HRF models were examined and test-retest reproducibility of HRF parameter estimates was assessed using computer simulations and fMRI data from 82 healthy young adult twins acquired on two occasions 3 to 4 months apart. The effects of systematically varying attributes of the block design paradigm were also examined. In our comparison of five HRF models, the model comprising the sum of two gamma functions with six free parameters had greatest parameter accuracy and identifiability. Hemodynamic response function height and time to peak were highly reproducible between studies and width was moderately reproducible but the reproducibility of onset time was low. This study established the feasibility and test-retest reliability of estimating HRF parameters using data from block design fMRI studies.
Project description:While most subunit malaria vaccines provide only limited efficacy, pre-erythrocytic and erythrocytic genetically attenuated parasites (GAP) have been shown to confer complete sterilizing immunity. We recently generated a Plasmodium berghei (PbNK65) parasite that lacks a secreted factor, the histamine releasing factor (HRF) (PbNK65 hrf?), and induces in infected mice a self-resolving blood stage infection accompanied by a long lasting immunity. Here, we explore the immunological mechanisms underlying the anti-parasite protective properties of the mutant PbNK65 hrf? and demonstrate that in addition to an up-regulation of IL-6 production, CD4<sup>+</sup> but not CD8<sup>+</sup> T effector lymphocytes are indispensable for the clearance of malaria infection. Maintenance of T cell-associated protection is associated with the reduction in CD4<sup>+</sup>PD-1<sup>+</sup> and CD8<sup>+</sup>PD-1<sup>+</sup> T cell numbers. A higher number of central and effector memory B cells in mutant-infected mice also plays a pivotal role in protection. Importantly, we also demonstrate that prior infection with WT parasites followed by a drug cure does not prevent the induction of PbNK65 hrf?-induced protection, suggesting that such protection in humans may be efficient even in individuals that have been infected and who repeatedly received antimalarial drugs.
Project description:Analysis of dynamical interactions between distributed brain areas is of fundamental importance for understanding cognitive information processing. However, estimating dynamic causal interactions between brain regions using functional magnetic resonance imaging (fMRI) poses several unique challenges. For one, fMRI measures Blood Oxygenation Level Dependent (BOLD) signals, rather than the underlying latent neuronal activity. Second, regional variations in the hemodynamic response function (HRF) can significantly influence estimation of causal interactions between them. Third, causal interactions between brain regions can change with experimental context over time. To overcome these problems, we developed a novel state-space Multivariate Dynamical Systems (MDS) model to estimate intrinsic and experimentally-induced modulatory causal interactions between multiple brain regions. A probabilistic graphical framework is then used to estimate the parameters of MDS as applied to fMRI data. We show that MDS accurately takes into account regional variations in the HRF and estimates dynamic causal interactions at the level of latent signals. We develop and compare two estimation procedures using maximum likelihood estimation (MLE) and variational Bayesian (VB) approaches for inferring model parameters. Using extensive computer simulations, we demonstrate that, compared to Granger causal analysis (GCA), MDS exhibits superior performance for a wide range of signal to noise ratios (SNRs), sample length and network size. Our simulations also suggest that GCA fails to uncover causal interactions when there is a conflict between the direction of intrinsic and modulatory influences. Furthermore, we show that MDS estimation using VB methods is more robust and performs significantly better at low SNRs and shorter time series than MDS with MLE. Our study suggests that VB estimation of MDS provides a robust method for estimating and interpreting causal network interactions in fMRI data.
Project description:Understanding non-stationary neuronal activity as seen in vivo requires estimation of both excitatory and inhibitory synaptic conductances from a single trial of recording. For this purpose, we propose a new intracellular recording method, called "firing clamp." Synaptic conductances are estimated from the characteristics of artificially evoked probe spikes, namely the spike amplitude and the mean subthreshold potential, which are sensitive to both excitatory and inhibitory synaptic input signals. The probe spikes, timed at a fixed rate, are evoked in the dynamic-clamp mode by injected meander-like current steps, with the step duration depending on neuronal membrane voltage. We test the method with perforated-patch recordings from isolated cells stimulated by external application or synaptic release of transmitter, and validate the method with simulations of a biophysically-detailed neuron model. The results are compared with the conductance estimates based on conventional current-clamp recordings.
Project description:It has previously been demonstrated that there is a negative correlation between the amplitude of the BOLD response and resting ? amino-butyric acid (GABA) concentration in visual cortex. The work here is the first to empirically characterize individual variability in the haemodynamic response functions (HRFs) in response to a simple visual stimulus and baseline GABA concentration in a population of young adult males (n = 15, aged 20-28 years). The results demonstrate that GABA concentration is negatively correlated with BOLD response amplitude (r = -0.64, P < 0.02) and positively correlated with HRF width (r = 0.67, P < 0.002), that is, individuals with higher resting GABA concentration tend to exhibit smaller and wider HRFs. No correlations were observed with resting cerebral blood flow and GABA concentration and similarly, no correlations were observed between GABA and the proportional tissue content of the MRS voxel. We argue that correlation of the height of the HRF is supportive of the view that the previously observed correlations between BOLD amplitudes and GABA are reflective of differences in neuronal activity. However, the changes in HRF shape in individuals with higher baseline GABA levels are suggestive that differing vascular response characteristics may also make a significant contribution. Our results reinforce the view that variability in endogenous factors, such as neurotransmitter concentration, can have a profound effect on the vascular haemodynamic response. This has important implications for between-cohort fMRI studies in which variation in parameters such as GABA concentration may lead to group differences in the BOLD signal.
Project description:<h4>Introduction</h4>Seizures occur rarely during EEG-fMRI acquisitions of epilepsy patients, but can potentially offer a better estimation of the epileptogenic zone than interictal activity. Independent component analysis (ICA) is a data-driven method that imposes minimal constraints on the hemodynamic response function (HRF). In particular, the investigation of HRFs with clear peaks, but varying latency, may be used to differentiate the ictal focus from propagated activity.<h4>Methods</h4>ICA was applied on ictal EEG-fMRI data from 15 patients. Components related to seizures were identified by fitting an HRF to the component time courses at the time of the ictal EEG events. HRFs with a clear peak were used to derive maps of significant BOLD responses and their associated peak delay. The results were then compared with those obtained from a general linear model (GLM) method. Concordance with the presumed epileptogenic focus was also assessed.<h4>Results</h4>The ICA maps were significantly correlated with the GLM maps for each patient (Spearman's test, p<0.05). The ictal BOLD responses identified by ICA always included the presumed epileptogenic zone, but were also more widespread, accounting for 20.3% of the brain volume on average. The method provided a classification of the components as a function of peak delay. BOLD response clusters associated with early HRF peaks were concordant with the suspected epileptogenic focus, while subsequent HRF peaks may correspond to ictal propagation.<h4>Conclusion</h4>ICA applied to EEG-fMRI can detect areas of significant BOLD response to ictal events without having to predefine an HRF. By estimating the HRF peak time in each identified region, the method could also potentially provide a dynamic analysis of ictal BOLD responses, distinguishing onset from propagated activity.
Project description:Biophysically detailed models of single cells are difficult to fit to real data. Recent advances in imaging techniques allow simultaneous access to various intracellular variables, and these data can be used to significantly facilitate the modelling task. These data, however, are noisy, and current approaches to building biophysically detailed models are not designed to deal with this. We extend previous techniques to take the noisy nature of the measurements into account. Sequential Monte Carlo ("particle filtering") methods, in combination with a detailed biophysical description of a cell, are used for principled, model-based smoothing of noisy recording data. We also provide an alternative formulation of smoothing where the neural nonlinearities are estimated in a non-parametric manner. Biophysically important parameters of detailed models (such as channel densities, intercompartmental conductances, input resistances, and observation noise) are inferred automatically from noisy data via expectation-maximization. Overall, we find that model-based smoothing is a powerful, robust technique for smoothing of noisy biophysical data and for inference of biophysical parameters in the face of recording noise.