Dynamic causal modeling applied to fMRI data shows high reliability

Sensitivity, specificity, and reproducibility are vital to interpret neuroscientific results from functional magnetic resonance imaging (fMRI) experiments. Here we examine the scan–rescan reliability of the percent signal change (PSC) and parameters estimated using Dynamic Causal Modeling (DCM) in scans taken in the same scan session, less than 5 min apart. We find fair to good reliability of PSC in regions that are involved with the task, and fair to excellent reliability with DCM. Also, the DCM analysis uncovers group differences that were not present in the analysis of PSC, which implies that DCM may be more sensitive to the nuances of signal changes in fMRI data.

Inverse problems in dynamic cognitive modeling

Inverse problems for dynamical system models of cognitive processes comprise the determination of synaptic weight matrices or kernel functions for neural networks or neural/dynamic field models, respectively. We introduce dynamic cognitive modeling as a three tier top-down approach where cognitive processes are first described as algorithms that operate on complex symbolic data structures. Second, symbolic expressions and operations are represented by states and transformations in abstract vector spaces. Third, prescribed trajectories through representation space are implemented in neurodynamical systems. We discuss the Amari equation for a neural/dynamic field theory as a special case and show that the kernel construction problem is particularly ill-posed. We suggest a Tikhonov-Hebbian learning method as regularization technique and demonstrate its validity and robustness for basic examples of cognitive computations.

A dynamic causal model of the coupling between pulse stimulation and neural activity

We present a dynamic causal model that can explain context-dependent changes in neural responses, in the rat barrel cortex, to an electrical whisker stimulation at different frequencies. Neural responses were measured in terms of local field potentials. These were converted into current source density (CSD) data, and the time series of the CSD sink was extracted to provide a time series response train. The model structure consists of three layers (approximating the responses from the brain stem to the thalamus and then the barrel cortex), and the latter two layers contain nonlinearly coupled modules of linear second-order dynamic systems. The interaction of these modules forms a nonlinear regulatory system that determines the temporal structure of the neural response amplitude for the thalamic and cortical layers. The model is based on the measured population dynamics of neurons rather than the dynamics of a single neuron and was evaluated against CSD data from experiments with varying stimulation frequency (1–40 Hz), random pulse trains, and awake and anesthetized animals. The model parameters obtained by optimization for different physiological conditions (anesthetized or awake) were significantly different. Following Friston, Mechelli, Turner, and Price (2000), this work is part of a formal mathematical system currently being developed (Zheng et al., 2005) that links stimulation to the blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) signal through neural activity and hemodynamic variables. The importance of the model described here is that it can be used to invert the hemodynamic measurements of changes in blood flow to estimate the underlying neural activity.

Dynamic hydrological modeling in drylands with TRMM based rainfall

This paper introduces and evaluates DryMOD, a dynamic water balance model of the key hydrological process in drylands that is based on free, public-domain datasets. The rainfall model of DryMOD makes optimal use of spatially disaggregated Tropical Rainfall Measuring Mission (TRMM) datasets to simulate hourly rainfall intensities at a spatial resolution of 1-km. Regional-scale applications of the model in seasonal catchments in Tunisia and Senegal characterize runoff and soil moisture distribution and dynamics in response to varying rainfall data inputs and soil properties. The results highlight the need for hourly-based rainfall simulation and for correcting TRMM 3B42 rainfall intensities for the fractional cover of rainfall (FCR). Without FCR correction and disaggregation to 1 km, TRMM 3B42 based rainfall intensities are too low to generate surface runoff and to induce substantial changes to soil moisture storage. The outcomes from the sensitivity analysis show that topsoil porosity is the most important soil property for simulation of runoff and soil moisture. Thus, we demonstrate the benefit of hydrological investigations at a scale, for which reliable information on soil profile characteristics exists and which is sufficiently fine to account for the heterogeneities of these. Where such information is available, application of DryMOD can assist in the spatial and temporal planning of water harvesting according to runoff-generating areas and the runoff ratio, as well as in the optimization of agricultural activities based on realistic representation of soil moisture conditions.

Prefrontal inhibition of threat processing reduces working memory interference

Bottom-up processes can interrupt ongoing cognitive processing in order to adaptively respond to emotional stimuli of high potential significance, such as those that threaten wellbeing. However it is vital that this interference can be modulated in certain contexts to focus on current tasks. Deficits in the ability to maintain the appropriate balance between cognitive and emotional demands can severely impact on day-to-day activities. This fMRI study examined this interaction between threat processing and cognition; 18 adult participants performed a visuospatial working memory (WM) task with two load conditions, in the presence and absence of anxiety induction by threat of electric shock. Threat of shock interfered with performance in the low cognitive load condition; however interference was eradicated under high load, consistent with engagement of emotion regulation mechanisms. Under low load the amygdala showed significant activation to threat of shock that was modulated by high cognitive load. A directed top-down control contrast identified two regions associated with top-down control; ventrolateral PFC and dorsal ACC. Dynamic causal modeling provided further evidence that under high cognitive load, top-down inhibition is exerted on the amygdala and its outputs to prefrontal regions. Additionally, we hypothesized that individual differences in a separate, non-emotional top-down control task would predict the recruitment of dorsal ACC and ventrolateral PFC during top-down control of threat. Consistent with this, performance on a separate dichotic listening task predicted dorsal ACC and ventrolateral PFC activation during high WM load under threat of shock, though activation in these regions did not directly correlate with WM performance. Together, the findings suggest that under high cognitive load and threat, top-down control is exerted by dACC and vlPFC to inhibit threat processing, thus enabling WM performance without threat-related interference.

Modeling the Stream Water Nitrate Dynamics in 60,000-km(2) European Catchment, the Garonne, Southwest France

The spatial and temporal dynamics in the stream water NO3-N concentrations in a major European river-system, the Garonne (62,700 km(2)), are described and related to variations in climate, land management, and effluent point-sources using multivariate statistics. Building on this, the Hydrologiska Byrans Vattenbalansavdelning (HBV) rainfall-runoff model and the Integrated Catchment Model of Nitrogen (INCA-N) are applied to simulate the observed flow and N dynamics. This is done to help us to understand which factors and processes control the flow and N dynamics in different climate zones and to assess the relative inputs from diffuse and point sources across the catchment. This is the first application of the linked HBV and INCA-N models to a major European river system commensurate with the largest basins to be managed tinder the Water Framework Directive. The simulations suggest that in the lowlands, seasonal patterns in the stream water NO3-N concentrations emerge and are dominated by diffuse agricultural inputs, with an estimated 75% of the river load in the lowlands derived from arable farming. The results confirm earlier European catchment studies. Namely, current semi-distrubuted catchment-scale dynamic models, which integrate variations in land cover, climate, and a simple representation of the terrestrial and in-stream N cycle, are able to simulate seasonal NO3-N patterns at large spatial (> 300 km(2)) and temporal (>= monthly) scales using available national datasets.

Modeling the Stream Water Nitrate Dynamics in 60,000-km(2) European Catchment, the Garonne, Southwest France

The spatial and temporal dynamics in the stream water NO3-N concentrations in a major European river-system, the Garonne (62,700 km(2)), are described and related to variations in climate, land management, and effluent point-sources using multivariate statistics. Building on this, the Hydrologiska Byrans Vattenbalansavdelning (HBV) rainfall-runoff model and the Integrated Catchment Model of Nitrogen (INCA-N) are applied to simulate the observed flow and N dynamics. This is done to help us to understand which factors and processes control the flow and N dynamics in different climate zones and to assess the relative inputs from diffuse and point sources across the catchment. This is the first application of the linked HBV and INCA-N models to a major European river system commensurate with the largest basins to be managed tinder the Water Framework Directive. The simulations suggest that in the lowlands, seasonal patterns in the stream water NO3-N concentrations emerge and are dominated by diffuse agricultural inputs, with an estimated 75% of the river load in the lowlands derived from arable farming. The results confirm earlier European catchment studies. Namely, current semi-distrubuted catchment-scale dynamic models, which integrate variations in land cover, climate, and a simple representation of the terrestrial and in-stream N cycle, are able to simulate seasonal NO3-N patterns at large spatial (> 300 km(2)) and temporal (>= monthly) scales using available national datasets.

Statistical modeling suggests that antiandrogens in effluents from wastewater treatment works contribute to widespread sexual disruption in fish living in English rivers.

BACKGROUND: The widespread occurrence of feminized male fish downstream of some wastewater treatment works has led to substantial interest from ecologists and public health professionals. This concern stems from the view that the effects observed have a parallel in humans, and that both phenomena are caused by exposure to mixtures of contaminants that interfere with reproductive development. The evidence for a "wildlife-human connection" is, however, weak: Testicular dysgenesis syndrome, seen in human males, is most easily reproduced in rodent models by exposure to mixtures of antiandrogenic chemicals. In contrast, the accepted explanation for feminization of wild male fish is that it results mainly from exposure to steroidal estrogens originating primarily from human excretion. OBJECTIVES: We sought to further explore the hypothesis that endocrine disruption in fish is multi-causal, resulting from exposure to mixtures of chemicals with both estrogenic and antiandrogenic properties. METHODS: We used hierarchical generalized linear and generalized additive statistical modeling to explore the associations between modeled concentrations and activities of estrogenic and antiandrogenic chemicals in 30 U.K. rivers and feminized responses seen in wild fish living in these rivers. RESULTS: In addition to the estrogenic substances, antiandrogenic activity was prevalent in almost all treated sewage effluents tested. Further, the results of the modeling demonstrated that feminizing effects in wild fish could be best modeled as a function of their predicted exposure to both anti-androgens and estrogens or to antiandrogens alone. CONCLUSION: The results provide a strong argument for a multicausal etiology of widespread feminization of wild fish in U.K. rivers involving contributions from both steroidal estrogens and xeno-estrogens and from other (as yet unknown) contaminants with antiandrogenic properties. These results may add farther credence to the hypothesis that endocrine-disrupting effects seen in wild fish and in humans are caused by similar combinations of endocrine-disrupting chemical cocktails.

Applying semiotic analysis to the design and modeling of distributed multimedia systems

Semiotics is the study of signs. Application of semiotics in information systems design is based on the notion that information systems are organizations within which agents deploy signs in the form of actions according to a set of norms. An analysis of the relationships among the agents, their actions and the norms would give a better specification of the system. Distributed multimedia systems (DMMS) could be viewed as a system consisted of many dynamic, self-controlled normative agents engaging in complex interaction and processing of multimedia information. This paper reports the work of applying the semiotic approach to the design and modeling of DMMS, with emphasis on using semantic analysis under the semiotic framework. A semantic model of DMMS describing various components and their ontological dependencies is presented, which then serves as a design model and implemented in a semantic database. Benefits of using the semantic database are discussed with reference to various design scenarios.

Generalized neurofuzzy network modeling algorithms using Bezier-Bernstein polynomial functions and additive decomposition

This paper introduces a new neurofuzzy model construction algorithm for nonlinear dynamic systems based upon basis functions that are Bezier-Bernstein polynomial functions. This paper is generalized in that it copes with n-dimensional inputs by utilising an additive decomposition construction to overcome the curse of dimensionality associated with high n. This new construction algorithm also introduces univariate Bezier-Bernstein polynomial functions for the completeness of the generalized procedure. Like the B-spline expansion based neurofuzzy systems, Bezier-Bernstein polynomial function based neurofuzzy networks hold desirable properties such as nonnegativity of the basis functions, unity of support, and interpretability of basis function as fuzzy membership functions, moreover with the additional advantages of structural parsimony and Delaunay input space partition, essentially overcoming the curse of dimensionality associated with conventional fuzzy and RBF networks. This new modeling network is based on additive decomposition approach together with two separate basis function formation approaches for both univariate and bivariate Bezier-Bernstein polynomial functions used in model construction. The overall network weights are then learnt using conventional least squares methods. Numerical examples are included to demonstrate the effectiveness of this new data based modeling approach.

Modeling building semantics: providing feedback and sustainability

Even minor changes in user activity can bring about significant energy savings within built space. Many building performance assessment methods have been developed, however these often disregard the impact of user behavior (i.e. the social, cultural and organizational aspects of the building). Building users currently have limited means of determining how sustainable they are, in context of the specific building structure and/or when compared to other users performing similar activities, it is therefore easy for users to dismiss their energy use. To support sustainability, buildings must be able to monitor energy use, identify areas of potential change in the context of user activity and provide contextually relevant information to facilitate persuasion management. If the building is able to provide users with detailed information about how specific user activity that is wasteful, this should provide considerable motivation to implement positive change. This paper proposes using a dynamic and temporal semantic model, to populate information within a model of persuasion, to manage user change. By semantically mapping a building, and linking this to persuasion management we suggest that: i) building energy use can be monitored and analyzed over time; ii) persuasive management can be facilitated to move user activity towards sustainability.

Spatial and temporal modeling of community non-timber forest extraction

This paper examines the interaction of spatial and dynamic aspects of resource extraction from forests by local people. Highly cyclical and varied across space and time, the patterns of resource extraction resulting from the spatial–temporal model bear little resemblance to the patterns drawn from focusing either on spatial or temporal aspects of extraction alone. Ignoring this variability inaccurately depicts villagers’ dependence on different parts of the forest and could result in inappropriate policies. Similarly, the spatial links in extraction decisions imply that policies imposed in one area can have unintended consequences in other areas. Combining the spatial–temporal model with a measure of success in community forest management—the ability to avoid open-access resource degradation—characterizes the impact of incomplete property rights on patterns of resource extraction and stocks.

Model estimation of cerebral hemodynamics between blood flow and volume changes: A data-based modeling approach

It is well known that there is a dynamic relationship between cerebral blood flow (CBF) and cerebral blood volume (CBV). With increasing applications of functional MRI, where the blood oxygen-level-dependent signals are recorded, the understanding and accurate modeling of the hemodynamic relationship between CBF and CBV becomes increasingly important. This study presents an empirical and data-based modeling framework for model identification from CBF and CBV experimental data. It is shown that the relationship between the changes in CBF and CBV can be described using a parsimonious autoregressive with exogenous input model structure. It is observed that neither the ordinary least-squares (LS) method nor the classical total least-squares (TLS) method can produce accurate estimates from the original noisy CBF and CBV data. A regularized total least-squares (RTLS) method is thus introduced and extended to solve such an error-in-the-variables problem. Quantitative results show that the RTLS method works very well on the noisy CBF and CBV data. Finally, a combination of RTLS with a filtering method can lead to a parsimonious but very effective model that can characterize the relationship between the changes in CBF and CBV.

A model of the dynamic relationship between blood flow and volume changes during brain activation

The temporal relationship between changes in cerebral blood flow (CBF) and cerebral blood volume (CBV) is important in the biophysical modeling and interpretation of the hemodynamic response to activation, particularly in the context of magnetic resonance imaging and the blood oxygen level-dependent signal. Grubb et al. (1974) measured the steady state relationship between changes in CBV and CBF after hypercapnic challenge. The relationship CBV proportional to CBFPhi has been used extensively in the literature. Two similar models, the Balloon (Buxton et al., 1998) and the Windkessel (Mandeville et al., 1999), have been proposed to describe the temporal dynamics of changes in CBV with respect to changes in CBF. In this study, a dynamic model extending the Windkessel model by incorporating delayed compliance is presented. The extended model is better able to capture the dynamics of CBV changes after changes in CBF, particularly in the return-to-baseline stages of the response.

An easily implemented agro-hydrological procedure with dynamic root simulation for water transfer in the crop-soil system: validation and application

Models for water transfer in the crop-soil system are key components of agro-hydrological models for irrigation, fertilizer and pesticide practices. Many of the hydrological models for water transfer in the crop-soil system are either too approximate due to oversimplified algorithms or employ complex numerical schemes. In this paper we developed a simple and sufficiently accurate algorithm which can be easily adopted in agro-hydrological models for the simulation of water dynamics. We used a dual crop coefficient approach proposed by the FAO for estimating potential evaporation and transpiration, and a dynamic model for calculating relative root length distribution on a daily basis. In a small time step of 0.001 d, we implemented algorithms separately for actual evaporation, root water uptake and soil water content redistribution by decoupling these processes. The Richards equation describing soil water movement was solved using an integration strategy over the soil layers instead of complex numerical schemes. This drastically simplified the procedures of modeling soil water and led to much shorter computer codes. The validity of the proposed model was tested against data from field experiments on two contrasting soils cropped with wheat. Good agreement was achieved between measurement and simulation of soil water content in various depths collected at intervals during crop growth. This indicates that the model is satisfactory in simulating water transfer in the crop-soil system, and therefore can reliably be adopted in agro-hydrological models. Finally we demonstrated how the developed model could be used to study the effect of changes in the environment such as lowering the groundwater table caused by the construction of a motorway on crop transpiration. (c) 2009 Elsevier B.V. All rights reserved.