PURPOSE: To investigate the fundamental connectivity architecture of neural structures involved in the goal-directed processing of target events. METHODS: Twenty healthy volunteers underwent event-related functional magnetic resonance imaging (fMRI) while performing a standard oddball task. In the task, two types of visual stimuli - rare (target) and frequent - were randomly presented, and subjects were instructed to mentally count the target stimuli. Dynamic causal modeling (DCM), in combination with Bayes factors was used to compare competing neurophysiological models with different intrinsic connectivity structures and input regions within the network of brain regions underlying target stimulus processing. RESULTS: Conventional analysis of fMRI data revealed significantly greater activation in response to the target stimuli (in comparison to the frequent stimuli) in several brain regions, including the intraparietal sulci and supramarginal gyri, the anterior and posterior cingulate gyri, the inferior and middle frontal gyri, the superior temporal sulcus, the precuneus/cuneus, and the subcortical grey matter (caudate and thalamus). The most extensive cortical activations were found in the right intraparietal sulcus (IPS), the anterior cingulate cortex (ACC), and the right lateral prefrontal cortex (PFC). These three regions were entered into the DCM. A comparison on a group level revealed that the dynamic causal models in which the ACC and alternatively the IPS served as input regions were superior to a model in which the PFC was assumed to receive external inputs. No significant difference was observed between the fully connected models with ACC and IPS as input regions. Subsequent analysis of the intrinsic connectivity within two investigated models (IPS and ACC) disclosed significant parallel forward connections from the IPS to the frontal areas and from the ACC to the PFC and the IPS. CONCLUSION: Our findings indicate that during target stimulus processing there is a bidirectional frontoparietal information flow, very likely reflecting parallel activation of two distinct but partially overlapping attentional or attentional/event-encoding neural systems. Additionally, a simple hierarchy within the right frontal lobe is suggested with the ACC exerting influence over the PFC.
Currently, mental disorders are usually conceptualized as a hidden causal factor, manifested by its symptoms. This notion rests upon the reflective latent model, which is implicitly at work every time complex symptomatology gets summarized by a single number or a categorical state. The present paper reflects on the quantitative, testable implications of this psychometric model and shows how its restraints are untenable for most mental disorders. The observed data are instead consistent with mental disorders being complex dynamic systems. Instead of being treated as interchangeable measures of the same latent factor, symptoms likely act as independent causal entities, directly affecting each other. In recent years, this shift in ontological stance toward psychopathology has laid a basis for adapting the network theory. Under this theory, a mental disorder is a relatively stable emergent state, which arises due to a pronounced and recurrent interaction of causally linked symptoms. It is discussed how models embedded within the network theory can help provide insight into the etiopathogenesis of mental disorders and address clinical intervention. In conclusion, limits and future challenges to the network theory are discussed.
Estimating causal interactions in complex dynamical systems is an important problem encountered in many fields of current science. While a theoretical solution for detecting the causal interactions has been previously formulated in the framework of prediction improvement, it generally requires the computation of high-dimensional information functionals-a situation invoking the curse of dimensionality with increasing network size. Recently, several methods have been proposed to alleviate this problem, based on iterative procedures for the assessment of conditional (in)dependences. In the current work, we bring a comparison of several such prominent approaches. This is done both by theoretical comparison of the algorithms using a formulation in a common framework and by numerical simulations including realistic complex coupling patterns. The theoretical analysis highlights the key similarities and differences between the algorithms, hinting on their comparative strengths and weaknesses. The method assumptions and specific properties such as false positive control and order-dependence are discussed. Numerical simulations suggest that while the accuracy of most of the algorithms is almost indistinguishable, there are substantial differences in their computational demands, ranging theoretically from polynomial to exponential complexity and leading to substantial differences in computation time in realistic scenarios depending on the density and size of networks. Based on the analysis of the algorithms and numerical simulations, we propose a hybrid approach providing competitive accuracy with improved computational efficiency.
... and Their Stability 24 -- 1.4 Functional Causal Models 26 -- 1.4.1 Structural Equations 27 -- 1.4.2 ... ... Probabilistic Predictions in Causal Models 30 -- 1.4.3 Interventions and Causal Effects in Functional ... ... Models 32 -- 1.4.4 Counterfactuals in Functional Models 33 -- 1.5 Causal versus Statistical Terminology ... ... and Structural Models in Social Science and Economics 133 -- 5.1 Introduction 134 -- 5.1.1 Causality ... ... in Linear Models 149 -- 5.3.2 Comparison to Nonparametric Identification 154 -- 5.3.3 Causal Effects ...
1st ed. xii, 384 s.
- MeSH
- Causality MeSH
- Probability MeSH
- Conspectus
- Přírodní vědy. Matematické vědy
- NML Fields
- přírodní vědy
- statistika, zdravotnická statistika
Identifying regions important for spreading and mediating perturbations is crucial to assess the susceptibilities of spatio-temporal complex systems such as the Earth's climate to volcanic eruptions, extreme events or geoengineering. Here a data-driven approach is introduced based on a dimension reduction, causal reconstruction, and novel network measures based on causal effect theory that go beyond standard complex network tools by distinguishing direct from indirect pathways. Applied to a data set of atmospheric dynamics, the method identifies several strongly uplifting regions acting as major gateways of perturbations spreading in the atmosphere. Additionally, the method provides a stricter statistical approach to pathways of atmospheric teleconnections, yielding insights into the Pacific-Indian Ocean interaction relevant for monsoonal dynamics. Also for neuroscience or power grids, the novel causal interaction perspective provides a complementary approach to simulations or experiments for understanding the functioning of complex spatio-temporal systems with potential applications in increasing their resilience to shocks or extreme events.
... Contents -- List of Contributors ix -- Part I Introduction 1 -- 1 Why look at causality in the sciences ... ... 3 -- Phyllis McKay Illari, Federica Russo, and Jon Williamson -- Part II Health sciences 23 -- 2 Causality ... ... models not good models? ... ... -- 30 A new causal power theory -- Kevin B. ... ... continuity 845 -- Jim Bogen and Peter Machamer -- 40 The causal-process-model theory of mechanisms 865 ...
xiii, 938 stran : ilustrace ; 24 cm
- MeSH
- Causality MeSH
- Probability MeSH
- Conspectus
- Věda. Všeobecnosti. Základy vědy a kultury. Vědecká práce
- NML Fields
- věda a výzkum
- NML Publication type
- studie
In this comparative study, six causality detection methods were compared, namely, the Granger vector autoregressive test, the extended Granger test, the kernel version of the Granger test, the conditional mutual information (transfer entropy), the evaluation of cross mappings between state spaces, and an assessment of predictability improvement due to the use of mixed predictions. Seven test data sets were analyzed: linear coupling of autoregressive models, a unidirectional connection of two Hénon systems, a unidirectional connection of chaotic systems of Rössler and Lorenz type and of two different Rössler systems, an example of bidirectionally connected two-species systems, a fishery model as an example of two correlated observables without a causal relationship, and an example of mediated causality. We tested not only 20000 points long clean time series but also noisy and short variants of the data. The standard and the extended Granger tests worked only for the autoregressive models. The remaining methods were more successful with the more complex test examples, although they differed considerably in their capability to reveal the presence and the direction of coupling and to distinguish causality from mere correlation.
- MeSH
- Time Factors MeSH
- Causality MeSH
- Systems Analysis MeSH
- Models, Theoretical * MeSH
- Publication type
- Research Support, Non-U.S. Gov't MeSH
- Comparative Study MeSH
This paper presents a new approach to inverting (fitting) models of coupled dynamical systems based on state-of-the-art (cubature) Kalman filtering. Crucially, this inversion furnishes posterior estimates of both the hidden states and parameters of a system, including any unknown exogenous input. Because the underlying generative model is formulated in continuous time (with a discrete observation process) it can be applied to a wide variety of models specified with either ordinary or stochastic differential equations. These are an important class of models that are particularly appropriate for biological time-series, where the underlying system is specified in terms of kinetics or dynamics (i.e., dynamic causal models). We provide comparative evaluations with generalized Bayesian filtering (dynamic expectation maximization) and demonstrate marked improvements in accuracy and computational efficiency. We compare the schemes using a series of difficult (nonlinear) toy examples and conclude with a special focus on hemodynamic models of evoked brain responses in fMRI. Our scheme promises to provide a significant advance in characterizing the functional architectures of distributed neuronal systems, even in the absence of known exogenous (experimental) input; e.g., resting state fMRI studies and spontaneous fluctuations in electrophysiological studies. Importantly, unlike current Bayesian filters (e.g. DEM), our scheme provides estimates of time-varying parameters, which we will exploit in future work on the adaptation and enabling of connections in the brain.
- MeSH
- Algorithms MeSH
- Hemodynamics physiology MeSH
- Image Interpretation, Computer-Assisted methods MeSH
- Humans MeSH
- Magnetic Resonance Imaging MeSH
- Monte Carlo Method MeSH
- Models, Neurological MeSH
- Brain blood supply physiology MeSH
- Neural Pathways physiology MeSH
- Neurons physiology MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
- Research Support, N.I.H., Extramural MeSH
Autoři objasňují obsah pojmu příčinnost, jehož podrobnější rozbor vede k realističtějšímu pohledu na nemoc jako na vychýlenou polohu komplexního adaptivního systému. Jeho podsystémy - genotyp, fenotyp, složky prostředí a životní styl - ve vzájemné interakci utváří komplexní architekturu zdraví-nemoc. Přirozeným zájmem je odhalování činitelů, které přímo zapříčiňují nemoc (kauzální faktory, příčiny), a těch, které alespoň zpřesňují předpověď jejího vzniku (rizikové faktory). Zjišťování skutečné příčiny, které je nutné pro zavedení racionálního léčebně- preventivního postupu, vyžaduje adekvátní vystižení složité reality, včetně popisu vztahu mezi úrovněmi expozice a následku. Tento proces bývá komplikován různými systematickými chybami, které vznikají v nejrůznějších etapách výzkumné práce. Získané poznatky by měly být zvažovány s ohledem na kritéria kauzality, aby byl výrok o příčinném vztahu věrohodnější. Epidemiologická metoda v důsledku pokročilejšího využívání konceptu příčinnosti proniká stále větší měrou do mnohých medicínských oborů, je hlavním nástrojem pro navrhování a ověřování preventivních a léčebných postupů v medicíně obecně. Ve světle přirozeného vývoje nemoci, který postupuje od působení podmiňujících (rizikových) faktorů přes latentní pre/subklinické stadium ke klinické manifestaci onemocnění, je účelné, aby se praktičtí lékaři, na nichž spočívá hlavní díl odpovědnosti za provádění primární a sekundární prevence,mohli seznámit s jedním ze současných pohledů na danou problematiku.
The authors try to explain the term causality, the more detailed analysis of which leads to a more realistic view of the disease, as a deviant position of a comprehensive adaptive system. Its sub-systems - genotype, phenotype, environmental constituents and lifestyle - in mutual interaction create the comprehensive architecture of health and disease. The natural interest is detection of factors which cause disease (causal factors, causes) and those which at least make the prognosis of its development more accurate (risk factors). Assessment of the true cause which is necessary for introducing a rational therapeutic and preventive procedure calls for adequate understanding of the complex reality, incl. description of the relationship between levels of exposure and consequence. This process is usually complicated by different systematic errors which occur in different stages of research. The assembled findings should be discussed with regard to criteria of causality to make the statement of a causal relationship more trustworthy. The epidemiological method penetrates due to the more advanced application of the principle of causality to an increasing extent into many medical disciplines, it is the main tool for suggesting and testing preventive and therapeutic procedures in medicine in general. It is expedient with regard to the natural development of disease which proceeds from the action of conditioning (risk) factors via the latent pre/subclinical stage to the clinical manifestation of the disease that general practitioners who have the main responsibility for primary and secondary prevention can become familiar with one of the present views of the given problem.
Applications of causal techniques to neural time series have increased extensively over last decades, including a wide and diverse family of methods focusing on electroencephalogram (EEG) analysis. Besides connectivity inferred in defined frequency bands, there is a growing interest in the analysis of cross-frequency interactions, in particular phase and amplitude coupling and directionality. Some studies show contradicting results of coupling directionality from high frequency to low frequency signal components, in spite of generally considered modulation of a high-frequency amplitude by a low-frequency phase. We have compared two widely used methods to estimate the directionality in cross frequency coupling: conditional mutual information (CMI) and phase slope index (PSI). The latter, applied to infer cross-frequency phase-amplitude directionality from animal intracranial recordings, gives opposite results when comparing to CMI. Both metrics were tested in a numerically simulated example of unidirectionally coupled Rössler systems, which helped to find the explanation of the contradictory results: PSI correctly estimates the lead/lag relationship which, however, is not generally equivalent to causality in the sense of directionality of coupling in nonlinear systems, correctly inferred by using CMI with surrogate data testing.
- MeSH
- Electroencephalography * methods MeSH
- Humans MeSH
- Models, Neurological MeSH
- Brain physiology MeSH
- Nonlinear Dynamics * MeSH
- Computer Simulation MeSH
- Signal Processing, Computer-Assisted MeSH
- Animals MeSH
- Check Tag
- Humans MeSH
- Animals MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
- Comparative Study MeSH
