6,337 research outputs found
Detecting non-causal artifacts in multivariate linear regression models
We consider linear models where potential causes are
correlated with one target quantity and propose a method to infer whether
the association is causal or whether it is an artifact caused by overfitting or
hidden common causes. We employ the idea that in the former case the vector of
regression coefficients has 'generic' orientation relative to the covariance
matrix of . Using an ICA based model for confounding, we show
that both confounding and overfitting yield regression vectors that concentrate
mainly in the space of low eigenvalues of .Comment: 7 figures, late
We Are Not Your Real Parents: Telling Causal from Confounded using MDL
Given data over variables we consider the problem of finding out whether jointly causes or whether they are all confounded by an unobserved latent variable . To do so, we take an information-theoretic approach based on Kolmogorov complexity. In a nutshell, we follow the postulate that first encoding the true cause, and then the effects given that cause, results in a shorter description than any other encoding of the observed variables. The ideal score is not computable, and hence we have to approximate it. We propose to do so using the Minimum Description Length (MDL) principle. We compare the MDL scores under the models where causes and where there exists a latent variables confounding both and and show our scores are consistent. To find potential confounders we propose using latent factor modeling, in particular, probabilistic PCA (PPCA). Empirical evaluation on both synthetic and real-world data shows that our method, CoCa, performs very well -- even when the true generating process of the data is far from the assumptions made by the models we use. Moreover, it is robust as its accuracy goes hand in hand with its confidence
Measuring autonomy and emergence via Granger causality
Concepts of emergence and autonomy are central to artificial life and related cognitive and behavioral sciences. However, quantitative and easy-to-apply measures of these phenomena are mostly lacking. Here, I describe quantitative and practicable measures for both autonomy and emergence, based on the framework of multivariate autoregression and specifically Granger causality. G-autonomy measures the extent to which the knowing the past of a variable helps predict its future, as compared to predictions based on past states of external (environmental) variables. G-emergence measures the extent to which a process is both dependent upon and autonomous from its underlying causal factors. These measures are validated by application to agent-based models of predation (for autonomy) and flocking (for emergence). In the former, evolutionary adaptation enhances autonomy; the latter model illustrates not only emergence but also downward causation. I end with a discussion of relations among autonomy, emergence, and consciousness
Connectivity Analysis in EEG Data: A Tutorial Review of the State of the Art and Emerging Trends
Understanding how different areas of the human brain communicate with each other is a crucial issue in neuroscience. The concepts of structural, functional and effective connectivity have been widely exploited to describe the human connectome, consisting of brain networks, their structural connections and functional interactions. Despite high-spatial-resolution imaging techniques such as functional magnetic resonance imaging (fMRI) being widely used to map this complex network of multiple interactions, electroencephalographic (EEG) recordings claim high temporal resolution and are thus perfectly suitable to describe either spatially distributed and temporally dynamic patterns of neural activation and connectivity. In this work, we provide a technical account and a categorization of the most-used data-driven approaches to assess brain-functional connectivity, intended as the study of the statistical dependencies between the recorded EEG signals. Different pairwise and multivariate, as well as directed and non-directed connectivity metrics are discussed with a pros-cons approach, in the time, frequency, and information-theoretic domains. The establishment of conceptual and mathematical relationships between metrics from these three frameworks, and the discussion of novel methodological approaches, will allow the reader to go deep into the problem of inferring functional connectivity in complex networks. Furthermore, emerging trends for the description of extended forms of connectivity (e.g., high-order interactions) are also discussed, along with graph-theory tools exploring the topological properties of the network of connections provided by the proposed metrics. Applications to EEG data are reviewed. In addition, the importance of source localization, and the impacts of signal acquisition and pre-processing techniques (e.g., filtering, source localization, and artifact rejection) on the connectivity estimates are recognized and discussed. By going through this review, the reader could delve deeply into the entire process of EEG pre-processing and analysis for the study of brain functional connectivity and learning, thereby exploiting novel methodologies and approaches to the problem of inferring connectivity within complex networks
Advancing functional connectivity research from association to causation
Cognition and behavior emerge from brain network interactions, such that investigating causal interactions should be central to the study of brain function. Approaches that characterize statistical associations among neural time series-functional connectivity (FC) methods-are likely a good starting point for estimating brain network interactions. Yet only a subset of FC methods ('effective connectivity') is explicitly designed to infer causal interactions from statistical associations. Here we incorporate best practices from diverse areas of FC research to illustrate how FC methods can be refined to improve inferences about neural mechanisms, with properties of causal neural interactions as a common ontology to facilitate cumulative progress across FC approaches. We further demonstrate how the most common FC measures (correlation and coherence) reduce the set of likely causal models, facilitating causal inferences despite major limitations. Alternative FC measures are suggested to immediately start improving causal inferences beyond these common FC measures
A blind deconvolution approach to recover effective connectivity brain networks from resting state fMRI data
A great improvement to the insight on brain function that we can get from
fMRI data can come from effective connectivity analysis, in which the flow of
information between even remote brain regions is inferred by the parameters of
a predictive dynamical model. As opposed to biologically inspired models, some
techniques as Granger causality (GC) are purely data-driven and rely on
statistical prediction and temporal precedence. While powerful and widely
applicable, this approach could suffer from two main limitations when applied
to BOLD fMRI data: confounding effect of hemodynamic response function (HRF)
and conditioning to a large number of variables in presence of short time
series. For task-related fMRI, neural population dynamics can be captured by
modeling signal dynamics with explicit exogenous inputs; for resting-state fMRI
on the other hand, the absence of explicit inputs makes this task more
difficult, unless relying on some specific prior physiological hypothesis. In
order to overcome these issues and to allow a more general approach, here we
present a simple and novel blind-deconvolution technique for BOLD-fMRI signal.
Coming to the second limitation, a fully multivariate conditioning with short
and noisy data leads to computational problems due to overfitting. Furthermore,
conceptual issues arise in presence of redundancy. We thus apply partial
conditioning to a limited subset of variables in the framework of information
theory, as recently proposed. Mixing these two improvements we compare the
differences between BOLD and deconvolved BOLD level effective networks and draw
some conclusions
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