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The arrow-of-time in neuroimaging time series identifies causal triggers of brain function
Related publications (47)
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Objective. Stereo-electroencephalography (SEEG) has recently gained importance in analyzing brain functions. Its high temporal resolution and spatial specificity make it a powerful tool to investigate the strength, direction, and spectral content of brain ...
IOP PUBLISHING LTD2021Separable Effects for Causal Inference in the Presence of Competing Events
In time-to-event settings, the presence of competing events complicates the definition of causal effects. Here we propose the new separable effects to study the causal effect of a treatment on an event of interest. The separable direct effect is the treatm ...
2020Causal inference in continuous time: an example on prostate cancer therapy
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In marginal structural models (MSMs), time is traditionally treated as a discrete parameter. In survival analysis on the other hand, we study processes that develop in continuous time. Therefore, Røysland (2011. A martingale approach to continuous-time mar ...
2020Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variables
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We consider the problem of learning causal models from observational data generated by linear non-Gaussian acyclic causal models with latent variables. Without considering the effect of latent variables, the inferred causal relationships among the observed ...
MICROTOME PUBL2020A causal framework for classical statistical estimands in failure-time settings with competing events
In failure-time settings, a competing event is any event that makes it impossible for the event of interest to occur. For example, cardiovascular disease death is a competing event for prostate cancer death because an individual cannot die of prostate canc ...
2020Dynamics Of Brain Activity Captured By Graph Signal Processing Of Neuroimaging Data To Predict Human Behaviour
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Joint structural and functional modelling of the brain based on multimodal imaging increasingly show potential in elucidating the underpinnings of human cognition. In the graph signal processing (GSP) approach for neuroimaging, brain activity patterns are ...
IEEE2020Exploring dynamic functional connectivity by incorporating prior knowledge of brain structure
The synchronized firing of distant neuronal populations gives rise to a wide array of functional brain networks that underlie human brain function. Given the enormous perception, learning, and cognition potential of the human brain, it is not surprising th ...
EPFL2020Development and application of dynamic functional connectivity methods to elucidate the neural underpinnings of human behaviour with functional magnetic resonance imaging
Human behaviour is exquisitely complex, because it is staggeringly multi-facetted and subtly varies across individuals. Characterising its yet incompletely understood underlying biological mechanisms has profound clinical implications. Here, our goal was t ...
EPFL2020A Graphical Description of Partial Exchangeability
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Partial exchangeability is sufficient for the identification of some causal effects of interest. Here we review the use of common graphical tools and the sufficient component cause model in the context of partial exchangeability. We illustrate the utility ...
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Non-invasive characterization of brain structure has been made possible by the introduction of magnetic resonance imaging (MRI). Graph modeling of structural connectivity has been useful, but is often limited to defining nodes as regions from a brain atlas ...
IEEE2019