93 research outputs found
MERLiN: Mixture Effect Recovery in Linear Networks
Causal inference concerns the identification of cause-effect relationships
between variables, e.g. establishing whether a stimulus affects activity in a
certain brain region. The observed variables themselves often do not constitute
meaningful causal variables, however, and linear combinations need to be
considered. In electroencephalographic studies, for example, one is not
interested in establishing cause-effect relationships between electrode signals
(the observed variables), but rather between cortical signals (the causal
variables) which can be recovered as linear combinations of electrode signals.
We introduce MERLiN (Mixture Effect Recovery in Linear Networks), a family of
causal inference algorithms that implement a novel means of constructing causal
variables from non-causal variables. We demonstrate through application to EEG
data how the basic MERLiN algorithm can be extended for application to
different (neuroimaging) data modalities. Given an observed linear mixture, the
algorithms can recover a causal variable that is a linear effect of another
given variable. That is, MERLiN allows us to recover a cortical signal that is
affected by activity in a certain brain region, while not being a direct effect
of the stimulus. The Python/Matlab implementation for all presented algorithms
is available on https://github.com/sweichwald/MERLi
Beta Power May Mediate the Effect of Gamma-TACS on Motor Performance
Transcranial alternating current stimulation (tACS) is becoming an important
method in the field of motor rehabilitation because of its ability to
non-invasively influence ongoing brain oscillations at arbitrary frequencies.
However, substantial variations in its effect across individuals are reported,
making tACS a currently unreliable treatment tool. One reason for this
variability is the lack of knowledge about the exact way tACS entrains and
interacts with ongoing brain oscillations. The present crossover stimulation
study on 20 healthy subjects contributes to the understanding of
cross-frequency effects of gamma (70 Hz) tACS over the contralateral motor
cortex by providing empirical evidence which is consistent with a role of low-
(12~-20 Hz) and high- (20-~30 Hz) beta power as a mediator of gamma-tACS on
motor performance.Comment: 7 pages, 5 figures, in Proceedings of IEEE Engineering in Medicine
and Biology Conference, July 2019 (IEEE license notice
Quantifying causal influences
Many methods for causal inference generate directed acyclic graphs (DAGs)
that formalize causal relations between variables. Given the joint
distribution on all these variables, the DAG contains all information about how
intervening on one variable changes the distribution of the other
variables. However, quantifying the causal influence of one variable on another
one remains a nontrivial question. Here we propose a set of natural, intuitive
postulates that a measure of causal strength should satisfy. We then introduce
a communication scenario, where edges in a DAG play the role of channels that
can be locally corrupted by interventions. Causal strength is then the relative
entropy distance between the old and the new distribution. Many other measures
of causal strength have been proposed, including average causal effect,
transfer entropy, directed information, and information flow. We explain how
they fail to satisfy the postulates on simple DAGs of nodes. Finally,
we investigate the behavior of our measure on time-series, supporting our
claims with experiments on simulated data.Comment: Published in at http://dx.doi.org/10.1214/13-AOS1145 the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Personalized Brain-Computer Interface Models for Motor Rehabilitation
We propose to fuse two currently separate research lines on novel therapies
for stroke rehabilitation: brain-computer interface (BCI) training and
transcranial electrical stimulation (TES). Specifically, we show that BCI
technology can be used to learn personalized decoding models that relate the
global configuration of brain rhythms in individual subjects (as measured by
EEG) to their motor performance during 3D reaching movements. We demonstrate
that our models capture substantial across-subject heterogeneity, and argue
that this heterogeneity is a likely cause of limited effect sizes observed in
TES for enhancing motor performance. We conclude by discussing how our
personalized models can be used to derive optimal TES parameters, e.g.,
stimulation site and frequency, for individual patients.Comment: 6 pages, 6 figures, conference submissio
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