Project I:
Group Causality
Prof. Jianfeng Feng, Dept. of Computer Science (http://www.dcs.warwick.ac.uk/~feng)
The analysis of the interrelationships among multiple simultaneously recorded time series is
always an important problem in a variety of fields such as economics, engineering, the physical
and the life sciences. Of particular interests are the dynamics relationships over time among the
series, which helps unravel the causally driving mechanisms of the underlying complex system.
1. In neuroscience, for instance, signal reflecting neural activity such as EEG or local field
potential recordings have been used to learn patterns of interactions between brain areas that
are activated during certain tasks and to improve thus our understanding of neural processing
of information.
2. In molecular biology, for another example, we can simultaneously record ten thousands gene
or protein activity over a time period. To single out the key or a small interacting circuit
which is responsible for some crucial physiological functions such as normal vs. cancer is of
primary interests in contemporary biology and applied mathematics, the so-called reverse
engineering approach.
Possibly the most commonly used approach for describing and inferring dynamic or causal
relationships in multivariate time series is based on vector autoregressive models and the concept
of Granger causality. This probabilistic concept of causality is based on the common sense
perception that causes always precede their effects in time: if one time series causes another series,
knowledge of the former series should help to predict future values of the latter series after
influences of other variables have been taken into account. Since the concept does not rely on an a
priori specification of a causal model, it is particularly suited for empirical investigations of
cause-effect relationships; being basically a measure of association.
In many cases, we intend to assess the causality between two or multi-group of measured variables.
This could be interesting in many cases of neurophysiological data, brain images and microarray
data (see Fig. 1).
Causality
between
groups
Fig. 1.
between
variables
Interactions
groups of
We have first-hand data of neurophysiology, brain image and gene available and you could test
your results on them.
[1] Uncovering interactions in frequency domains, PLOS Comp. Biol. (submitted).