Aapo Hyvärinen: Brain Imaging at rest: the ultimate neuroscience data set?

Aapo Hyvärinen from Helsinki University gave his plenary talk about the study of brains at rest. There are a lot of studies in neuroscience, but why is it so interesting to study brains at rest? Subject is said to be at rest, when he is not performing a given task or he is not stimulated in any other way by the researcher. First of all, there are not many analyses done yet on this topic, though the measurements are easy to repeat and there are no time-limits. In addition, it is more objective because it is free from the researchers experimental design. A new view point is to try to learn more of the brains internal dynamics with this study. Research shows that some parts of the brain, called the default network, are even more active during rest than during stimulation. Maybe this way it is possible find out the ultimate neuroscience data set.

The measurement for studying brains are traditionally done with Electroencephalography (EEG), Magnetoencephalography (MEG) or Functional magnetic resonance imaging (fMRI). Supervised methods can not be used to analyze this data and the most popular unsupervised method is independent component analyses (ICA). It is used to find components by maximizing sparsity of a given variable. Hyvärinen also presented a spatial version of ICA, that is often used with fMRI, and how it could be used in MEG. ICA has been used to find resting state networks in fMRI with good results. The results were very similar to ones acquired from research with very complex stimulation: movies.

Hyvärinen highlights the importance of testing significance of the results. ICA itself does not provide information of its result reliability, but there are ways to test it statistically: do a separate ICA on several subjects and pick the significant components which appear in two or more subjects. It is possible that all significant components won't appear in all subjects. Part of the analysis is seeking connections between the measured variables. Hyvärinen explained different approaches used, for example, structural equation models and when those can be estimated.

Exploratory data analysis with ICA could bring us a better understanding of functioning of brains. Hyvärinen proposes that these methods could be more used in studies, where complicated stimulations are used. He admits that speaking of the ultimate data set here is an overstatement, and until we can properly do two person neuroscience, we can not fully understand the human brains, like Riitta Hari said in her plenary talk.

Riitta Hari: Towards Two-Person Neuroscience

Prof. Riitta Hari kicked off ICANN'11 with her invited talk "Towards Two-Person Neuroscience". So far the research on human brain has mostly focused on the study of a single brain. Humans, however, are social creatures, whose thoughts and actions are reflected by the other members in the community. In virtually any human culture, isolation is used as a punishment, not only for children but also for adults.


We all know that the interaction with other people affects our mood and thoughts very strongly. While an individual is interacting with another person, the brains of the two persons become coupled as one's brain analyzes the behavior of the other and vice versa. This is why the neuroscience community is now looking towards a pair instead of an individual as a proper unit of analysis.



There have already been studies on humans under controlled interaction, such as a movie or a computer game. While watching a movie, brains of individual viewers have been shown to be activated in a very synchronous fashion. Game against a human opponent activates the brain differently from a game against computer, which is also reflected in the reported feelings of the players.

Mirroring is a phenomenon which has been possible to study with existing technology. We feel pain when we are shown a picture of a suffering person. Already Ludwig Wittgenstein noted that "The human body is the best picture of the human soul". How individual's feelings tune into other person's feelings, is a more complicated question. It is a combination of the following factors:

• similar senses, motor systems and the brain that the individuals have


• the experience that they collect throughout their lives, and


• the beliefs they test by acting in the community.

Machine learning steps in for the analysis of the high-dimensional data produced by the functional measurement technologies. Dimensionality reduction methods such as independent component analysis (ICA) extract noise-free components that can potentially be biologically interpreted.

So far in most of the studies of human interaction, only the activity of one brain has been measured regardless of the presence of the other interacting person. Soon, however, accurate measurements of several subjects at a time will be possible, and that will most likely push for a leap in the development of computational data fusion techniques. Then, we will not only have a link between a stimulus and a brain image but between a stimulus and images of several subjects' brains.

When the focus of brain research moves towards the analysis of two or more interacting subjects, efficient multi-view methods will be needed. Thus, multi-view learning is currently a hot area of machine learning research.

Prof. Hari's message to the ICANN audience was that the analysis remains the bottleneck in brain research. As methodological researchers, we should next consider the opportunities opened by the new experiment settings and measurement technologies, and see how to learn more from the data.