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.