Abstract
Inferring the interactions between different brain areas is an important step towards understanding brain activity. Most often, signals can only be measured from some specific brain areas (e.g., cortex in the case of scalp electroencephalograms). However, those signals may be affected by brain areas from which no measurements are available (e.g., deeper areas such as hippocampus). In this paper, the latter are described as hidden variables in a graphical model; such model quantifies the statistical structure in the neural recordings, conditioned on hidden variables, which are inferred in an automated fashion from the data.
As an illustration, electroencephalograms (EEG) of Alzheimer’s disease patients are considered. It is shown that the number of hidden variables in AD EEG is not significantly different from healthy EEG. However, there are fewer interactions between the brain areas, conditioned on those hidden variables. Explanations for these observations are suggested.
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Dauwels, J. et al. (2012). Inferring Brain Networks through Graphical Models with Hidden Variables. In: Langs, G., Rish, I., Grosse-Wentrup, M., Murphy, B. (eds) Machine Learning and Interpretation in Neuroimaging. Lecture Notes in Computer Science(), vol 7263. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34713-9_25
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DOI: https://doi.org/10.1007/978-3-642-34713-9_25
Publisher Name: Springer, Berlin, Heidelberg
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