Abstract
Communication across the brain networks is dependent on neuronal oscillations. Detection of the synchronous activation of neurons can be used to determine the well-being of the connectivity in the human brain networks. Well-connected highly synchronous activity can be measured by MEG, EEG, fMRI, and PET and then analyzed with several types of mathematical algorithms. Coherence is one mathematical method that can detect how well 2 or more sensors or brain regions have similar oscillatory activity with each other. Phase synchrony can be used to determine if these oscillatory activities are in sync or out of sync with each other. Correlation is used to determine the strength of interaction between two locations or signals. Granger causality can be used to determine the direction of the information flow in the neuronal brain networks. Statistical analysis can be performed on the connectivity results to verify evidence of normal or abnormal network activity in a patient.
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