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It is commonly accepted, that the resting-state activity of the human brain - as captured by electroencephalography (EEG) - can be segmented into a sequence of just a few discrete states, called microstates, each of them being defined by a unique spatial pattern of scalp potentials. Recently, it has also come to light, that functional connectivity (FC) networks within the brain are dynamically changing, even in resting-state recordings, contradicting the assumption of temporal stationarity applied by most FC-based studies. In this paper, it is theorized, that functional connectivity networks, specifically the dynamic functional connectivity (dFC) time-series can be represented by a sequence of discrete connectivity states, at least to the same extent, as raw EEG recordings can be described using a sequence of microstates. If this assumption is proven to be true, it can enable a new class of functional brain network descriptors, that complement existing approaches, which aim to better understand and predict the development of psychiatric and neurodegenerative disorders in a clinical setting. Notably, these conditions include Alzheimer’s Disease (AD) and schizophrenia, which are already associated with altered brain network activity and microstate characteristics, respectively.
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