Fast dynamics of brain regional networks underlying cognition

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Cognitive processes are implemented in the brain by regional networks that perform a variety of computations on incoming (sensory) and stored (memory) information based on intricate connectivity derived from both genetic programming and environmentally-directed plasticity (learning). It is clear that during each cognitive “operation” information and control signals must be passed among these brain regions, e.g., the feedforward sweep of new sensory information, error feedback, attentional signals turning up or down readiness for incoming stimuli, etc. Some of the regional networks are becoming fairly well understood, at least in terms of the sort of computations they might be doing. We have begun to characterize the fast dynamics of these networks using advanced techniques, for example the reading network and the attention orienting network. By “fast dynamics” I mean the transfer of information and control signals, at various oscillatory frequencies, between the regions involved in the network on a time scale of tens to hundreds of ms. I will expand this work in several important directions. First, I intend to investigate the effects of disruptions of various sorts on the fast dynamics both of the networks we have looked at in the past, e.g. reading, attention, perceptual consciousness, and of some that are new to us, e.g., working memory, visual and auditory imagery. The types of disruptions include natural ones, such as dyslexia, habitual cannabis use, dying, and transcranial stimulation, both cathodal direct current stimulation (tDCS) and noise (tRNS). Second I intend to investigate how brain stimulation might affect the fast dynamics in these networks, both normal and challenged. In the case of normal networks there might be improved temporal precision as well as improved performance, and for challenged networks there might be partial restoration of normal temporal dynamics. Third, I will begin to characterize the fast dynamics of cortico-subcortical circuits. I will employ techniques that directly involve particular subcortical circuits, such as amygdala and pulvinar and MD thalamus and that might have reliable cortical mirrors (e.g., prefrontal for amygdala, PPC for pulvinar). I will do co-registered fMRI-EEG studies, using fMRI to verify the subcortical involvement and to map out the functional and effective connectivity so as to identify cortical mirror regions, and EEG to study the fast dynamics of this network. Finally, I will continue theoretical studies of computational modeling of oscillatory brain networks with the goal of characterizing the interactions between oscillating brain regions that support cognition. I expect this work will not only be fruitful for a fundamental understanding of how brain-regional neural networks implement cognitive processes, but also will eventually find application in ameliorating brain challenges such as dyslexia. aging and drug use.