Inferring Cortical Feed-Forward and Feedback Processes with Human Neuroimaging

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DESCRIPTION (provided by applicant): A central question related to information flow in the human brain is: how do bottom-up and top-down influences interact in the cerebral cortex? A hypothesis is proposed that could form the basis for a novel way to use non-invasive neuroimaging to address this question. Specifically, the direction of the current dipoles detected by magneto- and electroencephalography (MEG, EEG) is expected to be a function of whether the measured activation is a result of top-down (feedback) or bottom-up (feed-forward) flow in the cortex. This hypothesis is founded on the principle that feed-forward and feedback connections into a cortical area have characteristic laminar pattern of synaptic inputs. Consequently, knowing which of the cortical layers received a certain input could be highly informative about the source of this input, and more generally about inter- cortical communication. Such insights about the laminar structure are beyond the resolution of human neuroimaging. However, the proposal that the different types of laminar inputs might result in macroscopic dipoles with different polarities has the potential to provide a powerful non-invasive solution. The overall hypothesis is evaluated using three different approaches: biophysically realistic computational modeling (Aim 1), comparison of somatosensory MEG/EEG responses with intracranial primate recordings (Aim 2), and evaluation of experimental predictions about the polarity of MEG/EEG sources derived from a cognitive neuroscience theory of visual object perception (Aim 3). This research is anticipated to enable non-invasive inference of information flow in networks of cortical areas. It will provide a novel way to apply MEG/EEG recordings to studies of cognitive processing and interpret MEG/EEG in the context of large-scale integrative theories of the brain. It could lead to a better understanding of the neural mechanisms underlying cognitive functions, as well as to potential applications for revealing mechanisms of neural disorders.