Interpretation of mammalian brain rhythms of sensorimotor processing
- Author(s): Whitmer, Diane J.;
- et al.
A fundamental goal of neuroscience is to relate neural signals with external sensory stimuli and with complex behaviors such as movement. In many systems and brain regions, brain oscillations correlate with movement. The body of work presented here examines the role of oscillations in both sensory representation and motor output, spanning multiple scales of measurement from local field potential recordings to the large-scale electrical activity of the whole human brain. The vibrissa system of rats is an active sensory motor system where the whiskers are actively moved to explore the environment. The work described in Chapter II uses a behavioral paradigm to test coding strategies within the rat vibrissa system. We ask whether rats can discriminate the position of objects in the plane within which the whiskers move and whether discrimination can be accomplished with a single vibrissa. We report that rats can locate the position of objects in space relative to its body position with a single whisker, suggesting a neural code based on timing of the whisking cycle. Chapter III examines a salient, widespread oscillation associated with movements in rats (the theta rhythm), to determine whether this signal might drive whisking behavior. We find that hippocampal theta and the whisking rhythm are not coherent although they are oscillatory signals within the same spectral band. In humans, invasive brain measurements are possible in the cases of focal refractory epilepsy patients who are undergoing neurosurgical evaluation. Chapter IV uses intracranial measurements from epilepsy patients who performed a visually-cued finger movement task, to understand the electrical signals that enable a complex sensory motor action. We analyze signals spectrally, examining oscillations with a linear systems approach, specifically using independent component analysis (ICA) to interpret the signals. We find that ICA can separate pathological brain signals from motor signals and decompose intracranial signals into its underlying sources. Together, these results demonstrate that oscillations of peripheral sensors can encode the representation of spatial information, that neural rhythms in overlapping frequency bands are not necessarily entrained, and that ICA is a useful tool for ummixing motor and other signals in the human brain