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Learning may Spindle Tiny Parts of the Sleeping Brain


For release: Thursday, April 4, 2013

Precise Learning during Sleep

The red spot represents the brain area used by an epilepsy patient while learning to use a brain-computer interface to control a cursor. Colored spots represent changes in spindle activity recorded with electrodes after learning. The greatest changes (green spots) occurred closest to the learning area. Courtesy of Dr. Jeffrey Ojemann’s lab, University of Washington, Seattle

How does the brain remember?  A growing body of evidence suggests that sleep is important, especially a sleep stage called nonrapid eye movement (NREM) sleep.  During NREM, the brain undergoes unique waves of electrical activity called sleep spindles.  Previous studies suggested that spindles represent learning activity.  Currently scientists are debating whether spindles occur synchronously, throughout the entire brain, or locally in the areas involved with something new.

Researchers published results in Proceedings of the National Academy of Sciences supporting the idea that spindles occur locally.  The researchers performed experiments on patients with intractable epilepsy whose brains had been implanted with a grid of electrodes used to measure brain activity in preparation for surgery.  Spindles were measured before and after the patients learned to move a cursor on a computer screen with a device, called a brain-computer interface (BCI).  BCIs directly connect a patient’s brain to a computer with wires and electrodes.  These remarkable devices converted the brain activity generated by the patients’ thoughts into cursor movements.  Spindle activity in every patient increased in small areas surrounding the brain cells that were active when the patients learned to move the cursor with their thoughts.  The results are consistent with the idea that sleep spindles represent precise changes in brain activity associated with learning and memory.

Reference:

Johnson et al., “Sleep spindles are locally modulated by training on a brain-computer interface.” PNAS, November 6, 2012, Vol. 109, pp. 18583-18588. DOI: 10.1073/pnas.1207532109

www.ncbi.nlm.nih.gov/pubmed/23091013

Last Modified April 10, 2013