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There is a discrepancy between the results of imaging studies in which subjects learn motor sequences. Some experiments have shown decreases in the activation of some areas as learning increased, whereas others have reported learning-related increases as learning progressed. We have exploited fMRI to measure changes in blood oxygen leve-dependent (BOLD) signal throughout the course of learning. T2*-weighted echo-planar images were acquired over the whole brain for 40 min while the subjects learned a sequence eight moves long by trial and error. The movements were visually paced every 3.2 s and visual feedback was provided to the subjects. A baseline period followed each activation period. The effect due to the experimental conditions was modeled using a square-wave function, time locked to their occurrence. Changes over time in the difference between activation and baseline signal were modeled using a set of polynomial basis functions. This allowed us to take into account linear as well as nonlinear changes over time. Low-frequency changes over time common to both activation and baseline conditions (and thus not learning related) were modeled and removed. Linear and nonlinear changes of BOLD signal over time were found in prefrontal, premotor, and parietal cortex and in neostriatal and cerebellar areas. Single-unit recordings in nonhuman primates during the learning of motor tasks have clearly shown increased activity early in learning, followed by a decrease as learning progressed. Both phenomena can be observed at the population level in the present study.

Original publication

DOI

10.1006/nimg.1998.0349

Type

Journal article

Journal

Neuroimage

Publication Date

07/1998

Volume

8

Pages

50 - 61

Keywords

Adult, Arousal, Basal Ganglia, Brain, Brain Mapping, Cerebellum, Dominance, Cerebral, Female, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Middle Aged, Motor Cortex, Motor Skills, Oxygen Consumption, Parietal Lobe, Prefrontal Cortex, Psychomotor Performance, Reaction Time, Serial Learning