title: Reorganization of brain activity for multiple internal models after short but intensive training.
abstract: Internal models are neural mechanisms that can mimic the input-output properties of controlled objects. Our studies have shown that: 1) an internal model for a novel tool is acquired in the cerebellum (Imamizu et al., 2000); 2) internal models are modularly organized in the cerebellum (Imamizu et al., 2003); 3) their outputs are sent to the premotor regions after learning (Tamada et al., 1999); and 4) the prefrontal and parietal regions contribute to the blending of the outputs (Imamizu et al., 2004). Here, we investigated changes in global neural networks resulting from the acquisition of a new internal model. Human subjects manipulated three types of rotating joystick whose cursor appeared at a position rotated 60 degrees, 110 degrees, or 160 degrees around the screen's center. In a pre-test after long-term training (5 days) for the 60 degrees and 160 degrees joysticks, brain activation was scanned during manipulation of the three joysticks. The subjects were then trained for the 110 degrees for only 25 min. In a post-test, activation was scanned using the same method as the pre-test. Comparisons of the post-test to the pre-test revealed that the volume of activation decreased in most of the regions where activation for the three rotations was observed. However, there was an increase in volume at a marginally significant level (p < .08) only in the inferior-lateral cerebellum and only for the 110 degrees joystick. In the cerebral cortex, activation related to 110 degrees decreased in the prefrontal and parietal regions but increased in the premotor and supplementary motor area (SMA) regions. These results can be explained by a model in which outputs of the 60 degrees and 160 degrees internal models are blended by prefrontal and parietal regions to cope with the novel 110 degrees joystick before the 25-minute training; after the acquisition within the cerebellum of an internal model for the 110 degrees, output is directly sent to the premotor and SMA regions, and activation in these regions increases.
authors.affiliation: ATR Computational Neuroscience Laboratories, Kyoto, Japan. imamizu@atr.jp
authors.forename: Hiroshi
authors.initials: H
authors.lastname: Imamizu
authors.forename: Satomi
authors.initials: S
authors.lastname: Higuchi
authors.forename: Akihiro
authors.initials: A
authors.lastname: Toda
authors.forename: Mitsuo
authors.initials: M
authors.lastname: Kawato
mesh_terms: D000328:Adult; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping; D002531:Cerebellum / Q000502:physiology; D005625:Frontal Lobe / Q000502:physiology; D006801:Humans; D008279:Magnetic Resonance Imaging; D008959:Models, Neurological; D009048:Motor Skills / Q000502:physiology*; D009434:Neural Pathways / Q000502:physiology*; D010296:Parietal Lobe / Q000502:physiology; D011214:Practice, Psychological; D011340:Problem Solving / Q000502:physiology*; D011434:Proprioception / Q000502:physiology; D012016:Reference Values; D013028:Space Perception / Q000502:physiology; D013997:Time Factors; D014584:User-Computer Interface