title: Functional networks in motor sequence learning: abnormal topographies in Parkinson's disease.
abstract: We examined the neural circuitry underlying the explicit learning of motor sequences in normal subjects and patients with early stage Parkinson's disease (PD) using 15O-water (H2 15O) positron emission tomography (PET) and network analysis. All subjects were scanned while learning motor sequences in a task emphasizing explicit learning, and during a kinematically controlled motor execution reference task. Because different brain networks are thought to subserve target acquisition and retrieval during motor sequence learning, we used separate behavioral indices to quantify these aspects of learning during the PET experiments. In the normal cohort, network analysis of the PET data revealed a significant covariance pattern associated with acquisition performance. This topography was characterized by activations in the left dorsolateral prefrontal cortex (PFdl), rostral supplementary motor area (preSMA), anterior cingulate cortex, and in the left caudate/putamen. A second independent covariance pattern was associated with retrieval performance. This topography was characterized by bilateral activations in the premotor cortex (PMC), and in the right precuneus and posterior parietal cortex. The normal learning-related topographies failed to predict acquisition performance in PD patients and predicted retrieval performance less accurately in the controls. A separate network analysis was performed to identify discrete learning-related topographies in the PD cohort. In PD patients, acquisition performance was associated with a covariance pattern characterized by activations in the left PFdl, ventral prefrontal, and rostral premotor regions, but not in the striatum. Retrieval performance in PD patients was associated with a covariance pattern characterized by activations in the right PFdl, and bilaterally in the PMC, posterior parietal cortex, and precuneus. These results suggest that in early stage PD sequence learning networks are associated with additional cortical activation compensating for abnormalities in basal ganglia function.
authors.affiliation: Center for Neurosciences, North Shore-Long Island Jewish Research Institute, Manhasset, New York 11030, USA.
authors.forename: T
authors.initials: T
authors.lastname: Nakamura
authors.forename: M F
authors.initials: MF
authors.lastname: Ghilardi
authors.forename: M
authors.initials: M
authors.lastname: Mentis
authors.forename: V
authors.initials: V
authors.lastname: Dhawan
authors.forename: M
authors.initials: M
authors.lastname: Fukuda
authors.forename: A
authors.initials: A
authors.lastname: Hacking
authors.forename: J R
authors.initials: JR
authors.lastname: Moeller
authors.forename: C
authors.initials: C
authors.lastname: Ghez
authors.forename: D
authors.initials: D
authors.lastname: Eidelberg
mesh_terms: D000328:Adult; D000368:Aged; D001931:Brain Mapping; D015331:Cohort Studies; D005260:Female; D006801:Humans; D007858:Learning / Q000502:physiology; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D009043:Motor Activity / Q000502:physiology; D009044:Motor Cortex / Q000503:physiopathology*; D010300:Parkinson Disease / Q000000981:diagnostic imaging* / Q000503:physiopathology*; D011597:Psychomotor Performance / Q000502:physiology; D011601:Psychophysics; D014055:Tomography, Emission-Computed