title: Striatum forever, despite sequence learning variability: a random effect analysis of PET data.
abstract: This PET study is concerned with the what, where, and how of implicit sequence learning. In contrast with previous studies imaging the serial reaction time (SRT) task, the sequence of successive locations was determined by a probabilistic finite-state grammar. The implicit acquisition of statistical relationships between serially ordered elements (i.e., what) was studied scan by scan, aiming to evidence the brain areas (i.e., where) specifically involved in the implicit processing of this core component of sequential higher-order knowledge. As behavioural results demonstrate between- and within-subjects variability in the implicit acquisition of sequential knowledge through practice, functional PET data were modelled using a random-effect model analysis (i.e., how) to account for both sources of behavioural variability. First, two mean condition images were created per subject depending on the presence or not of implicit sequential knowledge at the time of each of the 12 scans. Next, direct comparison of these mean condition images provided the brain areas involved in sequential knowledge processing. Using this approach, we have shown that the striatum is involved in more than simple pairwise associations and that it has the capacity to process higher-order knowledge. We suggest that the striatum is not only involved in the implicit automatization of serial information through prefrontal cortex-caudate nucleus networks, but also that it plays a significant role for the selection of the most appropriate responses in the context created by both the current and previous stimuli, thus contributing to better efficiency and faster response preparation in the SRT task.
authors.affiliation: Centre de Recherches du Cyclotron, Université de Liège, Belgium. Philippe.Peigneux@ulg.ac.be
authors.forename: P
authors.initials: P
authors.lastname: Peigneux
authors.forename: P
authors.initials: P
authors.lastname: Maquet
authors.forename: T
authors.initials: T
authors.lastname: Meulemans
authors.forename: A
authors.initials: A
authors.lastname: Destrebecqz
authors.forename: S
authors.initials: S
authors.lastname: Laureys
authors.forename: C
authors.initials: C
authors.lastname: Degueldre
authors.forename: G
authors.initials: G
authors.lastname: Delfiore
authors.forename: J
authors.initials: J
authors.lastname: Aerts
authors.forename: A
authors.initials: A
authors.lastname: Luxen
authors.forename: G
authors.initials: G
authors.lastname: Franck
authors.forename: M
authors.initials: M
authors.lastname: Van der Linden
authors.forename: A
authors.initials: A
authors.lastname: Cleeremans
mesh_terms: D000328:Adult; D001931:Brain Mapping; D003342:Corpus Striatum / Q000000981:diagnostic imaging* / Q000502:physiology; D005260:Female; D014644:Genetic Variation; D006801:Humans; D007858:Learning / Q000502:physiology*; D008297:Male; D008959:Models, Neurological; D011336:Probability; D011597:Psychomotor Performance / Q000502:physiology; D011930:Reaction Time; D012044:Regression Analysis; D014055:Tomography, Emission-Computed