Structural and temporal organization of the functional network underlying motor learning

1. INTRODUCTION METHODS REAL DATASETS fMRI Day 1 Training (Day 1 and 2) fMRI Day 2 1 2 3 4 Global performance index #run Structural and temporal organization of the functional network underlying motor learning Pierre Bellec1, Roberto Toro2, Guillaume Marrelec1,3, Mélanie Pélégrini-Issac1, Habib Benali1, Yves Burnod2, Julien Doyon1,3 1 INSERM/UPMC U678, Paris, France, 2CNRS UMR 5015, Lyon, France, 3 Université de Montréal, Canada Inserm Institut national de la santé et de la recherche médicale Context We investigate through fMRI and functional connectivity analysis the spatio-temporal dynamics of the networks mediating the learning of a motor adaptation task (1). IssueThe networks of functionally connected regions are complex (31 regions, 465 links). It is therefore difficult to analyze both their structural organization and their changes during the four stages of the learning process under investigation. PurposeWe use a representation in a functional space (2) to simplify the visual analysis of the functional networks and their changes over time. • For each run, the regional time series were extracted by averaging the time series of all voxels within a region, and were then corrected to have a zero mean and a unit variance. The time series were concatenated within each time block: the 2 first runs of Day 1 (block 1), three last runs of Day 1 (block 2), the 2 first runs of day 2 (block 3), and the 3 last runs of day 2 (block 4). These time series were used to measure the functional connectivity at each time block using the inter-regional correlation matrix R. • Five healthy volunteers were included in our study. For each subject, ten functional runs of 70 T2*-weighted volumes each were acquired (Bruker 3 T scanner; TR: 3,500 ms; TE: 35 ms; voxel size: 333 mm3; 42 slices). Functional data were spatially co-registered and smoothed. • Subjects were scanned on two consecutive days while performing a target-reaching task: they used a joystick to move a cursor from the center of a screen to one of eight targets following an elliptical trajectory. The relationship between the joystick and the direction were inverted. • mds…. • A set of regions was obtained from activation maps using a clustering algorithm (3). These regions were then used as seeds to look for strongly correlated regions not included in the activation map. We obtained a set of 31 regions involved in the task. • The solution of NMDS is not unique: even if the underlying networks were identical at two time blocks, their representation in the functional space could differ by an isometric and/or scaling transformation. This issue is addressed by applying a procruste analysis, which finds the best combination of translation/rotation/reflection/scaling to fit the network at a given time block onto the network at time block 1. L R RESULTS 2 3 4 1 • At the very beginning of learning,the functional connectivity network is organized into anatomically and functionally homogeneous sub-systems[?] where for example symmetric regions are represented together. • At the end of the fast learning phase on Day 1, visual and parietal associative regions increased their correlation with the ventral and dorsal central motor clusters. • After further practice on Day 2, the regions of the prefrontal cortex overlapped in turn the ventral and dorsal central motor clusters. Furthermore, cerebellar regions got closer to the thalami, the motor cortex and the striatum. • When subjects reached asymptotic performance at the end of Day 2, the position of all the subsystems in the representation were close to their initial configuration. CONCLUSION REFERENCES • Use of the functional space to represent the graph of functional interactions within a large set of distributed regions allows to clarify both the structure of these graphs and their changes over time. • Interactions within and between sub-systems including basal ganglia, cerebellum, and motor cortex are necessary early in the process of motor adaptation. • Specialization within sub-systems is probably sufficient for maintaining this skilled behavior over time. Doyon et al. HBM04,CDROM:?-? (2004) Friston et al. Cereb. Cortex, ?:?-? (1996) Bellec et al. HBM04, CDROM:?-? (2004) Toro et al. HBM05, poster number ? (2005) Welchew et al., Neuroimage, 17:1227-1239 (2002) L R