INTRODUCTION Recent models of visual perception suggested that scene recognition is processed in terms of spatial frequencies: in adults, low spatial frequencies (LSF) rapidly reach high-order cortical areas to allow initial scene recognition and high spatial frequencies (HSF) subsequently carry fine details analysis (Kauffmann, 2014). Paradigms using compound stimuli (large global forms, supposed to convey HSF, composed of arrangements of small local forms, supposed to convey HSF) had indicated an evolution from a local (HSF) preference in young children (Poirel, 2008) evolving toward a global (LSF) preference, from 6 years of age until the end of adolescence (Mondloch, 2003). The present MRI study investigated for the first time the relationship between cortical thickness and behavioral performances to LSF/HSF during this childhood transitional period. METHODS Sixteen children (M=10 years, SD=7 months, 6 boys) and 16 adults (M=30 years, SD=5 years, 9 boys) were presented with 80 pictures of natural scenes filtered either in LSF or in HSF (Fig1). Each scene was presented during 40ms and participants had to indicate as rapidly as possible whether the scene was outdoor or indoor. Reaction times were recorded. Participants were scanned with a 3-Tesla MRI scanner (T1-weighted, FOV: 256mm; slice thickness: 1.33 mm; 128 slices; matrix size 192×192 voxels; 5min7s duration). Mean cortical thickness values were extracted with Freesurfer software using the Destrieux Atlas (2010). For each group, regression analyses were carried to investigate the relationship between cortical thickness and LSF/HSF reaction times (RTs). All results reported are statistically significant at 0.05. RESULTS Behavioral data indicated that adults showed faster RTs than children (495±12ms and 867±34ms respectively, p<.0001). RTs did not differed between LSF and HSF conditions (672±41ms and 689±43ms respectively, p=.23). There was no interaction between the two experimental factors (p=.92). Regression analyses revealed that, in adults, faster RTs were always associated with an increase in cortical thickness (Fig2). Regardless of spatial frequency, correlations between faster RTs and cortical thickness were found in left middle temporal, right middle frontal and right parahippocampal regions. Concerning LSF only, faster RTs were associated with an increase in cortical thickness in the right insula. Concerning HSF only, faster RTs were associated with an increase in cortical thickness in left fronto-marginal and supramarginal gyri. In sharp contrast, in children, faster RTs were associated with both increases and decreases in cortical thickness (Fig2). Concerning LSF, faster RTs were associated with a decreased cortical thickness in anterior areas: right middle frontal, right lateral orbital and bilateral insula regions. Concerning HSF, faster RTs were associated with an increased cortical thickness in posterior areas: left parietal and left calcarine regions. CONCLUSION In adults, the increase in cortical thickness could correspond to an expansion mechanism (Draganski, 2004) linked to the efficiency in processing visual scenes (e.g., parahippocampal gyrus, known to be strongly involved during scene processing, Kauffman, 2014). In our study, children exhibited both increase and decrease in cortical thickness. We hypothesize that, as for adults, the increase could correspond to an expansion mechanism specifically for HSF processing, a process that is efficient at 10 years of age. On the other hand, decrease in cortical thickness associated to LSF efficiency could correspond to a pruning mechanism (Edelman, 1993) reflecting an ongoing maturational process, particularly present in anterior regions of the brain. These assumptions are in line with the view of a postero-anterior gradient in brain development during childhood (Sowell, 2004). Altogether, the present study highlights the neural mechanisms involved in spatial frequency processing in children and adults. REFERENCES Destrieux, C., Fischl, B., Dale, A., & Halgren, E. (2010). Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature. NeuroImage, 53(1), 1‑15. doi:10.1016/j.neuroimage.2010.06.010 Draganski, B., Gaser, C., Busch, V., Schuierer, G., Bogdahn, U., & May, A. (2004). Neuroplasticity: changes in grey matter induced by training. Nature, 427(6972), 311‑312. Edelman, G. M. (1993). Neural Darwinism: selection and reentrant signaling in higher brain function. Neuron, 10(2), 115‑125. Kauffmann, L., Ramanoël, S., & Peyrin, C. (2014). The neural bases of spatial frequency processing during scene perception. Frontiers in Integrative Neuroscience, 8, 37. doi:10.3389/fnint.2014.00037 Mondloch, C. J., Geldart, S., Maurer, D., & de Schonen, S. (2003). Developmental changes in the processing of hierarchical shapes continue into adolescence. J.Exp.Child Psychol., 84(1), 20‑40. Poirel, N., Mellet, E., Houdé, O., & Pineau, A. (2008). First came the trees, then the forest: developmental changes during childhood in the processing of visual local-global patterns according to the meaningfulness of the stimuli. Developmental Psychology, 44(1), 245‑253. Sowell, E. R., Thompson, P. M., Leonard, C. M., Welcome, S. E., Kan, E., & Toga, A. W. (2004). Longitudinal mapping of cortical thickness and brain growth in normal children. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 24(38), 8223‑8231. doi:10.1523/JNEUROSCI.1798-04.2004