infonet-economy

One of the main challenges in working memory research has been to understand the degree of separation and overlap between the neural systems involved in encoding and maintenance. In the current study we used a variable load version of the Sternberg item recognition test (two, four, six, or eight letters) and a functional connectivity method based on constrained principal component analysis to extract load-dependent neural systems underlying encoding and maintenance, and to characterize their anatomical overlap and functional interaction. Based on the pattern of functional connectivity, constrained principal component analysis identified a load-dependent encoding system comprising bilateral occipital (Brodmann's area (BA) 17, 18), bilateral superior parietal (BA 7), bilateral dorsolateral prefrontal (BA 46), and dorsal anterior cingulate (BA 24, 32) regions. For maintenance, in contrast, constrained principal component analysis identified a system that was characterized by both load-dependent increases and decreases in activation. The structures in this system jointly activated by maintenance load involved left posterior parietal (BA 40), left inferior prefrontal (BA 44), left premotor and supplementary motor areas (BA 6), and dorsal cingulate regions (BA 24, 32), while the regions displaying maintenance-load-dependent activity decreases involved bilateral occipital (BA 17, 18), posterior cingulate (BA 23) and rostral anterior cingulate/orbitofrontal (BA 10, 11, 32) regions. The correlation between the encoding and maintenance systems was strong and negative (Pearson's r = -.55), indicting that some regions important for visual processing during encoding displayed reduced activity during maintenance, while subvocal rehearsal and phonological storage regions important for maintenance showed a reduction in activity during encoding. In summary, our analyses suggest that separable and complementary subsystems underlie encoding and maintenance in verbal working memory, and they demonstrate how constrained principal component analysis can be employed to characterize neuronal systems and their functional contributions to higher-level cognition.