Functional networks and structural connectivity of visuospatial and visuoperceptual working memory - Sala-Llonch, Roser; Palacios, Eva M.; Junqué, Carme; Bargalló, Núria; Vendrell, Pere (Frontiers in Human Neuroscience, 2015)
Neural correlates of working memory (WM) in healthy subjects have been extensively investigated using functional MRI (fMRI). However it still remains unclear how cortical areas forming part of functional WM networks are also connected by white matter fiber bundles, and whether DTI measures, used as indices of microstructural properties and directionality of these connections, can predict individual differences in task performance. FMRI data were obtained from 23 healthy young subjects while performing one visuospatial (square location) and one visuoperceptual (face identification) 2-back task. Diffusion tensor imaging (DTI) data were also acquired. We used independent component analysis (ICA) of fMRI data to identify the main functional networks involved in WM tasks. Voxel-wise DTI analyses were performed to find correlations between structural white matter and task performance measures, and probabilistic tracking of DTI data was used to identify the white matter bundles connecting the nodes of the functional networks. We found that functional recruitment of the fusiform and the inferior frontal cortex was specific for the visuoperceptual working memory task, while there was a high overlap in brain activity maps in parietal and middle frontal areas for both tasks. Axial diffusivity and fractional anisotropy, of the tracts connecting the fusiform with the inferior frontal areas correlated with processing speed in the visuoperceptual working memory task. Although our findings need to be considered as exploratory, we conclude that both tasks share a highly-overlapping pattern of activity in areas of frontal and parietal lobes with the only differences in activation between tasks located in the fusiform and inferior frontal regions for the visuoperceptual task. Moreover, we have found that the DTI measures are predictive of the processing speed.