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From ATOM to GradiATOM: Cortical gradients support time and space processing as revealed by a meta-analysis of
Giorgia Cona1, Martin Wiener2, Cristina Scarpazza3
1Department of General Psychology, University of Padua, Via Venezia 8, 35131, Padua, Italy; Padova Neuroscience Center, University of Padua, Italy.
Abstract:
According to the ATOM (A Theory Of Magnitude), formulated by Walsh more than fifteen years ago, there is a general system of magnitude in the brain that comprises regions, such as the parietal cortex, shared by space, time and other magnitudes. The present meta-analysis of neuroimaging studies used the Activation Likelihood Estimation (ALE) method in order to determine the set of regions commonly activated in space and time processing and to establish the neural activations specific to each magnitude domain. Following PRISMA guidelines, we included in the analysis a total of 112 and 114 experiments, exploring space and time processing, respectively. We clearly identified the presence of a system of brain regions commonly recruited in both space and time that includes: bilateral insula, the pre-supplementary motor area (pre-SMA), the right frontal operculum and the intraparietal sulci. These regions might be the best candidates to form the core magnitude neural system. Surprisingly, along each of these regions but the insula, ALE values progressed in a cortical gradient from time to space. The SMA exhibited an anterior-posterior gradient, with space activating more-anterior regions (i.e., pre-SMA) and time activating more-posterior regions (i.e., SMA-proper). Frontal and parietal regions showed a dorsal-ventral gradient: space is mediated by dorsal frontal and parietal regions, and time recruits ventral frontal and parietal regions. Our study supports but also expands the ATOM theory. Therefore, we here re-named it the 'GradiATOM' theory (Gradient Theory of Magnitude), proposing that gradient organization can facilitate the transformations and integrations of magnitude representations by allowing space- and time-related neural populations to interact with each other over minimal distances.
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