Data-driven identification of a left anterior hippocampus’ morphological network associated with self-regulation

Abstract The human hippocampus is a key region in cognitive and emotional processing, but also a vulnerable and plastic region. Accordingly, there is a great interest in understanding how variability in hippocampus’ structure relates to variability in behavior in healthy and clinical populations. In this study, we aimed to link interindividual variability in subregional hippocampal networks (i.e. the brain grey matter networks of hippocampal subregions) to variability in behavioral phenotype. To do so, we used a multi-block multivariate approach mapping association between grey matter volume in hippocampal subregions, grey matter volume in the whole brain regions and behavioral variables in healthy adults. Implementing this approach in a machine learning framework enabled us to identify stable patterns (latent dimensions) and its application to two large adult datasets further enabled us to focus on a replicable latent dimension that thus captures a general aspect of hippocampal-brain-behavior phenotype in the population. Our results highlighted a left anterior hippocampal morphological network including the left amygdala and the posterior midline structures whose expression relates to higher self-regulation, life satisfaction and better performance at standard neuropsychological tests. Future studies should investigate the structural development of this morphological network across childhood, the genetic and exposome factors influencing it and its relationship to neurocognitive phenotypes in different brain diseases.