Data-driven brain-types and their cognitive consequences

The canonical approach to exploring brain-behaviour relationships is to group individuals according to a phenotype of interest, and then explore the neural correlates of this grouping. A limitation of this approach is that multiple aetiological pathways could result in a similar phenotype, so the role of any one brain mechanism may be substantially underestimated. Building on advances in network analysis, we used a data-driven community-clustering algorithm to identify robust subgroups based on white-matter microstructure in childhood and adolescence (total N=313, mean age: 11.24 years). The algorithm indicated the presence of two equal-size groups that show a critical difference in FA of the left and right cingulum. These different brain types had profoundly different cognitive abilities with higher performance in the higher FA group. Further, a connectomics analysis indicated reduced structural connectivity in the low FA subgroup that was strongly related to reduced functional activation of the default mode network.\n\nGraphical abstract\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=155 SRC=\"FIGDIR/small/237859_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (34K):\norg.highwire.dtl.DTLVardef@135e807org.highwire.dtl.DTLVardef@145673org.highwire.dtl.DTLVardef@137cd67org.highwire.dtl.DTLVardef@8e2cff_HPS_FORMAT_FIGEXP M_FIG C_FIG