BACKGROUND. In the last decades, neuroimaging studies have attempted to unveil the neurobiological markers underlying pediatric psychiatric disorders. However, children diagnosed with such disorders are likely to receive an additional diagnosis in the following years. Yet, the vast majority of neuroimaging studies focus on a single nosological category, which limit our understanding of the shared/specific neural correlates between these disorders. Therefore, we aimed to investigate the transdiagnostic neural signatures through a novel meta-analytical method. METHOD. A data-driven meta-analysis was carried out which grouped similar experiments topographic map together, irrespectively of nosological categories and task-characteristics. Then, activation likelihood estimation meta-analysis was performed on each group of experiments to extract spatially convergent brain regions. RESULTS. One hundred forty-seven experiments were retrieved (3199 subjects): 79 attention-deficit/hyperactivity disorder, 32 conduct/oppositional defiant disorder, 14 anxiety disorders, 22 major depressive disorders. Four significant groups of experiments were observed. Functional characterization suggested that these groups of aberrant brain regions may be implicated internally/externally directed processes, attentional control of affect, somato-motor and visual processes. Furthermore, despite that some differences in rates of studies involving major depressive disorders were noticed, nosological categories were evenly distributed between these four sets of regions. Additionally, main effects of task characteristics were observed. CONCLUSIONS. By using a data-driven meta-analytic method, we observed four significant groups of aberrant brain regions that may reflect transdiagnostic neural signature of pediatric psychiatric disorders. Overall, results of this study underscore the importance of studying pediatric psychiatric disorders simultaneously rather than independently.
The neural signatures of social hierarchy-related learning and interaction: a coordinate- and connectivity-based meta-analysis
