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| Home > Publications database > Category- and Data-driven Meta-analyses - Comparison of Different Approaches for Identification of Consistent Aberrant Brain Activation in Psychiatric Disorders |
| Conference Presentation (After Call) | FZJ-2026-04497 |
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2026
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Please use a persistent id in citations: doi:10.34734/FZJ-2026-04497
Abstract: Neuroimaging meta-analyses are widely used to identify convergent aberrant brain activation in patients. Among clinical features, emotional dysfunction is one of prominent symptoms in several psychiatric diseases. Transdiagnostic investigation of emotion processing deficits may thus provide valuable insights into potential general psychiatric biomarkers. However, classic transdiagnostic meta-analyses might miss relevant effects that are shared across specific subgroups (e.g. shared symptoms) of studies different to known categorization systems. To address this issue, we used in addition to classical meta-analyses, a data-driven approach that combines neuroimaging meta-analyses with hierarchical clustering. We compared data- and category-driven meta-analyses, investigating if the results of these approaches differ or complement each other. In total, 302 studies reporting 510 experiments were included. Across all diagnostic groups consistent aberrant activity during emotional processing were found in bilateral amygdala, anterior and posterior cingulate cortex and left precentral gyrus. Classic category driven analyses per disease category revealed only few regions of convergence. Data-driven analyses pointed to clustering of experiments into several distinct groups, with one large group consisting of multiple disease categories sharing convergent aberrant activation in a similar but more extensive network than the overall ungrouped analysis. The remaining clusters were more specific but still consisted of multiple disease categories sharing convergent aberrant activation in visual-parietal areas. In summary, results point to shared aberrant activation during emotion processing across several disorders found in both types of meta-analytical approaches and indicate that data-driven clustering of experiments may reveal additional insights that are not captured by the classical category driven approach.
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