bioRxivNeurosciencePreprint
Common default mode network dysfunction across psychopathologies: A neuroimaging meta-analysis of the n-back working memory paradigm
Michael C. FarruggiaiD, Angela R. LairdiD, Aaron T. MattfeldiD
Abstract
The National Institute of Mental Healths (NIMH) Research Domain Criteria (RDoC) classifies disorders based on shared aspects of behavioral and neurobiological dysfunction. One common behavioral deficit observed in various psychopathologies, namely ADHD, addiction, bipolar disorder, depression, and schizophrenia, is a deficit in working memory performance. However, it is not known to what extent, if any, these disorders share common neurobiological abnormalities that contribute to decrements in performance. The goal of the present study was to examine convergence and divergence of working memory networks across psychopathologies. We used the Activation Likelihood Estimate (ALE) meta-analytic technique to collapse prior data obtained from published studies using the n-back working memory paradigm in individuals with a DSM-criteria diagnosis of the aforementioned disorders. These studies examined areas in the brain that showed increases in activity as a function of working memory-related load compared to a baseline condition, both within subjects and between healthy individuals and those with psychiatric disorder. A meta-analysis of 281 foci covering 81 experiments and 2,629 participants found significant convergence of hyperactivity in medial prefrontal cortex (mPFC) for DSM-diagnosed individuals compared to healthy controls. Foci from ADHD, addiction, bipolar disorder, schizophrenia, and major depression studies contributed to the formation of this cluster. These results provide evidence that default-mode intrusion may constitute a shared seed of dysregulation across multiple psychopathologies, ultimately resulting in poorer working memory performance.
- organism: human
- assay: imaging
- research focus: cell type annotation
- research focus: clustering
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 14
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