PT  - JOURNAL ARTICLE
AU  - Feiwen Wang
AU  - Chang Xi
AU  - Zhening Liu
AU  - Mengjie Deng
AU  - Wen Zhang
AU  - Hengyi Cao
AU  - Jie Yang
AU  - Lena Palaniyappan
TI  - Load-dependent inverted U–shaped connectivity of the default mode network in schizophrenia during a working-memory task: evidence from a replication functional MRI study
AID  - 10.1503/jpn.220053
DP  - 2022 Sep 27
TA  - Journal of Psychiatry and Neuroscience
PG  - E341--E350
VI  - 47
IP  - 5
4099  - http://jpn.ca/content/47/5/E341.short
4100  - http://jpn.ca/content/47/5/E341.full
SO  - JPN2022 Sep 27; 47
AB  - Background: Working-memory deficit is associated with aberrant degree distribution of the brain connectome in schizophrenia. However, the brain neural mechanism underlying the degree redistribution pattern in schizophrenia is still uncertain.Methods: We examined the functional degree distribution of the connectome in 81 patients with schizophrenia and 77 healthy controls across different working-memory loads during an n-back task. We tested the associations between altered degree distribution and clinical symptoms, and we conducted functional connectivity analyses to investigate the neural mechanism underlying altered degree distribution. We repeated these analyses in a second independent data set of 96 participants. In the second data set, we employed machine-learning analysis to study whether the degree distribution pattern of one data set could be used to discriminate between patients with schizophrenia and controls in the other data set.Results: Patients with schizophrenia showed decreased centrality in the dorsal posterior cingulate cortex (dPCC) for the “2-back versus 0-back” contrast compared to healthy controls. The dPCC centrality pattern across all working-memory loads was an inverted U shape, with a left shift of this pattern in patients with schizophrenia. This reduced centrality was correlated with the severity of delusions and related to reduced functional connectivity between the dPCC and the dorsal precuneus. We replicated these results with the second data set, and the machine-learning analyses achieved an accuracy level of 71%.Limitations: We used a limited n-back paradigm that precluded the examination of higher working-memory loads.Conclusion: Schizophrenia is characterized by a load-dependent reduction of centrality in the dPCC, related to the severity of delusions. We suggest that restoring dPCC centrality in the presence of cognitive demands might have a therapeutic effect on persistent delusions in people with schizophrenia.