Load-dependent inverted U-shaped connectivity of the default mode network in schizophrenia during a working-memory
Feiwen Wang1, Chang Xi1, Zhening Liu1
1From the Department of Psychiatry, Second Xiangya Hospital of Central South University, Changsha, Hunan, China (Wang, Xi, Liu, Deng, Zhang, Yang); the National Clinical Research Centre for Mental Disorders, Changsha, Hunan, China (Wang, Xi, Liu, Deng, Zhang, Yang); the Centre for Psychiatric Neuroscience, Feinstein Institutes for Medical Research, Manhasset, New York (Cao); the Division of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, New York (Cao); the Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Que. (Palaniyappan); the Department of Medical Biophysics, Western University, London, Ont. (Palaniyappan); the Robarts Research Institute, Western University, London, Ont. (Palaniyappan).
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.
More Related Videos
12:09Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy
Published on: August 5, 2014
08:36Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms
Published on: March 21, 2019