Reduced coupling between offline neural replay events and default mode network activation in schizophrenia

Matthew M Nour; Yunzhe Liu; Cameron Higgins; Mark W Woolrich; Raymond J Dolan

doi:10.1093/braincomms/fcad056

Reduced coupling between offline neural replay events and default mode network activation in schizophrenia

Brain Commun. 2023 Mar 3;5(2):fcad056. doi: 10.1093/braincomms/fcad056. eCollection 2023.

Authors

Matthew M Nour^{1

2

3

4}, Yunzhe Liu^{1

5

6}, Cameron Higgins^{3

4}, Mark W Woolrich^{3

4}, Raymond J Dolan^{1

2

5}

Affiliations

¹ Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, London WC1B 5EH, UK.
² Wellcome Trust Centre for Human Neuroimaging, University College London, London WC1N 3AR, UK.
³ Department of Psychiatry, University of Oxford, Oxford OX3 7JX, UK.
⁴ Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford OX9 9DU, UK.
⁵ State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China.
⁶ Chinese Institute for Brain Research, Beijing 102206, China.

Abstract

Schizophrenia is characterized by an abnormal resting state and default mode network brain activity. However, despite intense study, the mechanisms linking default mode network dynamics to neural computation remain elusive. During rest, sequential hippocampal reactivations, known as 'replay', are played out within default mode network activation windows, highlighting a potential role of replay-default mode network coupling in memory consolidation and model-based mental simulation. Here, we test a hypothesis of reduced replay-default mode network coupling in schizophrenia, using magnetoencephalography and a non-spatial sequence learning task designed to elicit off-task (i.e. resting state) neural replay. Participants with a diagnosis of schizophrenia (n = 28, mean age 28.2 years, range 20-40, 6 females, 13 not taking antipsychotic medication) and non-clinical control participants (n = 29, mean age 28.1 years, range 18-45, 6 females, matched at group level for age, intelligence quotient, gender, years in education and working memory) underwent a magnetoencephalography scan both during task completion and during a post-task resting state session. We used neural decoding to infer the time course of default mode network activation (time-delay embedding hidden Markov model) and spontaneous neural replay (temporally delayed linear modelling) in resting state magnetoencephalography data. Using multiple regression, we then quantified the extent to which default mode network activation was uniquely predicted by replay events that recapitulated the learned task sequences (i.e. 'task-relevant' replay-default mode network coupling). In control participants, replay-default mode network coupling was augmented following sequence learning, an augmentation that was specific for replay of task-relevant (i.e. learned) state transitions. This task-relevant replay-default mode network coupling effect was significantly reduced in schizophrenia (t(52) = 3.93, P = 0.018). Task-relevant replay-default mode network coupling predicted memory maintenance of learned sequences (ρ(52) = 0.31, P = 0.02). Importantly, reduced task-relevant replay-default mode network coupling in schizophrenia was not explained by differential replay or altered default mode network dynamics between groups nor by reference to antipsychotic exposure. Finally, task-relevant replay-default mode network coupling during rest correlated with stimulus-evoked default mode network modulation as measured in a separate task session. In the context of a proposed functional role of replay-default mode network coupling, our findings shed light on the functional significance of default mode network abnormalities in schizophrenia and provide for a consilience between task-based and resting state default mode network findings in this disorder.

Keywords: decoding; default mode network; replay; resting state network; schizophrenia.