Relationship between resting-state theta phase-gamma amplitude coupling and neurocognitive functioning in patients with first-episode psychosis
Introduction
Patients with schizophrenia suffer from dysfunction in various cognitive domains, which is one of the core symptoms of schizophrenia, and has been related to the underlying pathophysiology (Kahn and Keefe, 2013; Mesholam-Gately et al., 2009). Although it has been reported that cognitive dysfunction persisted after symptomatic improvement and had a significant negative effect on the quality of life and prognosis of patients with schizophrenia (Alptekin et al., 2005; Ueoka et al., 2011), current treatments including antipsychotic medication, neuromodulation, and cognitive remediation have not been successful enough (Goff et al., 2011; Palm et al., 2016; Tripathi et al., 2018). This frustration may be partly due to the insufficient understanding of the neural mechanisms underlying cognitive dysfunction, which constitute a core element of schizophrenia pathophysiology and a treatment target. Therefore, the neural correlates of cognitive dysfunction in patients with schizophrenia need to be further investigated to advance the current understanding of schizophrenia pathophysiology and to inform the development of effective treatments.
Although previous studies on human cognitive functioning have primarily focused on brain activities during the performance of a particular cognitive task, neural activity during the resting state also plays an important role in cognitive functioning (Buckner et al., 2008; Li et al., 2015; Vlahou et al., 2014). The most consistently reported brain activity in the resting state is that of the default mode network (DMN) assessed by functional magnetic resonance imaging (fMRI). The DMN showed relatively reduced activity during task execution, suggesting its relationship with the preparation of task performance and the modulation of switching from the resting state to the task-performance state (Buckner et al., 2008; Mason et al., 2007; Smith et al., 2018). In patients with schizophrenia, the hyperactivation or hyperconnectivity of the brain regions comprising the DMN and its relationship with poorer performance in neurocognitive function tests (NCFTs) have been reported (Cole et al., 2011; Hunt et al., 2017; Wang et al., 2014; Whitfield-Gabrieli et al., 2009). In addition, task-related DMN deactivation was reported to be less prominent in patients with schizophrenia, suggesting that impaired downregulation of the DMN may contribute to cognitive dysfunction in schizophrenia patients (Anticevic et al., 2013; Hahn et al., 2017; Sheffield and Barch, 2016).
Although previous fMRI studies have suggested abnormal DMN activity as one of a neural correlates of cognitive dysfunction (Bassett et al., 2012; Cole et al., 2011; Unschuld et al., 2014; Wang et al., 2014), the moment-by-moment changes in neural activity in the resting state could not be fully understood due to the low temporal resolution of fMRI. In this regard, electroencephalography (EEG) is a better approach to investigate instantaneous neural processing in the resting state because EEG has a very high temporal resolution at the millisecond level (Bell and Cuevas, 2012). For example, Khanna et al. suggested a microstate analysis of resting-state EEG to investigate changes in neural activity in every moment, which were called “the atoms of thoughts” (Khanna et al., 2015). Although surface EEG data have low spatial resolution, which limits the exact location in the brain where neural activity occurs, this can be complemented with the source analysis method using the structural information acquired with MRI (Mantini et al., 2007; Yuan et al., 2016). Therefore, an investigation of spatiotemporal psychopathology using the source analysis of resting-state EEG data was proposed to estimate both temporal and spatial features of the neural substrates of cognitive dysfunction in schizophrenia (Cabral et al., 2014; Northoff and Duncan, 2016).
Among the EEG measures, theta phase-gamma amplitude coupling (TGC) processes information by modulating the amplitude of the fast oscillating (i.e., gamma band) activity based on the phase of the slow oscillating (i.e., theta band) activity (Lisman and Jensen, 2013). TGC provides a mechanism for information processing and communication coordination between brain regions (Canolty and Knight, 2010; Lisman and Jensen, 2013; Mehta et al., 2002). In patients with schizophrenia, abnormal TGC during task performance has been consistently reported (Barr et al., 2017; Lynn et al., 2016; Popov et al., 2015). In particular, N-methyl-d-aspartate receptor (NMDAr) hypofunction and decreased gamma-aminobutyric acid (GABA)-mediated signaling have been suggested as the molecular mechanisms of cognitive dysfunction in schizophrenia in relationship with TGC abnormalities (Kahn et al., 2015; Uhlhaas and Singer, 2015). Therefore, TGC has been suggested as a new approach to reveal the neural correlates of cognitive dysfunction in schizophrenia (Lisman and Buzsaki, 2008). Despite the suggested relationship of altered DMN activity with cognitive dysfunction in schizophrenia patients, resting-state TGC in DMN-related regions in patients with schizophrenia has not yet been reported. Only one study by Won et al. investigated resting-state TGC; resting-state TGC was larger in schizophrenia patients than healthy controls (HCs), however, structural MRI data to improve spatial resolution was not used (Won et al., 2018).
We aimed to investigate the spatiotemporal neural correlates of cognitive dysfunction in schizophrenia using resting-state TGC analysis with source signals from the brain areas comprising the DMN. To minimize the effect of illness duration, medication exposure, and disease chronicity, we compared TGC in patients with first-episode psychosis (FEP) to that in HCs. The correlation between TGC showing significant group differences and the performances on the NCFTs in each group of participants was also estimated. We hypothesized that FEP patients would show elevated resting-state TGC in brain regions comprising the DMN and that altered TGC would show a significant relationship with behavioral performance on the NCFTs.
Section snippets
Participants, clinical assessments, and neurocognitive function tests
Fifty-nine patients with FEP and 50 HCs participated in this study. Patients with FEP were recruited from the inpatient and outpatient clinic of the Department of Neuropsychiatry at the Seoul National University Hospital (SNUH). FEP patients were defined as patients diagnosed with schizophrenia, schizoaffective disorder, or schizophreniform disorder using the Structured Interview for the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition Axis I Disorders (SCID-I) with an
Results
The demographic and clinical characteristics as well as the NCFT results of patients with FEP and HCs are summarized in Table 1. There was no significant difference between groups in age, handedness, and education years. However, the ratio of females to males was higher in the FEP group than in the HC group (χ2 = 9.026, df = 1, P = 0.003), and the patients with FEP had lower IQ (t = −5.766, df = 107, P < 0.001) than the HCs. The patients with FEP performed more poorly in all NCFTs than the HCs:
Discussion
In this study, TGC of the resting-state EEG in DMN-related brain areas was used to reveal the neural correlates of cognitive dysfunction in patients with FEP. We found that the patients with FEP showed larger TGC in the left PCC than the HCs. In addition, the larger TGC in the left PCC showed significant correlations with better performances in the TMT-A and -B as well as in the immediate and delayed recall tests of the CVLT in the FEP patients. On the other hand, no significant correlations
Data availability statement
The data that support the findings of this study are available from the corresponding author upon reasonable request.
Contributors
Authors Tak Hyung Lee, Minah Kim, and Jun Soo Kwon contributed to study design and study procedures. Authors Tak Hyung Lee, Minah Kim, Yoo Bin Kwak, Wu Jeong Hwang, and Taekwan Kim performed data analysis. Authors Tak Hyung Lee and Minah Kim wrote the first draft of the manuscript. Authors Yoo Bin Kwak, Wu Jeong Hwang, Taekwan Kim, and Jun Soo Kwon contributed to data interpretation and manuscript revision. Author Minah Kim managed and supervised all study procedures. All of the authors have
Role of the funding source
This research was supported by the Brain Research Program and the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Science, ICT & Future Planning (Grant no. 2017M3C7A1029610 and 2019R1C1C1002457).
Declaration of competing interest
None.
Acknowledgement
The principal investigators wish to thank all of the participants who participated in the study for their time and effort, and the anonymous reviewers whose observations improved the manuscript.
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