EEG Microstates Reveal Altered Default-mode Network Dynamics Related to Attention in Narcolepsy
Poster No:
413
Submission Type:
Abstract Submission
Authors:
Xiao Han1, Feiyan CHEN1
Institutions:
1Bio-X Laboratory, School of Physics, Zhejiang University, Hangzhou, China
First Author:
Co-Author:
Introduction:
Narcolepsy is a chronic neurological disorder characterized by the dysfunction of hypocretin system. In addition to cataplexy and excessive daytime sleepiness, patients always exhibit cognitive disturbances, such as difficulty in attention sustaining and decision making, accompanied by depression, anxiety(Bassetti et al., 2019). Previous neuroimaging studies in narcolepsy have reported the functional abnormalities of default-mode network (DMN) in resting-state functional magnetic resonance imaging (fMRI) (Fulong et al., 2020). In this study, we aim to investigate abnormalities of DMN by the electroencephalographic microstates. Moreover, we seek to explore the relationship between the anomalous DMN and the sustaining attention disturbances, neuropsychological assessment, clinical data in narcolepsy.
Methods:
Narcolepsy type 1 patients (NT1, n=40) and control group (Control, n=40) were recruited to collect their resting-state EEG data before and after their participations in Sustained Attention to Response Task (SART) (each lasting three minutes). Before EEG recordings, neuropsychological assessment was applied. We used resting-state EEG data before the task for data analysis, and the data after the task as the validation set. EEG data was filtered with a 0.5-30 Hz band-pass for further microstates analysis. EEG microstates were analyzed by the k-means clustering, which yielded seven prototypes for group comparisons (Figure 1.A). Four of the prototypes correspond to the classic microstate prototypes used in other brain disease studies (da Cruz et al., 2020; Lei et al., 2022). Previous studies suggested that microstate C and microstate F mainly contributed to the posterior cingulate cortex (PCC) and the dorsal anterior cingulate cortex (ACC) which belong to DMN. Microstate E also involves DMN and Microstate G might be associated with the sensorimotor network (Custo et al., 2017). the relationship between microstates properties and the sustaining attention disturbances, neuropsychological assessment, clinical data was analyzed with correlation analyses.
Results:
At the group level, the NT1 exhibits a significant decrease in microstate C properties, including time coverage and occurrence (Figure 1). It is also accompanied by an increase in microstates E and F properties significantly, indicating that the disrupted temporal dynamics of DMN. In correlation analyses, the occurrence of MS C in the NT1 was negatively correlated with the reaction time variability in the SART task (r = 0.65, p < 0.001). It revealed that the decline of PCC activity in DMN might impair the stability of sustained attention. The coverage and duration of MS F in the NT1 was positively associated with Patient Health Questionnaire-9 (PHQ-9) (r = 0.42, p = 0.010) and Barratt Impulsiveness Scale Version 11 (BIS11) (r = 0.49, p = 0.002). This result suggested the abnormal activity in the ACC, which might result in patients more prone to impulsivity and emotional dysregulation. Furthermore, the occurrences of microstates E and G showed significantly positive correlations with N3 sleep latency (r = 0.45, =0.011) and REM latency (r = 0.56, p < 0.001) respectively. These results encouraged future study to pay more attention to the relationship between microstates and sleep indicators for clinical treatment in the narcolepsy.
Conclusions:
Our study utilized the property of EEG MS as a biomarker for narcolepsy. We demonstrated that narcolepsy presented anomalous temporal dynamics of DMN. Further, the differential characteristics of microstates suggest that distinct regions of DMN exhibit divergent abnormalities, implying a complex pathological landscape within DMN in NT1. Additionally, the DMN may play an essential role in the sustained attention, emotional regulation, sleep rhythms. It suggests that abnormalities in DMN may cause patients poorer sustained attention ability and more prone to impulsivity and depression, along with abnormalities of sleep.
Disorders of the Nervous System:
Neurodevelopmental/ Early Life (eg. ADHD, autism) 1
Psychiatric (eg. Depression, Anxiety, Schizophrenia)
Modeling and Analysis Methods:
EEG/MEG Modeling and Analysis
Perception, Attention and Motor Behavior:
Sleep and Wakefulness
Perception and Attention Other 2
Keywords:
Attention Deficit Disorder
Electroencephaolography (EEG)
Sleep
Treatment
Other - default mode network, narcolepsy
1|2Indicates the priority used for review
Provide references using author date format
Custo, A., Van De Ville, D., Wells, W. M., Tomescu, M. I., Brunet, D., & Michel, C. M. (2017). Electroencephalographic Resting-State Networks: Source Localization of Microstates. Brain Connectivity, 7(10), 671–682. https://doi.org/10.1089/brain.2016.0476
da Cruz, J. R., Favrod, O., Roinishvili, M., Chkonia, E., Brand, A., Mohr, C., Figueiredo, P., & Herzog, M. H. (2020). EEG microstates are a candidate endophenotype for schizophrenia. Nature Communications, 11(1), Article 1. https://doi.org/10.1038/s41467-020-16914-1
Fulong, X., Spruyt, K., Chao, L., Dianjiang, Z., Jun, Z., & Fang, H. (2020). Resting-state brain network topological properties and the correlation with neuropsychological assessment in adolescent narcolepsy. Sleep, 43(8), zsaa018. https://doi.org/10.1093/sleep/zsaa018
Lei, L., Liu, Z., Zhang, Y., Guo, M., Liu, P., Hu, X., Yang, C., Zhang, A., Sun, N., Wang, Y., & Zhang, K. (2022). EEG microstates as markers of major depressive disorder and predictors of response to SSRIs therapy. Progress in Neuro-Psychopharmacology and Biological Psychiatry, 116, 110514. https://doi.org/10.1016/j.pnpbp.2022.110514