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Disruption of Frontal Lobe Neural Synchrony During Cognitive Control by Alcohol Intoxication
Published on: February 6, 2019
Multilayer network analysis of dynamic network reconfiguration in alcohol use disorder and its association with
Guanmao Chen1, Xiaoxing Huang2, Jinming Yu3
1Medical Imaging Center, Zhongshan City People's Hospital, Zhongshan, China; Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou 510630, China.
Background:
Alcohol abuse represents the primary direct and indirect factor responsible for preventable mortality. Although aberrant static functional brain network activity has been reported in alcohol use disorder (AUD), the neural mechanisms of AUD and the brain mechanism of its cognitive function impairments are still not fully understood.
Methods:
The resting-state functional magnetic resonance imaging data were acquired from 38 patients with AUD and 31 healthy controls (HC). Multilayer network analysis was applied to calculate the network switching rates between brain states, and its correlations between cognitive impairments. JuSpace toolbox was used to calculate the spatial correlations between network switching rates and neurotransmitter activity in AUD group.
Results:
Compared to HC, AUD patients showed significantly increased modularity (FDRp = 0.009) and switching rates at global level (FDRp = 0.004), as well as exhibited increased switching rates at somatic motor network (SMN, FDRp = 0.014), dorsal attention network (DAN, FDRp = 0.007), ventral attention network (VAN, FDRp = 0.007), frontoparietal network (FPN, FDRp = 0.007), default mode network (DMN, FDRp = 0.009), and subcortical network (SCN, FDRp = 0.014) and node levels. Exploratory correlation analyses revealed associations between network switching rates and clinical measures: global, SMN, DAN, and DMN switching rates showed positive correlations with Michigan Alcohol Screening Test (MAST) scores, while DAN and DMN switching rates showed negative correlations with MoCA scores in the AUD group. However, these correlations did not survive FDR correction for multiple comparisons and should be interpreted as preliminary findings. Furthermore, switching rates in AUD patients were spatial correlated with serotonergic, dopaminergic, glutamatergic, and GABAergic neurotransmitter system.
Conclusions:
Multilayer network analysis revealed instability of brain dynamics in AUD patients, correlating with AUD severity, cognitive impairment and multiple neurotransmitter system. These findings provide preliminary evidence of spatial associations between AUD-related network dynamic abnormalities and neurotransmitter receptor distributions, as well as nominal exploratory correlations with clinical severity and cognitive performance.
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