ABSTRACT
The diagnosis of mild cognitive impairment (MCI) is very helpful for early therapeutic interventions of Alzheimer's disease (AD). MCI has been proven to be correlated with disorders in multiple brain areas. In this paper, we used information from resting brain networks at different EEG frequency bands to reliably recognize MCI. Because EEG network analysis is influenced by the reference that is used, we also evaluate the effect of the reference choices on the resting scalp EEG network-based MCI differentiation. The conducted study reveals two aspects: (1) the network-based MCI differentiation is superior to the previously reported classification that uses coherence in the EEG; and (2) the used EEG reference influences the differentiation performance, and the zero approximation technique (reference electrode standardization technique, REST) can construct a more accurate scalp EEG network, which results in a higher differentiation accuracy for MCI. This study indicates that the resting scalp EEG-based network analysis could be valuable for MCI recognition in the future.
Subject(s)
Brain/physiopathology , Cognitive Dysfunction/diagnosis , Cognitive Dysfunction/physiopathology , Electroencephalography/methods , Signal Processing, Computer-Assisted , Aged , Female , Humans , Male , ROC Curve , Rest , ScalpABSTRACT
OBJECTIVE: To determine the role of altered brain connectivity in patients with psychogenic non-epileptic seizures (PNES). METHODS: Patients with PNES and age- and sex-matched healthy control subjects were enrolled. Participants underwent neuropsychological evaluation (anxiety, depression and dissociation) and interictal scalp electroencephalography (EEG). A brain network was constructed. Between-group differences in clustering coefficient and global efficiency were analysed. RESULTS: Patients with PNES (n = 15) had significantly decreased clustering coefficients in the gamma band compared with controls (n = 15). Difference topology revealed that patients with PNES had decreased long linkage between the frontal region and other regions compared with controls. There were no significant between-group differences in global efficiency. Neuropsychological scores were significantly higher in patients than controls, but there were no correlations with network properties. CONCLUSION: Altered brain connectivity in patients with PNES suggests an underlying pathophysiological mechanism. EEG and network analysis allow noninvasive exploration of the neurological processes of this disease.
