Source-level MEG analysis of the intrinsic temporal properties of neural networks in Schizophrenia

Biological systems tend to display complex behaviour with a power-law (1/f - like) distribution. In the brain, this translates into neural activity that exhibits scale-free, temporal or spatial, properties (He, 2014). Scaleinvariance has been observed across different neuroimaging modalities and conditions (Linkenkaer-Hansen, 2001; He, 2014; Ciuciu et al. 2012). Beyond previously used features, recent electrophysiology studies have shown the presence of long-range temporal correlations (LRTCs) in the amplitude dynamics of alpha and beta oscillations (Nikulin et al. 2012). Disease, such as psychosis, can alter the temporal properties of neuronal activity and, consequently, potentially affect information integration (Fernandez et al. 2013). The goals of this study were to: (a) measure and compare scale-free dynamics in schizophrenia patients (SZ) and controls using magnetoencephalography (MEG), and (b) classify subjects using machine-learning tools. Five minutes of resting-state MEG were acquired for 25 SZ patients and 25 controls during eyes open and eyes closed conditions. Detrended Fluctuation analysis (DFA) was applied to resting alpha and beta band oscillatory amplitudes to investigate LRTCs in each subject group, at both the sensor and the source levels. Permutation tests with maximum statistics correction were used to explore group differences (p < 0.01). Finally, machine-learning, using support vector machine (SVM) and a 10-fold cross-validation technique, was applied to classify controls and patients based on DFA values. Statistically significant decoding was assessed using binomial law statistics and permutation tests (p < 0.001). Results/Discussion: Significant group differences were observed between the two groups at both sensor and source levels. Specifically, sensormeasured DFA were found to be significantly attenuated in SZ patients compared to controls over the temporo-parietal areas in the alpha-band, and over central brain regions in the beta-band. Source-level analysis improved anatomical. Relative group differences in DFA were found up to 20% in both alpha and beta bands. Specifically, compared to controls, attenuated DFA values were observed in the frontal, temporal and occipital poles and the cuneus in the alpha band, and in the occipital pole, cuneus and mid frontal gyrus in the beta band. Finally, the machine-learning algorithm successfully classified the groups using the measure of DFA with up to 76% decoding accuracy. The combination of classical statistical measures and machine learning tools in our study illustrate the interest of using features of scale-free dynamics to enhance our understanding of schizophrenia and potentially find a new path for early clinical diagnosis.