Intra- and inter-frequency brain network structure in health and schizophrenia

Abstract

Empirical studies over the past two decades have provided support for the hypothesis that schizophrenia is characterized by altered connectivity patterns in functional brain networks. These alterations have been proposed as genetically mediated diagnostic biomarkers and are thought to underlie altered cognitive functions such as working memory. However, the nature of this dysconnectivity remains far from understood. In this study, we perform an extensive analysis of functional connectivity patterns extracted from MEG data in 14 subjects with schizophrenia and 14 healthy controls during a 2-back working memory task. We investigate uni-, bi- and multivariate properties of sensor time series by computing wavelet entropy of and correlation between time series, and by constructing binary networks of functional connectivity both within and between classical frequency bands ([Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text]). Networks are based on the mutual information between wavelet time series, and estimated for each trial window separately, enabling us to consider both network topology and network dynamics. We observed significant decreases in time series entropy and significant increases in functional connectivity in the schizophrenia group in comparison to the healthy controls and identified an inverse relationship between these measures across both subjects and sensors that varied over frequency bands and was more pronounced in controls than in patients. The topological organization of connectivity was altered in schizophrenia specifically in high frequency [Formula: see text] and [Formula: see text] band networks as well as in the [Formula: see text]-[Formula: see text] cross-frequency networks. Network topology varied over trials to a greater extent in patients than in controls, suggesting disease-associated alterations in dynamic network properties of brain function. Our results identify signatures of aberrant neurophysiological behavior in schizophrenia across uni-, bi- and multivariate scales and lay the groundwork for further clinical studies that might lead to the discovery of new intermediate phenotypes.

Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Figure 1. Group Differences in Activity and Connectivity.

Wavelet entropy (A) and intra-frequency strength (B), averaged over sensors, in the -, -, - and -bands for healthy controls and people with schizophrenia. Asterisks indicate significant group differences as measured by non-parametric permutation tests (, corrected for multiple comparisons using the Holm-Bonferroni method).

Figure 2. Entropy and Strength: Inter-Subject Level.

Scatterplots of strength and complexity of the time series, as measured by wavelet entropy, for -, -, - and -bands. Single data points represent values for each individual averaged over trials and sensors. Red markers denote subjects with schizophrenia spectrum diagnosis; blue markers denote healthy subjects. Lines indicate best linear fits for the two groups separately (red, SZ; and blue, HC) and we provide values as indicators of goodness of fit. Similar scatterplots that code each experimental block separately are provided in Figure S1 in the SI.

Figure 3. Entropy and strength: Sensor Level.

Scatterplots of strength and wavelet entropy in -, -, - and -bands. Single data points represent values for each sensor averaged over trials and individuals. Red markers denote data from the SZ group; blue markers denote data from the HC group. Lines indicate linear fits for the two groups separately (red, SZ; and blue, HC) and we provide values as indicators of goodness of fit.

Figure 4. Cost-Efficiency of Functional Networks in Health and Disease.

(A) Cost-efficiency in the -, -, -, -, and cross-frequency band networks for healthy controls (blue) and people with schizophrenia (red). Error bars indicate the standard error over subjects. The gray shaded line indicates the expected values of cost-efficiency for an ensemble of random (ER) graphs (see Methods).

Figure 5. Binary Network Organization in Health and Disease.

(A–B) Example binary network diagnostic curves as a function of threshold: the modularity for controls (HC; black) and people with schizophrenia (SZ; red) (A), and the robustness to targeted attack for controls (black) and people with schizophrenia (blue) for -band networks. Individual curves indicate average values for each individual over the 66 trial-specific networks. (C) Significant group differences in graph diagnostic versus cost curves for 12 graph diagnostics (y-axis) in both intra- and inter-frequency bands (x-axis). Warm colors indicate that the diagnostics values were higher in people with schizophrenia than in healthy controls; cool colors indicate that the values were higher in healthy controls than in people with schizophrenia. Both warm and cool colors are two different shades corresponding to different levels of stringency for significance testing: false discovery rate (), and uncorrected (). See Figures S2 and S3 in the SI for the full graph diagnostic versus cost curves for all subjects, frequency bands, and diagnostics.

Figure 6. Temporal Variability of Network Diagnostics for binary network diagnostics in the and intra-frequency networks and in the inter-frequency networks.

CV values indicate temporal variability over trials for healthy (blue) and schizophrenic subjects (red), averaged over the entire range of cost values. Error bars indicate the mean squared error over subjects and costs. Values and error bars for synchronizability are scaled down by a factor of 10 for visualization purposes. Results for other frequency bands can be found in Figure S4 in the SI.