Disturbed resting state EEG synchronization in bipolar disorder: A graph-theoretic analysis.
- Authors
- Kim, Dae-Jin; Bolbecker, Amanda R; Howell, Josselyn; Rass, Olga; Sporns, Olaf; Hetrick, William P; Breier, Alan; O'Donnell, Brian F
- Year
- 2013
- Journal
- NeuroImage. Clinical
- PMID
- 24179795
- DOI
- 10.1016/j.nicl.2013.03.007
- PMCID
- PMC3777715
Disruption of functional connectivity may be a key feature of bipolar disorder (BD) which reflects disturbances of synchronization and oscillations within brain networks. We investigated whether the resting electroencephalogram (EEG) in patients with BD showed altered synchronization or network properties. Resting-state EEG was recorded in 57 BD type-I patients and 87 healthy control subjects. Functional connectivity between pairs of EEG channels was measured using synchronization likelihood (SL) for 5 frequency bands (δ, θ, α, β, and γ). Graph-theoretic analysis was applied to SL over the electrode array to assess network properties. BD patients showed a decrease of mean synchronization in the alpha band, and the decreases were greatest in fronto-central and centro-parietal connections. In addition, the clustering coefficient and global efficiency were decreased in BD patients, whereas the characteristic path length increased. We also found that the normalized characteristic path length and small-worldness were significantly correlated with depression scores in BD patients. These results suggest that BD patients show impaired neural synchronization at rest and a disruption of resting-state functional connectivity.
The workflow of all preprocessing for network analysis. (A) The raw eye-closed resting EEG data, first, was band-pass filtered with 0.1 < f < 200-Hz, and corrected for the eye movement with references of vertical and horizontal EOG. Then, the EEG was segmented into 2000-ms epochs, where the epochs with voltage samples exceeding ± 150-μV were excluded. (B) Ten epochs (20-sec) were down-sampled from 1000 Hz to 250 Hz, resulting in the time series of 5000 samples for further analysis. The preprocessed EEG data was classified into 5 frequency bands (δ, θ, α, β, and γ), and the SL was computed between EEG channels resulting in the connectivity matrix for each frequency band. Finally, graph-theoretic analysis was performed, and global/local network measures were calculated.
LLM interpretation
This figure is a workflow diagram illustrating the preprocessing and analysis pipeline for EEG network analysis. Panel A shows the initial stages: band-pass filtering (0.1–200 Hz), ocular correction using VEOG and HEOG, segmentation into 2-second epochs, and artifact rejection of samples exceeding $\pm 150\mu\text{V}$. Panel B depicts the subsequent steps: down-sampling from 1000 to 250 Hz, band-pass filtering into five frequency bands ($\delta, \theta, \alpha, \beta, \gamma$), computation of synchronization likelihood (SL) to create a connectivity matrix, and final graph-theoretical analysis.
Synchronization matrices across the bipolar disorder (BD) patients (N = 57) and the normal healthy control (NC) participants (N = 87). The number of EEG channels is 29, resulting in the 29 × 29 square matrix whose elements represent the average strength of SL values across the whole subjects between a pair of EEG channels.
LLM interpretation
This figure consists of ten synchronization heatmaps arranged in a 2x5 grid, comparing normal healthy controls (NC) and bipolar disorder (BD) patients across five EEG frequency bands: delta, theta, alpha, beta, and gamma. Each 29x29 matrix represents the average synchronization strength (SL values) between pairs of EEG channels, with a color scale ranging from 0 (yellow) to 0.6 (dark red). The matrices show similar overall patterns of connectivity across both groups and all frequency bands, with the strongest synchronization appearing along the diagonal.
Mean SL of BD patients was decreased (p = 0.019, permutation test) in alpha-band as compared to controls.
LLM interpretation
This bar chart compares the synchronization likelihood (SL) across five frequency bands (delta, theta, alpha, beta, and gamma) between normal controls (NC, white bars) and bipolar disorder patients (BD, grey bars). The y-axis represents synchronization likelihood, ranging from 0.0 to 0.5. A statistically significant decrease in SL is indicated for the BD group compared to the NC group in the alpha band, marked by an asterisk and a bracket.
Clustered connections from network-based statistics (NBS). The nodes consisted of F4, FC3, FC4, Cz, and Cpz comprised decreased synchronization in alpha-band of BD subjects compared to controls (p < 0.05, corrected).
LLM interpretation
This figure is a schematic diagram of an EEG electrode layout on a head map, illustrating network-based statistics (NBS). It highlights a cluster of connections between five specific nodes (F4, FC3, FC4, Cz, and Cpz), indicated by solid black arrows. According to the legend, these connections represent decreased alpha-band synchronization in BD subjects compared to controls (p < 0.05, corrected).
(A) Weighted clustering coefficient C, (B) weighted path length L, and (C) global efficiency Eg in alpha-band (8–12Hz) for the bipolar disorder patients (BD: red) and healthy controls (NC: black) as a function of connection density. Error bars represent 95% confidence interval, and the asterisks denote where the group difference is significant (p < 0.05, permutation test). Vertical dashed line represents the connection density (≈ 24%) from Erdös–Rényi model for 29 nodes, which predicts that most of nodes are fully connected.
LLM interpretation
This figure consists of three line graphs (A, B, and C) comparing network metrics—weighted clustering coefficient (C), weighted path length (L), and global efficiency (Eg)—between bipolar disorder patients (BD, red) and healthy controls (NC, black) across varying connection densities (0–100%). In all three plots, the NC group generally exhibits higher values for C and Eg and lower values for L compared to the BD group. Statistical significance (p < 0.05) is indicated by asterisks above the data points, showing significant group differences across most density levels.
Decreased node-specific network measures in bipolar disorder (BD) patients (p < 0.01, uncorrected); red = strength s, blue = local efficiency El, and gray = nodes of decreased functional subnetwork from the network-based statistics (NBS) in Fig. 4. All nodes but CP4 correspond to alpha-band. Results were computed from the SL matrix at the threshold of 30% connection density, where the clustering coefficient and characteristic path length of the SL network have the maximum values in Fig. 5.
LLM interpretation
This is a schematic diagram of an EEG electrode layout on a head map showing decreased network measures in bipolar disorder patients. Gray shading identifies nodes of a decreased functional subnetwork, while red and blue outlines highlight nodes with decreased strength ($s$) and local efficiency ($El$), respectively. The affected nodes include FC3, F4, FC4, Cz, and CPz.
Partial correlations between depressive score of MADRS and (A) normalized characteristic path length (λ), and (B) small-worldness (σ) in gamma-band for bipolar disorder patients. Correlations for C, L, γ, and Eg were not significant for other frequency bands. Correlations were computed at the threshold of 30% connection density.
LLM interpretation
This figure consists of two scatter plots with linear regression lines showing the relationship between MADRS depressive scores (x-axis) and network metrics in the gamma-band for bipolar disorder patients. Plot A shows a significant positive correlation between MADRS and normalized characteristic path length ($\lambda$; $r=0.353, p=0.008$). Plot B shows a significant negative correlation between MADRS and small-worldness ($\sigma$; $r=-0.292, p=0.030$).
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