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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| Predictive coding and neurocomputational psychiatry: a mechanistic framework for understanding mental disorders. | Shaw AD et al. | — | 2025 | → |
| Electroencephalography-based endogenous phenotype of diagnostic transition from major depressive disorder to bipolar disorder. | Jang KI et al. | — | 2024 | → |
| Electrophysiological biomarkers in dual pathology. | Rojas Bernal LA et al. | — | 2024 | → |
| Fractal Analysis of Electrophysiological Signals to Detect and Monitor Depression: What We Know So Far? | Čukić M et al. | — | 2024 | → |
| High-altitude exposure leads to increased modularity of brain functional network with the increased occupation of attention resources in early processing of visual working memory. | Zhou J et al. | — | 2024 | → |
| Point of Care Testing (POCT) in Psychopathology Using Fractal Analysis and Hilbert Huang Transform of Electroencephalogram (EEG). | Khan MSI et al. | — | 2024 | → |
| Predictive Value of qEEG in Manic Switch of Depressed Patients. | Arıkan MK et al. | — | 2024 | → |
| The Pathophysiological Underpinnings of Gamma-Band Alterations in Psychiatric Disorders. | Palmisano A et al. | — | 2024 | → |
| Altered Functional Brain Network Structure between Patients with High and Low Generalized Anxiety Disorder. | Qi X et al. | — | 2023 | → |
| Functional brain network features specify DBS outcome for patients with treatment resistant depression. | Ghaderi AH et al. | — | 2023 | → |
| Functional connectivity learning via Siamese-based SPD matrix representation of brain imaging data. | Tang Y et al. | — | 2023 | → |
| Alteration of cortical functional networks in mood disorders with resting-state electroencephalography. | Kim S et al. | — | 2022 | → |
| Association between abnormal brain oscillations and cognitive performance in patients with bipolar disorder: Molecular mechanisms and clinical evidence. | Lu Z et al. | — | 2022 | → |
| Electroconvulsive Therapy-Induced Changes in Functional Brain Network of Major Depressive Disorder Patients: A Longitudinal Resting-State Electroencephalography Study. | Sun S et al. | — | 2022 | → |
| Hypofunction of directed brain network within alpha frequency band in depressive patients: a graph-theoretic analysis. | Liu S et al. | — | 2022 | → |
| Impaired Self-Referential Cognitive Processing in Bipolar Disorder: A Functional Connectivity Analysis. | Zhang J et al. | — | 2022 | → |
| Recent insights into respiratory modulation of brain activity offer new perspectives on cognition and emotion. | Heck DH et al. | — | 2022 | → |
| Schizophrenia, Bipolar Disorder and Pre-Attentional Inhibitory Deficits. | Vlcek P et al. | — | 2022 | → |
| Aberrant brain network topology in the frontoparietal-limbic circuit in bipolar disorder: a graph-theory study. | Zhang L et al. | — | 2021 | → |
| Classification of Gamers Using Multiple Physiological Signals: Distinguishing Features of Internet Gaming Disorder. | Ha J et al. | — | 2021 | → |
| EEG response to game-craving according to personal preference for games. | Ha J et al. | — | 2021 | → |
| Information Optimized Multilayer Network Representation of High Density Electroencephalogram Recordings. | Font-Clos F et al. | — | 2021 | → |
| Prediction of Depression Severity Scores Based on Functional Connectivity and Complexity of the EEG Signal. | Mohammadi Y et al. | — | 2021 | → |
| Structural dysconnectivity in offspring of individuals with bipolar disorder: The effect of co-existing clinical-high-risk for bipolar disorder. | Bora E et al. | — | 2021 | → |
| The Proposition for Bipolar Depression Forecasting Based on Wearable Data Collection. | Llamocca P et al. | — | 2021 | → |
| Workplace design-related stress effects on prefrontal cortex connectivity and neurovascular coupling. | Alyan E et al. | — | 2021 | → |
| Alterations of Cerebral Hemodynamics and Network Properties Induced by Newsvendor Problem in the Human Prefrontal Cortex. | Wanniarachchi H et al. | — | 2020 | → |
| Altered Cortical Functional Networks in Patients With Schizophrenia and Bipolar Disorder: A Resting-State Electroencephalographic Study. | Kim S et al. | — | 2020 | → |
| Classification of Depression Through Resting-State Electroencephalogram as a Novel Practice in Psychiatry: Review. | Čukić M et al. | — | 2020 | → |
| Electro-Encephalography and Electro-Oculography in Aeronautics: A Review Over the Last Decade (2010-2020). | Belkhiria C et al. | — | 2020 | → |
| Frequency-Specific Resting Connectome in Bipolar Disorder: An MEG Study. | Sunaga M et al. | — | 2020 | → |
| Magnetoencephalography resting-state spectral fingerprints distinguish bipolar depression and unipolar depression. | Jiang H et al. | — | 2020 | → |
| Modulation of functional network properties in major depressive disorder following electroconvulsive therapy (ECT): a resting-state EEG analysis. | Hill AT et al. | — | 2020 | → |
| Nonlinear analysis of EEG complexity in episode and remission phase of recurrent depression. | Čukić M et al. | — | 2020 | → |
| The successful discrimination of depression from EEG could be attributed to proper feature extraction and not to a particular classification method. | Čukić M et al. | — | 2020 | → |
| Aberrant brain network topology in fronto-limbic circuitry differentiates euthymic bipolar disorder from recurrent major depressive disorder. | Dvorak J et al. | — | 2019 | → |
| A Magnetoencephalography Study of Pediatric Interictal Neuromagnetic Activity Changes and Brain Network Alterations Caused by Epilepsy in the High Frequency (80-1000 Hz). | Meng L | — | 2019 | → |
| Current understanding of bipolar disorder: Toward integration of biological basis and treatment strategies. | Kato T | — | 2019 | → |
| Disrupted rich club organization and structural brain connectome in unmedicated bipolar disorder. | Wang Y et al. | — | 2019 | → |
| Electrophysiological Brain Connectivity: Theory and Implementation. | He B et al. | — | 2019 | → |
| Higuchi's fractal dimension, but not frontal or posterior alpha asymmetry, predicts PID-5 anxiousness more than depressivity. | Kawe TNJ et al. | — | 2019 | → |
| Machine learning: assessing neurovascular signals in the prefrontal cortex with non-invasive bimodal electro-optical neuroimaging in opiate addiction. | Ieong HF et al. | — | 2019 | → |
| Multiparametric graph theoretical analysis reveals altered structural and functional network topology in Alzheimer's disease. | Lin SY et al. | — | 2019 | → |
| The Reason Why rTMS and tDCS Are Efficient in Treatments of Depression. | Čukić M | — | 2019 | → |
| Abnormal functional connectivity of high-frequency rhythms in drug-naïve schizophrenia. | Takahashi T et al. | — | 2018 | → |
| Are resting state spectral power measures related to executive functions in healthy young adults? | Gordon S et al. | — | 2018 | → |
| Electroencephalographic delta/alpha frequency activity differentiates psychotic disorders: a study of schizophrenia, bipolar disorder and methamphetamine-induced psychotic disorder. | Howells FM et al. | — | 2018 | → |
| Maturation trajectories of cortical resting-state networks depend on the mediating frequency band. | Khan S et al. | — | 2018 | → |
| Maturation Trajectories of Cortical Resting-State Networks Depend on the Mediating Frequency Band | Khan S et al. | — | 2018 | — |
| Possible Biological Mechanisms Linking Mental Health and Heat-A Contemplative Review. | Lõhmus M | — | 2018 | → |
| Progressive topological disorganization of brain network in focal epilepsy. | Park KM et al. | — | 2018 | → |
| Randomized EEG functional brain networks in major depressive disorders with greater resilience and lower rich-club coefficient. | Zhang M et al. | — | 2018 | → |
| The Abnormality of Topological Asymmetry in Hemispheric Brain Anatomical Networks in Bipolar Disorder. | Wang B et al. | — | 2018 | → |
| Alterations of Intrinsic Brain Connectivity Patterns in Depression and Bipolar Disorders: A Critical Assessment of Magnetoencephalography-Based Evidence. | Alamian G et al. | — | 2017 | → |
| Automated Detection of Epileptic Biomarkers in Resting-State Interictal MEG Data. | Soriano MC et al. | — | 2017 | → |
| Effect of Field Spread on Resting-State Magneto Encephalography Functional Network Analysis: A Computational Modeling Study. | Silva Pereira S et al. | — | 2017 | → |
| On characterizing population commonalities and subject variations in brain networks. | Ghanbari Y et al. | — | 2017 | → |
| Topologically convergent and divergent functional connectivity patterns in unmedicated unipolar depression and bipolar disorder. | Wang Y et al. | — | 2017 | → |
| Aberrant Global and Regional Topological Organization of the Fractional Anisotropy-weighted Brain Structural Networks in Major Depressive Disorder. | Chen JH et al. | — | 2016 | → |
| Analysis of infant cortical synchrony is constrained by the number of recording electrodes and the recording montage. | Tokariev A et al. | — | 2016 | → |
| Automated voxel classification used with atlas-guided diffuse optical tomography for assessment of functional brain networks in young and older adults. | Li L et al. | — | 2016 | → |
| Brain Network Connectivity and Topological Analysis During Voluntary Arm Movements. | Storti SF et al. | — | 2016 | → |
| Disrupted Resting-State Functional Connectivity in Nonmedicated Bipolar Disorder. | Wang Y et al. | — | 2016 | → |
| Elucidating neural network functional connectivity abnormalities in bipolar disorder: toward a harmonized methodological approach. | Chase HW et al. | — | 2016 | → |
| Functional brain network alterations in epilepsy: A magnetoencephalography study. | Wang B et al. | — | 2016 | → |
| Functional Connectivity and Quantitative EEG in Women with Alcohol Use Disorders: A Resting-State Study. | Herrera-Díaz A et al. | — | 2016 | → |
| Gibbs distribution for statistical analysis of graphical data with a sample application to fcMRI brain images. | La Rosa PS et al. | — | 2016 | → |
| Graph theoretical analysis of EEG effective connectivity in vascular dementia patients during a visual oddball task. | Wang C et al. | — | 2016 | → |
| Spatiotemporal psychopathology I: No rest for the brain's resting state activity in depression? Spatiotemporal psychopathology of depressive symptoms. | Northoff G | — | 2016 | → |
| Advances in Electrophysiological Research. | Kamarajan C et al. | — | 2015 | → |
| Altered emotionality and neuronal excitability in mice lacking KCTD12, an auxiliary subunit of GABAB receptors associated with mood disorders. | Cathomas F et al. | — | 2015 | → |
| Altered structure of dynamic electroencephalogram oscillatory pattern in major depression. | Fingelkurts AA et al. | — | 2015 | → |
| Disruption of cortical connectivity during remifentanil administration is associated with cognitive impairment but not with analgesia. | Khodayari-Rostamabad A et al. | — | 2015 | → |
| EEG power, cordance and coherence differences between unipolar and bipolar depression. | Tas C et al. | — | 2015 | → |
| Electrical Brain Responses to an Auditory Illusion and the Impact of Musical Expertise. | Ioannou CI et al. | — | 2015 | → |
| The Structural Connectivity Pattern of the Default Mode Network and Its Association with Memory and Anxiety. | Tao Y et al. | — | 2015 | → |
| TRPM2, a Susceptibility Gene for Bipolar Disorder, Regulates Glycogen Synthase Kinase-3 Activity in the Brain. | Jang Y et al. | — | 2015 | → |
| What graph theory actually tells us about resting state interictal MEG epileptic activity. | Niso G et al. | — | 2015 | → |
| A mathematical model of dysfunction of the thalamo-cortical loop in schizophrenia. | Rosjat N et al. | — | 2014 | → |
| Brain electrical source imaging in manic and depressive episodes of bipolar disorder. | Painold A et al. | — | 2014 | → |
| Disrupted modular architecture of cerebellum in schizophrenia: a graph theoretic analysis. | Kim DJ et al. | — | 2014 | → |