Increased functional connectivity within alpha and theta frequency bands in dysphoria: A resting-state EEG study

AbstractRecent studies have shown how MEG can reveal spatial patterns of functional connectivity using frequency-specific oscillatory coupling measures and that these may be modified in disease. However, there is a need to understand both how repeatable these patterns are across participants and how these measures relate to the moment-to-moment variability (or ‘irregularity’) of neural activity seen in healthy brain function. In this study, we used Multi-scale Rank-Vector Entropy (MRVE) to calculate the dynamic timecourses of signal variability over a range of temporal scales. The correlation of MRVE timecourses was then used to detect functional connections in resting state MEG recordings that were robust over 183 participants and varied with temporal scale. We then compared these MRVE connectivity patterns to those derived using more standard amplitude-amplitude coupling measures, using methods designed to quantify the consistency of these patterns across participants.Using oscillatory amplitude envelope correlation (AEC), the most consistent connectivity patterns, across the cohort, were seen in the alpha and beta frequency bands. At fine temporal scales (corresponding to ‘scale frequencies’, fS = 30-150Hz), MRVE correlation detected mostly occipital and parietal connections and these showed high similarity with the networks identified by AEC in the alpha and beta frequency bands. The most consistent connectivity profiles between participants were given by MRVE correlation at fS = 75Hz and AEC in the beta band.It was also found that average mid-to fine scale variability within each region (fS ∼ 10-150Hz) negatively correlated with the region’s overall connectivity strength with other brain areas, as measured by fine scale MRVE correlation (fS ∼ 30-150Hz) and by alpha and beta band AEC. These findings suggest that local activity at frequencies fS ≳ 10Hz becomes more regular when a region exhibits high levels of resting state connectivity.

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Evaluating the Contribution of EEG Power Profiles to Characterize and Discriminate Posttraumatic Stress Symptom Factors in a Combat-Exposed Population

Clinical EEG and Neuroscience ◽

10.1177/1550059418767583 ◽

2018 ◽

Vol 49 (6) ◽

pp. 379-387 ◽

Cited By ~ 3

Author(s):

Christina M. Sheerin ◽

Laura M. Franke ◽

Steven H. Aggen ◽

Ananda B. Amstadter ◽

William C. Walker

Keyword(s):

Posttraumatic Stress ◽

Resting State ◽

Ptsd Symptom ◽

Factor Model ◽

Common Factor ◽

Frequency Bands ◽

Eyes Closed ◽

Exposed Population ◽

Theta Frequency ◽

Alpha Beta

This study investigated the use of resting-state electroencephalography (EEG) data to help differentiate posttraumatic stress disorder (PTSD) symptom factors. The sample, 147 combat-exposed OIF/OEF (Operation Iraqi Freedom/Operation Enduring Freedom) Veterans and service members, was a polytrauma population with variable PTSD and mild traumatic brain injury (mTBI) diagnoses. Participants completed the PTSD Checklist (PCL) and resting-state EEG was assessed for 10 minutes, with eyes closed. Regional averages of absolute power in alpha, beta, delta, and theta frequency bands were computed to estimate a single EEG common factor per band. An oblique 4 common-factor model was then fit to the 17 PCL items that included a residual EEG factor as an exogenous predictor with the group mean effect of mTBI on the EEG factor removed. Separate comparative model testing sequences for the alpha, beta, delta, and theta EEG factor frequency bands were conducted. An inverse relationship of delta and theta frequency bands on avoidance and numbing symptom factors (but not re-experiencing and hyperarousal) was found. Results provide evidence for possible neurobiological basis for the 4 PTSD symptom factors.

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Measuring robust functional connectivity from resting-state MEG using amplitude and entropy correlation across frequency bands and temporal scales

NeuroImage ◽

10.1016/j.neuroimage.2020.117551 ◽

2021 ◽

Vol 226 ◽

pp. 117551

Author(s):

Megan Godfrey ◽

Krish D. Singh

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Temporal Scales ◽

Frequency Bands

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Identifying Mild Cognitive Impairment in Parkinson’s Disease With Electroencephalogram Functional Connectivity

Frontiers in Aging Neuroscience ◽

10.3389/fnagi.2021.701499 ◽

2021 ◽

Vol 13 ◽

Author(s):

Min Cai ◽

Ge Dang ◽

Xiaolin Su ◽

Lin Zhu ◽

Xue Shi ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Cognitive Impairment ◽

Functional Connectivity ◽

Pathophysiological Mechanism ◽

Frequency Bands ◽

Eyes Closed ◽

Theta Frequency ◽

Connectivity Patterns ◽

Left And Right

ObjectiveCognitive impairment occurs frequently in Parkinson’s disease (PD) and negatively impacts the patient’s quality of life. However, its pathophysiological mechanism remains unclear, hindering the development of new therapies. Changes in brain connectivity are related to cognitive impairment in patients with PD, with the dorsolateral prefrontal cortex (DLPFC) being considered the essential region related to PD cognitive impairment. Nevertheless, few studies have focused on the global connectivity responsible for communication with the DLPFC node, the posterior division of the middle frontal gyrus (PMFG) in patients with PD; this was the focus of this study.MethodsWe applied resting-state electroencephalography (EEG) and calculated a reliable functional connectivity measurement, the debiased weighted phase lag index (dWPLI), to examine inter-regional functional connectivity in 68 patients with PD who were classified into two groups according to their cognitive condition.ResultsWe observed that altered left and right PMFG-based functional connectivity associated with cognitive impairment in patients with PD in the theta frequency bands under the eyes closed condition (r = −0.426, p < 0.001 and r = −0.437, p < 0.001, respectively). Exploratory results based on the MoCA subdomains indicated that poorer visuospatial function was associated with higher right PMFG-based functional connectivity (r = −0.335, p = 0.005), and poorer attention function was associated with higher left and right PMFG-based functional connectivity (r = −0.380, p = 0.001 and r = −0.256, p = 0.035, respectively). Further analysis using logistic regression and receiver operating characteristic (ROC) curves found that this abnormal functional connectivity was an independent risk factor for cognitive impairment [odds ratio (OR): 2.949, 95% confidence interval (CI): 1.294–6.725, p = 0.01 for left PMFG; OR: 11.278, 95% CI: 2.578–49.335, p = 0.001 for right PMFG, per 0.1 U], and provided moderate classification power to discriminate between cognitive abilities in patients with PD [area under the ROC curve (AUC) = 0.770 for left PMFG; AUC = 0.809 for right PMFG].ConclusionThese preliminary findings indicate that abnormal PMFG-based functional connectivity patterns associated with cognitive impairment in the theta frequency bands under the eyes closed condition and altered functional connectivity patterns have the potential to act as reliable biomarkers for identifying cognitive impairment in patients with PD.

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