An exploratory study of EEG connectivity during the first year of life in preterm and full-term infants

Aim: to evaluate EEG connectivity during the first year of age in healthy full-term infants and preterm infants with prenatal and perinatal risk factors for perinatal brain damage. Methods: Three groups of infants were studied: healthy at full-term infants (n = 71), moderate and late preterm infants (n = 54), and very preterm infants (n = 56). All preterm infants had perinatal or/and perinatal risk factors for brain damage. EEG was obtained during phase II of natural NREM sleep. EEG analysis was performed in 24 segments of 2.56 s free of artifacts. For the calculation of EEG sources, the spectral Structured Sparse Bayesian Learning (sSSBL) was used. Connectivity was computed by the phase-lag index. Results: In healthy full-term infants, EEG interhemispheric connectivity in the different frequency bands followed similar trends with age to those reported in each frequency band: delta connectivity decreases, theta increases at the end of the year, in the alpha band, different trends were observed according to the region studied, and beta interhemispheric connectivity decreases with age. EEG connectivity in preterm infants showed differences from the results of the term group. Discussion: Important structural findings may explain the differences observed in EEG connectivity between the term and preterm groups. Conclusion: The study of EEG connectivity during the first year of age gives essential information on normal and abnormal brain development.

Altered electroencephalography resting state network coherence in remitted MDD

Individuals with remitted depression are at greater risk for subsequent depression and therefore may provide a unique opportunity to understand the neurophysiological correlates underlying the risk of depression. Research has identified abnormal resting-state electroencephalography (EEG) power metrics and functional connectivity patterns associated with major depression, however little is known about these neural signatures in individuals with remitted depression. We investigate the spectral dynamics of 64-channel EEG surface power and source-estimated network connectivity during resting states in 37 individuals with depression, 56 with remitted depression, and 49 healthy adults that did not differ on age, education, and cognitive ability across theta, alpha, and beta frequencies. Average reference spectral EEG surface power analyses identified greater left and midfrontal theta in remitted depression compared to healthy adults. Using Network Based Statistics, we also demonstrate within and between network alterations in LORETA transformed EEG source-space coherence across the default mode, fronto-parietal, and salience networks where individuals with remitted depression exhibited enhanced coherence compared to those with depression, and healthy adults. This work builds upon our currently limited understanding of resting EEG connectivity in depression, and helps bridge the gap between aberrant EEG power and brain network connectivity dynamics in this disorder. Further, our unique examination of remitted depression relative to both healthy and depressed adults may be key to identifying brain-based biomarkers for those at high risk for future, or subsequent depression.

Predicting upper extremity motor improvement following therapy using EEG-based connectivity in chronic stroke

BACKGROUND: Uncertain prognosis presents a challenge for therapists in determining the most efficient course of rehabilitation treatment for individual patients. Cortical Sensorimotor network connectivity may have prognostic utility for upper extremity motor improvement because the integrity of the communication within the sensorimotor network forms the basis for neuroplasticity and recovery. OBJECTIVE: To investigate if pre-intervention sensorimotor connectivity predicts post-stroke upper extremity motor improvement following therapy. METHODS: Secondary analysis of a pilot triple-blind randomized controlled trial. Twelve chronic stroke survivors underwent 2-week task-practice therapy, while receiving vibratory stimulation for the treatment group and no stimulation for the control group. EEG connectivity was obtained pre-intervention. Motor improvement was quantified as change in the Box and Block Test from pre to post-therapy. The association between ipsilesional sensorimotor connectivity and motor improvement was examined using regression, controlling for group. For negative control, contralesional/interhemispheric connectivity and conventional predictors (initial clinical motor score, age, time post-stroke, lesion volume) were examined. RESULTS: Greater ipsilesional sensorimotor alpha connectivity was associated with greater upper extremity motor improvement following therapy for both groups (p < 0.05). Other factors were not significant. CONCLUSION: EEG connectivity may have a prognostic utility for individual patients’ upper extremity motor improvement following therapy in chronic stroke.

On the Specificity and Permanence of Electroencephalography Functional Connectivity

Functional connectivity, representing a statistical coupling relationship between different brain regions or electrodes, is an influential concept in clinical medicine and cognitive neuroscience. Electroencephalography-derived functional connectivity (EEG-FC) provides relevant characteristic information about individual differences in cognitive tasks and personality traits. However, it remains unclear whether these individual-dependent EEG-FCs remain relatively permanent across long-term sessions. This manuscript utilizes machine learning algorithms to explore the individual specificity and permanence of resting-state EEG connectivity patterns. We performed six recordings at different intervals during a six-month period to examine the variation and permanence of resting-state EEG-FC over a long period. The results indicated that the EEG-FC networks are quite subject-specific with a high-precision identification accuracy of greater than 90%. Meanwhile, the individual specificity remained stable and only varied slightly after six months. Furthermore, the specificity is mainly derived from the internal connectivity of the frontal lobe. Our work demonstrates the existence of specific and permanent EEG-FC patterns in the brain, providing potential information for biometric applications.

Stronger connectivity and higher extraversion protect against stress-related deterioration of cognitive functions

Here we attempted to define the relationship between: EEG activity, personality and coping during lockdown. We were in a unique situation since the COVID-19 outbreak interrupted our independent longitudinal study. We already collected a significant amount of data before lockdown. During lockdown, a subgroup of participants willingly continued their engagement in the study. These circumstances provided us with an opportunity to examine the relationship between personality/cognition and brain rhythms in individuals who continued their engagement during lockdown compared to control data collected well before pandemic. The testing consisted of a one-time assessment of personality dimensions and two sessions of EEG recording and deductive reasoning task. Participants were divided into groups based on the time they completed the second session: before or during the COVID-19 outbreak ‘Pre-pandemic Controls’ and ‘Pandemics’, respectively. The Pandemics were characterized by a higher extraversion and stronger connectivity, compared to Pre-pandemic Controls. Furthermore, the Pandemics improved their cognitive performance under long-term stress as compared to the Pre-Pandemic Controls matched for personality traits to the Pandemics. The Pandemics were also characterized by increased EEG connectivity during lockdown. We posit that stronger EEG connectivity and higher extraversion could act as a defense mechanism against stress-related deterioration of cognitive functions.