[Instant adjustive effect of auricular electroacupuncture on brain default model network of patients with primary insomnia]

OBJECTIVE

To investigate the immediate brain effect of auricular electroacupuncture (EA) in the treatment of primary insomnia (PI).

METHODS

In this study, 15 subjects with PI who were diagnosed according to Pitsburgh Sleep Quality Index (PSQI), and other 15 age- and gender-matched subjects without insomnia were recruited in the present study. The PI patients received EA (4 Hz/20 Hz, a tolerable electrical current strength) of auricular concha for 30 min, and their resting state functional magnetic resonance imaging (fMRI) data before and after treatment were collected. The healthy subjects received no any treatment and their resting state fMRI data were collected. The diffe-rence of default mode network functional connectivity between the patients and healthy subjects, and changes of the patient's brain functional connectivity after EA treatment were estimated by using seed-point-based analysis (SPBA).

RESULTS

Analysis by taking the posterior cingulate gyrus as the seed-point showed that compared with the healthy participants before treatment, the patient's brain functional connectivity between the posterior cingulate gyrus and the right insula, or the inferior frontal gyrus of the right opercularis region, or the right rolandic operculum was increased. After 30 minutes' EA treatment, the functional connectivity between the posterior cingulate gyrus and the precuneus, the left angular gyrus, the left frontal superior gyrus, the left frontal middle cortex, the right temporalis inferior gyrus, the right temporalis middle gyrus or the left medial orbitofrontal cortex was decreased, while the functional connectivity between the posterior cingulate gyrus and the right lingual gyrus, or the cortex surrounding the right calcarine fissure was increased.

CONCLUSION

EA of auricular concha has an instant effect in modulating the brain default mode network in PI patients, which may be its brain mechanism underlying improvement of PI.

Investigation of global brain dynamics depending on emotion regulation strategies indicated by graph theoretical brain network measures at system level

In the present study, new findings reveal the close association between graph theoretic global brain connectivity measures and cognitive abilities the ability to manage and regulate negative emotions in healthy adults. Functional brain connectivity measures have been estimated from both eyes-opened and eyes-closed resting-state EEG recordings in four groups including individuals who use opposite Emotion Regulation Strategies (ERS) as follow: While 20 individuals who frequently use two opposing strategies, such as rumination and cognitive distraction, are included in 1st group, 20 individuals who don't use these cognitive strategies are included in 2nd group. In 3rd and 4th groups, there are matched individuals who use both Expressive Suppression and Cognitive Reappraisal strategies together frequently and never use them, respectively. EEG measurements and psychometric scores of individuals were both downloaded from a public dataset LEMON. Since it is not sensitive to volume conduction, Directed Transfer Function has been applied to 62-channel recordings to obtain cortical connectivity estimations across the whole cortex. Regarding well defined threshold, connectivity estimations have been transformed into binary numbers for implementation of Brain Connectivity Toolbox. The groups are compared to each other through both statistical logistic regression models and deep learning models driven by frequency band specific network measures referring segregation, integration and modularity of the brain. Overall results show that high classification accuracies of 96.05% (1st vs 2nd) and 89.66% (3rd vs 4th) are obtained in analyzing full-band ( 0.5 - 45 H z ) EEG. In conclusion, negative strategies may upset the balance between segregation and integration. In particular, graphical results show that frequent use of rumination induces the decrease in assortativity referring network resilience. The psychometric scores are found to be highly correlated with brain network measures of global efficiency, local efficiency, clustering coefficient, transitivity and assortativity in even resting-state.