Neural consequences of 5-Hz transcranial alternating current stimulation over right hemisphere: An eLORETA EEG study.

INTRODUCTION: Transcranial alternating current stimulation (tACS) at 5-Hz to the right hemisphere can effectively alleviate anxiety symptoms. This study aimed to explore the neural mechanisms that drive the therapeutic benefits. METHODS: We collected electroencephalography (EEG) data from 24 participants with anxiety disorders before and after a tACS treatment session. tACS was applied over the right hemisphere, with 1.0 mA at F4, 1.0 mA at P4, and 2.0 mA at T8 (10-10 EEG convention). With eLORETA, we transformed the scalp signals into the current source density in the cortex. We then assessed the differences between post- and pre-treatment brain maps across multiple spectra (delta to low gamma) with non-parametric statistics. RESULTS: We observed a trend of heightened power in alpha and reduced power in mid-to-high beta and low gamma, in accord with the EEG markers of anxiolytic effects reported in previous studies. Additionally, we observed a consistent trend of de-synchronization at the stimulating sites across spectra. CONCLUSION: tACS 5-Hz over the right hemisphere demonstrated EEG markers of anxiety reduction. The after-effects of tACS on the brain are intricate and cannot be explained solely by the widely circulated entrainment theory. Rather, our results support the involvement of plasticity mechanisms in the offline effects of tACS.

Tripod transcranial alternating current stimulation at 5-Hz to alleviate anxiety symptoms: A preliminary report.

INTRODUCTION: One of the most common applications of transcranial electrical stimulation (tES) at low current intensity is to induce a relaxed state or reduce anxiety. With technical advancement, different waveforms, montages, and parameters can be incorporated into the treatment regimen. We developed a novel protocol to treat individuals with anxiety disorders by transcranial alternating current stimulation (tACS). METHODS: A total of 27 individuals with anxiety disorders underwent tACS treatment for 12 sessions, with each session lasting 25 min. tACS at 5 Hz was applied to F4 (1.0 mA), P4 (1.0 mA), and T8 (2.0 mA) EEG lead positions (tripod), with sinewave oscillation between T8 and F4/P4. We evaluated the primary and secondary outcomes using the Beck Anxiety Inventory (BAI) and neuropsychological assessments. RESULTS: Of the 27 patients, 19 (70.4 %) experienced a reduction in symptom severity >50 %, with an average reduction of BAI 58.5 %. All reported side effects were mild, with itching or tingling being the most common complaint. No significant differences were noted in attention, linguistic working memory, visuospatial working memory, or long-term memory in neuropsychological assessments. CONCLUSION: The results suggest the potential of this novel tripod tACS design as a rapid anxiety alleviator and the importance of a clinical trial to verify its efficacy.

Concordant dynamic changes of global network properties in the frontoparietal and limbic compartments: An EEG study.

INTRODUCTION: Despite its complexity, deciphering nodal interaction is imperative to understanding a neural network. Network interaction is an even more complicated topic that must be addressed. This study aimed to examine the relationship between the brain waves of two canonical brain structures, i.e., the frontoparietal and limbic compartments, during a resting state. METHODS: Electroencephalography (EEG) of 51 subjects in eye-closed condition was analyzed, and the eLORETA method was applied to convert the signals from the scalp to the brain. By way of community detection, representative neural nodes and the associated mean activities were retrieved. Total and lagged coherences were computed to indicate functional connectivity between those neural nodes. Two global network properties were elucidated based on the connectivity measures, i.e., global efficiency and mean functional connectivity strength. The temporal correlation of the global network indices between the two studied networks was explored. RESULTS: It was found that there was a significant trend of positive correlation across the four metrics (lagged vs. total coherence x global efficiency vs. average connectivity). In other words, when the neural interaction in the FP network was stronger, so did that in the limbic network, and vice versa. Notably, the above interaction was not spectrally specific and only existed at a finer temporal scale (under hundreds of milliseconds level). CONCLUSION: The concordant change in network properties indicates an intricate balance between FP and LM compartments. Possible mechanisms and implications for the findings are discussed.

Regional spectral ratios as potential neural markers to identify mild cognitive impairment related to Alzheimer's disease.

OBJECTIVE: Alzheimer's disease (AD) has prolonged asymptomatic or mild symptomatic periods. Given that there is an increase in treatment options and that early intervention could modify the disease course, it is desirable to devise biological indices that may differentiate AD and nonAD at mild cognitive impairment (MCI) stage. METHODS: Based on two well-acknowledged observations of background slowing (attenuation in alpha power and enhancement in theta and delta powers) and early involvement of posterior cingulate cortex (PCC, a neural hub of default-mode network), this study devised novel neural markers, namely, spectral ratios of alpha1 to delta and alpha1 to theta in the PCC. RESULTS: We analysed 46 MCI patients, with 22 ADMCI and 24 nonADMCI who were matched in age, education, and global cognitive capability. Concordant with the prediction, the regional spectral ratios were lower in the ADMCI group, suggesting its clinical application potential. CONCLUSION: Previous research has verified that neural markers derived from clinical electroencephalography may be informative in differentiating AD from other neurological conditions. We believe that the spectral ratios in the neural hubs that show early pathological changes can enrich the instrumental assessment of brain dysfunctions at the MCI (or pre-clinical) stage.

Automatic parcellation of resting-state cortical dynamics by iterative community detection and similarity measurements.

To investigate the properties of a large-scale brain network, it is a common practice to reduce the dimension of resting state functional magnetic resonance imaging (rs-fMRI) data to tens to hundreds of nodes. This study presents an analytic streamline that incorporates modular analysis and similarity measurements (MOSI) to fulfill functional parcellation (FP) of the cortex. MOSI is carried out by iteratively dividing a module into sub-modules (via the Louvain community detection method) and unifying similar neighboring sub-modules into a new module (adjacent sub-modules with a similarity index <0.05) until the brain modular structures of successive runs become constant. By adjusting the gamma value, a parameter in the Louvain algorithm, MOSI may segment the cortex with different resolutions. rs-fMRI scans of 33 healthy subjects were selected from the dataset of the Rockland sample. MOSI was applied to the rs-fMRI data after standardized pre-processing steps. The results indicate that the parcellated modules by MOSI are more homogeneous in content. After reducing the grouped voxels to representative neural nodes, the network structures were explored. The resultant network components were comparable with previous reports. The validity of MOSI in achieving data reduction has been confirmed. MOSI may provide a novel starting point for further investigation of the network properties of rs-fMRI data. Potential applications of MOSI are discussed.