Functional Connectivity States of Alpha Rhythm Sources in the Human Cortex at Rest: Implications for Real-Time Brain State Dependent EEG-TMS

Tabarelli, Davide; Brancaccio, Arianna; Zrenner, Christoph; Belardinelli, Paolo

doi:10.3390/brainsci12030348

Cited by 10 publications

(11 citation statements)

References 51 publications

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“…In this experiment, we focused on the alpha band, because it is predominant, and alpha connectivity patterns can be different according to different cortical generators depending on the state of the brain [ 32 ]. Therefore, the signals were filtered to 8–13 Hz according to [ 32 , 42 , 43 , 44 ].…”

Section: Resultsmentioning

confidence: 99%

“…The higher-order dynamic functional connectivity time series were computed in [ 31 ] using the constant sliding window analysis method. In [ 32 ], the wPLI was computed based on predefined 2 s epochs, and constant sliding window analysis was proposed to extract information about connectivity patterns in the future, focusing on determining the tradeoff between temporal resolution and estimation error, i.e., determining the optimal window size. In [ 33 ], it is mentioned that the window size should be short enough to represent a good tradeoff between the ability to capture dynamic connectivity and sensitivity to noise.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic Connectivity Analysis Using Adaptive Window Size

Šverko

Vrankić

Vlahinić

et al. 2022

Sensors

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In this paper, we propose a new method to study and evaluate the time-varying brain network dynamics. The proposed RICI-imCPCC method (relative intersection of confidence intervals for the imaginary component of the complex Pearson correlation coefficient) is based on an adaptive window size and the imaginary part of the complex Pearson correlation coefficient. It reduces the weaknesses of the existing method of constant sliding window analysis with narrow and wide windows. These are the low temporal precision and low reliability for short connectivity periods for wide windows, and high susceptibility to noise for narrow windows, all resulting in low estimation accuracy. The proposed method overcomes these shortcomings by dynamically adjusting the window width using the RICI rule, which is based on the statistical properties of the area around the observed sample. In this paper, we compare the RICI-imCPCC with the existing constant sliding window analysis method and describe its advantages. First, the mathematical principles are established. Then, the comparison between the existing and the proposed method using synthetic and real electroencephalography (EEG) data is presented. The results show that the proposed RICI-imCPCC method has improved temporal resolution and estimation accuracy compared to the existing method and is less affected by the noise. The estimation error energy calculated for the RICI-imCPCC method on synthetic signals was lower by a factor of 1.22 compared to the error of the constant sliding window analysis using narrow window size imCPCC, by a factor of 2.87 compared to using wide window size imCPCC, by a factor of 6.69 compared to using narrow window size wPLI, and by a factor of 4.72 compared to using wide window size wPLI. Analysis of the real signals shows the ability of the proposed method to detect a P300 response and to detect a decrease in dynamic connectivity due to desynchronization and blockage of mu-rhythms.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dynamic Connectivity Analysis Using Adaptive Window Size

Šverko

Vrankić

Vlahinić

et al. 2022

Sensors

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“…Both NFT and tACS have shown some improvement in the alpha amplitude, sensory perceptivity, and cognitive performance. [ 14 - 24 ]…”

Section: Cortical Excitability and Pulsed Inhibitionmentioning

confidence: 99%

The Electroencephalographic Brainwave Spectrum, Mindful Meditation, and Awareness

Deshmukh

2023

International Journal of Yoga

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It is hypothesized that being mindfully aware is a spontaneous state of being. It is imbued with joy, peace and happiness. Such a state is periodically revealed during restful attentiveness or presence. It is also associated with spontaneous brain alpha activity of 8–13 Hz. In deep nonrapid eye movement sleep, there is synchronous delta activity at a coherent frequency of 0.1 Hz. Both of these brainwave ground states are spontaneous, calm and effortless. When any physical or mental effort is made, the alpha rhythm is desynchronized, and it is superposed by faster brain waves of beta (13–30 Hz) and gamma frequencies (30–150 Hz). This is associated with a stream of dualistic conscious experiences with contents. During deep sleep, delta activity is superposed by beta and gamma activity with microarousals resulting in dream experiences. During effortless, meditative awareness, the whole family of alpha rhythm is synchronized including (a) Occipital-parietal alpha with visual clarity, formless color, and the absence of visual imagery (b) Frontal eye-field alpha with relatively motionless eyes, and the absence of voluntary actions or plans to move the eyes in some direction, along with nonactive working memory, (c) Somatosensory alpha or Mu rhythm from the somatic motor-sensory cortex with the resultant stillness of the body including head, face, larynx, spine, hands and legs, (d) Mid-temporal auditory alpha with vocal quietness and internal verbal silence ( Maunam ) with a feeling of spontaneous silence and serenity, (e) Cingulate and precuneus alpha resulting in freedom from autobiographical memories and the sense of agency or ego. The insular cortex serves as a gatekeeper, a hierarchical controller to switch between conscious engagement or disengagement from the internal or the external world. It switches between the default mode network and the executive frontoparietal networks, between the sequential and the parallel modes of functioning. Mindful consciousness is local and dualistic, whereas mindful awareness is nonlocal and nondual.

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“…To address this issue, Phase Lag Index (PLI) is an optimal analytical approach that can reveal the relationships and interactions within brain regions. The PLI quantifies the asymmetry of the phase differences between different EEG channels, allowing for insights into the functional connectivity and synchronization between brain regions [ 11 , 33 , 34 ]. Specifically, the focus of the PLI is on the phase information contained within the EEG signals, as this is believed to reflect the underlying neural communication and coordination.…”

Section: Introductionmentioning

confidence: 99%

Neuroimaging Study of Brain Functional Differences in Generalized Anxiety Disorder and Depressive Disorder

Qi,

Xu,

2023

Brain Sciences

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Generalized anxiety disorder (GAD) and depressive disorder (DD) are distinct mental disorders, which are characterized by complex and unique neuroelectrophysiological mechanisms in psychiatric neurosciences. The understanding of the brain functional differences between GAD and DD is crucial for the accurate diagnosis and clinical efficacy evaluation. The aim of this study was to reveal the differences in functional brain imaging between GAD and DD based on multidimensional electroencephalogram (EEG) characteristics. To this end, 10 min resting-state EEG signals were recorded from 38 GAD and 34 DD individuals. Multidimensional EEG features were subsequently extracted, which include power spectrum density (PSD), fuzzy entropy (FE), and phase lag index (PLI). Then, a direct statistical analysis (i.e., ANOVA) and three ensemble learning models (i.e., Random Forest (RF), Light Gradient Boosting Machine (LightGBM), eXtreme Gradient Boosting (XGBoost)) were used on these EEG features for the differential recognitions. Our results showed that DD has significantly higher PSD values in the alpha1 and beta band, and a higher FE in the beta band, in comparison with GAD, along with the aberrant functional connections in all four bands between GAD and DD. Moreover, machine learning analysis further revealed that the distinct features predominantly occurred in the beta band and functional connections. Here, we show that DD has higher power and more complex brain activity patterns in the beta band and reorganized brain functional network structures in all bands compared to GAD. In sum, these findings move towards the practical identification of brain functional differences between GAD and DD.

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Functional Connectivity States of Alpha Rhythm Sources in the Human Cortex at Rest: Implications for Real-Time Brain State Dependent EEG-TMS

Cited by 10 publications

References 51 publications

Dynamic Connectivity Analysis Using Adaptive Window Size

Dynamic Connectivity Analysis Using Adaptive Window Size

The Electroencephalographic Brainwave Spectrum, Mindful Meditation, and Awareness

Neuroimaging Study of Brain Functional Differences in Generalized Anxiety Disorder and Depressive Disorder

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