A new method of comb spectrum signal generating based on Morlet wavelet

The connectivity of the brain network allows the brain to perform a variety of complex functions (Friston, 2011). Functional connectivity (FC) represents the temporal correlations that suggest direct or indirect interactions between brain regions (Friston et al., 1993;Song et al., 2008). Studying FC and dynamics in neural networks allows us to understand how the brain performs various complex functions (He et al., 2019). It has also been reported that the brain network connectivity is affected by mental illness (Leistedt et al., 2009;van den Heuvel et al., 2013).

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“…The phase is extracted using a combined Morlet wavelet, which is a high-precision frequency division method (Wang et al, 2011).…”

Section: Fc Analysismentioning

confidence: 99%

Characterization of specific spatial functional connectivity difference in depression during sleep

Lian

Song

Zhang

et al. 2021

J of Neuroscience Research

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“…“∗” represents conjugation. The process and calculation formula of Morlet wavelet phase extraction can be obtained in the manuscript of Wang et al (2011) .…”

Section: Methodsmentioning

confidence: 99%

Graph Theory Analysis of the Cortical Functional Network During Sleep in Patients With Depression

Song

Wang

et al. 2022

Front. Physiol.

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Depression, a common mental illness that seriously affects the psychological health of patients, is also thought to be associated with abnormal brain functional connectivity. This study aimed to explore the differences in the sleep-state functional network topology in depressed patients. A total of 25 healthy participants and 26 depressed patients underwent overnight 16-channel electroencephalography (EEG) examination. The cortical networks were constructed by using functional connectivity metrics of participants based on the weighted phase lag index (WPLI) between the EEG signals. The results indicated that depressed patients exhibited higher global efficiency and node strength than healthy participants. Furthermore, the depressed group indicated right-lateralization in the δ band. The top 30% of connectivity in both groups were shown in undirected connectivity graphs, revealing the distinct link patterns between the depressed and control groups. Links between the hemispheres were noted in the patient group, while the links in the control group were only observed within each hemisphere, and there were many long-range links inside the hemisphere. The altered sleep-state functional network topology in depressed patients may provide clues for a better understanding of the depression pathology. Overall, functional network topology may become a powerful tool for the diagnosis of depression.

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A new method of comb spectrum signal generating based on Morlet wavelet

Cited by 2 publications

References 1 publication

Characterization of specific spatial functional connectivity difference in depression during sleep

Characterization of specific spatial functional connectivity difference in depression during sleep

Graph Theory Analysis of the Cortical Functional Network During Sleep in Patients With Depression

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