Identification of spatial patterns with maximum association between power of resting state neural oscillations and trait anxiety

Vidaurre, Carmen; Nikulin, Vadim V. and Herrojo Ruiz, Maria. 2023. Identification of spatial patterns with maximum association between power of resting state neural oscillations and trait anxiety. Neural Computing and Applications, 35(8), pp. 5737-5749. ISSN 0941-0643 [Article]

Vidaurre_Spoc_NeuralComputingApplications22.pdf - Published Version
Available under License Creative Commons Attribution.

Download (1MB) | Preview

Abstract or Description

Anxiety affects approximately 5–10% of the adult population worldwide, placing a large burden on the health systems. Despite its omnipresence and impact on mental and physical health, most of the individuals affected by anxiety do not receive appropriate treatment. Current research in the field of psychiatry emphasizes the need to identify and validate biological markers relevant to this condition. Neurophysiological preclinical studies are a prominent approach to determine brain rhythms that can be reliable markers of key features of anxiety. However, while neuroimaging research consistently implicated prefrontal cortex and subcortical structures, such as amygdala and hippocampus, in anxiety, there is still a lack of consensus on the underlying neurophysiological processes contributing to this condition. Methods allowing non-invasive recording and assessment of cortical processing may provide an opportunity to help identify anxiety signatures that could be used as intervention targets. In this study, we apply Source-Power Comodulation (SPoC) to electroencephalography (EEG) recordings in a sample of participants with different levels of trait anxiety. SPoC was developed to find spatial filters and patterns whose power comodulates with an external variable in individual participants. The obtained patterns can be interpreted neurophysiologically. Here, we extend the use of SPoC to a multi-subject setting and test its validity using simulated data with a realistic head model. Next, we apply our SPoC framework to resting state EEG of 43 human participants for whom trait anxiety scores were available. SPoC inter-subject analysis of narrow frequency band data reveals neurophysiologically meaningful spatial patterns in the theta band (4–7 Hz) that are negatively correlated with anxiety. The outcome is specific to the theta band and not observed in the alpha (8–12 Hz) or beta (13–30 Hz) frequency range. The theta-band spatial pattern is primarily localised to the superior frontal gyrus. We discuss the relevance of our spatial pattern results for the search of biomarkers for anxiety and their application in neurofeedback studies.

Item Type:


Identification Number (DOI):

Additional Information:

Acknowledgements The work of CV was partially supported by MINECO (Grant RyC-2014-15671) and MCIN (Grant PID2020- 118829RB-I00). CV was also partially supported by IKERBASQUE. MHR and VVN were partially supported by the Basic Research Program of the National Research University Higher School of Economics (Russian Federation).

Data Access Statement:

Data availability The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.


EEG/MEG oscillations, Anxiety, Supervised spatial patterns, Affective neurofeedback, Affective interface Emotion neurofeedback

Departments, Centres and Research Units:



3 November 2021Submitted
14 September 2022Accepted
1 October 2022Published Online
March 2023Published

Item ID:


Date Deposited:

07 Jan 2022 11:57

Last Modified:

15 Mar 2023 16:31

Peer Reviewed:

Yes, this version has been peer-reviewed.


View statistics for this item...

Edit Record Edit Record (login required)