Top-down selection of visual working memory contents is supported by alpha-band phase-synchronized oscillatory networks

Visual working memory (VWM) maintenance depends on oscillatory network dynamics across multiple frequency bands throughout fronto-parietal and sensory brain areas. However, whether these networks reflect the active maintenance of visual information content or serve top-down control processes has remained unresolved. To address this, we used concurrent magneto- and electroencephalography (M/EEG) to measure brain activity during VWM tasks, in which the memory content was parametrically controlled. Using new edge-level analysis for source-connectivity networks, we disentangled connections and subnetworks underlying the maintenance of specific contents from those supporting feature-general VWM. We show here that long-range high-alpha band (α, 11– 13 Hz) phase- synchronization networks carry out a dual role in these VWM functions. α-band subgraphs localized to the visual areas are feature-selective and maintain the contents of VWM. In contrast, the high α-band subgraph in the fronto-parietal..., Recording and processing of this data is described in the article.
Haque H, et al.: \"Top-down selection of visual working memory contents is supported by alpha-band phase-synchronized oscillatory networks. Imaging Neuroscience.\"
, , Data archive for: **Top-down selection of visual working memory contents is supported by alpha-band phase-synchronized oscillatory networks**

Authors: Hamed Haque, Sheng H. Wang, Felix Siebenhühner, Edwin M. Robertson, J. Matias Palva, Satu Palva

Year: 2025

Contact: Hamed Haque, [[e-mail]](mailto:[e-mail]); [[e-mail]](mailto:[e-mail])

This data archive contains synchronization data of twenty healthy human participants performing a visual working memory (VWM) task while being scanned using concurrent electroencephalography (EEG) and magnetoencephalography (MEG). For each analysis, the relevant input and supporting files that allow replication of the workflow and results are included. Scripts used for these analysis can be found in GitHub ([https://github.com/palvalab/vwm_synchronization]()).

In the present work we analyse large-scale synchronization networks in multiple frequency bands during a VWM task. We identified d..., We have received explicit consent from the participants that de-identified data could be published in the public domain. To de-identify the data, all personal information that may be used to identify a participant has been removed. Each subject is only labelled via a numbered code (e.g. S001) and subject data only contains connectome data (400 x 400 matrices of complex valued numbers). Any physiological data that could be used to identify the subjects (e.g. raw EEG/MEG traces, MRIs etc.) are not included in the dataset.